Virtual Library

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    ES01 - Advances in Lung Cancer Screening Through Imaging (ID 769)

    • Event: WCLC 2018
    • Type: Educational Session
    • Track: Screening and Early Detection
    • Presentations: 4
    • Now Available
    • Moderators:
    • Coordinates: 9/24/2018, 13:30 - 15:00, Room 206 F
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      ES01.01 - Image Quality Characteristics and Nodule Growth Measurement, Medical Physics and Machine Parameters (ID 11351)

      13:30 - 13:50  |  Presenting Author(s): Ricardo S Avila

      • Abstract

      Abstract

      Modern CT scanners are routinely being used to determine the malignancy potential of small sub-centimeter pulmonary nodules. Increasingly, this involves CT scanning and quantitative volume measurement of lung nodules over short time intervals (e.g. 3 or 6 months) to determine whether a change in nodule size consistent with malignant growth has occurred. Although it may appear that current CT scanners are more than capable of reliably performing these quantitative measurements with high quality due to their ability to obtain sub-millimeter resolution lung images, many clinical sites are not taking the steps needed to achieve consistent high quality small lung nodule measurement results. A study of volume measurement performance in a phase II clinical trial observed multiple clinical sites using CT scanners which resulted in errors in volume change measurements as high as 43% [1]. In addition, a 2016 crowd-sourcing study of CT scanner image quality performance using the site’s low dose CT lung cancer screening acquisition protocol revealed that 37% of sites used insufficient slice thickness (<= 1.25mm slice thickness is needed) and only 19% of sites used the needed slice thickness and a reconstruction kernel that avoided excessive smoothing and avoided high levels of edge enhancement [2]. Poor CT image acquisition performance has the potential to result in poor lung nodule volume measurement performance which can negatively impact patient care by contributing to unnecessary biopsies and delays in early lung cancer diagnosis.

      To address these issues the RSNA’s Quantitative Imaging Biomarkers Alliance (QIBA®) has developed the QIBA CT Small Lung Nodule Profile that provides a comprehensive set of specifications to ensure that a clinical site attains a minimum level of quantitative imaging performance necessary to achieve a specified lung nodule volume measurement accuracy. The Small Lung Nodule Profile outlines six fundamental image quality characteristics that are needed throughout the full scanner field of view to support precise volumetric measurement of small lung nodules. These characteristics are (1) Edge Enhancement, (2) Three-Dimensional Resolution, (3) Resolution Aspect Ratio, (4) CT linearity, (5) Spatial Warping, and (6) Noise. In general, CT scanners achieve highest fundamental image quality performance at scanner iso-center with some scanners and image acquisition protocols exhibiting large losses in image quality performance as a function of distance from scanner iso-center [3]. These fundamental image quality properties can now be quickly and easily measured by a technologist at any clinical site using a new image quality measurement phantom and fully automated and cloud-based phantom analysis software.

      To determine the clinical impact of achieved CT image quality performance, a new set of modeling and simulation tools has been developed that can create simulated CT images given the image quality characteristics for a CT scanner and image acquisition protocol [4]. Quantitative measurement software can then be applied to these images resulting in expected measurement performance for a clinical task, such as the bias and precision of solid lung nodule volume change measurement for virtual lung nodules of different sizes. Having these estimates of a CT scanners performance can further be used to one day quantitatively determine the minimum time interval needed in order to be able to distinguish malignant nodule volume growth from a stable lung nodule. Regularly performing these measurements also has the potential to offer numerous advantages to lung cancer screening sites including the ability to determine if scans from two different CT scanner models will produce sufficiently similar image quality and measurement performance.

      In summary, a new set of phantoms and cloud-based software tools is available that enables more careful control and optimization of CT lung cancer imaging performance based on fundamental image quality properties. These new tools provide several new opportunities for clinical sites to more precisely perform CT lung cancer imaging studies and measurements.

      References

      [1] Henschke CI, Yankelevitz DF, Yip R, Archer A, Zahlmann G, Krishnan K, Helba B, Avila R, “Tumor volume measurement error using computed tomography imaging in a phase II clinical trial in lung cancer.” Journal of Medical Imaging 3(3), 035505 (Jul–Sep 2016).

      [2] Avila R, Yankelevitz D, Yip R, Henschke C, “P1.03-021 Initial Results from A Novel and Low Cost Method For Measuring CT Image Quality,” January 2017. Journal of Thoracic Oncology 12(1):S554-S555.

      [3] Avila R, Subramaniam R, Henschke C, Yankelevitz D, “Hot Topic: Clinical Implications of CT Image Quality Variation in Low Dose Lung Cancer Screening Scans,” 4th World Congress of Thoracic Imaging Proceedings, Journal of Thoracic Imaging, accepted for oral presentation, 2017.

      [4] Avila R, Jirapatnakul A, Subramaniam R, Yankelevitz D, “A new method for predicting CT lung nodule volume measurement performance,” SPIE Medical Imaging Proceedings, 2017.

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      ES01.02 - Image Interpretation and Advances from the Perspective of the Radiologist (Now Available) (ID 11352)

      13:50 - 14:10  |  Presenting Author(s): David F Yankelevitz

      • Abstract
      • Presentation
      • Slides

      Abstract

      To a large extent the success of the screening process depends on the algorithm used to manage the findings. Therefore, even though the image production might be identical for two different screening programs, if they differ in the way they manage findings, especially nodules, the comparative results between them would be quite different. It is with this in mind that screening programs develop and choose their workup algorithm. In regard to how these algorithms are chosen, several features must be balanced including the rate of false positives and also potential delays in diagnosing lung cancer. Each of these is associated with a potential cost, the fewer the false positives might be associated with an increase in the number of cases where lung cancer diagnosis is delayed. As we have moved from 5 mm size thresholds to 8 mm size thresholds on the baseline scan we can clearly see how these factors are balanced against each other. The number of false positives dramatically declines as the number of cancers where diagnosis will be delayed by nine months will increase. While it is generally assumed that there is a substantial downside to delaying diagnosis, the challenge is understanding how much the delay actually costs in terms of decrease in the curability of the cancer and this is then considered in terms of how often this might occur.

      Another major feature of management protocols is their dependence on change in size over time to guide the management protocol. Different protocols apply different criteria for measuring change. One of the main differences is that some recommend the use of 3D volumetric analysis while others still rely on 2D measurements. As a general rule, the 3D approach has inherent advantages in that boundaries for the nodule are automatically chosen by the computer, asymmetric growth can be more easily recognized, and the proportional change for a given amount of change is far greater for volume then for diameter measurements. Nevertheless, there may still be circumstances where volume measures can still be misleading and the radiologist still has a very important role in visually inspecting the nodule to confirm whether change has occurred. Along with the measurement of size change, the time interval between measurements is also important in determining whether growth is meaningful. It is not simply enough to say that a nodule is growing, but rather the intent is to understand its growth rate, and this depends on time, with shorter time intervals between scans introducing greater uncertainty.

      There are currently many algorithms that have been developed. Some focus solely on screening such as Lung-RADS, while others are designed primarily for the incidentally detected nodules. Differences between the algorithms focus primarily on the size thresholds used to define a positive result, the time intervals between repeat scans, the choice of management for a positive finding, differences in the management of nodule subtypes (solid, part-solid, nonsolid), and differences between baseline rounds and repeat rounds. These different algorithms will be compared and data will be presented in terms of the influence on the rate of positive results. An additional consideration here is also how we define a positive result. Some algorithms define the positivity based on a size threshold, whereas others consider this based on a growth threshold or a combination of size and growth. When these growth thresholds are used, the rate of positive results dramatically decreases.

      In addition to the finding of lung nodules there are many other findings that commonly occur on the scans such as micro-nodules, areas of atelectasis, perifissural nodules, waxing and waning nodules, endobronchial nodules, presumed pneumonias, that might be found by the radiologist but there are no specific guidance rules for management. Here again the radiologist is confronted with the challenge of attempting to balance excess workup against obtaining a firm clinical diagnosis. While many of these examples have no authoritative guidelines as to how they should be managed, some practical guidance is presented.

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      ES01.03 - Deep Machine Learning for Screening LDCT (Now Available) (ID 11353)

      14:10 - 14:30  |  Presenting Author(s): Bram Van Ginneken

      • Abstract
      • Presentation
      • Slides

      Abstract

      The first computer algorithms to automatically detect pulmonary nodules in CT scans, based on classical machine learning approaches, were developed almost two decades ago. These systems appeared in commercially available computer-aided detection packages. However, a recent study concluded that such older software systems fail to flag a substantial number of cancerous lesions and have a fairly high false positive rate.

      Recently, algorithms based on deep learning, in particular, convolutional neural networks, have been developed that report high sensitivity with low false positive rates. Similar deep learning algorithms have been successful in classifying nodules as solid, subsolid or part-solid with accuracy comparable to radiologists, and in estimating the probability of malignancy of nodules.

      The 2017 Kaggle Data Science Bowl combined these tasks into a single challenge where 2000 teams developed methods to predict, on the basis of a single screening CT scan, whether a patient would be diagnosed with lung cancer within one year of the date of the scan. The 10 best performing solutions are now available under an open source license and form the basis of commercial solutions that show, in recent validation studies, a performance comparable to radiologists.

      Thorough validation studies are now needed to investigate if the good performance of these deep learning systems can be replicated, independent of CT parameters, and how such systems can be implemented in a lung cancer screening setting. Possibilities include the use of AI software as a second reader, as a concurrent reader, or even a stand-alone reader for a fraction of the cases, when widespread implementation of screening will put a too large burden on scarce radiological resources.

      In this lecture, I will review the currently available computer solutions and discuss their validation and integration into CT lung screening worksflows.

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      ES01.04 - Multi-Phasic Screening - Can We Address Competing Causes of Morbidity * Mortality Such as Coronary Artery Disease and COPD (Now Available) (ID 11355)

      14:30 - 14:50  |  Presenting Author(s): Rozemarijn Vliegenthart

      • Abstract
      • Presentation
      • Slides

      Abstract

      Lung cancer, chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) are highly prevalent in the Western population (annual incidences in the Netherlands: lung cancer n=12,200, COPD n=53,300, and CVD n=101,700). This results in a high burden on the health care system and associated costs, with annual costs of 10 billion euros in the Netherlands alone. Furthermore, lung cancer, COPD and CAD are responsible for a high burden of morbidity, with disability adjusted live year reduction of 2.9, 3.4 and 5.0, respectively. For these so-called Big-3 diseases, treatment is most often initiated at late stages due to late diagnosis after development of symptoms. Early detection and treatment will cure many patients in time, and delay or stop disease progression. Therefore, prevention and early detection are crucial.

      Currently, no screening is performed for the Big-3. The impact of low-dose computed tomography (CT) lung cancer screening on lung cancer stage shift and reduction of lung cancer mortality has been demonstrated.(1,2) These results have led to recommendations to implement CT screening in high-risk individuals in the USA. Population-based studies have shown the strong relationship between CT-derived extent of CAD and COPD, and mortality, also in lung cancer screening setting.(3-8) However, there is as yet no evidence from randomized controlled trials regarding benefit of CT screening for COPD or CAD. As the high-risk population for the Big-3 is comparable (namely long-term [ex-]smokers), combining imaging biomarkers will likely improve CT screening efficiency.

      Technological developments in CT allow the determination of early imaging biomarkers for the Big-3, namely lung nodule volume, coronary artery calcium score and lung density/ bronchial wall thickness with low-dose CT (see Figure). Combined evaluation of early signs of the Big-3 diseases has not been extensively explored yet. Major advantages of integrated Big-3 screening can be anticipated due to shared risk factors (in particular long-term smoking) and thus overlapping at-risk population, simultaneous presence of B3 diseases, and the health economic yield compared to a single disease. However, at this moment there is no single CT acquisition that allows for accurate assessment of all Big-3 biomarkers. In particular, calcium scoring based on low-dose chest CT, while providing a good correlation on a population basis, is inaccurate for determining the score on an individual basis.(9)

      biomarkers.jpg

      Furthermore, there are several challenges that need to be addressed in the preparation and establishment of a B3 screening program. These include the need for evidence of morbidity/mortality reduction for screening of COPD and CAD. Also, B3 imaging biomarkers, particularly for COPD, need validation and standardization. Another hurdle is the labour-intensive work required to obtain B3 imaging biomarkers. Also, education and training for evaluation of B3 CT screening examinations is lacking. Finally, the cost-efficiency of integral B3 screening has not been established.

      The presentation includes discussion of the background of interest in Big-3 screening, estimated health economic consequences of Big-3 screening, status of imaging biomarker development for the Big-3 diseases, screening population and CT scan protocol, and impediments to Big-3 screening implementation.

      References:

      1. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395–409.

      2. van Klaveren RJ, Oudkerk M, Prokop M, et al. Management of lung nodules detected by volume CT scanning. N Engl J Med 2009;361:2221-9.

      3. Oudkerk M, Stillman AE, Halliburton SS, et al; European Society of Cardiac Radiology; North American Society for Cardiovascular Imaging. 2008. Coronary artery calcium screening: current status and recommendations from the European Society of Cardiac Radiology and North American Society for Cardiovascular Imaging. Eur Radiol. 18:2785-807.

      4. Hecht HS, Cronin P, Blaha MJ, et al. 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: A report of the Society of Cardiovascular Computed Tomography and Society of Thoracic Radiology. J Cardiovasc Comput Tomogr. 2017;11:74-84.

      5. Mets OM, Vliegenthart R, Gondrie MJ, et al. Lung cancer screening CT-based prediction of cardiovascular events. JACC Cardiovasc Imaging. 2013;6:899-907.

      6. Oelsner EC, Smith BM, Hoffman EA, et al. Prognostic Significance of Large Airway Dimensions on Computed Tomography in the General Population. The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study. Ann Am Thorac Soc. 2018;15:718-27.

      7. Mets OM, Buckens CF, Zanen P, et al. Identification of chronic obstructive pulmonary disease in lung cancer screening computed tomographic scans. JAMA. 2011;306:1775-81.

      8. Oelsner EC, Carr JJ, Enright PL, et al. Per cent emphysema is associated with respiratory and lung cancer mortality in the general population: a cohort study. Thorax. 2016;71:624-32.

      9. Xie X, Zhao Y, de Bock GH, et al. Validation and prognosis of coronary artery calcium scoring in nontriggered thoracic computed tomography: systematic review and meta-analysis. Circ Cardiovasc Imaging. 2013 Jul;6(4):514-21.

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    GR01 - Thymic Malignancies Tumor Board (ID 777)

    • Event: WCLC 2018
    • Type: Grand Rounds Session
    • Track: Thymoma/Other Thoracic Malignancies
    • Presentations: 5
    • Now Available
    • Moderators:
    • Coordinates: 9/25/2018, 10:30 - 12:00, Room 206 F
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      GR01.01 - Pathology (Now Available) (ID 11386)

      10:30 - 10:45  |  Presenting Author(s): Anja C Roden

      • Abstract
      • Presentation
      • Slides

      Abstract

      The pathologist is an integral member of the thymic malignancies tumor board team as he/she provides information on diagnosis, tumor stage, completeness of resection, and potential performance of biomarkers which is necessary for the team to decide on the optimal treatment for each individual patient. Information that will be expected from the pathologist differs dependent on whether a preoperative biopsy, a resection specimen or a specimen from a recurrence/metastasis is discussed (Table 1).

      For preoperative biopsies it is important to establish a histologic diagnosis including thymoma, thymic carcinoma, thymic neuroendocrine tumor, or benign thymic gland. Morphologic mimickers of thymic epithelial tumors (TET) such as lymphoma or germ cell tumors also need to be considered. Thymoma are not further subtyped on biopsies given the potential heterogeneity of these tumors. Moreover, the subtype of a thymoma in general does not play a role for treatment decisions. In contrast, the subtype of a thymic carcinoma might be of value at the time of biopsy as some subtypes behave in a very aggressive manner and might be treated differently from a squamous cell carcinoma, the most common subtype of thymic carcinomas. For instance NUT carcinoma commonly have already metastasized at time of diagnosis(1). Similarly, SMARCA4-deficient tumors (even though not included in the current WHO) are highly aggressive tumors(2). Lymphoepithelioma-like carcinoma can also behave more aggressively(3). Although these subtypes of thymic carcinoma are quite rare, they should be kept in mind and the threshold of ordering ancillary tests such as immunohistochemical stains for NUT and/or BRG1 or an EBV in situ hybridization should be low. As some TET are unresectable, suggestions for biomarker testing might also be expected from the pathologist.

      If a resection specimen of a TET is discussed stage and resection status are critical as they are the most important prognostic parameters and guide additional treatment decisions. To stage TET the recently introduced 8th edition of the UICC/AJCC TNM should be used. Currently, in many centers, both, Masaoka-Koga stage(4) and TNM stage(5) are reported simultaneously as some treatment protocols are still based on the Masaoka-Koga stage. The histologic classification also plays a role especially if the TET was not previously biopsied (which is the most common scenario in thymoma). In resection specimens the histologic classification should include the distinction between thymoma, thymic carcinoma and thymic neuroendocrine tumor vs benign thymic gland (i.e., thymic follicular hyperplasia, true thymic hyperplasia) vs mimickers of TET. In resection specimens the thymoma should be further subtyped, which is usually performed according to the 2015 WHO classification.(6) If the patient underwent neoadjuvant therapy, a comment on treatment effect of the resection specimen might be made(7).

      Although uncommon, biopsies or resections of recurrences or metastases of TET are performed and are discussed during tumor board. Based on the type of specimen (biopsy vs resection specimen) similar issues as described above will be discussed. In addition, especially in resection specimens, the WHO subtype of the TET should be mentioned as it might differ from the original specimen.

      In conclusion, the pathologist will contribute important information in regards to histologic diagnosis, stage, completeness of resection and biomarker testing of TET to the tumor board discussion which will be crucial for further treatment decision.

      References:

      1. Bauer DE, Mitchell CM, Strait KM, Lathan CS, Stelow EB, Luer SC, et al. Clinicopathologic features and long-term outcomes of NUT midline carcinoma. Clin Canc Res. 2012;18(20):5773-9.

      2. Sauter JL, Graham RP, Larsen BT, Jenkins SM, Roden AC, Boland JM. SMARCA4-deficient thoracic sarcoma: a distinctive clinicopathological entity with undifferentiated rhabdoid morphology and aggressive behavior. Mod Pathol. 2017;30(10):1422-32.

      3. Gomez JMD, Syed G, Co MLF, Bayoumi M, Abrams R. A rare highly aggressive tumour: lymphoepithelioma-like thymic carcinoma. BMJ Case Rep. 2017;2017.

      4. Koga K, Matsuno Y, Noguchi M, Mukai K, Asamura H, Goya T, et al. A review of 79 thymomas: modification of staging system and reappraisal of conventional division into invasive and non-invasive thymoma. Pathol Int. 1994;44(5):359-67.

      5. Amin MB, American Joint Committee on Cancer., American Cancer Society. AJCC cancer staging manual. Eight edition / editor-in-chief, Mahul B. Amin, MD, FCAP ; editors, Stephen B. Edge, MD, FACS and 16 others ; Donna M. Gress, RHIT, CTR - Technical editor ; Laura R. Meyer, CAPM - Managing editor. ed. Chicago IL: American Joint Committee on Cancer, Springer; 2017. xvii, 1024 pages p.

      6. Travis WD, Brambilla E, Burke AP, Marx A, Nicholson AG. WHO Classification of tumours of the lung, pleura, thymus and heart. 4th ed. Lyon: International Agency for Research on Cancer; 2015.

      7. Johnson GB, Aubry MC, Yi ES, Koo CW, Jenkins SM, Garces YI, et al. Radiologic Response to Neoadjuvant Treatment Predicts Histologic Response in Thymic Epithelial Tumors. J Thorac Oncol. 2016.

      Table 1: Pathology discussion points at thymic malignancies tumor boards
      Time / type of specimen Important information from pathology
      Presurgical / initial biopsy

      Histologic subtype of thymic epithelial tumor

      Subtype of thymic carcinoma

      Mimicker of thymic epithelial tumor

      Biomarker testing

      Resection specimen

      Stage

      Resection status

      Thymic epithelial tumor histologic subtype including thymoma subtype

      Treatment effect

      Biomarker testing
      Recurrence / metastasis

      Histologic subtype of thymic epithelial tumor including thymoma subtype (especially if resection)

      Resection status if excision

      Biomarker testing

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      GR01.02 - Surgical Oncology (Now Available) (ID 11387)

      10:45 - 11:00  |  Presenting Author(s): Pier Luigi Filosso  |  Author(s): Enrico Ruffini, Francesco Guerrera, Paolo Olivo Lausi, Paraskevas Lyberis, Alberto Oliaro

      • Abstract
      • Presentation
      • Slides

      Abstract

      The treatment strategy for Thymic Epithelial Tumors (TETs) is based first on the tumour resectability. If complete resection is deemed feasible upfront, as it is the case in Masaoka-Koga stage I/II as well as for some stage III tumours, surgery represents the first step of the treatment, sometimes followed by postoperative radiotherapy and, less frequently, chemotherapy. Pretreatment biopsy is not always required, if the diagnosis of thymic tumour is highly probable and upfront surgical resection is achievable. Biopsy is therefore required in all other clinical situations: approaches may consist of percutaneous core-needle biopsy or incisional surgical biopsy through mediastinotomy or mini-thoracotomy. Pleural spaces should be respected to avoid tumour cell seeding. Fine-needle aspiration is generally not recommended.

      The standard approach is median sternotomy, which allows the complete opening of the mediastinum and both pleural cavities, and the evaluation of tumour macroscopic capsular invasion, infiltration of perithymic and mediastinal fat, peritumoural and pleural adherences and possible involvement of surrounding anatomical structures. Generally, complete thymectomy including the tumour, the residual thymus gland and perithymic fat is preferred because local recurrences have been sometimes observed after partial thymectomy, when a part of the thymus gland is left behind. However, thymomectomy alone is an option in stage I tumours and in non-myasthenic patients. If the tumour is widely invasive (stage III/IV), en bloc removal of all affected structures, including lung parenchyma (usually through limited resection), pericardium, great vessels, nerves and pleural implants, should be carried out. Resection of venous vascular structures (innominate vein(s) and superior vena cava) includes partial resection with suturing or complete resection and vessel reconstruction using vascular prosthesis. Areas of uncertain resection margins are marked with clips to allow a precise postoperative radiotherapy delivery.. Phrenic nerve preservation does not affect survival, but increases the risk of local recurrence, and should be balanced with the achievement of a complete resection, especially in patients with severe and uncompensated myasthenia gravis. Frozen sections to assess tumour involvement of resection margins are not always recommended, since the risk of false-negative results is high. Minimally invasive surgery is an option for presumed stage I and possibly stage II tumours in the hands of appropriately trained thoracic surgeons. This includes transcervical, extended transcervical, video-assisted thoracoscopy (VATS) and robotic approaches (right or left, right and left, right and cervical, left and cervical, subxiphoid and right and left, cervical and subxiphoid); furthermore, robotic surgery may allow a better visualisation of the tumour when compared with VATS. The choice for minimally invasive resection should not jeopardise or change the principles that are deemed appropriate for an open approach, especially the achievement of complete resection that may ultimately require switching to an open procedure. Minimally invasive surgery is not recommended for stage III tumours, because the lack of long-term follow-up. Lymphadenectomy has historically rarely performed after resection of thymic tumours. The new IASLC/ITMIG TNM staging system of thymic tumours, however, leads to the recommendation that locoregional lymphoadenectomy should be carried out during resection of all types of thymic tumours. A proposed nodal map is available from ITMIG. Routine removal of anterior mediastinal nodes and anterior cervical nodes is also recommended. Systematic sampling of other intrathoracic sites is encouraged (i.e. paratracheal, aortopulmonary window and subcarinal areas, depending on tumour location) in stage III/IV tumours. Systematic lymphadenectomy (N1 + N2) is strongly recommended in case of thymic carcinoma due to the high rate of lymphatic spread (20% versus 3% in thymomas)

      If complete resection is deemed not to be achievable upfront on the basis of imaging studies, as it is frequently the case in Masaoka-Koga stage III/IVA tumours, a biopsy should be carried out, followed by primary/induction chemotherapy as part of a curative-intent sequential strategy that integrates subsequent surgery or radiotherapy. Patients not eligible for local treatment should receive palliative chemotherapy only.

      Recurrences of thymic epithelial tumours are not uncommon (10%–15% of all-stage resected tumours) and should be managed according to the same strategy as newly diagnosed tumours. Complete resection of recurrent lesions represents a major predictor of favourable outcome, and surgery is then recommended in case of resectable lesions.

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      GR01.03 - Surgical Oncology (Now Available) (ID 11388)

      11:00 - 11:15  |  Presenting Author(s): David Waller

      • Abstract
      • Presentation
      • Slides

      Abstract

      In this section we will address the controversial areas of surgical management in the more advanced stages of thymoma.

      Using case-based discussion we will debate the following clinical scenarios :

      Stage III Thymic tumours ( with invasion of great vessels)

      Is there a role for primary surgical debulking leaving an intentional R2 resection ?

      There is little survival evidence to support intentional debulking but such procedures may reduce the dose and extent of radiotherapy subsequently required and therefore the associated morbidity [1]. However, there is a lack of supportive evidence for debulking surgery in thymic carcinoma. [2,3]

      Should primary treatment be chemoradiotherapy followed by consolidation surgery ?

      Induction therapy is feasible in locally advanced thymic tumours and has been reported to achieve around a 50% partial response. A complete pathological response has not been seen but such treatment can facilitate a high rate (over75%) of R0 resection.[4]

      Stage IVa Thymic tumours – pleural/pericardial deposits

      Is there a role for radical surgery ?

      The International Thymic Malignancies Interest Group have recommended that in locally-advanced Stage IVa patients with pleural involvement, major pleural resections, including pleurectomy/decortication or extrapleural pneumonectomy are indicated, provided a complete resection of the pleural deposits is anticipated, usually in a multidisciplinary setting [5]

      Should this be extrapleural pneumonectomy or thymectomy and extended pleurectomy/decortication ?

      As in other disease, extrapleural pneumonectomy (EPP) is associated with a high 30 day mortality of up to 17% (6). Providing a complete resection can be achieved there is no difference between EPP and extended pleurectomy decortication (EPD) (7] and median survival may exceed 4 years. The contribution of occult nodal metastases must be recognized and radical resection must include lymph node dissection.

      Stage migration due to lymph node metastases, WHO-classification type C, and T3/4-status are associated with inferior survival but extended surgery has been found to be the only independent significant prognosticator in multivariate analysis [8,9].

      Which surgical incision is best ?

      Radical resections can be facilitated by extended approaches which are well tolerated and adequate exposure is necessary to ensure a complete resection

      Recurrent thymic tumour – after previous resection

      Is there evidence that extending local control prolongs overall survival over systemic therapy alone ?

      Survival is acceptable and superior to non surgical treatment if complete resection of recurrence is achieved. There is no evidence to support debulking of recurrent thymoma [10]

      A significant poorer prognosis is associated with multiple versus single relapses, Masaoka stage III primary tumour versus Masaoka stage I-II primary tumour, distant versus loco-regional relapses and B3 histotype versus other. On multivariate analysis, completeness of resection, number of metastases, Masaoka stage of primary tumour and site of relapse were identified as the only independent predictors of prognosis [10]

      Conclusions

      The relative rarity of thymic neoplasms has contributed to the lack of high grade evidence from randomized controlled trials of large numbers of patients. Most supportive evidence for radical surgery in advanced thymic malignancies has therefore been provided by relatively small selected case series. However, the formation of larger collaborative groups with cumulative databases has provided more robust support for extended surgical procedures that many have avoided previously. The superiority of resection as part of multimodality treatment over non-surgical treatment alone seems to be justified provided high quality surgical standards are maintained.

      References

      1. Ried M et al. Extended surgical resections of advanced thymoma Masaoka stages III and IVa facilitate outcome. Thorac Cardiovasc Surg. (2014)

      2. Hamaji M et al A meta-analysis of debulking surgery versus surgical biopsy for unresectable thymoma.. Eur J Cardiothorac Surg. (2015)

      3. Attaran S et al , Does surgical debulking for advanced stages of thymoma improve survival? Interact Cardiovasc Thorac Surg. 2012 Sep;15(3):494-7

      4. Korst RJ, et a. lNeoadjuvant chemoradiotherapy for locally advanced thymic tumours: a phase II, multi-institutional clinical trial. J Thorac Cardiovasc Surg. 2014 Jan;147(1):36- 44

      5. Ruffini E et al, Optimal surgical approach to thymic malignancies: New trends challenging old dogmas. Lung Cancer. 2018 Apr;118:161-170.

      6. Fabre Det al.Long-term outcome of pleuropneumonectomy for Masaoka stage IVa thymoma. Eur J Cardiothorac Surg. 2011 ;39:e133-8

      7. Moser B et al, Surgical therapy of thymic tumours with pleural involvement: an ESTS Thymic Working Group Project. Eur J Cardiothorac Surg. 2017 Aug 1;52(2):346-355

      8. Kaba E et al, Role of Surgery in the Treatment of Masaoka Stage IVa Thymoma. Ann Thorac Cardiovasc Surg. 2018:20;24:6-12.

      9.. Bölükbas S et al, Extended thymectomy including lung-sparing pleurectomy for the treatment of thymic malignancies with pleural spread. Thorac Cardiovasc Surg. 2015 Apr;63(3):217-22.

      10. Dai J et al, Is it valuable and safe to perform reoperation for recurrent thymoma? Interact Cardiovasc Thorac Surg. 2015 Oct;21(4):526-31

      11. Marulli G et al, Surgical treatment of recurrent thymoma: is it worthwhile? Eur J Cardiothorac Surg. 2016 Jan;49(1):327-32

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      GR01.04 - Medical Oncology (Now Available) (ID 11389)

      11:15 - 11:30  |  Presenting Author(s): Nicolas Girard

      • Abstract
      • Presentation
      • Slides

      Abstract

      Thymic malignancies represent a heterogeneous group of cancers, which are classified according to the World Health Organization (WHO) histopathologic classification, that distinguishes thymomas from thymic carcinomas; thymomas are further subdivided into different types (so-called A, AB, B1, B2, and B3) based upon the relative proportion of the non-tumoral lymphocytic component, and the resemblance to normal thymic architecture. Thymic carcinomas are similar to their extra-thymic counterpart, the most frequent subtype being squamous cell carcinoma. Neuroendocrine tumors may occur in the thymus, for which the management is similar to that of advanced neuroendocrine tumors originating from other anatomic locations.

      The management of thymic epithelial tumors is a paradigm of cooperation between clinicians, surgeons, and pathologists from establishing the diagnosis to organizing the multimodal therapeutic strategy. Data related to the systemic treatment of thymic malignancies are mostly based on non-randomized studies, retrospective data, and recommendations rely on expert opinion; this is related to the rarity of the disease, precluding large clinical trials to be developed.

      Systemic treatment may be delivered in a curative-intent approach, for patients presenting with locally-advanced tumor at time of diagnosis, with invasion of intra-thoracic neighboring structures, and/or dissemination to the pleura and the pericardium, precluding upfront complete resection to be achieved. In such cases, chemotherapy has been used both to reduce the tumor burden - possibly allowing subsequent surgery and/or radiotherapy- and to achieve prolonged disease control. In this setting, cisplatin-based combination regimens should be administered; combinations of cisplatin, adriamycin, and cyclophosphamide, and cisplatin and etoposide have been recommended, based on historical studies.

      When the patient is not deemed to be a surgical candidate - either because R0 resection is not thought to be achievable, or because of poor performance status or co-existent medical condition, definitive radiotherapy is recommended part of a sequential chemoradiotherapy strategy. Combination with chemotherapy may be considered as well.

      Chemotherapy is also a palliative-intent treatment of unresectable, metastatic, and recurrent tumors, which are more frequently thymic carcinomas than thymomas. Again, cisplatin-based combination regimens with anthracyclins and/or etoposide are standard. No randomized studies have been conducted, and it is unclear which regimens are best; multi-agent combination regimens and anthracycline-based regimens appear to have improved response rates compared to others, especially the etoposide, ifosfamide and cisplatin combination; however, the effect of corticosteroids to deplete the lymphocytic component of thymomas, without any antitumor effect, may significantly impact radiologic response assessment, and hamper comparisons between chemotherapy regimens. Combination of carboplatin and paclitaxel is an option for thymic carcinoma, based on results of recent phase II trials.

      Recurrences of thymic epithelial tumors should be managed according to the same strategy as newly diagnosed tumors. In non-resectable recurrences, several consecutive lines of chemotherapy may be administered when the patient presents with tumor progression. The re-administration of a previously effective regimen has to be considered, especially in case of previous response, late occurring recurrence. Preferred regimens for second-line treatment include carboplatin plus paclitaxel, and platin plus etoposide; capecitabine plus gemcitabine is an option. These regimens were evaluated in dedicated phase II trials. Options for subsequent lines include pemetrexed, oral etoposide. Sunitinib is an off-label option in the second-line setting, based on its antiangiogenic activity. Everolimus may be another option for refractory disease.

      Several trials assessing the efficacy of PD-1 checkpoint inhibitors are currently ongoing. Phase II studies of pembrolizumab were recently reported, collectively enrolling 63 patients, showing response rates of 24%, but occurrence of serious, autoimmune adverse events in 20% to 30% of patients. The off-label use of checkpoint inhibitors is currently not recommended.

      The management of patients requires continuous multidisciplinary expertise at any step of the disease. A dramatic improvement in our knowledge has occurred in the last few years, through the development of databases, translational research programs, and clinical trials. While access to innovative strategies represents a major challenge, as the rarity of the tumor precludes specific approval of drugs to be obtained, patient-centered initiatives, such as the establishment of dedicated networks to provide expertise for the actual management of patients, are warranted.

      In France, RYTHMIC (Réseau tumeurs THYMiques et Cancer; www.rythmic.org) is a nationwide network for thymic malignancies, which was appointed in 2012 by the French National Cancer Institute, as part of its rare cancer program. Since then, the management of all patients diagnosed with thymic tumors has been discussed on a real-time basis at a national multidisciplinary tumor board (MTB), which is organized twice a month basis using a web-based conferencing system. Decision-making is based on consensual recommendations, that were originally established based on available evidence, and are updated and approved each year by all members of the network. A prospective database of all patients is hosted by the French Thoracic Cancer Intergroup. Overall, more than 2,500 patients have been enrolled, demonstrating the feasibility of a national MTB for thymic malignancies, that, besides ensuring patients an equal access to highly specialized management, provides with a comprehensive tool to monitor dedicated actions to improve the management of patients, and enroll patients in clinical trials. Similar thymoma-dedicated and mesothelioma-dedicated networks are now being implemented in France and in other European countries, such as Spain and Italy (the TYME collaborative group).

      Within the European Reference Network EURACAN, the rare thoracic tumor domain – referred as to G8 domain - handles a network of 20+ healthcare providers; the objectives of EURACAN include the updating and the assessment of current guidelines, the development of educational programs, dissemination and communication with patients groups, and the establishment of research projects, from the diagnosis workup of the disease to the therapeutic strategies. Achieving the highest quality of patient care is the main objective of EURACAN, and the RYTHMIC model provides some practical tools to be implemented at the European level, including Clinical Patient Management System. The European network also provides an infrastructure for collaboration with diagnosis and pharmaceutical companies; one example may be the opening of dedicated cohorts in basket studies assessing new drugs, for which the network allows a better identification of patients and facilitates the recruitment in the trials.

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      GR01.05 - Radiation Oncology (Now Available) (ID 11390)

      11:30 - 11:45  |  Presenting Author(s): Anthony Brade

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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    MA20 - Implementation of Lung Cancer Screening (ID 923)

    • Event: WCLC 2018
    • Type: Mini Oral Abstract Session
    • Track: Screening and Early Detection
    • Presentations: 11
    • Now Available
    • Moderators:
    • Coordinates: 9/25/2018, 15:15 - 16:45, Room 206 F
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      MA20.01 - Lung Cancer Screenee Selection by USPSTF versus PLCOm2012 Criteria – Preliminary ILST Findings (Now Available) (ID 14331)

      15:15 - 15:20  |  Presenting Author(s): Martin Tammemägi  |  Author(s): Renelle L Myers, Mamta Ruparel, Niloofar Taghizadeh, Sukhinder Atkar-Khattra, Jennifer Dickson, Samantha Quaife, Angshu Bhowmik, Paul Burrowes, Paul Maceachern, Eric Bedard, John Yee, John Mayo, Jing Liu, Kwun M Fong, Alain Tremblay, Sam M Janes, Stephen Lam

      • Abstract
      • Presentation
      • Slides

      Background

      Background

      The National Lung Screening Trial showed that lung cancer screening of high-risk individuals with low dose computed tomography can reduce lung cancer mortality by 20%. Critically important is enrolling high-risk individuals. Most current guidelines including the United States Preventive Services Task Force (USPSTF) and Center for Medicare and Medicaid Services (CMS) recommend screening using variants of the NLST eligibility criteria: smoking ≥30 pack-years, smoking within 15 years, and age 55-80 and 55-77 years. Many studies indicate that using accurate risk prediction models is superior for selecting individuals for screening, but these findings are based on retrospective analyses. The International Lung Screen Trial(ILST) was implemented to prospectively identify which approach is superior.

      Method

      Methods

      ILST is a multi-centred trial enrolling 4000 participants. Individuals will be offered screening if they are USPSTF criteria positive or have PLCOm2012 model 6-year risk ≥1.5%. Participants will receive two annual screens and will be followed for six years for lung cancer outcomes. Individuals not qualifying by either criteria will not be offered screening, but samples of them will be followed for lung cancer outcomes. Outcomes in discordant groups, USPSTF+ve/PLCOm2012-ve and USPSTF-ve/PLCOm2012+ve, are informative. Numbers of lung cancers, abnormal suspicious for lung cancer scans (a marker of future lung cancers) and individuals enrolled, and sensitivity and specificity and positive predictive values of the two criteria will be compared.

      Result

      Results

      As of March 2018, ILST centers in Canada (British Columbia and Alberta), Australia, and the United Kingdom had enrolled and scanned 1938 individuals. Study results are summarized in Figure 1.

      fig1.jpg

      Conclusion

      Conclusion

      Interim analysis of ILST data, suggests that classification accuracy of lung cancer screening outcomes support the PLCOm2012 criteria over the USPSTF criteria. Individuals who are USPSTF+ve and PLCOm2012-ve appear to be at such low baseline risk (0.46%) that they may be unlikely to benefit from screening.

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      MA20.02 - “Reduced” HUNT Lung Cancer Model for Predicting Lung Cancer in the Prospective Danish Lung Cancer Screening Study - Comparison with the NLST. (Now Available) (ID 12802)

      15:20 - 15:25  |  Presenting Author(s): Oluf Dimitri Røe  |  Author(s): Maria Markaki, Ioannis Tsmardinos, Vincenzo Lagani, Jesper Holst Pedersen, Zaigham Saghir, Haseem Ashraf

      • Abstract
      • Presentation
      • Slides

      Background

      Risk prediction is important for selection of individuals for lung cancer screening programs. We have recently published a validated calculator, the HUNT Lung Cancer Risk Model (https://www.ebiomedicine.com/article/S2352-3964(18)30114-2/fulltext) for all ages and smoking patterns.

      The Danish Lung Cancer Screening Trial (DLCST) was a randomized prospective study that included 4104 heavy smokers with a median age of 57.6, 33.8 pack-years and maximum 9 years quit time and 10 years follow-up. We tested the value of the HUNT model in this prospective Danish lung cancer study and compared with the NLST.

      Method

      The DLCST study only had 5 of the 7 variables in the original HUNT Model, so we trained a “reduced” HUNT Model in the Norwegian HUNT2 cohort of 12 091 ever-smokers ages between 49-71 years (as the age span in the Danish cohort) based on age, pack-years, smoking intensity, quit time and BMI where sex was added for adjustment. The model was applied blindly in the 4051 individuals of the Danish cohort that had all 5 variables.

      Result

      In the population selected by the "reduced" HUNT Model, 148/149 (99.33%) lung cancer cases were predicted (sensitivity 99.33%, negative predictive value 99.23%), thus only one individual that developed lung cancer was lost among those 52 not selected for screening. If the the NLST criteria were used (age 55-74, >30 pack-years and <15 years quit time), less than half of the Danish cohort (n=1870 (46.2%)) would have been considered eligible for screening, and 104/149 (69.80%) lung cancer cases would have been predicted. With these criteria, one would lose 45 (32.7%) lung cancer cases, and the sensitivity would be lower (69.80%).

      Conclusion

      We were able to predict 99.33% of those that were diagnosed with lung cancer in 10 years, only one future lung cancer case was not included. Therefore, even the “reduced” HUNT model was highly sensitive in selecting persons at high risk for lung cancer in a screening cohort. The objection one could have for preferring the NLST criteria is that one screened about half of this population but at the same time lost 1/3 of the future lung cancers. In a health system that can afford to screen more people than those included by the NLST criteria, the HUNT model may be useful, preferably the validated HUNT Lung Cancer Model, for the selection of high risk individuals to a screening programme.

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      MA20.03 - Surprising Implications of Proposed Risk-Thresholds for Selecting US Ever-Smokers into CT Lung-Cancer Screening (Now Available) (ID 13834)

      15:25 - 15:30  |  Presenting Author(s): Hormuzd Katki  |  Author(s): Christine Berg, Anil Chaturvedi, Rebecca Landy, Hilary Robbins, Li C Cheung

      • Abstract
      • Presentation
      • Slides

      Background

      Many studies show that using risk-models to select ever-smokers for screening may be more effective and efficient than current US Preventive Service Task Force (USPSTF) guidelines. Current National Comprehensive Cancer Network (NCCN) guidelines permit screening ever-smokers with at least 1.3% 6-year lung-cancer risk. Here we re-evaluate assertions, originally based on pre-2015 or non-representative data, that currently proposed risk-thresholds would screen no more ever-smokers than current USPSTF guidelines and at higher effectiveness and efficiency.

      Method

      Using the 2015 US National Health Interview Survey (NHIS), we estimate the number of ever-smokers eligible for lung-cancer screening according to 3 lung-cancer risk-thresholds: 1.3% (NCCN) and 1.51% 6-year risks by the PLCOM2012 risk-model and 1.9% 5-year risk by the Lung Cancer Risk Assessment Tool (LCRAT). The NCCN and 1.51% thresholds were based on data from the Prostate Lung Colorectal and Ovarian (PLCO) cancer screening trial, a non-representative sample of US ever-smokers 1993-2001. The 1.9% threshold was based on pre-2015 but representative data (NHIS 2010-2012). Using previously published methodology to estimate 5-year outcomes following 3 annual CT lung-screens, we calculate screening effectiveness (the number needed to screen (NNS) to prevent 1 lung-cancer death) and efficiency (the number of false-positive CT screens per prevented lung-cancer death).

      Result

      8.0M US ever-smokers were eligible for lung-screening by USPSTF guidelines in 2015. Surprisingly, millions more were eligible according to risk-thresholds: 12.6M, 11.3M, and 9.2M ever-smokers were eligible at the 1.3% (NCCN), 1.51%, and 1.9% thresholds, respectively. We estimated effectiveness by USPSTF guidelines as NNS=194, which worsened to 222 and 207 for the 1.3% (NCCN) and 1.51% thresholds respectively, but improved to 172 for the 1.9% threshold. We estimated screening efficiency by USPSTF guidelines as 133 false-positives per prevented death, which worsened to 150 and 141 for the 1.3% (NCCN) and 1.51% thresholds respectively, but improved to 122 for the 1.9% threshold. The PLCOm2012 risk threshold that would select 8.0M 2015 ever-smokers is substantially higher than NCCN guidelines (2.28% vs. 1.3% 6-year risk).

      Conclusion

      Compared to current USPSTF guidelines, the 1.3% (NCCN) and 1.51% risk thresholds (6-year risks by PLCOm2012) would screen many millions more US ever-smokers and likely at lower effectiveness and efficiency. Although the 1.9% threshold (5-year risk by LCRAT) also chose more ever-smokers than USPSTF guidelines, it may screen them more effectively and efficiently. Our findings demonstrate that risk-thresholds developed using older or non-representative data should be re-evaluated using current and representative data.

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      MA20.04 - Discussant - MA 20.01, MA 20.02, MA 20.03 (Now Available) (ID 14633)

      15:30 - 15:45  |  Presenting Author(s): Amanda Tufman

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MA20.05 - Who Gets Screened for Lung Cancer? A Simple Adjustment to Current Guidelines to Reduce Racial Disparities (Now Available) (ID 13992)

      15:45 - 15:50  |  Presenting Author(s): Melinda Aldrich  |  Author(s): Sarah Mercaldo, Kim L. Sandler, William J Blot, Eric Grogan, Jeffrey Blume

      • Abstract
      • Presentation
      • Slides

      Background

      Background. The United States Preventive Services Task Force (USPSTF) recommends low-dose computed tomography screening for lung cancer in current or former smokers age 55-80 years with a minimum 30 pack-year history and having quit no more than 15 years ago. However, these guidelines were developed in the predominantly white population of the National Lung Screening Trial and therefore may not be generalizable to African Americans who have different smoking patterns. We evaluated USPSTF lung cancer screening guidelines in a predominantly African American prospective cohort with an elevated incidence of lung cancer.

      Method

      Methods. The Southern Community Cohort Study (SCCS) is a prospective observational cohort of approximately 86,000 adults (two-thirds African American) aged 40-79 years enrolled primarily at community health centers from 2002-2009 across 12 Southern U.S. states. Former and current smokers were included in the present study and were followed for up to 9 years. We examined the impact of race and smoking history on eligibility for lung cancer screening using USPSTF guidelines.

      Result

      Results. Among N=50,524 adult (67% African American, 33% white) ever smokers at baseline (64% current smokers, median age 50 years at enrollment) in the SCCS, we identified 1,359 incident lung cancers. Among lung cancer patients, 32% of African Americans were eligible for screening following USPTSF criteria compared with 55% of whites (p<0.001). The lower percentage of eligible African Americans was primarily due to African Americans having smoked fewer pack-years than whites (14 vs 27 median pack-years, respectively, p<0.001). Lowering the smoking pack-year eligibility criteria to a minimum 20-29 pack-year history, increased the number of African Americans eligible for screening by 38%. With a lower smoking pack-year criterion for African Americans, sensitivity increased from 32% to 50% and specificity decreased from 87% to 78% yielding sensitivity and specificity values that were similar to whites (55% sensitivity, 75% specificity) using the USPSTF guidelines.

      Conclusion

      Conclusion. Current lung cancer screening guidelines are too conservative for African Americans. A greater percentage of African Americans are excluded from screening opportunities primarily due to lower smoking histories. Adjustment of pack-years in lung cancer screening guidelines by race will result in more equitable screening for smokers at high risk for lung cancer.

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      MA20.06 - Lung Cancer Screening Pilot for People at High Risk: Early Results on Cancer Detection and Staging (Now Available) (ID 13890)

      15:50 - 15:55  |  Presenting Author(s): Martin Tammemägi  |  Author(s): Gail Elizabeth Darling, Heidi Schmidt, Monica Yu, Marianne Luettschwager, Bogdan Pylypenko, Tanvi Patel, Veronica Rivera, Katharine Pearson, Katie Larson

      • Abstract
      • Presentation
      • Slides

      Background

      In June 2017, Cancer Care Ontario initiated organized lung cancer screening for people at high risk of developing lung cancer, using annual low-dose computed tomography (LDCT), at three pilot sites in Ontario. A key indicator of pilot success is detection of lung cancers at early stages. Ontario Cancer Registry (OCR) is used to track lung cancer diagnosis, stage and histology.

      Method

      Patient abstracts were created using Registry Plus CDC abstracting software for pilot participants and patient-level data were collected from hospital data submissions, hospital electronic medical records via remote access, OCR pathology database (eMaRC) and OCR clinical source records (Resolink). Confirmed lung cancer cases were reviewed by a team of cancer staging analysts to achieve consensus on stage group using AJCC TNM 8th edition. A post-staging review was conducted for all staged cases to ensure accuracy and completeness.

      Result

      As of February 2018, 1086 participants received a baseline LDCT scan. 37% (n=404) of participants had Lung-RADS™ scores of 1; 45% (n=487) had Lung-RADS™ scores of 2; 10% (n=112) had Lung-RADS™ scores of 3; and 8% (n=83) had Lung-RADS™ scores of 4A, 4B or 4X, which triggered additional follow-up or diagnostic workup. 18 lung cancers were confirmed and 11 were fully staged.

      Of the 11 staged cases: 45% (n=5) was stage I; 9% (n=1) stage II; 9% (n=1) stage III; and 36% (n=4) stage IV. This represents a statistically significant increase in the proportion of early stage lung cancers (stage I and II) compared to historical proportions (p<0.05). 73% (n=8) were adenocarcinoma. The median risk score (i.e., PLCOm2012 risk prediction model probability of developing lung cancer in 6 years) was 8.1%, considerably higher than the median risk score of the overall pilot cohort (2.9%). 82% (n=9) had baseline Lung-RADS™ scores of 4X and 18% (n=2) had 4B. The average age at diagnosis was 67. 45% (n=5) were male; 55% (n=6) were current smokers; and 55% (n=6) had high school education or less. In addition, the screening pilot facilitated the successful transition by the OCR from AJCC TNM 7th to TNM 8th edition in lung cancer staging. Results will be updated in the conference presentation.

      Conclusion

      Early pilot results demonstrate success in detecting early stage lung cancers and a statistically significant stage shift to earlier cancer stages. We anticipate a greater proportion of early stage lung cancers on annual recall LDCT scans. The OCR efficiently enabled capturing important incidence, staging and histological pilot data.

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      MA20.07 - Lung Cancer Screening for Limited-Resource Patients: Preliminary Findings from a Low-dose CT Pilot Program (Now Available) (ID 13910)

      15:55 - 16:00  |  Presenting Author(s): Lesley Watson  |  Author(s): Megan May Cotter, Robert A. Smith, Katherine Sharpe

      • Abstract
      • Presentation
      • Slides

      Background

      Low-dose CT (LDCT) screening for lung cancer in adults at high-risk is associated with a reduction in lung cancer mortality in high-risk adults, yet screening rates remain low. Increasing access to high-quality lung cancer screening is critical to further reducing deaths from the disease. In 2015 the American Cancer Society implemented a pilot program to identify successful strategies for improving access to LDCT in Memphis, Tennessee and Charleston, West Virginia through partnerships with Federally Qualified Health Centers (FQHCs) that serve limited resource patients. The program focused on developing systems within FQHCs to identify and refer patients for LDCT, and helping FQHCs build relationships with local accredited screening facilities to deliver lung cancer screening and navigate patients through the screening process and any necessary follow-up. This program is novel because it brings emerging technology in lung cancer screening and early detection to low-resource FQHCs that typically do not have access to state-of-the-art interventions. As such, the pilot affords important opportunities to identify facilitators and barriers to conducting LDCT in under-resourced areas. This presentation focuses on evaluation results for the program to-date, emphasizing barriers to implementation experienced by FQHCs and their screening partners.

      Method

      Participating FQHCs submitted quarterly monitoring reports tracking the number of: patients assessed for LDCT eligibility, shared decision-making (SDM) visits, patients screened, and screening results. Evaluators conducted site visits and stakeholder interviews with staff from FQHCs and their screening partners in summer 2017 and 2018 to capture nuanced information about program implementation.

      Result

      Participating FQHCs conducted 387 SDM discussions and have screened 252 patients to date. Participants expressed uncertainty about the definition and process of SDM, and difficulty with tracking and billing for these patient-provider encounters. During the project period, both sites established processes for follow-up screening and referrals based on initial screening results (LRADs 1-4). Interview data provided insight into the major challenges and successes to piloting and implementing a new protocol. Both sites struggled to agree on the correct follow-up for LRADS 3 and 4 patients. Through piloting and discussion with clinic leadership, one site successfully implemented clear, logical follow-up procedures based on staff capacity and clinical guidelines.

      Conclusion

      Our evaluation findings, including key lessons learned and recommendations, add to the growing knowledge base of effective lung cancer screening practices and may be used to inform and guide health systems looking to initiate similar programs, particularly those in low-resource settings.

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      MA20.08 - Discussant - MA 20.05, MA 20.06, MA 20.07 (Now Available) (ID 14634)

      16:00 - 16:15  |  Presenting Author(s): Annette Maree McWilliams

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MA20.09 - Improved Lung Cancer and Mortality Prediction Accuracy Using Survival Models Based on Semi-Automatic CT Image Measurements (Now Available) (ID 12074)

      16:15 - 16:20  |  Presenting Author(s): Anton Schreuder  |  Author(s): Colin Jacobs, Nikolas Lessmann, Ernst Scholten, Ivana Isgum, Mathias Prokop, Cornelia Schaefer-Prokop, Bram Van Ginneken

      • Abstract
      • Presentation
      • Slides

      Background

      In a lung cancer CT screening setting, imaging biomarkers are typically extracted by experienced human readers. We found that adding semi-automatic computer-aided detection (CAD) measurements to a base model significantly improved lung cancer and mortality risk prediction accuracy.

      Method

      Participants’ baseline CT scans, characteristics, and 7-year follow-up outcomes were obtained from the National Lung Screening Trial. The selection included all 1810 deceased and a random selection of 4190 surviving participants from the CT screening arm with an image available; the latter subcohort was sampled with replacement up to 24432 to approximate the full CT arm. Seventeen patient characteristics variables endorsed by literature were considered for each model. CAD was used to automatically measure normalized emphysema score, coronary calcium volume, and thoracic aorta calcium volume. Pulmonary nodule consistency, volume, solid core volume (if part-solid), and upper lobe location were annotated by an experienced radiologist with CAD support. Only the largest noncalcified nodule was considered for the model; having no nodules was the reference.

      Cox proportional hazard regression was performed on patient characteristics variables only (base model) and combined with CAD variables (new model). This was done for three outcomes: lung cancer diagnosis, lung cancer mortality, and overall mortality. The average continuous net reclassification improvements (NRI) between the base and new models were calculated for each year following the baseline scan. To calculate NRI, the net percentages of subjects with and without the event of interest correctly reclassified as high and low risk, respectively, are summed (maximum range: -2 to 2); positive scores indicate that the new model is more accurate.

      Result

      CAD measures were successfully computed for 5575 baseline scans. After sampling, the test cohort consisted of 24370 participants. 3.9% were diagnosed with lung cancer (940/24370) and 6.9% died (1681/24370), of which 24.9% due to lung cancer (418/1681). For all outcomes, the new models were significantly superior to the base model. With lung cancer diagnosis as the outcome, the NRI at 1, 4, and 7 years follow-up were 0.628 (95% confidence interval: 0.373–0.700), 0.331 (0.261–0.390), and 0.349 (0.293–0.389), respectively. The respective NRIs were 0.501 (0.290–0.642), 0.288 (0.221–0.374), and 0.255 (0.218–0.339) when predicting lung cancer mortality and 0.496 (0.295–0.610), 0.301 (0.239–0.376), and 0.270 (0.201–0.320) when predicting overall mortality.

      Conclusion

      CAD measures of emphysema and atherosclerosis and CAD-supported pulmonary nodule annotations are of added value for predicting lung cancer and mortality. These new models may be used to further personalize lung cancer CT screening follow-up protocols.

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      • Abstract
      • Presentation
      • Slides

      Background

      Artificial Intelligence software has shown promise in predicting malignancy in indeterminate CT detected pulmonary nodules. This study aimed to assess the accuracy of a convolutional neural network (CNN) based lung cancer prediction software on an independent dataset of indeterminate incidentally detected nodules in a retrospective European multicentre trial.

      Method

      The software was trained using the US National Lung Screening Trial (NLST) dataset which was manually curated, such that each reported nodule and cancer was located, contoured and diagnostically characterised (9310 benign nodule patients; 1058 cancer patients). From this complete dataset, a training set was built by selecting all patients with solid and part-solid lesions of 6mm and above, where benign nodules and cancers could be confidently identified by clinicians (5972 patients, of which 575 were cancer patients). A CNN classifier was trained using Deep Learning on this data to produce a malignancy score per nodule. We defined a benign nodule rule-out test by calculating thresholds on the malignancy score that achieve 100% and 99.5% sensitivity on the NLST data.

      The study was set up so that a malignancy score for each nodule was generated. Overall performance was evaluated using Area-Under-the-ROC-Curve analysis (AUC) and rule-out performance measured the specificity at the two thresholds, i.e. the proportion of benign nodules correctly stratified at each threshold.

      There were 2201 nodules, measuring between 5-15mm from 1719 patients from three tertiary referral centres in the UK, Germany and Netherlands. The CT data included heterogeneous scan parameters, scanner manufacturers and clinical indications. Diagnostic ground-truth was established according to Fleischner or British Thoracic Society guidelines. The dataset contained 222 unique cancers from 215 patients.

      Result

      AUC on all-site data was 0.92 (95%CI = 0.89-0.93) and broken down per-site the AUC was 0.97 (Netherlands, n=883, 26 cancers), 0.93 (UK, n= 698, 51 cancers), and 0.84 (Germany, 620, 145 cancers).

      The score thresholds used for the target sensitivity of 100% and 99.5% were the same and achieved an overall sensitivity on the data of 99.1% with a specificity of 25.0%. Per-site results were 25.6% (Netherlands), 27.8% (UK) and 20.6% (Germany) specificity with 100%, 100% and 98.6% sensitivity respectively.

      Conclusion

      Performance of the AI software on independent European multicentre data was comparable to that achieved on the NLST training data, although there was some variability in the performance of the system across the three centres, potentially providing an opportunity for further optimisation.

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      MA20.12 - Discussant - MA 20.09, MA 20.10 (Now Available) (ID 14635)

      16:25 - 16:40  |  Presenting Author(s): Heidi Schmidt

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      Abstract not provided

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    MS01 - Cancer Pathways, Targeted Therapy and Resistance (ID 780)

    • Event: WCLC 2018
    • Type: Mini Symposium
    • Track: Biology
    • Presentations: 4
    • Now Available
    • Moderators:
    • Coordinates: 9/24/2018, 10:30 - 12:00, Room 206 F
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      MS01.01 - Defects of the SWI/SNF OR MYC/MAX Pathways: Effects in Cell Differentiation and Therapeutic Opportunities (Now Available) (ID 11401)

      10:30 - 10:50  |  Presenting Author(s): Montse Sanchez-Cespedes

      • Abstract
      • Presentation
      • Slides

      Abstract

      The SWI/SNF complexes are ATP-dependent remodelers of the chromatin structure, by disrupting of DNA–histone interactions to activate or repress gene expression (Wilson et al. 2011). In healthy adults and during embryonic development, the complex is involved in the control of cell differentiation and in the specification of different tissues. Components of the SWI/SNF complex bind to various nuclear receptors, such as those of estrogen, progesterone, androgen, glucocorticoids and retinoic acid, thereby adapting the gene expression programs to the demands of the cell environmental requirements. The effect of the SWI/SNF complex on some of these processes is, at least in part, related to its involvement in regulating hormone-responsive promoters (reviewed Romero et al. 2014).

      A few years ago, we discovered that, in lung cancer, the SWI/SNF component, SMARCA4 (also called BRG1), is genetically inactivated in about thirty per cent of non-small cell lung cancers (NSCLC), and that its inactivation occurs in a background of wild type MYC (Medina et al. 2008). Nowadays, it is well established that other components of the complex are also commonly inactivated in most cancer types, including lung cancer (reviewed in Romero et al. 2014). Gene alterations of the SWI/SNF complex are significantly more common in NSCLC, as compared to small cell lung cancers (SCLC), and tend to associated with smoking habit. In addition, we reported the presence of tumor-specific inactivation of the MYC-associated factor X gene, MAX, in about ten percent of SCLC (Romero et al. 2014). The two events are mutually exclusive among them and with alterations at the MYC-family of genes. We also demonstrated that SMARCA4 regulates the expression of MAX and that depletion of SMARCA4 specifically in MAX-deficient cells strongly decreased cell growth, heralding a synthetic lethal interaction with potential therapeutic implications. Furthermore, MAX required of SMARCA4 to activate neuroendocrine transcriptional programs and to up-regulate MYC-targets, such as glycolytic-related genes. Finally, we observed genetic inactivation of the MAX dimerization protein, MGA, in lung cancers with wild type components of the SWI/SNF or MYC pathways.

      The widespread occurrence of alterations at genes encoding different components of the SWI/SNF complex reveals an important new feature that sustains cancer development. Retinoic acid (RA) and glucorticoids (GC) are well known modulators of cell differentiation, embryonic development and morphogenesis. GCs and RA are part of the curative treatment of some malignancies, mostly leukemias (Collins et al. 2002; Rutz et al. 2002; Pottier et al. 2008). However, most solid tumors, including lung cancers, are refractory to GC- and RA-based therapies. Underlying some cases of refractoriness to GC and RA is a dysfunctional SWI/SNF complex, for example due to alterations at SMARCA4 (Romero et al. 2002). On the other hand, compounds that modulate the structure of the chromatin are currently used to treat cancer. These include histone deacetylase (HDAC) inhibitors, in hematological malignancies and cutaneous T-cell lymphomas, and inhibitors of DNA methylation such as azacytidine for myelodysplasic syndrome (Liu et al. 2013). HDACs and DNA methylation inhibitors promote gene transcription by increasing DNA accessibility through the inhibition of histone deacetylation and DNA methylation, respectively. In a preliminary study, these drugs, in combination, have shown promising results in the treatment of lung cancer patients. In lung cancer cell lines, we observed that GC plus RA (GC/RA) in combination with the epigenetic drugs azacytidine and SAHA (A/S) reduced growth, triggered pro-differentiation gene expression signatures and downregulated MYC, in MYC-amplified but not in most SMARCA4-mutant cells (Romero et al. 2017). In vivo, treatments with GC/RA improved overall survival of mice implanted with MYC-amplified cells and reduced tumor-cell viability and cell proliferation. We also found some effect of the SAHA treatment, alone in reducing the cell growth of MYC-amplified lung cancer cells but not those that are SMARCA4-deficient. Thus, we propose that the combination of retinoids, corticoids and epigenetic treatments of lung tumors with MYC amplification constitute a strategy for therapeutic intervention in this otherwise incurable disease.

      Altogether, the genetic observations coupled with the functional evidence demonstrate that an aberrant SWI/SNF-MYC network is essential for lung cancer development and open novel therapeutic possibilities for the treatment of lung cancer patients.

      REFERENCES

      Collins SJ. The role of retinoids and retinoic acid receptors in normal hematopoiesis. Leukemia 2002; 16, 1896–905.

      Liu SV, Fabbri M, Gitlitz BJ, Laird-Offringa IA. Epigenetic therapy in lung cancer. Front Oncol 2013; 3, 135.

      Medina PP et al. Frequent BRG1/SMARCA4-inactivating mutations in human lung cancer cell lines. Hum Mut 2008; 29, 617-22a.

      Pottier N et al. The SWI/SNF chromatin-remodeling complex and glucocorticoid resistance in acute lymphoblastic leukemia. J Natl Cancer Inst 2008; 100, 1792-803.

      Romero OA et al. The tumour suppressor and chromatin-remodelling factor BRG1 antagonizes Myc activity and promotes cell differentiation in human cancer. EMBO Mol Med 2012; 4, 603-16.

      Romero OA et al. MAX inactivation in small cell lung cancer disrupts MYC-SWI/SNF programs and is synthetic lethal with BRG1. Cancer Discov 2014; 4, 292-303.

      Romero OA, Sanchez-Cespedes M. The SWI/SNF genetic blockade: effects in cell differentiation, cancer and developmental diseases. Oncogene 2014; 33, 2681-9.

      Romero OA et al. Sensitization of retinoids and corticoids to epigenetic drugs in MYC-activated lung cancers by antitumor reprogramming. Oncogene 2017; 36, 1287-96.

      Rutz HP. Effects of corticosteroid use on treatment of solid tumours. Lancet 2002; 360, 1969–70.

      Wilson GB, Roberts CWM. SWI/SNF nucleosome remodellers and cancer. Nat Rev Cancer 2011; 11, 481-92.

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      MS01.02 - Targeting Negative Feedback Regulators to Hyperactivate Oncogenic Signaling (Now Available) (ID 11402)

      10:50 - 11:10  |  Presenting Author(s): William Lockwood

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      Abstract not provided

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      MS01.03 - Stimulating Anti-Tumor Immunity Through Enhancing T-Cell Activation (Now Available) (ID 11403)

      11:10 - 11:30  |  Presenting Author(s): Kwok-Kin Wong

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      • Presentation
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      Abstract not provided

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      MS01.04 - Addressing Drug Resistance Beyond Kinase Domain Mutations (Now Available) (ID 11404)

      11:30 - 11:50  |  Presenting Author(s): Robert C. Doebele

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      • Presentation
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      Abstract not provided

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    MS10 - Part Solid Nodules, GGN and STAS (ID 789)

    • Event: WCLC 2018
    • Type: Mini Symposium
    • Track: Treatment of Early Stage/Localized Disease
    • Presentations: 5
    • Now Available
    • Moderators:
    • Coordinates: 9/24/2018, 15:15 - 16:45, Room 206 F
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      MS10.01 - Radiographic Differences Between Presumed AIS and MIA (Now Available) (ID 11440)

      15:15 - 15:30  |  Presenting Author(s): Mini Pakkal

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      • Presentation
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      Abstract not provided

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      MS10.02 - Importance of CT in the Pathologic Assessment of Tumor Sized in Subsolid and Part-Solid Adenocarcinoma (Now Available) (ID 11441)

      15:30 - 15:45  |  Presenting Author(s): Erik Thunnissen

      • Abstract
      • Presentation
      • Slides

      Abstract

      Importance of CT in the Pathologic Assessment of Tumor Sized in Subsolid and Part-Solid Adenocarcinoma

      Non-solid nodules (previously also called ground glass opacity) is a finding on thin-section CT that is defined as “hazy increased attenuation of the lung with preservation of bronchial and vascular margins”.1This is in contrast to consolidation that is defined as a “homogeneous increase in pulmonary parenchymal attenuation that obscures the margins of vessels and airway walls” (also called ‘solid´ component).

      The resolution of the CT differs few orders of magnitude from the resolution of the microscope. Therefore, non-solid nodule is not one disease: pathological examination reveals several different diseases. The radiological GGO change is actually due to a reduction of air, while a certain amount of air remains present. At the microscopic level this may be caused by either i) partial filling of the alveolar airspaces, ii) thickening of the parenchymal interstitium and alveolar walls, iii) relative increase in perfusion, or iv) any combination of these factors.1,2Alveolar spaces may become partially filled by several ways, such as transudative fluid, blood, inflammatory cells or debris, or amorphous material as seen in cardiogenic pulmonary edema, diffuse alveolar hemorrhage, pneumonia, and pulmonary alveolar proteinosis. Alveolar walls and septal interstitium may become thickened secondary to edema, neoplastic proliferation, fibrosis, and noncaseating granulomatous deposition as seen in cardiogenic pulmonary edema, lung adenocarcinoma other malignancies, nonspecific interstitial pneumonia, and sarcoidosis. Partial alveolar filling and interstitial thickening coexist in many disease entities. Thus the non-solid nodule is a non-specific finding that may be caused by various disorders, including inflammatory disease, pulmonary fibrosis, alveolar haemorrhage or neoplasms3.

      Part-solid nodules (PSNs): Nodules with a solid component obscuring the underlying lung parenchyma other than blood vessels on thin-section CT scans viewed on CT lung window settings. Subsolid nodules comprise the non-solid and part-solid nodules.

      The histopathology of the solid component may be inflammation1) e.g. aspergillosis, organising pneumonia3,4, non-specific fibrosis and invasive adenocarcinomawith prominent lepidic component3,56and rarely lymphoma4or a combination of both. As subsolid nodules are an appearance on CT that may histologically represent different diseases, the term “Natural history of subsolid or part solid nodule” is a misnomer. Spread through air spaces is an immature concept under debate, where an underlying artifact is far from unrealistic.7

      The chance on lymph node metastases is very low in GGO with total and solid size < 1cm. Only one case with solid size between 0.5 and 1.0 cm8has been reported so far.

      Usually, in patients with multiple AIS sufficient molecular differences are detected to classify the individual lesions as multiple primary tumors. However, in an occasional patient with multiple GGO/AIS the differences in two of several comparisons were so limited that an argument for ‘early metastases was formulated9.

      Although it may be important for prognosis to measure the solid size for radiologists and invasive size for pathologists for prognostic reasons, sufficient reproducibility of these parameters has not been proven. These challenges emphasize the need for further standardization10.

      References

      1. El-Sherief, A. H. et al.Clear Vision Through the Haze: A Practical Approach to Ground-Glass Opacity. Curr. Probl. Diagn. Radiol.43,140–158 (2014).

      2. Hewitt, M. G., Miller, W. T., Reilly, T. J. & Simpson, S. The relative frequencies of causes of widespread ground-glass opacity: A retrospective cohort. Eur. J. Radiol.83,1970–1976 (2014).

      3. Kim, H. Y. et al.Persistent Pulmonary Nodular Ground-Glass Opacity at Thin-Section CT: Histopathologic Comparisons 1. Radiology245,267–275 (2007).

      4. Lee, H. J. et al.Nodular ground-glass opacities on thin-section CT: size change during follow-up and pathological results. Korean J. Radiol.8,22–31 (2007).

      5. Son, J. Y. et al.Quantitative CT Analysis of Pulmonary Ground-Glass Opacity Nodules for the Distinction of Invasive Adenocarcinoma from Pre-Invasive or Minimally Invasive Adenocarcinoma. PLoS One9,e104066 (2014).

      6. Kakinuma, R. et al.Natural history of pulmonary subsolid nodules: A prospective multicenter study.J. Thorac. Oncol.11,1012–1028 (2016).

      7. Blaauwgeers, H., Russell, P. A., Jones, K. D., Radonic, T. & Thunnissen, E. Lung Cancer. Pulmonary loose tumor tissue fragments and spread through air spaces ( STAS ): Invasive pattern or artifact ? A critical review. 123,107–111 (2018).

      8. Seok, Y. et al.Frequency of Lymph Node Metastasis According to the Size of Tumors in Resected Pulmonary Adenocarcinoma with a Size of 30 mm or Smaller. J. Thorac. Oncol.9,818–824 (2014).

      9. Li, R. et al.Early metastasis detected in patients with multifocal pulmonary ground-glass opacities (GGOs). Thorax73,290–292 (2018).

      10. Yip, R. et al.Controversies on lung cancers manifesting as part-solid nodules. Eur. Radiol.28,747–759 (2018).

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      MS10.03 - CT Versus Needle Biopsy Assessment Before Resection of Part Solid Nodules (Now Available) (ID 11442)

      15:45 - 16:00  |  Presenting Author(s): Young Tae Kim

      • Abstract
      • Presentation
      • Slides

      Abstract

      Increasing number of lung cancer screening program detects pulmonary ground glass nodules (GGN) and those are frequently found to be an adenocarcinoma or its precursors, particularly if they are persistent or increase in size.

      Preoperative diagnosis of GGNs by trans-bronchial or trans-thoracic fine-needle aspiration biopsy can be performed with reasonable diagnostic accuracy 1. However, they have limitation for the pathologic confirmation of GGNs as they can fail to sample small invasive foci 2, 3. In addition, hemoptysis has been reported to occur more frequently after needle biopsy in GGNs 4. Accordingly, needle biopsy for GGNs are not used in our current practice and if the suspicion for malignancy is high on CT, surgical biopsy is performed without preoperative needle biopsy. Recently published Fleischner recommendations for the management of GGNs may help to make a clinical decision.

      During the surgery, especially in minimally invasive surgery, GGNs are often difficult to palpate. Thus, preoperative marking techniques have been utilized for localization of the GGNs using various methods 5. That can be done either percutaneously or transbronchially, using various materials including a dye, colored collagen, barium, lipiodol, micro coil, metallic wire, or fiducial 6. In our center, we have been using various methods for preoperative localization and but currently, we favor to use electromangetive navigational bronchoscopy (ENB) guided dye marking technique.

      Once surgery is decided, deciding the extent of resection is another issue. The size of solid portion measured on the CT can be helpful. If it is less than 5 mm, a simple wide wedge resection can be performed by which the goal of diagnosis and treatment can be achieved. If it is larger than 5 mm, the specimen is examined by frozen section and if the malignancy is confirmed, anatomic lung resection is conducted.
      However, the CT findings have not yet proven sufficiently reliable to guide the management plan. Intraoperative frozen section diagnosis is an alternative that can guide the extent of the subsequent surgical procedure. The problem of frozen section is, however, the fact that deflated lung specimens often makes the correct diagnosis difficult. To obviate this problem, the technique of inflating the lung specimen with the embedding medium for frozen section (EMIT) has been used, which allows better interpretation, and facilitated correct diagnosis in the frozen section 7. In our center, we have been using EMIT and found a high diagnostic accuracy with the concordance rate of 90.6% between EMIT and permanent pathology. Based on our experience, it is our current practice to perform a wide wedge resection of the GGNs and send the specimen for EMIT. If the result of EMIT is pre-invasive lesions (benign, AAH, AIS, or MIA), we do not perform additional resection. If the invasive adenocarcinoma is diagnosed, we prefer to proceed anatomic lung resection with systematic lymph node dissection.

      Several studies showed that limited resection could be beneficial, especially in early stage lung adenocarcinoma, including GGN 8. On the contrary, in one prospective study that reported a long-term outcome, limited resection of GGNs showed a low disease-control rate. They reported adenocarcinomas developed in four out of 26 patients in the surrounding area of initial resection site after more than five years 9. However, as GGNs usually show favorable prognosis, limited resection could be generally recommended 10. Additionally, in cases of deeply located GGNs, where wedge resection is not technically feasible, direct segmentectomy without wedge biopsy for the purpose of diagnosis and treatment, is recommended. For the segmentectomy, various technics can be used, but it is our current practice to use ENB guided dye marking to define an adequate parenchymal resection margin during the segmentectomy.

      To summarize, although there are several CT findings that can differentiate between pre-invasive and invasive lesions, those findings have not yet proven sufficiently reliable to guide the management plan for GGNs. In addition, attempt to sample solid component in GGNs using a biopsy needle is often not feasible and therefore, not helpful for being used in clinical decision. Currently, the best practice for the management of GGNs is to carefully follow the patient with CT, and if malignancy is suspected, to perform a surgical biopsy with the guide of various localization methods and/or other innovative methods to differentiate between pre-invasive versus invasive adenocarcinoma.

      1. Yamagami T, Yoshimatsu R, Miura H, et al. Diagnostic performance of percutaneous lung biopsy using automated biopsy needles under CT-fluoroscopic guidance for ground-glass opacity lesions. Br J Radiol 2013;86:20120447.
      2. Kim TJ, Lee JH, Lee CT, et al. Diagnostic accuracy of CT-guided core biopsy of ground-glass opacity pulmonary lesions. AJR Am J Roentgenol 2008;190:234-239.
      3. Lu CH, Hsiao CH, Chang YC, et al. Percutaneous computed tomography-guided coaxial core biopsy for small pulmonary lesions with ground-glass attenuation. J Thorac Oncol 2012;7:143-150.
      4. Choi JW, Park CM, Goo JM, et al. C-arm cone-beam CT-guided percutaneous transthoracic needle biopsy of small (</= 20 mm) lung nodules: diagnostic accuracy and complications in 161 patients. AJR Am J Roentgenol 2012;199:W322-330.
      5. Ikeda K, Nomori H, Mori T, et al. Impalpable pulmonary nodules with ground-glass opacity: Success for making pathologic sections with preoperative marking by lipiodol. Chest 2007;131:502-506.
      6. Zaman M, Bilal H, Woo CY, et al. In patients undergoing video-assisted thoracoscopic surgery excision, what is the best way to locate a subcentimetre solitary pulmonary nodule in order to achieve successful excision? Interactive cardiovascular and thoracic surgery 2012;15:266-272.
      7. Marchevsky AM, Changsri C, Gupta I, et al. Frozen section diagnoses of small pulmonary nodules: accuracy and clinical implications. The Annals of thoracic surgery 2004;78:1755-1759.
      8. Cao C, Gupta S, Chandrakumar D, et al. Meta-analysis of intentional sublobar resections versus lobectomy for early stage non-small cell lung cancer. Ann Cardiothorac Surg 2014;3:134-141.
      9. Nakao M, Yoshida J, Goto K, et al. Long-term outcomes of 50 cases of limited-resection trial for pulmonary ground-glass opacity nodules. J Thorac Oncol 2012;7:1563-1566.
      10. Shao G, Ren W, Feng Z, et al. The role of video-assisted thoracoscopic surgery in management of the multiple ground-glass nodules. Indian J Cancer 2015;52 Suppl 2:e75-79

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      MS10.04 - Therapeutic Implications of Spread Through Air Spaces (STAS) (Now Available) (ID 11443)

      16:00 - 16:15  |  Presenting Author(s): William D Travis  |  Author(s): Rania Gaber, Shaohua Lu, Takashi Eguchi, Natasha Rekhtman, Prasad S. Adusumilli

      • Abstract
      • Presentation
      • Slides

      Abstract

      Spread through air spaces (STAS) is a recently recognized pattern of invasion in lung cancer defined as spread beyond the edge of the main tumor into the air spaces surrounding the tumor. It was originally described as a poor prognostic factor in Stage I lung adenocarcinoma.1 STAS has been observed in 15-62% of lung adenocarcinomas and associated with poor prognosis in multiple independent cohorts worldwide.2-4 In addition, it has now been shown to occur with prognostic significance in most all major types of lung cancer including squamous cell carcinoma (SQCC),5 small cell carcinoma SCLC),6 large cell neuroendocrine carcinoma (LCNEC),6 atypical carcinoid (AC)6 and pleomorphic carcinoma.7 Three dimensional evaluation has shown most STAS clusters are attached to alveolar walls rather than floating in air spaces suggesting a mechanism of detachment then reattachment perhaps by vessel co-option.8

      Criteria for STAS

      The original definition of STAS by Kadota et al and the 2015 WHO consisted of tumor cells within the first alveolar air spaces in the lung parenchyma beyond the edge of the main tumor. It can occur as one of three morphologic patterns including 1) micropapillary structures within air spaces; 2) solid nests or tumor islands and 3) scattered discohesive single cells.1, 9 The solid nest pattern is characteristic in other lung cancer histologies. Although other criteria have been proposed our group has used these same criteria for STAS to demonstrate its prognostic significance in SQCC, LCNEC, SCLC and AC. Warth et al defined STAS with different criteria including a detachment of small solid cell nests of least 5 tumor cells where < 3 alveolar spaces were regarded as limited STAS and tumor cells nests >3 alveolar spaces away from the tumor as extensive STAS.4

      Distance of and Quantitation of STAS

      Gaber R et al found that circumferential STAS was associated with a higher risk of recurrence free probability (RFP) than focal STAS (5yr RFP in circumferential vs focal; 67% vs 87%, p=0.027) and that longer distance of STAS was associated with a higher risk of recurrence (5yr RFP >7 alveoli vs ≤ alveoli, 69% vs 91%, p=0.003).9 However, Quantitation of STAS was not prognostic (5yr RFP in >3/HPFs vs ≤3/HPF, 75% vs 88%, p=0.15).9 Uruga H et al found that high vs low STAS (≥5 vs 1-4 single cells or clusters) was an independent predictor of worse (p=0.015).2 Warth did not find a prognostic difference between extensive vs limited STAS as described above.4

      Implications of STAS for Radiation Therapy

      In the setting of sterotactic body radiation therapy (SBRT) for lung cancer, the documentation of microscopic extension has been appreciated for many years.10 Radiologic and pathologic studies have shown that tumor cells can extend beyond the edge of the tumor from 1.3 centimeters to 2.6 cm.10 Although the concept of STAS emerged many years later, it provides morphologic and clinical support to radiation therapists concerns to address microscopic extension and STAS in planning the radiation field.

      Implications of STAS for Surgical Management

      There is limited data evaluating pathologists ability to recognize STAS in frozen section. Kameda et al found the sensitivity and specificity of frozen section for prediction of STAS were 71%, 92.4% respectively and the accuracy was 80%.11 Kappa statistics for interobserver agreement were 0.4-0.74.

      Walts AE et al studied frozen section for evaluation of STAS and recommended that current evidence did not warrant frozen section evaluation for STAS.12 However, frozen section sensitivity to detect STAS positivity was 50%, with a 100% positive predictive value and an 8% negative predictive value. So from the two studies, it appears if a pathologist sees STAS on a frozen section there is a 92-100% likelihood it will be present on permanent sections. Both of these were retrospective studies where tissue sampling for frozen sections was not made to include the tumor edge and adjacent lung to search for STAS. More studies are needed to evaluate the potential role of frozen section in detecting STAS and guiding intraoperative decisions by surgeons.

      REFERENCES

      1. Kadota K, et al. Tumor Spread through Air Spaces is an Important Pattern of Invasion and Impacts the Frequency and Location of Recurrences after Limited Resection for Small Stage I Lung Adenocarcinomas. J Thorac Oncol 2015;10:806-14.

      2. Uruga H, et al. Semiquantitative Assessment of Tumor Spread through Air Spaces (STAS) in Early-Stage Lung Adenocarcinomas. J Thorac Oncol 2017;12:1046-51.

      3. Toyokawa G, et al. Significance of Spread Through Air Spaces in Resected Pathological Stage I Lung Adenocarcinoma. Ann Thorac Surg 2018.

      4. Warth A, et al. Prognostic Impact of Intra-alveolar Tumor Spread in Pulmonary Adenocarcinoma. The American journal of surgical pathology 2015;39:793-801.

      5. Lu S, et al. Spread through Air Spaces (STAS) Is an Independent Predictor of Recurrence and Lung Cancer-Specific Death in Squamous Cell Carcinoma. J Thorac Oncol 2017;12:223-34.

      6. Aly RG, et al. Spread through air apsaces (STAS) correlates with prognosis in lung neuroendocrine tumors (LNET). Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2018;31:724.

      7. Shintaro Y, et al. Tumor spread through air spaces identifies a distinct subgroup with poor prognosis in surgically resected lung pleomorphic carcinoma. Chest 2018;in press.

      8. Yagii Y, et al. Three-Dimensional Assessment of Spread Through Air Spaces in Lung Adenocarcinoma: Insights and Implications. J Thoracic Oncol 2017;12 (Suppl 2): S1797, 2017.

      9. Gaber R, et al. Circumferential distribution and distance from main tumor of tumor spread through air spaces (STAS) are prognostic. J Thoracic Oncol 2017;12:S1864.

      10. van Loon J, et al. Microscopic disease extension in three dimensions for non-small-cell lung cancer: development of a prediction model using pathology-validated positron emission tomography and computed tomography features. Int J Radiat Oncol Biol Phys 2012;82:448-56.

      11. Kameda K, et al. Can tumor spread through air spaces (STAS) in lung adenocarcinomas be predicted pre- and intraoperatively? J Thoracic Oncol 2017;12:S209.

      12. Walts AE, et al. Current Evidence Does Not Warrant Frozen Section Evaluation for the Presence of Tumor Spread Through Alveolar Spaces. Arch Pathol Lab Med 2018;142:59-63.

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      MS10.05 - Should We Resect GGNs (Now Available) (ID 11444)

      16:15 - 16:30  |  Presenting Author(s): Kenji Suzuki

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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    MS16 - Implementation of Lung Cancer Screening (ID 795)

    • Event: WCLC 2018
    • Type: Mini Symposium
    • Track: Screening and Early Detection
    • Presentations: 5
    • Now Available
    • Moderators:
    • Coordinates: 9/25/2018, 13:30 - 15:00, Room 206 F
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      MS16.01 - CMS Approved ACR Lung Cancer Screening Registry in the United States (Now Available) (ID 11468)

      13:30 - 13:45  |  Presenting Author(s): Ella Kazerooni

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MS16.02 - NELCIN B3 Screening Program in China (Now Available) (ID 11469)

      13:45 - 14:00  |  Presenting Author(s): Qinghua Zhou

      • Abstract
      • Presentation
      • Slides

      Abstract

      Lung cancer is the leading cause of cancer-related death in the world, and lung cancer mortality has steadily declined in some developed countries due to effective tobacco control and improvement of early detection and treatment. In U.S, Lung cancer death rates declined 45% between 1990 and 2015 among males and 19% between 2002 and 2011 among females[1]. However, as the sequence of population age, high smoking prevalence and serious air pollution, Lung cancer in China increased 465% during the past 30 years, and has ranked the highest among all types of cancer since the beginning of this century[2]. As a highly lethal disease, the 5-year survival of lung cancer in China was 19.5% and 11.2% in urban areas and in rural areas respectively[3].

      Though smoking control is the most effective measure for the primary prevention of lung cancer, an upward trend of lung cancer incidence and mortality is still expected in future decades in China because of the high prevalence of smoking and severe air pollution. In addition, Lung cancer survival is closely related to the stage at diagnosis, that is, its prognosis is more favorable when diagnosed at an earlier stage. Accordingly, as a measure of secondary prevention, screening and early detection play a important role in lung cancer control.

      With the increasing disease burden from lung cancer, the widespread availability of spiral CT scanners in China, and the excellent survival of early lung cancer cases detected by LDCT, in 2009, lung cancer screening with LDCT were included into program of early detection and treatment of cancer which was the public health special subsidy from the central government(33). And later, this prospective, multi-centre observational program was renamed as early detection and treatment of cancer in rural China[4]. From 2009 to 2015, lung cancer screening in this program has expanded from 2 centers to 10 centers from 6 provinces/ Municipalities. Up to July 2007, a total of 54164 LDCT scans among about 13000 high risk individuals were conducted which including 19068 baseline screens and 35096 annual screens. The detection rate and early detection rate were 1% and 40% in baseline screening and were 0.4% and 56% in annual screening respectively(table 1 and 2).
      The early detection rates in this screening program whether in baseline or annual screening were significantly improved compared to the detection rate of early lung cancers diagnosed at hospital in China(about 7%)(figure 1). Besides, another government-sponsored LDCT lung cancer screening program were initiated in urban China in 2012[5].
      table 1.jpg
      table 2.jpg

      figure 1.jpg
      Based on the protocol of lung cancer screening program in rural China, experts developed lung cancer screen guideline in China in 2015, and revised it recently[6, 7]. Besides LDCT screening, the above two screening program involved several other items including health promotion to increase screening acceptance, technical training for local doctors and technical personnel, delivery of smoking cessation intervention, biomarker discovery and validation to evaluate whether early lung cancer biomarker can refined high risk population and augment LDCT accuracy through classify nodules detected by LDCT. In addition, to keep a sustainable development of a national screening program, the two program has been included into the special program of medical insurance system reform in China to explore the possibility of incorporating lung cancer screening in the routine health insurance system in China.

      References

      [1] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018. 68(1): 7-30.

      [2] Chen W, Zheng R, Zhang S, et al. Annual report on status of cancer in China, 2010. Chin J Cancer Res. 2014. 26(1): 48-58.

      [3] Zeng H, Zheng R, Guo Y, et al. Cancer survival in China, 2003-2005: a population-based study. Int J Cancer. 2015.

      136(8): 1921-30.

      [4] Zhou Q, Fan Y, Wu N, et al. Demonstration program of population-based lung cancer screening in China: Rationale

      and study design. Thorac Cancer. 2014. 5(3): 197-203.

      [5] Dai M SSJ, Li N. Design and goal of urban cancer early diagnosis and treatment project in China. Chin J Prev Med.

      2013. 47(2): 179-82.

      [6] Zhou QH, Fan YG, Bu H, et al. China national lung cancer screening guideline with low-dose computed tomography (2015 version). Thorac Cancer. 2015. 6(6): 812-8.

      [7] Zhou Q, Fan Y, Wang Y, et al. [China National Lung Cancer Screening Guideline with Low-dose Computed 
Tomography (2018 version)]. Zhongguo Fei Ai Za Zhi. 2018. 21(2): 67-75.

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      MS16.03 - Recruitment Strategies for the Lung Cancer Screening (Now Available) (ID 11470)

      14:00 - 14:15  |  Presenting Author(s): Gail Elizabeth Darling

      • Abstract
      • Presentation
      • Slides

      Abstract

      The National Lung Cancer Screening Trial (NLST) randomized current or former smokers who had quit within 15 years, 55-75 years of age, to low dose CT or chest x-ray and demonstrated a 20% reduction in lung cancer mortality.[1] Screening for lung cancer is different from other screening programs as those at risk are not clearly defined by age or sex. Potential recruitment strategies include provider referral; media or internet campaigns; or mass mailing to individuals identified through mailing lists.

      Direct mailing was used by the NLST, ITALUNG, NELSON, and the Prostate, Lung, Colorectal and Ovarian (PLCO) Screening trials [ 2,3,4,5]. This reached large numbers of potential participants but it was inefficient and costly. Almost 35% of the total enrollment in the NLST was recruited by direct mailing but this is only 0.2-3.7% of those contacted by mail[ 2]. Media advertising and community outreach were used in PLCO, but were ineffective [4]. The Lung Screening Study (LSS) mailed 653,417 potential participants, and 4,828 (0.7% of the original mailing) were eligible [6]. The Pan Canadian Early Detection of Lung Cancer Study (PanCan) used mass mailing but also media, posters, websites, and general practitioners and a toll- free number. Of 7044 individuals initially considered, 36% were ultimately eligible for screening [7].

      The Veterans Affairs Lung Cancer Screening Program used the Electronic Medical Record (EMR) to identify potential participants but primary care physicians determined if patients were appropriate for screening. Of 18,083 potential candidates, only 5035 were assessed by their primary care physician. Of those considered appropriate, 50% went on to be screened. Tthe participation of the primary care physician was key to successful recruitment [8].

      The UK Lung Screen (ULKLS) used a population based approach through local primary care records, 23,794 (26.8%) of those contacted indicated willingness to participate in a screening study. Ex-smokers, those of higher socioeconomic status and in the 66-70 year age group were more likely to participate. Men and women were equally willing to participate[9].

      Wake Forest University School of Medicine identified that their NLST participants` did not reflect local demographics as only 3% of participants were black whereas blacks represented 25% of the local population [10]. PLCO strategies to increase participation among the black population included support from a prominent black business owner and local churches, including black individuals in the planning team, in community outreach and as interviewers. Focus groups or semi-structured interviews have identified that using members of ethnic minorities to promote or recruit are more likely to be successful in these groups [11]. Recruitment through primary care or other respected individuals in the community is also important. [12]

      Observations from NLST included: “go to where the smokers are”; build trust through local physicians; and use recruiters of the same ethnicity as target populations [3]. Interviews with current smokers identified themes that may contribute to reduced participation including fatalism, fear of diagnosis/ treatment, pessimism about survival and stigma that lung cancer was a self-inflicted disease [13]

      Cancer Care Ontario High Risk Lung Cancer Screening Pilot
      Cancer Care Ontario (CCO) launched a pilot screening program on June 1, 2017, at three centers that differed based on demographics, geography and academic or community hospital. Individuals are recruited, assessed for eligibility, screened and followed. The Tammemӓgi Risk Prediction model was used to identify eligible individuals. Those with a risk score ≥2% were eligible. Provider and public-led recruitment strategies were used. A major aim of the pilot was to recruit individuals who are known to have the highest rates of cigarette smoking: lower socioeconomic status (SES) and First Nations, Inuit and Metis.

      Areas of predicted high risk populations were identified within the catchment area of each pilot site. Market research was used to recommend recruitment modalities ( eg TV, radio or print) for specific sub-groups such as lower SES, older middle-income suburbanites, and rural populations. An accredited Continuing Professional Development course was developed for primary care and collaborative educational sessions were held with primary care providers and First Nations, Inuit and Métis provider groups.

      In the first year, 3294 individuals were recruited. The majority 3294 (81%) were physician referred (Table 1). The leading methods of recruitment were physician referrals (65%), newspaper advertisements (11%), word of mouth (6%) and nurse practitioners (6%). Only 4% of individuals identified as First Nations, Inuit or Métis. Level of education at high school or lower was self-reported by 48% of individuals. Based on early results, June – November 2017, approximately 27% of eligible individuals were recruited from low income postal codes (average annual household income < $70,000 CAD).

      Health Sciences North used primary care providers to identify and refer potential participants leading to the highest participation rate across all three centres. The Ottawa Hospital utilized media recruitment methods which led to a high number of applicants but eligibility was lower than for physician referred participants. Provider-led recruitment was more successful at reaching target populations and enlisting eligible participants. At Lakeridge Heath, provider-led strategies were less successful, so public-led recruitment strategies were increased. Public-led methods such as road shows and newspaper were used and led to a boost in volumes.

      Conclusions
      First year CCO pilot results have shown that provider-led recruitment strategies have been effective in enrolling appropriate individuals and is the primary source of recruitment for the pilot. Importantly, the proportion of eligible individuals recruited through their primary care physician is double that reported in the PANCAN study. Providers were also important in the VHA study. Use of
      Emr is helpful in identification of potentially eligible individuals. Mass mailing may reach more individuals, but is costly and inefficient. Our results demonstrate that support from primary care physicians is important in successful recruitment to lung cancer screening.

      Recruitment of First Nations, Inuit or Métis and those with a lower socioeconomic status remains a challenge. Utilizing previously identified strategies such as respected individuals in FNIM communities as well as members of ethnic minorities to promote the program and recruit participants will likely improve recruitment in these hard to reach populations.


      Table 1:

      2017-2018 The Ottawa Hospital/ Renfrew Victoria Hospital Lakeridge Health Health Sciences North Total
      # Recruited 1898 650 746 3294
      # Physician - referred 1533 (81%) 508 (78%) 640 (86%) 2681 (81%)
      How individuals learned about the pilot
      Family Doctor 67% 68% 56% 65%
      Newspaper 15% 10% 4% 11%
      Social Media 2% 2% 2% 2%
      Nurse Practitioner 1% 1% 21% 6%
      Word of Mouth 6% 2% 8% 6%
      Other 3% 11% 1% 4%
      First Nations, Inuit or Métis 3% 2% 7% 4%
      High School Education or Lower 39% 55% 60% 48%
      Age 55-64 vs. 65+ 59% vs. 41% 62% vs. 38% 61% vs. 39% 60% vs. 40%
      Male vs. Female 48% vs. 52% 49% vs. 51% 53% vs. 47% 50% vs. 50%
      Current vs. Former Smoker 47% vs. 53% 62% vs. 38% 62% vs. 38% 54% vs. 46%


      References
      1. Aberle DR, Adams AM, Berg CD et al. Reduced lung cancer mortality with low-dose computed tomographic screening, NEng J Med. 2011; 365:395-409
      2. Marcus PM, Sammons D, Balc W, Garg K. Recruitment methods employed in the National Lung Screening Trial. J Med Screen 2012; 19:94-102. Doi: 10.1256/jms.2012.012016.
      3, Simpson NK, Johnson CC, Trocky N, et al. Recruitment Strategies in the Prostate, Lung, Colorectal and Ovarian ( PLCO ) Cancer Screening Trial: The first six years. Control Clin Trials 2000; 21: 356S-378S.
      4. Lopes PA, Picozzi G, Mascalchi M, et al. Design, recruitment and baseline results of the ITALUNG trial for lung cancer screening with low-dose CT. Lung Cancer. 2009; 64: 34-40.
      doi: 10.1016/j.lungcan.2008.07.003
      5. Yousaf-Khan U, Horeweg N, van der Aalst C, et al. Baseline characteristics and mortality outcomes of control group participants and eligible non-responders in the NELSON Lung Cancer Screening Study. JThorac Oncol 2015; 10:747-53. doi: 10.1097/JTO.0000000000000488.]
      6. Gohagan J, Marcus P, Fagerstrom R, et al. Baseline findings of a randomized feasibility trial of lung cancer screening with spiral CT scan vs chest radiograph. Chest 2004; 126: 114-121.
      7. Tammemagi, MC, Schmidt H, Martel S, et al. Participant selection for lung cancer screening by risk modelling ( the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study. Lancet Oncol. 2017; 18: 1523-31. Doi: 10.1016/S1470-2045(17)30597-1.
      8. Kisinger LS, Anderson C, Kim J et al. Implementation of Lung Cancer Screening in the Veterans Health Administration. JAMA Intern Med. 2017; 177: 399-406. Doi: 10.001/jamainternmed.2016.9022.
      9. McRonald FE, Yadegarfar G, Baldwin DR. et al. The UK Lung Screen ( UKLS): Demographic Profile of first 88,897 approaches provides recommendation for population screening. Cancer Prev Res. 2014;7: 362-371. doi: 1-.1158/1940-6206.
      10. Hinshaw LB, Jackson SA, Chen MY. Direct mailing was a successful recruitment strategy for a lung-cancer screening trial. J Clin Epidem. 2007; 60:853-857.
      11. Stallings FL, Ford ME, Simpson NK, et al. Black Participation in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Control Clin Trials 2000; 21: 379S-389S.
      12. das Nair R, Orr KS, Vedhara K, Kendrick D. Exploring recruitment barriers and facilitators in early cancer detection trials: the use of pre-trial focus groups. Trials 2014; 15: 98.
      13. Quaife SL, Marlow LAV, McEwen A, Janes SM, Wardle J. Attitudes towards lung cancer screening in socioeconomically deprived and heavy smoking communities: informing screening communication. Health Expectations 2017; 20: 563-573. Doi:10.1111/hex.12481

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      MS16.04 - National Lung Screening Program in Taiwan (Now Available) (ID 11471)

      14:15 - 14:30  |  Presenting Author(s): Pan-Chyr Yang

      • Abstract
      • Presentation
      • Slides

      Abstract

      Lung cancer remains the leading cause of cancer mortality worldwide. Early-stage lung cancer is usually asymptomatic and most patients present with symptoms are diagnosed at advanced stages with poor treatment outcome. Early detection is the most effective way to improve survival of lung cancer patients. Low-dose computed tomography (LDCT) scan is a proven tool for lung cancer screening1. The National Lung Screening Trial (NLST) demonstrated a 20% mortality rate reduction in high-risk smokers who had undergone LDCT screening as compared to conventional chest X-ray2. The effectiveness of LDCT screening among never-smokers is unknown. Evidence showed that the incidence of lung cancer among never-smokers is increasing. It is estimated that 10-25% of the lung cancer occurs in never-smokers and the prevalence is relatively high in East Asian women3. In Taiwan, more than half of the lung cancer patients are never-smokers. We hypothesized that the diagnostic consensus reached and the risk models developed in non-Asian populations may not be suitable for Asians. Based on this unmet clinical need, we conducted a four-year LDCT screening program that enrolled 12,000 subjects with a three-year follow-up period. We also tempted to validate the risk SNPs we previously identified that associated with lung cancer susceptibility in never-smokers4-6.

      Methods: This is a prospective, nationwide, a multicenter study sponsored by the Ministry of Health and Welfare, Taiwan. The study protocol was approved by the institutional review boards. Informed consent was obtained from every participating subject. Individuals who fulfill the following criteria are eligible for the study: (1) aged between 55-75 years, (2) being a never-smoker, and (3) having one of the following risk factors: (i) family history of lung cancer within third-degree relatives, (ii) passive smoke exposure, (iii) history of pulmonary tuberculosis or COPD, (iv) cooking index ≥ 110, or (v) not using ventilator during cooking. The LDCT was conducted according to the guideline suggested by the American College of Radiology. A solid or part-solid (PS) nodule larger than 6 mm in diameter or a pure ground glass nodule (GGN) larger than 5 mm in diameter was designated as positive on LDCT. The LDCT were performed annually for three consecutive years. If LDCT was positive, short-term follow-up (3-6 months) or tissue diagnosis would be arranged. DNAs were extracted from peripheral blood mononuclear cells for SNP typing (TERT, TP63, VTI1A, BPTF, HLA-DRB1/HLA-DQA1, HLA-DRB9/HLA-DRB5, DCBLD1 and YAP1) in every enrolled subject. In addition, a risk score prediction model integrated effects from family history and SNPs were developed and the risk of each individual was calculated.

      Results: This is the interim report. The data cut-off date was May 13, 2018. A total of 10,397 subjects were enrolled (Table1), with 4,498 subjects completed in Stage 1(2014-2015), 4,679 in Stage 2(2016-2017), and 1,220 in Stage 3(2018), respectively. A total of 329 subjects received biopsy for tissue poof (31 by bronchoscopy or CT guide, 298 by surgery) prior to the date of data cut-off. The final pathology diagnoses revealed 12 atypical adenomatous hyperplasias, 42 (17.3%) adenocarcinoma in situ, 46 (19.0%) minimally invasive adenocarcinoma, 152 (62.6%) invasive lung adenocarcinoma, one adenosquamous cell carcinoma, one squamous cell carcinoma, and one small cell carcinoma. In 243 lung cancer patients, 240 patients were adenocarcinoma, 208 patients were stage 1A and 23 patients were stage 1B. The lung cancer detection rate was 2.34% (243/10,397) with 95.1% being stage I. The SNP typing validated 4 SNPs(TERT, TP63, HLA-DRB9/HLA-DRB5, HLA-DRB1/HLA-DQA1) that were significantly associated with the risk of lung cancer. The risk score point was significantly different between normal (mean±SD= 50.1±28.84 ) and case (mean±SD= 59.4±28.23)(p <0.0001). In addition,the group with risk point < 25 as the reference, odds ratios of 3 groups (risk point 25-50, 50-75, and 75-100) were 1.71 (p= 0.046), 1.98 (p= 0.006), and 2.54 (p< 0.0001), respectively.

      Conclusions: In the NLST study, the LDCT detection rate in the first stage was 1.03% (270/26,309). Our interim results showed that, in our pre-defined never-smoking high-risk population, the LDCT lung cancer detection rate was higher than NLST study (2.34% vs 1.03%). Previously reported 4 SNPs were still significant in this cohort. In addition, risk score model showed a high odds ratio of the high score group, suggesting that may be useful to predict the risk of lung cancer in never-smokers.

      References:

      1. Oudnerk M, Devaraj A, Vliegenthart R, et al.European position statement on lung cancer screening. Lancet Oncol 2017;18(12):e754-e766.

      2. The National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409.

      3. Couraud S, Zalcman G, Milleron B, Morin F, Souquet PJ. Lung cancer in never smokers -- a review. Eur J Cancer 2012;48(9):1299-311.

      4. Hsiung CA, Lan Q, Hong YC, et al. The 5p15.33 locus is associated with risk of lung adenocarcinoma in never-smoking females in Asia. PLoS Genet 2010;6(8) pii: e1001051.

      5. Lan Q, Hsiung CA, Matsuo K, et al. Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia. Nat Genet. 2012;44:1330-5.

      6. Chen HY, Yu SL, Ho BC, et al. R331W missense mutation of oncogene YAP1 Is a germline risk allele for lung adenocarcinoma with medical actionability. J Clin Oncol. 2015;33:2303-10.

      Table1. Selected characteristics of the study subjects

      Characteristic

      Number(%)

      P-valuee

      All participants

      10,397(100)

      Sex

      Male

      2,694(25.9)

      Female

      7,703(74.1)

      Agea

      61.2±6.0yr

      Male

      61.6±6.1yr

      Female

      61.1±6.0yr

      Risk factorb

      Family history of lung cancer

      4,449(42.8)

      Environmental smoking exposure

      7,924(76.2)

      TB/COPD

      917(8.8)

      Cooking index≧110

      4,176(40.2)

      Not using ventilator during cooking

      572(5.5)

      Invasive procedure

      329(3.2)

      Histologic diagnosis

      Adenocarcinoma in situ

      42

      Minimally invasive adenocarcinoma

      46

      Invasive adenocarcinoma

      152

      Adenosquamous cell carcinoma

      1

      Squamous cell carcinoma

      1

      Small Cell lung cancer

      1

      Atypical adenomatous hyperplasia

      12

      Benign

      66

      Others

      8

      Lung cancer detection rate

      243(2.34)

      Stage

      Stage IA

      208

      Stage IB

      23

      Stage IIA

      2

      Stage IIB

      2

      Stage IIIA

      3

      Stage IV

      5

      Unknown

      0

      SNP allelec and MAFd

      Control
      (n=5837)

      Case
      (n=176)

      rs2395185 (HLA-DRB9/HLA-DRB5)

      G/T

      33.17%

      38.64%

      0.0324

      rs2736100 (TERT)

      A/C

      42.17%

      48.30%

      0.0223

      rs28366298 (HLA-DRB1/HLA-DQA1)

      A/C

      31.98%

      39.77%

      0.0022

      rs4488809 (TP63)

      T/C

      49.85%

      38.07%

      <0.0001

      rs7086803 (VTI1A)

      G/A

      30.36%

      34.94%

      0.0661

      rs7216064 (BPTF)

      G/A

      67.22%

      69.60%

      0.348

      rs9387478 (LOC100132917, DCBLD1)

      A/C

      48.18%

      50.28%

      0.435

      rs193100333 (YAP1)

      C/T

      0.27%

      0%

      0.963

      Risk score point

      50.1±28.8

      59.4±28.2

      p <0.001

      a Mean ± SD.

      b Some subjects may have more than one factor.

      c Risk allele listed second.

      d Major allele frequency.

      e Estimated from allelic model.

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      MS16.05 - Optimal Approach to Integrate Smoking Cessation into Screening Program (Now Available) (ID 11472)

      14:30 - 14:45  |  Presenting Author(s): Matthew Eric Callister

      • Abstract
      • Presentation
      • Slides

      Abstract

      Smoking cessation should be an integral part of low dose CT (LDCT) screening for lung cancer. Smoking cessation and LDCT screening have additive effects on reducing lung cancer mortality, with 7 years smoking abstinence delivering a mortality reduction comparable with the benefit of screening itself [1]. It is well known that a positive screening result increases the likelihood of quitting [2]. However, the majority of people undergoing screening receive a negative result (73% and 78% in the National Lung Screening Trial at prevalence and incidence rounds, rising to 86% and 94% when scans were re-evaluated using the Lung-RADS classification) [3].

      A negative result has the potential to falsely reassure participants and thus reduce the incentive to quit - the so-called ‘licence to smoke’ effect. This effect can only be properly assessed by comparison with an unscreened control population. Analysis from the NELSON study suggested such an effect may exist by demonstrating a higher quit rate in the unscreened control group compared to screened participants, although the effect lost statistical significance following intention to treat analysis [4]. However, this finding has not been replicated in two subsequent European studies. LDCT screening appeared to have no effect on smoking behaviours in the Danish Lung Cancer Screening Trial [5], and the LDCT screened arm had a higher quit rate than non-screened controls in the UK Lung Screening Pilot [6]. All three studies showed higher quit rates in both study arms compared to the background population, although such comparisons are likely to be subject to significant bias related to participation in a CT screening study.

      Thus although there is no convincing evidence of a licence-to-smoke effect, smoking cessation interventions in screening programmes are vital to guard against this, and to maximise the mortality benefits of these two interventions combined. One of the key questions regarding smoking cessation and screening is whether interventions that have worked in other settings should simply be transferred to screening programmes, or whether bespoke smoking cessation interventions are needed for this particular scenario.

      LDCT screening for lung cancer is itself a relatively recent phenomenon, having been introduced in the US and Canada following recommendations in 2013 and 2016 respectively. Accordingly, research into Smoking Cessation in the context of Lung Cancer Screening is in its relative infancy, a point acknowledged by the joint guideline on this topic produced by the Association for the Treatment of Tobacco Use and Dependence, and the Society for Research on Nicotine and Tobacco published in 2016 [7]. Most of the active research in this area is occurring within the SCALE collaboration (Smoking Cessation within the Context of Lung Cancer Screening) [8], constituting 8 clinical trials, 7 funded by the National Cancer Institute and 1 by the Veterans Health Administration. These trials are assessing various strategies of smoking cessation support including on-site individual counselling, use of a telephone quit-line, digital cessation tools, motivational interviewing, nicotine replacement medications and message framing (gain vs. loss). Many of these studies are assessing the cost-effectiveness of these interventions, as incorporation of successful smoking cessation interventions into screening is likely to significantly improve the cost effectiveness of the bundle.

      Whilst outcome data is awaited from these studies, interventions that have been shown to be effective in other settings should be applied to lung cancer screening programmes. Brief interventions by clinicians should cover the 5As of smoking cessation (ask, advise, assess, assist, and arrange follow-up). Analysis of primary care-delivered smoking cessation in NLST showed that arranging follow-up was associated with the highest chance of quitting (OR 1.46; 95% CI 1.19-1.76) but was only delivered in 10% of cases [9]. The UK National Institute for Clinical Excellence (NICE) has produced evidence based guidelines for Stop Smoking Interventions and Services [10]. These include providing behavioural support by trained stop smoking staff together with provision of bupropion, varenicline or nicotine replacement therapy; setting quit dates; and checking self-reported abstinence using carbon monoxide monitoring at 4 weeks after the quit date. Studies mentioned above are testing whether less labour intensive alternatives might achieve similar results to on-site individual smoking cessation counselling. However, until such proof of equivalence is available, it might be argued that given the importance of combining screening with effective smoking cessation, the ‘gold-standard’ described by NICE or equivalent bodies should be routinely available to any current smoker undergoing LDCT screening.

      1. Tanner, N.T., et al., The Association between Smoking Abstinence and Mortality in the National Lung Screening Trial. Am J Respir Crit Care Med, 2016. 193(5): p. 534-41.

      2. Tammemagi, M.C., et al., Impact of lung cancer screening results on smoking cessation. J Natl Cancer Inst, 2014. 106(6): p. dju084.

      3. Pinsky, P.F., et al., Performance of Lung-RADS in the National Lung Screening Trial: a retrospective assessment. Ann Intern Med, 2015. 162(7): p. 485-91.

      4. van der Aalst, C.M., et al., Lung cancer screening and smoking abstinence: 2 year follow-up data from the Dutch-Belgian randomised controlled lung cancer screening trial. Thorax, 2010. 65(7): p. 600-5.

      5. Ashraf, H., et al., Smoking habits in the randomised Danish Lung Cancer Screening Trial with low-dose CT: final results after a 5-year screening programme. Thorax, 2014. 69(6): p. 574-9.

      6. Brain, K., et al., Impact of low-dose CT screening on smoking cessation among high-risk participants in the UK Lung Cancer Screening Trial. Thorax, 2017. 72(10): p. 912-918.

      7. Fucito, L.M., et al., Pairing smoking-cessation services with lung cancer screening: A clinical guideline from the Association for the Treatment of Tobacco Use and Dependence and the Society for Research on Nicotine and Tobacco. Cancer, 2016. 122(8): p. 1150-9.

      8. Joseph, A.M., et al., Lung Cancer Screening and Smoking Cessation Clinical Trials. SCALE (Smoking Cessation within the Context of Lung Cancer Screening) Collaboration. Am J Respir Crit Care Med, 2018. 197(2): p. 172-182.

      9. Park, E.R., et al., Primary Care Provider-Delivered Smoking Cessation Interventions and Smoking Cessation Among Participants in the National Lung Screening Trial. JAMA Intern Med, 2015. 175(9): p. 1509-16.

      10. Stop smoking interventions and services, 2018, National Institute for Clinical Excellence.

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    MS22 - Biology of the Lung and Lung Cancer (ID 800)

    • Event: WCLC 2018
    • Type: Mini Symposium
    • Track: Biology
    • Presentations: 6
    • Now Available
    • Moderators:
    • Coordinates: 9/26/2018, 10:30 - 12:00, Room 206 F
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      MS22.01 - Lung Development and Stem Cells (Now Available) (ID 11490)

      10:30 - 10:45  |  Presenting Author(s): Samuel Rowbotham  |  Author(s): Joo-Hyeon Lee, Carla Kim

      • Abstract
      • Presentation
      • Slides

      Abstract

      REGULATION OF PROGENITOR CELLS IN THE ADULT LUNG AND IN LUNG CANCER

      Our laboratory has pioneered the use of stem cell biology approaches for the study of adult lung progenitor cells and lung cancer. Through a combination of mouse genetics and cell biology, we have developed tools to identify and characterize cells with progenitor cell activity in adult lung tissue. We have also applied our expertise to the study of lung cancer, which resulted in our definition of the cancer stem cell populations in the two most common types of lung cancer. We have examined the mechanisms that regulate lung progenitor cell self-renewal and differentiation in the normal lung and in the context of lung cancer.

      One major focus in our lab has been the creation of three-dimensional co-culture and co-transplantation organoid systems that have begun to define the cell-cell crosstalk between epithelial progenitors, endothelial cells and mesenchymal cells in the lung. I will discuss how we have recently used our organoid system to define lung mesenchymal cell types that specifically regulate airway or alveolar epithelial cells. I will also present new studies in which we have examined how epigenetic factors, particularly H3K9 methyltransferases and demethylases, affect lung injury repair, lung tumorigenesis and response to therapy in lung cancer.

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      MS22.02 - Epigenetic Alterations in Lung Cancer Development (Now Available) (ID 11491)

      10:45 - 11:00  |  Presenting Author(s): Wan Lam

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MS22.03 - Non-Coding RNA in Lung Cancer (Now Available) (ID 11492)

      11:00 - 11:15  |  Presenting Author(s): Sven Diederichs

      • Abstract
      • Presentation
      • Slides

      Abstract

      Lung cancer is a disease of the genome caused by the aberrant activation of oncogenes and inhibition of tumor suppressor genes. However, the vast majority of the human genome is only transcribed into RNA and not translated into proteins. Hence, one major class of products derived from the genome are long non-coding RNAs (lncRNAs). These recently discovered class of molecules can execute a wide range of functions in the cell. Their versatility is based on their capability of specifically interacting with DNA, RNA or proteins, while often their molecular mechanisms of action are unknown. The role of lncRNAs in lung cancer is only beginning to emerge.

      As one of the first lncRNAs linked to cancer at all, we identified the conserved transcript MALAT1 as a marker significantly associated with development of distant metastasis and overall survival in early stages of lung adenocarcinoma (1). Although MALAT1 has been found deregulated in a plethora of pathological conditions after its discovery (reviewed in 2), it remained unknown whether it would only be a molecular marker or an active player in lung cancer metastasis. Hence, we generated a genetic loss-of-function model in human lung cancer cells using a then innovative approach combining gene editing and RNA degradation (3). Employing this model, we showed that MALAT1 was essential for tumor cell migration in vitro and metastasis formation in vivo in a mouse xenograft model. Moreover, an inhibitor for MALAT1 could also pharmacologically suppress metastasis formation in a mouse model (4). Thereby, the nuclear lncRNA MALAT1 acts as an epigenetic regulator of a signature of multiple genes associated with migration, invasion and metastasis mediating the aggressive cellular phenotype (4).

      Beyond MALAT1, we have screened for additional lncRNAs relevant for lung adenocarcinoma using gene expression profiling as well as RNAi screening with a customized library of 3200 siRNA targeting cancer-associated lncRNAs. Thereby, we identified the important role of linc00673 / LUCAIR1 for the suppression of p53-mediated senescence in lung cancer cells. Moreover, we discovered the lncRNA VELUCT which is essential for lung cancer cell viability despite its extremely low abundance (5).

      In summary, non-coding RNAs can play important roles in lung carcinogenesis and metastasis (reviewed in 6-8)

      References:

      (1) P Ji*, S Diederichs* et al.: MALAT-1, a novel non-coding RNA, and Thymosin b4 predict Metastasis and Survival in early-stage Non-Small Cell Lung Cancer. Oncogene (2003)

      (2) T Gutschner et al.: MALAT1 - A paradigm for long non-coding RNA function in cancer. Journal of Molecular Medicine (2013)

      (3) T Gutschner et al.: Non-coding RNA Gene Silencing through genomic integration of RNA destabilizing elements using Zinc Finger Nucleases. Genome Research (2011)

      (4) T Gutschner et al.: The non-coding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells. Cancer Research (2013)

      (5) J Seiler et al.: The lncRNA VELUCT strongly regulates viability of lung cancer cells despite its extremely low abundance. Nucleic Acids Research (2017)

      (6) T Gutschner et al.: The Hallmarks of Cancer: A long non-coding RNA point of view. RNA Biology (2012)

      (7) A Roth et al.: Long Noncoding RNAs in Lung Cancer. Current Topics in Microbiology & Immunology (2016)

      (8) S Dhamija et al.: From junk to master regulators of invasion: lncRNA functions in migration, EMT and metastasis. International Journal of Cancer (2016)

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      MS22.04 - Genomics of Resistance and Response in Lung Cancer (Now Available) (ID 11493)

      11:15 - 11:30  |  Presenting Author(s): Marc Ladanyi

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MS22.05 - Inflammation in Lung Cancer (Now Available) (ID 11494)

      11:30 - 11:45  |  Presenting Author(s): Katerina Politi

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MS22.06 - Transcriptomic-Metabolomic Reprograming as Resistant Mechanism (Now Available) (ID 11495)

      11:45 - 12:00  |  Presenting Author(s): Patrick C Ma  |  Author(s): XIaoliang Wu, Zuan-Fu Lim, Satoshi Komo

      • Abstract
      • Presentation
      • Slides

      Abstract

      Genomics-guided precision therapy is now a standard of care in lung cancer, particularly in lung adenocarcinoma. Currently known actionable genomic-molecular alterations in lung cancer include EGFR, HER2 and BRAF mutations, ALK-, ROS1- and RET-translocations, MET amplification and MET exon 14 splicing variants. Other novel emerging promising actionable targets include NTRK1/2/3-translocations. While molecular precision therapy often results in excellent response rates upfront, one of the key obstacle hindering long term clinical outcome improvement relates invariably to acquired drug resistance. This is true even in the remarkable responders in those with initial near-complete to complete response. Classic studies of clinical resistance to precision therapy were based on tumor rebiopsies late during clinical tumor progression on therapy. These research effort has defined a variety of mutational resistant mechanisms including T790M-EGFR against erlotinib/gefitinib and C797S against osimertinib. Although genetic/genomic heterogeneity can partially account for aspects of incomplete upfront drug response in these patients with oncogene-addicted lung tumors, it is now clear that non-genetic tumor cell state reprogramming and plasticity can play significant roles in promoting drug escape and resistance, even very soon after drug therapy initiation. Using lung cancer cell lines and patient-derived ex vivo cell lines both in vitro and in vivo (CDX and PDX), we have investigated and characterized in EGFR-mutant and ALK translocation lung cancer, the non-mutational transcriptomic-metabolomic mechanisms of early adaptive drug resistance emergence in a subpopulation of parent drug sensitive cells known as drug persisters. In EGFR-mutant lung tumor model, there is an early-onset MET-independent TKI drug-escape cell states emerging among the respondent cell populations. When these drug-escaping persisters were analyzed by integrated transcriptomic and metabolomics profiling, we uncovered a central role for autocrine TGFβ2 in mediating cellular plasticity through profound cellular adaptive omic reprogramming, with common mechanistic link to prosurvival mitochondrial BCL-2/BCL-xL priming. Cells undergoing early adaptive drug escape take on proliferative-metabolic quiescence, and are transformed into enhanced EMT-ness with stem cell signaling, global bioenergetics suppression, susceptibility to glutamine deprivation and TGFβ2 inhibition. In the ALK translocation driven (ALK+) lung adenocarcinoma model, we also verified the emergence of ALK-TKIs induced early adaptive drug persister cells. Using various ALK inhibitors (crizotinb, ceritinib, alectinib), we identified that tumoral TGFβ2 autocrine induction regulates through epigenetic chromatin remodeling in EZH2/UTX/HOXB3 cascade in the drug persister cells which also were found to have upregulated cancer stemness and EMT markers. RNA-seq revealed a rapid-onset adaptive global transcriptome reprogramming in the drug-escaping persister tumor cells. Finally, our study data using mass spectrometry imaging (MSI) and biomolecular profiling platform in the ALK+ PDX model with a histology-guided MS (HGMS) approach will be presented. Overall, the MALDI and LAESI-MSI profiling analysis interestingly raised the specter of a rapid-onset proteomic-lipidomic reprogramming as early as 7 days post-TKI initiation during the adaptive drug escape. In summary, the drug persister cell state is characterized by autocrine TGFβ2 upregulation and a transcriptomic-proteomic-lipidomic-metabolomic cellular reprogramming to exploit the tumor plasticity ultimately to fully achieve rapid drug escape. This mechanism would eventually enable the drug persister cells to have enough time and space to further evolve into proliferative and genomically more heterogeneous long-term drug resisters.

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    MS29 - Selection into Screening Programs: Interplay of Risk Algorithms, Genetic Markers and Biomarkers (ID 807)

    • Event: WCLC 2018
    • Type: Mini Symposium
    • Track: Screening and Early Detection
    • Presentations: 6
    • Now Available
    • Moderators:
    • Coordinates: 9/26/2018, 13:30 - 15:00, Room 206 F
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      MS29.00 - Introduction with Poll Slides (Now Available) (ID 14933)

      13:30 - 13:35  |  Presenting Author(s): Betty Tong, John R Goffin

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MS29.01 - Assessment of Risk Prediction Algorithms for Entry into Screening Programs (Now Available) (ID 11525)

      13:35 - 13:50  |  Presenting Author(s): Martin Tammemägi

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      MS29.02 - Defining Screening Frequency &amp; Duration Using Risk Prediction Algorithms and CT Image Findings (Now Available) (ID 11526)

      13:50 - 14:05  |  Presenting Author(s): Kevin ten Haaf

      • Abstract
      • Presentation
      • Slides

      Abstract

      Lung cancer screening guidelines generally recommend annual screening, similar to the design of the National Lung Screening Trial.1, 2 However, results from European trials indicate that longer time-intervals between screenings can still yield a stage-shift.3, 4 This has led to discussion on the feasibility of screening programs with longer time-intervals between screenings.

      Findings from trials has led to improvements in interpreting and managing nodules found on CT screens. These findings have been essential in improving nodule management protocols and reducing the number of false-positive results.5, 6 But, the results of CT screens have also been shown to inform an individual’s future lung cancer risk.5, 7, 8

      The NLST showed that individuals with a negative baseline screen had a lower risk of developing lung cancer compared to participants with a positive baseline screen.8 Analyses of the ongoing Dutch-Belgian Lung Cancer Screening Trial (NELSON) indicate that the results of the first three screening rounds were indicative for detection of lung cancer in the fourth screening round.7 In addition, NELSON showed that the characteristics of screen-detected nodules can be used to estimate the two-year risk for developing lung cancer.5

      Risk prediction models have been suggested for selecting individuals for lung cancer screening. Analyses of different risk prediction models have been shown to be superior compared to participant selection based on age and pack-years.9 Combining these models with the information provided by CT screens may allow the personalization of an individual’s screening regimen.

      This session will consider the evidence on the effects of varying time-intervals between screenings. In addition, it will discuss the potential for personalizing the screening regimen based on screening results and other risk factors.

      References

      1. Aberle DR, Adams AM, Berg CD, et al. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. New England Journal of Medicine 2011;365:395-409.

      2. Moyer VA, on behalf of the USPSTF. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Annals of Internal Medicine 2014;160:330-338.

      3. Yousaf-Khan U, van der Aalst C, de Jong PA, et al. Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5-year screening interval. Thorax 2016.

      4. Sverzellati N, Silva M, Calareso G, et al. Low-dose computed tomography for lung cancer screening: comparison of performance between annual and biennial screen. Eur Radiol 2016;26:3821-3829.

      5. Horeweg N, van Rosmalen J, Heuvelmans MA, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. The Lancet Oncology;15:1332-1341.

      6. Callister MEJ, Baldwin DR, Akram AR, et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules: accredited by NICE. Thorax 2015;70:ii1.

      7. Yousaf-Khan U, van der Aalst C, de Jong PA, et al. Risk stratification based on screening history: the NELSON lung cancer screening study. Thorax 2017.

      8. Patz Jr EF, Greco E, Gatsonis C, et al. Lung cancer incidence and mortality in National Lung Screening Trial participants who underwent low-dose CT prevalence screening: a retrospective cohort analysis of a randomised, multicentre, diagnostic screening trial. The Lancet Oncology 2016;17:590-599.

      9. ten Haaf K, Jeon J, Tammemägi MC, et al. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study. PLOS Medicine 2017;14:e1002277.

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      MS29.03 - Polygenic Risk Score for Risk Assessment (Now Available) (ID 11527)

      14:05 - 14:20  |  Presenting Author(s): Rayjean J. Hung  |  Author(s): Yonathan Brhane, Nilanjan Chatterjie, David C Christiani, Neil Caporaso, Maria Teresa Landi, Loic Le Marchand, Geoffrey Liu, Stephen Lam, John Kirkpatrick Field, Paul Brennan, Christopher Ian Amos

      • Abstract
      • Presentation
      • Slides

      Abstract

      Background: Genome-wide association studies uncovered multiple lung cancer susceptibility genes, and consortium efforts greatly increased our ability to investigate the genetic architecture of histological subtypes. However the clinical utility of these genomic discoveries remains unclear. Method: We therefore constructed a risk prediction model with polygenic risk score (PRS) based on 18,316 lung cancer patients and 14,025 controls with European ancestry, via 10-fold cross-validation with elastic net penalized regression. Model calibration was assessed, and was validated with UK biobank data (N=336,911 unrelated participants with European ancestry). To evaluate its potential clinical utility, the PRS distribution was simulated in the National Lung Screening Trial (NLST, N=50,772 participants). Absolute risk was estimated based on age-specific lung cancer incidence and all-cause mortality as competing risk. Added value of PRS to the risk prediction model was assessed by Net Reclassification Index. Results: A PRS was constructed based on 128 independent lung cancer variants using regularized penalized regression. The lung cancer ORs for individuals at the bottom 5% and top 5% of the PRS distribution were 0.49 (95%CI=0.43-0.56, P=2.7e-26) and 2.23 (95%CI=1.93-2.58, P=2.3e-27) in the training set, and 0.46 (95%CI=0.34-0.64, P=2.50e-6) and 1.33 (95%CI=1.08-1.64, P=7.10e-3) in the testing set, versus those at 40 to 60% as the referent group. The OR per standard deviation of PRS was 1.43 (95%CI=1.39-1.47. P=7.8e-138) for overall lung cancer risk in the training set and 1.24 (95%CI=1.18-1.30, P=2.59-e19) in the testing set. When considering age as the time scale, PRS separated out the curve of 5-year absolute risk and cumulative risk. When simulating the PRS distribution in the NLST population, we estimated 47.4% of cases occurred in the top 20% of the individuals with highest lifetime cumulative risk. Discussion: Including well-established genomic information in the risk model can contribute to the risk stratification of the population.

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      MS29.04 - LuCID Exhaled Breath Analysis (Now Available) (ID 11528)

      14:20 - 14:35  |  Presenting Author(s): Marc Phillipe Van Der Schee  |  Author(s): Edoardo Gaude, Jasper Boschmans, Billy Boyle, Robert Campbell Rintoul, Robert Smith, Anna Battista

      • Abstract
      • Presentation
      • Slides

      Abstract

      The Breath Biopsy platform enables the collection and analysis of breath samples, in order to look for volatile organic compound (VOC) biomarkers of disease. The LuCID (lung cancer indicator detection) project is one of the first major deployments of the platform in a research setting, and is currently the largest breath-based trial in the world, recruiting up to 4000 patients. The aim of the LuCID project is to discover VOC biomarkers in breath for early detection of lung cancer, which could improve patient outcomes and save lives. In this talk, we will give an introduction to the LuCID program, then go on to describe the biology underlying the VOC biomarkers, before discussing the inherent challenges associated with breath sampling and analysis. We will conclude by giving an update on the progress of the LuCID trial to date.

      The LuCID Study

      LuCID is an international multi-centre prospective case-control cohort study (ClinicalTrials.gov ID NCT02612532) currently in progress, evaluating breath VOCs in patients with a clinical suspicion of lung cancer. A clinical suspicion is based on symptoms and/or suspicious finding on incidental imaging. Using tidal breathing, patients breathe into the ReCIVA Breath Sampler to collect breath samples on stable sorbent tubes for later analysis by Gas Chromatography-Mass Spectrometry and Field Asymmetric Ion Mobility Spectrometry (FAIMS, Owlstone Medical Ltd). One arm of the study is focused on early detection of lung cancer, with the aim of increasing the number of cases diagnosed at Stages 1 and 2, while an additional arm is currently being initiated looking at differences in breath profiles pre- and post-surgery which has the advantage of allowing the patient to act as their own control.

      The Biology of VOCs

      So why would we believe that VOC biomarkers for lung cancer could be discovered during LuCID? Cancer cells undergo profound changes of their metabolism in order to support high energetic demands of uncontrolled proliferation. Several oncogenic mutations have been shown to affect metabolism of cancer cells by converging to common metabolic pathways linked to cell cycle and anabolic growth. The Warburg effect is among well-established cancer metabolic hallmarks and entails the activation of aerobic glycolysis as main pathway for biosynthetic purposes, as opposed to normal cells that exploit mitochondrial metabolism for their energetic needs. These changes in cellular metabolism favor survival in an oxygen deprived environment and result in altered metabolic intermediates that function as the building blocks for new cells, both enabling the growth of rapidly dividing cancer cells, and also altering the profile of VOCs in breath. As these processes are fundamental to cancer cell survival, such altered metabolism occurs as one of the earliest stages of tumorigenesis, hence VOCs are excellent candidate biomarkers for early detection of cancer.

      Breath Sampling: Challenges and Solutions

      The potential of using breath sampling to identify markers of disease has long been recognised, but has to date seen almost no adoption into clinical practice, with only FeNO and H. pylori breath tests in widespread use. This has largely been due to practical considerations that have made large-scale clinical trials impractical to carry out. Most previous tests have involved collecting breath in bags, which

      - suffer from chemical losses over time

      - are vulnerable to contamination from ambient air if they are reused incorrectly

      - are difficult to transport and store.

      - only allow the collection of smaller volumes, limiting the sensitivity of the analysis.

      In this section, we will discuss how the ReCIVA breath sampler, a key part of the Breath Biopsy platform, allows these problems to be overcome, and we will present data, including VOC washout curves monitoring changes in VOC levels over time following ingestion of a peppermint capsule, that demonstrate how the ReCIVA performs in practice.

      reciva.jpg

      Image 1: The ReCIVA breath sampler

      Current Status of the LuCID trial

      In the concluding section, we will provide an update on the LuCID trial. We have identified some VOCs for which we have observed associations with certain disease processes in our biomarker discovery phase, and we are currently investigating what the mechanisms associating these VOCs with lung cancer initiation and progression might be.

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      MS29.05 - Early Detection Biomarker Development: Is Success on the Horizon? (Now Available) (ID 11529)

      14:35 - 14:50  |  Presenting Author(s): Peter Mazzone

      • Abstract
      • Presentation
      • Slides

      Abstract

      There is a recognized need for biomarkers capable of assisting with the early diagnosis of lung cancer. We need tools to improve our ability to identify patients at risk of developing cancer; to identify patients with asymptomatic early stage lung cancer; to identify patients who are more likely to die of causes other than lung cancer; to help us characterize the nature of a patient’s lung cancer; and to improve our management of lung nodules (expedite diagnosis, reduce the risks of evaluation, reduce the cost of evaluation).

      To have clinical utility a biomarker result must affect a clinical decision in a manner that leads to improved patient care. Both the benefit of clinical decisions influenced by true positive and negative results and the harms of clinical decisions influenced by false positive and negative results must be considered. In the screening setting a biomarker should lead to fewer lung cancer deaths without substantially increasing harms or expense, or a similar number of lung cancer deaths with fewer harms or less expense. When used to characterize a lung nodule a biomarker should lead to earlier diagnosis of malignant nodules without substantially increasing the number of procedures performed on patients with benign nodules or fewer procedures for patients with benign nodules without substantially delaying the diagnosis of cancer in patients with malignant nodules.(1)

      Biomarker development moves through a series of phases. In the discovery phase, a molecule or pattern of molecules are found to be associated with the presence or absence of the condition in question. If the association appears to be strong enough the biomarker may be taken to validation stages. Technical validation refers to the assessment of the accuracy (precision, reproducibility) of the assay that will measure the biomarker. Clinical validation refers to determining the accuracy (sensitivity, specificity, AUC, NRI) of a biomarker, with a fixed threshold for result interpretation, when applied to the intended use population. Through each phase of development standard operating procedures that include pre-analytical, analytical, and post-analytical processes, must be in place. Even accurate biomarkers may not be clinically useful. Prior to clinical use, it is important that the validated biomarker move through the clinical utility phase of assessment.

      To determine if the accuracy of a validated biomarker is high enough to invest in a clinical utility study one must consider the potential impact of true and false positive and negative results. In a screening context true positive results may lead to individuals with lung cancer being identified at curable stages while false positive results may lead to individuals without lung cancer (or at low risk of developing lung cancer) being enrolled in screening programs where they will be exposed to the associated harms of screening. True negative results could lead to individuals without lung cancer (or at low risk of developing lung cancer) avoiding the harms associated with low-dose CT screening while false negative results could prevent individuals with (or who will develop) lung cancer from being enrolled in a low-dose CT based lung cancer screening program, and thus not have an opportunity to benefit.

      A judgment about an acceptable tradeoff of benefit and harm from using the biomarker will have to be made. The biomarker should be more accurate at identifying patients with (or who will develop) potentially curable lung cancer than current eligibility criteria and available clinical risk prediction calculators, alone or in combination. What is considered an acceptable tradeoff in a screening context is likely to vary based on whether the biomarker is being applied to a cohort already eligible for LDCT screening or to a cohort that is currently not eligible for LDCT screening. In the former it is most important that patients with lung cancer are not excluded from being screened (rule out test) while in the latter it may be more important not to screen individuals without lung cancer (rule in test). Calculations exist that use currently accepted benefit:harm ratios when available (e.g. the currently eligible cohort for screening had an incidence of lung cancer of 0.83% in the NLST trial) to help determine if the validated accuracy supports pursuit of clinical utility studies.(2) Biomarker-stratified, enrichment, and biomarker-strategy study designs are acceptable approaches to determine clinical utility.

      Ongoing research trials are moving potential biomarkers through the phases of development. A handful of biomarkers have completed discovery level work and are working on, or have published or presented clinical validation study results. Others continue discovery level work. Few have entered formal clinical utility testing. Oncimmune’s panel of autodantibodies, the EarlyCDT-Lung test, is being assessed as part of a 12,000 person randomized controlled screening study (the ECLS study). An exciting amount of high quality discovery and clinical validation work is ongoing. Some companies are in the process of planning true clinical utility studies for early lung cancer detection (Table).

      Company

      Biomarker

      Target

      Completed

      Ongoing

      Plans

      ANCON

      VOC – NBT

      N/A

      N/A

      Discovery

      N/A

      bioAffinity

      Sputum flow cytometry

      N/A

      N/A

      Discovery

      N/A

      Exact Sciences

      DNA methylation

      Nodule

      N/A

      Discovery

      Clinical Validation

      Genesys

      Antigens, Auto-antibody

      Screening

      Clinical Validation

      N/A

      N/A

      GRAIL

      cfDNA assays

      Screening

      N/A

      Discovery

      Clinical Validation

      InDi

      2 proteins + clinical

      Nodule

      Clinical Validation

      N/A

      Clinical Utility

      MagArray

      Antigens and Autoantibodies

      Nodule

      Discovery

      Clinical Validation

      Clinical Utility

      Nucleix

      DNA methylation

      Screening

      Discovery

      N/A

      Clinical Validation

      Oncimmune

      Autoantibodies

      Nodule, Screening

      Clinical Validation

      ECLS study

      Clinical Utility

      Oncocyte

      mRNA + size

      Nodule

      Discovery

      Clinical Validation

      Clinical Utility

      Owlstone

      VOC - FAIMS

      N/A

      N/A

      Discovery

      N/A

      Synergenz

      SNPs + clinical

      Screening

      Clinical Validation

      N/A

      N/A

      Veracyte

      Airway genome signature

      Nodule

      Clinical Validation

      Registry study

      Discovery

      Mazzone PJ, Sears CR, Arenberg DA, Gaga M, Gould MK, Massion PP, Nair VS, Powell CA, Silvestri GA, Vachani A, Wiener RS. Evaluating molecular biomarkers for the early detection of lung cancer: When is a biomarker ready for clinical use? An official American Thoracic Society policy statement: Executive summary. Am J Respir Crit Care Med 2017;196(7):911-919.

      Pepe MS, Janes H, Li CI, Bossuyt PM, Feng Z, Hilden J. Early-phase studies of biomarkers: what target sensitivity and specificity values might confer clinical utility? Clin Chem 2016;62:737–742.

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    MTE01 - Preclinical Models of Lung Cancer (Ticketed Session) (ID 811)

    • Event: WCLC 2018
    • Type: Meet the Expert Session
    • Track: Biology
    • Presentations: 2
    • Now Available
    • Moderators:
    • Coordinates: 9/24/2018, 07:00 - 08:00, Room 206 F
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      MTE01.01 - GEMM of Lung Cancer (Now Available) (ID 11546)

      07:00 - 07:30  |  Presenting Author(s): Sarah Ann Best

      • Abstract
      • Presentation
      • Slides

      Abstract

      The primary induction of lung cancer is difficult to study in humans because patients often present very late in the course of their disease. Genetically engineered mouse models (GEMMs) have therefore emerged as crucial bridging strategies between understanding pathogenic mechanisms and clinical translation. Importantly, they reveal insights on the events and processes underlying tumor initiation and progression, studies which are not possible when employing transplantation or chemically-induced model systems.

      The recent advent of next-generation sequencing technologies has provided us with an in-depth characterization of the cancer genome of lung adenocarcinoma (LUAD) (1), squamous cell carcinoma (LUSC) (2) and small cell lung cancer (SCLC) (3). While these studies have highlighted the genetic complexities of lung cancers, attention is now focused on elucidating “driver” mutations that confer a growth advantage, from “passenger” mutations that have little impact on malignant transformation. Investigating the loss or gain-of-function of individual genes, alone or in combination, can be directly addressed using GEMM systems.

      The “gold-standard” lung cancer models are based on Cre-LoxP recombination technology that enable the formation of autochthonous tumors from a limited number of somatic cells in a spatial and temporal fashion. Critically, tumors arise sporadically within the lung, in the setting of an intact immune microenvironment. GEMMs are designed to harbor genetic mutations frequently identified in human lung cancer. Cre-inducible alleles are engineered to disrupt tumor suppressor genes (LoxP sites flanking key exons (floxed), that are removed upon recombination) and/or activate oncogenes (LoxP-flanked stop codons (lox-stop-lox) that result in gene expression upon recombination). Cre-recombinase is delivered to the lung via inhalation or intra-tracheal injection of a recombinant adenovirus (Ad5) expressing Cre-recombinase under the control of a ubiquitous cytomegalovirus (CMV) promoter. Expression of Cre-recombinase directs the recombination of floxed alleles in a variety of epithelial cell types in the adult mouse lung (4,5). Utilizing this approach enabled investigators to interrogate the functional consequences of genetic alterations found in human lung cancer through the generation of models of LUAD, SCLC and more recently lung LUSC (6). Moreover, the recent advent of CRISPR-Cas9 gene-editing technology now enables us to interrogate the functional interaction between multiple genetic alterations in a high-throughput setting (7). Furthermore, the generation of cell type specific Ad5-Cre viruses, that restrict Cre expression, and thus recombination, to alveolar type II (ATII) (Ad5-SPC-Cre), club (Ad5-CC10-Cre), neuroendocrine (Ad5-CGRP-Cre) and basal (Ad5-K5-Cre, Ad5-K14-Cre) (8) cells, have provided insights into the cellular origins of different subtypes of lung cancer (9,10). Critically, unlike patient-derived xenograft (PDX) models, one additional advantage of GEMMs is the ability to interrogate the interplay between tumor cells and immune cells present in the tumor microenvironment. Such studies are crucial given the success of immune checkpoint inhibitors in lung cancer patients.

      This presentation will outline lung cancer GEMMs commonly used in the field and how these models can be utilized to identify cancer initiating cells, understand the molecular pathways underlying tumorigenesis, the immune microenvironment of lung cancer, and importantly to identify vulnerabilities that can be exploited for the design of improved treatment modalities for patients.

      References

      1. The Cancer Genome Atlas Research Network, Comprehensive molecular profiling of lung adenocarcinoma (2014) Nature, 511 (7511) 543-550.

      2. The Cancer Genome Atlas Research Network, Comprehensive genomic characterization of squamous cell lung cancers (2012) Nature, 489 (7417) 519-525.

      3. George et al., Comprehensive genomic profiles of small cell lung cancer (2015) Nature, 524 (7563) 47-53.

      4. Best et al., Combining cell type-restricted adenoviral targeting with immunostaining and flow cytometry to identify cells-of-origin of lung cancer (2018) Methods in Molecular Biology, 1725 15-29.

      5. DuPage et al., Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase (2009) Nature Protocols, 4 (7) 1064-1072.

      6. Farago et al., SnapShot: Lung cancer models (2012) Cell, 149 (1) 246-246e1.

      7.Rogers et al., A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo (2017) Nature Methods, 14 (7) 737-742.

      8. Ferone et al., SOX2 is the determining oncogenic switch in promoting lung squamous cell carcinoma from different cells of origin (2016) Cancer Cell, 30 (4) 519-532.

      9. Sutherland et al., Cell of origin of small cell lung cancer: inactivation of Trp53 and Rb1 in distinct cell types of adult mouse lung (2011) Cancer Cell, 19 (6) 754-764.

      10. Sutherland et al., Multiple cells-of-origin of mutant K-Ras-induced mouse lung adenocarcinoma (2014) Proc. Natl. Acad. Sci. USA, 111 (13) 4952-2957.

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      • Abstract
      • Presentation
      • Slides

      Abstract

      Establishment of preclinical lung cancer models that closely match patient tumor biology is imperative for developing therapeutic strategies with the most translational relevance. Non small cell lung cancer (NSCLC) cell lines grown under 2D conditions or as cell line-derived xenografts (CDXs) are the most widely used models. They have been complemented with murine models engineered to develop lung cancer after introduction of specific genetic alterations (GEMMs). Lung cancer cell lines are readily amenable to mechanistic studies and economical high-throughput drug screening. However, for many NSCLC cell lines, the spectrum of mutations and copy number alterations have drifted considerably relative to patient tumors 1. This finding, in conjunction with long-term adaption to heterologous in vitro growth conditions, raise concerns about the extent to which cell line biology and potential drug responses may have deviated from clinical tumors. GEMMs are powerful tools for studying specific oncogenic mechanisms in isolation in vivo, but these models lack the intratumoral heterogeneity of patient tumors, which is thought to play a major role in the development of drug resistance. Furthermore, ideally, GEMMs should be constructed using the appropriate cell of origin context, which is challenging, as there are differences in the compositions of human and murine airways, and the cellular origins for most forms of lung cancer have not been established.

      NSCLC patient-derived xenografts (PDXs) overcome some of the limitations of these other models. They show much less genetic drift than cell lines, and their mRNA expression and the phospho-tyrosyl proteome more closely match patient tumors 1, 2. We have established a large collection of NSCLC PDXs from surgically resected tumors and endobronchial ultrasound-guided (EBUS) and CT-guided biopsies. Tumor specimens were initially implanted in the subcutaneous flanks of NSG mice (NOD SCID gamma, non-obese diabetic severe combined immunodeficiency, gamma). The PDX tumors have been viably cryopreserved and can be serially passaged in NOD SCID mice. Most of our collection comprises the major histologic subtypes of NSCLC [52 adenocarcinomas (LUAD) and 62 squamous cell carcinomas (LUSC)]. They, along with the primary patient tumors, are being molecularly profiled at multiple levels so that they can be optimally used for personalized medicine studies and novel integrated approaches to understand NSCLC pathogenesis, prognosis, and treatment. These levels include copy number variations, exome mutations, DNA methylation, mRNA and miRNA expression, and proteomics. In general, the PDX models recapitulate the mutation spectrum, copy number variations, and gene expression of matched patient histologies. They also recapitulate sensitivity and resistance to known targeted therapeutics (e.g. EGFR inhibitors), and thus, can be used to dissect mechanisms underlying differential drug responses. Such studies are ongoing, including investigation of potentially new biomarker-targeted therapeutic combinations. We have also found that not all patient tumor fragments engraft successfully, and that successful engraftment correlates with poor prognosis of the patient 3. We are using this relationship to discover a new molecular fingerprint to predict clinical outcome, as well as understand the bases that distinguish less and more aggressive tumor behavior.

      In parallel, we have developed methods to grow organoids from primary patient tumors and PDX models in 3D culture using Matrigel (PDO and XDO, respectively). For LUAD, both the PDO and XDO success rate of establishing bona fide organoid models is ~20%. Our stringent criteria include a minimum capacity of 10 passages and a split ratio of at least 1:3. LUSC has been more difficult to establish as organoid models, with a success rate of 17%, and only from PDXs, so far. Using these methods, we have established 4 models of each histology, which we have confirmed form tumors when transplanted into mice. Molecular profiling indicates that the organoids maintain the same mutation spectrum and copy number variations of their parental tumor tissue. These models offer distinct advantages over PDXs and cell lines. As compared to standard 2D cultures, they recapitulate the appropriate tissue histology, and thus, possibly clinically relevant growth control mechanisms, even while growing ex vivo. This notion is further supported by the ex vivo conditions supporting gene expression patterns, which allows the organoids to be segregated into their respective tumor histologies when using signatures derived from patient or PDX material. Given the low cost, rapid growth rates, and ease of in vitro manipulation, these models are ideally suited for rapid discovery and testing of new therapeutic strategies that can be matched to specific patient molecular profiles.

      In summary, generation of molecularly profiled PDX and organoid models offer great opportunity for translational and personalized medicine in NSCLC.

      1. Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318-1325 (2015).

      2. Wang, D. et al. Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors. Int. J. Cancer 140, 662-673 (2017).

      3. John, T. et al. The ability to form primary tumor xenografts is predictive of increased risk of disease recurrence in early-stage non-small cell lung cancer. Clin. Cancer Res. 17, 134-141 (2011).

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    MTE14 - Nodule Management (Pro Con Debate and Case Presentations) (Ticketed Session) (ID 824)

    • Event: WCLC 2018
    • Type: Meet the Expert Session
    • Track: Screening and Early Detection
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/25/2018, 07:00 - 08:00, Room 206 F
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      MTE14.01 - Nodule Management (Pro Con Debate and Case Presentations) (Now Available) (ID 11570)

      07:00 - 08:00  |  Presenting Author(s): Stephen Lam, Matthijs Oudkerk

      • Abstract
      • Presentation
      • Slides

      Abstract

      A recent observation study on the management of lung nodules 8 mm to 20 mm by community pulmonologists in the US showed a high benign biopsy rate of 62% and benign surgical resection rate of 35%. Furthermore, the surgical resection rates were similar irrespective of the pre-test probability of malignancy risk.1 The study suggested there is a lack of adherence to nodule management guidelines. However, there are a number of lung nodule management guidelines and lung nodule malignancy risk prediction tools.2-6 Some are based on 2D diameter size measurement while others used volumetric measurement or a combination of both. New nodules with a prior negative CT have a higher probability of malignancy even at a smaller size. 7-9 Compares to baseline screen, the malignancy risk of new nodules is higher for nodules <8mm.9 Computer assisted diagnostic (CAD) tools facilitates volume measurement and reduce inter-observer variability but they may not be generally available. Volumetric measurement is particularly useful for comparison of serial scans for evidence of growth. Growth independent nodule characteristics such as right upper lung and central distribution may further improve volume based new nodule malignancy prediction. Nodule size and growth are the most important parameters for malignancy.

      To measure size accurately especially to determine changes in volume, it is necessary to address standardization of technical requirements related to the scanners and image acquisition protocols.10 The action thresholds for early recall CT imaging study, PET/CT or biopsy vary in different guidelines with major differences for non-solid nodules making it difficult for clinicians to remember or apply. Therefore, a lack of adherence to guideline recommendations could be related to a lack of clarity of guidelines. To facilitate implementation, there is a need to have an integrated nodule malignancy risk tool that takes into account prior LDCT history when there is more than a baseline LDCT.

      In this session, the important issues regarding which risk model should be applied and which nodule management approach should be used (e.g. diameter or volume) for baseline and new nodules will be discussed through case presentations.

      References:

      1. Tanner NT, Aggarwal J, Gould MK, Kearney P, Diette G, Anil Vachani A, Fang KC, Silvestri GA. Management of Pulmonary Nodules by Community Pulmonologists. A Multicenter Observational Study. Chest 2015:148(6):1405-1414.

      2. MacMahon H, Naidich DP, Goo JM, et al (2017). Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology; 284; 228-243.

      3. American College of Radiology. Lung CT Screening Reporting and Data System (Lung-RADS). Accessed 23rd June 2017 from www.acr.og/quality-saftey/resources/lungRADS.

      4. Callister MEJ, Baldwin DR, Akram AR et al (2015). BTS guidelines for investigation and management of pulmonary nodules. Thorax; 70:ii1-ii54. Doi: 10.1136/thoraxjnl-2015-207168.

      5. Horeweg N, van der Aalst CM, Vliegenthart R, et al. Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial. Eur Respir J 2013;42:1659-67.

      6. McWilliams AM, Tammemägi MC, Mayo JR, et al (2013). Probability of cancer in pulmonary nodules detected on first screening CT. N Eng J Med; 369: 910-919.

      7. Walter JE, Heuvelmans MA, Bock GH, Yousaf-Khan U, Groen HJM, Aalst CMV, Nackaerts K, Ooijen PMAV, Koning HJ, Vliegenthart R, Oudkerk M. Characteristics of new solid nodules detected in incidence screening rounds of low-dose CT lung cancer screening: the NELSON study.Thorax. 2018 Apr 16. pii: thoraxjnl-2017-211376. doi: 10.1136/thoraxjnl-2017-211376. [Epub ahead of print]

      8. Walter JE, Heuvelmans MA, de Jong PA, Vliegenthart R, van Ooijen PMA, Peters RB, Ten Haaf K, Yousaf-Khan U, van der Aalst CM, de Bock GH, Mali W, Groen HJM, de Koning HJ, Oudkerk M. Occurrence and lung cancer probability of new solid nodules at incidence screening with low-dose CT: analysis of data from the randomised, controlled NELSON trial. Lancet Oncol. 2016 Jul;17(7):907-916.

      9. Paul F. Pinsky PF, Gierada DF, Nath PH, Munden R. Lung Cancer Risk Associated With New Solid Nodules in the National Lung Screening Trial. AJR 2017; 209:1009–1014.

      10. Alexander A. Bankier AA, MacMahon H, Goo JM, Rubin GD, Schaefer-Prokop CM, Naidich DP. Recommendations for measuring pulmonary nodules at CT: A Statement from the Fleischner Society. Radiology 2017 Nov;285(2):584-600.

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