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John Kirkpatrick Field

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    S01 - IASLC CT Screening Symposium: Forefront Advances in Lung Cancer Screening (Ticketed Session) (ID 853)

    • Event: WCLC 2018
    • Type: Symposium
    • Track: Screening and Early Detection
    • Presentations: 7
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      S01.04 - Lung Cancer Screening: 1999 to Date – What Have We Learnt? (ID 11885)

      07:35 - 07:50  |  Presenting Author(s): David F Yankelevitz

      • Abstract

      Abstract

      In 1999, ELCAP published their initial results from baseline screening. It found that in a cohort of 1000 participants approximately 85% of the cancers could be diagnosed as clinical Stage I, and that compared with chest radiography found many more of the cancers. In a subsequent study the expanded I-ELCAP found that the long term survival as a measure of cure rate approached 80%. The publicity associated with this initial study was quite large and led to the initiation of several other trials including the NLST. The NLST published their results in 2011 and based this, screening was endorsed by insurers in the US and now other countries are similarly following suit. However, despite the positive result of the NLST, and reimbursement from insurers, screening has had extremely limited uptake in the US, with only approximately 2% of those eligible (among a restricted population) are being screened. Thus, we face a situation where the most common cancer killer has been studied in the most expensive screening trial ever performed which had a positive result, insurers are reimbursing for it, and few people are having it done.

      With lung cancer screening being touted as a major breakthrough in the war on cancer the question naturally arises as to why it is not being performed more frequently. There have been many reasons to explain the poor uptake, ranging from merely a slow start but expected steady increase, lack of awareness by the clinician or potential screenee, obstacles such as the shard decision making requirement, too many potential harms, and lack of significant benefits.

      This lack of perceived significant benefit is perhaps the most important aspect, since without a substantial benefit, even if the harms were minimized, why would anyone get screened and why would a clinician recommend it. It seems that this is clearly influencing the decision not to be screened as many experts and even guideline organizations consider the benefits to not be sufficient enough so as to recommend the screening. Even CMS considered the balance of the risks and benefits so tenuous that they took the unique step of requiring a shared decision making process to be included as necessary for reimbursement so that a person could balance the risks and benefits.

      It is this aspect of benefit that needs to be considered more carefully when explaining it to a potential screenee. Current decision aids, which are required as part of a shared decision making process, in the US and Canada rely almost exclusively on the NLST result and attempt to convert its findings into more visual aids. However, in translating those NLST results, it needs to be understood that they were highly dependent on the design parameters of the study itself, namely 3 rounds of screening and 6.5 years of follow-up. When these parameters change so do the benefits. In the US, current recommendations for screening include annual screening over the period of eligibility for the participant (although for Canada it is restricted to 3 years). Under the circumstance of continued annual screening, the reduction in mortality begins to approach the estimated cure rate for the cancer. It is this feature of cure rate that is really what is most important to any person interested in being screened, and it is substantially higher than the mortality reduction seen in a randomized trial where by necessity the mortality reduction is diluted by the time interval after screening has stopped and cancers are still being followed, and also by not including those cancers that are relatively slow growing and cured as a result of early treatment but not counted towards the mortality reduction because the trial has concluded before their counterpart in the control arm has died. Based on these considerations, it is possible to have a cancer that is 100% curable when screen detected, yet the trial may only show a 20% (or even lower) mortality reduction. Thus, there is inherently no incompatibility between the 80% cure rate seen in the I-ELCAP compared with the 20% mortality reduction seen in the NLST. The simple conversion of the 20% mortality reduction found in NLST into a cure rate as is so commonly done when explaining the benefit to a person interested in screening is highly misleading. The cure rate, which is the clinically relevant feature, is higher. This coupled with the way in which harms are currently expressed, based again almost solely on those NLST results has the effect of amplifying harms at the same time the benefits are being underestimated and surely affect the perception of overall value of CT screening both for physicians as well as people who might be interested.

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      S01.07 - The U19 Plans for Integration of Biomarkers into Future Lung Cancer Screening (ID 11888)

      08:00 - 08:50  |  Presenting Author(s): Rayjean J. Hung, Paul Brennan, Christopher Ian Amos

      • Abstract

      Abstract

      We are performing a series of three integrated research projects with the unifying goal of reducing mortality from LC by applying targeted approaches to its prevention or early detection. These projects study (1) genetic susceptibility to nicotine dependence and lung cancer, (2) biomarkers for early detection, and (3) application of the results for LC screening. This proposal leverages an extensive collaborative framework and wealth of data from the International Lung Cancer Consortium (ILCCO), the Transdisciplinary Research in Cancer of the Lung (TRICL) Consortium and the Lung Cancer Cohort Consortium (LC3). Epidemiological data from 60 LC studies have been harmonized within ILCCO including 71,000 cases and more than 1 million cohort individuals.

      Aims and Results

      Project 1: Genomic Predictors of Smoking and Lung Cancer Risk. This project extends and augments genomic analyses that have been completed on 16,000 LC cases and 50,000 controls and extensively characterizes the contribution that genetic variation makes to LC susceptibility. The four aims are. Aim 1: To precisely characterize the contribution of common genetic variation to LC etiology. We will analyze a GWAS of LC of 47,506 genotyped LC cases and 63,687 controls. Aim 2: To investigate uncommon genetic variants using imputation approaches. Aim 3: To identify genetic effects on smoking behavior. Aim 4: To characterize joint effects of environmental and genetic interactions on LC risk. For this aim we will take advantage of novel statistical approaches (Mendelian Randomization, Mediation analysis, gene by environment interactions and pathway based analyses) developed by our team to provide a comprehensive approach to evaluating the impact of environmental factors according to genetic background. Recent findings from project 1 include identification of 10 new loci influencing lung cancer risk, the identification of 3 novel gene-smoking interactions contributing to lung cancer risk, identification and validation of two rare variants that convey an over four fold higher risk for lung cancer among carriers, and Mendelian randomization studies that show excess BMI and shorter telomere lengths increase lung cancer risk in a histology-dependent fashion.

      Project 2: Biomarkers of Lung Cancer Risk. Multiple preliminary studies have implicated a wide range of circulating biomarkers in risk prediction for lung cancer. In Project 2, we hypothesize that a comprehensive and extensively validated risk prediction model that incorporates such risk biomarkers has the potential to substantially improve the selection of subjects at a high risk of developing LC and that these individuals are most likely to benefit from CT screening. This project involves three aims. Aim 1: To organize the LC3, including identifying the study population of 2,300 former and current smoking LC cases that were diagnosed within 5 years of donating their blood sample along with one smoking-matched control per case; and organize sample shipments and database preparation. Aim 2: To replicate a comprehensive panel of promising risk biomarkers and identify those that may be useful for risk prediction. This will involve assaying pre-diagnostic plasma samples for immune biomarkers, protein biomarkers such as pro-surfactant protein B, micro RNAs, methylation markers, and 34 additional promising biomarkers implicated in lung cancer. We will base this initial analysis on 800 case-control pairs from three LC3 cohorts, and define a panel of replicated risk biomarkers that provide non-redundant information on disease risk. Aim 3: To extensively evaluate all replicated risk biomarkers from Aim 2, identifying a minimum set of validated risk biomarkers, and ultimately evaluate the extent to which they improve risk prediction models. This will involve performing additional assays for 1,500 additional case-control pairs selected from 16 separate LC3 cohorts. The final outcome of this work will be risk prediction models incorporating a distinct set of biomarkers that provide meaningful information on disease risk, and these biomarkers will finally be evaluated in CT screening studies in collaboration with Project 3. This project recently completed analysis of a set of 4 biomarkers that improve the classification accuracy in prediction of lung cancer risk by 14% compared with a model that only included demographic and smoking information.

      Project 3: Translating Molecular and Clinical Data to Population Lung Cancer Risk Assessment will evaluate radiographic models using data from the National Lung Screening Trial (NLST), lung cancer CT screening programs in British Columbia Cancer Agency (BCCA), Early Detection of Lung Cancer – a Pan-Canadian Study (PanCan), and the International Early Lung Cancer Action Program- Toronto (IELCAP-Toronto) along with the UK Lung Screen Trial (UKLS), and the Dutch Belgian randomized Lung Cancer Screening trial (NELSON) trial. Data from Projects 1-2 will be used to improve the risk prediction model and the nodule probability models. There are 2 specific aims. Aim 1 will establish an integrated risk prediction model to identify individuals at high risk of lung cancer, initially analyzing epidemiological and smoking related phenotypes and then integrating targeted biomarker, genomic profile, and lung function data applied to LC CT screening populations. We will study 950 CT-detected LC patients with biosamples from 46,057 screening individuals. Specific Aim 2 will establish a comprehensive LC probability models for individuals with LDCT-detected non-calcified pulmonary nodules. In this aim we will (a) first establish the 2D diameter-based probability model in N. American CT programs based on 36,481 participants, and then validate it based on 9,576 participants in the European LDCT programs; (b) establish the volume 3D and radiomics-based probability model in European CT programs based on 9,576 participants in European CT programs, and then validate it in the North American CT screening populations; and (c) assess the added predictive value and clinical usefulness of targeted genomic and molecular profiles in both the 2D diameter- and 3D and radiomics volume-based LC probability models based on risk stratification table analysis and decision curve analysis. Finally we will (d) compare the model performance with the existing classification system such as Lung-RADS. This project has developed and evaluated a polygenic risk score using data from project 1 which highly significantly improves risk prediction for lung cancer risk, but has a limited impact on prediction accuracy.

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      S01.10 - EU Position Statement on Lung Cancer Screening (ID 11891)

      09:00 - 09:20  |  Presenting Author(s): Matthijs Oudkerk

      • Abstract
      • Slides

      Abstract not provided

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      S01.14 - Coordination of the Lung Cancer CT Screening Experience (ID 11895)

      09:50 - 10:05  |  Presenting Author(s): Joelle Thirsk Fathi

      • Abstract

      Abstract

      Coordination of the Lung Cancer CT Screening Experience

      Tobacco use, including cigarette smoking is the most preventable cause of cancer in the entire world, contributing to one third of all cancers. While 80-90% of all lung cancers are directly correlated with cigarette smoking, tobacco is also identified as playing a direct role in a multitude of other malignancies and chronic diseases, and resides in the top ten contributors to human suffering, disability and death (U.S. Department of Health & Human Services, 2014). Tobacco will shorten the lives of 50% of its users, resulting in approximately 17,000 people dying every day in the world (Cahn, 2018)

      Since the National Lung Screening Trial data demonstrated that lung cancer screening provides a reduction in mortality in high-risk patients, (National Lung Screening Trial Research Team et al., 2011) interest and momentum in the adoption of lung cancer screening in the U.S. and abroad has been on a slow but upward trajectory. Yet only 2-4% of eligible people are getting screened (Jemal & Fedewa, 2017).

      Lung cancer screening patients, by having met high-risk criteria, are a defined and select population of people who could greatly benefit from a sophisticated and well-orchestrated lung cancer screening experience. Coordination of successful, high quality, and comprehensive care of patients in the screening environment is challenging, but screening represents an enormous opportunity to reduce disability and death from tobacco use. It is critical that transformation and refinement of screening practices occurs, to adapt a comprehensive model which encompasses a broader scope of diagnoses, treatments, and patient education.

      Uptake of lung cancer screening has been slow in the U.S., and education around screening needs to be continually promoted. Additionally, we need to continue to develop and refine the roles and responsibilities of all involved in the screening process, including the patient. Current diagnostic and health information technology allows for more precise, easier, faster, and safer care. In the setting of lung cancer screening, low dose computed tomography of the lung can often provide a snapshot into a patient’s overall health and has the potential to alert the healthcare team and the patient to additional potential disease states, to which we are obligated to address.

      Additionally, lung cancer screening is a unique and ideal opportunity to address tobacco cessation with patients. Technology is critical, but can’t replace coordination of care, patient engagement, and education with this invaluable opportunity for detection of tobacco related diseases and tobacco cessation efforts. Screening requirements and the high incidence of abnormal findings on screening scans represents the need for interprofessional collaboration, and a concert of sequential events and highly coordinated care potentially involving many members of different healthcare teams.

      In the setting of healthcare today, with an emphasis on collaboration and coordination of care, screening should be viewed and treated as a long-term commitment by all parties, and engagement and partnership with patients and fellow referring providers is critical in redefining the patient experience and delivery of care. It is not just about the chest CT; in fact, this is minor compared to the potential to intervene, and even halt disease progression and reduce risk with health behavior modification, while realizing earlier diagnosis and intervention, and saving money and lives.

      Cahn, Z., Drope, J., Hamill, S., Gomeshtapeh, F., Liber, Al, Nargis, N., Stoklosa, M.,. (2018). Health Effects. In J. Drope, Schluger, N., (Ed.), The tobacco atlas (pp. 24-25). Atlanta, Georgia: American Cancer Society.

      Jemal, A., & Fedewa, S. A. (2017). Lung Cancer Screening With Low-Dose Computed Tomography in the United States-2010 to 2015. JAMA Oncol, 3(9), 1278-1281. doi:10.1001/jamaoncol.2016.6416

      National Lung Screening Trial Research Team, Aberle, D. R., Adams, A. M., Berg, C. D., Black, W. C., Clapp, J. D., . . . Sicks, J. D. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 365(5), 395-409. doi:10.1056/NEJMoa1102873

      U.S. Department of Health & Human Services. (2014). The health consequences of smoking—50 years of progress: A report of the Surgeon General 2014, executive summary. Retrieved from http://www.surgeongeneral.gov/library/reports/50-years-of-progress/

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      S01.15 - Integration of Smoking Cessation into Lung Cancer Screening (ID 11896)

      10:05 - 10:20  |  Presenting Author(s): Kate Brain

      • Abstract
      • Slides

      Abstract

      Cigarette smoking is the largest preventable risk factor for lung cancer, disproportionately affecting people from socioeconomically disadvantaged communities. Low dose computed tomography (CT) screening for high risk smokers is now the standard of care in the United States, with implementation pending in Europe. The potential health gains from combined CT lung screening and smoking cessation are considerable. Recent evidence disputes the notion that CT screening offers a “license to smoke” and reveals that engaging with lung screening can give smokers an opportunity to access smoking cessation support at a time when they are likely to be receptive to offers of help. However, considerable challenges remain in identifying methods of engaging high risk smokers in lung screening, and little evidence exists on the optimal design and delivery of effective smoking cessation interventions in this setting. Findings from studies including the United Kingdom Lung Screening trial (UKLS1) will be presented to highlight patient barriers and facilitators to successful integration of smoking cessation within the lung screening pathway, including beliefs and attitudes towards lung screening among high risk smokers, and the impact of abnormal lung scan results. Using the UK-wide Lung Symptom Awareness and Health (LUSH) study example, reflections will also be made on contextual barriers to engaging smokers with comorbid lung conditions living in areas of socioeconomic deprivation. Emerging issues and trends will be presented in methods of recruiting high risk smokers using community-based strategies, and developing personalised materials to support smoking cessation. Novel methods of designing, delivering and testing smoking cessation interventions embedded in the lung screening context will be considered. This presentation will be relevant to clinicians and scientists who are interested in the contribution of behavioural science to optimising lung cancer screening protocols as a teachable moment for smoking cessation, designing evidence-based clinical services to deliver the maximum health benefits for current and future generations.

      1 Brain K, Carter B, Lifford KJ, Burkes O, Devaraj A, Baldwin D, Duffy S, Field JK. Impact of low-dose CT screening on smoking cessation among high-risk participants in the UK Lung Cancer Screening trial. Thorax 2017 72:912–918. http://dx.doi.org/10.1136/thoraxjnl-2016-209690.

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      S01.17 - Session V: Panel Discussion: Next Steps for Lung Screening? (ID 11898)

      10:30 - 11:30  |  Presenting Author(s): Claudia I Henschke, Kwun M Fong, Motoyasu Sagawa, Matthew Eric Callister, Nasser Altorki, Bruce Pyenson, Andrea Katalin Borondy Kitts

      • Abstract
      • Slides

      Abstract not provided

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      S01.18 - IASLC Leads the International Collaboration on Data Sharing (IASLC- ELIC-CCTRR) (ID 11899)

      11:30 - 11:50  |  Presenting Author(s): John Kirkpatrick Field, James L Mulshine

      • Abstract
      • Slides

      Abstract

      The IASLC ELIC-CCTR vision is to create a globally-accessible, privacy-secured environment to enable the analysis and study of extremely large collections of quality-controlled internationally assembled CT lung cancer images and associated biomedical data for research and healthcare delivery. This initiative will rapidly accelerate improvements to the multi-disciplinary management of early curable, lung cancer and other major thoracic diseases. This new research environment will be deployed and used to conduct global studies within the first two years of this project and is designed to one day scale to enabling coherent analysis across millions of cases.

      The current problem is that the implementation and advancement of lung cancer low dose CT screening (LDCT) screening requires large and high-quality collections of data obtained from global populations with currently deployed scanning equipment 1-5. Furthermore, there are new opportunities to develop deep learning methods for lung cancer imaging, which requires large quality-controlled datasets. As a community we have to very aware of the privacy challenges around data sharing. Lack of high quality data has been a barrier to LDCT screening progress.

      The way forward has been developed at the recent IASLC Confederation of CT Screened Patients Registry & Resource (CCTRR) Roundtable Workshop, as outlined in figure 1.

      figure 1.jpg

      IASLC will develop and run a new international collaborative (the ELIC framework) building on the processes established in the successful TNM Staging project. An internationally-federated Hub and Spoke system will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal data itself (i.e. privacy-protecting). No identifiable data ever leaves sources under local governance (PI) control. Existing imaging collections remain in the geographic regions where they were collected, so the resulting environment remains consistent with local regulations without privacy or data disclosure risk. In addition to connecting the world’s largest lung cancer screening registries, which is necessary for exploiting advanced computing capabilities with trustworthy security, enabling the rapid ramp up and participation of new global screening groups.

      The structure will provide the ability to interrogate large, high-quality, and internationally sourced image data sets will allow the lung cancer screening community to identify key insights, publish studies, and make lung cancer recommendations based on potentially millions of screening participants. By validating and distributing common data standards for CT imaging as well as for additional clinical follow-up information, the framework can be applied to the collaborative study of related intrathoracic disease processes.

      1. Henschke CI, McCauley DI, Yankelevitz DF, et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999; 354(9173): 99-105.

      2. National Lung Screening Trial Research T, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011; 365(5): 395-409.

      3. van Klaveren RJ, Oudkerk M, Prokop M, et al. Management of lung nodules detected by volume CT scanning. N Engl J Med 2009; 361(23): 2221-9.

      4. 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(11): 1523-31.

      5. Field JK, Duffy SW, Baldwin DR, et al. The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer. Health Technol Assess 2016; 20(40): 1-146.

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Author of

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    MA06 - PDL1, TMB and DNA Repair (ID 903)

    • Event: WCLC 2018
    • Type: Mini Oral Abstract Session
    • Track: Biology
    • Presentations: 1
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      MA06.10 - Germline Mutation in ATM Affect Lung Cancer Risk with High Effect (ID 12792)

      14:35 - 14:40  |  Author(s): John Kirkpatrick Field

      • Abstract

      Background

      Genome wide association studies have identified several lung cancer susceptibility regions and common variants influencing lung cancer risk. However, few previous studies investigated the association between germline mutations and lung cancer risk.

      Method

      We analyzed data from a case-control study with 19053 lung cancer cases and 15446 healthy controls of European ancestry in a discovery phase and performed a validation analysis using a case-control study comprising 4261 lung cancer cases and 4152 healthy controls of European ancestry for replication. Logistic regression was used to identify germline mutations with high effect within exome regions associated with lung cancer risk.

      Result

      We found rs56009889 in ATM was statistically associated with lung cancer risk in the discovery set (OR = 3.05, P = 3.68 × 10−8) and was nonsignificantly associated with lung cancer risk in the validation set (OR = 1.83, P = 0.16). Stratified analyses by gender with adjustment for age and smoking status showed that females carrying at least one mutated allele of rs56009889 (T/C + T/T) had an increased risk of lung cancer with ORs being 7.77 (95% CI 3.45 - 17.47) in discovery and 6.73 (95% CI 1.46–30.98) in replication, compared to C/C homozygotes among females. Individuals carrying at least one T allele showed a significant 6.9-fold increased risk for lung adenocarcinoma in discovery (adjusted OR = 6.85; 95% CI 4.37 – 10.75) and approximately a 4.9-fold increased risk in replication (adjusted OR = 4.89; 95% CI 2.01 – 11.91). Never smokers with combined genotypes (T/C + T/T) had a greater than 8-fold increased risk of lung cancer in discovery (adjusted OR = 8.03, 95% CI 4.00 – 16.13), while smokers only showed a 2.13-fold increased risk (adjusted OR = 2.13, 95% CI 1.25 – 3.65). In replication, however, the risks from this variant were comparable between smokers and nonsmokers, although the sample size is small for nonsmokers (adjusted OR = 2.16; 95% CI 0.48 – 9.79 for never-smokers and adjusted OR = 2.07; 95% CI 0.66 – 6.52 for smokers). All the T/T homozygotes of rs56009889 developed lung adenocarcinoma in discovery (P = 0.036). The association exhibited a dose-response relationship between the number of T allele of rs56009889 and lung cancer risk in discovery (Ptrend = 1.07 x 10 -9).

      Conclusion

      rs56009889 highly affected the risk of lung cancer, mainly of lung adenocarcinoma, primarily in women and never smokers. These germline mutations provide important insights for the prevention of lung cancer.

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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: 1
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      MS29.03 - Polygenic Risk Score for Risk Assessment (ID 11527)

      14:00 - 14:15  |  Author(s): John Kirkpatrick Field

      • Abstract

      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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    P1.04 - Immunooncology (Not CME Accredited Session) (ID 936)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/24/2018, 16:45 - 18:00, Exhibit Hall
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      P1.04-24 - Digital Core Needle-Biopsy to Assess PD-L1 Expression in Non-Small Cell Lung Cancer: Optimal Sampling and Need for Re-Biopsy (ID 12059)

      16:45 - 18:00  |  Author(s): John Kirkpatrick Field

      • Abstract

      Background

      Assessing expression of PD-L1 on tumour cell membranes by immunochemistry is an important complementary or crucial companion diagnostic test to guide the use of immune modulating checkpoint inhibitors in the treatment of non-small cell lung cancer (NSCLC). Unfortunately, the known temporal and spatial heterogeneity of PD-L1 expression raises the important question of how to ensure that the small biopsy specimens with which this assessment is usually made are adequately representative of PD-L1 expression by the whole tumour.

      Method

      Expression of PD-L1 was assessed in sections of 94 tissue blocks from 50 primary pulmonary adenocarcinomas using the Ventana SP263 antibody and a validated protocol. Scoring was performed by two appropriately-trained pathologists with extensive experience in its interpretation. After conventional assessment, slides were digitally scanned and divided into squares of 1mm² area to form a digital database (mean of 150 data-points per tumour), which were assigned co-ordinates and re-scored. By these means, multiple, “digital core biopsies” (DCBx) approximating a 17 gauge needle were simulated in sequential fashion, and expression in these was compared to that in the whole tumour and categorised by current UK prescribing guidelines*

      Result

      PD-L1 score (%)

      Total number of cases

      Cases where PD-L1% from single DCBx changed scoring category vs whole tumour*

      Cases where PD-L1% from two DCBx changed scoring category vs whole tumour*

      Cases where PD-L1% from three DCBx changed scoring category vs whole tumour*

      Cases where PD-L1% from four DCBx changed scoring category vs whole tumour*

      Cases where PD-L1% from five DCBx changed scoring category vs whole tumour*

      Focal expression primary pattern in non-correlative cases

      <1

      14

      2

      2

      2

      2

      2

      Y

      1-10

      13

      6

      3

      1

      1

      0

      Y

      11-49

      10

      1

      1

      0

      0

      0

      Y

      50-100

      13

      0

      0

      0

      0

      0

      n/a

      All

      50

      9 (18%)

      6 (12%)

      3 (6%)

      3 (6%)

      2 (4%)

      PD-L1, programmed death ligand 1; DCBx, Digital Core Biopsy

      *Based on pembrolizumab categories as: 1st line ≥50%, 2nd line 1-49%; nivolumab categories as: ≥1% (for adenocarcinoma)

      Conclusion

      In the majority of cases, three digital core biopsies achieved closest correlation with the whole tumour, with little greater accuracy achieved by assessing four cores or more. Correlation was weakest when expression was low and very focal, an important consideration in view of the importance of the ‘1% cut-off’ used commonly to guide immune checkpoint therapy. Using this model as a guide, a single good quality biopsy (2x10mm² area) is sufficient for most tumours scoring 11% or greater PD-L1 expression. However, in the lower range of expression, re-biopsy might be routinely considered if there is doubt about specimen adequacy.

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    P2.09 - Pathology (Not CME Accredited Session) (ID 958)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall
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      P2.09-19 - Utilising Heterogeneity: Using a Digital Database of Lung Cancers and Immune Profile to Complement Subjective Assessment (ID 12594)

      16:45 - 18:00  |  Author(s): John Kirkpatrick Field

      • Abstract

      Background

      Traditional pathological assessment of tissue sections involves subjective analysis of complex and heterogeneous features, typified by the challenge of ‘measuring’ PD-L1 expression in non-small cell lung cancer (NSCLC) as a guide to its treatment with immune checkpoint inhibitors. Such heterogeneity is generally perceived as a problem but might, in fact, reflect not only biologically important epitope variation, but also important features of the tumour microenvironment and, by extension, be a tool for predicting behaviour. In-depth analysis of a single slide of a tumour by digital pathology, image analysis and machine learning makes more accurate and meaningful analysis a possibility.

      Method

      Expression of PD-L1 was assessed by immunochemistry in 250 sections from 137 resected NSCLCs using the Ventana SP263 antibody and a validated protocol and its distribution compared with morphology as revealed by corresponding H&E-stained sections. Slides were scanned to create a digital image using Aperio Scanscope with division of images into 1mm² squares using QuPath opensource software, each of which was assigned x and y co-ordinates. Squares were assessed subjectively by two pathologists for morphological features and PD-L1 expression and also subject to automatic image analysis including cell counting and membrane detection. Co-ordinates and values were stored in Microsoft Excel and a digital database was generated for every slide. In-depth analysis of digital data points was achieved using “R” software custom algorithms that included simulating biopsy sampling and applying spatial analysis packages.

      Result

      The resulting database, comprising approximately 30,000 data points from the 137 tumours, is being used to simulate needle-core biopsies, assess heterogeneity of PD-L1 expression and relate this to the tumour micro-environment including immune cell populations, immune signature and tumour mutational burden.

      Conclusion

      The vast amount of information in every NSCLC cannot be extracted by conventional histopathological analysis. By utilising new technologies and considering alternative paradigms for data acquisition, powerful new approaches may be developed that give information pertaining to not just diagnostic and prognostic features of a tumour, but behavioural traits including likely responses and resistances to novel drugs such as immune checkpoint inhibitors. The methodology described here is an attempt to extract these data in a more objective way and complement the still crucial subjective analysis that is traditionally the prerogative of the histopathologist.

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    P3.11 - Screening and Early Detection (Not CME Accredited Session) (ID 977)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
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      P3.11-13 - Liverpool Identifies the Hard to Reach Population at Risk of Developing Lung Cancer. (ID 13173)

      12:00 - 13:30  |  Author(s): John Kirkpatrick Field

      • Abstract

      Background

      The Liverpool Healthy Lung Programme (LHLP) is an initiative aimed at improving respiratory health and diagnosing respiratory disease at a more treatable stage, taken by the Liverpool Clinical Commissioning Group (CCG) working with communities across Liverpool. Liverpool has one of the highest respiratory morbidity rates in England, with double the national lung cancer incidence, particularly in lower socioeconomic groups. The Liverpool Healthy Lung Programme was initiated in response to both the clinical problem and the health inequality.

      Method

      General practice records targeted ever-smokers and subjects with chronic obstructive pulmonary disease (COPD), 58-70y and were invited for 45-minute lung health check. Positive lifestyle messages were promoted; 5-year personal lung cancer risk calculated (www.MyLungRisk.org using LLPv2 risk model). Those who trigger the 5% threshold were offered a LDCT-scan. Spirometry was used to assess lung function (FEV1/FVC); those with abnormal results referred for potentially definitive diagnosis of COPD. Smoking advice and referrals to smoking cessation clinics were provided. Patients CT detected nodules were managed, based on BTS guidelines; referred to MDT for work-up and significant other findings (SoF) were analysed in detail.

      Result

      3,591 Healthy Lung Programme consultations (consented). 11,526 people were invited, 4,566 (40%) attended. 1,853 (52%) were male, 2,897 (81%) in the most deprived IMD quintile. 832 (23%) subjects had an existing diagnosis of COPD and 527 (15%) had a previous diagnosis of cancer. 1,173 (33%) subjects had a family history of cancer.

      1,548 (99.3% meeting LLPv2 5% risk criterion) were offered CT scan. 119 (9%) patients required further investigations (follow-up CT scan at 3 or 12 months, or immediate MDT referral), 25 (1.9% undergoing CT scan) were diagnosed with lung cancer (11 have suspected lung cancer, undergoing further investigations). Analysis of a sub-set of the SoF findings were followed up and indicated benefit to participants.

      Conclusion

      The results suggest that it is feasible to achieve similar clinical outcome benefits to those observed in the US trial of LDCT screening for lung cancer, with lesser harms in terms of unnecessary diagnostic activity. However, this needs confirmation with extended follow-up, larger numbers of lung cancers diagnosed, and the addition of mortality data. Additional randomised trial results would also add to the precision of estimation of benefits and harms, in particular mortality results from the large European trial, NELSON. In the meantime, the results of LHLP suggest that it is succeeding in early detection of both COPD and lung cancer.

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    PL02 - Presidential Symposium - Top 5 Abstracts (ID 850)

    • Event: WCLC 2018
    • Type: Plenary Session
    • Track: Advanced NSCLC
    • Presentations: 1
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      PL02.06 - Discussant - PL02.05 (ID 14738)

      08:55 - 09:00  |  Presenting Author(s): John Kirkpatrick Field

      • Abstract

      Abstract not provided

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    S01 - IASLC CT Screening Symposium: Forefront Advances in Lung Cancer Screening (Ticketed Session) (ID 853)

    • Event: WCLC 2018
    • Type: Symposium
    • Track: Screening and Early Detection
    • Presentations: 1
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      S01.18 - IASLC Leads the International Collaboration on Data Sharing (IASLC- ELIC-CCTRR) (ID 11899)

      11:30 - 11:50  |  Presenting Author(s): John Kirkpatrick Field

      • Abstract
      • Slides

      Abstract

      The IASLC ELIC-CCTR vision is to create a globally-accessible, privacy-secured environment to enable the analysis and study of extremely large collections of quality-controlled internationally assembled CT lung cancer images and associated biomedical data for research and healthcare delivery. This initiative will rapidly accelerate improvements to the multi-disciplinary management of early curable, lung cancer and other major thoracic diseases. This new research environment will be deployed and used to conduct global studies within the first two years of this project and is designed to one day scale to enabling coherent analysis across millions of cases.

      The current problem is that the implementation and advancement of lung cancer low dose CT screening (LDCT) screening requires large and high-quality collections of data obtained from global populations with currently deployed scanning equipment 1-5. Furthermore, there are new opportunities to develop deep learning methods for lung cancer imaging, which requires large quality-controlled datasets. As a community we have to very aware of the privacy challenges around data sharing. Lack of high quality data has been a barrier to LDCT screening progress.

      The way forward has been developed at the recent IASLC Confederation of CT Screened Patients Registry & Resource (CCTRR) Roundtable Workshop, as outlined in figure 1.

      figure 1.jpg

      IASLC will develop and run a new international collaborative (the ELIC framework) building on the processes established in the successful TNM Staging project. An internationally-federated Hub and Spoke system will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal data itself (i.e. privacy-protecting). No identifiable data ever leaves sources under local governance (PI) control. Existing imaging collections remain in the geographic regions where they were collected, so the resulting environment remains consistent with local regulations without privacy or data disclosure risk. In addition to connecting the world’s largest lung cancer screening registries, which is necessary for exploiting advanced computing capabilities with trustworthy security, enabling the rapid ramp up and participation of new global screening groups.

      The structure will provide the ability to interrogate large, high-quality, and internationally sourced image data sets will allow the lung cancer screening community to identify key insights, publish studies, and make lung cancer recommendations based on potentially millions of screening participants. By validating and distributing common data standards for CT imaging as well as for additional clinical follow-up information, the framework can be applied to the collaborative study of related intrathoracic disease processes.

      1. Henschke CI, McCauley DI, Yankelevitz DF, et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999; 354(9173): 99-105.

      2. National Lung Screening Trial Research T, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011; 365(5): 395-409.

      3. van Klaveren RJ, Oudkerk M, Prokop M, et al. Management of lung nodules detected by volume CT scanning. N Engl J Med 2009; 361(23): 2221-9.

      4. 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(11): 1523-31.

      5. Field JK, Duffy SW, Baldwin DR, et al. The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer. Health Technol Assess 2016; 20(40): 1-146.

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