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Erik Thunnissen
Moderator of
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MS17 - Pathology of the Future (ID 80)
- Event: WCLC 2019
- Type: Mini Symposium
- Track: Pathology
- Presentations: 5
- Now Available
- Moderators:Erik Thunnissen, Teh-Ying Chou
- Coordinates: 9/10/2019, 14:30 - 16:00, Interlaken (1988)
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MS17.01 - Role of Liquid Biopsy in the Pathology Diagnosis Workflow (Including DNA, RNA Ad Exosomes) (Now Available) (ID 3537)
14:30 - 16:00 | Presenting Author(s): Fernando Lopez-Rios
- Abstract
- Presentation
Abstract
Although liquid biopsy approaches have considerable potential to improve patient care, integrating them into the pathology diagnosis workflow could be a challenge. In the presentation I will address the most frequent barriers for inmediate implementation of the recommendations released by the IASLC and other academic groups. Briefly, plasma is preferred over serum for ctDNA and ctRNA extraction. The choice of analytical methodology should balance availability, cost, turnaround time, sensitivity and specificity. Recent cross-platform comparisons will be presented because technical factors could explain both discordance between assays and with tissue-based genotyping. The results of liquid biopsies should follow the standards of molecular pathology reporting. Taking into consideration that the number of pathology and biomarker reports per patient will be growing over the next few years, it is important to integrate all of them before discussion of treatment options take place at the molecular tumour board. The recent tier classifications of molecular alterations released by professional organisations could help implement this strategy.
References
Abbosh C, Birkbak NJ, Swanton C. Early stage NSCLC - challenges to implementing ctDNA-based screening and MRD detection. Nat Rev Clin Oncol 2018; 15: 577-586.
Aggarwal C, Thompson JC, Black TA, et al. Clinical Implications of Plasma-Based Genotyping With the Delivery of Personalized Therapy in Metastatic Non-Small Cell Lung Cancer. JAMA Oncol 2018. doi: 10.1001/jamaoncol.2018.4305.
Laufer-Geva S, Rozenblum AB, Twito T, et al. The Clinical Impact of Comprehensive Genomic Testing of Circulating Cell-Free DNA in Advanced Lung Cancer. J Thorac Oncol 2018; 13: 1705-1716.
Leighl NB, Page RD, Raymond VM, et al. Clinical Utility of Comprehensive Cell-free DNA Analysis to Identify Genomic Biomarkers in Patients with Newly Diagnosed Metastatic Non-small Cell Lung Cancer. Clin Cancer Res 2019. doi: 10.1158/1078-0432.
Li BT, Janku F, Jung B, et al. Ultra-deep next-generation sequencing of plasma cell-free DNA in patients with advanced lung cancers: results from the Actionable Genome Consortium. Ann Oncol 2019; 30: 597-603.
Rolfo C, Mack PC, Scagliotti GV, et al. Liquid Biopsy for Advanced Non-Small Cell Lung Cancer (NSCLC): A Statement Paper from the IASLC. J Thorac Oncol 2018; 13: 1248-1268.
Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol 2017; 14: 531-548.
Torga G, Pienta KJ. Patient-Paired Sample Congruence Between 2 Commercial Liquid Biopsy Tests. JAMA Oncol 2018; 4: 868-870.
Sabari JK, Offin M, Stephens D, et al. A Prospective Study of Circulating Tumor DNA to Guide Matched Targeted Therapy in Lung Cancers. J Natl Cancer Inst 2018. doi: 10.1093/jnci/djy156.
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MS17.02 - Major Pathological Evaluation in Neoadjuvant Immunotherapy (Now Available) (ID 3538)
14:30 - 16:00 | Presenting Author(s): Jose Ramirez
- Abstract
- Presentation
Abstract
The pathologists play different roles in the care of cancer patients. The main part of this activity is to make the diagnosis of the tumor type, followed by the support to the clinicians in the staging of the patient.
In the group of patients that receive Chemotherapy (CT) and / or Radiotherapy (RT) before surgery, the pathologist has to evaluate the results of these treatments by evaluating the tumor bed and the response of the lung.
There is a lot of experience in the literature referred to the different types of pathological response to lung cancer treatment after the use of either CT or RT. There are guidelines for the pathologists in order to give an objective evaluation of the treatment results (1). All of them ask the pathologists to evaluate the percentage of viable tumor cells, necrosis and stromal reaction. We can also find different attempts to find molecular markers useful for predicting survival after these traditional treatments (2).
In order to have an objective evaluation of the lung cancer patients after CT in 2014 emerged the concept of Major Pathological Response (MPR) as a possible predictor of overall survival (3). This term was applied to describe those patients with a viability of 10% of the tumor cells after neoadjuvant CT. In this report, they propose the MPR as a surrogate endpoint. Since that, the concept of MPR has been used in different reports and assays, becoming a usual parameter for patients with lung resections after receiving chemotherapy.
The percentage of viable cells became an important data that has to be evaluated with accuracy in order to avoid subjective results. One of the best ways is to use any of the commercial systems to make measurements on the slides. In our department, we use digital slides to get objective and reproducible data for this type of evaluation.
The recent implementation of immunotherapy (IT) for NSCLC that is being applied to an increasing number of patients all over the world makes it necessary to study deeply its morphological effects over de tumor in lung specimens. As most of the patients will never go to the operating room, it becomes important to be aware of the limited knowledge that we have so far.
There are some reports comparing the effects of the traditional chemotherapy with immunotherapy. One of these studies shows that the pathologist should look for the same features that are routinely evaluated in the cases after CT (4). The pathologist might evaluate the classic features (viable cells, necrosis and stromal reaction) and other characteristics such as macrophages, cholesterol clefts, lymphoid aggregates, giant cells and neovascularization. In this review, they did not find important differences between the type of morphological changes in both types of treatment, so they propose to use the same parameters in CT and in IT
A recent report proposes the concept of Immuno related Pathological Response Criteria (irPRC) (5). They pay attention to the Immune activation with dense lymphoid infiltrate, macrophages and tertiary lymphoid structures as the main characteristics. They also evaluate the massive tumor cell death with destructive features such as cholesterol clefts and those indicative of tissue repair with neovascularization and fibrosis.
Last year a review collected different assays done with neoadjuvant IT in patients with resectable lung cancer (6). There is no enough experience in these special groups of patients, as today the IT is given only to advanced lung carcinoma patients.
Conclusions: At the present time there are no guidelines for the evaluation of lung tissues after IT. The pathologists have to be aware of the effects of these new biological treatments for lung cancer patients, in order to give as much information as possible in the short number of cases that are seen in the real clinical situation. The previous experience in resected lungs after neoadjuvant therapy is not enough to evaluate the cases after IT. The data obtained from these lung specimens have to give more information in order to improve the knowledge of the effects of these new therapies.
References:
1. Pataer A, Kalhor N, Correa AM et al. Histopathologic Response Criteria Predict survival of Patients with Resected Lung Cancer After Neoadjuvant Chemotherapy. J Thorac Oncol. 2012;7: 825–832.
2. Pataer A, Shao R, Correa AM et al. Major pathologic response and RAD51 predict survival in lung cancer patients receiving neoadjuvant chemotherapy. Cancer Medicine 2018; 7(6):2405–2414.
3. Hellman MD, Chaft JE, William NW et al. Pathologic response after neoadjuvant chemotherapy in resectable non-small cell lung cancers: proposal for the use of “major pathologic response” as a surrogate endpoint. Lancet Oncol. 2014 January ; 15(1): e42–e50.
4. Weissferdt A, Sepesi B, Pataer A et al. Pathologic assessment following neoadjuvant immunotherapy or chemotherapy demonstrates similar patterns in non-small cell lung cancer (NSCLC). Ann Oncol 2018: 29(S8).
5. Cottrell TR, Thompson ED, Forde PM et al. Pathologic features of response to neoadjuvant anti-PD-1 in resected non-small-cell lung carcinoma: a proposal for quantitative immune-related pathologic response criteria (irPRC). Annals of Oncology 2018; 29: 1853–1860.
6. Owen D, Chaft JE. Immunotherapy in surgically resectable non-small cell lung cancer. J Thorac Dis 2018; 10(S3): 404-411.
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MS17.03 - Quantitative Image Analysis, Image-Based Profiling Including 3D Digital Printing and AI (Now Available) (ID 3539)
14:30 - 16:00 | Presenting Author(s): Sylvie Lantuejoul
- Abstract
- Presentation
Abstract
Digital pathology and AI
Digital pathology refers to the use of computer stations to analyze images of whole slides scanned at high resolution for teaching or research purposes; these images are analyzed by algorithms that allow standardization and reproducibility of the analyses. Recently, artificial intelligence has been used to build even more powerful algorithms. The idea is not to reproduce the visual analysis of pathologists but to improve it and to identify new predictive or prognostic morphological characteristics or to couple them with genomic analysis to better stratify tumours (1,2).
Technical considerations
The digital workflow requires specific equipement (slide scanner, image storage and digital pathology workstation). Image analysis needs well-standardized pre-analytical conditions, especially if IHC stainings are analyzed, where colour variations and absence of background noise must be controlled during different validation steps. Some methods exist to correct artifacts such as section fold, fuzzy area. SOPs (standardised operating procedures) and regulations must also be respected concerning the scanning platforms, which must be CE IVD or FDA approved, the construction of databases, anonymisation, sending and image analysis. Algorithms used for analyses perform different operations on digital images: quality improvement, filtering, recording and segmentation and they must also be validated by correlation measuring the reproducibility between pathologists and algorithms. In addition, The large amount of data requires significant computer processing with accelerated calculations by CNNs (Convolutional Neural Networks) and large image storage capacity.
Applications :
Quantitative analyses and Image-Based Profiling: most image analyses proposed by commercial softwares (VisionPharm, Definiens Tissue Studio, Indica Labs HALO), or open source softwares such as QuPath Open Source Software for Quantitative pathology or ImageJ, are area or cells-based measurements. Area- based measurements include quantification of stained zones (using IHC stain for example) ; cell_based measurements require segmentation steps to delineate tissue compartments, to distinguish for example tumor from begnin regions or to identify subcellular structures (such as nuclei). Some modules or algorithms are CE-IVD or FDA approved such as those used to quantify the expression of ER, PR, Her2 and KI67 in breast cancers. Other algorithms enable the estimation of the percentage of tumour cells before molecular analysis for the detection of genes mutations (Tissue Mark, Philipps pathology). They can also enable to localise and quantify the immune infiltrate ( CD3 and CD8+ T Cells) within the tumor stroma (Immunoscore , Laboratory of Integrative cancer Immunology INSERM Paris). Some machine Learning methods can also automatically differentiate tumor cells from stroma cells and inflammatory cells or identify lymph node metastases. In thoracic pathology, automated whole slide scoring of PDL1 has been proposed in NSCLC with an excellent agreement for tumor cells scoring and a good concordance for immune cells (3). Image based analysis has been used for quantification of immune checkpoints molecules co expression in NSCLC (4). Several studies have showed the interest of automatic image-based analysis for the optimization of histological or cytological classification of lung cancer and prediction of prognosis (5,6). Deep learning technology has been used to classify lung tumour subtypes from the virtual slides available in the TCGA (7), or to classifiy adenocarcinoma according to the predominant pattern with a kappa score of 0.525 and an agreement of 66.6% with three pathologists (8). Deep learning was also proposed for genomic profiling of tumours, with a prediction of STK11, EGFR, FAT1, SETBP1, KRAS and TP53 mutations from pathology images showing an AUCs ranging from 0.733 to 0.856 (9,10).
Applications: 3D dimensions modeling and digital Printing
Three-dimensional (3D) photogrammetry is a method of image-based modeling in which data points in digital images, taken from offset viewpoints, are analyzed to generate a 3D model. This technique can be used to generate 3D representation sof surgical specimens, for routine gross examination, in multidisciplinary meetings to improve clinicopathologic correlation between surgeon and pathologists, and for education purposes via 3D printing specimen models (11).
References
1. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. mai 2019;20(5):e253‑61.
2. Aeffner F, Zarella M, Buchbinder N, Bui M, Goodman M, Hartman D, et al. Introduction to digital image analysis in whole-slide imaging: A white paper from the digital pathology association. J Pathol Inform. 2019;10(1):9.
3. Taylor CR, Jadhav AP, Gholap A, Kamble G, Huang J, Gown A, et al. A Multi-Institutional Study to Evaluate Automated Whole Slide Scoring of Immunohistochemistry for Assessment of Programmed Death-Ligand 1 (PD-L1) Expression in Non–Small Cell Lung Cancer: Appl Immunohistochem Mol Morphol. avr 2019;27(4):263‑9.
4. Parra ER, Villalobos P, Zhang J, Behrens C, Mino B, Swisher S, et al. Immunohistochemical and Image Analysis-Based Study Shows That Several Immune Checkpoints are Co-expressed in Non–Small Cell Lung Carcinoma Tumors. J Thorac Oncol. juin 2018;13(6):779‑91.
5. Yu K-H, Zhang C, Berry GJ, Altman RB, Ré C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun [Internet]. nov 2016 [cité 10 juin 2019];7(1). Disponible sur: http://www.nature.com/articles/ncomms12474
6. Luo X, Zang X, Yang L, Huang J, Liang F, Rodriguez-Canales J, et al. Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis. J Thorac Oncol. mars 2017;12(3):501‑9.
7. Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. EBioMedicine. janv 2018;27:317‑28.
8. Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep [Internet]. déc 2019 [cité 10 juin 2019];9(1). Disponible sur: http://www.nature.com/articles/s41598-019-40041-7
9. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med. oct 2018;24(10):1559‑67.
10. Patil PD, Hobbs B, Pennell NA. The promise and challenges of deep learning models for automated histopathologic classification and mutation prediction in lung cancer. J Thorac Dis. févr 2019;11(2):369‑72.
11. Turchini J, Buckland ME, Gill AJ, Battye S. Three-Dimensional Pathology Specimen Modeling Using “Structure-From-Motion” Photogrammetry: A Powerful New Tool for Surgical Pathology. Arch Pathol Lab Med. nov 2018;142(11):1415‑20.
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MS17.04 - Multiplex Immunohistochemistry (Now Available) (ID 3540)
14:30 - 16:00 | Presenting Author(s): Ignacio Wistuba | Author(s): Edwin Parra, Alejandro Francisco Cruz
- Abstract
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Abstract
Multiplexed imaging platforms to simultaneously detect multiple epitopes in the same tissue section emerged in the last years as very powerful tools to study tumor immune contexture. These revolutionary technologies are providing a deep methodology for tumor evaluation in formalin-fixed and paraffin-embedded (FFPE) to improve the understanding of tumor microenvironment, new targets for treatment, prognostic and predictive biomarkers, and translational studies. Multiplexed imaging platforms allow the identification of several antigens simultaneously from a single tissue section, core needle biopsies, and tissue microarrays. In recent years, multiplexed immunohistochemistry, immunofluorescence, mass spectometry and other imaging modalities have improved the abilities to characterize the different types of cell populations in malignant and non-malignant tissues, and their spatial distribution in relationship to clinical outcomes. Multiplexed technologies associated with digital image analysis software offer a high-quality throughput assay to study cancer specimens, inc;luding lung cancer, at multiple timepoints before, during and after treatment. The aim of this resentation is to provide a review of multiplexed tissue imaging applied to lung cancer focusing in the use of multiplex immunofluorescence with tyramine signal amplification staining for lung cancer immune profiling and translational research.
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MS17.05 - Controversies in Pathologic Staging (Now Available) (ID 3541)
14:30 - 16:00 | Presenting Author(s): Masaya Yotsukura
- Abstract
- Presentation
Abstract
The 8th edition of the TNM classification system was implemented in January 2017, except in the United States, where it was delayed until January 2018. The 8th edition was proposed by the Staging and Prognostic Factors Committee (SPFC) of the International Association for the Study of Lung Cancer (IASLC), and accepted by the Union for International Cancer Control and American Joint Committee on Cancer. The SPFC’s proposed changes from the 7th to 8th edition were essentially based on prognostic data from the IASLC database, which included 70,697 evaluable patients with non-small cell lung cancer and 6,189 patients with small cell lung cancer.
Although the TNM system is the result of a scientific analysis of the anatomical extent of the tumor, some of the classification points are still controversial.
1. Ground glass opacity
Regarding the T descriptor, the main issue revolves around ground glass opacity (GGO)-related matters. The invasive area can pathologically be well-defined under microscopic evaluation, sometimes in combination with Elastica van Gieson stain. However, the difference between pathological and clinical staging has been a problem. Clinical staging depends on how to measure the GGO component by computed tomography (CT), which has not been defined very well.
Another point to be addressed related to GGO is the separation of GGO-containing tumors from solid tumors. For example, if a tumor has a solid component of 1.5 cm without any surrounding GGO component, it will be defined as clinical T1b, whereas if a tumor has a solid component of 1.5 cm along with 1.0 cm of surrounding GGO component (total tumor diameter of 1.5 + 1.0 = 2.5 cm), it would also be defined as clinical T1b. However, the malignant capacity of a purely solid tumor is reported to be worse than that of GGO-containing tumors. Thus, from a prognostic viewpoint, it might be better to consider these two types of tumors separately.
2. Visceral pleural invasion
Another controversy regarding the T descriptor in pathologic staging involves visceral pleural invasion (VPI). First, data should be collected using a standardized definition. A standardized definition of VPI was incorporated into the 7th edition of TNM and maintained in the 8th edition. PL1, 2, and 3 are pathologically evaluated based on tumor invasion to the elastic layer, pleural surface, and parietal pleura. In case of doubt regarding VPI, the use of elastic stains is recommended. The collection of data using this definition and the use of elastic stains are important for accurate evaluation in future revisions.
Second, the difficulty of clinical evaluation of VPI might be a problem. In the current staging system, clinicians have to speculate on the presence of VPI, based solely on the findings of CT imaging. A clear imaging-based definition of VPI might be beneficial.
Third, in the current staging system, interlobar PL3 is classified as T2a. Since little is known about the prognosis of this interlobar PL3, data should be collected for use in a future revision of the TNM classification.
3. Nodal evaluation
Regarding the N descriptor, as previously reported in the literature, tumors with nodal metastases to multiple lymph node stations have a worse prognosis than those with single-station metastasis. Counting the number of positive nodes instead of the number of stations might be considered as well, especially from the viewpoint of emphasizing prognostic impact. However, a standardized method for pathological nodal evaluation has been lacking. How should pathologic slides be made for an accurate evaluation of nodal status? The number of nodes that are incorporated on the same slide, and the number and depth of sections to be evaluated per slide, are not well defined. It is also not clear how to handle an intraoperatively separated node, when counting the number of metastatic nodes. It would be desirable to have some consensus about the evaluation methodology to achieve a more accurate prognostic analysis.
4. Molecular information
There are some disagreements about how to use molecular information, in relation to TNM classification. Over the past decades, dramatic advances have been made in the fields of molecular diagnosis and precision medicine. The therapeutic strategy has been developed in detail depending on the molecular status, especially in advanced tumors. Accordingly, the prognosis has been influenced by the molecular status of the tumor, and thus molecular biomarkers have become some of the most important prognostic factors.
In principle, the TNM classification describes the anatomic extent of tumors, arranged according to prognostic differences. From the perspective of the prognostic impact, molecular biomarkers might be as important as TNM staging. The combination of molecular status and TNM classification could be considered. However, based on the principal concept of the anatomic classification of the TNM system, it might be better to consider molecular factors as a different methodology of the classification system. Further discussions will be needed regarding the relationship between molecular markers and the anatomic TNM classification system.
These issues reflect only some of the controversies surrounding the TNM staging system. Along with other issues, they are expected to be discussed in detail in the SPFC meetings for the forthcoming update of the TNM classification system.
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Author of
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MA12 - New Frontiers from Pathology to Genomics (ID 138)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Mesothelioma
- Presentations: 1
- Now Available
- Moderators:Prasad S Adusumilli, Francisco Perez Ochoa
- Coordinates: 9/09/2019, 14:00 - 15:30, Melbourne (1991)
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MA12.04 - Discussant - MA12.01, MA12.02, MA12.03 (Now Available) (ID 3771)
14:00 - 15:30 | Presenting Author(s): Erik Thunnissen
- Abstract
- Presentation
Abstract not provided
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P1.04 - Immuno-oncology (ID 164)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Immuno-oncology
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.04-12 - Tumor Uptake and Biodistribution of 89Zr-Labeled Pembrolizumab in Patients with Metastatic Non-Small-Cell Lung Cancer (Now Available) (ID 2429)
09:45 - 18:00 | Author(s): Erik Thunnissen
- Abstract
Background
Pembrolizumab, a monoclonal antibody targeting PD-1, is approved as monotherapy for the treatment of advanced NSCLC, depending on tumor PD-L1 (T-PD-L1) expression level. However, T-PD-L1 expression correlates moderately with response to pembrolizumab treatment. Therefore, there is need for biomarkers that accurately discriminate between responders and non-responders. Whole body 89Zr-pembrolizumab PET/CT allows to quantify pembrolizumab uptake in tumor lesions and to study pembrolizumab biodistribution in non-malignant tissues.
Method
Patients with advanced stage NSCLC and eligible for pembrolizumab monotherapy were enrolled. Following tracer injection (standard dose: 2mg 89Zr-pembrolizumab, 37 MBq), the first 3 patients received whole body PET/CT-scans at 1h, 72h, 120h and 168h post-injection. Subsequent 10 patients were scanned at 72h and 144h post-injection. Biodistribution and tumor uptake were assessed visually by an experienced nuclear physician. Quantitative uptake was calculated as SUVpeak for delineable lesions and correlated to T-PD-L1/PD-1 IHC and response. A tumor biopsy after the last line of prior treatment was required to assess T-PD-L1/PD-1 IHC status (22C3 assay). Response was defined as partial or complete, after 6 months, according to RECIST criteria.
Result
Thirteen patients (5 ≥50%, 4 1-49%, 4 negative by PD-L1 IHC) were enrolled (6 first line, 7 second/third line treatment). One patient experienced grade 3 myalgia after tracer injection. 89Zr-pembrolizumab biodistribution was comparable to 89Zr-nivolumab biodistribution with relatively high uptake in the spleen, likely due to binding to PD-1 receptors on lymphocytes/dendritic cells, and in the liver likely due to catabolism of the tracer. Imaging revealed tumor uptake in all patients, but not in all lesions. Visual assessment confirmed 70 lesions (including non-malignant lymph nodes) at 72h post-injection (SUVpeak range 1.7-13.0). Not all tumor lesions showed tracer uptake and uptake was heterogeneous within and between patients. In 3 patients response was observed. Pretreatment T-PD-L1≥50% was predictive for response (p=0.023). SUVpeak was higher in responding patients than in non-responding patients (median SUVpeak of all visible lesions 8.0 vs 5.5, p=0.033). In line with these results, responding lesions (n=10) had a higher SUVpeak compared to non-responding lesions (n=24, median 8.1 vs 5.5, p=0.025). Tumor tracer uptake did not correlate with PD-1 expression (low PD-1 expression SUVpeak 6.0 vs high PD-1 expression SUVpeak 7.7, p=0.3).
Conclusion
89Zr-pembrolizumab injection was safe with only one grade 3 possibly related adverse event. Tumor 89Zr-pembrolizumab uptake correlated with response to pembrolizumab treatment. Further research is needed to study the value of this biomarker as standalone biomarker or as added information to T-PD-L1 expression by immunohistochemistry.
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P2.05 - Interventional Diagnostic/Pulmonology (ID 168)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Interventional Diagnostics/Pulmonology
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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P2.05-03 - Tumor Atelectasis Gives Rise to Solid Appearance in Pulmonary Adenocarcinomas (ID 758)
10:15 - 18:15 | Author(s): Erik Thunnissen
- Abstract
Background
Ground glass opacities (GGO) on CT scan, if malignant, correlate with adenocarcinoma in situ in histology. Solid appearance on HRCT, either ground glass nodule (GGN) or a solid nodule (SN), is considered to be invasive carcinoma. Histological recognition of invasion in pulmonary adenocarcinoma revealed low kappa scores among expert pathologists. A likely explanation is that morphological collapse is not taken into account in the WHO lung cancer classification.
This study aims to compare radiological features in HRCT and histological features of primary adenocarcinomas in resection specimen for the presence of collapse.
Method
Patients with primary adenocarcinoma in the lung, resected between November 2016 and November 2018, were selected. HRCT scans and CT-PET imaging were evaluated for GGO, GGN , and SN. For pathology diagnosis the 2015 WHO classification was used. Both evaluations were blinded for other information. Lepidic growth pattern with collapse was considered if reduction of air in the histological section was present, while underlying pulmonary architecture was maintained (organoid pattern without invasion).
Result
In total 47 lesions of 41 patients were evaluated. The number of GGO, GGN and solid nodules was 2, 8 and 37, respectively. Lepidic growth pattern with collapse was present in both GGO lesions, 7 out of 8 (88%) GGN lesions and 24 out of 37 (65%) solid lesions.
Remarkably, pre-existing pulmonary architecture with air spaces between alveoli covered with epithelial tumor cells (lepidic pattern) can be detected in over 50% of the adenocarcinomas with solid appearance on HRCT. This can only be explained by tumor related atelectasis (collapse) in vivo.
Conclusion
Tumor related atelectasis is a frequent finding in pulmonary adenocarcinomas and may be a feature in a solid appearance on HRCT. Therefore, a solid appearance on HRCT cannot be solely attributed to invasion, as has been the assumption until now.
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P2.09 - Pathology (ID 174)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Pathology
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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P2.09-24 - IASLC Global Survey for Pathologists on PD-L1 Testing for Non-Small Cell Lung Cancer (ID 906)
10:15 - 18:15 | Author(s): Erik Thunnissen
- Abstract
Background
PD-L1 immunohistochemistry (IHC) is now performed for advanced non-small cell lung cancer (NSCLC) patients to examine their eligibility for pembrolizumab treatment, as well as in Europe for durvalumab therapy after chemoradiation for stage III NSCLC patients. Four PD-L1 clinical trial validated assays (commercial assays) have been FDA/EMA approved or are in vitro diagnostic tests in multiple countries, but high running costs have limited their use; thus, many laboratories utilize laboratory-developed tests (LDTs). Overall, the PD-L1 testing seems to be diversely implemented across different countries as well as across different laboratories.
Method
The Immune biomarker working group of the IASLC international pathology panel conducted an international online survey for pathologists on PD-L1 IHC testing for NSCLC patients from 2/1/2019 to 5/31/2019. The goal of the survey was to assess the current prevalence and practice of the PD-L1 testing and to identify issues to improve the practice globally. The survey included more than 20 questions on pre-analytical, analytical and post-analytical aspects of the PDL1 IHC testing, including the availability/type of PD-L1 IHC assay(s) as well as the attendance at a training course(s) and participation in a quality assurance program(s).
Result
344 pathologists from 310 institutions in 64 countries participated in the survey. Of those, 38% were from Europe (France 13%), 23% from North America (US 17%) and 17% from Asia. 53% practice thoracic pathology and 36%, cytopathology. 11 pathologists from 10 countries do not perform PD-L1 IHC and 7.6% send out to outside facility. Cell blocks are used by 75% of the participants and cytology smear by 9.9% along with biopsies and surgical specimens. Pre-analytical conditions are not recorded in 45% of the institutions. Clone 22C3 is the most frequently used (61.5%) (59% with the commercial assay; 41% with LDT) followed by clone SP263 (45%) (71% with the commercial assay; 29% with LDT). Overall, one or several LDTs are used by 57% of the participants. A half of the participants reported turnaround time as 2 days or less, while 13% reported it as 5 days or more. Importantly, 20% of the participants reported no quality assessment, 15%, no formal training session for PD-L1interpretation and 14%, no standardized reporting system.
Conclusion
There is marked heterogeneity in PD-L1 testing practice across individual laboratories. In addition, the significant minority reported a lack of quality assurance, formal training and/or standardized reporting system that need to be established to improve the PD-L1 testing practice globally.
