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Sylvie Lantuejoul



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    MA08 - Pawing the Way to Improve Outcomes in Stage III NSCLC (ID 127)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Treatment of Locoregional Disease - NSCLC
    • Presentations: 1
    • Now Available
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      MA08.02 - Durvalumab Impact in the Treatment Strategy of Stage III Non-Small Cell Lung Cancer (NSCLC): An EORTC Young Investigator Lung Cancer Group Survey (Now Available) (ID 608)

      15:15 - 16:45  |  Author(s): Sylvie Lantuejoul

      • Abstract
      • Presentation
      • Slides

      Background

      Stage III NSCLC represents a very heterogeneous population with extremely different treatment modalities including surgery, chemotherapy (CT) and radiotherapy (RT), mostly in combination. The results of the PACIFIC trial have now been reported in full including an overall survival (OS) benefit with durvalumab in addition to concomitant CT-RT. An electronic European survey was circulated to evaluate the impact of durvalumab in the staging and treatment strategy of stage III disease.

      Method

      A Young Investigator EORTC Lung Cancer Group survey containing 31 questions, was distributed between 31/01/18 and 31/03/19 to EORTC LCG and several European thoracic oncology societies’ members

      Result

      206 responses were analyzed (radiation oncologist: 50% [n=103], pulmonologist: 26.7% [n=55], medical oncologist: 22.3% [n=46]; 81.5% with >5 years experience in treating NSCLC). Italy (27.7%, n=57), Netherlands (22.8%, n=47), France (13.6%, n=28), and Spain (11.6%, n=24) contributed most. 83.5% (n=172) confirmed that they had access to durvalumab at the time of the survey. 97.6% (n=201) report that treatment decision is made by a multidisciplinary board. Regarding staging, 76.7% (n=158) support the need of a mediastinal pathological staging in case of suspect lymph-nodes, with a preference for EBUS/EUS (61.2%, n=126). 81.6% (n=168) treated more than half of patients with a concomitant CT-RT with the 1st cycle of chemotherapy in 39.7% (n=81). 95.1% consider durvalumab as practice changing, especially given the OS results (77.9%, n=152/195). 30% (n=119/395) will give patients concomitant CT-RT if PD-L1 >1%, and in borderline resectable cases 17.7% (n=70/395) will propose concomitant CT-RT instead of surgery. Durvalumab administration will be given regardless of PDL1 status in 13.1% (n=27) and 28.6% (n=59) would consider the possibility of a rebiopsy after CT-RT in case of negative PD-L1. 38.8% (n=80) foresee some problems with PD-L1 testing in this population due to availability of cytologic or small histologic samples. About 53.8% (n=105/195) normally will start durvalumab within 6 weeks after CT-RT and 48.5% (n=100) would also use durvalumab after sequential CT-RT

      Conclusion

      Durvalumab results are changing the treatment approach to stage III unresectable (and maybe resectable) NSCLC and planned strict adherence to the patient population as recruited to the PACIFIC study, was not demonstrated. This survey was released after the EMA approval of durvalumab and PD-L1 status seems to play a role in the treatment strategies, but surprisingly almost half of the clinicians will use durvalumab after sequential CT-RT without safety or efficacy data.

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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
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      MA12.01 - Redefining Malignant Pleural Mesothelioma Types as a Continuum Uncovers Immune-Vascular Interactions (Now Available) (ID 1773)

      14:00 - 15:30  |  Author(s): Sylvie Lantuejoul

      • Abstract
      • Presentation
      • Slides

      Background

      Malignant Pleural Mesothelioma (MPM) is a deadly disease. The current histopathologycal classification recognises three major types (epithelioid, biphasic, and sarcomatoid) with different prognosis, but showes high interobserver variability. This classification also has a role in the clinical decision-making although, ultimately, MPM becomes refractory to all conventional treatment modalities, and alternative therapeutic options have been evaluated with limited success.

      Method

      We have performed unsupervised analyses of publicly available RNA-seq data of 284 MPM tumours1,2 with no assumption of discreteness. We have performed an orthogonal validation in a subset of 187 samples, and we have replicated the findings in an independent series of 77 MPM from the French MESOBANK.

      Result

      A continuum of molecular profiles appeared to explain the prognosis of this disease better than discrete models based on the histopathological classification or on expression data. We identified the immune and vascular pathways as major sources of molecular variation, with strong differences in the expression of immune checkpoints and pro-angiogenic genes across samples; the extrema of this continuum had very specific molecular profiles: a "hot" bad-prognosis profile (median survival of 7 months), with high lymphocyte infiltration, and high expression of immune checkpoints and pro-angiogenic genes; a "cold" bad-prognosis profile (median survival of 10 months), with low lymphocyte infiltration and high expression of pro-angiogenic genes; and a better-prognosis profile (VEGFR2+/VISTA+, median survival of 36 months), with high expression of the immune checkpoint VISTA and the pro-angiogenic VEGFR2 gene. We selected five genes belonging to the immune and vascular pathways (CD8A, PDL1, VEGFR3, VEGFR2, and VISTA), which expression was enough to capture the three molecular profiles, to validate the expression of these genes at the protein level by immunohistochemistry on a subset of 187 samples from the discovery cohort, and to replicate the molecular profiles as well as their prognostic value in an independent series of 77 MPMs.

      picture copy.jpg

      Conclusion

      In this study we found that the prognosis of MPM is best explained by a continuous model, which extremes show characteristic molecular profiles with specific expression patterns of genes involved in the angiogenesis and immune response3. These data may inform future classifications of MPM and provides insights that may assist the clinical management of this disease.

      1Bueno et al., Nat Genet 2016; 2Hmeljak et al., Cancer Discov 2018; 3Alcala et al., under review in Cancer Res; NA and LM equally contributed to this work; MF, FGS, and LFC jointly supervised this work

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    MA21 - Non EGFR/MET Targeted Therapies (ID 153)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Targeted Therapy
    • Presentations: 1
    • Now Available
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      MA21.03 - The International Association for the Study of Lung Cancer (IASLC) Global Survey on Molecular Testing in Lung Cancer (Now Available) (ID 1198)

      14:30 - 16:00  |  Author(s): Sylvie Lantuejoul

      • Abstract
      • Presentation
      • Slides

      Background

      Evidence-based standards for molecular testing of lung cancer have been established, but the global frequency and practice of testing are not well understood. The IASLC conducted an international survey to evaluate current practice and barriers to molecular testing.

      Method

      Distributed to IASLC members and other healthcare professionals, content included: 7-question introduction, 32 questions for those requesting tests/treating patients, 45 questions on performing/interpreting assays, and 24 questions on tissue acquisition. All respondents were asked to provide 3-5 barriers to implementing/offering molecular testing.

      Respondents’ countries were grouped by geography or developing/developed using IASLC and World Bank criteria. Surveys were available in 7 languages. Regional comparisons used the Chi-squared test or ANOVA; free-text was analyzed with Nvivo.

      Result

      We obtained 2,537 responses from 102 countries. Respondents were 45% Medical Oncologists, 12% Pulmonologists, 12% Thoracic Surgeons, 9% Pathologists, and 22% scientists or other. 56% of responses were from developing countries, 44% developed. Regions included: 52% Asia, 19% Europe, 11% Latin America, 11% US/Canada, 7% Other.

      1683 (66%) chose the requesting/treating track (50% government, 42% academic, 8% other). 61% reported most patients in their country do not receive molecular testing, with the lowest rates in Latin America/Other (p<0.0001). 39% were not satisfied with the conditions of molecular testing in their country. Indications for requesting testing included: adenocarcinoma (89%), never-smoker (61%), female (57%), and young (54%) (variable by region, p<0.0001). 99% ordered EGFR, 95% ALK, 84% PDL1, 79% ROS1, all other tests <50%. 56% typically received results within 10 days. Only 67% were aware of CAP/IASLC/AMP guidelines, least frequently in Asia/Other (p=0.041). 37% have trouble understanding molecular testing result reports, most of whom cited a need for more technical and scientific knowledge. 75% had multidisciplinary tumor boards, but 23% met <1/month.

      The 316 (12%) testing track respondents were from laboratories that were 49% academic, 35% government, and 16% private/other. 94% of laboratories offered EGFR, 83% ALK, 69% KRAS, 68% BRAF, 64% ROS1, 56% HER2, and others <50%; 68% tested for PDL1. 57% offered Multiplex assays, less frequently in Latin America/Asia (p=0.0294). 69% tested blood-derived DNA, less frequently in US/Canada/Other (0.0013). 23% of respondents reported >10% of cases are rejected due to inadequate samples; however, 47% stated there is no policy or strategy to improve the quality of the tissue samples in their country. 52% reported patients/physicians are not satisfied with the state of molecular testing in their country. Respondents performing/interpreting assays (334, 14%) were typically informed of biopsy results (91%), and notified when the sample was inadequate (84%).

      The most frequent barrier to molecular testing in every region was cost, followed by quality/standards, turnaround-time, access, and awareness. After cost, time was the most common barrier in developed countries, while it was quality in developing countries. The second largest barrier was quality in Asia, access in Europe/Latin America/Other, and turn-around time in US/Canada.

      Conclusion

      These preliminary analyses show molecular testing usage varies across the globe. Barriers vary by region, and one-third of respondents were unaware of evidence-based guidelines. Global and regional strategies should be developed to address barriers.

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    MS17 - Pathology of the Future (ID 80)

    • Event: WCLC 2019
    • Type: Mini Symposium
    • Track: Pathology
    • Presentations: 1
    • Now Available
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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
      • Slides

      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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    OA08 - Advanced Models and "Omics" for Therapeutic Development (ID 133)

    • Event: WCLC 2019
    • Type: Oral Session
    • Track: Biology
    • Presentations: 1
    • Now Available
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      OA08.02 - A Multidisciplinary Multi-Omics Study of Spatial and Temporal Tumor Evolution in Thoracic Cancers with Clinical Implications (Now Available) (ID 2365)

      11:00 - 12:30  |  Author(s): Sylvie Lantuejoul

      • Abstract
      • Presentation
      • Slides

      Background

      In the context of the MESOMICS and lungNENomics projects1, we generated comprehensive molecular profiles of Malignant Pleural Mesothelioma (MPM)2 and pulmonary carcinoids (PCa)3. We showed that a continuous molecular model can better explain the prognosis of MPM than the three histologies, with strong differences in the expression of immune checkpoints and pro-angiogenic genes across samples. We also identified a new entity of PCa (supra-carcinoids) with carcinoid-like morphology yet the molecular and clinical features of LCNEC, which challenges the general believe that PCa have no relationship or genetic, epidemiologic, and clinical traits in common with LCNEC and SCLC. These two studies suggest an important role of heterogeneity in the biology of these tumors.

      Method

      Much progress has been made in revealing the evolutionary history of individual cancers, in particular using multi-region sequencing. However, most studies focused on a single ‘omic technique, and lacked temporal samples. Here we present the results of an innovative approach to study spatial and temporal tumor evolution based on (i) integration of whole-genome and transcriptome sequencing and EPIC 850K methylation arrays on multiple regions from 12 MPM, and (ii) a novel tumor-derived organoid-based strategy for studying the evolution of PCa.

      mesomics_example.png

      Figure 1. Multi-omic multi-regional profiling of a MPM patient. A) Somatic Copy Number Variants (CNV), somatic Structural Variants (SV), kernel density plots of (top) somatic single nucleotide variants (SNVs) allelic fractions, (middle) expression normalized read counts, and (bottom) methylation array M-values. B) Projection of the transcriptomic profile of two tumoral regions into the Principal Component Analysis (PCA) space computed from 284 malignant pleural mesotheliomas2C) Expression (z-score of normalized read counts) for two clinically relevant genes with substantial inter-regional differences.

      Biorepositories: French MESOBANK; LungNEN Network

      Result

      In the data analyses of the 12 MPM we detected significant intra-tumor heterogeneity (ITH) in the expression of immune checkpoints and pro-angiogenic genes (see example in Fig. 1). This might explain the modest and variable response to treatment in clinical trials assessing immunotherapies and antiangiogenic drugs. In the case of PCa, we are currently analysing the organoids genomic data and we will present the preliminary data for the temporal evolution of these diseases.

      Conclusion

      We found that our approach can detect clinically and biologically meaningful ITH. All the computational methods we developed for these evolutionary studies are available to the scientific community4.

      1RareCancersGenomics.com
      2Alcala et al., under review in Cancer Res
      3Alcala et al., under review in Nat Commun
      4https://github.com/IARCbioinfo

      LFC and MF co-supervised this work

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    P2.09 - Pathology (ID 174)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Pathology
    • Presentations: 2
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.09-17 - Real-World Concordance Across Pathologists for PD-L1 Scoring in Non-Small Cell Lung Cancer: Results from a Large Nationwide Initiative (ID 898)

      10:15 - 18:15  |  Author(s): Sylvie Lantuejoul

      • Abstract

      Background

      PD-L1 immunohistochemistry (IHC) is an important routine biomarker in patients with metastatic and locally advanced non resectable non-small cell lung cancer (NSCLC). Currently, the thresholds of ≥1% and ≥50% of tumor cells stained are clinically relevant. Scoring concordance across pathologists was reported only in small groups of pathologists or across thoracic pathology experts. Here, we provide real-world concordance data in a large group of pathologists (n=161) with various experience of PD-L1 testing and practice in thoracic pathology.

      Method

      Twenty-nine NSCLC samples, mostly biopsies, stained in routine clinical pathology practice with PD-L1 IHC standardized assays (22C3, 28-8 and SP263), were selected to represent various PD-L1 expression levels. Slides were digitalized and scored for the percentage of tumor cells with membranous staining by 161 pathologists using an online digital platform. A consensus score was defined for each case by a group 15 expert pathologists. Data regarding experience, training and practice of PD-L1 testing were also collected for each pathologist.

      Result

      Consensus score determined by the expert group highly correlated with the median of scores for each case (correlation coefficient=0.992). Overall concordance across pathologists was moderate, higher for the ≥50% cutoff (K=0.64) than the ≥1% cutoff (K=0.58). A higher concordance was achieved in the expert group (15 pathologists) as compared to the other pathologists (146 pathologists), in particular for the ≥1% cutoff. Concordance across pathologists correlated with training to PD-L1 scoring as well as the number of PD-L1 tests evaluated weekly. No correlation was found with the number of years of thoracic pathology practice or the type of pathology practice (private laboratory, community hospital, university hospital). The issues observed in the most discrepant cases were evaluated and described.

      Conclusion

      Concordance across pathologists for PD-L1 scoring in NSCLC was higher in the expert group of pathologists as compared to other pathologists, in particular for the ≥1% cutoff. Training to PD-L1 scoring and experience in routine pathology practice correlated with higher concordance. These data emphasize the importance of training to achieve a high concordance across pathologists in the real-world setting.

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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): Sylvie Lantuejoul

      • Abstract
      • Slides

      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.

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