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Teh-Ying Chou
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
- Presentation
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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MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Pathology
- Presentations: 1
- Now Available
- Moderators:John Le Quesne, Noriko Motoi
- Coordinates: 9/09/2019, 15:45 - 17:15, Tokyo (1982)
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MA15.01 - Cellular Prion Protein Transcriptionally Regulated by NFIL3 Enhances Lung Cancer Cell Lamellipodium Formation and Migration (Now Available) (ID 151)
15:45 - 17:15 | Author(s): Teh-Ying Chou
- Abstract
- Presentation
Background
Tumor invasion and metastasis are the major causes of treatment failure and mortality in lung cancer patients. However, the precise molecular targets responsible for tumor invasion remain unclear.
Method
In this study, we identified a group of genes with differential expression in in situ and invasive lung adenocarcinoma tissues by cDNA microarray analysis; among these genes we further characterized the association of the upregulation of PRNP, the gene encoding cellular Prion protein (PrPc), with lung adenocarcinoma invasiveness through immunohistochemistry and in situ hybridization analysis on clinical tissues. The roles of PrPc in lung cancer cell lines were also verified by using immunofluorescence staining, in vitro transwell assay and in vivo metastasis mouse model. In addition, the impact of PrPc on the activation of the JNK signaling pathway was investigated by Western blot analysis. Finally, luciferase reporter assay and chromatin immunoprecipitation assay were used to identify the transcriptional activators of PRNP.
Result
Immunohistochemistry on clinical specimens showed association of PrPc expression with invasive but not in situ lung adenocarcinoma. Consistently, the expression of PrPc was higher in the highly invasive than in the lowly invasive lung adenocarcinoma cell lines. Knockdown of PrPc expression in cultured lung adenocarcinoma cells decreased their lamellipodium formation, in vitro migration and invasion, and in vivo experimental lung metastasis. Phosphorylation of JNKs was found to correlate with PrPc expression and the inhibition of JNKs suppressed the PrPc-induced up-regulation of lamellipodium formation, cell migration, and invasion. Moreover, we identified the nuclear factor, interleukin 3 regulated (NFIL3) protein as a transcriptional activator of the PRNP promoter. Accordingly, NFIL3 promoted lung cancer cell migration and invasion in a PrPc-dependent manner. High NFIL3 expression in clinical specimens of lung adenocarcinoma was also associated with tumor invasiveness and poor survival of patients.
Conclusion
Our observations suggest that PRNP expression is associated with the invasiveness of lung adenocarcinoma, and cell line model demonstrated that PrPc serves as a critical factor for lung cancer cell lamellipodia formation, migration and invasion via JNK signaling. A novel transcription factor, NFIL3, was identified to upregulate PRNP expression in lung cancer cells; further characterizations showed that NFIL3 promotes lung cancer cell migration through PrPc-dependent manner. Moreover, high NFIL3 expression was found to be associated with lung cancer invasiveness in clinical tissues. Overall, NFIL3/PrPc axis plays a critical role in lung cancer invasiveness and metastasis, and may be the potential therapeutic targets in the future.
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P1.09 - Pathology (ID 173)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Pathology
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.09-30 - Molecular Characterization of Preinvasive and Invasive Lesions in Multifocal Pulmonary Adenocarcinomas (ID 1526)
09:45 - 18:00 | Author(s): Teh-Ying Chou
- Abstract
Background
Multifocal pulmonary adenocarcinomas typically present as multiple ground-glass opacities (GGOs) on computed tomography (CT) scan. Pathologically, GGOs in majority represent preinvasive lesions including atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) and rarely, invasive lesions such as minimally invasive adenocarcinoma (MIA). Given that a stepwise progression sequence from AAH to invasive adenocarcinoma (AIV) has been proposed in lung carcinogenesis, we would like to investigate the molecular features between preinvasive and invasive lesions in patients with multifocal pulmonary adenocarcinomas.
Method
Result
From 2011 to 2015, thirty-three patients with surgically resected preinvasive lesions of lung adenocarcinoma diagnosed in their tumor(s) were included. There were 25 female and 8 male, with a total of 87.9% being never smokers. Twenty-nine patients (87.9%) had at least one preinvasive and one invasive lesions. One hundred and nine tumor lesions composed of 18 AAH, 73 AIS and 18 AIV were analyzed for EGFR, KRAS and TP53 mutations.
Among them, 4 (12%) patients were found to harbor one lesion, 14 (42%) two lesions, 6 (18%) three lesions, and 9 more than four lesions. In patients with more than 3 tumor lesions, these lesions were more likely to localize in the right lateral lung compared to those with lesions less than 2 (p=0.02). Of 109 tumor lesions analyzed, EGFR, KRAS and TP53 mutations were 22.2%, 0% and 22.2% in AAH, 24.7%, 6.8% and 15.1% in AIS, while 66.7%, 11.1% and 16.7% in AIV. EGFR mutation rate of AIV, especially L8585R mutation, was higher than that of AAH and AIS (p =0.004). On the contrary, mutations on KRAS and TP53 were randomly distributed between preinvasive and invasive lesions (p > 0.05). The discordant pattern of EGFR, KRAS and TP53 mutations between AAH, AIS and AIV lesions within the same patients was also observed.
Figure 1. The prevalence of EGFR, KRAS and TP53 mutations in preinvasive and invasive tumor lesions. EGFR mutations are more associated with invasive components.
Conclusion
Our results showed that EGFR, KRAS and TP53 mutations occur early in preinvasive lesions, and only EGFR mutation is significantly associated with invasive components. These findings suggested that EGFR mutation may contribute to the invasiveness and progression of lung adenocarcinoma. Additionally, presence of distinct mutation profiles in separate preinvasive and invasive lesions from the same patient demonstrated that the emergence of these lesions may come from independent events, implying that genetic heterogeneity does occur in patients with multifocal pulmonary adenocarcinomas.
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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): Teh-Ying Chou
- 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.
