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Bontoux C, Hofman V, Chamorey E, Schiappa R, Lassalle S, Long-Mira E, Zahaf K, Lalvée S, Fayada J, Bonnetaud C, Goffinet S, Ilié M, Hofman P. Reproducibility of c-Met Immunohistochemical Scoring (Clone SP44) for Non-Small Cell Lung Cancer Using Conventional Light Microscopy and Whole Slide Imaging. Am J Surg Pathol 2024; 48:1072-1081. [PMID: 38980727 DOI: 10.1097/pas.0000000000002274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Emerging therapies for non-small cell lung cancer targeting c-Met overexpression have recently demonstrated promising results. However, the evaluation of c-Met expression can be challenging. We aimed to study the inter and intraobserver reproducibility of c-Met expression evaluation. One hundred ten cases with non-small cell lung cancer (40 biopsies and 70 surgical specimens) were retrospectively selected in a single laboratory (LPCE) and evaluated for c-Met expression. Six pathologists (4 seniors and 2 juniors) evaluated the H-score and made a 3-tier classification of c-Met expression for all cases, using conventional light microscopy (CLM) and whole slide imaging (WSI). The interobserver reproducibility with CLM gave global Cohen Kappa coefficients (ƙ) ranging from 0.581 (95% CI: 0.364-0.771) to 0.763 (95% CI: 0.58-0.92) using the c-Met 3-tier classification and H-score, respectively. ƙ was higher for senior pathologists and biopsy samples. The interobserver reproducibility with WSI gave a global ƙ ranging from 0.543 (95% CI: 0.33-0.724) to 0.905 (95% CI: 0.618-1) using the c-Met H-score and 2-tier classification (≥25% 3+), respectively. ƙ for intraobserver reproducibility between CLM and WSI ranged from 0.713 to 0.898 for the c-Met H-score and from 0.600 to 0.779 for the c-Met 3-tier classification. We demonstrated a moderate to excellent interobserver agreement for c-Met expression with a substantial to excellent intraobserver agreement between CLM and WSI, thereby supporting the development of digital pathology. However, some factors (scoring method, type of tissue samples, and expertise level) affect reproducibility. Our findings highlight the importance of establishing a consensus definition and providing further training, particularly for inexperienced pathologists, for c-Met immunohistochemistry assessment in clinical practice.
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Affiliation(s)
- Christophe Bontoux
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Véronique Hofman
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Emmanuel Chamorey
- Department of Statistics, Antoine Lacassagne Cancer Center, Nice, France
| | - Renaud Schiappa
- Department of Statistics, Antoine Lacassagne Cancer Center, Nice, France
| | - Sandra Lassalle
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Elodie Long-Mira
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Katia Zahaf
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Salomé Lalvée
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Julien Fayada
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Christelle Bonnetaud
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | | | - Marius Ilié
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
| | - Paul Hofman
- Laboratory of Clinical and Experimental Pathology
- Hospital-Integrated Biobank
- Team 4, Institute of Research on Cancer and Aging of Nice Inserm U1081, CNRS UMR7284, Côte d'Azur University
- FHU OncoAge, Côte d'Azur University
- University Hospital Institute RespirERA, Côte d'Azur University, Pasteur Hospital, CHU of Nice
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Ito H, Yoshizawa A, Terada K, Nakakura A, Rokutan-Kurata M, Sugimoto T, Nishimura K, Nakajima N, Sumiyoshi S, Hamaji M, Menju T, Date H, Morita S, Bise R, Haga H. A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score? Mod Pathol 2024; 37:100485. [PMID: 38588885 DOI: 10.1016/j.modpat.2024.100485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/08/2024] [Accepted: 04/01/2024] [Indexed: 04/10/2024]
Abstract
Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-assisted systems could help pathologists determine the tumor proportion score (TPS). In this study, we developed an AI model to calculate the TPS of the PD-L1 22C3 assay and evaluated whether and how this AI-assisted system could help pathologists determine the TPS and analyze how AI-assisted systems could affect pathologists' assessment accuracy. We assessed the 4 methods of the AI-assisted system: (1 and 2) pathologists first assessed and then referred to automated AI scoring results (1, positive tumor cell percentage; 2, positive tumor cell percentage and visualized overlay image) for final confirmation, and (3 and 4) pathologists referred to the automated AI scoring results (3, positive tumor cell percentage; 4, positive tumor cell percentage and visualized overlay image) while determining TPS. Mixed-model analysis was used to calculate the odds ratios (ORs) with 95% CI for AI-assisted TPS methods 1 to 4 compared with pathologists' scoring. For all 584 samples of the tissue microarray, the OR for AI-assisted TPS methods 1 to 4 was 0.94 to 1.07 and not statistically significant. Of them, we found 332 discordant cases, on which the pathologists' judgments were inconsistent; the ORs for AI-assisted TPS methods 1, 2, 3, and 4 were 1.28 (1.06-1.54; P = .012), 1.29 (1.06-1.55; P = .010), 1.28 (1.06-1.54; P = .012), and 1.29 (1.06-1.55; P = .010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS method compared with the others was 0.99 to 1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases in which pathologists had difficulty determining the PD-L1 TPS.
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Affiliation(s)
- Hiroaki Ito
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Akihiko Yoshizawa
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Nara Medical University, Nara, Japan.
| | - Kazuhiro Terada
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Akiyoshi Nakakura
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - Tatsuhiko Sugimoto
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Kazuya Nishimura
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Naoki Nakajima
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Toyooka Hospital, Hyogo, Japan
| | - Shinji Sumiyoshi
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Tenri Hospital, Nara, Japan
| | - Masatsugu Hamaji
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Toshi Menju
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Hiroshi Date
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Hironori Haga
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
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Kim H, Kim S, Choi S, Park C, Park S, Pereira S, Ma M, Yoo D, Paeng K, Jung W, Park S, Ock CY, Lee SH, Choi YL, Chung JH. Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer. JCO Precis Oncol 2024; 8:e2300556. [PMID: 38723233 DOI: 10.1200/po.23.00556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/11/2023] [Accepted: 04/03/2024] [Indexed: 05/15/2024] Open
Abstract
PURPOSE Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS The AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS. RESULTS In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS <1% group better than the pathologist's reading, with the TPS ≥50% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS <1% group). CONCLUSION PD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.
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Affiliation(s)
- Hyojin Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Changhee Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | | | | | - Minuk Ma
- Lunit Inc., Seoul, Republic of Korea
| | | | | | | | - Sehhoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yoon-La Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jin-Haeng Chung
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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Wang X, He J, Li J, Wu C, Yue M, Niu S, Jia Y, Jia Z, Cai L, Liu Y. Concordance of assessments of four PD-L1 immunohistochemical assays in esophageal squamous cell carcinoma (ESCC). J Cancer Res Clin Oncol 2024; 150:43. [PMID: 38280970 PMCID: PMC10821831 DOI: 10.1007/s00432-023-05595-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 12/23/2023] [Indexed: 01/29/2024]
Abstract
OBJECTIVE Given real-world limitations in programmed death-ligand 1 (PD-L1) testing, concordance studies between PD-L1 assays are needed. We undertook comparisons of PD-L1 assays (DAKO22C3, Ventana SP263, Ventana SP142, E1L3N) among observers in esophageal squamous cell carcinoma (ESCC) to provide information on the analytical and clinical comparability of four PD-L1 IHC assays. METHODS Paraffin embedded samples of 50 cases of esophageal squamous cell carcinoma were obtained, satined with all four PD-L1 assays. PD-L1 was evaluated by 68 pathologists from 19 different hospitals. PD-L1 expression was assessed for combined positive score (CPS). RESULTS The expression sensitivity of SP263 was the highest in ESCC, followed by 22C3, E1L3N and SP142. Taking CPS 10 as the critical value, inter-observer concordance for CPS scores among 68 physicians was assessed for the 22C3, SP263, SP142, and E1L3N assays, yielding values of 0.777, 0.790, 0.758, and 0.782, respectively. In the comparison between assays, the overall CPS scores concordance rates between 22C3 and SP263, SP142, and E1L3N were 0.896, 0.833, and 0.853, respectively. 22C3 and SP263 have high concordance, with OPA of 0.896, while E1L3N and SP142 have the highest concordance, with OPA of 0.908. CONCLUSION In ESCC, the concordance of PD-L1 evaluation among observers is good, and the immune cell score is still an important factor affecting the concordance of interpretation among observers. Cases near the specific threshold are still the difficult problem of interpretation. SP263 had the highest CPS score of the four assays. SP263 cannot identify all 22C3 positive cases, but had good concordance with 22C3.E1L3N and SP142 showed high concordance.
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Affiliation(s)
- Xinran Wang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Jiankun He
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Jinze Li
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Chun Wu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Meng Yue
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Shuyao Niu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Ying Jia
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Zhanli Jia
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Lijing Cai
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China
| | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, Shijiazhuang, 050011, Hebei, China.
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Ligero M, Serna G, El Nahhas OS, Sansano I, Mauchanski S, Viaplana C, Calderaro J, Toledo RA, Dienstmann R, Vanguri RS, Sauter JL, Sanchez-Vega F, Shah SP, Ramón y Cajal S, Garralda E, Nuciforo P, Perez-Lopez R, Kather JN. Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression. CANCER RESEARCH COMMUNICATIONS 2024; 4:92-102. [PMID: 38126740 PMCID: PMC10782919 DOI: 10.1158/2767-9764.crc-23-0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. SIGNIFICANCE The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.
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Affiliation(s)
- Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Garazi Serna
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Omar S.M. El Nahhas
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Irene Sansano
- Pathology Department, Vall d'Hebron University Hospital (VHUH), Barcelona, Spain
| | - Siarhei Mauchanski
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Cristina Viaplana
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Département de Pathologie, CHU Henri Mondor, Créteil, France
- Université Paris-Est Créteil, Faculté de Médecine, Créteil, France
| | - Rodrigo A. Toledo
- Biomakers and Clonal Dynamics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rodrigo Dienstmann
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rami S. Vanguri
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jennifer L. Sauter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Sohrab P. Shah
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Elena Garralda
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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Hacıhasanoglu E, Bambul Sıgırcı B, Usul G, Savlı TC. PD-L1 Assessment in Needle Core Biopsies of Non-Small Cell Lung Cancer: Interpathologist Agreement and Potential Associated Histopathological Features. Turk Patoloji Derg 2024; 40:37-44. [PMID: 37614090 PMCID: PMC10823782 DOI: 10.5146/tjpath.2023.01609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023] Open
Abstract
OBJECTIVE Immune checkpoint inhibitors are used in the treatment of non-small cell lung cancer (NSCLC). Programmed cell death-ligand 1 (PD-L1) immunohistochemistry (IHC) assessed by pathologists is subject to interobserver variability. In advanced/metastatic disease and inoperable patients, PD-L1 assessment relies on biopsy specimens, commonly needle core biopsies (NCB). We aimed to determine the interobserver agreement for PD-L1 tumor proportion score (TPS) in NSCLC NCBs and identify histopathological features that may be related to interobserver variability. MATERIAL AND METHODS Sixty NSCLC NCBs with PD-L1 IHC were evaluated independently by four pathologists from different institutions. PD-L1 TPS was evaluated in three categories: no/low expression ( < 1%), intermediate expression (1%49%), and high expression (≥50%). Histological tumor type, necrosis, tumor-infiltrating lymphocytes, tumor length/percentage in the biopsy, and crush/squeeze artifact was evaluated. RESULTS The statistical analysis of the three PD-L1 TPS categories demonstrated moderate agreement (Fleiss Kappa 0.477) in the no/low category, fair agreement (Fleiss Kappa 0.390) in the intermediate category, and almost perfect agreement (Fleiss Kappa 0.952) in the high category. A significant correlation (p=0.003) was found between the crush/squeeze artifact in NCB and rate of discordant TPS categories. There was no significant correlation between pathologists' agreement in the TPS categories and histological tumor type, tumor length, tumor ratio, necrosis, and tumor-infiltrating lymphocytes. CONCLUSION Our results demonstrated moderate agreement among pathologists for the PD-L1 TPS 1% cut-off in NSCLC NCB, which is lower than that reported in resection materials. The presence of crush/squeeze artifact in NCBs is significantly related to the rate of discordant TPS categories, suggesting that PD-L1 assessment of pulmonary NCBs requires an awareness of this artifact.
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Affiliation(s)
- Ezgi Hacıhasanoglu
- Department of Pathology, 1Yeditepe University, School of Medicine, İstanbul, Turkey
| | - Buket Bambul Sıgırcı
- University of Health Sciences, Sisli Hamidiye Etfal Training Hospital, İstanbul, Turkey
| | - Gamze Usul
- Basaksehir Cam and Sakura City Hospital, İstanbul, Turkey
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7
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Barberà A, González J, Martin M, Mate JL, Oriol A, Martínez-Soler F, Santalucia T, Fernández PL. Impact of Prolonged Ischemia on the Immunohistochemical Expression of Programmed Death Ligand 1 (PD-L1). Appl Immunohistochem Mol Morphol 2023; 31:607-612. [PMID: 37668435 DOI: 10.1097/pai.0000000000001153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 08/03/2023] [Indexed: 09/06/2023]
Abstract
Antibodies targeting programmed death receptor 1 or programmed death ligand 1 (PD-L1) have become a standard of care to treat different cancers; for some of these tumors, there is a correlation between tissue expression of PD-L1 and response rates in patients. Although most of the analytical challenges in the evaluation of PD-L1 expression have been standardized, preanalytical issues have been less explored. The objective of this study was to evaluate the impact of time of ischemia on the performance of 2 commonly used antibodies against PD-L1. Sixteen tonsillectomy samples were kept in ischemia for <30 minutes from sample obtention (control) and 1, 3, 6, 12, and 24 hours at room temperature before formalin fixation and paraffin embedding. Selected areas were inserted into TMA paraffin recipient blocks stained with SP142 and SP263 antibodies and evaluated by 2 blind observers. The proportion of suboptimally stained samples was significantly higher for samples with cold ischemia times 6 hours or over ( P <0.0001). False-negative results were 25% in samples exposed to 6 hours of ischemia and raised to 34% for samples remaining in ischemia for 12 or 24 hours. When all observations were pooled, SP142 provided suboptimal results in 24% of observations and SP263 in 12.5%; this is a statistically significant difference ( P =0.042). In conclusion, the quality of staining for PD-L1 in tonsil samples varies with the time of cold ischemia. The SP142 antibody presented a significantly lower tolerance to prolonged cold ischemia than SP263. These results reveal the relevance of controlled preanalytical processing of samples.
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Affiliation(s)
- Angels Barberà
- Department of Fundamental Care and Medical-Surgical Nursing, School of Nursing, Faculty of Medicine and Health Sciences, Barcelona University
- Faculty of Medicine and Health Sciences, Autonomous Barcelona University, Barcelona
- Department of Pathology, Germans Trias i Pujol Hospital and IGTP
| | - Juan González
- Faculty of Medicine and Health Sciences, Autonomous Barcelona University, Barcelona
- Department of Pathology, Germans Trias i Pujol Hospital and IGTP
| | - Montserrat Martin
- Faculty of Medicine and Health Sciences, Autonomous Barcelona University, Barcelona
- Department of Pathology, Germans Trias i Pujol Hospital and IGTP
| | - Jose L Mate
- Faculty of Medicine and Health Sciences, Autonomous Barcelona University, Barcelona
- Department of Pathology, Germans Trias i Pujol Hospital and IGTP
| | - Albert Oriol
- Josep Carreras Leukemia Research Institute, Badalona, Spain
| | - Fina Martínez-Soler
- Department of Fundamental Care and Medical-Surgical Nursing, School of Nursing, Faculty of Medicine and Health Sciences, Barcelona University
| | - Tomas Santalucia
- Department of Fundamental Care and Medical-Surgical Nursing, School of Nursing, Faculty of Medicine and Health Sciences, Barcelona University
| | - Pedro Luis Fernández
- Faculty of Medicine and Health Sciences, Autonomous Barcelona University, Barcelona
- Department of Pathology, Germans Trias i Pujol Hospital and IGTP
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8
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Naso JR, Vrana JA, Koepplin JW, Molina JR, Roden AC. EZH2 and POU2F3 Can Aid in the Distinction of Thymic Carcinoma from Thymoma. Cancers (Basel) 2023; 15:cancers15082274. [PMID: 37190202 DOI: 10.3390/cancers15082274] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Thymic carcinoma is an aggressive malignancy that can be challenging to distinguish from thymoma using histomorphology. We assessed two emerging markers for these entities, EZH2 and POU2F3, and compared them with conventional immunostains. Whole slide sections of 37 thymic carcinomas, 23 type A thymomas, 13 type B3 thymomas, and 8 micronodular thymomas with lymphoid stroma (MNTLS) were immunostained for EZH2, POU2F3, CD117, CD5, TdT, BAP1, and MTAP. POU2F3 (≥10% hotspot staining), CD117, and CD5 showed 100% specificity for thymic carcinoma versus thymoma with 51%, 86%, and 35% sensitivity, respectively, for thymic carcinoma. All POU2F3 positive cases were also positive for CD117. All thymic carcinomas showed >10% EZH2 staining. EZH2 (≥80% staining) had a sensitivity of 81% for thymic carcinoma and a specificity of 100% for thymic carcinoma versus type A thymoma and MNTLS but had poor specificity (46%) for thymic carcinoma versus B3 thymoma. Adding EZH2 to a panel of CD117, TdT, BAP1, and MTAP increased cases with informative results from 67/81 (83%) to 77/81 (95%). Overall, absent EZH2 staining may be useful for excluding thymic carcinoma, diffuse EZH2 staining may help to exclude type A thymoma and MNTLS, and ≥10% POU2F3 staining has excellent specificity for thymic carcinoma versus thymoma.
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Affiliation(s)
- Julia R Naso
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55902, USA
| | - Julie A Vrana
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55902, USA
| | - Justin W Koepplin
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55902, USA
| | - Julian R Molina
- Division of Medical Oncology, Mayo Clinic, Rochester, MN 55902, USA
| | - Anja C Roden
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55902, USA
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9
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Angerilli V, Fassan M, Parente P, Gullo I, Campora M, Rossi C, Sacramento ML, Pennelli G, Vanoli A, Grillo F, Mastracci L. A practical approach for PD-L1 evaluation in gastroesophageal cancer. Pathologica 2023; 115:57-70. [PMID: 36537078 PMCID: PMC10462995 DOI: 10.32074/1591-951x-836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022] Open
Abstract
PD-L1 is an established predictive immunohistochemical biomarker of response to immune checkpoint inhibitors. At present, PD-L1 is routinely assessed on biopsy samples of advanced gastroesophageal cancer patients before initiating first-line treatment. However, PD-L1 is still a suboptimal biomarker, due to changing cut-off values and scoring systems, interobserver and interlaboratory variability. This practical illustrated review discusses the range of staining patterns of PD-L1 and the potential pitfalls and challenges that can be encountered when evaluating PD-L1, focusing on gastric and gastroesophageal adenocarcinoma (G/GEA) and esophageal squamous cell carcinoma (ESCC).
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Affiliation(s)
- Valentina Angerilli
- Department of Medicine (DIMED), Surgical Pathology Unit, University Hospital of Padua, Padua (PD), Italy
| | - Matteo Fassan
- Department of Medicine (DIMED), Surgical Pathology Unit, University Hospital of Padua, Padua (PD), Italy
- Veneto Institute of Oncology IOV - IRCCS, Padua (PD), Italy
| | - Paola Parente
- Unit of Pathology, Fondazione IRCCS Ospedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, FG, Italy
| | - Irene Gullo
- Department of Pathology, Centro Hospitalar Universitário de São João (CHUSJ), Porto, Portugal
- Department of Pathology, Faculty of Medicine of the University of Porto (FMUP), Portugal
- i3S - Instituto de Investigação e Inovação em Saúde da Universidade do Porto, Portugal
| | - Michela Campora
- Public Healthcare Trust of the Autonomous Province of Trento, Santa Chiara Hospital, Department of Laboratory Medicine, Pathology Unit, Trento, Italy
| | - Chiara Rossi
- Anatomic Pathology Unit, Department of Molecular Medicine, University of Pavia, and IRCCS San Matteo Hospital, Pavia, Italy
| | - Maria Luisa Sacramento
- Department of Pathology, Centro Hospitalar Universitário de São João (CHUSJ), Porto, Portugal
| | - Gianmaria Pennelli
- Department of Medicine (DIMED), Surgical Pathology Unit, University Hospital of Padua, Padua (PD), Italy
| | - Alessandro Vanoli
- Anatomic Pathology Unit, Department of Molecular Medicine, University of Pavia, and IRCCS San Matteo Hospital, Pavia, Italy
| | - Federica Grillo
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Anatomic Pathology, Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genova, Italy
| | - Luca Mastracci
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Anatomic Pathology, Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genova, Italy
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10
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Avilés‐Salas A, Flores‐Estrada D, Lara‐Mejía L, Catalán R, Cruz‐Rico G, Orozco‐Morales M, Heredia D, Bolaño‐Guerra L, Soberanis‐Piña PD, Varela‐Santoyo E, Cardona AF, Arrieta O. Modifying factors of PD-L1 expression on tumor cells in advanced non-small-cell lung cancer. Thorac Cancer 2022; 13:3362-3373. [PMID: 36317227 PMCID: PMC9715877 DOI: 10.1111/1759-7714.14695] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Programmed death ligand-1 (PD-L1) expression predicts immunotherapy utility in nononcogenic addictive lung adenocarcinoma (ADC). However, its reproducibility and reliability may be compromised outside clinical trials. This study aimed to evaluate factors associated with PD-L1 expression in lung ADC. METHODS This observational study assessed 547 tumor samples with advanced lung ADC from January 2016 to December 2020 in a single cancer institution. Tumor samples were stained by at least one approved PD-L1 clone, SP263 (Ventana) or 22C3 (Dako), and stratified in tumor proportion score (TPS) <1%, 1-49%, or ≥50%. RESULTS Of all the tumor samples, positive PD-L1 staining was higher in poorly differentiated tumors (67.3% vs. 32.7%, p < 0.001). Analytical factors associated with a PD-L1 high expression (TPS ≥ 50%) were the SP263 clone (19.6% vs. 8.2%, p < 0.001), time of archival tumor tissue <12 months (15.3% vs. 3.8%, p = 0.024), whenever the analysis was performed in the most recent years (2019-2020) (19.0% vs. 8.3%, p < 0.001), and whenever the analysis was performed by pathologists in the academic setting (Instituto Nacional de Cancerologia, INCan) (19.9% vs. 11.9%, p = 0.001). In the molecular analysis, EGFR wild-type tumors had an increased proportion of PD-L1 positive and PD-L1 high cases (60.2% vs. 47.9%, p = 0.006 and 17.4% vs.8.5%, p = 0.004). A moderate correlation (r = 0.69) in the PD-L1 TPS% was observed between the two different settings (INCan vs. external laboratories). CONCLUSION Clinicopathological factors were associated with an increased PD-L1 positivity rate. These differences were significant in the PD-L1 high group and associated with the academic setting, the SPS263 clone, time of archival tumor tissue <12 months, and a more recent period in the PD-L1 analysis.
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Affiliation(s)
- Alejandro Avilés‐Salas
- Thoracic Oncology Unit, Department of PathologyInstituto Nacional de CancerologíaMexico CityMexico
| | - Diana Flores‐Estrada
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
| | - Luis Lara‐Mejía
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
| | - Rodrigo Catalán
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
| | - Graciela Cruz‐Rico
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
| | - Mario Orozco‐Morales
- Laboratory of Personalized MedicineInstituto Nacional de CancerologíaMexico CityMexico
| | - David Heredia
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
| | - Laura Bolaño‐Guerra
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
| | | | - Edgar Varela‐Santoyo
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
| | - Andrés F. Cardona
- Clinical and Translational Oncology GroupFundación Santa Fe de BogotáBogotáColombia
| | - Oscar Arrieta
- Thoracic Oncology Unit, Department of Thoracic OncologyInstituto Nacional de CancerologíaMexico CityMexico
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11
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Nuti S, Zhang Y, Zerrouki N, Roach C, Bänfer G, Kumar GL, Manna E, Diezko R, Kersch K, Rüschoff J, Jasani B. High interobserver and intraobserver reproducibility among pathologists assessing PD-L1 CPS across multiple indications. Histopathology 2022; 81:732-741. [PMID: 35993150 DOI: 10.1111/his.14775] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/18/2022] [Accepted: 08/14/2022] [Indexed: 11/30/2022]
Abstract
AIMS A common concern among pathologists scoring PD-L1 immunohistochemical staining is interobserver and intraobserver variability. We assessed interobserver and intraobserver reproducibility of PD-L1 scoring among trained pathologists using combined positive score (CPS; tumour cell and tumour-associated immune cell staining). METHODS AND RESULTS Data were collected for 2 years (2017-2019) from 456 pathologists worldwide. Digital training encompassed unique, tumour-specific training and test sets. Samples were stained using PD-L1 IHC 22C3 pharmDx and evaluated at specific CPS cut-offs for gastric cancer (GC), cervical cancer (CC), urothelial cancer (UC), oesophageal cancer (OC), and head and neck squamous cell carcinoma (HNSCC). Pathologists underwent expert-to-peer training and scored 20 blinded samples on day 1 and 25 blinded samples on day 2 (including 15 of the day 1 samples). Interobserver and intraobserver reproducibility were assessed. For GC (120 observers) and CC (32 observers) samples assessed at CPS ≥1, average interobserver agreement was 91.5% and 91.0%, respectively, and average intraobserver agreement was 90.2% and 96.6%, respectively. For UC (139 observers) and OC (52 observers) samples measured at CPS ≥10, average interobserver agreement was 93.4% and 93.7%, respectively, and average intraobserver agreement was 92.0% and 92.5%, respectively. For HNSCC samples (113 observers), average interobserver agreement was 94.1% at CPS ≥1 and 86.5% at CPS ≥20; intraobserver agreement was 94.7% at CPS ≥1 and 90.5% at CPS ≥20. CONCLUSION The consistently high interobserver and intraobserver concordance rates support the effectiveness of face-to-face training of many global pathologists for scoring PD-L1 CPS across multiple indications at several specific cut-offs.
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Affiliation(s)
- Shanthy Nuti
- Biomarkers and Diagnostics, Oncology, Global Medical and Scientific Affairs, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA
| | - Yiwei Zhang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, USA
| | - Nabila Zerrouki
- Biomarkers and Diagnostics, Oncology, Global Medical and Scientific Affairs, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA
| | - Charlotte Roach
- Companion Diagnostics, R&D, Agilent Technologies, Inc., Carpinteria, CA, USA
| | - Gudrun Bänfer
- Training & Consulting, Targos Molecular Pathology GmbH, Kassel, Germany
| | - George L Kumar
- Scientific Affairs, Targos Inc, San Bruno, CA, USA.,Current affiliation: Bristol Myers Squibb, Princeton, NJ, USA
| | - Edward Manna
- CDx Pathology, Agilent Technologies, Inc., Carpinteria, CA, USA
| | - Rolf Diezko
- Training & Consulting, Targos Molecular Pathology GmbH, Kassel, Germany
| | - Kristopher Kersch
- Companion Diagnostics, Agilent Technologies, Inc., Carpinteria, CA, USA
| | - Josef Rüschoff
- Department of Pathology, Targos Molecular Pathology GmbH, Kassel, Germany.,Current affiliation: Discovery Life Sciences, Kassel, Germany
| | - Bharat Jasani
- Department of Pathology, Targos Molecular Pathology GmbH, Kassel, Germany.,Current affiliation: Discovery Life Sciences, Kassel, Germany
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12
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Cheng G, Zhang F, Xing Y, Hu X, Zhang H, Chen S, Li M, Peng C, Ding G, Zhang D, Chen P, Xia Q, Wu M. Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer. Front Immunol 2022; 13:893198. [PMID: 35844508 PMCID: PMC9286729 DOI: 10.3389/fimmu.2022.893198] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/27/2022] [Indexed: 12/12/2022] Open
Abstract
Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.
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Affiliation(s)
- Guoping Cheng
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | | | | | - Xingyi Hu
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - He Zhang
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | | | | | | | - Guangtai Ding
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Dadong Zhang
- 3D Medicines Inc., Shanghai, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Peilin Chen
- 3D Medicines Inc., Shanghai, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Qingxin Xia
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Meijuan Wu
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
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13
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Choi S, Cho SI, Ma M, Park S, Pereira S, Aum BJ, Shin S, Paeng K, Yoo D, Jung W, Ock CY, Lee SH, Choi YL, Chung JH, Mok TS, Kim H, Kim S. Artificial intelligence–powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non–small cell lung cancer with better prediction of immunotherapy response. Eur J Cancer 2022; 170:17-26. [DOI: 10.1016/j.ejca.2022.04.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/10/2022] [Accepted: 04/04/2022] [Indexed: 12/23/2022]
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14
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5-hmC loss is another useful tool in addition to BAP1 and MTAP immunostains to distinguish diffuse malignant peritoneal mesothelioma from reactive mesothelial hyperplasia in peritoneal cytology cell-blocks and biopsies. Virchows Arch 2022; 481:23-29. [DOI: 10.1007/s00428-022-03336-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/02/2022] [Accepted: 05/05/2022] [Indexed: 10/18/2022]
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15
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Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer. Mod Pathol 2022; 35:403-411. [PMID: 34518630 DOI: 10.1038/s41379-021-00904-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 02/07/2023]
Abstract
Standardized programmed death-ligand 1 (PD-L1) assessment in non-small cell lung cancer (NSCLC) is challenging, owing to inter-observer variability among pathologists and the use of different antibodies. There is a strong demand for the development of an artificial intelligence (AI) system to obtain high-precision scores of PD-L1 expression in clinical diagnostic scenarios. We developed an AI system using whole slide images (WSIs) of the 22c3 assay to automatically assess the tumor proportion score (TPS) of PD-L1 expression based on a deep learning (DL) model of tumor detection. Tests were performed to show the diagnostic ability of the AI system in the 22c3 assay to assist pathologists and the reliability of the application in the SP263 assay. A robust high-performance DL model for automated tumor detection was devised with an accuracy and specificity of 0.9326 and 0.9641, respectively, and a concrete TPS value was obtained after tumor cell segmentation. The TPS comparison test in the 22c3 assay showed strong consistency between the TPS calculated with the AI system and trained pathologists (R = 0.9429-0.9458). AI-assisted diagnosis test confirmed that the repeatability and efficiency of untrained pathologists could be improved using the AI system. The Ventana PD-L1 (SP263) assay showed high consistency in TPS calculations between the AI system and pathologists (R = 0.9787). In conclusion, a high-precision AI system is proposed for the automated TPS assessment of PD-L1 expression in the 22c3 and SP263 assays in NSCLC. Our study also indicates the benefits of using an AI-assisted system to improve diagnostic repeatability and efficiency for pathologists.
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16
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Choi S, Park S, Kim H, Kang SY, Ahn S, Kim KM. Gastric Cancer: Mechanisms, Biomarkers, and Therapeutic Approaches. Biomedicines 2022; 10:543. [PMID: 35327345 PMCID: PMC8945014 DOI: 10.3390/biomedicines10030543] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/22/2022] [Accepted: 02/22/2022] [Indexed: 12/13/2022] Open
Abstract
Gastric cancer (GC) remains one of the most common deadly malignancies worldwide. Recently, several targeted therapeutics for treating unresectable or metastatic GC have been developed. Comprehensive characterization of the molecular profile and of the tumor immune microenvironment of GC has allowed researchers to explore promising biomarkers for GC treatment and has enabled a new paradigm in precision-targeted immunotherapy. In this article, we review established and promising new biomarkers relevant in GC, with a focus on their clinical implications, diagnostic methods, and the efficacy of targeted agents.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (S.C.); (S.P.); (H.K.); (S.Y.K.); (S.A.)
| | - Sujin Park
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (S.C.); (S.P.); (H.K.); (S.Y.K.); (S.A.)
| | - Hyunjin Kim
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (S.C.); (S.P.); (H.K.); (S.Y.K.); (S.A.)
- Center of Companion Diagnostics, Samsung Medical Center, Seoul 06351, Korea
| | - So Young Kang
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (S.C.); (S.P.); (H.K.); (S.Y.K.); (S.A.)
| | - Soomin Ahn
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (S.C.); (S.P.); (H.K.); (S.Y.K.); (S.A.)
- Center of Companion Diagnostics, Samsung Medical Center, Seoul 06351, Korea
| | - Kyoung-Mee Kim
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (S.C.); (S.P.); (H.K.); (S.Y.K.); (S.A.)
- Center of Companion Diagnostics, Samsung Medical Center, Seoul 06351, Korea
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17
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Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab. Mod Pathol 2022; 35:1529-1539. [PMID: 35840720 PMCID: PMC9596372 DOI: 10.1038/s41379-022-01119-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 11/08/2022]
Abstract
Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1-positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1-positive compared with PD-L1-negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1-positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.
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18
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Bencze J, Szarka M, Kóti B, Seo W, Hortobágyi TG, Bencs V, Módis LV, Hortobágyi T. Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry. Biomolecules 2021; 12:biom12010019. [PMID: 35053167 PMCID: PMC8774232 DOI: 10.3390/biom12010019] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/12/2021] [Accepted: 12/20/2021] [Indexed: 12/27/2022] Open
Abstract
Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.
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Affiliation(s)
- János Bencze
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary;
- ELKH-DE Cerebrovascular and Neurodegenerative Research Group, Department of Neurology, University of Debrecen, 4032 Debrecen, Hungary
| | - Máté Szarka
- Horvath Csaba Laboratory of Bioseparation Sciences, Research Center for Molecular Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary;
- Vitrolink Kft., 4033 Debrecen, Hungary;
- Institute for Nuclear Research, 4026 Debrecen, Hungary
| | | | - Woosung Seo
- Department of Surgical Sciences, Radiology, Uppsala University, 751 85 Uppsala, Sweden;
| | - Tibor G. Hortobágyi
- Institute of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, 6725 Szeged, Hungary;
| | - Viktor Bencs
- Department of Neurology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary;
| | - László V. Módis
- Department of Behavioural Sciences, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary;
| | - Tibor Hortobágyi
- ELKH-DE Cerebrovascular and Neurodegenerative Research Group, Department of Neurology, University of Debrecen, 4032 Debrecen, Hungary
- Institute of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, 6725 Szeged, Hungary;
- Department of Old Age Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- Centre for Age-Related Medicine, SESAM, Stavanger University Hospital, 4011 Stavanger, Norway
- Correspondence:
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19
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Hondelink LM, Hüyük M, Postmus PE, Smit VTHBM, Blom S, von der Thüsen JH, Cohen D. Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer. Histopathology 2021; 80:635-647. [PMID: 34786761 PMCID: PMC9299490 DOI: 10.1111/his.14571] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/08/2021] [Accepted: 09/21/2021] [Indexed: 12/24/2022]
Abstract
AIMS Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning-based PD-L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD-L1 (22C3, laboratory-developed test)-stained samples. METHODS AND RESULTS We designed a fully supervised deep learning algorithm for whole-slide PD-L1 assessment, consisting of four sequential convolutional neural networks (CNNs), using aiforia create software. We included 199 whole slide images (WSIs) of 'routine diagnostic' histology samples from stage IV NSCLC patients, and trained the algorithm by using a training set of 60 representative cases. We validated the algorithm by comparing the algorithm TPS with the reference score in a held-out validation set. The algorithm had similar concordance with the reference score (79%) as the pathologists had with one another (75%). The intraclass coefficient was 0.96 and Cohen's κ coefficient was 0.69 for the algorithm. Around the 1% and 50% cutoff points, concordance was also similar between pathologists and the algorithm. CONCLUSIONS We designed a new, deep learning-based PD-L1 TPS algorithm that is similarly able to assess PD-L1 expression in daily routine diagnostic cases as pathologists. Successful validation on routine diagnostic WSIs and detailed visual feedback show that this algorithm meets the requirements for functioning as a 'scoring assistant'.
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Affiliation(s)
- Liesbeth M Hondelink
- Department of Pathology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Melek Hüyük
- Department of Pulmonology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Pieter E Postmus
- Department of Pulmonology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Vincent T H B M Smit
- Department of Pathology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Sami Blom
- Aiforia Technologies Oy, Helsinki, Finland
| | | | - Danielle Cohen
- Department of Pathology, Leiden University Medical Centre, Leiden, The Netherlands
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20
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Rosenbaum MW, Gonzalez RS. Immunohistochemistry as predictive and prognostic markers for gastrointestinal malignancies. Semin Diagn Pathol 2021; 39:48-57. [PMID: 34740486 DOI: 10.1053/j.semdp.2021.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/28/2021] [Indexed: 11/11/2022]
Abstract
Biomarkers play a key role in the comprehensive pathologic evaluation of gastrointestinal malignancies. These biomarkers can be predictive, indicating whether a tumor is likely to respond to a particular therapy, or prognostic, providing information about the likely course and outcome of a disease. This review article will discuss available immunohistochemical stains for assessing these markers, including staining rationale, scoring criteria, associated systemic therapies, and pictorial examples. PD-L1, HER2, and mismatch repair status can be evaluated via immunohistochemistry for esophageal, gastric, and colorectal carcinomas. Biomarkers currently play a more limited role in evaluation of pancreatic and small bowel malignancies. Immunohistochemistry can also be used to evaluate biomarker status in gastrointestinal stromal tumors, gastrointestinal malignancies with NTRK gene fusions, and undifferentiated carcinomas with switch-sucrose non-fermentable complex abnormalities.
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Affiliation(s)
- Matthew W Rosenbaum
- Department of Pathology, Beth Israel Deaconess Medical Center, United States
| | - Raul S Gonzalez
- Department of Pathology, Beth Israel Deaconess Medical Center, United States.
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21
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Lee KS, Choe G. Programmed cell death-ligand 1 assessment in urothelial carcinoma: prospect and limitation. J Pathol Transl Med 2021; 55:163-170. [PMID: 33823566 PMCID: PMC8141973 DOI: 10.4132/jptm.2021.02.22] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/22/2021] [Indexed: 11/17/2022] Open
Abstract
Programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibition has revolutionized the treatment paradigm of urothelial carcinoma (UC). Several PD-L1 assays are conducted to formulate appropriate treatment decisions for PD-1/PD-L1 target therapy in UC. However, each assay has its own specific requirement of antibody clones, staining platforms, scoring algorithms, and cutoffs for the determination of PD-L1 status. These prove to be challenging constraints to pathology laboratories and pathologists. Thus, the present article comprehensively demonstrates the scoring algorithm used and differences observed in each assay (22C3, SP142, and SP263). Interestingly, the SP142 score algorithm considers only immune cells and not tumor cells (TCs). It remains controversial whether SP142 expressed only in TCs truly accounts for a negative PD-L1 case. Moreover, the scoring algorithm of each assay is complex and divergent, which can result in inter-observer heterogeneity. In this regard, the development of artificial intelligence for providing assistance to pathologists in obtaining more accurate and objective results has been actively researched. To facilitate efficiency of PD-L1 testing, several previous studies attempted to integrate and harmonize each assay in UC. The performance comparison of the various PD-L1 assays demonstrated in previous studies was encouraging, the exceptional concordance rate reported between 22C3 and SP263. Although these two assays may be used interchangeably, a clinically validated algorithm for each agent must be applied.
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Affiliation(s)
- Kyu Sang Lee
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Korea.,Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Gheeyoung Choe
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Korea.,Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
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22
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Naso JR, Povshedna T, Wang G, Banyi N, MacAulay C, Ionescu DN, Zhou C. Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software. Pathol Oncol Res 2021; 27:609717. [PMID: 34257575 PMCID: PMC8262183 DOI: 10.3389/pore.2021.609717] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/10/2021] [Indexed: 12/23/2022]
Abstract
PD-L1 expression in non-small cell lung cancer (NSCLC) is predictive of response to immunotherapy, but scoring of PD-L1 immunohistochemistry shows considerable interobserver variability. Automated methods may allow more consistent and expedient PD-L1 scoring. We aimed to assess the technical concordance of PD-L1 scores produced using free open source QuPath software with the manual scores of three pathologists. A classifier for PD-L1 scoring was trained using 30 NSCLC image patches. A separate test set of 207 image patches from 69 NSCLC resection cases was used for comparison of automated and manual scores. Automated and average manual scores showed excellent correlation (concordance correlation coeffecient = 0.925), though automated scoring resulted in significantly more 1–49% scores than manual scoring (p = 0.012). At both 1% and 50% thresholds, automated scores showed a level of concordance with our ‘gold standard’ (the average of three pathologists’ manual scores) similar to that of individual pathologists. Automated scoring showed high sensitivity (95%) but lower specificity (84%) at a 1% threshold, and excellent specificity (100%) but lower sensitivity (71%) at a 50% threshold. We conclude that our automated PD-L1 scoring system for NSCLC has an accuracy similar to that of individual pathologists. The detailed protocol we provide for free open source scoring software and our discussion of the limitations of this technology may facilitate more effective integration of automated scoring into clinical workflows.
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Affiliation(s)
- Julia R Naso
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Gang Wang
- Department of Pathology, BC Cancer, Vancouver, BC, Canada
| | - Norbert Banyi
- Department of Pathology, BC Cancer, Vancouver, BC, Canada
| | - Calum MacAulay
- Department of Integrative Oncology, British Columbia Cancer Research Center, Vancouver, BC, Canada
| | | | - Chen Zhou
- Department of Pathology, BC Cancer, Vancouver, BC, Canada
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23
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Barberà A, Marginet Flinch R, Martin M, Mate JL, Oriol A, Martínez-Soler F, Santalucia T, Fernández PL. The Immunohistochemical Expression of Programmed Death Ligand 1 (PD-L1) Is Affected by Sample Overfixation. Appl Immunohistochem Mol Morphol 2021; 29:76-81. [PMID: 32134754 DOI: 10.1097/pai.0000000000000847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Humanized antibodies targeting programmed death receptor 1 (PD-1) or its ligand (PD-L1) have been approved for the treatment of different cancers. Some of these antibodies show a correlation between the tissue expression of PD-L1 and response. Evaluation of PD-L1 expression presents multiple challenges, but some preanalytical issues such as tissue fixation have been scarcely evaluated. With the hypothesis that immunohistochemical staining of PD-L1 may be impacted by the time of specimen fixation, we evaluated differences in its expression in tonsil samples exposed to predefined fixation times. Random nontumoral tonsillectomy specimens were blindly evaluated in tissue microarray slides after staining with SP142 and SP263 antibodies. With fixation times ranging from 12 to 72 hours, between 2.8% and 6.1% of the samples were considered to be suboptimally stained, with no differences between the 2 antibodies within these fixation times. A significantly higher proportion of samples exposed to a fixation time of 96 hours presented suboptimal immunostaining (15.6%, P<0.0001). In addition, suboptimally stained spots were 20.8% using SP142 and 10.4% using SP263 after 96 hours of fixation (P=0.046). In conclusion, the quality of staining for PD-L1 in tonsil samples decreased with overfixation of the specimen at times >72 hours. Samples exposed to formaldehyde for longer periods presented suboptimal results for both clones, but the SP142 antibody presented a significantly lower tolerance to formalin overexposure than SP263. These results indicate the relevance of a controlled preanalytical processing of samples and particularly the length of fixation of tumor specimens.
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Affiliation(s)
- Angels Barberà
- Fundamental Care and Medical-Surgical Nursing Department, School of Nursing, Faculty of Medicine and Health Sciences, Barcelona University
- Pathology Department, Germans Trias i Pujol Hospital and Institute, Faculty of Medicine and Health Sciences, Universitat Autònoma de Barcelona
| | - Ruth Marginet Flinch
- Pathology Department, Germans Trias i Pujol Hospital and Institute, Faculty of Medicine and Health Sciences, Universitat Autònoma de Barcelona
| | - Montserrat Martin
- Pathology Department, Germans Trias i Pujol Hospital and Institute, Faculty of Medicine and Health Sciences, Universitat Autònoma de Barcelona
| | - Jose L Mate
- Pathology Department, Germans Trias i Pujol Hospital and Institute, Faculty of Medicine and Health Sciences, Universitat Autònoma de Barcelona
| | - Albert Oriol
- Josep Carreras Leukemia Research Institute, Barcelona, Spain
| | - Fina Martínez-Soler
- Fundamental Care and Medical-Surgical Nursing Department, School of Nursing, Faculty of Medicine and Health Sciences, Barcelona University
| | - Tomas Santalucia
- Fundamental Care and Medical-Surgical Nursing Department, School of Nursing, Faculty of Medicine and Health Sciences, Barcelona University
| | - Pedro L Fernández
- Pathology Department, Germans Trias i Pujol Hospital and Institute, Faculty of Medicine and Health Sciences, Universitat Autònoma de Barcelona
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24
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Sinclair W, Kobalka P, Ren R, Beshai B, Lott Limbach AA, Wei L, Mei P, Li Z. Interobserver agreement in programmed cell death-ligand 1 immunohistochemistry scoring in nonsmall cell lung carcinoma cytologic specimens. Diagn Cytopathol 2020; 49:219-225. [PMID: 33104298 DOI: 10.1002/dc.24651] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 09/28/2020] [Accepted: 10/14/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND The evaluation of PD-L1 expression in nonsmall cell lung carcinoma (NSCLC) is becoming increasingly important given the effectiveness of PD-L1 inhibitors. Although cytologic specimens have been shown to be compatible with surgical specimens to evaluate PD-L1 immunohistochemistry (IHC), evidence of the reproducibility of PD-L1 in cytologic specimens is lacking. The aim of this study is to evaluate interobserver agreement in PD-L1 IHC in cytologic specimens. METHODS PD-L1 IHC was performed on 86 NSCLC cytology specimens using Dako PD-L1 IHC 22C3 pharmDx. The digitally scanned whole slide images (WSI) were read by five pathologists. Each case was given a Tumor Proportion Score (TPS) and the results were compared between the observers. The interobserver concordance was assessed using 1% and 50% as cutoffs. RESULTS TPSs were highly correlated among observers (Spearman correlation coefficient, 0.86-0.94). Using greater than 1% as a cutoff, interobserver agreement measured by Fleiss Kappa was 0.74 for all pathologists and Cohen's Kappa coefficient ranged from 0.49 to 0.83, consistent with moderate to substantial agreement. With a cutoff of greater than 50%, Fleiss Kappa was 0.79 for all pathologists and the kappa values ranged from 0.63 to 0.90, consistent with substantial to almost perfect agreement. Several pitfalls were identified by reviewing discordant cases, including staining in macrophages, stromal cells, and intratumoral heterogeneity. CONCLUSION Our data suggest that TPS of PD-L1 IHC on cytology specimens is reproducible, with a better agreement when using 50% as the cutoff value. However, special attention is required when the TPS is near the 1% cutoff.
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Affiliation(s)
- William Sinclair
- Department of Pathology, Wexner Medical Center at The Ohio State University, Columbus, Ohio, USA
| | - Peter Kobalka
- Department of Pathology, Wexner Medical Center at The Ohio State University, Columbus, Ohio, USA
| | - Rongqin Ren
- Department of Pathology, Wexner Medical Center at The Ohio State University, Columbus, Ohio, USA
| | - Boulos Beshai
- Department of Pathology and Laboratory Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
| | - Abberly A Lott Limbach
- Department of Pathology, Wexner Medical Center at The Ohio State University, Columbus, Ohio, USA
| | - Lai Wei
- Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Ping Mei
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zaibo Li
- Department of Pathology, Wexner Medical Center at The Ohio State University, Columbus, Ohio, USA
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25
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Utility of PD-L1 testing on non-small cell lung cancer cytology specimens: An institutional experience with interobserver variability analysis. Ann Diagn Pathol 2020; 48:151602. [PMID: 32877833 DOI: 10.1016/j.anndiagpath.2020.151602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 08/17/2020] [Indexed: 12/22/2022]
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26
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Naso JR, Wang G, Banyi N, Derakhshan F, Shokoohi A, Ho C, Zhou C, Ionescu DN. Comparability of laboratory-developed and commercial PD-L1 assays in non-small cell lung carcinoma. Ann Diagn Pathol 2020; 50:151590. [PMID: 33157383 DOI: 10.1016/j.anndiagpath.2020.151590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/11/2020] [Accepted: 07/13/2020] [Indexed: 01/01/2023]
Abstract
PD-L1 expression in non-small cell lung cancer (NSCLC) is predictive of response to treatment with PD-1 and PD-L1 inhibitors. Different inhibitors have been developed with different PD-L1 assays, which use different PD-1 antibody clones on different immunohistochemistry platforms. Depending on instrument and reagent availability, laboratory-developed tests with cross-platform use of PD-L1 antibodies may have practical benefits over commercial assays. The 22C3 pharmDx Assay (referred to as 22C3 DAKO), the VENTANA PD-L1 SP263 Assay (referred to as SP263 VENTANA) and a lab-developed test using the 22C3 antibody on the VENTANA BenchMark ULTRA IHC/ISH system (referred to as 22C3 VENTANA) were performed on whole sections of 85 NSCLC surgical resections. All sections were independently scored by three pathologists using tumor proportion scores. Correlation coefficients for continuous scores in pairwise comparisons between assays ranged from 0.976 to 0.978. When using a 1% positivity threshold (dichotomous scores), the 22C3 DAKO assay and 22C3 VENTANA assays showed the greatest agreement (93% agreement, κ = 0.86, 95% CI 0.75-0.97), and the 22C3 DAKO and SP263 VENTANA assays tended to show slightly less agreement (84% agreement, κ = 0.66, 95% CI 0.50-0.82). When using a 50% positivity threshold (dichotomous scores), all pairwise comparisons showed similar agreement (96-99% agreement, κ = 0.89-0.97). Overall, there was no significant difference between assays at 1% or 50% thresholds (P = .77). These data are consistent with potential interchangeability of these assays, which may widen the scope of PD-L1 assays available to laboratories and reduce logistical barriers to testing.
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Affiliation(s)
- Julia R Naso
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Gang Wang
- Department of Pathology, British Columbia Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Norbert Banyi
- Department of Pathology, British Columbia Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Fatemeh Derakhshan
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Aria Shokoohi
- Department of Medical Oncology, British Columbia Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Cheryl Ho
- Department of Medical Oncology, British Columbia Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Chen Zhou
- Department of Pathology, British Columbia Cancer, Vancouver, BC V5Z 4E6, Canada
| | - Diana N Ionescu
- Department of Pathology, British Columbia Cancer, Vancouver, BC V5Z 4E6, Canada.
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27
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Lantuejoul S, Damiola F, Adam J. Selected highlights of the 2019 Pulmonary Pathology Society Biennial Meeting: PD-L1 test harmonization studies. Transl Lung Cancer Res 2020; 9:906-916. [PMID: 32676356 PMCID: PMC7354161 DOI: 10.21037/tlcr.2020.03.23] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Immune checkpoint inhibitors (ICI) including programmed death 1 (PD-1) inhibitors, such as nivolumab and pembrolizumab, or programmed death ligand 1 (PD-L1) inhibitors, such as atezolizumab and durvalumab, have recently emerged in advanced stage lung cancer as new standards of care. They are now indicated in first- line and second- or later-line treatment of metastatic or locally-advanced stage III non-small cell lung cancer (NSCLC), as well as for metastatic small cell lung cancer (SCLC), as single agent immunotherapy or in combination with chemotherapy. Four PD-L1 immunohistochemistry (IHC) assays have been established and validated in randomized trials, each for a specific ICI. They use different primary monoclonal antibodies, platforms and detection systems, as well as different scoring systems to assess PD-L1 expression either by tumor cells (TCs) and/or by infiltrating immune cells (ICs). Most studies have shown a close analytical performance of three of these clinically-validated standardized assays, but their use restricted to dedicated platforms, which are not all available in most laboratories, questions their applicability. In addition, the relative high costs of the assays have led to the development of in-house protocols in many pathology laboratories. Their use in clinical practice to assess the predictive value of PD-L1 expression for prescription of ICI raises the issue of their reliability and their validation as compared to standardized assays. This article discusses the main comparative studies available between LDT and assays, with clear evidence that LDT can reach a performance equivalent to the trial-validated assays. The requirements are an adequate validation as compared to an appropriate standard, and the participation to external quality assurance programs and training programs for PD-L1 IHC assessment for pathologists.
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Affiliation(s)
- Sylvie Lantuejoul
- Département de Biopathologie et Département de la Recherche Translationnelle et de l'Innovation, Centre Léon Bérard Unicancer, Lyon, France.,Université Grenoble Alpes, Grenoble, France
| | - Francesca Damiola
- Département de Biopathologie et Département de la Recherche Translationnelle et de l'Innovation, Centre Léon Bérard Unicancer, Lyon, France
| | - Julien Adam
- Département de biologie et pathologie médicales, Gustave-Roussy, Villejuif, France
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28
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Riccardo F, Barutello G, Petito A, Tarone L, Conti L, Arigoni M, Musiu C, Izzo S, Volante M, Longo DL, Merighi IF, Papotti M, Cavallo F, Quaglino E. Immunization against ROS1 by DNA Electroporation Impairs K-Ras-Driven Lung Adenocarcinomas . Vaccines (Basel) 2020; 8:vaccines8020166. [PMID: 32268572 PMCID: PMC7349290 DOI: 10.3390/vaccines8020166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 12/17/2022] Open
Abstract
Non-small cell lung cancer (NSCLC) is still the leading cause of cancer death worldwide. Despite the introduction of tyrosine kinase inhibitors and immunotherapeutic approaches, there is still an urgent need for novel strategies to improve patient survival. ROS1, a tyrosine kinase receptor endowed with oncoantigen features, is activated by chromosomal rearrangement or overexpression in NSCLC and in several tumor histotypes. In this work, we have exploited transgenic mice harboring the activated K-Ras oncogene (K-RasG12D) that spontaneously develop metastatic NSCLC as a preclinical model to test the efficacy of ROS1 immune targeting. Indeed, qPCR and immunohistochemical analyses revealed ROS1 overexpression in the autochthonous primary tumors and extrathoracic metastases developed by K-RasG12D mice and in a derived transplantable cell line. As proof of concept, we have evaluated the effects of the intramuscular electroporation (electrovaccination) of plasmids coding for mouse- and human-ROS1 on the progression of these NSCLC models. A significant increase in survival was observed in ROS1-electrovaccinated mice challenged with the transplantable cell line. It is worth noting that tumors were completely rejected, and immune memory was achieved, albeit only in a few mice. Most importantly, ROS1 electrovaccination was also found to be effective in slowing the development of autochthonous NSCLC in K-RasG12D mice.
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Affiliation(s)
- Federica Riccardo
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Giuseppina Barutello
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Angela Petito
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Lidia Tarone
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Laura Conti
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Maddalena Arigoni
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Chiara Musiu
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Stefania Izzo
- Department of Oncology, University of Torino, 10043 Orbassano, Italy; (S.I.); (M.V.); (M.P.)
| | - Marco Volante
- Department of Oncology, University of Torino, 10043 Orbassano, Italy; (S.I.); (M.V.); (M.P.)
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), 10126 Torino, Italy;
| | - Irene Fiore Merighi
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
| | - Mauro Papotti
- Department of Oncology, University of Torino, 10043 Orbassano, Italy; (S.I.); (M.V.); (M.P.)
| | - Federica Cavallo
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
- Correspondence: (F.C.); (E.Q.); Tel.: +39-011670-6457 (F.C. & E.Q.)
| | - Elena Quaglino
- Department of Molecular Biotechnology and Health Sciences, University of Torino, 10126 Torino, Italy; (F.R.); (G.B.); (A.P.); (L.T.); (L.C.); (M.A.); (C.M.); (I.F.M.)
- Correspondence: (F.C.); (E.Q.); Tel.: +39-011670-6457 (F.C. & E.Q.)
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