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L'Imperio V, Cazzaniga G, Mannino M, Seminati D, Mascadri F, Ceku J, Casati G, Bono F, Eloy C, Rocco EG, Frascarelli C, Fassan M, Malapelle U, Pagni F. Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline. Virchows Arch 2024:10.1007/s00428-024-03794-9. [PMID: 38532196 DOI: 10.1007/s00428-024-03794-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/19/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
The estimation of tumor cellular fraction (TCF) is a crucial step in predictive molecular pathology, representing an entry adequacy criterion also in the next-generation sequencing (NGS) era. However, heterogeneity of quantification practices and inter-pathologist variability hamper the robustness of its evaluation, stressing the need for more reliable results. Here, 121 routine histological samples from non-small cell lung cancer (NSCLC) cases with complete NGS profiling were used to evaluate TCF interobserver variability among three different pathologists (pTCF), developing a computational tool (cTCF) and assessing its reliability vs ground truth (GT) tumor cellularity and potential impact on the final molecular results. Inter-pathologist reproducibility was fair to good, with overall Wk ranging between 0.46 and 0.83 (avg. 0.59). The obtained cTCF was comparable to the GT (p = 0.129, 0.502, and 0.130 for surgical, biopsies, and cell block, respectively) and demonstrated good reliability if elaborated by different pathologists (Wk = 0.9). Overall cTCF was lower as compared to pTCF (30 ± 10 vs 52 ± 19, p < 0.001), with more cases < 20% (17, 14%, p = 0.690), but none containing < 100 cells for the algorithm. Similarities were noted between tumor area estimation and pTCF (36 ± 29, p < 0.001), partly explaining variability in the human assessment of tumor cellularity. Finally, the cTCF allowed a reduction of the copy number variations (CNVs) called (27 vs 29, - 6.9%) with an increase of effective CNVs detection (13 vs 7, + 85.7%), some with potential clinical impact previously undetected with pTCF. An automated computational pipeline (Qupath Analysis of Nuclei from Tumor to Uniform Molecular tests, QuANTUM) has been created and is freely available as a QuPath extension. The computational method used in this study has the potential to improve efficacy and reliability of TCF estimation in NSCLC, with demonstrated impact on the final molecular results.
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Affiliation(s)
- Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy.
| | - Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
| | - Mauro Mannino
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
| | - Davide Seminati
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
| | - Francesco Mascadri
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
| | - Joranda Ceku
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
| | - Gabriele Casati
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
| | - Francesca Bono
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
| | - Catarina Eloy
- Pathology Laboratory, Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP), Porto, Portugal
- Pathology Department, Medical Faculty of University of Porto, Porto, Portugal
| | - Elena Guerini Rocco
- Division of Pathology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Chiara Frascarelli
- Division of Pathology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Fassan
- Surgical Pathology and Cytopathology Unit, Department of Medicine, DIMED, University of Padua, Padua, Italy
- Veneto Institute of Oncology, IOV-IRCCS, Padua, Italy
| | - Umberto Malapelle
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo Dei Tintori, University of Milano-Bicocca, Milan, Italy
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Hofman P, Berezowska S, Kazdal D, Mograbi B, Ilié M, Stenzinger A, Hofman V. Current challenges and practical aspects of molecular pathology for non-small cell lung cancers. Virchows Arch 2024; 484:233-246. [PMID: 37801103 PMCID: PMC10948551 DOI: 10.1007/s00428-023-03651-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 10/07/2023]
Abstract
The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.
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Affiliation(s)
- Paul Hofman
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France.
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France.
| | - Sabina Berezowska
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Daniel Kazdal
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Baharia Mograbi
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
| | - Marius Ilié
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
| | - Albrecht Stenzinger
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Véronique Hofman
- Côte d'Azur University, FHU OncoAge, IHU RespirERA, Laboratory of Clinical and Experimental Pathology, BB-0033-00025, Louis Pasteur Hospital, 30 avenue de la voie romaine, BP69, 06001, Nice cedex 01, France
- Côte d'Azur University, IRCAN, Inserm, CNRS 7284, U1081, Nice, France
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Frei AL, Oberson R, Baumann E, Perren A, Grobholz R, Lugli A, Dawson H, Abbet C, Lertxundi I, Reinhard S, Mookhoek A, Feichtinger J, Sarro R, Gadient G, Dommann-Scherrer C, Barizzi J, Berezowska S, Glatz K, Dertinger S, Banz Y, Schoenegg R, Rubbia-Brandt L, Fleischmann A, Saile G, Mainil-Varlet P, Biral R, Giudici L, Soltermann A, Chaubert AB, Stadlmann S, Diebold J, Egervari K, Bénière C, Saro F, Janowczyk A, Zlobec I. Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study. Mod Pathol 2023; 36:100335. [PMID: 37742926 DOI: 10.1016/j.modpat.2023.100335] [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: 05/31/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023]
Abstract
Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems.
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Affiliation(s)
- Ana Leni Frei
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland.
| | - Raphaël Oberson
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Elias Baumann
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Aurel Perren
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Rainer Grobholz
- Medical Faculty University of Zurich, Institute of Pathology, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Alessandro Lugli
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Heather Dawson
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Christian Abbet
- Signal Processing Laboratory 5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ibai Lertxundi
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Stefan Reinhard
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Aart Mookhoek
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | | | - Rossella Sarro
- Istituto Cantonale di Patologia, Ente ospedaliero cantonale (EOC), Locarno, Switzerland
| | | | | | - Jessica Barizzi
- Istituto Cantonale di Patologia, Ente ospedaliero cantonale (EOC), Locarno, Switzerland
| | - Sabina Berezowska
- Institute of Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Katharina Glatz
- Institut of Pathology, University Hospital Basel, Basel, Switzerland
| | - Susanne Dertinger
- Institute of Pathology, Landeskrankenhaus Feldkirch, Feldkirch, Austria
| | - Yara Banz
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Rene Schoenegg
- Institute of Pathology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Laura Rubbia-Brandt
- Department of Pathology and Immunology, Geneva University Hospital, Genève, Switzerland
| | - Achim Fleischmann
- Institute of Pathology, Cantonal Hospital Thurgau, Münsterlingen, Switzerland
| | | | | | | | - Luca Giudici
- Istituto Cantonale di Patologia, Ente ospedaliero cantonale (EOC), Locarno, Switzerland
| | | | - Audrey Baur Chaubert
- FMH Pathology, Pathology Department of SYNLAB Switzerland SA, Lausanne, Switzerland
| | - Sylvia Stadlmann
- Institute of Pathology, Cantonal Hospital Baden, Baden, Switzerland
| | - Joachim Diebold
- Institute of Pathology, Cantonal Hospital Luzern, Luzern, Switzerland
| | - Kristof Egervari
- Department of Pathology and Immunology, Geneva University Hospital, Genève, Switzerland
| | | | - Francesca Saro
- Institute of Pathology and Molecular Pathology, University Hospital Zürich, Zürich, Switzerland
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University, Atlanta, Georgia; Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland; Department of Clinical Pathology, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Inti Zlobec
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland.
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Menzel M, Endris V, Schwab C, Kluck K, Neumann O, Beck S, Ball M, Schaaf C, Fröhling S, Lichtner P, Schirmacher P, Kazdal D, Stenzinger A, Budczies J. Accurate tumor purity determination is critical for the analysis of homologous recombination deficiency (HRD). Transl Oncol 2023; 35:101706. [PMID: 37327584 DOI: 10.1016/j.tranon.2023.101706] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 05/02/2023] [Accepted: 05/28/2023] [Indexed: 06/18/2023] Open
Abstract
Homologous recombination deficiency (HRD) is a predictive marker for response to poly (ADP-ribose) polymerase inhibitors (PARPi) in ovarian carcinoma. HRD scores have entered routine diagnostics, but the influence of algorithms, parameters and confounders has not been analyzed comprehensively. A series of 100 poorly differentiated ovarian carcinoma samples was analyzed using whole exome sequencing (WES) and genotyping. Tumor purity was determined using conventional pathology, digital pathology, and two bioinformatic methods. HRD scores were calculated from copy number profiles determined by Sequenza and by Sclust either with or without fixed tumor purity. Tumor purity determination by digital pathology combined with a tumory purity informed variant of Sequenza served as reference method for HRD scoring. Seven tumors had deleterious mutations in BRCA1/2, 12 tumors had deleterious mutations in other homologous recombination repair (HRR) genes, 18 tumors had variants of unknown significance (VUS) in BRCA1/2 or other HRR genes, while the remaining 63 tumors had no relevant alterations. Using the reference method for HRD scoring, 68 tumors were HRD-positive. HRDsum determined by WES correlated strongly with HRDsum determined by single nucleotide polymorphism (SNP) arrays (R = 0.85). Conventional pathology systematically overestimated tumor purity by 8% compared to digital pathology. All investigated methods agreed on classifying the deleterious BRCA1/2-mutated tumors as HRD-positive, but discrepancies were observed for some of the remaining tumors. Discordant HRD classification of 11% of the tumors was observed comparing the tumor purity uninformed default of Sequenza and the reference method. In conclusion, tumor purity is a critical factor for the determination of HRD scores. Assistance by digital pathology helps to improve accuracy and imprecision of its estimation.
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Affiliation(s)
- Michael Menzel
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany; Center for Personalized Medicine (ZPM) Heidelberg, Heidelberg 69120, Germany
| | - Volker Endris
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Constantin Schwab
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Klaus Kluck
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Olaf Neumann
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Susanne Beck
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany; Center for Personalized Medicine (ZPM) Heidelberg, Heidelberg 69120, Germany
| | - Markus Ball
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Christian Schaaf
- Center for Personalized Medicine (ZPM) Heidelberg, Heidelberg 69120, Germany; Institute of Human Genetics, Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Stefan Fröhling
- Center for Personalized Medicine (ZPM) Heidelberg, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; Division of Translational Medical Oncology, NCT Heidelberg and DKFZ, Heidelberg 69120, Germany; NCT Molecular Diagnostics Program, NCT Heidelberg and DKFZ, Heidelberg 69120, Germany
| | - Peter Lichtner
- Core Facility Genomics, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Peter Schirmacher
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany; Center for Personalized Medicine (ZPM) Heidelberg, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany
| | - Daniel Kazdal
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany; German Center for Lung Research (DZL), Heidelberg site, Heidelberg 69120, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany; Center for Personalized Medicine (ZPM) Heidelberg, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; German Center for Lung Research (DZL), Heidelberg site, Heidelberg 69120, Germany.
| | - Jan Budczies
- Institute of Pathology, Heidelberg University Hospital, Heidelberg 69120, Germany; Center for Personalized Medicine (ZPM) Heidelberg, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; German Center for Lung Research (DZL), Heidelberg site, Heidelberg 69120, Germany.
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5
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Wang Y, Luo F, Yang X, Wang Q, Sun Y, Tian S, Feng P, Huang P, Xiao H. The Swin-Transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04795-y. [PMID: 37097394 DOI: 10.1007/s00432-023-04795-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 04/15/2023] [Indexed: 04/26/2023]
Abstract
PURPOSE The classification of primary lung adenocarcinoma is complex and varied. Different subtypes of lung adenocarcinoma have different treatment methods and different prognosis. In this study, we collected 11 datasets comprising subtypes of lung cancer and proposed FL-STNet model to provide the assistance for improving clinical problems of pathologic classification in primary adenocarcinoma of lung. METHODS Samples were collected from 360 patients diagnosed with lung adenocarcinoma and other subtypes of lung diseases. In addition, an auxiliary diagnosis algorithm based on Swin-Transformer, which used Focal Loss for function in training, was developed. Meanwhile, the diagnostic accuracy of the Swin-Transformer was compared to pathologists. RESULTS The Swin-Transformer captures not only information in the overall tissue structure but also the local tissue details in the images of lung cancer pathology. Furthermore, training FL-STNet with the Focal Loss function can further balance the difference in the amount of data between different subtypes, improving recognition accuracy. The average classification accuracy, F1, and AUC of the proposed FL-STNet reached 85.71%, 86.57%, and 0.9903. The average accuracy of the FL-STNet was higher by 17% and 34%, respectively, than in the senior pathologist and junior pathologist group. CONCLUSION The first deep learning based on an 11-category classifier was developed for classifying lung adenocarcinoma subtypes based on WSI histopathology. Aiming at the deficiencies of the current CNN and Vit, FL-STNet model is proposed in this study by introducing Focal Loss and combining the advantages of Swin-Transformer model.
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Affiliation(s)
- Yujun Wang
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Furong Luo
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Xing Yang
- College of Computer and Cyber Security, Chengdu University of Technology, Chengdu, 610059, People's Republic of China
| | - Qiushi Wang
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Yunchun Sun
- Center of Digital Dentistry, School and Hospital of Stomatology, Peking University, Beijing, 100081, People's Republic of China
| | - Sukun Tian
- Center of Digital Dentistry, School and Hospital of Stomatology, Peking University, Beijing, 100081, People's Republic of China
| | - Peng Feng
- Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), College of Optoelectronic Engineering, Chongqing University, Chongqing, 400044, People's Republic of China
| | - Pan Huang
- Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), College of Optoelectronic Engineering, Chongqing University, Chongqing, 400044, People's Republic of China.
| | - Hualiang Xiao
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China.
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Henin D, Fiorin LG, Carmagnola D, Pellegrini G, Toma M, Cristofalo A, Dellavia C. Quantitative Evaluation of Inflammatory Markers in Peri-Implantitis and Periodontitis Tissues: Digital vs. Manual Analysis—A Proof of Concept Study. Medicina (B Aires) 2022; 58:medicina58070867. [PMID: 35888586 PMCID: PMC9318134 DOI: 10.3390/medicina58070867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Objectives: In dentistry, the assessment of the histomorphometric features of periodontal (PD) and peri-implant (PI) lesions is important to evaluate their underlying pathogenic mechanism. The present study aimed to compare manual and digital methods of analysis in the evaluation of the inflammatory biomarkers in PI and PD lesions. Materials and Methods: PD and PI inflamed soft tissues were excised and processed for histological and immunohistochemical analyses for CD3+, CD4+, CD8+, CD15+, CD20+, CD68+, and CD138+. The obtained slides were acquired using a digital scanner. For each marker, 4 pictures per sample were extracted and the area fraction of the stained tissue was computed both manually using a 594-point counting grid (MC) and digitally using a dedicated image analysis software (DC). To assess the concordance between MC and DC, two blinded observers analysed a total of 200 pictures either with good quality of staining or with non-specific background noise. The inter and intraobserver concordance was evaluated using the intraclass coefficient and the agreement between MC and DC was assessed using the Bland–Altman plot. The time spent analysing each picture using the two methodologies by both observers was recorded. Further, the amount of each marker was compared between PI and PD with both methodologies. Results: The inter- and intraobserver concordance was excellent, except for images with background noise analysed using DC. MC and DC showed a satisfying concordance. DC was performed in half the time compared to MC. The morphological analysis showed a larger inflammatory infiltrate in PI than PD lesions. The comparison between PI and PD showed differences for CD68+ and CD138+ expression. Conclusions: DC could be used as a reliable and time-saving procedure for the immunohistochemical analysis of PD and PI soft tissues. When non-specific background noise is present, the experience of the pathologist may be still required.
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Affiliation(s)
- Dolaji Henin
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20133 Milan, Italy; (D.H.); (L.G.F.); (G.P.); (M.T.); (A.C.); (C.D.)
| | - Luiz Guilherme Fiorin
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20133 Milan, Italy; (D.H.); (L.G.F.); (G.P.); (M.T.); (A.C.); (C.D.)
- Department of Diagnosis and Surgery, Division of Periodontics, School of Dentistry, Sao Paulo State University (UNESP), Aracatuba 16015-050, SP, Brazil
| | - Daniela Carmagnola
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20133 Milan, Italy; (D.H.); (L.G.F.); (G.P.); (M.T.); (A.C.); (C.D.)
- Correspondence:
| | - Gaia Pellegrini
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20133 Milan, Italy; (D.H.); (L.G.F.); (G.P.); (M.T.); (A.C.); (C.D.)
| | - Marilisa Toma
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20133 Milan, Italy; (D.H.); (L.G.F.); (G.P.); (M.T.); (A.C.); (C.D.)
| | - Aurora Cristofalo
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20133 Milan, Italy; (D.H.); (L.G.F.); (G.P.); (M.T.); (A.C.); (C.D.)
| | - Claudia Dellavia
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20133 Milan, Italy; (D.H.); (L.G.F.); (G.P.); (M.T.); (A.C.); (C.D.)
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7
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Sholl LM. Biomarkers of response to checkpoint inhibitors beyond PD-L1 in lung cancer. Mod Pathol 2022; 35:66-74. [PMID: 34608245 DOI: 10.1038/s41379-021-00932-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/23/2021] [Accepted: 09/07/2021] [Indexed: 12/23/2022]
Abstract
Immunotherapy, including use of checkpoint inhibitors against PD-1, PD-L1, and CTLA-4, forms the backbone of oncologic management for the majority of non-small cell lung carcinoma patients. However, response to these therapies varies widely, from patients who have complete resolution of metastatic disease and long-term remission, to those who rapidly progress and succumb to their cancer despite use of the newest checkpoint inhibitors. While PD-L1 protein expression by immunohistochemistry serves as the principle predictive biomarker for immunotherapy response, neither the sensitivity nor the specificity of this approach is optimal, and clinical PD-L1 testing is plagued by concerns around result reproducibility and confusion born from the proliferation of different companion diagnostic assays. At the same time, insights into tumor and host immune-specific factors that inform both prognosis and response prediction are beginning to define better immunotherapy biomarkers. Beyond immune checkpoint expression status, common themes in analyses of immunotherapy response prediction include cancer neoantigen production, the state of the antigen presentation pathway in both tumor and antigen presenting cells, the admixture of effector and suppressor immune cells in the tumor microenvironment, and the genomic drivers and comutations that can influence the all of these variables. This review will address the state of PD-L1 testing in lung cancer, the role for tumor mutation burden as a predictive biomarker, the evolving status of human leukocyte antigen/major histocompatibility complex expression as a marker of antigen presentation, approaches to tumor immune cell quantitation including by multiplex immunofluorescence, and the importance of tumor genomic profiling to ascertain oncogenic driver (EGFR, ALK, KRAS, MET, etc.) and co-mutation (STK11, KEAP1, SMARCA4) status.
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Affiliation(s)
- Lynette M Sholl
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
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