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Steffen C, Schallenberg S, Dernbach G, Dielmann A, Dragomir MP, Schweiger-Eisbacher C, Klauschen F, Horst D, Tinhofer I, Heiland M, Keilholz U. Spatial heterogeneity of tumor cells and the tissue microenvironment in oral squamous cell carcinoma. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137:379-390. [PMID: 38281880 DOI: 10.1016/j.oooo.2023.12.785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/07/2023] [Accepted: 12/10/2023] [Indexed: 01/30/2024]
Abstract
PURPOSE This study describes the morphologic and phenotypic spatial heterogeneity of tumor cells and the tissue microenvironment (TME), focusing on immune infiltration in OSCCs. STUDY DESIGN Patients with OSCCs and planned surgical tumor resection were eligible for the study. Two biopsies each from the tumor center and the tumor rim were obtained. Immunohistochemical characterization of tumor and immune cells was performed using a panel of immunohistochemical markers. RESULTS Thirty-six biopsies were obtained from the 9 patients. All patients showed an individual marker expression profile with ITH. Within the same biopsy, the CPS and TPS scores showed relevant variations in PD-L1 expression. Comparisons between the tumor center and rim revealed significant differences in the up/downregulation of p53. Marker expression of patients with recurrences clustered similarly, with the higher expression of FoxP3, IDO, CD4, CD68, and CD163 at the tumor rim. CONCLUSION OSCCs were found to exhibit relevant ITH involving both tumor cells and TME, suggesting that biomarker analysis of multiple tumor regions may be helpful for clinical decision making and tumor characterization. The analysis of multiple spots within a biopsy is recommended for a reliable determination of PD-L1 expression and other biomarkers, impacting current clinical assessments.
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Affiliation(s)
- Claudius Steffen
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.
| | - Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Gabriel Dernbach
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Anastasia Dielmann
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Mihnea P Dragomir
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany; Berlin Institute of Health (BIH), Berlin, Germany
| | | | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany; Berlin Institute of Health (BIH), Berlin, Germany; Institute of Pathology, Ludwig-Maximilians-University Munich, München, Germany
| | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ingeborg Tinhofer
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiooncology and Radiotherapy, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Max Heiland
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Ulrich Keilholz
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany; Berlin Institute of Health (BIH), Berlin, Germany
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Schallenberg S, Dernbach G, Dragomir MP, Schlachtenberger G, Boschung K, Friedrich C, Standvoss K, Ruff L, Anders P, Grohé C, Randerath W, Merkelbach-Bruse S, Quaas A, Heldwein M, Keilholz U, Hekmat JK, Rückert C, Büttner R, Horst D, Klauschen F, Frost N. TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading. Eur J Cancer 2024; 197:113474. [PMID: 38100920 DOI: 10.1016/j.ejca.2023.113474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/03/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023]
Abstract
OBJECTIVES Thyroid transcription factor 1 (TTF-1) is a well-established independent prognostic factor in lung adenocarcinoma (LUAD), irrespective of stage. This study aims to determine if TTF-1's prognostic impact is solely based on histomorphological differentiation (tumor grading) or if it independently relates to a biologically more aggressive phenotype. We analyzed a large bi-centric LUAD cohort to accurately assess TTF-1's prognostic value in relation to tumor grade. PATIENTS AND METHODS We studied 447 patients with resected LUAD from major German lung cancer centers (Berlin and Cologne), correlating TTF-1 status and grading with clinical, pathologic, and molecular data, alongside patient outcomes. TTF-1's impact was evaluated through univariate and multivariate Cox regression. Causal graph analysis was used to identify and account for potential confounders, improving the statistical estimation of TTF-1's predictive power for clinical outcomes. RESULTS Univariate analysis revealed TTF-1 positivity associated with significantly longer disease-free survival (DFS) (median log HR -0.83; p = 0.018). Higher tumor grade showed a non-significant association with shorter DFS (median log HR 0.30; p = 0,62 for G1 to G2 and 0.68; p = 0,34 for G2 to G3). In multivariate analysis, TTF-1 positivity resulted in a significantly longer DFS (median log HR -0.65; p = 0.05) independent of all other parameters, including grading. Adjusting for potential confounders as indicated by the causal graph confirmed the superiority of TTF-1 over tumor grading in prognostics power. CONCLUSIONS TTF-1 status predicts relapse and survival in LUAD independently of tumor grading. The prognostic power of tumor grading is limited to TTF-1-positive patients, and the effect size of TTF-1 surpasses that of tumor grading. We recommend including TTF1 status as a prognostic factor in the diagnostic guidelines of LUAD.
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Affiliation(s)
- Simon Schallenberg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany.
| | - Gabriel Dernbach
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany; Aignostics GmbH, 10555 Berlin, Germany; BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.
| | - Mihnea P Dragomir
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Berlin Institute of Health (BIH), Berlin, Germany
| | | | - Kyrill Boschung
- Bethanien Hospital, Clinic of Pneumology and Allergology, Center for Sleep Medicine and Respiratory Care, Institute of Pneumology at the University of Cologne, Solingen, Germany
| | - Corinna Friedrich
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany; Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany; Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Proteomics Platform, Berlin, Germany
| | | | | | - Philipp Anders
- Faculty of Medicine, Semmelweis University, 1085 Budapest, Hungary
| | - Christian Grohé
- Klinik für Pneumologie, Evangelische Lungenklinik Berlin Buch, Berlin, Germany
| | - Winfried Randerath
- Bethanien Hospital, Clinic of Pneumology and Allergology, Center for Sleep Medicine and Respiratory Care, Institute of Pneumology at the University of Cologne, Solingen, Germany
| | | | - Alexander Quaas
- Institute of Pathology, University Hospital Cologne, Germany
| | - Matthias Heldwein
- Department of Cardiothoracic Surgery, University Hospital Cologne, Germany
| | - Ulrich Keilholz
- Charite Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany
| | - Jens Khosro Hekmat
- Department of Cardiothoracic Surgery, University Hospital Cologne, Germany
| | - Carsten Rückert
- Department of General, Visceral, Vascular and Thoracic Surgery, Charité-Universitätsmedizin Berlin, Germany
| | | | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany
| | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany; BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Pathology, Ludwig-Maximilians-University Munich, Thalkirchner Str. 36, 80337 München, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich Partner Site, Heidelberg, Germany
| | - Nikolaj Frost
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany
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Keyl P, Bischoff P, Dernbach G, Bockmayr M, Fritz R, Horst D, Blüthgen N, Montavon G, Müller KR, Klauschen F. Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Res 2023; 51:e20. [PMID: 36629274 PMCID: PMC9976884 DOI: 10.1093/nar/gkac1212] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/16/2022] [Accepted: 12/06/2022] [Indexed: 01/12/2023] Open
Abstract
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.
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Affiliation(s)
- Philipp Keyl
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Philip Bischoff
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Gabriel Dernbach
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Department of Pediatric Hematology and Oncolog, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.,Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf Martinistr. 52, 20246 Hamburg, Germany
| | - Rebecca Fritz
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Institut für Biologie, Humboldt University, Free University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
| | - Grégoire Montavon
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
| | - Klaus-Robert Müller
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany.,Department of Artificial Intelligence, Korea University, Seoul 136-713, South Korea.,Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 4, 66123 Saarbrücken, Germany
| | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany.,BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Institute of Pathology, Ludwig-Maximilians-University Munich, Thalkirchner Str. 36, 80337 München, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich partner site, Germany
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Mrowiec T, Ruane S, Schallenberg S, Dernbach G, Todorova R, Böhm C, de Back W, Pablos B, Schulte-Sasse R, Trajanovska I, Creosteanu A, Barbuta E, Otte M, Ihling C, Grote HJ, Scheuenpflug J, Matyas V, Alber M, Klauschen F. Abstract 457: Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Automated cell-level characterization of the tumor microenvironment (TME) at scale is key to data-driven immuno-oncology. Artificial intelligence (AI)-powered analysis of hematoxylin and eosin (H&E) images scales and has recently been translated into diagnostics. However, robust TME analysis solely based on H&E data is bound by the stain's properties and by manual pathologist annotations, both in number and accuracy. In this study, we quantify the error introduced by pathologists' morphological assessment and mitigate this error by training AI-systems without manual pathologist annotations, using labels determined directly from IHC profiles.
Methods: The work was carried out on 239 clinical NSCLC cases. CK-KL1, CD3+CD20, and Mum1 were used for defining carcinoma (CA), lymphocyte (LY), and plasma (PL) cells. For evaluation, representative regions were annotated by 3 trained pathologists. The workflow is based on co-registration of same-section H&E and IHC stained images with single cell precision. Cells were detected in H&E and labelled using rule-based algorithms that incorporated IHC information. This H&E data was used to train neural networks (NN).
Results: (A) The inter-rater agreement of pathologists annotating on H&E is increased when information from registered IHC images is provided. (B) The concordance of pathologists on H&E-only compared to on H&E+IHC shows that pathologists miss or misclassify cells with a certain error. (C) NNs trained with IHC-based labels achieve similar performance for cell type classification on H&E as pathologists on H&E.
Conclusion: This study demonstrates the value of combining histomorphological and IHC data for improved cell annotation. Our novel workflow provides a quantitative benchmark and facilitates training of accurate AI models for quantitative characterization of tumor and TME from H&E sections.
A) Inter-rater agreement by metric, stain, and cell type By cell count, Pearson correlation By cell count, Pearson correlation By cell location, Krippendorff’s alpha By cell location, Krippendorff’s alpha Cell type H&E-only H&E+IHC H&E-only H&E+IHC CA 0.86 0.98 0.43 0.90 LY 0.88 0.99 0.21 0.76 PL 0.77 0.96 0.32 0.87 B) Performance of individual pathologists in H&E Against consensus in H&E+IHC Against own annotations in H&E+IHC Against own annotations in H&E+IHC Cell type By cell count, Pearson correlation By cell location, Precision By cell location, Recall CA 0.84 0.76 0.77 LY 0.78 0.70 0.60 PL 0.76 0.69 0.21 C) NN against annotator H&E+IHC consensus Cell Type By cell count, Pearson correlation CA 0.84 LY 0.92 PL 0.75
Citation Format: Thomas Mrowiec, Sharon Ruane, Simon Schallenberg, Gabriel Dernbach, Rumyana Todorova, Cornelius Böhm, Walter de Back, Blanca Pablos, Roman Schulte-Sasse, Ivana Trajanovska, Adelaida Creosteanu, Emil Barbuta, Marcus Otte, Christian Ihling, Hans Juergen Grote, Juergen Scheuenpflug, Viktor Matyas, Maximilian Alber, Frederick Klauschen. Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 457.
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