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Angeloni M, van Doeveren T, Lindner S, Volland P, Schmelmer J, Foersch S, Matek C, Stoehr R, Geppert CI, Heers H, Wach S, Taubert H, Sikic D, Wullich B, van Leenders GJLH, Zaburdaev V, Eckstein M, Hartmann A, Boormans JL, Ferrazzi F, Bahlinger V. A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing. J Pathol Clin Res 2024; 10:e12369. [PMID: 38504364 PMCID: PMC10951050 DOI: 10.1002/2056-4538.12369] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024]
Abstract
Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.
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Affiliation(s)
- Miriam Angeloni
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Thomas van Doeveren
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Sebastian Lindner
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Patrick Volland
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Jorina Schmelmer
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | | | - Christian Matek
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Robert Stoehr
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Hendrik Heers
- Department of UrologyPhilipps‐Universität MarburgMarburgGermany
| | - Sven Wach
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Helge Taubert
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Danijel Sikic
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Bernd Wullich
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Urology and Pediatric UrologyUniversity Hospital Erlangen, Friedrich‐Alexander Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Geert JLH van Leenders
- Department of PathologyErasmus MC Cancer Institute, University Medical CentreRotterdamthe Netherlands
| | - Vasily Zaburdaev
- Department of BiologyFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Max‐Planck‐Zentrum für Physik und MedizinErlangenGermany
| | - Markus Eckstein
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
| | - Joost L Boormans
- Department of UrologyErasmus MC Urothelial Cancer Research GroupRotterdamThe Netherlands
| | - Fulvia Ferrazzi
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of NephropathologyInstitute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Veronika Bahlinger
- Institute of Pathology, University Hospital Erlangen‐Nürnberg, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
- Comprehensive Cancer Center Erlangen‐EMN (CCC ER‐EMN)ErlangenGermany
- Bavarian Cancer Research Center (BZKF)ErlangenGermany
- Department of Pathology and NeuropathologyUniversity Hospital and Comprehensive Cancer Center TübingenTübingenGermany
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2
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Eckstein M, Matek C, Wagner P, Erber R, Büttner-Herold M, Wild PJ, Taubert H, Wach S, Sikic D, Wullich B, Geppert CI, Compérat EM, Lopez-Beltran A, Montironi R, Cheng L, van der Kwast T, Colecchia M, van Rhijn BWG, Amin MB, Netto GJ, Lehmann J, Stöckle M, Junker K, Hartmann A, Bertz S. Proposal for a Novel Histological Scoring System as a Potential Grading Approach for Muscle-invasive Urothelial Bladder Cancer Correlating with Disease Aggressiveness and Patient Outcomes. Eur Urol Oncol 2024; 7:128-138. [PMID: 37562993 DOI: 10.1016/j.euo.2023.07.011] [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: 05/20/2023] [Revised: 07/13/2023] [Accepted: 07/24/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Grading of muscle-invasive bladder cancer (MIBC) according to the current World Health Organization (WHO) criteria is controversial due to its limited prognostic value. All MIBC cases except a tiny minority are of high grade. OBJECTIVE To develop a prognostic histological scoring system for MIBC integrating histomorphological phenotype, stromal tumor-infiltrating lymphocytes (sTILs), tumor budding, and growth and spreading patterns. DESIGN, SETTING, AND PARTICIPANTS Tissue specimens and clinical data of 484 patients receiving cystectomy and lymphadenectomy with curative intent with or without adjuvant chemotherapy. Histomorphological phenotypes, sTILs, tumor budding, and growth and spreading patterns were evaluated and categorized into four grade groups (GGs). GGs were correlated with molecular subtypes, immune infiltration, immune checkpoint expression, extracellular matrix (ECM) remodeling, and epithelial-mesenchymal transition (EMT) activity. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS GGs were associated with overall (OS), disease-specific (DSS), and progression-free (PFS) survival in univariable and multivariable analyses. Association with biological features was analyzed with descriptive statistics. RESULTS AND LIMITATIONS Integration of two histomorphological tumor groups, three sTILs groups, three tumor budding groups, and four growth/spread patterns yielded four novel GGs that had high significance in the univariable survival analysis (OS, DSS, and PFS). GGs were confirmed as independent prognostic predictors with the greatest effect in the multivariable Cox regression analysis. Correlation with molecular data showed a gradual transition from basal to luminal subtypes from GG1 to GG4; a gradual decrease in survival, immune infiltration, and immune checkpoint activity; and a gradual increase in ECM remodeling and EMT activity. CONCLUSIONS We propose a novel, prognostically relevant, and biologically based scoring system for MIBC in cystectomies applicable to routine pathological sections. PATIENT SUMMARY We developed a novel approach to assess the aggressiveness of advanced bladder cancer, which allows improved risk stratification compared with the method currently proposed by the World Health Organization.
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Affiliation(s)
- Markus Eckstein
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Christian Matek
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Paul Wagner
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Maike Büttner-Herold
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany; Department of Nephropathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Peter J Wild
- Dr. Senckenberg Institute of Pathology (SIP), University Hospital Frankfurt & Frankfurt Cancer Institute (FCI), Goethe University Frankfurt, Frankfurt, Germany
| | - Helge Taubert
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sven Wach
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Danijel Sikic
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Bernd Wullich
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Eva M Compérat
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Antonio Lopez-Beltran
- Unit of Anatomical Pathology, Faculty of Medicine, Cordoba University, Cordoba, Spain
| | - Rodolfo Montironi
- Molecular Medicine and Cell Therapy Foundation, c/o Polytechnic University of the Marche Region, Ancona, Italy
| | - Liang Cheng
- Department of Pathology and Laboratory Medicine, Brown University Warren Alpert Medical School, Providence, RI, USA; The Legorreta Cancer Center at Brown University, Providence, RI, USA
| | - Theodorus van der Kwast
- Laboratory Medicine Program, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Maurizio Colecchia
- Vita-Salute San Raffaele University, IRCCS San Raffaele Hospital and Scientific Institute, Milan, Italy
| | - Bas W G van Rhijn
- Department of Urology, Caritas St. Josef Medical Center, University of Regensburg, Regensburg, Germany; Department of Surgical Oncology (Urology), Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Mahul B Amin
- Department of Pathology and Laboratory Medicine, University of Tennessee Health Science Center, Memphis, TN, USA; Department of Urology, USC Keck School of Medicine, Los Angeles, CA, USA
| | - George J Netto
- Department of Pathology and Laboratory Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jan Lehmann
- Urologische Gemeinschaftspraxis Prüner Gang, Kiel, Germany; Department of Urology, Städtisches Krankenhaus Kiel, Kiel, Germany
| | - Michael Stöckle
- Department of Urology and Pediatric Urology, Saarland University, Homburg/Saar, Germany
| | - Kerstin Junker
- Department of Urology and Pediatric Urology, Saarland University, Homburg/Saar, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Simone Bertz
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany.
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Wagner SJ, Matek C, Shetab Boushehri S, Boxberg M, Lamm L, Sadafi A, Winter DJE, Marr C, Peng T. Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology. Mod Pathol 2024; 37:100350. [PMID: 37827448 DOI: 10.1016/j.modpat.2023.100350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 02/10/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that, the codebase should be well-documented and easy to integrate into existing workflows and models should be robust toward noise and generalizable toward data from different sources. Strikingly, only a few computational pathology algorithms have been reused by other researchers so far, let alone employed in a clinical setting. To assess the current state of reproducibility and reusability of computational pathology algorithms, we evaluated peer-reviewed articles available in PubMed, published between January 2019 and March 2021, in 5 use cases: stain normalization; tissue type segmentation; evaluation of cell-level features; genetic alteration prediction; and inference of grading, staging, and prognostic information. We compiled criteria for data and code availability and statistical result analysis and assessed them in 160 publications. We found that only one-quarter (41 of 160 publications) made code publicly available. Among these 41 studies, three-quarters (30 of 41) analyzed their results statistically, half of them (20 of 41) released their trained model weights, and approximately a third (16 of 41) used an independent cohort for evaluation. Our review is intended for both pathologists interested in deep learning and researchers applying algorithms to computational pathology challenges. We provide a detailed overview of publications with published code in the field, list reusable data handling tools, and provide criteria for reproducibility and reusability.
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Affiliation(s)
- Sophia J Wagner
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Christian Matek
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Pathology, University Hospital Erlangen, Erlangen, Germany
| | - Sayedali Shetab Boushehri
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany; Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Germany
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany; Institute of Pathology Munich-North, Munich, Germany
| | - Lorenz Lamm
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; Helmholtz Pioneer Campus, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany
| | - Ario Sadafi
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany; Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany
| | - Dominik J E Winter
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany; School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany.
| | - Tingying Peng
- Helmholtz AI, Helmholtz Munich-German Research Center for Environmental Health, Neuherberg, Germany.
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Eckstein M, Matek C, Wagner P, Erber R, Büttner-Herold M, Wild PJ, Taubert H, Wach S, Sikic D, Wullich B, Geppert CI, Compérat EM, Lopez-Beltran A, Montironi R, Cheng L, van der Kwast T, Colecchia M, van Rhijn BWG, Amin MB, Netto GJ, Lehmann J, Stöckle M, Junker K, Hartmann A, Bertz S. Corrigendum to "Proposal for a Novel Histological Scoring System as a Potential Grading Approach for Muscle-invasive Urothelial Bladder Cancer Correlating with Disease Aggressiveness and Patient Outcomes" [European Urology Oncology (2023)]. Eur Urol Oncol 2023; 6:641. [PMID: 37813744 DOI: 10.1016/j.euo.2023.09.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Affiliation(s)
- Markus Eckstein
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Christian Matek
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Paul Wagner
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Maike Büttner-Herold
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany; Department of Nephropathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Peter J Wild
- Dr. Senckenberg Institute of Pathology (SIP), University Hospital Frankfurt & Frankfurt Cancer Institute (FCI), Goethe University Frankfurt, Frankfurt, Germany
| | - Helge Taubert
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sven Wach
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Danijel Sikic
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Bernd Wullich
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Eva M Compérat
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Antonio Lopez-Beltran
- Unit of Anatomical Pathology, Faculty of Medicine, Cordoba University, Cordoba, Spain
| | - Rodolfo Montironi
- Molecular Medicine and Cell Therapy Foundation, c/o Polytechnic University of the Marche Region, Ancona, Italy
| | - Liang Cheng
- Department of Pathology and Laboratory Medicine, Brown University Warren Alpert Medical School, Providence, RI, USA; The Legorreta Cancer Center at Brown University, Providence, RI, USA
| | - Theodorus van der Kwast
- Laboratory Medicine Program, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Maurizio Colecchia
- Vita-Salute San Raffaele University, IRCCS San Raffaele Hospital and Scientific Institute, Milan, Italy
| | - Bas W G van Rhijn
- Department of Urology, Caritas St. Josef Medical Center, University of Regensburg, Regensburg, Germany; Department of Surgical Oncology (Urology), Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Mahul B Amin
- Department of Pathology and Laboratory Medicine, University of Tennessee Health Science Center, Memphis, TN, USA; Department of Urology, USC Keck School of Medicine, Los Angeles, CA, USA
| | - George J Netto
- Department of Pathology and Laboratory Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jan Lehmann
- Urologische Gemeinschaftspraxis Prüner Gang, Kiel, Germany; Department of Urology, Städtisches Krankenhaus Kiel, Kiel, Germany
| | - Michael Stöckle
- Department of Urology and Pediatric Urology, Saarland University, Homburg/Saar, Germany
| | - Kerstin Junker
- Department of Urology and Pediatric Urology, Saarland University, Homburg/Saar, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Simone Bertz
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Comprehensive Cancer Center EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Bavarian Center for Cancer Research (BZKF), Erlangen, Germany.
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5
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Rubio CA, Matek C, Vieth M, Lang-Schwarz C. Sporadic Colorectal Tubular Adenomas Thrive in Symbiosis With Underlying Nondysplastic Branching Crypts. Anticancer Res 2023; 43:4947-4952. [PMID: 37909976 DOI: 10.21873/anticanres.16692] [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: 08/13/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND/AIM Nondysplastic crypt branching (NDCB), mostly asymmetric branching (NDCAB), was previously found beneath the dysplastic epithelium of colorectal tubular adenomas (TA) in Swedish patients. This study examined the frequency of NDCB and NDCAB beneath the dysplastic epithelium of TA, in German patients. PATIENTS AND METHODS From a collection of 305 TA, 121 TA fulfilled the prerequisites for inclusion. All NDCB were registered. RESULTS Of 673 NDBCs, 572 (85%) NDCABs and 101 (15%) NDCSs, were found beneath the neoplastic tissue in the 121 TA. When the frequency of NDCB was challenged against the TA size, a linear correlation was found in the 121 TA (p<0.05, p=0.020172). Most NDCB were NDCAB (p<0.05, p=0.00001). The frequency of NDCB correlated with increasing TA size, implying that the higher frequency of both NDCB, dysplastic crypt branching, and their dysplastic offspring crypts were the most probable sources of TA enlargement. The frequency of NDCB underneath TA was not influenced by increasing age, sex or TA localization. CONCLUSION Similar findings as those reported here were previously found in TA in Swedish patients. The similarity between these two populations, located in disparate geographical areas and subjected to dissimilar microenvironmental conditions suggests that NDBC in TA might be a ubiquitous unreported phenomenon. According to the literature, normal colon cells often harbor somatic mutations. Consequently, NDCB underneath TA may be mutated nondysplastic branching crypts upon which the dysplastic epithelium in TA eventually develops.
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Affiliation(s)
- Carlos A Rubio
- Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden;
| | - Christian Matek
- Institute of Pathology, Friedrich-Alexander-University Erlangen-Nuremberg, Klinikum Bayreuth, Bayreuth, Germany
| | - Michael Vieth
- Institute of Pathology, Friedrich-Alexander-University Erlangen-Nuremberg, Klinikum Bayreuth, Bayreuth, Germany
| | - Corinna Lang-Schwarz
- Institute of Pathology, Friedrich-Alexander-University Erlangen-Nuremberg, Klinikum Bayreuth, Bayreuth, Germany
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6
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Wagner SJ, Reisenbüchler D, West NP, Niehues JM, Zhu J, Foersch S, Veldhuizen GP, Quirke P, Grabsch HI, van den Brandt PA, Hutchins GGA, Richman SD, Yuan T, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Jonnagaddala J, Hawkins NJ, Ward RL, Morton D, Seymour M, Magill L, Nowak M, Hay J, Koelzer VH, Church DN, Matek C, Geppert C, Peng C, Zhi C, Ouyang X, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Hoffmeister M, Truhn D, Schnabel JA, Boxberg M, Peng T, Kather JN. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell 2023; 41:1650-1661.e4. [PMID: 37652006 PMCID: PMC10507381 DOI: 10.1016/j.ccell.2023.08.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/18/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023]
Abstract
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.
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Affiliation(s)
- Sophia J Wagner
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Daniel Reisenbüchler
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany
| | - Nicholas P West
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Jan Moritz Niehues
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Jiefu Zhu
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | | | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Heike I Grabsch
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Piet A van den Brandt
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Susan D Richman
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rupert Langer
- Institute of Pathology und Molecular Pathology, Johannes Kepler University Hospital Linz, Linz, Österreich
| | - Josien C A Jenniskens
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Kelly Offermans
- Department of Epidemiology, Maastricht University Medical Center+, Maastricht, the Netherlands
| | | | - Richard Gray
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Stephen B Gruber
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Joel K Greenson
- Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Gad Rennert
- Department of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Steve and Cindy Rasmussen Institute for Genomic Medicine, Lady Davis Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - Joseph D Bonner
- Department of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Daniel Schmolze
- Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Jitendra Jonnagaddala
- School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia
| | - Nicholas J Hawkins
- School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia
| | - Robyn L Ward
- School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Dion Morton
- University Hospital Birmingham, Birmingham, UK
| | | | - Laura Magill
- University of Birmingham Clinical Trials Unit, Birmingham, UK
| | - Marta Nowak
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jennifer Hay
- Glasgow Tissue Research Facility, University of Glasgow, Queen Elizabeth University Hospital, Glasgow, UK
| | - Viktor H Koelzer
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Oncology, University of Oxford, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, UK
| | - David N Church
- Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, UK; Oxford NIHR Comprehensive Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Christian Matek
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany
| | - Carol Geppert
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Erlangen, Germany
| | - Chaolong Peng
- Medical School, Jianggang Shan University, Jiangxi, China
| | - Cheng Zhi
- Department of Pathology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoming Ouyang
- Department of Pathology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, UK; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, UK; Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, UK; Integrated Pathology Unit, Institute for Cancer Research and Royal Marsden Hospital, London, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Julia A Schnabel
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany; Institute of Pathology Munich-North, Munich, Germany
| | - Tingying Peng
- Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany.
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg.
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7
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Rubio CA, Lang-Schwarz C, Matek C, Kamaradova K, Vieth M. The Number of Colon Crypts in Digital Mucosal Samples: A New Independent Parameter for Diagnosing Ulcerative Colitis. Cancer Diagn Progn 2023; 3:533-537. [PMID: 37671307 PMCID: PMC10475918 DOI: 10.21873/cdp.10251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/27/2023] [Indexed: 09/07/2023]
Abstract
Background/Aim It has been demonstrated that most routine biopsies from the colon and rectum display cross-cut crypts (CCC). The aim was to assess the number of CCC in microscopic isometric digital samples (0.500 mm2) from routine colon biopsies. Patients and Methods Colon biopsies from 224 patients were investigated: 99 in patients with ulcerative colitis (UC), 31 UC in remission (UCR), 28 infectious colitis (IC), 7 resolved IC (RIC), 19 diverticular sigmoiditis (DS), and 40 normal colon mucosa (NCM). Results A total of 8,024 CCC were registered: 2,860 (35.6%) in UC, 1,319 UCR (16.4%), 849 (10.6%) in IC, 340 (4.2%) in RIC, 795 (9.9%) in DS, and 1,861 (23.2%) in NCM. The CCC frequencies in UC and IC were significantly lower (p<0.05) than those in UCR, RIC, DS, and NCM. Conclusion By the simple algorithm of counting CCC in standardized isometric microscopic digital circles measuring 0.500 mm2, it was possible to differentiate between UC (long-lasting inflammation) and IC (short-lasting inflammation) on the one hand, and UCR, RIC, DS (persistent inflammation), and NCM, on the other. The counting of CCC in the algorithm by five pathologists working in three disparate European Countries, was found to be reproducible.
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Affiliation(s)
- Carlos A Rubio
- Department of Pathology, Karolinska Institute and University Hospital, Stockholm, Sweden
| | - Corinna Lang-Schwarz
- Institute of Pathology, Friedrich-Alexander University Erlangen-Nuremberg, Klinikum Bayreuth, Bayreuth, Germany
| | - Christian Matek
- Institute of Pathology, Friedrich-Alexander University Erlangen-Nuremberg, Klinikum Bayreuth, Bayreuth, Germany
| | - Katerina Kamaradova
- The Fingerland Department of Pathology, Faculty of Medicine and University Hospital, Charles University, Hradec Králové, Czech Republic
| | - Michael Vieth
- Institute of Pathology, Friedrich-Alexander University Erlangen-Nuremberg, Klinikum Bayreuth, Bayreuth, Germany
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8
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Matek C, Marr C, von Bergwelt-Baildon M, Spiekermann K. [Artificial Intelligence for computer-aided leukemia diagnostics]. Dtsch Med Wochenschr 2023; 148:1108-1112. [PMID: 37611575 DOI: 10.1055/a-1965-7044] [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] [Indexed: 08/25/2023]
Abstract
The manual examination of blood and bone marrow specimens for leukemia patients is time-consuming and limited by intra- and inter-observer variance. The development of AI algorithms for leukemia diagnostics requires high-quality sample digitization and reliable annotation of large datasets. Deep learning-based algorithms using these datasets attain human-level performance for some well-defined, clinically relevant questions such as the blast character of cells. Methods such as multiple - instance - learning allow predicting diagnoses from a collection of leukocytes, but are more data-intensive. Using "explainable AI" methods can make the prediction process more transparent and allow users to verify the algorithm's predictions. Stability and robustness analyses are necessary for routine application of these algorithms, and regulatory institutions are developing standards for this purpose. Integrated diagnostics, which link different diagnostic modalities, offer the promise of even greater accuracy but require more extensive and diverse datasets.
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Affiliation(s)
- Christian Matek
- Pathologisches Institut, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Deutschland
- Institute of AI for Health, Helmholtz Zentrum München, münchen
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München, münchen
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9
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Firmbach D, Benz M, Kuritcyn P, Bruns V, Lang-Schwarz C, Stuebs FA, Merkel S, Leikauf LS, Braunschweig AL, Oldenburger A, Gloßner L, Abele N, Eck C, Matek C, Hartmann A, Geppert CI. Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment. Cancers (Basel) 2023; 15:2675. [PMID: 37345012 DOI: 10.3390/cancers15102675] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/27/2023] [Accepted: 05/02/2023] [Indexed: 06/23/2023] Open
Abstract
The tumor-stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor-stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor-stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought.
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Affiliation(s)
- Daniel Firmbach
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Michaela Benz
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Petr Kuritcyn
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Volker Bruns
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Corinna Lang-Schwarz
- Institute of Pathology, Hospital Bayreuth, Preuschwitzer Str. 101, 95445 Bayreuth, Germany
| | - Frederik A Stuebs
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Obstetrics and Gynaecology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Susanne Merkel
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Surgery, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 12, 91054 Erlangen, Germany
| | - Leah-Sophie Leikauf
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Anna-Lea Braunschweig
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Angelika Oldenburger
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Laura Gloßner
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Niklas Abele
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christine Eck
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christian Matek
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
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10
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Hehr M, Sadafi A, Matek C, Lienemann P, Pohlkamp C, Haferlach T, Spiekermann K, Marr C. Explainable AI identifies diagnostic cells of genetic AML subtypes. PLOS Digit Health 2023; 2:e0000187. [PMID: 36921004 PMCID: PMC10016704 DOI: 10.1371/journal.pdig.0000187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 12/19/2022] [Indexed: 03/16/2023]
Abstract
Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient's blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms.
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Affiliation(s)
- Matthias Hehr
- Institute of AI for Health, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Laboratory of Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Ario Sadafi
- Institute of AI for Health, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany
| | - Christian Matek
- Institute of AI for Health, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Laboratory of Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | - Peter Lienemann
- Institute of AI for Health, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Laboratory of Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
| | | | | | - Karsten Spiekermann
- Laboratory of Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- * E-mail:
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11
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Wagner SJ, Matek C, Shetab Boushehri S, Boxberg M, Lamm L, Sadafi A, Waibel DJE, Marr C, Peng T. Make deep learning algorithms in computational pathology more reproducible and reusable. Nat Med 2022; 28:1744-1746. [PMID: 35941376 DOI: 10.1038/s41591-022-01905-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Sophia J Wagner
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Department of Informatics, Technical University of Munich, Garching, Germany
| | - Christian Matek
- Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Institute of Pathology, University Hospital Erlangen, Erlangen, Germany
| | - Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany.,Data Science, Pharmaceutical Research and Early Development Informatics (pREDi), Roche Innovation Center Munich (RICM), Penzberg, Germany
| | - Melanie Boxberg
- Institute of Pathology, Technical University Munich, Munich, Germany.,Institute of Pathology Munich-North, Munich, Germany
| | - Lorenz Lamm
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,Department of Informatics, Technical University of Munich, Garching, Germany.,Helmholtz Pioneer Campus, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Ario Sadafi
- Department of Informatics, Technical University of Munich, Garching, Germany.,Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany
| | - Dominik J E Waibel
- Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.,School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Carsten Marr
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany. .,Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
| | - Tingying Peng
- Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany. .,Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
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12
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Matek C. More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation. Patterns (N Y) 2022; 3:100426. [PMID: 35079721 PMCID: PMC8767290 DOI: 10.1016/j.patter.2021.100426] [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] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Label-efficient algorithms are of central importance for machine learning applications in many medical fields, where obtaining expert annotations is often expensive and time-consuming. Soni et al. show how contrastive learning can help build classifiers for one of the oldest and most revered methods of clinical medicine: auscultation of heart and lung sounds.
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Affiliation(s)
- Christian Matek
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
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13
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Oala L, Murchison AG, Balachandran P, Choudhary S, Fehr J, Leite AW, Goldschmidt PG, Johner C, Schörverth EDM, Nakasi R, Meyer M, Cabitza F, Baird P, Prabhu C, Weicken E, Liu X, Wenzel M, Vogler S, Akogo D, Alsalamah S, Kazim E, Koshiyama A, Piechottka S, Macpherson S, Shadforth I, Geierhofer R, Matek C, Krois J, Sanguinetti B, Arentz M, Bielik P, Calderon-Ramirez S, Abbood A, Langer N, Haufe S, Kherif F, Pujari S, Samek W, Wiegand T. Machine Learning for Health: Algorithm Auditing & Quality Control. J Med Syst 2021; 45:105. [PMID: 34729675 PMCID: PMC8562935 DOI: 10.1007/s10916-021-01783-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/11/2021] [Indexed: 01/26/2023]
Abstract
Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.
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Affiliation(s)
| | - Andrew G Murchison
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | | | - Jana Fehr
- Hasso-Plattner-Institute of Digital Engineering, Potsdam, Germany
| | - Alixandro Werneck Leite
- Machine Learning Laboratory in Finance and Organizations, Universidade de Brasília, Brasília, Brazil
| | | | | | | | | | | | | | | | | | | | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust & Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | | | | | | | - Shada Alsalamah
- Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.,Digital Health and Innovation Department, Science Division, World Health Organization, Winterthur, Switzerland
| | - Emre Kazim
- University College London, London, United Kingdom
| | | | | | | | | | | | | | - Joachim Krois
- Oral Diagnostics Digital Health Health Services Research, Charité-Universitätsmedizin, Berlin, Germany
| | | | - Matthew Arentz
- Department of Global Health, University of Washington, Washington, USA
| | | | | | | | - Nicolas Langer
- Department of Psychology, University of Zurich, Zürich, Switzerland
| | | | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sameer Pujari
- Digital Health and Innovation Department, Science Division, World Health Organization, Winterthur, Switzerland
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14
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Schouten JPE, Matek C, Jacobs LFP, Buck MC, Bošnački D, Marr C. Tens of images can suffice to train neural networks for malignant leukocyte detection. Sci Rep 2021; 11:7995. [PMID: 33846442 PMCID: PMC8042012 DOI: 10.1038/s41598-021-86995-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 04/06/2020] [Accepted: 03/09/2021] [Indexed: 11/09/2022] Open
Abstract
Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation relies on expert knowledge. Here, we analyze the binary classification performance of a CNN on two independent cytomorphology datasets as a function of training set size. Specifically, we train a sequential model to discriminate non-malignant leukocytes from blast cells, whose appearance in the peripheral blood is a hallmark of leukemia. We systematically vary training set size, finding that tens of training images suffice for a binary classification with an ROC-AUC over 90%. Saliency maps and layer-wise relevance propagation visualizations suggest that the network learns to increasingly focus on nuclear structures of leukocytes as the number of training images is increased. A low dimensional tSNE representation reveals that while the two classes are separated already for a few training images, the distinction between the classes becomes clearer when more training images are used. To evaluate the performance in a multi-class problem, we annotated single-cell images from a acute lymphoblastic leukemia dataset into six different hematopoietic classes. Multi-class prediction suggests that also here few single-cell images suffice if differences between morphological classes are large enough. The incorporation of deep learning algorithms into clinical practice has the potential to reduce variability and cost, democratize usage of expertise, and allow for early detection of disease onset and relapse. Our approach evaluates the performance of a deep learning based cytology classifier with respect to size and complexity of the training data and the classification task.
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Affiliation(s)
- Jens P E Schouten
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Matek
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.,Department of Internal Medicine III, University Hospital Munich, Ludwig-Maximilians-Universität München-Campus Großhadern, Munich, Germany
| | - Luuk F P Jacobs
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Michèle C Buck
- Department of Medicine III, Technische Universität München, Klinikum rechts der Isar, Munich, Germany
| | - Dragan Bošnački
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Carsten Marr
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
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15
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Kriegel F, Matek C, Dršata T, Kulenkampff K, Tschirpke S, Zacharias M, Lankaš F, Lipfert J. The temperature dependence of the helical twist of DNA. Nucleic Acids Res 2018; 46:7998-8009. [PMID: 30053087 PMCID: PMC6125625 DOI: 10.1093/nar/gky599] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 06/08/2018] [Accepted: 07/20/2018] [Indexed: 01/11/2023] Open
Abstract
DNA is the carrier of all cellular genetic information and increasingly used in nanotechnology. Quantitative understanding and optimization of its functions requires precise experimental characterization and accurate modeling of DNA properties. A defining feature of DNA is its helicity. DNA unwinds with increasing temperature, even for temperatures well below the melting temperature. However, accurate quantitation of DNA unwinding under external forces and a microscopic understanding of the corresponding structural changes are currently lacking. Here we combine single-molecule magnetic tweezers measurements with atomistic molecular dynamics and coarse-grained simulations to obtain a comprehensive view of the temperature dependence of DNA twist. Experimentally, we find that DNA twist changes by ΔTw(T) = (-11.0 ± 1.2)°/(°C·kbp), independent of applied force, in the range of forces where torque-induced melting is negligible. Our atomistic simulations predict ΔTw(T) = (-11.1 ± 0.3)°/(°C·kbp), in quantitative agreement with experiments, and suggest that the untwisting of DNA with temperature is predominantly due to changes in DNA structure for defined backbone substates, while the effects of changes in substate populations are minor. Coarse-grained simulations using the oxDNA framework yield a value of ΔTw(T) = (-6.4 ± 0.2)°/(°C·kbp) in semi-quantitative agreement with experiments.
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Affiliation(s)
- Franziska Kriegel
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, Amalienstr. 54, 80799 Munich, Germany
| | - Christian Matek
- Technical University of Munich and Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Tomáš Dršata
- Department of Informatics and Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
| | - Klara Kulenkampff
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, Amalienstr. 54, 80799 Munich, Germany
| | - Sophie Tschirpke
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, Amalienstr. 54, 80799 Munich, Germany
| | - Martin Zacharias
- Physics-Department T38, Technical University of Munich, James-Franck-Strasse 1, 85748 Garching, Germany
| | - Filip Lankaš
- Department of Informatics and Chemistry, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
| | - Jan Lipfert
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, Amalienstr. 54, 80799 Munich, Germany
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16
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Abstract
We study the behaviour of double-stranded RNA under twist and tension using oxRNA, a recently developed coarse-grained model of RNA. Introducing explicit salt-dependence into the model allows us to directly compare our results to data from recent single-molecule experiments. The model reproduces extension curves as a function of twist and stretching force, including the buckling transition and the behaviour of plectoneme structures. For negative supercoiling, we predict denaturation bubble formation in plectoneme end-loops, suggesting preferential plectoneme localisation in weak base sequences. OxRNA exhibits a positive twist-stretch coupling constant, in agreement with recent experimental observations.
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Affiliation(s)
- Christian Matek
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford OX1 3NP, United Kingdom
| | - Petr Šulc
- Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Avenue, New York, New York 10065, USA
| | - Ferdinando Randisi
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford OX1 3NP, United Kingdom
| | - Jonathan P K Doye
- Physical and Theoretical Chemistry Laboratory, Department of Chemistry, University of Oxford, South Parks Road, Oxford OX1 3QZ, United Kingdom
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford OX1 3NP, United Kingdom
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17
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Sutthibutpong T, Matek C, Benham C, Slade GG, Noy A, Laughton C, K Doye JP, Louis AA, Harris SA. Long-range correlations in the mechanics of small DNA circles under topological stress revealed by multi-scale simulation. Nucleic Acids Res 2016; 44:9121-9130. [PMID: 27664220 PMCID: PMC5100592 DOI: 10.1093/nar/gkw815] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [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: 03/11/2016] [Accepted: 09/03/2016] [Indexed: 12/14/2022] Open
Abstract
It is well established that gene regulation can be achieved through activator and repressor proteins that bind to DNA and switch particular genes on or off, and that complex metabolic networks determine the levels of transcription of a given gene at a given time. Using three complementary computational techniques to study the sequence-dependence of DNA denaturation within DNA minicircles, we have observed that whenever the ends of the DNA are constrained, information can be transferred over long distances directly by the transmission of mechanical stress through the DNA itself, without any requirement for external signalling factors. Our models combine atomistic molecular dynamics (MD) with coarse-grained simulations and statistical mechanical calculations to span three distinct spatial resolutions and timescale regimes. While they give a consensus view of the non-locality of sequence-dependent denaturation in highly bent and supercoiled DNA loops, each also reveals a unique aspect of long-range informational transfer that occurs as a result of restraining the DNA within the closed loop of the minicircles.
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Affiliation(s)
- Thana Sutthibutpong
- School of Physics and Astronomy, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.,Theoretical and Computational Science Center (TaCS), Science Laboratory Building, Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang Mod, Thrung Khru, Bangkok 10140, Thailand
| | - Christian Matek
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK
| | - Craig Benham
- UC Davis Genome Centre, Health Sciences Drive, Davis, CA 95616, USA
| | - Gabriel G Slade
- Department of Physics, São Paulo State University, Rua Cristovão, São José do Rio Preto, SP 15054-000, Brazil
| | - Agnes Noy
- Department of Physics, Biological Physical Sciences Institute, University of York, York, YO10 5DD, UK
| | - Charles Laughton
- School of Pharmacy and Centre for Biomolecular Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Jonathan P K Doye
- Physical and Theoretical Chemistry Laboratory, Department of Chemistry, University of Oxford, South Parks Road, Oxford OX1 3QZ, UK
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK
| | - Sarah A Harris
- School of Physics and Astronomy, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK .,Astbury Centre for Structural and Molecular Biology, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK
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18
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Doye JPK, Ouldridge TE, Louis AA, Romano F, Šulc P, Matek C, Snodin BEK, Rovigatti L, Schreck JS, Harrison RM, Smith WPJ. Coarse-graining DNA for simulations of DNA nanotechnology. Phys Chem Chem Phys 2013; 15:20395-414. [PMID: 24121860 DOI: 10.1039/c3cp53545b] [Citation(s) in RCA: 135] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To simulate long time and length scale processes involving DNA it is necessary to use a coarse-grained description. Here we provide an overview of different approaches to such coarse-graining, focussing on those at the nucleotide level that allow the self-assembly processes associated with DNA nanotechnology to be studied. OxDNA, our recently-developed coarse-grained DNA model, is particularly suited to this task, and has opened up this field to systematic study by simulations. We illustrate some of the range of DNA nanotechnology systems to which the model is being applied, as well as the insights it can provide into fundamental biophysical properties of DNA.
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Affiliation(s)
- Jonathan P K Doye
- Physical & Theoretical Chemistry Laboratory, Department of Chemistry, University of Oxford, South Parks Road, Oxford, OX1 3QZ, UK
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19
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Matek C, Ouldridge TE, Levy A, Doye JPK, Louis AA. DNA cruciform arms nucleate through a correlated but asynchronous cooperative mechanism. J Phys Chem B 2012; 116:11616-25. [PMID: 22931199 DOI: 10.1021/jp3080755] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Inverted repeat (IR) sequences in DNA can form noncanonical cruciform structures to relieve torsional stress. We use Monte Carlo simulations of a recently developed coarse-grained model of DNA to demonstrate that the nucleation of a cruciform can proceed through a cooperative mechanism. First, a twist-induced denaturation bubble must diffuse so that its midpoint is near the center of symmetry of the IR sequence. Second, bubble fluctuations must be large enough to allow one of the arms to form a small number of hairpin bonds. Once the first arm is partially formed, the second arm can rapidly grow to a similar size. Because bubbles can twist back on themselves, they need considerably fewer bases to resolve torsional stress than the final cruciform state does. The initially stabilized cruciform therefore continues to grow, which typically proceeds synchronously, reminiscent of the S-type mechanism of cruciform formation. By using umbrella sampling techniques, we calculate, for different temperatures and superhelical densities, the free energy as a function of the number of bonds in each cruciform arm along the correlated but asynchronous nucleation pathways we observed in direct simulations.
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Affiliation(s)
- Christian Matek
- Rudolph Peierls Centre for Theoretical Physics, 1 Keble Road, Oxford OX1 3NP, UK
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