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Wu Z, Yang JY, Yan CB, Zhang CG, Yang HC. Integrating SAM priors with U-Net for enhanced multiclass cell detection in digital pathology. Sci Rep 2025; 15:15641. [PMID: 40325120 PMCID: PMC12052837 DOI: 10.1038/s41598-025-99278-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2025] [Accepted: 04/18/2025] [Indexed: 05/07/2025] Open
Abstract
In digital pathology, the accurate detection, segmentation, and classification of cells are pivotal for precise pathological diagnosis. Traditionally, pathologists manually segment cells from pathological images to facilitate diagnosis based on these results and other critical indicators. However, this manual approach is not only time-consuming but also prone to subjective biases, which significantly hampers its efficiency and consistency in large-scale applications. While classic segmentation networks like U-Net have gained widespread adoption in medical imaging, their integration with external prior features remains limited, thereby restricting the potential enhancement of segmentation accuracy. Although the large model SAM, renowned for its capability to "segment anything", has shown promise, its application in the specialized field of medical image processing presents considerable challenges. Direct application of SAM to medical scenarios often fails to yield optimal results. To overcome these limitations, this paper proposes a novel prior-guided joint attention mechanism. This method effectively integrates the prior features generated by SAM with the foundational features extracted by U-Net, achieving superior cell segmentation and classification. Extensive experiments on public datasets reveal that the proposed method significantly surpasses both standalone U-Net and approaches that merely augment inputs by overlaying prior features onto color channels. This advancement not only enhances the utility of large models in medical applications but also lays the groundwork for the evolution of intelligent pathological diagnostic technologies.
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Affiliation(s)
- Zheng Wu
- College of Mathematics and Computer Science, Dali University, Dali, China
| | - Ji-Yun Yang
- People's Hospital of Dali Bai Autonomous Prefecture, Dali, China
| | - Chang-Bao Yan
- People's Hospital of Dali Bai Autonomous Prefecture, Dali, China
| | - Cheng-Gui Zhang
- National-Local Joint Engineering Research Center of Entomoceutics, Dali, Yunnan, China
| | - Hai-Chao Yang
- Yunnan Provincial Key Laboratory of Entomological Biopharmaceutical R&D, College of Mathematics and Computer Science, Dali University, Dali, China.
- National-Local Joint Engineering Research Center of Entomoceutics, Dali, Yunnan, China.
- College of Mathematics and Computer Science, Dali University, Dali, China.
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Taube JM, Sunshine JC, Angelo M, Akturk G, Eminizer M, Engle LL, Ferreira CS, Gnjatic S, Green B, Greenbaum S, Greenwald NF, Hedvat CV, Hollmann TJ, Jiménez-Sánchez D, Korski K, Lako A, Parra ER, Rebelatto MC, Rimm DL, Rodig SJ, Rodriguez-Canales J, Roskes JS, Schalper KA, Schenck E, Steele KE, Surace MJ, Szalay AS, Tetzlaff MT, Wistuba II, Yearley JH, Bifulco CB. Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing. J Immunother Cancer 2025; 13:e008875. [PMID: 39779210 PMCID: PMC11749220 DOI: 10.1136/jitc-2024-008875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 11/12/2024] [Indexed: 01/11/2025] Open
Abstract
OBJECTIVES Multiplex immunohistochemistry and immunofluorescence (mIHC/IF) are emerging technologies that can be used to help define complex immunophenotypes in tissue, quantify immune cell subsets, and assess the spatial arrangement of marker expression. mIHC/IF assays require concerted efforts to optimize and validate the multiplex staining protocols prior to their application on slides. The best practice guidelines for staining and validation of mIHC/IF assays across platforms were previously published by this task force. The current effort represents a complementary manuscript for mIHC/IF analysis focused on the associated image analysis and data management. METHODS The Society for Immunotherapy of Cancer convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the quantitative image analysis of mIHC/IF output and data management considerations. RESULTS Best-practice approaches for image acquisition, color deconvolution and spectral unmixing, tissue and cell segmentation, phenotyping, and algorithm verification are reviewed. Additional quality control (QC) measures such as batch-to-batch correction and QC for assembled images are also discussed. Recommendations for sharing raw outputs, processed results, key analysis programs and source code, and representative photomicrographs from mIHC/IF assays are included. Lastly, multi-institutional harmonization efforts are described. CONCLUSIONS mIHC/IF technologies are maturing and are routinely included in research studies and moving towards clinical use. Guidelines for how to perform and standardize image analysis on mIHC/IF-stained slides will likely contribute to more comparable results across laboratories and pave the way for clinical implementation. A checklist encompassing these two-part guidelines for the generation of robust data from quantitative mIHC/IF assays will be provided in a third publication from this task force. While the current effort is mainly focused on best practices for characterizing the tumor microenvironment, these principles are broadly applicable to any mIHC/IF assay and associated image analysis.
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Affiliation(s)
- Janis M Taube
- Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University SOM, Baltimore, Maryland, USA
- Bloomberg~Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University SOM, Baltimore, Maryland, USA
| | - Joel C Sunshine
- Bloomberg~Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University SOM, Baltimore, Maryland, USA
| | - Michael Angelo
- Department of Pathology, Stanford University School of Medicine, Palo Alto, California, USA
| | | | - Margaret Eminizer
- Johns Hopkins University, Baltimore, Maryland, USA
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Logan L Engle
- Bloomberg~Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University SOM, Baltimore, Maryland, USA
| | - Cláudia S Ferreira
- Pharma Research and Early Development (pRED), Roche Innovation Center Munich, Penzberg, Germany
| | - Sacha Gnjatic
- Icahn School of Medicine at Mount Sinai Tisch Cancer Institute, New York, New York, USA
| | - Benjamin Green
- Bloomberg~Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University SOM, Baltimore, Maryland, USA
| | - Shirley Greenbaum
- Department of Pathology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Noah F Greenwald
- Department of Pathology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Cyrus V Hedvat
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Travis J Hollmann
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Daniel Jiménez-Sánchez
- Bloomberg~Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University SOM, Baltimore, Maryland, USA
| | - Konstanty Korski
- Department of Personalized Healthcare, Data, Analytics and Imaging Group, F Hoffmann-La Roche AG, Basel, Switzerland
| | - Ana Lako
- Dana Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Edwin R Parra
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Scott J Rodig
- Dana Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Jeffrey S Roskes
- Bloomberg~Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University SOM, Baltimore, Maryland, USA
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kurt A Schalper
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA
| | | | | | | | - Alexander S Szalay
- Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University SOM, Baltimore, Maryland, USA
- Bloomberg~Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University SOM, Baltimore, Maryland, USA
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michael T Tetzlaff
- Departments of Pathology and Dermatology, University of California San Francisco, San Francisco, California, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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3
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Mi H, Sivagnanam S, Ho WJ, Zhang S, Bergman D, Deshpande A, Baras AS, Jaffee EM, Coussens LM, Fertig EJ, Popel AS. Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology. Brief Bioinform 2024; 25:bbae421. [PMID: 39179248 PMCID: PMC11343572 DOI: 10.1093/bib/bbae421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/11/2024] [Accepted: 08/09/2024] [Indexed: 08/26/2024] Open
Abstract
Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Shamilene Sivagnanam
- The Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, United States
- Department of Cell, Development and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, United States
| | - Won Jin Ho
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
| | - Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Daniel Bergman
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
| | - Atul Deshpande
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Alexander S Baras
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Pathology, Johns Hopkins University School of Medicine, MD 21205, United States
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Elizabeth M Jaffee
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Lisa M Coussens
- The Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, United States
- Department of Cell, Development and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, United States
- Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, OR 97201, United States
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, United States
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
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Liu Z, Cai Y, Tang Q. Nuclei detection in breast histopathology images with iterative correction. Med Biol Eng Comput 2024; 62:465-478. [PMID: 37914958 DOI: 10.1007/s11517-023-02947-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023]
Abstract
This work presents a deep network architecture to improve nuclei detection performance and achieve the high localization accuracy of nuclei in breast cancer histopathology images. The proposed model consists of two parts, generating nuclear candidate module and refining nuclear localization module. We first design a novel patch learning method to obtain high-quality nuclear candidates, where in addition to categories, location representations are also added to the patch information to implement the multi-task learning process of nuclear classification and localization; meanwhile, the deep supervision mechanism is introduced to obtain the coherent contributions from each scale layer. In order to refine nuclear localization, we propose an iterative correction strategy to make the prediction progressively closer to the ground truth, which significantly improves the accuracy of nuclear localization and facilitates neighbor size selection in the nonmaximum suppression step. Experimental results demonstrate the superior performance of our method for nuclei detection on the H&E stained histopathological image dataset as compared to previous state-of-the-art methods, especially in multiple cluttered nuclei detection, can achieve better results than existing techniques.
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Affiliation(s)
- Ziyi Liu
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, People's Republic of China
- Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264001, People's Republic of China
| | - Yu Cai
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, People's Republic of China
| | - Qiling Tang
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, People's Republic of China.
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Cooper M, Ji Z, Krishnan RG. Machine learning in computational histopathology: Challenges and opportunities. Genes Chromosomes Cancer 2023; 62:540-556. [PMID: 37314068 DOI: 10.1002/gcc.23177] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 06/15/2023] Open
Abstract
Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.
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Affiliation(s)
- Michael Cooper
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Zongliang Ji
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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Abousamra S, Gupta R, Kurc T, Samaras D, Saltz J, Chen C. Topology-Guided Multi-Class Cell Context Generation for Digital Pathology. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2023; 2023:3323-3333. [PMID: 38741683 PMCID: PMC11090253 DOI: 10.1109/cvpr52729.2023.00324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.
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Affiliation(s)
| | - Rajarsi Gupta
- Stony Brook University, Department of Biomedical Informatics, USA
| | - Tahsin Kurc
- Stony Brook University, Department of Biomedical Informatics, USA
| | | | - Joel Saltz
- Stony Brook University, Department of Biomedical Informatics, USA
| | - Chao Chen
- Stony Brook University, Department of Biomedical Informatics, USA
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Huss R, Raffler J, Märkl B. Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology. Cancer Rep (Hoboken) 2023:e1796. [PMID: 36813293 PMCID: PMC10363837 DOI: 10.1002/cnr2.1796] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/15/2023] [Accepted: 02/09/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way. RECENT FINDINGS Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker. CONCLUSIONS The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".
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Affiliation(s)
- Ralf Huss
- Medical Faculty University Augsburg, Augsburg, Germany
- Institute for Digital Medicine, University Hospital Augsburg, Augsburg, Germany
| | - Johannes Raffler
- Institute for Digital Medicine, University Hospital Augsburg, Augsburg, Germany
| | - Bruno Märkl
- Medical Faculty University Augsburg, Augsburg, Germany
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Huang PW, Ouyang H, Hsu BY, Chang YR, Lin YC, Chen YA, Hsieh YH, Fu CC, Li CF, Lin CH, Lin YY, Dah-Tsyr Chang M, Pai TW. Deep-learning based breast cancer detection for cross-staining histopathology images. Heliyon 2023; 9:e13171. [PMID: 36755605 PMCID: PMC9900267 DOI: 10.1016/j.heliyon.2023.e13171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/23/2023] Open
Abstract
Hematoxylin and eosin (H&E) staining is the gold standard for tissue characterization in routine pathological diagnoses. However, these visible light dyes do not exclusively label the nuclei and cytoplasm, making clear-cut segmentation of staining signals challenging. Currently, fluorescent staining technology is much more common in clinical research for analyzing tissue morphology and protein distribution owing to its advantages of channel independence, multiplex labeling, and the possibility of enabling 3D tissue labeling. Although both H&E and fluorescent dyes can stain the nucleus and cytoplasm for representative tissue morphology, color variation between these two staining technologies makes cross-analysis difficult, especially with computer-assisted artificial intelligence (AI) algorithms. In this study, we applied color normalization and nucleus extraction methods to overcome the variation between staining technologies. We also developed an available workflow for using an H&E-stained segmentation AI model in the analysis of fluorescent nucleic acid staining images in breast cancer tumor recognition, resulting in 89.6% and 80.5% accuracy in recognizing specific tumor features in H&E- and fluorescent-stained pathological images, respectively. The results show that the cross-staining inference maintained the same precision level as the proposed workflow, providing an opportunity for an expansion of the application of current pathology AI models.
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Affiliation(s)
- Pei-Wen Huang
- Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan
- Department of Pathology, Hsinchu Mackay Memorial Hospital, Hsinchu, Taiwan
| | - Hsu Ouyang
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Bang-Yi Hsu
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Yu-Ruei Chang
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Yu-Chieh Lin
- Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan
- JelloX Biotech Inc., Hsinchu, Taiwan
| | - Yung-An Chen
- Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan
| | | | - Chien-Chung Fu
- Department of Power Mechanical Engineering, National Tsing Hua University, Taiwan
| | - Chien-Feng Li
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
- National Institute of Cancer Research, National Health Research Institutes, Tainan, Taiwan
| | - Ching-Hung Lin
- National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan
| | | | - Margaret Dah-Tsyr Chang
- Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan
- JelloX Biotech Inc., Hsinchu, Taiwan
| | - Tun-Wen Pai
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
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Salmasi MY, Morris-Rosendahl D, Jarral OA, Rosendahl U, Asimakopoulos G, Raja S, Aragon-Martin JA, Child A, Pepper J, Oo A, Athanasiou T. Determining the genetic contribution in patients with non-syndromic ascending thoracic aortic aneurysms: Correlation with findings from computational pathology. Int J Cardiol 2022; 366:1-9. [PMID: 35830949 DOI: 10.1016/j.ijcard.2022.07.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 06/17/2022] [Accepted: 07/07/2022] [Indexed: 01/01/2023]
Abstract
OBJECTIVES This study aims to identify the clinical utility of targeted-genetic sequencing in a cohort of patients with TAA and establish a new method for regional histological characterisation of TAA disease. METHODS Fifty-four patients undergoing surgery for proximal TAA were recruited. EXCLUSIONS connective tissue disease, bicuspid aortic valves, redo surgery. All patients underwent next generation sequencing (NGS) using a custom gene panel containing 63 genes previously associated with TAA on Illumina MiSeqor NextSeq550 platforms. Explanted TAA tissue was obtained en-bloc from 34/54 patients, and complete circumferential strips of TAA tissue processed into whole slides which were subsequently digitalised. Computational pathology methods were employed to quantify elastin, cellularity and collagen in six equally divided regions across the whole aneurysm circumference. RESULTS Of 54 patients, clearly pathogenic or potentially pathogenic variants were found in 7.4%: namely LOX, PRKG1, TGFBR1 and SMAD3 genes. 55% had at least one variant of unknown significance (VUS) and seven of the VUSs were in genes with a strong disease association (category A) genes, whilst 15 were from moderate risk (category B) genes. Elastin and collagen abundance displayed high regional variation throughout the aneurysm circumference. In patients with <60% total elastin, the loss of elastin was more significant on the outer curve (38.0% vs 47.4%, p = 0.0094). The presence of VUS, higher pulse wave velocity and advancing age were predictors of elastin loss (regression analysis: p < 0.05). CONCLUSIONS These findings demonstrate the heterogeneity of TAA disease microstructure and the potential link between histological appearance and clinical factors, including genetic variation.
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Affiliation(s)
| | | | - Omar A Jarral
- Department of Surgery and Cancer, Imperial College London, UK
| | | | | | - Shahzad Raja
- Royal Brompton and Harefield Foundation Trust, UK
| | | | - Anne Child
- Guy Scadding Building, Marfan Trust, London, UK; Sonalee Laboratory, Imperial College, London, UK
| | - John Pepper
- Royal Brompton and Harefield Foundation Trust, UK
| | - Aung Oo
- Aortovascular Unit, Barts Heart Centre, UK
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10
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Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study. Pathol Res Pract 2022; 237:154014. [PMID: 35870238 DOI: 10.1016/j.prp.2022.154014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists' subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM. METHODS We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed. RESULTS The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion's silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information. CONCLUSION In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level.
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11
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Ruiz-Martinez A, Gong C, Wang H, Sové RJ, Mi H, Kimko H, Popel AS. Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model. PLoS Comput Biol 2022; 18:e1010254. [PMID: 35867773 PMCID: PMC9348712 DOI: 10.1371/journal.pcbi.1010254] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 08/03/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.
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Affiliation(s)
- Alvaro Ruiz-Martinez
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Chang Gong
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Richard J. Sové
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, Maryland, United States of America
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, United States of America
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12
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Vu T, Wrobel J, Bitler BG, Schenk EL, Jordan KR, Ghosh D. SPF: A spatial and functional data analytic approach to cell imaging data. PLoS Comput Biol 2022; 18:e1009486. [PMID: 35704658 PMCID: PMC9239468 DOI: 10.1371/journal.pcbi.1009486] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 06/28/2022] [Accepted: 05/16/2022] [Indexed: 11/19/2022] Open
Abstract
The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.
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Affiliation(s)
- Thao Vu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Julia Wrobel
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Benjamin G. Bitler
- University of Colorado Comprehensive Cancer Center, Aurora, Colorado, United States of America
- Department of OB/GYN, Division of Reproductive Sciences, The University of Colorado, Aurora, Colorado, United States of America
| | - Erin L. Schenk
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Kimberly R. Jordan
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
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13
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Frommeyer TC, Fursmidt RM, Gilbert MM, Bett ES. The Desire of Medical Students to Integrate Artificial Intelligence Into Medical Education: An Opinion Article. Front Digit Health 2022; 4:831123. [PMID: 35633734 PMCID: PMC9135963 DOI: 10.3389/fdgth.2022.831123] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Timothy C. Frommeyer
- Boonshoft School of Medicine at Wright State University, Dayton, OH, United States
| | - Reid M. Fursmidt
- Boonshoft School of Medicine at Wright State University, Dayton, OH, United States
| | - Michael M. Gilbert
- Boonshoft School of Medicine at Wright State University, Dayton, OH, United States
| | - Ean S. Bett
- Ohio University College of Osteopathic Medicine, Columbus, OH, United States
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14
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Jia Q, Wang A, Yuan Y, Zhu B, Long H. Heterogeneity of the tumor immune microenvironment and its clinical relevance. Exp Hematol Oncol 2022; 11:24. [PMID: 35461288 PMCID: PMC9034473 DOI: 10.1186/s40164-022-00277-y] [Citation(s) in RCA: 117] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/10/2022] [Indexed: 02/08/2023] Open
Abstract
During the course of tumorigenesis and subsequent metastasis, malignant cells gradually diversify and become more heterogeneous. Consequently, the tumor mass might be infiltrated by diverse immune-related components, including the cytokine/chemokine environment, cytotoxic activity, or immunosuppressive elements. This immunological heterogeneity is universally presented spatially or varies temporally along with tumor evolution or therapeutic intervention across almost all solid tumors. The heterogeneity of anti-tumor immunity shows a profound association with the progression of disease and responsiveness to treatment, particularly in the realm of immunotherapy. Therefore, an accurate understanding of tumor immunological heterogeneity is essential for the development of effective therapies. Facilitated by multi-regional and -omics sequencing, single cell sequencing, and longitudinal liquid biopsy approaches, recent studies have demonstrated the potential to investigate the complexity of immunological heterogeneity of the tumors and its clinical relevance in immunotherapy. Here, we aimed to review the mechanism underlying the heterogeneity of the immune microenvironment. We also explored how clinical assessments of tumor heterogeneity might facilitate the development of more effective personalized therapies.
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Affiliation(s)
- Qingzhu Jia
- Institute of Cancer, Xinqiao Hospital, Army Military Medical University, Xinqiao Main Street, Chongqing, 400037, China.,Chongqing Key Laboratory of Immunotherapy, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
| | - Aoyun Wang
- Institute of Cancer, Xinqiao Hospital, Army Military Medical University, Xinqiao Main Street, Chongqing, 400037, China.,Chongqing Key Laboratory of Immunotherapy, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
| | - Yixiao Yuan
- Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Army Military Medical University, Xinqiao Main Street, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.
| | - Haixia Long
- Institute of Cancer, Xinqiao Hospital, Army Military Medical University, Xinqiao Main Street, Chongqing, 400037, China. .,Chongqing Key Laboratory of Immunotherapy, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.
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15
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Wang YQ, Liu X, Xu C, Jiang W, Xu SY, Zhang Y, Liang YL, Li JY, Li Q, Chen YP, Zhao Y, Yun JP, Liu N, Li YQ, Ma J. Spatial heterogeneity of immune infiltration predicts the prognosis of nasopharyngeal carcinoma patients. Oncoimmunology 2021; 10:1976439. [PMID: 34721946 PMCID: PMC8555536 DOI: 10.1080/2162402x.2021.1976439] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Spatial information on the tumor immune microenvironment is of clinical relevance. Here, we aimed to quantify the spatial heterogeneity of lymphocytes and cancer cells and evaluated its prognostic value in patients with nasopharyngeal carcinoma (NPC). The scanned immunohistochemistry images of 336 NPC patients from two different hospitals were used to generate cell density maps for tumor and immune cells. Then, Getis-Ord hotspot analysis, a spatial statistic method used to describe species biodiversity in ecological habitats, was applied to identify cancer, immune, and immune-cancer hotspots. The results showed that cancer hotspots were not associated with any of the studied clinical outcomes, while immune-cancer hotspots predicted worse overall survival (OS) in the training cohort. In contrast, a high immune hotspot score was significantly associated with better OS (HR 0.41, 95% CI 0.22–0.77, P = .006), disease-free survival (DFS) (HR 0.43, 95% CI 0.24–0.75, P = .003) and distant metastasis-free survival (DMFS) (HR 0.40, 95% CI 0.20–0.81, P = .011) in NPC patients in the training cohort, and similar associations were also evident in the validation cohort. Importantly, multivariate analysis revealed that the immune hotspot score remained an independent prognostic indicator for OS, DFS, and DMFS in both cohorts. We explored the spatial heterogeneity of cancer cells and lymphocytes in the tumor microenvironment of NPC patients using digital pathology and ecological analysis methods and further constructed three spatial scores. Our study demonstrates that spatial variation may aid in the identification of the clinical prognosis of NPC patients, but further investigation is needed.
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Affiliation(s)
- Ya-Qin Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Xu Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Cheng Xu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Wei Jiang
- Department of Radiation Oncology, Guilin Medical University Affiliated Hospital, Guilin, China
| | - Shuo-Yu Xu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Yu Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Ye Lin Liang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Jun-Yan Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Qian Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Yu-Pei Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Yin Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Jing-Ping Yun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Na Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Ying-Qin Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
| | - Jun Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P.R, China
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16
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Corvo A, Caballero HSG, Westenberg MA, van Driel MA, van Wijk JJ. Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:3851-3866. [PMID: 32340951 DOI: 10.1109/tvcg.2020.2990336] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.
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17
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Abousamra S, Belinsky D, Van Arnam J, Allard F, Yee E, Gupta R, Kurc T, Samaras D, Saltz J, Chen C. Multi-Class Cell Detection Using Spatial Context Representation. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION 2021; 2021:3985-3994. [PMID: 38783989 PMCID: PMC11114143 DOI: 10.1109/iccv48922.2021.00397] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells, lymphocytes or stromal cells is particularly challenging. Existing methods focus on morphological appearance of individual cells, whereas in practice pathologists often infer cell classes through their spatial context. In this paper, we propose a novel method for both detection and classification that explicitly incorporates spatial contextual information. We use the spatial statistical function to describe local density in both a multi-class and a multi-scale manner. Through representation learning and deep clustering techniques, we learn advanced cell representation with both appearance and spatial context. On various benchmarks, our method achieves better performance than state-of-the-arts, especially on the classification task. We also create a new dataset for multi-class cell detection and classification in breast cancer and we make both our code and data publicly available.
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Affiliation(s)
| | | | | | | | - Eric Yee
- Stony Brook University, Stony Brook, NY 11794, USA
| | | | - Tahsin Kurc
- Stony Brook University, Stony Brook, NY 11794, USA
| | | | - Joel Saltz
- Stony Brook University, Stony Brook, NY 11794, USA
| | - Chao Chen
- Stony Brook University, Stony Brook, NY 11794, USA
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18
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Hong R, Liu W, DeLair D, Razavian N, Fenyö D. Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep Med 2021; 2:100400. [PMID: 34622237 PMCID: PMC8484685 DOI: 10.1016/j.xcrm.2021.100400] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/29/2021] [Accepted: 08/24/2021] [Indexed: 02/07/2023]
Abstract
The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Sequencing, albeit slower and more expensive, can provide additional information on molecular subtypes and mutations that can be used to better select treatments. Here, we implement a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes but also the molecular subtypes and 18 common gene mutations based on digitized H&E-stained pathological images. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that Panoptes, with further refinement, has the potential for clinical application to help pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing.
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Affiliation(s)
- Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Deborah DeLair
- Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Narges Razavian
- Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
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19
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Chervoneva I, Peck AR, Yi M, Freydin B, Rui H. Quantification of spatial tumor heterogeneity in immunohistochemistry staining images. Bioinformatics 2021; 37:1452-1460. [PMID: 33275142 DOI: 10.1093/bioinformatics/btaa965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 10/19/2020] [Accepted: 11/04/2020] [Indexed: 01/10/2023] Open
Abstract
MOTIVATION Quantitative immunofluorescence is often used for immunohistochemistry quantification of proteins that serve as cancer biomarkers. Advanced image analysis systems for pathology allow capturing expression levels in each individual cell or subcellular compartment. However, only the mean signal intensity within the cancer tissue region of interest is usually considered as biomarker completely ignoring the issue of tumor heterogeneity. RESULTS We propose using immunohistochemistry image-derived information on the spatial distribution of cellular signal intensity (CSI) of protein expression within the cancer cell population to quantify both mean expression level and tumor heterogeneity of CSI levels. We view CSI levels as marks in a marked point process of cancer cells in the tissue and define spatial indices based on conditional mean and conditional variance of the marked point process. The proposed methodology provides objective metrics of cell-to-cell heterogeneity in protein expressions that allow discriminating between different patterns of heterogeneity. The prognostic utility of new spatial indices is investigated and compared to the standard mean signal intensity biomarkers using the protein expressions in tissue microarrays incorporating tumor tissues from 1000+ breast cancer patients. AVAILABILITY AND IMPLEMENTATION: THE R CODE FOR COMPUTING THE PROPOSED SPATIAL INDICES IS INCLUDED AS SUPPLEMENTARY MATERIAL . SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Inna Chervoneva
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Amy R Peck
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Misung Yi
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Boris Freydin
- Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Hallgeir Rui
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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20
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Kalra J, Baker J, Song J, Kyle A, Minchinton A, Bally M. Inter-Metastatic Heterogeneity of Tumor Marker Expression and Microenvironment Architecture in a Preclinical Cancer Model. Int J Mol Sci 2021; 22:6336. [PMID: 34199298 PMCID: PMC8231937 DOI: 10.3390/ijms22126336] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/25/2021] [Accepted: 06/09/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Preclinical drug development studies rarely consider the impact of a candidate drug on established metastatic disease. This may explain why agents that are successful in subcutaneous and even orthotopic preclinical models often fail to demonstrate efficacy in clinical trials. It is reasonable to anticipate that sites of metastasis will be phenotypically unique, as each tumor will have evolved heterogeneously with respect to gene expression as well as the associated phenotypic outcome of that expression. The objective for the studies described here was to gain an understanding of the tumor heterogeneity that exists in established metastatic disease and use this information to define a preclinical model that is more predictive of treatment outcome when testing novel drug candidates clinically. METHODS Female NCr nude mice were inoculated with fluorescent (mKate), Her2/neu-positive human breast cancer cells (JIMT-mKate), either in the mammary fat pad (orthotopic; OT) to replicate a primary tumor, or directly into the left ventricle (intracardiac; IC), where cells eventually localize in multiple sites to create a model of established metastasis. Tumor development was monitored by in vivo fluorescence imaging (IVFI). Subsequently, animals were sacrificed, and tumor tissues were isolated and imaged ex vivo. Tumors within organ tissues were further analyzed via multiplex immunohistochemistry (mIHC) for Her2/neu expression, blood vessels (CD31), as well as a nuclear marker (Hoechst) and fluorescence (mKate) expressed by the tumor cells. RESULTS Following IC injection, JIMT-1mKate cells consistently formed tumors in the lung, liver, brain, kidney, ovaries, and adrenal glands. Disseminated tumors were highly variable when assessing vessel density (CD31) and tumor marker expression (mkate, Her2/neu). Interestingly, tumors which developed within an organ did not adopt a vessel microarchitecture that mimicked the organ where growth occurred, nor did the vessel microarchitecture appear comparable to the primary tumor. Rather, metastatic lesions showed considerable variability, suggesting that each secondary tumor is a distinct disease entity from a microenvironmental perspective. CONCLUSIONS The data indicate that more phenotypic heterogeneity in the tumor microenvironment exists in models of metastatic disease than has been previously appreciated, and this heterogeneity may better reflect the metastatic cancer in patients typically enrolled in early-stage Phase I/II clinical trials. Similar to the suggestion of others in the past, the use of models of established metastasis preclinically should be required as part of the anticancer drug candidate development process, and this may be particularly important for targeted therapeutics and/or nanotherapeutics.
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Affiliation(s)
- Jessica Kalra
- Experimental Therapeutics, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada;
- Applied Research Centre, Langara, Vancouver, BC V5Y 2Z6, Canada
- Department Anesthesia Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
| | - Jennifer Baker
- Integrative Oncology, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada; (J.B.); (A.K.)
| | - Justin Song
- Chemical and Biomolecular Engineering Department, Vanderbilt University, Nashville, TN 37235, USA;
| | - Alastair Kyle
- Integrative Oncology, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada; (J.B.); (A.K.)
| | - Andrew Minchinton
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- Integrative Oncology, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada; (J.B.); (A.K.)
| | - Marcel Bally
- Experimental Therapeutics, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada;
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Nanomedicine Innovation Network, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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21
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Abstract
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.
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22
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Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics. CURRENT PATHOBIOLOGY REPORTS 2020. [DOI: 10.1007/s40139-020-00217-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Abstract
Purpose of Review
Our goal is to show how readily available Pathomics tissue analytics can be used to study tumor immune interactions in cancer. We provide a brief overview of how Pathomics complements traditional histopathologic examination of cancer tissue samples. We highlight a novel Pathomics application, Tumor-TILs, that quantitatively measures and generates maps of tumor infiltrating lymphocytes in breast, pancreatic, and lung cancer by leveraging deep learning computer vision applications to perform automated analyses of whole slide images.
Recent Findings
Tumor-TIL maps have been generated to analyze WSIs from thousands of cases of breast, pancreatic, and lung cancer. We report the availability of these tools in an effort to promote collaborative research and motivate future development of ensemble Pathomics applications to discover novel biomarkers and perform a wide range of correlative clinicopathologic research in cancer immunopathology and beyond.
Summary
Tumor immune interactions in cancer are a fascinating aspect of cancer pathobiology with particular significance due to the emergence of immunotherapy. We present simple yet powerful specialized Pathomics methods that serve as powerful clinical research tools and potential standalone clinical screening tests to predict clinical outcomes and treatment responses for precision medicine applications in immunotherapy.
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23
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Valous NA, Moraleda RR, Jäger D, Zörnig I, Halama N. Interrogating the microenvironmental landscape of tumors with computational image analysis approaches. Semin Immunol 2020; 48:101411. [PMID: 33168423 DOI: 10.1016/j.smim.2020.101411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/13/2020] [Accepted: 09/04/2020] [Indexed: 02/07/2023]
Abstract
The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.
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Affiliation(s)
- Nektarios A Valous
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany.
| | - Rodrigo Rojas Moraleda
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany.
| | - Dirk Jäger
- Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Inka Zörnig
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Niels Halama
- Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD), Im Neuenheimer Feld 460, 69120 Heidelberg, Germany; Division of Translational Immunotherapy, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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24
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Stopsack KH, Huang Y, Tyekucheva S, Gerke TA, Bango C, Elfandy H, Bowden M, Penney KL, Roberts TM, Parmigiani G, Kantoff PW, Mucci LA, Loda M. Multiplex Immunofluorescence in Formalin-Fixed Paraffin-Embedded Tumor Tissue to Identify Single-Cell-Level PI3K Pathway Activation. Clin Cancer Res 2020; 26:5903-5913. [PMID: 32913135 DOI: 10.1158/1078-0432.ccr-20-2000] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/11/2020] [Accepted: 09/03/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE Identifying cancers with high PI3K pathway activity is critical for treatment selection and eligibility into clinical trials of PI3K inhibitors. Assessments of tumor signaling pathway activity need to consider intratumoral heterogeneity and multiple regulatory nodes. EXPERIMENTAL DESIGN We established a novel, mechanistically informed approach to assessing tumor signaling pathways by quantifying single-cell-level multiplex immunofluorescence using custom algorithms. In a proof-of-concept study, we stained archival formalin-fixed, paraffin-embedded (FFPE) tissue from patients with primary prostate cancer in two prospective cohort studies, the Health Professionals Follow-up Study and the Physicians' Health Study. PTEN, stathmin, and phospho-S6 were quantified on 14 tissue microarrays as indicators of PI3K activation to derive cell-level PI3K scores. RESULTS In 1,001 men, 988,254 tumor cells were assessed (median, 743 per tumor; interquartile range, 290-1,377). PI3K scores were higher in tumors with PTEN loss scored by a pathologist, higher Gleason grade, and a new, validated bulk PI3K transcriptional signature. Unsupervised machine-learning approaches resulted in similar clustering. Within-tumor heterogeneity in cell-level PI3K scores was high. During long-term follow-up (median, 15.3 years), rates of progression to metastases and death from prostate cancer were twice as high in the highest quartile of PI3K activation compared with the lowest quartile (hazard ratio, 2.04; 95% confidence interval, 1.13-3.68). CONCLUSIONS Our novel pathway-focused approach to quantifying single-cell-level immunofluorescence in FFPE tissue identifies prostate tumors with PI3K pathway activation that are more aggressive and may respond to pathway inhibitors.
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Affiliation(s)
- Konrad H Stopsack
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Ying Huang
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Svitlana Tyekucheva
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Travis A Gerke
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Clyde Bango
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Habiba Elfandy
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Michaela Bowden
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Kathryn L Penney
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Thomas M Roberts
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Giovanni Parmigiani
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Philip W Kantoff
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Massimo Loda
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts. .,Department of Pathology, Weill Cornell Medical College, New York, New York.,Broad Institute of Harvard and MIT, Cambridge, Massachusetts.,New York Genome Center, New York, New York
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25
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Boyce HB, Mallick P. Geostatistical visualization of ecological interactions in tumors. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2020; 2019:2741-2749. [PMID: 32368363 DOI: 10.1109/bibm47256.2019.8983076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Recent advances in our understanding of cancer progression have highlighted the roles played by molecular heterogeneity and by the tumor microenvironment in driving drug resistance and metastasis. The coupling of single-cell measurement technologies with algorithms, such as t-sne and SPADE, have enabled deep investigation of tumor heterogeneity. However, such techniques only capture molecular heterogeneity and do not enable the quantification nor visualization of intercellular interactions. They additionally do not allow the visualization of ecological niches that are critical to understanding tumor behavior. Novel computational tools to quantify and visualize spatial patterns in the tumor microenvironment are critically needed. Here, we take a tumor ecology perspective to examine how predation, mutualism, commensalism, and parasitism may impact tumor development and spatial patterning. We additionally quantify local spatial heterogeneity and the emergent global spatial behavior of the models using geostatistics. By visualizing emergent spatial patterns we demonstrate the potential utility of a geostatistical analysis in differentiating amongst cell-cell interactions in the tumor microenvironment. These studies introduce both an ecological framework for characterizing intercellular interactions in cancer and a novel way of quantifying and visualizing spatial patterns in cancer.
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Affiliation(s)
- Hunter Bryan Boyce
- Program in Biomedical Informatics, Stanford University, Stanford, CA, USA
| | - Parag Mallick
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Palo Alto, CA, USA
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26
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Tosun AB, Pullara F, Becich MJ, Taylor DL, Chennubhotla SC, Fine JL. HistoMapr™: An Explainable AI (xAI) Platform for Computational Pathology Solutions. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR DIGITAL PATHOLOGY 2020. [DOI: 10.1007/978-3-030-50402-1_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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27
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Nawaz S, Trahearn NA, Heindl A, Banerjee S, Maley CC, Sottoriva A, Yuan Y. Analysis of tumour ecological balance reveals resource-dependent adaptive strategies of ovarian cancer. EBioMedicine 2019; 48:224-235. [PMID: 31648981 PMCID: PMC6838425 DOI: 10.1016/j.ebiom.2019.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 07/02/2019] [Accepted: 10/01/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Despite treatment advances, there remains a significant risk of recurrence in ovarian cancer, at which stage it is usually incurable. Consequently, there is a clear need for improved patient stratification. However, at present clinical prognosticators remain largely unchanged due to the lack of reproducible methods to identify high-risk patients. METHODS In high-grade serous ovarian cancer patients with advanced disease, we spatially define a tumour ecological balance of stromal resource and immune hazard using high-throughput image and spatial analysis of routine histology slides. On this basis an EcoScore is developed to classify tumours by a shift in this balance towards cancer-favouring or inhibiting conditions. FINDINGS The EcoScore provides prognostic value stronger than, and independent of, known risk factors. Crucially, the clinical relevance of mutational burden and genomic instability differ under different stromal resource conditions, suggesting that the selective advantage of these cancer hallmarks is dependent on the context of stromal spatial structure. Under a high resource condition defined by a high level of geographical intermixing of cancer and stromal cells, selection appears to be driven by point mutations; whereas, in low resource tumours featured with high hypoxia and low cancer-immune co-localization, selection is fuelled by aneuploidy. INTERPRETATION Our study offers empirical evidence that cancer fitness depends on tumour spatial constraints, and presents a biological basis for developing better assessments of tumour adaptive strategies in overcoming ecological constraints including immune surveillance and hypoxia.
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Affiliation(s)
- Sidra Nawaz
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK; Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | - Nicholas A Trahearn
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK; Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | - Andreas Heindl
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK; Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | | | - Carlo C Maley
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK; Biodesign Center for Personalized Diagnostics, Arizona State University, Tempe, AZ, USA
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK; Division of Molecular Pathology, Institute of Cancer Research, London, UK
| | - Yinyin Yuan
- Centre for Evolution and Cancer, Institute of Cancer Research, London, UK; Division of Molecular Pathology, Institute of Cancer Research, London, UK.
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28
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Rojo MG. [Artificial intelligence in Pathological Anatomy]. REVISTA ESPAÑOLA DE PATOLOGÍA : PUBLICACIÓN OFICIAL DE LA SOCIEDAD ESPAÑOLA DE ANATOMÍA PATOLÓGICA Y DE LA SOCIEDAD ESPAÑOLA DE CITOLOGÍA 2019; 52:205-207. [PMID: 31530402 DOI: 10.1016/j.patol.2019.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 09/04/2019] [Indexed: 10/26/2022]
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29
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Cui Y, Zhang G, Liu Z, Xiong Z, Hu J. A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Med Biol Eng Comput 2019; 57:2027-2043. [PMID: 31346949 DOI: 10.1007/s11517-019-02008-8] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 06/24/2019] [Indexed: 12/12/2022]
Abstract
This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color-normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast, and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the-art methods. Moreover, it is efficient that one 1000×1000 image can be segmented in less than 5 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation . Graphical Abstract The neural network for nuclei segmentation.
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Affiliation(s)
- Yuxin Cui
- Department of Computer Science and Technology, University of South Carolina, Columbia, SC, 29208, USA
| | - Guiying Zhang
- Department of Medical Information Engineering, Zunyi Medical University, Zunyi, China
| | - Zhonghao Liu
- Department of Computer Science and Technology, University of South Carolina, Columbia, SC, 29208, USA
| | - Zheng Xiong
- Department of Computer Science and Technology, University of South Carolina, Columbia, SC, 29208, USA
| | - Jianjun Hu
- Department of Computer Science and Technology, University of South Carolina, Columbia, SC, 29208, USA.
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30
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Corredor G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, Rimm DL, Yang M, Romero E, Schalper KA, Velcheti V, Madabhushi A. Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non-Small Cell Lung Cancer. Clin Cancer Res 2019; 25:1526-1534. [PMID: 30201760 PMCID: PMC6397708 DOI: 10.1158/1078-0432.ccr-18-2013] [Citation(s) in RCA: 167] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/16/2018] [Accepted: 09/06/2018] [Indexed: 12/16/2022]
Abstract
PURPOSE The presence of a high degree of tumor-infiltrating lymphocytes (TIL) has been proven to be associated with outcome in patients with non-small cell lung cancer (NSCLC). However, recent evidence indicates that tissue architecture is also prognostic of disease-specific survival and recurrence. We show a set of descriptors (spatial TIL, SpaTIL) that capture density, and spatial colocalization of TILs and tumor cells across digital images that can predict likelihood of recurrence in early-stage NSCLC. EXPERIMENTAL DESIGN The association between recurrence in early-stage NSCLC and SpaTIL features was explored on 301 patients across four different cohorts. Cohort D1 (n = 70) was used to identify the most prognostic SpaTIL features and to train a classifier to predict the likelihood of recurrence. The classifier performance was evaluated in cohorts D2 (n = 119), D3 (n = 112), and D4 (n = 112). Two pathologists graded each sample of D1 and D2; intraobserver agreement and association between manual grading and likelihood of recurrence were analyzed. RESULTS SpaTIL was associated with likelihood of recurrence in all test sets (log-rank P < 0.02). A multivariate Cox proportional hazards analysis revealed an HR of 3.08 (95% confidence interval, 2.1-4.5, P = 7.3 × 10-5). In contrast, agreement among expert pathologists using tumor grade was moderate (Kappa = 0.5), and the manual TIL grading was only prognostic for one reader in D2 (P = 8.0 × 10-3). CONCLUSIONS A set of features related to density and spatial architecture of TILs was found to be associated with a likelihood of recurrence of early-stage NSCLC. This information could potentially be used for helping in treatment planning and management of early-stage NSCLC.See related commentary by Peled et al., p. 1449.
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Affiliation(s)
- Germán Corredor
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Xiangxue Wang
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
| | - Yu Zhou
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
| | - Cheng Lu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Konstantinos Syrigos
- Department of Medicine, University of Athens, Sotiria General Hospital, Athens, Greece
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Michael Yang
- Department of Pathology-Anatomic, University Hospitals, Cleveland, Ohio
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Kurt A Schalper
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Vamsidhar Velcheti
- Hematology and Medical Oncology Department, Cleveland Clinic, Cleveland, Ohio
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, Ohio.
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31
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The Impact of Tumor Eco-Evolution in Renal Cell Carcinoma Sampling. Cancers (Basel) 2018; 10:cancers10120485. [PMID: 30518081 PMCID: PMC6316833 DOI: 10.3390/cancers10120485] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 11/29/2018] [Accepted: 11/30/2018] [Indexed: 12/31/2022] Open
Abstract
Malignant tumors behave dynamically as cell communities governed by ecological principles. Massive sequencing tools are unveiling the true dimension of the heterogeneity of these communities along their evolution in most human neoplasms, clear cell renal cell carcinomas (CCRCC) included. Although initially thought to be purely stochastic processes, very recent genomic analyses have shown that temporal tumor evolution in CCRCC may follow some deterministic pathways that give rise to different clones and sub-clones randomly spatially distributed across the tumor. This fact makes each case unique, unrepeatable and unpredictable. Precise and complete molecular information is crucial for patients with cancer since it may help in establishing a personalized therapy. Intratumor heterogeneity (ITH) detection relies on the correctness of tumor sampling and this is part of the pathologist’s daily work. International protocols for tumor sampling are insufficient today. They were conceived decades ago, when ITH was not an issue, and have remained unchanged until now. Noteworthy, an alternative and more efficient sampling method for detecting ITH has been developed recently. This new method, called multisite tumor sampling (MSTS), is specifically addressed to large tumors that are impossible to be totally sampled, and represent an opportunity to improve ITH detection without extra costs.
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32
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Wickenden K, Nawaz N, Mamand S, Kotecha D, Wilson AL, Wagner SD, Ahearne MJ. PD1 hi cells associate with clusters of proliferating B-cells in marginal zone lymphoma. Diagn Pathol 2018; 13:74. [PMID: 30219078 PMCID: PMC6138907 DOI: 10.1186/s13000-018-0750-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 09/03/2018] [Indexed: 12/15/2022] Open
Abstract
Background Abnormally sustained immune reactions drive B-cell proliferation in some cases of marginal zone lymphoma but the CD4+ T-cell subsets, which are likely to contribute to the B-cell responses in the tumour microenvironment, are not well characterised and neither has the spatial distribution of the different subsets in involved lymph nodes been investigated. Methods Employing a workflow of multiplex semi-automated immunohistochemistry combined with image processing we investigated association between infiltrating T-cells and proliferating lymphoma B-cells. Results Both total numbers of activating follicular helper (Tfh) cells (defined by high expression of PD1) and suppressive regulatory (Treg) T-cells (defined by FOXP3+ expression) and the Tfh:Treg ratio, assessed over relatively large areas of tissue, varied among cases of marginal zone lymphoma. We determined spatial distribution and demonstrated that PD1hi cells showed significantly more clustering than did FOXP3+. To investigate the association of infiltrating T-cells with lymphoma B-cells we employed Pearson correlation and Morisita-Horn index, statistical measures of interaction. We demonstrated that PD1hi cells were associated with proliferating B-cells and confirmed this by nearest neighbour analysis. Conclusions The unexpected architectural complexity of T-cell infiltration in marginal zone lymphoma, revealed in this study, further supports a key role for Tfh cells in driving proliferation of lymphoma B-cells. We demonstrate the feasibility of digital analysis of spatial architecture of T-cells within marginal zone lymphoma and future studies will be needed to determine the clinical importance of these observations.
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Affiliation(s)
- Katherine Wickenden
- Leicester Cancer Research Centre and Ernest and Helen Scott Haematology Research Institute, University of Leicester, Lancaster Road, Leicester, LE1 7HB, UK
| | - Nadia Nawaz
- Leicester Cancer Research Centre and Ernest and Helen Scott Haematology Research Institute, University of Leicester, Lancaster Road, Leicester, LE1 7HB, UK
| | - Sami Mamand
- Leicester Cancer Research Centre and Ernest and Helen Scott Haematology Research Institute, University of Leicester, Lancaster Road, Leicester, LE1 7HB, UK
| | - Deevia Kotecha
- Leicester Cancer Research Centre and Ernest and Helen Scott Haematology Research Institute, University of Leicester, Lancaster Road, Leicester, LE1 7HB, UK
| | - Amy L Wilson
- Leicester Cancer Research Centre and Ernest and Helen Scott Haematology Research Institute, University of Leicester, Lancaster Road, Leicester, LE1 7HB, UK
| | - Simon D Wagner
- Leicester Cancer Research Centre and Ernest and Helen Scott Haematology Research Institute, University of Leicester, Lancaster Road, Leicester, LE1 7HB, UK.
| | - Matthew J Ahearne
- Leicester Cancer Research Centre and Ernest and Helen Scott Haematology Research Institute, University of Leicester, Lancaster Road, Leicester, LE1 7HB, UK
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33
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Pogrebniak KL, Curtis C. Harnessing Tumor Evolution to Circumvent Resistance. Trends Genet 2018; 34:639-651. [PMID: 29903534 DOI: 10.1016/j.tig.2018.05.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 05/16/2018] [Accepted: 05/21/2018] [Indexed: 02/08/2023]
Abstract
High-throughput sequencing can be used to measure changes in tumor composition across space and time. Specifically, comparisons of pre- and post-treatment samples can reveal the underlying clonal dynamics and resistance mechanisms. Here, we discuss evidence for distinct modes of tumor evolution and their implications for therapeutic strategies. In addition, we consider the utility of spatial tissue sampling schemes, single-cell analysis, and circulating tumor DNA to track tumor evolution and the emergence of resistance, as well as approaches that seek to forestall resistance by targeting tumor evolution. Ultimately, characterization of the (epi)genomic, transcriptomic, and phenotypic changes that occur during tumor progression coupled with computational and mathematical modeling of tumor evolutionary dynamics may inform personalized treatment strategies.
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Affiliation(s)
- Katherine L Pogrebniak
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; http://med.stanford.edu/curtislab.html
| | - Christina Curtis
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; http://med.stanford.edu/curtislab.html.
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34
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Heindl A, Lan C, Rodrigues DN, Koelble K, Yuan Y. Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer. Oncotarget 2018; 7:71123-71135. [PMID: 27661102 PMCID: PMC5342067 DOI: 10.18632/oncotarget.12106] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 08/24/2016] [Indexed: 12/29/2022] Open
Abstract
The tumor microenvironment is pivotal in influencing cancer progression and metastasis. Different cells co-exist with high spatial diversity within a patient, yet their combinatorial effects are poorly understood. We investigate the similarity of the tumor microenvironment of 192 local metastatic lesions in 61 ovarian cancer patients. An ecologically inspired measure of microenvironmental diversity derived from multiple metastasis sites is correlated with clinicopathological characteristics and prognostic outcome. We demonstrate a high accuracy of our automated analysis across multiple sites. A low level of similarity in microenvironmental composition is observed between ovary tumor and corresponding local metastases (stromal ratio r = 0.30, lymphocyte ratio r = 0.37). We identify a new measure of microenvironmental diversity derived from Shannon entropy that is highly predictive of poor overall (p = 0.002, HR = 3.18, 95% CI = 1.51-6.68) and progression-free survival (p = 0.0036, HR = 2.83, 95% CI = 1.41-5.7), independent of and stronger than clinical variables, subtype stratifications based on single cell types alone and number of sites. Although stromal influence in ovary tumors is known to have significant clinical implications, our findings reveal an even stronger impact orchestrated by diverse cell types. Quantitative histology-based measures can further enable objective selection of patients who are in urgent need of new therapeutic strategies such as combinatorial treatments targeting heterogeneous tumor microenvironment.
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Affiliation(s)
- Andreas Heindl
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.,Centre for Molecular Pathology, Royal Marsden Hospital, London, UK.,Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Chunyan Lan
- Department of Gynecologic Oncology, Sun Yat-sen University Cancer Centre, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangzhou, China
| | | | - Konrad Koelble
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK.,Department of Histopathology, Royal Marsden Hospital, London, UK
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.,Centre for Molecular Pathology, Royal Marsden Hospital, London, UK.,Division of Molecular Pathology, The Institute of Cancer Research, London, UK
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35
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López JI, Angulo JC. Pathological Bases and Clinical Impact of Intratumor Heterogeneity in Clear Cell Renal Cell Carcinoma. Curr Urol Rep 2018; 19:3. [PMID: 29374850 DOI: 10.1007/s11934-018-0754-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE OF REVIEW Intratumor heterogeneity is an inherent event in tumor development that is receiving much attention in the last years since it is responsible for most failures of current targeted therapies. The purpose of this review is to offer clinicians an updated insight of the multiple manifestations of a complex event that impacts significantly patient's life. RECENT FINDINGS Clear cell renal cell carcinoma is the most common renal tumor and a paradigmatic example of a heterogeneous neoplasm. Next-generation sequencing has demonstrated that intratumor heterogeneity encompasses genetic, epigenetic, and microenvironmental variability. Currently accepted protocols of tumor sampling seem insufficient in unveiling intratumor heterogeneity with reliability and need to be updated. This variability challenges the precise morphological diagnosis, its molecular characterization, and the selection of optimal personalized therapies in clear cell renal cell carcinoma, a neoplasm traditionally considered chemo- and radio-resistant. We review the state of the art of the different approaches to intratumor heterogeneity in clear cell renal cell carcinomas, from the simple morphology to the most sophisticated massive sequencing tools.
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Affiliation(s)
- José I López
- Department of Pathology, Cruces University Hospital, Biocruces Research Institute, University of the Basque Country (UPV/EHU), 48903, Barakaldo, Spain.
| | - Javier C Angulo
- Clinical Department, Urology, Hospital Universitario de Getafe, Universidad Europea de Madrid, 28905, Madrid, Spain
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Color-coded visualization of magnetic resonance imaging multiparametric maps. Sci Rep 2017; 7:41107. [PMID: 28112222 PMCID: PMC5255548 DOI: 10.1038/srep41107] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Accepted: 12/15/2016] [Indexed: 12/11/2022] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) data are emergingly used in the clinic e.g. for the diagnosis of prostate cancer. In contrast to conventional MR imaging data, multiparametric data typically include functional measurements such as diffusion and perfusion imaging sequences. Conventionally, these measurements are visualized with a one-dimensional color scale, allowing only for one-dimensional information to be encoded. Yet, human perception places visual information in a three-dimensional color space. In theory, each dimension of this space can be utilized to encode visual information. We addressed this issue and developed a new method for tri-variate color-coded visualization of mpMRI data sets. We showed the usefulness of our method in a preclinical and in a clinical setting: In imaging data of a rat model of acute kidney injury, the method yielded characteristic visual patterns. In a clinical data set of N = 13 prostate cancer mpMRI data, we assessed diagnostic performance in a blinded study with N = 5 observers. Compared to conventional radiological evaluation, color-coded visualization was comparable in terms of positive and negative predictive values. Thus, we showed that human observers can successfully make use of the novel method. This method can be broadly applied to visualize different types of multivariate MRI data.
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Abstract
Recent developments in studies of tumor heterogeneity have provoked new thoughts on cancer management. There is a desperate need to understand influence of the tumor microenvironment on cancer development and evolution. Applying principles and quantitative methods from ecology can suggest novel solutions to fulfil this need. We discuss spatial heterogeneity as a fundamental biological feature of the microenvironment, which has been largely ignored. Histological samples can provide spatial context of diverse cell types coexisting within the microenvironment. Advanced computer-vision techniques have been developed for spatial mapping of cells in histological samples. This has enabled the applications of experimental and analytical tools from ecology to cancer research, generating system-level knowledge of microenvironmental spatial heterogeneity. We focus on studies of immune infiltrate and tumor resource distribution, and highlight statistical approaches for addressing the emerging challenges based on these new approaches.
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Affiliation(s)
- Yinyin Yuan
- Centre for Evolution and Cancer and Division of Molecular Pathology, The Institute of Cancer Research, London; and Centre for Molecular Pathology, Royal Marsden Hospital, London
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Holst F. Estrogen receptor alpha gene amplification in breast cancer: 25 years of debate. World J Clin Oncol 2016; 7:160-173. [PMID: 27081639 PMCID: PMC4826962 DOI: 10.5306/wjco.v7.i2.160] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 01/05/2016] [Accepted: 02/16/2016] [Indexed: 02/06/2023] Open
Abstract
Twenty-five years ago, Nembrot and colleagues reported amplification of the estrogen receptor alpha gene (ESR1) in breast cancer, initiating a broad and still ongoing scientific debate on the prevalence and clinical significance of this genetic aberration, which affects one of the most important genes in breast cancer. Since then, a multitude of studies on this topic has been published, covering a wide range of divergent results and arguments. The reported prevalence of this alteration in breast cancer ranges from 0% to 75%, suggesting that ESR1 copy number analysis is hampered by technical and interpreter issues. To date, two major issues related to ESR1 amplification remain to be conclusively addressed: (1) The extent to which abundant amounts of messenger RNA can mimic amplification in standard fluorescence in situ hybridization assays in the analysis of strongly expressed genes like ESR1, and (2) the clinical relevance of ESR1 amplification: Such relevance is strongly disputed, with data showing predictive value for response as well as for resistance of the cancer to anti-estrogen therapies, or for subsequent development of cancers in the case of precursor lesions that display amplification of ESR1. This review provides a comprehensive summary of the various views on ESR1 amplification, and highlights explanations for the contradictions and conflicting data that could inform future ESR1 research.
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