Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer

Overwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These analyses have been applied to CD8+ T cells, but quantitative analyses of other important markers and their correlations are limited. In this study, a digital pathology computational workflow is formulated for characterizing the spatial distributions of five immune markers (CD3, CD4, CD8, CD20, and FoxP3) and then the functionality is tested on whole slide images from patients with TNBC. The workflow is initiated by digital image processing to extract and colocalize immune marker-labeled cells and then convert this information to point patterns. Afterward invasive front (IF), central tumor (CT), and normal tissue (N) are characterized. For each region, we examine the intra-tumoral heterogeneity. The workflow is then repeated for all specimens to capture inter-tumoral heterogeneity. In this study, both intra- and inter-tumoral heterogeneities are observed for all five markers across all specimens. Among all regions, IF tends to have higher densities of immune cells and overall larger variations in spatial model fitting parameters and higher density in cell clusters and hotspots compared to CT and N. Results suggest a distinct role of IF in the tumor immuno-architecture. Though the sample size is limited in the study, the computational workflow could be readily reproduced and scaled due to its automatic nature. Importantly, the value of the workflow also lies in its potential to be linked to treatment outcomes and identification of predictive biomarkers for responders/non-responders, and its application to parameterization and validation of computational immuno-oncology models.

[1]  Hanwen Wang,et al.  QSP‐IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno‐Oncology Applications , 2020, CPT: pharmacometrics & systems pharmacology.

[2]  A. Popel,et al.  A Quantitative Systems Pharmacology Model of T Cell Engager Applied to Solid Tumor , 2020, The AAPS Journal.

[3]  H. Wildiers,et al.  Computerised scoring protocol for identification and quantification of different immune cell populations in breast tumour regions by the use of QuPath software , 2020, Histopathology.

[4]  E. Jaffee,et al.  Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model With an Epigenetic Modulator and Immune Checkpoint Inhibitors , 2020, Frontiers in Bioengineering and Biotechnology.

[5]  C. Swanton,et al.  Cancer-Specific Loss of p53 Leads to a Modulation of Myeloid and T Cell Responses , 2020, Cell reports.

[6]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[7]  S. Astley,et al.  Spatial proximity between T and PD-L1 expressing cells as a prognostic biomarker for oropharyngeal squamous cell carcinoma , 2019, British Journal of Cancer.

[8]  D. Rimm,et al.  An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma , 2019, Nature Communications.

[9]  E. Winer,et al.  Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial. , 2019, The Lancet. Oncology.

[10]  S. Phinn,et al.  Australian vegetated coastal ecosystems as global hotspots for climate change mitigation , 2019, Nature Communications.

[11]  Jon C. Aster,et al.  Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging , 2019, Nature Protocols.

[12]  Xiaobo Zhou,et al.  Systematically understanding the immunity leading to CRPC progression , 2019, PLoS Comput. Biol..

[13]  Bing Wang,et al.  A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade , 2019, Scientific Reports.

[14]  A. Scialdone,et al.  Epstein–Barr virus reprograms human B lymphocytes immediately in the prelatent phase of infection , 2018, Proceedings of the National Academy of Sciences.

[15]  Jakob Nikolas Kather,et al.  CD163+ immune cell infiltrates and presence of CD54+ microvessels are prognostic markers for patients with embryonal rhabdomyosarcoma , 2019, Scientific Reports.

[16]  B. Wang,et al.  A Computational Model of Neoadjuvant PD-1 Inhibition in Non-Small Cell Lung Cancer , 2019, The AAPS Journal.

[17]  D. Mollura,et al.  Lymphocyte-driven regional immunopathology in pneumonitis caused by impaired central immune tolerance , 2019, Science Translational Medicine.

[18]  Jane P. F. Bai,et al.  Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices , 2019, The AAPS Journal.

[19]  B. de Strooper,et al.  PS917 SAFE TARGETING OF T-CELL ACUTE LYMPHOBLASTIC LEUKEMIA BY SUBUNIT SELECTIVE GAMMA-SECRETASE INHIBITION , 2019, HemaSphere.

[20]  M. Tsao,et al.  Amplifying Outcomes: Checkpoint Inhibitor Combinations in First-Line Non-Small Cell Lung Cancer. , 2019, The oncologist.

[21]  B. Wang,et al.  In silico simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model , 2019, Royal Society Open Science.

[22]  N. Shemesh,et al.  Susceptibility Perturbation MRI Maps Tumor Infiltration into Mesorectal Lymph Nodes. , 2019, Cancer research.

[23]  Aleksander S Popel,et al.  Tumor Ensemble-Based Modeling and Visualization of Emergent Angiogenic Heterogeneity in Breast Cancer , 2019, Scientific Reports.

[24]  R. Motzer,et al.  Avelumab plus Axitinib versus Sunitinib for Advanced Renal‐Cell Carcinoma , 2019, The New England journal of medicine.

[25]  H. Levine,et al.  Infiltration of CD8+ T cells into tumor cell clusters in triple-negative breast cancer , 2018, Proceedings of the National Academy of Sciences.

[26]  Bing Wang,et al.  Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors , 2019, Front. Oncol..

[27]  Daniel Zelterman,et al.  Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma , 2019, Clinical Cancer Research.

[28]  Aleksander S Popel,et al.  Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment , 2019, Processes.

[29]  Elisabeth Weiss,et al.  Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations , 2018, Advances in radiation oncology.

[30]  Horst K. Hahn,et al.  Data-Driven Discovery of Immune Contexture Biomarkers , 2018, Front. Oncol..

[31]  Dafydd G. Thomas,et al.  Association of B7-H4, PD-L1, and tumor infiltrating lymphocytes with outcomes in breast cancer , 2018, npj Breast Cancer.

[32]  Theodore R Rieger,et al.  Improving the generation and selection of virtual populations in quantitative systems pharmacology models. , 2018, Progress in biophysics and molecular biology.

[33]  David L Rimm,et al.  Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study , 2018, Laboratory Investigation.

[34]  A. Popel,et al.  Modeling triple-negative breast cancer heterogeneity: Effects of stromal macrophages, fibroblasts and tumor vasculature. , 2018, Journal of theoretical biology.

[35]  Mei Jiang,et al.  A Functional Spatial Analysis Platform for Discovery of Immunological Interactions Predictive of Low-Grade to High-Grade Transition of Pancreatic Intraductal Papillary Mucinous Neoplasms , 2018, Cancer informatics.

[36]  Wyeth W. Wasserman,et al.  Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer , 2018, Cell.

[37]  Jan G. Hengstler,et al.  Model Prediction and Validation of an Order Mechanism Controlling the Spatiotemporal Phenotype of Early Hepatocellular Carcinoma , 2018, Bulletin of Mathematical Biology.

[38]  C. Yuan,et al.  The predictive value and role of stromal tumor-infiltrating lymphocytes in pancreatic ductal adenocarcinoma (PDAC) , 2018, Cancer biology & therapy.

[39]  P. Fasching,et al.  Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. , 2018, The Lancet. Oncology.

[40]  Gerald Li,et al.  Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer , 2018, Laboratory Investigation.

[41]  Jan Poleszczuk,et al.  In Silico Modeling of Immunotherapy and Stroma-Targeting Therapies in Human Colorectal Cancer. , 2017, Cancer research.

[42]  Ehsan Kazemi,et al.  Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images , 2017, bioRxiv.

[43]  G. Parmigiani,et al.  Stromal and epithelial transcriptional map of initiation progression and metastatic potential of human prostate cancer , 2017, Nature Communications.

[44]  A. Popel,et al.  A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition , 2017, Journal of The Royal Society Interface.

[45]  Carsten Denkert,et al.  Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method from the International Immuno-Oncology Biomarkers Working Group: Part 2: TILs in Melanoma, Gastrointestinal Tract Carcinomas, Non-Small Cell Lung Carcinoma and Mesothe , 2017, Advances in anatomic pathology.

[46]  Aleksander S. Popel,et al.  An agent-based model of triple-negative breast cancer: the interplay between chemokine receptor CCR5 expression, cancer stem cells, and hypoxia , 2017, BMC Systems Biology.

[47]  Manish Gupta,et al.  QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models , 2017, The AAPS Journal.

[48]  Emanuele Giorgi,et al.  Spatial point patterns:methodology and applications with R , 2017 .

[49]  V. Band,et al.  Corrigendum: Endocytic recycling protein EHD1 regulates primary cilia morphogenesis and SHH signaling during neural tube development , 2017, Scientific Reports.

[50]  S. H. Sabzpoushan,et al.  An agent-based model of avascular tumor growth: Immune response tendency to prevent cancer development , 2017, Simul..

[51]  Peter Bankhead,et al.  QuPath: Open source software for digital pathology image analysis , 2017, Scientific Reports.

[52]  K. Gaus,et al.  Clus-DoC: a combined cluster detection and colocalization analysis for single-molecule localization microscopy data , 2016, Molecular biology of the cell.

[53]  K. Flaherty,et al.  Overall Survival and Durable Responses in Patients With BRAF V600-Mutant Metastatic Melanoma Receiving Dabrafenib Combined With Trametinib. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[54]  Yinyin Yuan.  Spatial Heterogeneity in the Tumor Microenvironment. , 2016, Cold Spring Harbor perspectives in medicine.

[55]  Christos Hatzis,et al.  Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer , 2016, Breast Cancer Research.

[56]  Jingzhou Liu,et al.  Visualizing Large-scale and High-dimensional Data , 2016, WWW.

[57]  Toby C. Cornish,et al.  PD-L1 (B7-H1) expression and the immune tumor microenvironment in primary and metastatic breast carcinomas. , 2016, Human pathology.

[58]  A. Ladányi,et al.  Prognostic and predictive significance of immune cells infiltrating cutaneous melanoma , 2015, Pigment cell & melanoma research.

[59]  Florian Klemm,et al.  Microenvironmental regulation of therapeutic response in cancer. , 2015, Trends in cell biology.

[60]  Dirk Brockmann,et al.  Spatial and Functional Heterogeneities Shape Collective Behavior of Tumor-Immune Networks , 2015, PLoS Comput. Biol..

[61]  D. Rimm,et al.  Multiplexed Quantitative Analysis of CD3, CD8, and CD20 Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer , 2014, Clinical Cancer Research.

[62]  David Ben-Arieh,et al.  Agent-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System , 2014 .

[63]  Hulin Wu,et al.  Multi-scale agent-based modeling on melanoma and its related angiogenesis analysis , 2013, Theoretical Biology and Medical Modelling.

[64]  C. Bianca,et al.  The triplex vaccine effects in mammary carcinoma: A nonlinear model in tune with SimTriplex ☆ , 2012 .

[65]  Shaimaa Al-Janabi,et al.  Digital pathology: current status and future perspectives , 2012, Histopathology.

[66]  Nicolas Chenouard,et al.  Icy: an open bioimage informatics platform for extended reproducible research , 2012, Nature Methods.

[67]  Andrew H. Beck,et al.  Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.

[68]  Axel Benner,et al.  Localization and density of immune cells in the invasive margin of human colorectal cancer liver metastases are prognostic for response to chemotherapy. , 2011, Cancer research.

[69]  G. Kroemer,et al.  Prognostic and predictive impact of intra- and peritumoral immune infiltrates. , 2011, Cancer research.

[70]  Beatriz Pateiro-López,et al.  Generalizing the Convex Hull of a Sample: The R Package alphahull , 2010 .

[71]  C. Lambert,et al.  Variability of CD3 membrane expression and T cell activation capacity , 2009, Cytometry. Part B, Clinical cytometry.

[72]  Masao Tanaka,et al.  Intratumoral CD8+ T/FOXP3+ cell ratio is a predictive marker for survival in patients with colorectal cancer , 2010, Cancer Immunology, Immunotherapy.

[73]  Zlatko Trajanoski,et al.  In situ cytotoxic and memory T cells predict outcome in patients with early-stage colorectal cancer. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[74]  Dmitry I. Strokotov,et al.  Is there a difference between T- and B-lymphocyte morphology? , 2009, Journal of biomedical optics.

[75]  Doron Levy,et al.  Modeling and simulation of the immune system as a self-regulating network. , 2009, Methods in enzymology.

[76]  A. Gemma,et al.  Predominant infiltration of macrophages and CD8+ T Cells in cancer nests is a significant predictor of survival in stage IV nonsmall cell lung cancer , 2008, Cancer.

[77]  Dietrich Stoyan,et al.  Parameter Estimation and Model Selection for Neyman‐Scott Point Processes , 2008, Biometrical journal. Biometrische Zeitschrift.

[78]  R. Waagepetersen.  An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes , 2007, Biometrics.

[79]  Philippos K. Tsourkas,et al.  Mechanisms of B-cell synapse formation predicted by Monte Carlo simulation. , 2006, Biophysical journal.

[80]  Roland Hess,et al.  The Essential Blender: Guide to 3D Creation with the Open Source Suite Blender , 2007 .

[81]  Stephen B Fox,et al.  Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[82]  E. Bloemena,et al.  Melanoma-specific tumor-infiltrating lymphocytes but not circulating melanoma-specific T cells may predict survival in resected advanced-stage melanoma patients , 2006, Cancer Immunology, Immunotherapy.

[83]  Christophe Claramunt,et al.  A Spatial Form of Diversity , 2005, COSIT.

[84]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[85]  Hartmut Bossel,et al.  Modeling and simulation , 1994 .

[86]  Jean-Pierre Royet,et al.  Stereology: A method for analyzing images , 1991, Progress in Neurobiology.

[87]  F. Davey,et al.  Cell volumes of normal and malignant mononuclear cells. , 1981, Journal of clinical pathology.

[88]  B. Efron.  Nonparametric standard errors and confidence intervals , 1981 .

[89]  E. Weibel,et al.  PRACTICAL STEREOLOGICAL METHODS FOR MORPHOMETRIC CYTOLOGY , 1966, The Journal of cell biology.

[90]  M. Thomas.  A generalization of Poisson's binomial limit for use in ecology. , 1949, Biometrika.