Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
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N. Halama | J. A. van der Laak | N. Rajpoot | M. Salto‐Tellez | J. Hartman | E. Thompson | C. Sotiriou | S. Loi | D. Larsimont | L. Pusztai | P. V. van Diest | A. Madabhushi | H. Horlings | M. Cheang | J. Reis-Filho | A. Salomon | E. Hytopoulos | D. Rimm | L. Cooper | S. Dudgeon | Yinyin Yuan | K. AbdulJabbar | P. Savas | F. Ciompi | D. Moore | J. Lennerz | Pawan Kirtani | G. Pruneri | S. Demaria | S. Adams | S. Loibl | Z. Kos | M. Hanna | S. Michiels | R. Salgado | A. Hida | A. Grigoriadis | A. Laenkholm | B. Ács | E. Bellolio | G. Broeckx | J. Giltnane | K. Siziopikou | K. Blenman | K. Korski | S. Ely | S. Irshad | S. Fineberg | Wentao Yang | W. Tran | Z. Husain | T. Taxter | S. Tejpar | F. Symmans | J. Saltz | S. Hart | T. Rau | A. Harbhajanka | A. Coosemans | S. Sayed | E. Janssen | S. Gnjatic | Germán Corredor | T. Tramm | W. Gallagher | J. Teuwen | Arman Rahman | C. Jahangir | I. Alvarado-Cabrero | A. Khramtsov | N. Wahab | A. Kovács | Shachi Mittal | B. Rapoport | L. Kodach | A. Ly | Guray Akturk | D. Marks | Thomas Walter | F. Penault-Llorca | E. McDonald | M. Sughayer | Xiaoxian Li | Sara Verbandt | J. Bartlett | T. Kataoka | Daniel G Sur | K. Kawaguchi | G. Acosta Haab | H. Wen | Reena Khiroya | M. Kahila | T. Papathomas | G. Viale | Y. Waumans | Umay Kiraz | O. Burgués | Sunao Tanaka | Najat Bouchmaa | S. Badve | S. Fox | C. Lang-Schwarz | M. Lerousseau | C. Pinard | Durga Kharidehal | A. Hardas | Ravi Mehrotra | C. Denkert | V. Bheemaraju | F. Minhas | J. Thagaard | Rashindrie Perera | Farid Azmoudeh-Ardalan | Shamim Mushtaq | Jonas S. Almeida | F. Deman | Paula I. González-Ericsson | S. Maley | Stephen M. Hewitt | Elisabeth Specht Stovgaard | A. Stenzinger | Claudio Fernandez-Martín | M. Vieth | D. B. Page | Nurkhairul Bariyah Baharun | Luciana Botinelly Mendonça Fujimoto | Flavio Luis Dantas Portela | Mahmoud Elghazawy | Vidya Manur Narasimhamurthy | Hussain Nighat | Juan Carlos Pinto-Cardenas | J. M. Ribeiro | G. Verghese | Rajarsi R Gupta
[1] Rajarsi R. Gupta,et al. Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer , 2023, The Journal of pathology.
[2] S. Pinder,et al. Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large‐scale studies , 2023, The Journal of pathology.
[3] I. Soubeyran,et al. Standardized Pathology Screening of Mature Tertiary Lymphoid Structures in Cancers. , 2023, Laboratory investigation; a journal of technical methods and pathology.
[4] Yu Guang Wang,et al. Cell graph neural networks enable the precise prediction of patient survival in gastric cancer , 2022, npj Precision Oncology.
[5] Rajarsi R. Gupta,et al. Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression , 2022, Cancers.
[6] I. Ellis,et al. Breast tumor microenvironment structures are associated with genomic features and clinical outcome , 2022, Nature Genetics.
[7] I. Soubeyran,et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression , 2021, Nature Cancer.
[8] Shuoyu Xu,et al. A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer , 2021, EBioMedicine.
[9] Volker Bruns,et al. Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification , 2021, Journal of medical imaging.
[10] Ludmila V. Danilova,et al. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade , 2021, Science.
[11] G. Litjens,et al. Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics , 2021, Breast.
[12] Rajarsi R. Gupta,et al. Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma , 2021, Scientific reports.
[13] Yinyin Yuan,et al. SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images , 2021, Frontiers in Oncology.
[14] E. Cerami,et al. Network for Biomarker Immunoprofiling for Cancer Immunotherapy: Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC) , 2021, Clinical Cancer Research.
[15] Brady Bernard,et al. Multiplex immunofluorescence to measure dynamic changes in tumor-infiltrating lymphocytes and PD-L1 in early-stage breast cancer , 2021, Breast cancer research : BCR.
[16] P. Tan,et al. Delineating the breast cancer immune microenvironment in the era of multiplex immunohistochemistry/immunofluorescence , 2021, Histopathology.
[17] A. Szalay,et al. Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer , 2020, Frontiers in Physiology.
[18] I. Osman,et al. Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma , 2020, Modern Pathology.
[19] Angela E. Leek,et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma , 2020, Nature Medicine.
[20] S. Loi,et al. KEYNOTE-355: Randomized, double-blind, phase III study of pembrolizumab + chemotherapy versus placebo + chemotherapy for previously untreated locally recurrent inoperable or metastatic triple-negative breast cancer. , 2020 .
[21] Andrew H. Beck,et al. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer , 2020, npj Breast Cancer.
[22] Andrew H. Beck,et al. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group , 2020, npj Breast Cancer.
[23] J. Taube,et al. The Society for Immunotherapy in Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation , 2020, Journal for immunotherapy of cancer.
[24] 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.
[25] D. Rimm,et al. Advances in quantitative immunohistochemistry and their contribution to breast cancer , 2020, Expert review of molecular diagnostics.
[26] V. Ostapenko,et al. Immunogradient indicators for anti-tumor response assessment by automated tumor-stroma interface zone detection. , 2020, The American journal of pathology.
[27] A. Gown,et al. The path to a better biomarker: application of a risk management framework for the implementation of PD‐L1 and TILs as immuno‐oncology biomarkers in breast cancer clinical trials and daily practice , 2020, The Journal of pathology.
[28] P. Fasching,et al. Pembrolizumab for Early Triple-Negative Breast Cancer. , 2020, The New England journal of medicine.
[29] E. Winer,et al. Abstract PD5-03: Relationship between tumor-infiltrating lymphocytes (TILs) and outcomes in the KEYNOTE-119 study of pembrolizumab vs chemotherapy for previously treated metastatic triple-negative breast cancer (mTNBC) , 2020 .
[30] M. Hall. National Heart, Lung, and Blood Institute , 2020, The Grants Register 2021.
[31] R. Rabadán,et al. Linking transcriptomic and imaging data defines features of a favorable tumor immune microenvironment and identifies a combination biomarker for primary melanoma. , 2020, Cancer research.
[32] Danielle Park,et al. A FIJI macro for quantifying pattern in extracellular matrix , 2019, Life Science Alliance.
[33] D. Rimm,et al. An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma , 2019, Nature Communications.
[34] A. Ravaud,et al. Clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial , 2019, Nature Medicine.
[35] Thomas J. Fuchs,et al. Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation , 2019, Comput. Medical Imaging Graph..
[36] E. Winer,et al. Estimating the Benefits of Therapy for Early Stage Breast Cancer The St Gallen International Consensus Guidelines for the Primary Therapy of Early Breast Cancer 2019. , 2019, Annals of oncology : official journal of the European Society for Medical Oncology.
[37] K. Syrigos,et al. High-Plex Predictive Marker Discovery for Melanoma Immunotherapy–Treated Patients Using Digital Spatial Profiling , 2019, Clinical Cancer Research.
[38] M. Scott,et al. Comparison of patient populations identified by different PD-L1 assays in in triple-negative breast cancer (TNBC). , 2019, Annals of oncology : official journal of the European Society for Medical Oncology.
[39] 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.
[40] A. Debucquoy,et al. The Link between the Multiverse of Immune Microenvironments in Metastases and the Survival of Colorectal Cancer Patients. , 2018, Cancer cell.
[41] A. Madabhushi,et al. Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer , 2018, Clinical Cancer Research.
[42] F. Marincola,et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study , 2018, The Lancet.
[43] Souptik Barua,et al. Spatial interaction of tumor cells and regulatory T cells correlates with survival in non-small cell lung cancer. , 2018, Lung cancer.
[44] Jack Cuzick,et al. Relevance of Spatial Heterogeneity of Immune Infiltration for Predicting Risk of Recurrence After Endocrine Therapy of ER+ Breast Cancer , 2018, Journal of the National Cancer Institute.
[45] Tuan Bui,et al. Multiparametric immune profiling in HPV- oral squamous cell cancer. , 2017, JCI insight.
[46] G. Saldanha,et al. A Novel Numerical Scoring System for Melanoma Tumor-infiltrating Lymphocytes Has Better Prognostic Value Than Standard Scoring , 2017, The American journal of surgical pathology.
[47] Nektarios A. Valous,et al. Detailed resolution analysis reveals spatial T cell heterogeneity in the invasive margin of colorectal cancer liver metastases associated with improved survival , 2017, Oncoimmunology.
[48] Yinyin Yuan,et al. Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score , 2016, Scientific Reports.
[49] M. Disis,et al. Clinical significance of tumor-infiltrating lymphocytes in breast cancer , 2016, Journal of Immunotherapy for Cancer.
[50] Yinyin Yuan. Spatial Heterogeneity in the Tumor Microenvironment. , 2016, Cold Spring Harbor perspectives in medicine.
[51] M. Koch,et al. Tumoral Immune Cell Exploitation in Colorectal Cancer Metastases Can Be Targeted Effectively by Anti-CCR5 Therapy in Cancer Patients. , 2016, Cancer cell.
[52] Fei Yang,et al. Interobserver Agreement Between Pathologists Assessing Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Using Methodology Proposed by the International TILs Working Group , 2016, Annals of Surgical Oncology.
[53] N. Linder,et al. Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples , 2016, Journal of pathology informatics.
[54] Carlo C. Maley,et al. An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer , 2015, Breast Cancer Research.
[55] Sidra Nawaz,et al. Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer , 2015, Modern Pathology.
[56] N. Brockton,et al. Fractal analysis of nuclear histology integrates tumor and stromal features into a single prognostic factor of the oral cancer microenvironment , 2015, BMC Cancer.
[57] S. H. van der Burg,et al. Consensus nomenclature for CD 8 T cell phenotypes in cancer , 2015 .
[58] Yinyin Yuan,et al. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer , 2015, Journal of The Royal Society Interface.
[59] T. Nielsen,et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.
[60] Chichung Wang,et al. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. , 2014, Methods.
[61] R. Emerson,et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance , 2014, Nature.
[62] D. Rimm,et al. Multiplexed Quantitative Analysis of CD3, CD8, and CD20 Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer , 2014, Clinical Cancer Research.
[63] F. Markowetz,et al. Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling , 2012, Science Translational Medicine.
[64] 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.
[65] Edzer J. Pebesma,et al. Applied Spatial Data Analysis with R - Second Edition , 2008, Use R!.
[66] Z. Trajanoski,et al. Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome , 2006, Science.
[67] B O Palsson,et al. Effective intercellular communication distances are determined by the relative time constants for cyto/chemokine secretion and diffusion. , 1997, Proceedings of the National Academy of Sciences of the United States of America.
[68] E. Elgabry,et al. Long-term Clinical Outcomes and Biomarker Analyses of Atezolizumab Therapy for Patients With Metastatic Triple-Negative Breast Cancer: A Phase 1 Study , 2019, JAMA oncology.
[69] 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.
[70] Carsten Denkert,et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.