TY - JOUR
T1 - Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
AU - Amgad, Mohamed
AU - Stovgaard, Elisabeth Specht
AU - Balslev, Eva
AU - Thagaard, Jeppe
AU - Chen, Weijie
AU - Dudgeon, Sarah
AU - Sharma, Ashish
AU - Kerner, Jennifer K.
AU - Denkert, Carsten
AU - Yuan, Yinyin
AU - AbdulJabbar, Khalid
AU - Wienert, Stephan
AU - Savas, Peter
AU - Voorwerk, Leonie
AU - Beck, Andrew H.
AU - Madabhushi, Anant
AU - Hartman, Johan
AU - Sebastian, Manu M.
AU - Horlings, Hugo M.
AU - Hudecek, Jan
AU - Ciompi, Francesco
AU - Moore, David A.
AU - Singh, Rajendra
AU - Roblin, Elvire
AU - Balancin, Marcelo Luiz
AU - Mathieu, Marie-Christine
AU - Lennerz, Jochen K.
AU - Kirtani, Pawan
AU - Chen, I-Chun
AU - Braybrooke, Jeremy P.
AU - Pruneri, Giancarlo
AU - Demaria, Sandra
AU - Adams, Sylvia
AU - Schnitt, Stuart J.
AU - Lakhani, Sunil R.
AU - Rojo, Federico
AU - Comerma, Laura
AU - Badve, Sunil S.
AU - Khojasteh, Mehrnoush
AU - Symmans, W. Fraser
AU - Sotiriou, Christos
AU - Gonzalez-Ericsson, Paula
AU - Pogue-Geile, Katherine L.
AU - Kim, Rim S.
AU - Rimm, David L.
AU - Viale, Giuseppe
AU - Hewitt, Stephen M.
AU - Bartlett, John M. S.
AU - Penault-Llorca, Frederique
AU - Kooreman, Loes F. S.
AU - International Immuno-Oncology Biomarker Working Group
AU - Cooper, Lee A. D.
AU - Salgado, Roberto
N1 - Funding Information:
L.A.D.C. is supported in part by the National Institutes of Health National Cancer Institute (NCI) grants U01CA220401 and U24CA19436201. R.S. is supported by the Breast Cancer Research Foundation (BCRF), grant No. 17-194. J.S. is supported in part by NCI grants UG3CA225021 and U24CA215109. A.M. is supported in part by NCI grants 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01, National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University. S.G. is supported by Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance. T.O.N. receives funding support from the Canadian Cancer Society. M.M.S. is supported by P30 CA16672 DHHS/NCI Cancer Center Support Grant (CCSG). A.S. is supported in part by NCI grants 1UG3CA225021, 1U24CA215109, and Leidos 14 × 138. This work includes contributions from, and was reviewed by, individuals at the F.D.A. This work has been approved for publication by the agency, but it does not necessarily reflect official agency policy. Certain commercial materials and equipment are identified in order to adequately specify experimental procedures. In no case does such identification imply recommendation or endorsement by the FDA, nor does it imply that the items identified are necessarily the best available for the purpose. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the US Department of Veterans Affairs, the Department of Defense, the United States Government, or other governments or entities. The following is a list of current members of the International Immuno-Oncology Working Group (TILs Working Group). Members contributed to the manuscript through discussions, including at the yearly TIL-WG meeting, and have reviewed and provided input on the manuscript. The authors alone are responsible for the views expressed in the work of the TILs Working Group and they do not necessarily represent the decisions, policy or views of their employer.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/5/12
Y1 - 2020/5/12
N2 - Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.
AB - Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.
KW - BREAST-CANCER
KW - STANDARDIZED METHOD
KW - DIGITAL PATHOLOGY
KW - QUALITY-CONTROL
KW - IMAGE-ANALYSIS
KW - SOLID TUMORS
KW - T-CELLS
KW - IN-SITU
KW - TILS
KW - CLASSIFICATION
U2 - 10.1038/s41523-020-0154-2
DO - 10.1038/s41523-020-0154-2
M3 - (Systematic) Review article
C2 - 32411818
SN - 2374-4677
VL - 6
JO - npj Breast Cancer
JF - npj Breast Cancer
IS - 1
M1 - 16
ER -