Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
Mohamed Amgad, Elisabeth Specht Stovgaard, Eva Balslev, Jeppe Thagaard, Weijie Chen, Sarah Dudgeon, Ashish Sharma, Jennifer K. Kerner, Carsten Denkert, Yinyin Yuan, Khalid AbdulJabbar, Stephan Wienert, Peter Savas, Leonie Voorwerk, Andrew H. Beck, Anant Madabhushi, Johan Hartman, Manu Sebastian, Hugo M. Horlings, Jan Hudeček, Francesco Ciompi, David Moore, Rajendra Singh, Elvire Roblin, Marcelo Luiz Balancin, Marie‐Christine Mathieu, Jochen K. Lennerz, Pawan Kirtani, I‐Chun Chen, Jeremy Braybrooke, Giancarlo Pruneri, Sandra Demaria, Sylvia Adams, Stuart J. Schnitt, Sunil R. Lakhani, Federico Rojo, Laura Comerma, Sunil Badve, Mehrnoush Khojasteh, W. Fraser Symmans, Christos Sotiriou, Paula I. González-Ericsson, Katherine L. Pogue–Geile, Rim S. Kim, David L. Rimm, Giuseppe Viale, Stephen M. Hewitt, John M. S. Bartlett, Frédérique Penault–Llorca, Shom Goel, Huang‐Chun Lien, Sibylle Loibl, Zuzana Kos, Sherene Loi, Matthew G. Hanna, Stefan Michiels, Marleen Kok, Torsten O. Nielsen, Alexander J. Lazar, Zsuzsanna Bagó-Horváth, Loes Kooreman, Jeroen van der Laak, Joel Saltz, Brandon D. Gallas, Uday Kurkure, Michael Barnes, Roberto Salgado, Lee Cooper, International Immuno-Oncology Biomarker Working Group, Aini Hyytiäinen, Akira I. Hida, Alastair M. Thompson, Alexis Lefevre, Allen M. Gown, Amy Lo, Anna Sapino, André L. Moreira, Andrea L. Richardson, Andrea Vingiani, Andrew M. Bellizzi, Andrew Tutt, Ángel Guerrero‐Zotano, Anita Grigoriadis, Anna Ehinger, Ana C. Garrido-Castro, Anne Vincent-Salomon, Anne‐Vibeke Lænkholm, Ashley Cimino‐Mathews, Ashok Srinivasan, Balázs Ács, Baljit Singh, Benjamin C. Calhoun, Benjamin Haibe-Kans, Benjamin Solomon, Bibhusal Thapa, Brad H. Nelson, Carlos Castaneda, Carmen Ballesteroes-Merino, Carmen Criscitiello, Carolien Boeckx
Abstract
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.