TY - JOUR

T1 - Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer

T2 - a report of the international immuno-oncology biomarker working group

AU - Thagaard, Jeppe

AU - Broeckx, Glenn

AU - Page, David B.

AU - Jahangir, Chowdhury Arif

AU - Verbandt, Sara

AU - Kos, Zuzana

AU - Gupta, Rajarsi

AU - Khiroya, Reena

AU - Abduljabbar, Khalid

AU - Acosta Haab, Gabriela

AU - Acs, Balazs

AU - Akturk, Guray

AU - Almeida, Jonas S.

AU - Alvarado-Cabrero, Isabel

AU - Amgad, Mohamed

AU - Azmoudeh-Ardalan, Farid

AU - Badve, Sunil

AU - Baharun, Nurkhairul Bariyah

AU - Balslev, Eva

AU - Bellolio, Enrique R.

AU - Bheemaraju, Vydehi

AU - Blenman, Kim R.M.

AU - Botinelly Mendonça Fujimoto, Luciana

AU - Bouchmaa, Najat

AU - Burgues, Octavio

AU - Chardas, Alexandros

AU - Chon U Cheang, Maggie

AU - Ciompi, Francesco

AU - Cooper, Lee A.D.

AU - Coosemans, An

AU - Corredor, Germán

AU - Dahl, Anders B.

AU - Dantas Portela, Flavio Luis

AU - Deman, Frederik

AU - Demaria, Sandra

AU - Doré Hansen, Johan

AU - Dudgeon, Sarah N.

AU - Ebstrup, Thomas

AU - Elghazawy, Mahmoud

AU - Fernandez-Martín, Claudio

AU - Fox, Stephen B.

AU - Gallagher, William M.

AU - Giltnane, Jennifer M.

AU - Gnjatic, Sacha

AU - Gonzalez-Ericsson, Paula I.

AU - Grigoriadis, Anita

AU - Halama, Niels

AU - Hanna, Matthew G.

AU - Harbhajanka, Aparna

AU - Hart, Steven N.

AU - Hartman, Johan

AU - Hauberg, Søren

AU - Hewitt, Stephen

AU - Hida, Akira I.

AU - Horlings, Hugo M.

AU - Husain, Zaheed

AU - Hytopoulos, Evangelos

AU - Irshad, Sheeba

AU - Janssen, Emiel A.M.

AU - Kahila, Mohamed

AU - Kataoka, Tatsuki R.

AU - Kawaguchi, Kosuke

AU - Kharidehal, Durga

AU - Khramtsov, Andrey I.

AU - Kiraz, Umay

AU - Kirtani, Pawan

AU - Kodach, Liudmila L.

AU - Korski, Konstanty

AU - Kovács, Anikó

AU - Laenkholm, Anne Vibeke

AU - Lang-Schwarz, Corinna

AU - Larsimont, Denis

AU - Lennerz, Jochen K.

AU - Lerousseau, Marvin

AU - Li, Xiaoxian

AU - Ly, Amy

AU - Madabhushi, Anant

AU - Maley, Sai K.

AU - Manur Narasimhamurthy, Vidya

AU - Marks, Douglas K.

AU - McDonald, Elizabeth S.

AU - Mehrotra, Ravi

AU - Michiels, Stefan

AU - Minhas, Fayyaz ul Amir Afsar

AU - Mittal, Shachi

AU - Moore, David A.

AU - Mushtaq, Shamim

AU - Nighat, Hussain

AU - Papathomas, Thomas

AU - Penault-Llorca, Frederique

AU - Perera, Rashindrie D.

AU - Pinard, Christopher J.

AU - Pinto-Cardenas, Juan Carlos

AU - Pruneri, Giancarlo

AU - Pusztai, Lajos

AU - Rahman, Arman

AU - Rajpoot, Nasir Mahmood

AU - Rapoport, Bernardo Leon

AU - Rau, Tilman T.

AU - Reis-Filho, Jorge S.

AU - Ribeiro, Joana M.

AU - Rimm, David

AU - Roslind, Anne

AU - Vincent-Salomon, Anne

AU - Salto-Tellez, Manuel

AU - Saltz, Joel

AU - Sayed, Shahin

AU - Scott, Ely

AU - Siziopikou, Kalliopi P.

AU - Sotiriou, Christos

AU - Stenzinger, Albrecht

AU - Sughayer, Maher A.

AU - Sur, Daniel

AU - Fineberg, Susan

AU - Symmans, Fraser

AU - Tanaka, Sunao

AU - Taxter, Timothy

AU - Tejpar, Sabine

AU - Teuwen, Jonas

AU - Thompson, E. Aubrey

AU - Tramm, Trine

AU - Tran, William T.

AU - van der Laak, Jeroen

AU - van Diest, Paul J.

AU - Verghese, Gregory E.

AU - Viale, Giuseppe

AU - Vieth, Michael

AU - Wahab, Noorul

AU - Walter, Thomas

AU - Waumans, Yannick

AU - Wen, Hannah Y.

AU - Yang, Wentao

AU - Yuan, Yinyin

AU - Zin, Reena Md

AU - Adams, Sylvia

AU - Bartlett, John

AU - Loibl, Sibylle

AU - Denkert, Carsten

AU - Savas, Peter

AU - Loi, Sherene

AU - Salgado, Roberto

AU - Specht Stovgaard, Elisabeth

N1 - Funding Information: The authors would like to thank Jeannette Parrodi, PA assistant to Professor Sherene Loi, for het extensive help and administrative support for the International Immuno‐Oncology Biomarker Working Group (TIL working group). Without her, this working group would not even exist. Furthermore, the authors make the following acknowledgments regarding support and funding. GB: Funded by Gilead Breast Cancer Research Grant 2023. SV: Supported by Interne Fondsen KU Leuven/Internal Funds KU Leuven. BA: supported by the Swedish Society for Medical Research (Svenska Sällskapet för Medicinsk Forskning) postdoctoral grant, Swedish Breast Cancer Association (Bröstcancerförbundet) Research grant 2021. GC: Peer Reviewed Cancer Research Program (Award W81XWH‐21‐1‐0160) from the US Department of Defense and the Mayo Clinic Breast Cancer SPORE grant P50 CA116201 from the National Institutes of Health (NIH). CF‐M: Funded by the Horizon 2020 European Union Research and Innovation Programme under the Marie Sklodowska Curie Grant agreement No. 860627 (CLARIFY Project). SBF: NHMRC GNT1193630. WMG: Support by the Higher Education Authority, Department of Further and Higher Education, Research, Innovation and Science, and the Shared Island Fund [AICRIstart: A Foundation Stone for the All‐Island Cancer Research Institute (AICRI): Building Critical Mass in Precision Cancer Medicine, https://www.aicri.org/aicristart ]: Irish Cancer Society (Collaborative Cancer Research Centre BREAST‐PREDICT; CCRC13GAL; https://www.breastpredict.com ), the Science Foundation Ireland Investigator Programme (OPTi‐PREDICT; 15/IA/3104), the Science Foundation Ireland Strategic Partnership Programme (Precision Oncology Ireland; 18/SPP/3522; https://www.precisiononcology.ie ). SG: Partially supported by NIH grants CA224319, DK124165, CA263705, and CA196521. AG: Supported by Breast Cancer Now (and their legacy charity Breakthrough Breast Cancer) and Cancer Research UK (CRUK/07/012, KCL‐BCN‐Q3). TRK: Japan Society for the Promotion of Science (JSPS) KAKENHI (21K06909). UK: Funded by Horizon 2020 European Union Research and Innovation Programme under the Marie Sklodowska Curie Grant agreement 860627 (CLARIFY Project). JKL: This work is in part supported by NIH R37 CA225655 to JKL. AM: Research reported in this publication was supported by the National Cancer Institute under award numbers R01CA268287A1, U01CA269181, R01CA26820701A1, R01CA249992‐01A1, R01CA202752‐01A1, R01CA208236‐01A1, R01CA216579‐01A1, R01CA220581‐01A1, R01CA257612‐01A1, 1U01CA239055‐01, 1U01CA248226‐01, and 1U54CA254566‐01, National Heart, Lung and Blood Institute 1R01HL15127701A1, R01HL15807101A1, National Institute of Biomedical Imaging and Bioengineering 1R43EB028736‐01, VA Merit Review Award IBX004121A from the US Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH‐19‐1‐0668), the Prostate Cancer Research Program (W81XWH‐20‐1‐0851), the Lung Cancer Research Program (W81XWH‐18‐1‐0440, W81XWH‐20‐1‐0595), the Peer Reviewed Cancer Research Program (W81XWH‐18‐1‐0404, W81XWH‐21‐1‐0345, W81XWH‐21‐1‐0160), the Kidney Precision Medicine Project (KPMP) Glue Grant, and sponsored research agreements from Bristol Myers‐Squibb, Boehringer‐Ingelheim, Eli‐Lilly, and Astrazeneca. SKM: Kay Pogue‐Geile, Director of Molecular Profiling at NSABP for her constant support and encouragement, Roberto Salgado, for initiating me into the wonderful subject of Immuno‐Oncology and its possibilities. FuAAM: Funding from EPSRC EP/W02909X/1 and PathLAKE consortium. FP‐L: Research grants from Fondation ARC, La Ligue contre le Cancer. RDP: The Melbourne Research Scholarship and a scholarship from the Peter MacCallum Cancer Centre. JSR‐F: Funded in part by the Breast Cancer Research Foundation, by a Susan G. Komen Leadership grant, and by the NIH/NCI grant P50 CA247749 01. JS: NIH/NCI grants UH3CA225021 and U24CA215109. ST: Supported by Interne Fondsen KU Leuven/Internal Funds KU Leuven. JT: Supported by institutional grants of the Dutch Cancer Society and the Dutch Ministry of Health, Welfare and Sport. EAT: Breast Cancer Research Foundation grant 22‐161. GEV: Supported by Breast Cancer Now (and their legacy charity Breakthrough Breast Cancer) and Cancer Research UK (CRUK/07/012, KCL‐BCN‐Q3). TW: Support by the French government under management of Agence Nationale de la Recherche as part of the Investissements d'avenir’ program, reference ANR‐19‐P3IA‐0001 (PRAIRIE 3IA Institute), and by Q‐Life (ANR‐17‐CONV‐0005). HYW: Funded in part by the NIH/NCI grant P50 CA247749 01. YY: Funding from Cancer Research UK Career Establishment Award (CRUK C45982/A21808). PS: Funding support from the National Health and Medical Research Council, Australia. SL: Supported by the National Breast Cancer Foundation of Australia (NBCF) (APP ID: EC‐17‐001), the Breast Cancer Research Foundation, New York [BCRF (APP ID: BCRF‐21‐102)], and a National Health and Medical Council of Australia (NHMRC) Investigator Grant (APP ID: 1162318). RS: Supported by the Breast Cancer Research Foundation (BCRF, grant 17‐194). Publisher Copyright: © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

PY - 2023

Y1 - 2023

N2 - The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

AB - The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

KW - deep learning

KW - digital pathology

KW - guidelines

KW - image analysis

KW - machine learning

KW - pitfalls

KW - prognostic biomarker

KW - triple-negative breast cancer

KW - tumor-infiltrating lymphocytes

U2 - 10.1002/path.6155

DO - 10.1002/path.6155

M3 - Review

C2 - 37608772

AN - SCOPUS:85167995389

VL - 260

SP - 498

EP - 513

JO - Journal of Pathology

JF - Journal of Pathology

SN - 0022-3417

IS - 5

ER -