Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) 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 in TILs 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 TNBC

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: 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.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: 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

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

Quantitative measurement of epidermal growth factor receptor is a negative predictive factor for tamoxifen response in hormone receptor - Positive premenopausal breast cancer

Purpose Although there is evidence for interaction between epidermal growth factor receptor ( EGFR) and estrogen receptor ( ER), it is still not clear how this affects response to endocrine therapies like tamoxifen. Here we assess the relationship between EGFR expression and tamoxifen response, with a new quantitative technology. Patients and Methods A tissue microarray was constructed from breast cancer from a cohort of 564 patients enrolled in a randomized clinical trial for adjuvant tamoxifen treatment in early breast cancer, with a median follow-up of 14 years. EGFR expression was measured using automated quantitative analysis, a fluorescence-based method for quantitative analysis of in situ protein expression. Results In ER-positive patients, tamoxifen-treated patients with low EGFR expression ( n = 113) showed a significant effect by 2 years of adjuvant tamoxifen ( P = .01), in contrast to no treatment effect in the EGFR-high group ( n = 73, P = .69). The untreated group showed 49% v 57% 10-year recurrence-free survival for EGFR low versus high ( P = .466) in the corresponding group of ER-positive patients. A significant beneficial effect of tamoxifen treatment was seen in the EGFR-low group ( hazard ratio [ HR] = 0.43 ( 95% CI, 0.22 to 0.84; P = .013) in contrast to no effect in the EGFR-high group ( HR = 1.14; 95% CI, 0.59 to 2.22; P = .7) by using a Cox model. Conclusion This study provides clinical evidence that confirms the basic work that has shown high EGFR can indicate resistance to tamoxifen. It suggests that careful measurement of EGFR protein expression might define a subset of low-stage patients that could benefit from an alternative therapy

The ERα coactivator, HER4/4ICD, regulates progesterone receptor expression in normal and malignant breast epithelium

<p>Abstract</p> <p>The HER4 intracellular domain (4ICD) is a potent estrogen receptor (ERα) coactivator with activities in breast cancer and the developing mammary gland that appear to overlap with progesterone receptor (PgR). In fact, 4ICD has recently emerged as an important regulator and predictor of tamoxifen response, a role previously thought to be fulfilled by PgR. Here we investigated the possibility that the 4ICD coactivator regulates PgR expression thereby providing a mechanistic explanation for their partially overlapping activities in breast cancer. We show that 4ICD is both sufficient and necessary to potentiate estrogen stimulation of gene expression. Suppression of HER4/4ICD expression in the MCF-7 breast tumor cell line completely eliminated estrogen stimulated expression of PgR. In addition, the HER4/4ICD negative MCF-7 variant, TamR, failed to express PgR in response to estrogen. Reintroduction of wild-type HER4 but not the γ-secretase processing mutant HER4V673I into the TamR cell line restored PgR expression indicating that 4ICD is an essential PgR coactivator in breast tumor cells. These results were substantiated <it>in vivo </it>using two different physiologically relevant experimental systems. In the mouse mammary gland estrogen regulates expression of PgR-A whereas expression of PgR-B is estrogen independent. Consistent with a role for 4ICD in estrogen regulated PgR expression <it>in vivo</it>, PgR-A, but not PgR-B, expression was abolished in HER4-null mouse mammary glands during pregnancy. Coexpression of PgR and 4ICD is also commonly observed in ERα positive breast carcinomas. Using quantitative AQUA IHC technology we found that 4ICD potentiated PgR expression in primary breast tumors and the highest levels of PgR expression required coexpression of ERα and the 4ICD coactivator. In summary, our results provide compelling evidence that 4ICD is a physiologically important ERα coactivator and 4ICD cooperates with ERα to potentiate PgR expression in the normal and malignant breast. We propose that direct coupling of these signaling pathways may have important implications for mammary development, breast carcinogenesis, and patient response to endocrine therapy.</p