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

Longitudinal Assessment of Tumor-Infiltrating Lymphocytes in Primary Breast Cancer Following Neoadjuvant Radiation Therapy

PURPOSE

Tumor-infiltrating lymphocytes (TILs) have prognostic significance in several cancers, including breast cancer. Despite interest in combining radiation therapy with immunotherapy, little is known about the effect of radiation therapy itself on the tumor-immune microenvironment, including TILs. Here, we interrogated longitudinal dynamics of TILs and systemic lymphocytes in patient samples taken before, during, and after neoadjuvant radiation therapy (NART) from PRADA and Neo-RT breast clinical trials.

METHODS AND MATERIALS

We manually scored stromal TILs (sTILs) from longitudinal tumor samples using standardized guidelines as well as deep learning-based scores at cell-level (cTIL) and cell- and tissue-level combination analyses (SuperTIL). In parallel, we interrogated absolute lymphocyte counts from routine blood tests at corresponding time points during treatment. Exploratory analyses studied the relationship between TILs and pathologic complete response (pCR) and long-term outcomes.

RESULTS

Patients receiving NART experienced a significant and uniform decrease in sTILs that did not recover at the time of surgery (P < .0001). This lymphodepletive effect was also mirrored in peripheral blood. Our SuperTIL deep learning score showed good concordance with manual sTILs and importantly performed comparably to manual scores in predicting pCR from diagnostic biopsies. The analysis suggested an association between baseline sTILs and pCR, as well as sTILs at surgery and relapse, in patients receiving NART.

CONCLUSIONS

This study provides novel insights into TIL dynamics in the context of NART in breast cancer and demonstrates the potential for artificial intelligence to assist routine pathology. We have identified trends that warrant further interrogation and have a bearing on future radioimmunotherapy trials.

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

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. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology

Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.

Immunogenomic landscape analyses of immune molecule signature-based risk panel for patients with triple-negative breast cancer

Triple-negative breast cancer (TNBC) presented as high heterogeneous immunogenicity that lacks useful clinical signatures to risk-stratify immune-benefit subtypes. We hypothesized that molecular-based phenotypic characterization of TNBC tumors and their immunity may overcome these challenges. We enrolled 1,145 patients with TNBC for analysis. Through combining algorithm integration analysis and TNBC datasets, a tumor immune risk score (TIRS) panel consisting of 8 potential biomarkers was identified. The TIRS panel represented excellent effectiveness as an independent predictor. High- and low risk stratification of patients was further achieved by TIRS, and significant survival and immune-infiltration pattern differences were found in each cohort, both at the transcriptome and protein levels. Non-negative matrix factorization clustering further identified four different tumor immune microenvironment types (TIMTs), among which TIMT-II was associated with the best prognosis and immune status, whereas TIMT-IV had the opposite effect, TIMT-III was associated with highly unstable genomes, and TIMT-I displayed stem-cell-related characteristics along with high stromal scores and may have extensive enrichment of tumor-associated fibroblasts and vascular cells. In conclusion, our TIRS panel could serve as a robust prognostic signature and provide therapeutic benefits for immunotherapy. Additionally, coordinating four TIMTs may be helpful for clinical decision-making in TNBC patients.

Incorporation of TILs in daily breast cancer care: how much evidence can we bear?

One of the most important developments in the breast cancer field has been an improved understanding of prognostic and predictive biomarkers, of which TILs are increasingly gaining importance. The evaluation of TILs by light microscopy on a H&E-stained section is workable in a daily practice setting. Reproducibility of reporting TILs is good, but heterogeneity is a cause of variation. TILs provide clinicians with important prognostic information for patients with TNBC, as early-stage TNBC with high TILs have > 98% 5-year survival and TILs predict benefit to immunotherapy. Importantly, while TILs do not have level of evidence IA, TILs should be used as a prognostic factor with caution and with other accepted prognostic variables, such as tumour size and lymph node status, to inform clinicians and patients on their treatment options. A framework on how to use the TILs in daily practice is proposed, including a co-assessment with PD-L1 for its predictive role to immunotherapy.

Practical Tools and Guidelines for Young Oncologists From Resource-Limited Settings to Publish Excellence and Advance Their Career

Cancer research is evolving worldwide. However, publishing high-quality academic literature in oncology remains challenging for authors in the developing world. Young oncologists in low- and middle-income countries experience several barriers including lack of funding and research facilities, as well as inadequate training. Publication best practices, science integrity, and ethics are required to improve oncology research quality and therefore, improve patients' care in these countries. To achieve this goal, we propose some basic principles and tools that may help young oncologists especially in developing countries overcome these issues and boost their academic careers.

The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72-0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.

Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer

Purpose

To assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer.

Methods

In total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10^th, 25^th, 50^th, 75^th, and 90^th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests.

Results

A statistically significant difference existed in the 10^th and 25^th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10^th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029).

Conclusions

Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.

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

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.