Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer

TL;DR

This study uses explainable AI to identify key immune cells in the tumor microenvironment that are linked to better prognosis in breast cancer patients, providing new insights for treatment strategies.

Contribution

The paper introduces an XAI model that uncovers critical TME features, especially B and CD4+ T cells, associated with improved survival in breast cancer, with specific threshold insights.

Findings

B-cell fraction > 0.018 predicts 100% 5-year survival in NTNBC

CD4+ T cells and B cells are key prognostic features in TME

XAI models reveal critical thresholds for immune cell fractions

Abstract

We investigated the data-driven relationship between features in the tumor microenvironment (TME) and the overall and 5-year survival in triple-negative breast cancer (TNBC) and non-TNBC (NTNBC) patients by using Explainable Artificial Intelligence (XAI) models. We used clinical information from patients with invasive breast carcinoma from The Cancer Genome Atlas and from two studies from the cbioPortal, the PanCanAtlas project and the GDAC Firehose study. In this study, we used a normalized RNA sequencing data-driven cohort from 1,015 breast cancer patients, alive or deceased, from the UCSC Xena data set and performed integrated deconvolution with the EPIC method to estimate the percentage of seven different immune and stromal cells from RNA sequencing data. Novel insights derived from our XAI model showed that CD4+ T cells and B cells are more critical than other TME features for enhanced prognosis for both TNBC and NTNBC patients. Our XAI model revealed the critical inflection points (i.e., threshold fractions) of CD4+ T cells and B cells above or below which 5-year survival rates improve. Subsequently, we ascertained the conditional probabilities of $\geq$ 5-year survival in both TNBC and NTNBC patients under specific conditions inferred from the inflection points. In particular, the XAI models revealed that a B-cell fraction exceeding 0.018 in the TME could ensure 100% 5-year survival for NTNBC patients. The findings from this research could lead to more accurate clinical predictions and enhanced immunotherapies and to the design of innovative strategies to reprogram the TME of breast cancer patients.