  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.
