Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer.

This study addresses the limited mechanistic understanding behind medical imaging for tumor microenvironment (TME) assessment. We developed a novel framework that analyzes tumor imaging heterogeneity index (TIHI)-correlated genes to uncover underlying TME biology and therapeutic vulnerabilities. DCE-MRI and mRNA data from 987 high-risk breast cancer patients in the I-SPY2 trial, together with mRNA data from 508 patients in GSE25066, were analyzed. TIHI-associated genes were identified via Pearson correlation, clustered via weighted gene co-expression network analysis (WGCNA), and subgroups were defined via non-negative matrix factorization (NMF). The clinical relevance of the image-to-gene comprehensive (I2G-C) subtype defined by subgroups was assessed using logistic regression and Cox analysis. I2G-C comprised four clusters with distinct immune and replication/repair functions. It further stratified receptor, PAM50, and RPS5 subtypes. The "immune+/replication+" was more likely to achieve pathological complete response (pCR) (OR = 2.587, p < 0.001), while the "immune-/replication-" was the least likely to achieve pCR (OR = 0.402, p < 0.001). The "immune+/replication+" showed sensitivity to pembrolizumab (OR = 10.192, p < 0.001) and veliparib/carboplatin (OR = 5.184, p = 0.006), while "immune-/replication-" responded poorly to pembrolizumab (OR = 0.086, p < 0.001). Additionally, "immune+/replication-" had the best distant recurrence-free survival (DRFS), whereas "immune-/replication+" had the worst (log-rank p = 6 × 10-4, HR = 5.45). By linking imaging heterogeneity directly to molecular subtypes and therapeutic response, this framework provides a robust, non-invasive surrogate for genomic profiling and a strategic tool for personalized neoadjuvant therapy selection.

Duke Scholars

Author Jing Cai Radiation Oncology