Transcriptome predictors of second breast events in ductal carcinoma in situ: a case‒control study including Black and White women

BACKGROUND: Black women diagnosed with ductal carcinoma in situ (DCIS) experience disproportionately higher risk of second breast cancer events (SBEs), including both ipsilateral and contralateral recurrences, yet remain underrepresented in molecular biomarker studies compared to non-Hispanic White women. METHODS: We performed RNA sequencing on 200 archival DCIS samples from the Resource of Archival Breast Tissue cohort, including 33% self-reported Black women. We correlated transcriptomic and clinical data to identify predictors of SBEs. Cases (n = 100) who developed SBEs between 1999 and 2019 were matched 1:1 to controls (n = 100) on age, race, and margin status, who remained event-free during a comparable follow-up period in this observational study. RNA-seq was conducted using the Illumina NovaSeq 6000 platform, which yielded high-quality transcriptomic profiles (13,460 protein-coding genes) from 141 out of 200 samples. Differential gene expression and pathway analyses were used to identify molecular predictors of SBE risk. RESULTS: Principal component analysis demonstrated that transcriptomic variance was associated with race and follow-up duration. Four genes, CHGB, RBM20, SYP, and SYNJ2BP, were significantly associated with ipsilateral SBEs (FDR P value < 0.05). Interferon-alpha signaling was the most significantly enriched pathway in DCIS cases compared with controls. Gene signatures varied by recurrence site (e.g., contralateral SBEs) and covariates, including self-reported race. Our findings demonstrate the feasibility and value of RNA-seq from diverse archival DCIS specimens. CONCLUSION: We identified novel transcriptomic alterations associated with second breast cancer events in DCIS. Our results support the inclusion of racially diverse biospecimens in biomarker discovery. These findings warrant validation in independent cohorts. These molecular insights have the potential to refine risk prediction tools and inform equitable strategies for DCIS management.