Monoallelic expression characterizes a distinct molecular and clinical group of breast tumors.

In diploid cells, allelic imbalance occurs when gene alleles are expressed at different levels. To investigate the allelic imbalance landscape in tumor samples, we developed Interval-Based Allelic Imbalance Detection (IB-Aid), a quantitative framework that uses interval arithmetic to robustly distinguish monoallelic from biallelic gene expression by computing confidence intervals that account for sequencing measurement uncertainty. We applied this approach to The Cancer Genome Atlas Breast Invasive Carcinoma cohort and, through unsupervised gene enrichment analyses, identified a group of patients with a distinct monoallelic gene expression signature. These tumors were not previously classified into any established molecular subtype and exhibited mixed immunohistochemical (IHC) profiles. Notably, this group was enriched for Black/African American patients. Clinically, tumors in this subgroup were associated with poor overall survival, with outcomes comparable to the aggressive basal subtype. Together, these findings suggest a link between allelic imbalance and breast cancer development. Our results further implicate genetic and epigenetic mechanisms driving allelic imbalance as potential biomarkers for prognosis and the design of targeted treatment strategies.