Unraveling breast cancer response to neoadjuvant chemotherapy through integrated genomic, transcriptomic, and circulating tumor DNA analysis.

Introduction: Neoadjuvant chemotherapy (NAC) is a standard treatment for breast cancer (BC) to shrink tumors and facilitate surgery. However, the molecular underpinnings of response to NAC and prognosis have not been well characterized.

Methods: We enrolled 73 stage II/III BC patients who received NAC followed by surgery. Tumor tissue samples were available from 36 patients at baseline and 38 at the time of surgery. Plasma circulating tumor DNA (ctDNA) was collected at three time points: before NAC (n = 63), during NAC (n = 42), and after NAC (n = 40). Comprehensive genomic, transcriptomic, and ctDNA analyses were performed to identify biomarkers associated with pathological complete response (pCR) and survival outcomes.

Results: Nine baseline mutations, including DNHD1 and PLEC, along with HIPPO pathway alterations, were associated with pCR. Responsive tumors exhibited immune activation and downregulated PI3K-Akt and AGE-RAGE pathways, while non-pCR tumors showed reduced cytokine and immune receptor activity. Undetectable ctDNA during and after NAC was predictive of treatment efficacy and correlated with improved survival. Baseline mutations in USH2A were associated with shorter disease-free survival (hazard ratio: 11.9; 95% confidence interval: 2.8-50.8; P < 0.001), with a consistent trend observed for overall survival. Elevated NHSL1 expression in baseline tumors indicated an initial treatment response but was later associated with tumor relapse and poor overall survival (P = 0.026 and P = 0.023, respectively), findings that were validated in an independent clinical cohort (N = 30) through immunohistochemistry staining.

Conclusion: Our comprehensive multi-omics analysis identified promising biomarkers predictive of treatment response and survival in BC patients receiving NAC followed by surgery. These findings underscore the importance of early tumor assessment for improved patient stratification and prognostication.