National Cancer Institute 5R01CA229409-07

Optimizing prognostic and predictive algorithms using residual disease (RD) genomic biomarkers in HER2+ and TNBC

This award funds research to develop prognostic and predictive algorithms using genomic biomarkers in HER2+ and triple negative breast cancer patients with residual disease after neoadjuvant therapy, aiming to improve treatment decisions and reduce overtreatment.

Description

We propose to comprehensively examine the role of tumor and microenvironmental factors determining outcome in patients with residual disease after neoadjuvant therapy in HER2-positive and triple negative nonmetastatic breast cancer. This will be achieved by integrating RNA sequencing and clinical features of over 1500 patients with HER2+ breast cancer, and over 1200 patients with triple negative breast cancer from multiple neoadjuvant clinical trials. The aims include determining if pre-treatment or residual tumor biology is more prognostic, developing prognostic algorithms for outcomes in patients with residual disease, identifying patients with good prognosis who may not need additional postoperative adjuvant therapy, and examining B and T cell interactions with tumor cells using spatial transcriptomics. This research aims to improve prognostication and tailor adjuvant therapy, reducing overtreatment and associated toxicities.

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