Abstract 476: Predicting response to treatment in early breast cancer using dynamic integrative multi-omic profiling

Abstract

Abstract Aim: We recently published the first machine learning framework that integrates multi-omic data derived from the pre-therapy breast tumor ecosystem to accurately predict response to neoadjuvant systemic therapy (Sammut et al, Nature 2021). We aimed to extend this framework to incorporate serially acquired multi-omic data to further improve response predictions and model tumor biology as it is perturbed by treatment. Methods: Breast tumor core biopsies were acquired at diagnosis from 168 women that went on to receive pre-operative chemotherapy (or chemotherapy plus anti-HER2 targeted therapy). Serial tumor core biopsies were obtained midway (n=78) and on completion (n=69) of neoadjuvant systemic therapy. Response was assessed at surgery using the Residual Cancer Burden score. Core biopsies were molecularly profiled by shallow whole genome, exome and RNA sequencing and their histological architecture characterized using digital pathology. Baseline clinical, molecular, and digital pathology imaging features associated with response were identified and their dynamics modelled throughout therapy. Results: At baseline, a total of 34 features derived from multi-omic data were associated with response to neoadjuvant therapy. These included: tumor mutation and neoantigen burden, subclonal diversity, HRD and chromosomal instability. A suppressed immune response, typified by the presence of T-cell dysfunction and immune exclusion, was associated with extensive chemoresistance. The changes in abundance of these features across the serially sampled on-therapy tumors were then mapped to response outcomes. An early increase in adaptive and innate immune infiltration and activation was associated with a linear decrease in expressed neoantigens, tumor proliferation and loss of subclonal mutational and copy number diversity, indicating early response to therapy. Conversely, a stable tumor and microenvironment transcriptional landscape throughout treatment, corresponding to a minimal change in tumor clonal architecture and microenvironment composition, was associated with a poor response to therapy. Notably, tumors with LOH HLA failed to engage a productive immune response during treatment and this was associated with resistance. A dynamic framework that models the change of these on-therapy features and extends the functionality of the published static machine learning model is being developed. Conclusion: Response to neoadjuvant therapy is determined by the baseline characteristics of the tumor ecosystem. During therapy, both the tumor and its microenvironment follow distinct evolutionary trajectories that can be mapped to outcome. The change in enrichment of features derived from the tumor and its microenvironment can be integrated within machine learning frameworks that leverage dynamic data from the entire therapy time course for more accurate response prediction. Citation Format: Stephen-John Sammut, Mireia Crispin-Ortuzar, Suet-Feung Chin, Elena Provenzano, Wei Cope, Ali Dariush, Sarah-Jane Dawson, Paul D. Pharoah, Florian Markowetz, Oscar M. Rueda, Helena M. Earl, Carlos Caldas. Predicting response to treatment in early breast cancer using dynamic integrative multi-omic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 476.