Drug-Gene Network Signature Modeling Predicts Breast Cancer Patient Response to Neoadjuvant Chemotherapy

Neoadjuvant chemotherapy (NAC) has been a staple treatment for breast cancer (BRCA) patients regardless of the tumor histological type. While this treatment can be effective on a population level, the pathologic complete response (pCR) rate post-NAC for individual patients varies widely throughout various clinical demographic groups and has not dramatically changed in practice. Improving stratification methods for therapeutic interventions could avoid the physical side effects as well as the psychological stress of undergoing NAC treatment if a patient is unlikely to respond [1, 2]. Given the rapid advancements in sequencing technologies and the availability of RNA expression data, medical solutions based on transcriptomics data are becoming increasingly prevalent [3]. Here, we present a novel method to stratify the prognosis for individual breast cancer patients for NAC therapy using RNA expression data from pre-treatment tumor biopsies by relying on network biology interactions rather than individual gene panels. We processed the datasets through the BioNAV™ pipeline to generate BioNAV™ network signatures (BioNAV™ NS) combined with a random forest machine learning model and incorporating demographic and other metadata, including patient race, specific drugs used in NAC treatment, and tumor histological subtyping. These network signatures offer insights into the gene-gene and drug-gene interactions occurring within each patient’s biopsy. This study demonstrates the capability of BioNAV™ NS to help guide BRCA prognoses through a comprehensive, network-level view of the gene expression data. Using BioNAV™ NS, we were able to accurately predict patient response to NAC with a mean area under the receiver operator characteristic (AUROC) of 82.4%. The addition of demographic and tumor receptor type stratification further increased performance to as high as an AUROC of 93.7% for patients who are progesterone receptor positive (PR+). Additionally, classifier performance was maintained when combining datasets from multiple studies and various transcriptomics platforms and heterogeneous preprocessing steps prior to BioNAV™ pipeline processing. Stratification by histological subgroups enhanced the predictive accuracy and AUROC of BioNAV™, outperforming two leading models in recent literature by 18.6% and 12.9%, respectively. BioNAV™ NS significantly enhances the predictive value of transcriptomic data to determine patient response to NAC. This approach offers the integration of multiple biological data and clinical metadata layers to improve clinical outcome prediction, highlighting potentially novel therapeutic mechanisms that have been hidden inside a heterogeneous patient population. A transition towards personalized treatment plans and adjuvant treatments may further enhance efficacy and reduce adverse events.