Prediction of ROR1 and CAV1 expression from whole-exome sequencing of tumor tissue and plasma cfDNA to infer the efficacy of B013 plus nab-paclitaxel in triple-negative breast cancer

Abstract

Accurate prediction of response to targeted anticancer therapy frequently depends on the expression level of the drug target. However, transcriptomic profiling requires tumor tissue that is often unavailable, and circulating cell-free DNA (cfDNA) sequencing carries no expression information. Here, in an in silico companion study to a trial of ROR1 antibody plus nab-paclitaxel in triple-negative breast cancer (TNBC), we transformed whole-exome sequencing (WES) data into somatic activity profiles of signaling pathways (sAPSPs) using the previously published DAGM framework, and used them together with matched expression data to develop regression model sets predicting ROR1 and CAV1 expression. The model sets were applied to 10 trial participants (4 with tumor tissue WES, 6 with plasma cfDNA WES). Patients were classified as predicted-sensitive only when both ROR1 and CAV1 were predicted to be highly expressed. The prediction matched the observed best response in 9 of 10 patients; all three predicted-sensitive patients achieved a partial response. These exploratory findings support the feasibility of WES-based expression prediction for non-invasive response stratification and warrant prospective validation.