Improving personalised therapy in metastatic breast cancer by implementing RNA sequencing based gene expression signatures

Abstract

Abstract Background Breast cancer is one of the most common cancers worldwide and - despite significant advances in prevention and treatment - contributes significantly to the overall mortality from cancer. This emphasizes the need for better treatments and improved patient stratification.Histologic as well as genetic testing is able to substantially improve treatment selection and benefit as well as patient management. Within the CATCH (Comprehensive assessment of clinical features and biomarkers to identify patients with advanced or metastatic breast cancer (mBC) for marker-driven trials in humans) program, whole-genome and RNA sequence analyses are performed to identify biomarkers. With these high-throughput approaches, genetic variants and/or expression events are frequently discovered for which the predictive value is not yet known. Methods The R-packages GSVA (Gene Set Variation Analysis) and PROGENy (Pathway RespOnsive GENes) were used to calculate enrichment scores from RNA sequencing data. Results Here, we show that RNA sequencing based pathway analyses of metastatic breast cancer samples has the potential to guide patient stratification by providing an additional layer of information that may help to interpret the observed events. This approach is particularly interesting for challenging clinical samples greatly extending the impact of single-gene-based analyses. We analyzed 115 metastases by RNA sequencing for which histologic subtype and matched whole genome sequencing data are available. We test the usefulness of several published predictive gene expression signatures in metastatic breast cancer, which we exemplify by the IFN-y signature that has the potential to stratify patients for immune checkpoint inhibitor therapy. However, there are important limitations when these approaches are applied: Both breast cancer subtypes as well as the amount of normal tissue present in the material used can affect the results. Therefore, careful comparison to subtype and tissue matched reference cohorts is critical. Conclusions In the future, integration with clinical outcome data will add further information on the sensitivity and specificity of the approach. Pathway enrichment analysis may emerge as a fast and cost-effective tool to identify patients that benefit from certain targeted therapies such as immune checkpoint inhibitors. Legal entity responsible for the study The authors. Funding German Cancer Research Center and University Hospital Heidelberg. Disclosure All authors have declared no conflicts of interest.