Functional Exploration of Copy Number Alterations in a Drosophila Model of Triple Negative Breast Cancer

Abstract

Accounting for 10-20% of breast cancer cases, TNBC is associated with a disproportionate number of breast cancer deaths. Despite recent progress, many patients fail to respond to current targeted therapies. Responses to chemotherapy are variable, and the tumor characteristics that determine response are poorly understood. One challenge in studying TNBC is its genomic profile: outside of TP53 loss, most cases are characterized by copy number alterations (CNAs), making modeling the disease in whole animals challenging. We analyzed 186 previously identified CNA regions in breast cancer to rank genes within each region by likelihood of acting as a tumor driver. We characterized a Drosophila p53-Myc model of TNBC, demonstrating aspects of transformation. We then used this model to assess highly ranked genes, identifying 48 as functional drivers. To demonstrate the utility of this functional database, we combined six of these drivers with p53-Myc to generate six 3-hit genotypes. These 3-hit models showed increased aspects of transformation as well as resistance to the standard-of-care chemotherapeutic drug fluorouracil. Our work provides a functional database of CNA-associated TNBC drivers, and uses this database to support the model that increased genetic complexity leads to increased therapeutic resistance. Further, we provide a template for an integrated computational/whole animal approach to identify functional drivers of transformation and drug resistance within CNAs for other tumor types.