Accounting for 10-20% of breast cancer cases, TNBC is associated with a disproportionate number of breast cancer deaths. 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 computationally analyzed 186 previously identified CNA regions in breast cancer to rank genes within each region by likelihood of acting as a tumor driver. We then used a Drosophila p53-Myc TNBC model to identify 48 genes as functional drivers. To demonstrate the utility of this functional database, we established six 3-hit models; altering candidates led to increased aspects of transformation as well as resistance to the chemotherapeutic drug fluorouracil. Our work provides a functional database of CNA-associated TNBC drivers, and a template for an integrated computational/whole animal approach to identify functional drivers of transformation and drug resistance within CNAs for other tumor types.