Triple-negative breast cancer (TNBC) accounts for roughly 15% of all breast cancers and is the most aggressive subtype. TNBC is molecularly heterogenous, yet advances in sequencing and computational analysis have identified extensive genomic copy number alterations (CNAs) in the majority of patients with TNBC. Better understanding of how genes within regions of CNAs drive TNBC will likely improve prognostic and therapeutic abilities.
Diaz, Cagan and colleagues used a combination of computational approaches and modelling in Drosophila to identify TNBC-driving genes in common CNA regions. They used several computational methods that harnessed The Cancer Genome Atlas (TCGA) data to confirm that a high proportion of TNBC tumours harbour mutations within TP53 and comprise CNAs in the locus containing MYC. Expanding this computational analysis and using a ranking protocol the authors assessed the 186 most common TNBC CNA regions to create a set of candidate driver genes. They then translated these data to a whole-animal model of TNBC by first generating Drosophila lines with overexpression of Myc and loss of p53 expression, and then combining this model with knockdown of tumour suppressor candidates or overexpression of oncogene candidates to create a library of ‘3-hit’ Drosophila lines. The Myc/p53 mutant flies had reduced survival compared with controls and displayed signs of tumorigenic transformation in wing disc, including cell translocation and tissue overgrowth. Of the 222 ‘3-hit’ fly lines tested, 100 lines – representing 69 genes – increased lethality and 48 of these genes increased cell translocation or tissue overgrowth compared with flies carrying Myc/p53 alteration alone.
The authors then focused on six of these TNBC-driver genes and used their corresponding ‘3-hit’ Drosophila lines to test an extensive library of anti-cancer drugs. They found that the ‘3-hit’ TNBC models – but not the Myc/p53 mutant flies – failed to respond to the chemotherapeutic fluorouracil, which is used to treat TNBC.
This study has generated a functional database of 49 (including MYC) TNBC-driving genes in common CNA regions. As a result, Diaz et al. provide a partial, functional ‘map’ to deconvolute the complex genetics of TNBC, as well as a useful tool for researchers to better understand the drivers of individual tumours. Interestingly, the authors also found that increased genetic complexity leads to drug resistance and identified candidate resistance genes that merit further research. This research could inform patient prognosis and treatment based on their genetic profile.
