UCLA

Focal amplifications (FAs) are important contributors to genomic instability within cancer cells and can influence their response to targeted therapies. FAs can take on two different forms: double minutes (DMs), which are circular segments of extrachromosomal DNA (ecDNA), or homogeneously staining regions (HSRs), which are intrachromosomal regions of amplification. DMs, or ecDNAs, are particularly known for enrichment of oncogenes and their contribution to cell plasticity, especially in conjunction with BRD4 which promotes overexpression of the oncogenes. However, while FAs confer advantages upon the cells, there are also weaknesses that are introduced due to the increased instability of the genome. This project aims to identify weak points in cells with FAs through large-scale computational analysis of drug screens. The area under the dose response curve (AUC) was chosen as a measure of sensitivity for this analysis since a smaller AUC indicates that the sample responded more quickly to the drug. We performed linear regressions for hundreds of drugs to identify those with a smaller AUC value when treating samples with FA compared to samples without FA. The targets associated with these drugs were then compared to differential expression analysis and network correlation analysis modules to identify if they were differentially expressed or part of a larger network specific to cells with DMs and HSRs. We found several targets that appeared to be consistently associated with our criteria for sensitivity including AKT1, BRD2, BRD3, BRD4, DNMT3A, FLT3, KIT, and PDGFRB. These targets appeared even when removing samples with known driver genes and reducing tissue-specific effects on the linear regressions. A subset of these targets has been demonstrated to be associated with DNA repair and genomic instability in cancer cells from previous studies. Most importantly, AKT1 and FLT3 were found to be effective targets for joint inhibition with BRD4 in previous research, which introduces potential for targeting ecDNA and DM hubs. Transcriptome (RNA-seq) data found that numerous genes scoring in the drug sensitivity analysis were differentially regulated. Network analysis of co-expressed gene modules identified by network correlation analysis showed interactions between identified drug targets and differentially expressed genes and support the existence of gene interactions that were not apparent from the sensitivity analysis alone. These results provide more insight into potential targeted therapies for cells with FA and the potential weak points within the cells.