Hebrew University of Jerusalem Jerusalem, Yerushalayim, Israel
Abstract: Anticancer drugs are at the frontline of cancer therapy. However, innate resistance to these drugs occurs in up to 50% of patients, exposing them to the side effects of these drugs with no meaningful benefit. To identify the genes and pathways that confer resistance to such therapies, we performed a genome-wide screen in haploid human embryonic stem cells (hESCs). These cells possess the advantages of having only one copy of each gene, a normal karyotype, and lack any underlying point mutations. Initially, we show a close correlation between the potency of anticancer drugs in cancer cell lines to those in hESCs. We then exposed a genome-wide loss-of-function DNA library of mutations in all protein-coding genes to a dozen selected anticancer drugs, which represent six different mechanisms of drug therapies. The genetic screening enabled us to identify genes and pathways which can confer resistance to these drugs, demonstrating several common pathways. We validated several of the resistance-conferring genes, showing a significant shift in the effective drug concentrations to indicate a drug-specific effect to these genes. To highlight the clinical relevance of our findings, we focused on the screen results for paclitaxel and carboplatin, two of the most used anticancer drugs in treatment. After confirming the hESC results for these two drugs, further validation in cancer cell lines was performed. Finally, an algorithm for predicting resistance to paclitaxel or carboplatin was developed. Applying the algorithm to DNA mutation profiles of patient tumors enabled the separation of sensitive from resistant patients, thereby providing a prediction tool. As the anticancer drugs arsenal can offer alternatives in case of drug resistance, early prediction can provide a significant advantage and improvement to treatment. Our results show a capacity to identify relevant genetic mutations for cancer in haploid hESCs, demonstrating the applicability of these findings in generating a predictive algorithm to assist the unmet need for preemptive identification of tumor resistance to drug treatments. These findings may have clinically actionable application, improving both efficacy and quality of cancer treatment.
