A genome-scale CRISPR knock-out screen in chronic myeloid leukemia identifies novel drug resistance mechanisms along with intrinsic apoptosis and MAPK signaling.

Understanding resistance mechanisms in cancer is of utmost importance for the discovery of novel "druggable" targets. Efficient genetic screening, now even more possible with CRISPR-Cas9 gene-editing technology, next-generation sequencing and bioinformatics, is an important tool for deciphering novel cellular processes, such as resistance to treatment in cancer. Imatinib specifically eliminates chronic myeloid leukemia (CML) cells by targeting and blocking the kinase activity of BCR-ABL1; however, resistance to treatment exists. In order to discover BCR-ABL1 independent mechanisms of imatinib resistance, we utilized the genome-scale CRISPR knock-out library to screen for imatinib-sensitizing genes in vitro on K562 cells. We revealed genes that seem essential for imatinib-induced cell death, such as proapoptotic genes (BIM, BAX) or MAPK inhibitor SPRED2. Specifically, reestablishing apoptosis in BIM knock-out (KO) cells with BH3 mimetics, or inhibiting MAPK signaling in SPRED2 KO cells with MEK inhibitors restores sensitivity to imatinib. In this work, we discovered previously identified pathways and novel pathways that modulate response to imatinib in CML cell lines, such as the implication of the Mediator complex, mRNA processing and protein ubiquitinylation. Targeting these specific genetic lesions with combinational therapy can overcome resistance phenotypes and paves the road for the use of precision oncology.

Novel analytical methods to interpret large sequencing data from small sample sizes.

BACKGROUND: Targeted therapies have greatly improved cancer patient prognosis. For instance, chronic myeloid leukemia is now well treated with imatinib, a tyrosine kinase inhibitor. Around 80% of the patients reach complete remission. However, despite its great efficiency, some patients are resistant to the drug. This heterogeneity in the response might be associated with pharmacokinetic parameters, varying between individuals because of genetic variants. To assess this issue, next-generation sequencing of large panels of genes can be performed from patient samples. However, the common problem in pharmacogenetic studies is the availability of samples, often limited. In the end, large sequencing data are obtained from small sample sizes; therefore, classical statistical analyses cannot be applied to identify interesting targets. To overcome this concern, here, we described original and underused statistical methods to analyze large sequencing data from a restricted number of samples. RESULTS: To evaluate the relevance of our method, 48 genes involved in pharmacokinetics were sequenced by next-generation sequencing from 24 chronic myeloid leukemia patients, either sensitive or resistant to imatinib treatment. Using a graphical representation, from 708 identified polymorphisms, a reduced list of 115 candidates was obtained. Then, by analyzing each gene and the distribution of variant alleles, several candidates were highlighted such as UGT1A9, PTPN22, and ERCC5. These genes were already associated with the transport, the metabolism, and even the sensitivity to imatinib in previous studies. CONCLUSIONS: These relevant tests are great alternatives to inferential statistics not applicable to next-generation sequencing experiments performed on small sample sizes. These approaches permit to reduce the number of targets and find good candidates for further treatment sensitivity studies.