Drivers of intrinsic resistance

Abstract

Precision oncology requires an understanding of the genes and pathways that dictate therapeutic response. Through specialized analysis of drug sensitivity patterns across hundreds of genomically annotated cancer cell lines, specific and actionable drivers of intrinsic resistance have been identified.

Figure 1.. Discovery of actionable genes driving intrinsic resistance.

Using data from large pharmacogenomic datasets describing the responses of hundreds of genomically credentialed human cancer cell lines to diverse drugs (A), investigators identified those genes whose expression levels across the dataset positively correlate with measures of drug potency such as area under the curve (AUC) (B, left), in contrast with most genes whose expression levels are uncorrelated (B, right). Down-selection steps enabled the removal of genes whose expression levels serve as proxies of other co-expressed genes and those that are correlated with sensitivity to large numbers of drugs. The remaining, high priority candidate genes can then be credentialed to identify those that functionally drive intrinsic resistance, enabling the potential identification of combination therapies that overcome this resistance, leading to more penetrant and durable therapeutic responses (C).