Functional Interrogation Of Anti-Cancer Drug Resistance

Abstract

<p>Targeted therapeutics are among the most promising approaches for treating diverse forms of malignancies. Indeed, as sequencing prices and technology continue to improve it will be possible to achieve a precise map of each individual cancer’s genomic lesions, providing insights into the best strategies for treatment. However, these approaches will be undermined by cancer’s ability to resist upfront target inhibition (intrinsic resistance) as well as, in cases where tumors are initially sensitive, develop resistance over the course of drug treatment (acquired resistance). The literature to date reveals a problem as complex as the tumor itself; the heterogeneity of cancer as a disease is matched by the myriad ways in which it evades treatment. </p><p>Understanding drug resistance as a whole quickly becomes a problem of scale. Not only is there cancer subtype-associated variation to consider, but also intrinsic and acquired resistance profiles can differ based on the type of inhibitor used and at what node the offending pathway is inhibited. Assigning proper treatments to account for these mechanisms adds an additional layer of complexity as the number of FDA-approved and late-stage clinical candidate molecules increases. Here, when applied appropriately, high throughput methods offer the ability to screen thousands of perturbations in parallel, quickly narrowing the search space for a phenotype of interest. </p><p>This work applies such methods to the cell-autonomous complexity of drug resistance and seeks to understand (1) mechanisms by which cancer cells evade drug treatment, (2) design concepts for the most effective combinatorial drugging strategies, and (3) how it might be possible to account for resistance-associated heterogeneity by targeting the evolutionary liabilities of resistant cells. Using a combination of open reading frame (ORF), clustered regularly spaced short palindromic repeats (CRISPR), and pharmacologic screening technologies, this work attains the resolution and throughput necessary to address 1-3 above and begins to unravel the complexity of drug resistance.</p>