Computational Analyses Connect Small-Molecule Sensitivity to Cellular Features Using Large Panels of Cancer Cell Lines

Abstract

We recently pioneered several analyses of small-molecule sensitivity data collected from large-scale perturbation of hundreds of cancer cell lines with hundreds of small molecules, with cell viability measured as a readout of compound sensitivity. We performed these studies using cancer cell lines previously annotated with cellular, genomic, and basal gene-expression features. By combining small-molecule sensitivity data with these other datasets, we identified new candidate biomarkers of sensitivity, gained insights into small-molecule mechanisms of action, and proposed candidate hypotheses for cancer dependencies (including candidate combination therapies). Nevertheless, given the size of these datasets, we expect that many connections between cellular features and small-molecule sensitivity remain under-explored. In this chapter, we provide a step-by-step account of foundational data-analysis methods underlying our published studies, including working MATLAB code applied to our own public datasets. These procedures will allow others to repeat analyses of our data with new parameters, in additional contexts, and to adapt our procedures to their own datasets.

The Why, How, and When of Representations for Complex Systems

“…As an example, one could encode common properties (mutation status, chromatin rearrangements, etc.) as layers in a multiplex network of cancer cell lines in order to better understand drug response [168]. The multilayer variation is readily applicable whenever researchers have access to and want to model two different fragments of the same system.…”

Identification of biological targets through the correlation between cell line chemosensitivity and protein expression pattern

“…The advantage of this method is the free availability of data with a detailed explanation of the analytical tool made available online by the Cancer Therapeutics Response Portal. [53][54][55] This protocol led to the identification of PDE3A as a potential target for the compound DNMDP, demonstrating that predictive chemogenomics based on the correlation between protein expression and chemosensitivity could represent an important resource to gain insight into small-molecule targets. 56 Recently, taking advantage of this method, the putative molecular targets/mechanism of action of several non-oncology drugs (e.g., disulfiram) endowed with anticancer properties have been proposed.…”