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. 2019:1888:233-254.
doi: 10.1007/978-1-4939-8891-4_14.

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

Matthew G Rees  1 , Brinton Seashore-Ludlow  2 , Paul A Clemons  3
Affiliations

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

Matthew G Rees et al. Methods Mol Biol. 2019.

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 underexplored. 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.

Keywords: Biomarkers; Cancer dependencies; Chemical biology; Combination therapy; Computational biology; Data sharing; Pharmacogenomics; Public datasets; Reproducibility.

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Figures

Figure 1:
Figure 1:
Representative curve-fit visualization showing the differential sensitivity of two cell lines. Two plots are presented for each of two cell lines, one more sensitive (red) and one less sensitive (blue), to navitoclax, a compound annotated in the Cancer Therapeutics Response Portal (CTRP; http://portals.broadinstitute.org/ctrp) as an inhibitor of BCL2, BCL-xL, and BCL-W. Unconnected crosses represent the original data and are labeled in the MATLAB figure legend with the cell-line name. Line plots with error bars represent the corresponding fit curves and are labeled in the MATLAB figure legend with the computed area-under-curve (AUC).
Figure 2:
Figure 2:
Representative visualization of enrichment analysis for a single compound tested in multiple cell lines of the same type. In this case, 25 breast-derived cancer lines were tested with RAF265 (annotated in CTRP as an inhibitor of VEGFR2 and BRAF), and then sorted by area-under concentration-response curve (AUC) in the top left panel (increasing red color represents lower AUCs below the mean and therefore more sensitivity). Enrichment analysis resulted in an optimal cutoff of AUC < 12.2 which corresponds to 8 total cell lines in the bottom left panel, of which 6 carry a coding mutation in TNRC6B (red = has mutation; pink = lacks mutation). These were the only 6 TNRC6B mutants in this subset of 25 breast cancer-derived cell lines. The right panel depicts an alternative representation (box-whisker plot) and statistical analysis (t-test) of the same information, showing the relative distribution of AUC values for cell lines with or without coding mutations in TNRC6B.
Figure 3:
Figure 3:
Representative visualization of correlation analysis for a single compound tested in multiple cell lines of the same type. In this case, 15 bone-derived cancer lines were tested with gemcitabine (annotated in CTRP as an inhibitor of CMPK1, RRM1, TYMS). Sensitivity to gemcitabine (low AUC) is correlated with low expression of SERPINE1 in these cell lines, and each of the AUC and gene-expression distributions exhibit good dynamic ranges as described in the text (see also Note 57).
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