A new study using computational tools to combine multiple sets of previously generated biomedical big data has identified drugs that can be repurposed to fight cancer. Dr. Bin Chen, a Big Data to Knowledge (BD2K) Mentored Career Development Awardee, and colleagues took advantage of multiple existing data sets, including the NIH Common Fund’s Library of Integrated Network-based Cellular Signatures (LINCS) L1000 data set and the National Cancer Institute’s The Cancer Genome Atlas (TCGA) data set to generate a list of potential drugs predicted to inhibit the growth of liver cancer cells. Dr. Chen and colleagues compared the global cellular changes between normal and cancerous liver cells in the TCGA data set with changes caused by treating cells with a panel of 12,442 distinct chemical compounds in the LINCS data sets. They tested the hypothesis that compounds causing inverse changes to those seen when a cell becomes cancerous would be strong candidates for treating cancer. Using this approach, four compounds were selected that had the potential to reverse the changes seen in liver cancer patients. When these compounds were tested against liver cancer cell lines grown in the laboratory, all four inhibited the growth of these cells. The most potent of these drugs was also tested in a mouse liver cancer model and was shown to reduce the size of tumors. This drug (pyrvinium pamoate), which has been previously FDA approved to treat pinworm infections, will require further experimentation to determine if it can be repurposed to treat liver cancer in humans.
The growing number of large, robust experimental data sets in biomedical research offers unprecedented opportunities for new discoveries using computational approaches, which is a continuing goal of the BD2K program. This study, also demonstrates the power of using the LINCS data sets to discover novel therapeutics by focusing on global cellular changes instead of a singular molecular target. Similar approaches have the potential to yield candidate drugs for repurposing to treat a wide spectrum of diseases.
Read the press release from UCSF here.
Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Chen B, Ma L, Paik H1, Sirota M, Wei W, Chua MS, So S, Butte AJ. Nat Commun. 2017 Jul 12. 8:16022.
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Contacts: Jin Paik - email@example.com, Aravind Subramanian - firstname.lastname@example.org
Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Hafner M, Niepel M, Chung M, Sorger PK. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nature Methods 2016.
Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes. Abelin JG, Patel J, Lu X, Feeney CM, Fagbami L, Creech AL, Hu R, Lam D, Davison D, Pino L, Qiao JW, Kuhn E, Officer A, Li J, Abbatiello S, Subramanian A, Sidman R, Snyder E, Carr SA, Jaffe JD. Molecular & Cellular Proteomics. 2016 May; 15(5): 1622-41.
Santagata S, Mendillo ML, Tang YC, Subramanian A, Perley CC, Roche SP, Wong B, Narayan R, Kwon H, Koeva M, Amon A, Golub TR, Porco JA Jr, Whitesell L, Lindquist S. Tight coordination of protein translation and HSF1 activation supports the anabolic malignant state. Science, July 19, 2013; 341. PMID: 23869022.
More effective therapeutics are needed for numerous conditions affecting people, yet drug development remains inefficient. Investigators supported by the Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program have discovered new insights into the pharmacological properties of drugs that should help us design better therapeutics and more accurately predict their effects. While most studies of drug effects focus on measures of drug potency, e.g., by emphasizing the dose needed to experimentally reduce cell numbers in half, Fallahi-Sichani and colleagues at Harvard Medical School and the Oregon Health and Science University found that other measures of the response of cells to drugs can provide additional insights. They found that different measures are more informative at different doses of the drug response, e.g., some are more informative at higher doses and some at lower doses. They also found that the different measures do not always correlate with each other, e.g., when compared across different drugs or different cell types. Yet some measures correlate with cell type and others with drug class. In addition, the different measures each reveal unique information that contributes insights into the action of the drug. The findings indicate that it is worthwhile to compare multiple parameters when examining the variability of drug effects, and expand the way we should think about parameters of drug activity. In some cases the underlying explanation comes from how individual cells might behave differently from a population of cells.
Fallahi-Sichani M, Honarnejad S, Heiser LM, Gray JW, Sorger PK. Metrics other than potency reveal systematic variation in responses to cancer drugs. Nat Chem Biol. 2013 Sep 8. PMID 24013279.
The Common Fund is supporting two awards totaling $12.7 million dollars in the Library of Integrated Network-Based Cellular Signatures (LINCS) program. The awards support the high-throughput collection and integrative computational analysis of molecular activity and cellular signatures generated in response to a variety of perturbing agents in a multiple cell types. The new knowledge generated by this program will serve as a long-term resource for the scientific community.
- One new award, given to Drs. Todd Golub and Wendy Winckler of the Broad Institute, supports the application of a novel approach to analysis of genome-wide expression to catalogue the cellular consequences of 4,000 diverse small molecules and genetic perturbations in an array of twenty different human cell types representing biological diversity and interest in the broad scientific community. The work will establish the information resource needed to support the discovery of unknown components of the genome, annotate the function of small molecules, and link disease states with small-molecule or signatures of genetic perturbation to provide insight into the biological basis of disease and potential new therapeutics (1U54-HG006093-01)
- The second award given to Drs. Timothy Mitchinson and Peter Sorger of Harvard University, supports the development of a new research center to advance the knowledge of disease processes, drug mechanisms and selectivity, and ultimately patient-specific responses to therapy. The center will focus on small molecule kinase inhibitors as versatile perturbagens which may be targeted in new therapies. The researchers will characterize the response to these perturbagens in 45 different cell lines known for diverse drug responses and for which genomic data are available, and then in more than 1000 human tumor cell lines (1U54-HG006097-01)
Biomedical research data generated from genomics analyses, imaging, biochemistry and other assays are abundant yet difficult to integrate using conventional approaches and databases. To address this need, Dr. Peter Sorger and colleagues at Harvard Medical School, Massachusetts Institute of Technology, and the University of Applied Sciences in Germany, researchers supported through the Common Fund’s Library of Integrated Network Based Cellular Signatures (LINCS) program, have developed an innovative new adaptable method that allows different types of complex data sets to be stored, analyzed and extended. In a recent paper in Nature Methods, they demonstrate the utility of the approach, which exploits useful aspects of two data file formats (HDF5 and XML), for analyzing a complex imaging data set reflecting 160 experimental conditions in over a million different single cells. The approach led to the discovery of new pharmacological relationships between compounds that bind and inhibit epidermal growth factor receptors (EGFR), providing insights into cell-to-cell variability in response to drugs.
Millard BL, Niepel M, Menden MP, Muhlich JL, and Sorger PK. Adaptive informatics for multifactorial and high-content biological data. Nat Methods. 2011. Vol 8(6):487-493.
This page last reviewed on August 3, 2017