Harnessing chemical and genomic data to fight cancer
In the years since the human genome was first sequenced, a trove of genomic data has been amassed, aiding not only in our understanding of how the body works, but also in the search for disease-fighting drugs. Indeed, finding connections between idiosyncrasies within the genome and effective treatments is the hallmark of “precision medicine,” a growing movement that aims to target therapeutics to those who would most likely benefit from their use.
In this drug discovery effort, one of the most valuable pieces of information has been gene expression data – gene-by-gene analyses that reveal how the genetic code is being read in specific cell types. One way to use these data is to conduct screens using small molecular compounds-of-interest (which are “of interest” as potential drugs) and then to analyze them in context of vast databases that contain gene expression data, such as the Cancer Cell Line Encyclopedia, and others, such as the Cancer Therapeutics Response Portal (CTRP), that contain “chemical sensitivities” of cancer cell lines to small-molecule compounds.
This process has allowed researchers to compare patterns of sensitivities with patterns of basal gene expression. When correlations are detected, researchers can uncover new insights with implications for therapeutics and for the understanding of the inner workings of cancer cells. While this approach has been used the past, it has not been possible to apply it systematically.
This week in Nature Chemical Biology, Broad scientists provide a means to use the approach on a large scale and report on several specific insights that have already emerged. In both cases, the researchers work backward in a sense, using the correlations between the gene expression data and chemical sensitivity data to figure out how these molecules are working within cells. To use a police procedural analogy, the teams start from the point at which they have identified a person-of-interest (or, in this case, their compound-of-interest) using a “feature recognition” approach – matching key features in their person-of-interest against a vast database of individuals whose likenesses and modus operandi are known. From there, their case turns from a “whodunit” into a “howdunit”; what’s left for the detectives to figure out are means and motive – or, in scientific parlance, “mechanisms of action.”
One paper, led by co-senior authors Stuart Schreiber, Alykhan Shamji, and Paul Clemons of the Broad’s Center for the Science of Therapeutics, and postdoctoral associate and first author Matthew Rees (now a research scientist in the Broad’s Cancer Program), reports on a new resource that enables this type of study. Their Cancer Therapeutics Response Portal, which is open online to the scientific community, allows researchers to investigate correlations between basal gene expression – gene expression in cells during their resting state – and cell sensitivity data in order to make biological insights.