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News

Sift, Sort, Seize Transcriptomic Gold

Monday, August 1, 2016

This is an excerpt of a story that appeared in Genetic Engineering and Biotechnolgy News. Read the full article here. 

The transcriptome is less a tranquil pool than a turbulent stream that keeps shifting its course and adopting new patterns of gene expression. To survey this stream, one must repeatedly troop up and down its banks. Otherwise, one will never identify its developmental headwaters, homeostatic meanders, or pathogenic cascades.

Along with surveying comes prospecting, that is, dipping into the sediment beneath the flow to assess the cellular and molecular bases of gene expression. Here it is important to distinguish between materials that may seem to be homogeneous or intractably jumbled, but are in fact heterogeneous and, more important, separable. In fact, the growing appreciation of the inter-individual variability that defines cellular populations has become the driving force of efforts to capture and analyze the transcriptome in single cells.

Something of a transcriptomic gold rush is taking shape. The gold, however, may escape all but the best prospectors, those capable of deftly panning unpromising deposits and glimpsing the merest transcriptomic glints in heterogeneous cell populations.

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Computational Matchmaking

Photo of Olivier ElementoOlivier Elemento “Much of our work is focusing on mutations in transcription factors,” says Olivier Elemento, Ph.D., associate professor of physiology and biophysics at Weill Cornell Medical College. “Mutations of this type are very relevant in cancer.”

In cancer research, such mutations have been described even more frequently than other well-studied mutation types including mutations in kinase genes. Yet mutations in transcription factor genes, notes Dr. Elemento, pose a special difficulty: “Kinases can be targeted pharmacologically in many ways using small molecules, but transcription factors are not druggable because it is difficult to find a small molecule that blocks their binding to DNA.”

The pharmacological targeting of oncogenic transcription factors, Dr. Elemento and colleagues decided, could be accomplished by means of a computational approach, one that would search for molecules that capable of modulating transcription factor activity. “We wanted an approach,” explains Dr. Elemento, “that would be agnostic to the mechanism of action.”

The approach developed in Dr. Elemento’s laboratory is based on an in silico screening for chemicals that could specifically disrupt the expression of many genomic targets of a transcription factor, without disrupting the expression of nontarget genes. Using the genetic profiles of cell lines treated with over 1,300 different drugs, Dr. Elemento and colleagues identified genes that are up- or down-regulated in response to the drug. Then the investigators generated connectivity maps reflecting the transcriptional impact of each individual small molecule.

“We used the information about transcription factors and the genes that are modulated to carry out a sort of matchmaking  procedure,” details Dr. Elemento. That is, the investigators considered potential matches between drugs and transcription factor genomic targets.

In a proof-of-concept study, Dr. Elemento and colleagues applied this approach to study ERG, an oncogenic pro-invasive transcription factor that is frequently overexpressed in prostate cancer. “We mapped the ERG binding sites in the genome,” recalls Dr. Elemento. “We found about 2,000 target genes. Then, with reference to the Broad Institute’s Connectivity Map, we asked whether there were any drugs that could inhibit most of those 2,000 genes.”

This strategy identified eight candidate drugs that could inhibit many of the targets, and when ChIP-seq combined with drug-induced expression profiling was used to prioritize the list, dexamethasone emerged as the compound with the highest prediction score. ”We showed that dexamethasone is able to disrupt or reverse all the phenotypes induced by ERG,” informs Dr. Elemento.

Building on this finding, Dr. Elemento and colleagues used clinical database electronic medical data to retroactively identify patients who received dexamethasone. Then the investigators determined the likelihood these patients had for developing prostate cancer later in life. “Patients treated with dexamethasone,” reports Dr. Elemento, “had a much lower risk for prostate cancer later in life, reinforcing our experimental data.”