Several comments addressing the recent Nicholas Roberts et al. study that sought to assess the predictive capacity of personal genome sequencing appear in Science Translational Medicine this week. In one comment, Eric Topol from the Scripps Research Institute in La Jolla, Calif., says that "contrary to the impression given by Roberts et al. and its coverage in the media, the notion that genetic information cannot with certainty predict disease in an individual is well known, but its precision will improve as more kinds of genetic variability can be ascertained." He adds that scientists "cannot know the predictive capacity of whole-genome sequencing until we have sequenced a large number of individuals with like conditions." Separately, the Memorial Sloan-Kettering Cancer Center's Colin Begg and Malcolm Pike present "an alternative calculation of the predictive capacity of genomic sequencing and an analysis based on the occurrence of cancer in the second breast of breast cancer patients." Meanwhile, David Golan and Saharon Rosset at Tel Aviv University propose a recalculation of the maximal estimates reported by Roberts et al. that "shows that the true predictive capacity of genomes may be higher," they write.
In response to those three comments, lead author Bert Volegstein — co-author of the Roberts et al. study — and his colleagues say that their "group was the first to show that unbiased genome-wide sequencing could illuminate the basis for a hereditary disease," and add that they are "acutely aware of its immense power to elucidate disease pathogenesis." However, Vogelstein and his colleagues say that recognizing the potential limitations of personal genome sequencing is critical to "minimize false expectations and foster the most fruitful investigations."
In a Science paper published online in advance this week, a team led by investigators at Brigham and Women's Hospital in Boston reports on its search for genes with cancer-relevant properties within hemizygous focal deletions in tumors. Through this, the researchers report having identified STOP and GO genes, which they say "negatively and positively regulate proliferation, respectively." Further, in its paper, the team proposes "the Cancer Gene Island model, whereby gene islands encompassing high densities of STOP genes and low densities of GO genes are hemizygously deleted to maximize proliferative fitness through cumulative haploinsufficiencies."
Over in this week's issue, an international team led by researchers at the Broad Institute presents its mass spectrometry-based analysis of the consumption and release profiles of 219 metabolites from media across the NCI-60 cancer cell lines, through which it found that "glycine consumption and expression of the mitochondrial glycine biosynthetic pathway [is] strongly correlated with rates of proliferation across cancer cells." A perspective article to accompany the study from Keio University's Masaru Tomita and Kenjiro Kami notes that advances in data-driven systems biology research have been "facilitated by the emergence of metabolomics technologies."