In Science this week, a team of Swedish scientists reports a new open-access database that enables the genome-wide examination of the effects of individual proteins on cancer outcomes. Using systems-level approaches, the researchers analyzed the genome-wide transcriptome of protein-coding genes of 17 major cancer types with respect to clinical outcome. They found that shorter patient survival was associated with upregulation of genes involved in cell growth and with downregulation of genes involved in cellular differentiation. With metabolic modeling, they showed that cancer patients have widespread metabolic heterogeneity, "highlighting the need for precise and personalized medicine for cancer treatment." Data from the study have been made available through the Human Protein Atlas. GenomeWeb has more on this, here.
Also in Science, a group of Stanford University researchers describes a new computational framework for analyzing peoples' genomic data for diagnostic applications without compromising privacy. To do so, DNA sequences are converted into vectors to represent whether changes are present at the more than 28 million known disease-associated positions within the three billion base pair genome as strings of 0s and 1s. A series of manipulations then anonymizes the vectors and allows for comparison. The investigators also describe three mathematical operations that successfully identified causal changes for a variety of conditions without disclosing other information in several validation cohorts. GenomeWeb also covers this, here.
And in Science Translational Medicine, an international group of scientists details a new sequencing-based method for noninvasively detecting early-stage cancers. Called targeted error correction sequencing, or TEC-Seq, the technique uses massively parallel sequencing for ultrasensitive direct evaluation of sequence changes in circulating cell-free DNA. Using the approach, they were able to distinguished between cancer-associated alterations and normal variation in cfDNA with a false positive rate of fewer than one per three million DNA base pairs. Additionally, cancer patients were found to have, on average, more than four times more cfDNA in their blood versus healthy individuals, with these higher levels correlating to more aggressive disease. The method represents "a broadly applicable approach for noninvasive detection of early-stage tumors that may be useful for screening and management of patients with cancer," according to the researchers. GenomeWeb has its take on this work, here.