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In a paper published in Science Translational Medicine November 2018 (PMID: 30404863), Mouilere et al. surveyed circulating tumour DNA (ctDNA) fragment sizes in 344 plasma samples from 200 patients using low-pass whole-genome sequencing, to test the hypothesis that differences in fragment lengths of ctDNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for non-invasive genomic analysis of cancer. They found that selecting fragments between 90 and 150 bp improved detection of tumour DNA with more than twofold median enrichment in > 95% of cases and more than fourfold enrichment in > 10% of cases. Analysis of size-selected cell-free DNA (cfDNA) identified clinically actionable mutations and copy number alterations that were otherwise not detected. Their findings show that fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA.

In a paper published in Nature Genetics August 2018 (PMID: 30104763), Macintyre et al. derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. They used big data processing techniques to look for broad patterns in the genetic readouts from ovarian cancer cells and found seven distinct patterns: each representing a different mechanism of DNA mutation. All patents studied showed more than one signature, suggesting multiple mechanisms change ovarian cancer cells during the cancer life. The signatures were also linked to how well the patients responded to different treatments and whether they were likely to become resistant to chemotherapy. Two of the signatures predicted particularly poor survival outcomes, and two signatures identified patients with good outcomes. The researchers are now planning a clinical trial to test whether the seven signatures can be used to choose the best treatment for patients.