Daily Digest | March 27, 2020

Integrating genomic features for non-invasive early lung cancer detection | Nature

Radiologic screening of high-risk adults reduces lung-cancer-related mortality; however, a small minority of eligible individuals undergo such screening in the United States. The availability of blood-based tests could increase screening uptake. Here researchers introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq), a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. They show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. They develop and prospectively validate a machine-learning method termed ‘lung cancer likelihood in plasma’ (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls.

Research paper

 

A genomic and epigenomic atlas of prostate cancer in Asian populations | Nature

Prostate cancer is the second most common cancer in men worldwide. Here researchers produced and analysed whole-genome, whole-transcriptome and DNA methylation data for 208 pairs of tumour tissue samples and matched healthy control tissue from Chinese patients with primary prostate cancer. Systematic comparison with published data from 2,554 prostate tumours revealed that the genomic alteration signatures in Chinese patients were markedly distinct from those of Western cohorts: specifically, 41% of tumours contained mutations in FOXA1 and 18% each had deletions in ZNF292 and CHD1.

Research paper

 

Decode-seq: a practical approach to improve differential gene expression analysis | Genome Biology

Many differential gene expression analyses are conducted with an inadequate number of biological replicates. Researchers describe an easy and effective RNA-seq approach using molecular barcoding to enable profiling of a large number of replicates simultaneously. This approach significantly improves the performance of differential gene expression analysis.

Research paper

 

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