[PMID: 28720581] [Genome Research]

HIT’nDRIVE: Patient-specific multi-driver gene prioritization for precision oncology

“a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT’nDRIVE aims to solve the “random walk facility location” (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: “multi-hitting time”, the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene.”

TCGA Neoantigens

[PMID: 28694034] [Lancet Oncology]

Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis

“analysed whole-exome sequencing data from 5777 solid tumours, spanning 19 cancer types from The Cancer Genome Atlas.” “Analysis of tumour-specific neoantigens showed that enrichment of indel mutations for high-affinity binders was three times that of non-synonymous SNV mutations. Furthermore, neoantigens derived from indel mutations were nine times enriched for mutant specific binding, as compared with non-synonymous SNV derived neoantigens.”


[PMID:28658208] [Nature]

Recurrent and functional regulatory mutations in breast cancer

“deep sequencing in 360 primary breast cancers and develop computational methods to identify significantly mutated promoters.” “promoter regions harbour recurrent mutations in cancer with functional consequences and that the mutations occur at similar frequencies as in coding regions.” Work from Gad Getz.

[PMID:28658210] [Nature]

Cancer genomics: Less is more in the hunt for driver mutations

“The authors’ power analysis (statistical calculations estimating the sample numbers needed to detect an effect of a given size) indicated that more than 90% of drivers could be reliably identified if they occurred in at least 10% of the 360 samples studied, but only 70% of drivers present in 5%of patients would be identified”

DNA Methylation Markers

[PMID:28652331] [Proceedings of the National Academy of Sciences]

DNA methylation markers for diagnosis and prognosis of common cancers

“We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival.”