[PMID: 28783718] [Nature]
Integrative clinical genomics of metastatic cancer
“whole-exome and -transcriptome sequencing of 500 adult patients with metastatic solid tumours of diverse lineage and biopsy site. The most prevalent genes somatically altered in metastatic cancer included TP53, CDKN2A, PTEN, PIK3CA, and RB1. Putative pathogenic germline variants were present in 12.2% of cases of which 75% were related to defects in DNA repair.”
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.
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”
Whole-genome landscapes of major melanoma subtypes
“Significantly mutated genes included BRAF, CDKN2A, NRAS and TP53 in cutaneous melanoma, BRAF, NRAS and NF1 in acral melanoma and SF3B1 in mucosal melanoma. Mutations affecting the TERT promoter were the most frequent of all; however, neither they nor ATRX mutations, which correlate with alternative telomere lengthening, were associated with greater telomere length.” “Telomere length was not correlated with melanoma subtype, chromothripsis or breakage–fusion–bridge events.”
[PMID:28445112] [The New England Journal of Medicine]
Tracking the Evolution of Non–Small-Cell Lung Cancer
“multiregion whole-exome sequencing on 100 early-stage NSCLC tumors that had been resected before systemic therapy.” TRACERx Consortium.
[PMID:28424200] [Clinical Cancer Research]
Whole exome sequencing of metaplastic breast carcinoma indicates monoclonality with associated ductal carcinoma component
In eight patients. “In each case, the tumor components have nearly identical landscapes of somatic mutation, implying that the differing histologies do not derive from genetic clonal divergence.”
[PMID:28429735] [Nature Communications]
Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations
“using whole-exome sequencing and copy number profiling of primary and multiple-matched metastatic tumours from ten autopsied patients” “two modes of disease progression. In some patients, all distant metastases cluster on a branch separate from their primary lesion. Clonal frequency analyses of somatic mutations show that the metastases have a monoclonal origin and descend from a common ‘metastatic precursor’. Alternatively, multiple metastatic lesions are seeded from different clones present within the primary tumour.”
DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification
It demonstrates that “many so-called low-frequency genetic variants in large public databases may be due to DNA damage” — DNA material itself, not the calling. “To estimate the extent of damage in public data sets, we determined the GIV scores of individual sequencing runs from the 1000 Genomes Project and a subset of The Cancer Genome Atlas (TCGA) data set. Both data sets showed widespread damage, particularly those leading to an excess of G-to-T variants. Specifically, 41% of the 1000 Genomes Project data sets had a GIVG_T score ≥ 1.5, indicative of damaged samples. Furthermore, 73% of the TCGA sequencing runs showed extensive damage, with a GIVG_T > 2. This indicates that the majority of G-to-T observations are erroneous and establishes damage as a pervasive cause of errors in these data sets.”
[PMID:28190455] [American Journal of Human Genetics]
Who’s Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy
“to identify and facilitate the remediation of such errors via interactive visualizations and reports comparing the stated sex, relatedness, and ancestry to what is inferred from the individual genotypes derived from whole-genome (WGS) or whole-exome (WES) sequencing. Peddy predicts a sample’s ancestry using a machine learning model trained on individuals of diverse ancestries from the 1000 Genomes Project reference panel.” Work from Aaron Quinlan.