Pan-cancer analysis of whole genomes

Abstract

The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

RET Gene Fusions in Malignancies of the Thyroid and Other Tissues

“…Different inter-chromosomal (translocations) or intra-chromosomal (inversions, tandem duplications, and interstitial deletions) structural variations can result in gene fusion events [87,93]. As discussed below, the most common RET fusions, in papillary thyroid carcinoma (PTC) and in lung adenocarcinoma (LADC) are CCDC6-RET and NCOA4-RET (primarily in PTC) and KIF5B-RET (primarily in LADC).…”

“…This is followed by chains of rearrangements, resulting in the simultaneous shuffling of several chromosomal fragments. Chromoplexy is typically found in prostate carcinoma and lymphoid malignancies [93]. However, in the recent pan-cancer analysis of whole genomes, it was found that 4 of the 13 fusion genes identified in thyroid carcinoma, including 2 RET fusions, were caused by chromoplexy [93].…”

Pan-cancer detection of driver genes at the single-patient resolution

“…Starting from the GI canonical drivers 11 damaged in each sample, sysSVM2 predictions were progressively added based on their rank to reach five drivers per patient (Methods). This was based on the assumption that each cancer requires at least five driver events to fully develop, in concordance with recent quantifications of the amount of excess mutations arising from positive selection in cancer 8,25 . One-hundred and fifty-four patients had damaging alterations in five or more GI canonical drivers, while 503 patients (77%) needed at least one prediction ( Figure 4B).…”

“…For example, transcriptomic and epigenomic data could enhance the ability of sysSVM2 to identify driver events. Additionally, recent efforts have identified a large number of driver events in non-coding genomic elements 25 . Given such a training set of true positives, sysSVM2 could be further developed to identify non-coding drivers in individual patients, as long as appropriate features could be identified.…”

Probabilistic Approach to Understand Errors in Sequencing and its based Applications

“…Next Generation Sequencing has been applied in many areas of biology, including quantification of gene expression, Genome Wide Association Studies (GWAS), Gene finding, Motif discovery and much more. Studies such as PANCANCER (recently published in Feb'20) are totally based on large scale sequencing [13] . Due to the vast area of application and importance in key findings in the field, massive genomic data is being generated using high throughput sequencing.…”