Pathway and network analysis of more than 2500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments

Sex differences in oncogenic mutational processes.

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Global network construction.

<p><b>(a)</b> Conversion of binary data to a network representation. All continuous data are mapped to a binary representation with ‘1’ (colored squares) corresponding to a gene with a value deviating from normal for a particular sample. Each ‘1’ in the binary datasets is converted to an undirected link (solid line) between a gene node and a sample node. Prior knowledge, derived from public gene interaction repositories, is available in the form of undirected links (dashed grey line) between genes. Characters a-g correspond to gene IDs, S₁-S₃ represent sample IDs. <b>(b)</b> Construction of the global network. The network representations of the binary datasets and the prior knowledge network are merged to constitute a single comprehensive network representation. Gene nodes originating from the input datasets are connected to the corresponding gene in the prior knowledge interaction network (dashed yellow lines). <b>(c)</b> The resulting adjacency matrix representation of the undirected global network. For clarity, individual gene and sample identifiers are omitted. NET (grey) = genes from the prior knowledge interaction network, S (dark blue) = samples, EXP (green) = genes from the gene expression dataset, CNV (pink) = genes from the copy number dataset, MUT (light blue) = mutated genes, MET (orange) = methylated genes. <b>(d)</b> The similarity matrix derived from the adjacency matrix, indicating the parts of the similarity matrix that are relevant for the pathway ranking task.</p

Ratio of bad-outcome pathway scores and the corresponding good-outcome scores.

<p>A ratio of ‘1’ indicates that the pathway scores equally high for patients in the bad-outcome group and patients in the good-outcome group. Values larger than 1 indicate higher pathway importance / activity for the bad-outcome group. Pathways shown are limited to the top-20 highest scoring pathways in the bad-outcome group.</p

Remote sensing meets psychology: a concept for operator performance assessment

An often undervalued but inevitable component in remote sensing image analysis is human perception and interpretation. Human intervention is a requisite for visual image interpretation, where the interpreter actually performs the analysis. Although image processing became more and more automated, human screening and interpretation remained indispensable at certain stages. One particular stage where the operator plays a crucial role is in the development of reference maps. This is often done by a visual interpretation of an image by an operator. Although the result is crucial for adequately assessing automated systems' performance, the work of the human operator is rarely questioned. No variability is considered and the possibility of errors is not mentioned. This is an implicit assumption that operator performance approaches perfection and that infrequent errors are randomly distributed across time, operators and image types. Given that the existence of operator variability has been proven in several other related domains, for example, screening of medical images, this assumption may be questioned. This letter brings the issue to the attention of the remote sensing community and introduces a new concept quantifying operator variability. As the WAVARS project (web-based assessment of operator performance variability within remote sensing image interpretation tasks) will gain from a high amount of data, we kindly invite interested researchers to access the website http://wavars.ugent.be and take part in the test

The 20 highest ranking pathways for each of the four breast cancer subtypes.

<p>The aggregate score assigned to each pathway can be decomposed into 4 probabilistic components. The contribution of each component to the total score is indicated in a different color bar: mRNA expression (dark blue), copy number (light blue), mutation (green) and methylation (yellow).</p