A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.

A major goal of cancer biology is to understand the mechanisms underlying tumorigenesis driven by somatically acquired mutations. Existing computational approaches focus on either scoring the pathogenicity of mutations or characterizing their effects at specific scales. Here, we established a unified computational framework, NetFlow3D, that systematically maps the multiscale mechanistic effects of somatic mutations in cancer. The establishment of NetFlow3D hinges upon the Human Protein Structurome, a complete repository we first compiled that incorporates the 3D structures of every single protein as well as the binding interfaces for all known PPIs in humans. The vast majority of 3D structural information was resolved by recent deep learning algorithms. By applying NetFlow3D to 415,017 somatic protein-altering mutations in 5,950 TCGA tumors across 19 cancer types, we identified 1,656 intra- and 3,343 inter-protein 3D clusters of mutations throughout the Human Protein Structurome, of which ~50% would not have been found if using only experimentally-determined protein structures. These 3D clusters have converging effects on 377 cellular subnetworks. Compared to canonical PPI network analyses, NetFlow3D achieved a 5.5-fold higher statistical power for identifying significantly dysregulated subnetworks. The majority of identified subnetworks were previously obscured by the overwhelming background noise of non-clustered passenger mutations, including portions of non-canonical PRC1, mediator complex, MCM2-7 complex, neddylation of cullins, complement system, TRiC, etc. NetFlow3D and our pan-cancer results can be accessed from http://netflow3d.yulab.org. This work shows that mapping how individual mutations act across scales requires the integration of their local spatial organization on protein structures and their global topological organization in the PPI network.

Genome maps across 26 human populations reveal population-specific patterns of structural variation.

Large structural variants (SVs) in the human genome are difficult to detect and study by conventional sequencing technologies. With long-range genome analysis platforms, such as optical mapping, one can identify large SVs (>2 kb) across the genome in one experiment. Analyzing optical genome maps of 154 individuals from the 26 populations sequenced in the 1000 Genomes Project, we find that phylogenetic population patterns of large SVs are similar to those of single nucleotide variations in 86% of the human genome, while ~2% of the genome has high structural complexity. We are able to characterize SVs in many intractable regions of the genome, including segmental duplications and subtelomeric, pericentromeric, and acrocentric areas. In addition, we discover ~60 Mb of non-redundant genome content missing in the reference genome sequence assembly. Our results highlight the need for a comprehensive set of alternate haplotypes from different populations to represent SV patterns in the genome.

Genome-Wide Structural Variation Detection by Genome Mapping on Nanochannel Arrays.

Comprehensive whole-genome structural variation detection is challenging with current approaches. With diploid cells as DNA source and the presence of numerous repetitive elements, short-read DNA sequencing cannot be used to detect structural variation efficiently. In this report, we show that genome mapping with long, fluorescently labeled DNA molecules imaged on nanochannel arrays can be used for whole-genome structural variation detection without sequencing. While whole-genome haplotyping is not achieved, local phasing (across >150-kb regions) is routine, as molecules from the parental chromosomes are examined separately. In one experiment, we generated genome maps from a trio from the 1000 Genomes Project, compared the maps against that derived from the reference human genome, and identified structural variations that are >5 kb in size. We find that these individuals have many more structural variants than those published, including some with the potential of disrupting gene function or regulation.