A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes.

Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10-6), EOMES (P = 6.0 × 10-6) and BTNL2 (P = 2.1 × 10-3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice.

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we develop PCR-mediated Linkage of barcoded Adapters To nucleic acid Elements for sequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome for Oryza sativa covering 2,300 genes and constructing a high-quality protein-protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein-protein interactions in a wide range of organisms.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations.

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

Germline Lysine-Specific Demethylase 1 (LSD1/KDM1A) Mutations Confer Susceptibility to Multiple Myeloma.

Given the frequent and largely incurable occurrence of multiple myeloma, identification of germline genetic mutations that predispose cells to multiple myeloma may provide insight into disease etiology and the developmental mechanisms of its cell of origin, the plasma cell (PC). Here, we identified familial and early-onset multiple myeloma kindreds with truncating mutations in lysine-specific demethylase 1 (LSD1/KDM1A), an epigenetic transcriptional repressor that primarily demethylates histone H3 on lysine 4 and regulates hematopoietic stem cell self-renewal. In addition, we found higher rates of germline truncating and predicted deleterious missense KDM1A mutations in patients with multiple myeloma unselected for family history compared with controls. Both monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma cells have significantly lower KDM1A transcript levels compared with normal PCs. Transcriptome analysis of multiple myeloma cells from KDM1A mutation carriers shows enrichment of pathways and MYC target genes previously associated with myeloma pathogenesis. In mice, antigen challenge followed by pharmacologic inhibition of KDM1A promoted PC expansion, enhanced secondary immune response, elicited appearance of serum paraprotein, and mediated upregulation of MYC transcriptional targets. These changes are consistent with the development of MGUS. Collectively, our findings show that KDM1A is the first autosomal-dominant multiple myeloma germline predisposition gene providing new insights into its mechanistic roles as a tumor suppressor during post-germinal center B-cell differentiation.Significance: KDM1A is the first germline autosomal dominant predisposition gene identified in multiple myeloma and provides new insights into multiple myeloma etiology and the mechanistic role of KDM1A as a tumor suppressor during post-germinal center B-cell differentiation. Cancer Res; 78(10); 2747-59. ©2018 AACR.

Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk.

The high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance-a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691* and p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly and p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.

Interactome INSIDER: a structural interactome browser for genomic studies.

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

GeMSTONE: orchestrated prioritization of human germline mutations in the cloud.

Integrative analysis of whole-genome/exome-sequencing data has been challenging, especially for the non-programming research community, as it requires simultaneously managing a large number of computational tools. Even computational biologists find it unexpectedly difficult to reproduce results from others or optimize their strategies in an end-to-end workflow. We introduce Germline Mutation Scoring Tool fOr Next-generation sEquencing data (GeMSTONE), a cloud-based variant prioritization tool with high-level customization and a comprehensive collection of bioinformatics tools and data libraries (http://gemstone.yulab.org/). GeMSTONE generates and readily accepts a shareable 'recipe' file for each run to either replicate previous results or analyze new data with identical parameters and provides a centralized workflow for prioritizing germline mutations in human disease within a streamlined workflow rather than a pool of program executions.

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution.

A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations.

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, "Clone-seq", leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.

Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks.

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface "hot-spots," but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.

Somatic mutations in MAP3K5 attenuate its proapoptotic function in melanoma through increased binding to thioredoxin.

Patients with advanced metastatic melanoma have poor prognosis and the genetics underlying its pathogenesis are poorly understood. High-throughput sequencing has allowed comprehensive discovery of somatic mutations in cancer samples. Here, on analysis of our whole-genome and whole-exome sequencing data of 29 melanoma samples, we identified several genes that harbor recurrent nonsynonymous mutations. These included MAP3K5 (mitogen-activated protein kinase kinase kinase-5), which in a prevalence screen of 288 melanomas was found to harbor a R256C substitution in 5 cases. All MAP3K5-mutated samples were wild type for BRAF, suggesting a mutual exclusivity for these mutations. Functional analysis of the MAP3K5 R256C mutation revealed attenuation of MKK4 (mitogen-activated protein kinase kinase 4) activation through increased binding of the inhibitory protein thioredoxin (TXN/TRX-1/Trx), resulting in increased proliferation and anchorage-independent growth of melanoma cells. This mutation represents a potential target for the design of new therapies to treat melanoma.

Integrative annotation of variants from 1092 humans: application to cancer genomics.

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Whole-genome sequencing identifies a recurrent functional synonymous mutation in melanoma.

Synonymous mutations, which do not alter the protein sequence, have been shown to affect protein function [Sauna ZE, Kimchi-Sarfaty C (2011) Nat Rev Genet 12(10):683-691]. However, synonymous mutations are rarely investigated in the cancer genomics field. We used whole-genome and -exome sequencing to identify somatic mutations in 29 melanoma samples. Validation of one synonymous somatic mutation in BCL2L12 in 285 samples identified 12 cases that harbored the recurrent F17F mutation. This mutation led to increased BCL2L12 mRNA and protein levels because of differential targeting of WT and mutant BCL2L12 by hsa-miR-671-5p. Protein made from mutant BCL2L12 transcript bound p53, inhibited UV-induced apoptosis more efficiently than WT BCL2L12, and reduced endogenous p53 target gene transcription. This report shows selection of a recurrent somatic synonymous mutation in cancer. Our data indicate that silent alterations have a role to play in human cancer, emphasizing the importance of their investigation in future cancer genome studies.

Dissecting disease inheritance modes in a three-dimensional protein network challenges the "guilt-by-association" principle.

To better understand different molecular mechanisms by which mutations lead to various human diseases, we classified 82,833 disease-associated mutations according to their inheritance modes (recessive versus dominant) and molecular types (in-frame [missense point mutations and in-frame indels] versus truncating [nonsense mutations and frameshift indels]) and systematically examined the effects of different classes of disease mutations in a three-dimensional protein interactome network with the atomic-resolution interface resolved for each interaction. We found that although recessive mutations affecting the interaction interface of two interacting proteins tend to cause the same disease, this widely accepted "guilt-by-association" principle does not apply to dominant mutations. Furthermore, recessive truncating mutations in regions encoding the same interface are much more likely to cause the same disease, even for interfaces close to the N terminus of the protein. Conversely, dominant truncating mutations tend to be enriched in regions encoding areas between interfaces. These results suggest that a significant fraction of truncating mutations can generate functional protein products. For example, TRIM27, a known cancer-associated protein, interacts with three proteins (MID2, TRIM42, and SIRPA) through two different interfaces. A dominant truncating mutation (c.1024delT [p.Tyr342Thrfs*30]) associated with ovarian carcinoma is located between the regions encoding the two interfaces; the altered protein retains its interaction with MID2 and TRIM42 through the first interface but loses its interaction with SIRPA through the second interface. Our findings will help clarify the molecular mechanisms of thousands of disease-associated genes and their tens of thousands of mutations, especially for those carrying truncating mutations, often erroneously considered "knockout" alleles.

Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways.

The fission yeast Schizosaccharomyces pombe has more metazoan-like features than the budding yeast Saccharomyces cerevisiae, yet it has similarly facile genetics. We present a large-scale verified binary protein-protein interactome network, "StressNet," based on high-throughput yeast two-hybrid screens of interacting proteins classified as part of stress response and signal transduction pathways in S. pombe. We performed systematic, cross-species interactome mapping using StressNet and a protein interactome network of orthologous proteins in S. cerevisiae. With cross-species comparative network studies, we detected a previously unidentified component (Snr1) of the S. pombe mitogen-activated protein kinase Sty1 pathway. Coimmunoprecipitation experiments showed that Snr1 interacted with Sty1 and that deletion of snr1 increased the sensitivity of S. pombe cells to stress. Comparison of StressNet with the interactome network of orthologous proteins in S. cerevisiae showed that most of the interactions among these stress response and signaling proteins are not conserved between species but are "rewired"; orthologous proteins have different binding partners in both species. In particular, transient interactions connecting proteins in different functional modules were more likely to be rewired than conserved. By directly testing interactions between proteins in one yeast species and their corresponding binding partners in the other yeast species with yeast two-hybrid assays, we found that about half of the interactions that are traditionally considered "conserved" form modified interaction interfaces that may potentially accommodate novel functions.

Comparative exome sequencing of metastatic lesions provides insights into the mutational progression of melanoma.

BACKGROUND: Metastasis is characterized by spreading of neoplastic cells to an organ other than where they originated and is the predominant cause of death among cancer patients. This holds true for melanoma, whose incidence is increasing more rapidly than any other cancer and once disseminated has few therapeutic options. Here we performed whole exome sequencing of two sets of matched normal and metastatic tumor DNAs. RESULTS: Using stringent criteria, we evaluated the similarities and differences between the lesions. We find that in both cases, 96% of the single nucleotide variants are shared between the two metastases indicating that clonal populations gave rise to the distant metastases. Analysis of copy number variation patterns of both metastatic sets revealed a trend similar to that seen with our single nucleotide variants. Analysis of pathway enrichment on tumor sets shows commonly mutated pathways enriched between individual sets of metastases and all metastases combined. CONCLUSIONS: These data provide a proof-of-concept suggesting that individual metastases may have sufficient similarity for successful targeting of driver mutations.

Mutational signatures of de-differentiation in functional non-coding regions of melanoma genomes.

Much emphasis has been placed on the identification, functional characterization, and therapeutic potential of somatic variants in tumor genomes. However, the majority of somatic variants lie outside coding regions and their role in cancer progression remains to be determined. In order to establish a system to test the functional importance of non-coding somatic variants in cancer, we created a low-passage cell culture of a metastatic melanoma tumor sample. As a foundation for interpreting functional assays, we performed whole-genome sequencing and analysis of this cell culture, the metastatic tumor from which it was derived, and the patient-matched normal genomes. When comparing somatic mutations identified in the cell culture and tissue genomes, we observe concordance at the majority of single nucleotide variants, whereas copy number changes are more variable. To understand the functional impact of non-coding somatic variation, we leveraged functional data generated by the ENCODE Project Consortium. We analyzed regulatory regions derived from multiple different cell types and found that melanocyte-specific regions are among the most depleted for somatic mutation accumulation. Significant depletion in other cell types suggests the metastatic melanoma cells de-differentiated to a more basal regulatory state. Experimental identification of genome-wide regulatory sites in two different melanoma samples supports this observation. Together, these results show that mutation accumulation in metastatic melanoma is nonrandom across the genome and that a de-differentiated regulatory architecture is common among different samples. Our findings enable identification of the underlying genetic components of melanoma and define the differences between a tissue-derived tumor sample and the cell culture created from it. Such information helps establish a broader mechanistic understanding of the linkage between non-coding genomic variations and the cellular evolution of cancer.

Three-dimensional reconstruction of protein networks provides insight into human genetic disease.

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.