A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.
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ABSTRACT:
SUBMITTER: Chen S
PROVIDER: S-EPMC8744679 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Here an analysis is presented of the roles of recombination and mutation in shaping previously determined haplotype variation in 9.7 kb of genomic DNA sequence from the human lipoprotein lipase gene (LPL), scored in 71 individuals from three populations: 24 African Americans, 24 Finns, and 23 non-Hispanic whites. Recombination and gene-conversion events inferred from data on 88 haplotypes that were defined by 69 variable sites were tested. The analysis revealed 29 statistically significant recombination events and one gene-conversion event. The recombination events were concentrated in a 1.9-kb region, near the middle of the segment, that contains a microsatellite and a pair of tandem and complementary mononucleotide runs; both the microsatellite and the runs show length variation. An analysis of site variation revealed that 9.6% of the nucleotides at CpG sites were variable, as were 3% of the nucleotides found in mononucleotide runs of >/=5 nucleotides, 3% of the nucleotides found </=3 bp from certain putative polymerase alpha-arrest sites, and 0. 5% of the remaining nucleotides. This nonhomogeneous distribution of variation suggests that multiple mutational hits at certain sites are common, an observation that challenges the fundamental assumption of the infinite-sites-mutation model. The nonrandom patterns of recombination and mutation suggest that randomly chosen single-nucleotide polymorphisms may not be optimal for disequilibrium mapping of this gene. Overall, these results indicate that both recombinational and mutational hotspots have played significant roles in shaping the haplotype variation at the LPL locus.
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Project description:RNA Binding Proteins (RBPs) are a class of post-transcriptional regulatory molecules which are increasingly documented to be dysfunctional in cancer genomes. However, our current understanding of these alterations is limited. Here, we delineate the mutational landscape of ?1300 RBPs in ?6000 cancer genomes. Our analysis revealed that RBPs have an average of ?3 mutations per Mb across 26 cancer types. We identified 281 RBPs to be enriched for mutations (GEMs) in at least one cancer type. GEM RBPs were found to undergo frequent frameshift and inframe deletions as well as missense, nonsense and silent mutations when compared to those that are not enriched for mutations. Functional analysis of these RBPs revealed the enrichment of pathways associated with apoptosis, splicing and translation. Using the OncodriveFM framework, we also identified more than 200 candidate driver RBPs that were found to accumulate functionally impactful mutations in at least one cancer. Expression levels of 15% of these driver RBPs exhibited significant difference, when transcriptome groups with and without deleterious mutations were compared. Functional interaction network of the driver RBPs revealed the enrichment of spliceosomal machinery, suggesting a plausible mechanism for tumorogenesis while network analysis of the protein interactions between RBPs unambiguously revealed the higher degree, betweenness and closeness centrality for driver RBPs compared to non-drivers. Analysis to reveal cancer-specific Ribonucleoprotein (RNP) mutational hotspots showed extensive rewiring even among common drivers between cancer types. Knockdown experiments on pan-cancer drivers such as SF3B1 and PRPF8 in breast cancer cell lines, revealed cancer subtype specific functions like selective stem cell features, indicating a plausible means for RBPs to mediate cancer-specific phenotypes. Hence, this study would form a foundation to uncover the contribution of the mutational spectrum of RBPs in dysregulating the post-transcriptional regulatory networks in different cancer types.
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Project description:BackgroundCofactors, biomarkers, and the mutational status of genes such as TP53, EGFR, IDH1/2, or PIK3CA have been used for patient stratification. However, many genes exhibit recurrent mutational positions known as hotspots, specifically linked to varying degrees of survival outcomes. Nevertheless, few hotspots have been analyzed (e.g., TP53 and EGFR). Thus, many other genes and hotspots remain unexplored.MethodsWe systematically screened over 1400 hotspots across 33 TCGA cancer types. We compared the patients carrying a hotspot against (i) all cases, (ii) gene-mutated cases, (iii) other mutated hotspots, or (iv) specific hotspots. Due to the limited number of samples in hotspots and the inherent group imbalance, besides Cox models and the log-rank test, we employed VALORATE to estimate their association with survival precisely.ResultsWe screened 1469 hotspots in 6451 comparisons, where 314 were associated with survival. Many are discussed and linked to the current literature. Our findings demonstrate associations between known hotspots and survival while also revealing more potential hotspots. To enhance accessibility and promote further investigation, all the Kaplan-Meier curves, the log-rank tests, Cox statistics, and VALORATE-estimated null distributions are accessible on our website.ConclusionsOur analysis revealed both known and putatively novel hotspots associated with survival, which can be used as biomarkers. Our web resource is a valuable tool for cancer research.
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