Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers

Large‐scale cancer genome sequencing has uncovered thousands of gene mutations, but distinguishing tumor driver genes from functionally neutral passenger mutations is a major challenge. We analyzed 800 cancer genomes of eight types to find single‐nucleotide variants (SNVs) that precisely target phosphorylation machinery, important in cancer development and drug targeting. Assuming that cancer‐related biological systems involve unexpectedly frequent mutations, we used novel algorithms to identify genes with significant phosphorylation‐associated SNVs (pSNVs), phospho‐mutated pathways, kinase networks, drug targets, and clinically correlated signaling modules. We highlight increased survival of patients with TP53 pSNVs, hierarchically organized cancer kinase modules, a novel pSNV in EGFR, and an immune‐related network of pSNVs that correlates with prolonged survival in ovarian cancer. Our findings include multiple actionable cancer gene candidates (FLNB, GRM1, POU2F1), protein complexes (HCF1, ASF1), and kinases (PRKCZ). This study demonstrates new ways of interpreting cancer genomes and presents new leads for cancer research.     

Signatures of accelerated somatic evolution in gene promoters in multiple cancer types

Cancer-associated somatic mutations outside protein-coding regions remain largely unexplored. Analyses of the TERT locus have indicated that non-coding regulatory mutations can be more frequent than previously suspected and play important roles in oncogenesis. Using a computational method called SASE-hunter, developed here, we identified a novel signature of accelerated somatic evolution (SASE) marked by a significant excess of somatic mutations localized in a genomic locus, and prioritized those loci that carried the signature in multiple cancer patients. Interestingly, even when an affected locus carried the signature in multiple individuals, the mutations contributing to SASE themselves were rarely recurrent at the base-pair resolution. In a pan-cancer analysis of 906 samples from 12 tumor types, we detected SASE in the promoters of several genes, including known cancer genes such as MYC, BCL2, RBM5 and WWOX. Nucleotide substitution patterns consistent with oxidative DNA damage and local somatic hypermutation appeared to contribute to this signature in selected gene promoters (e.g. MYC). SASEs in selected cancer gene promoters were associated with over-expression, and also correlated with the age of onset of cancer, aggressiveness of the disease and survival. Taken together, our work detects a hitherto under-appreciated and clinically important class of regulatory changes in cancer genomes.     

Systematic identification of dysregulated lncRNAs associated with platinum-based chemotherapy response across 11 cancer types

Aberrant expression of long non-coding RNAs (lncRNAs) leads to the development of chemoresistance by regulating a series of biological processes, which is one of the major obstacles in the cancer treatment. This study aimed to identify some key lncRNAs that are associated with platinum-based chemoresistance in multiple cancers. Regulating the expression levels of these lncRNAs can enhance the sensitivity of patients to chemotherapy drugs and improve the therapeutic effect of cancer. By systematically analyzing 648 samples regarding platinum drug response from the Cancer Genome Atlas (TCGA), we have identified 32 dysregulated lncRNAs across 11 cancer types that could affect platinum-based chemotherapy response, of which 78.125% (25/32) were significantly down-regulated in drug-resistant samples. Drug response prediction model that had been constructed based on the expression pattern of these dysregulated lncRNAs could accurately predict the chemotherapy response of tumor patients, and the area under the curve (AUC) was between 0.8034 and 0.9984. In particular, all of these dysregulated lncRNAs that we identified were cancer-specific. They were significantly associated with the survival of tumor patients and could serve as cancer-specific biomarkers for prognosis. In conclusion, this study will contribute to improving the drug resistance of tumor patients during chemotherapy, and it is of real significance for selecting effective chemotherapy drugs and achieving precision medicine.     

Systematic evaluation of the effects of genetic variants on PIWI-interacting RNA expression across 33 cancer types

PIWI-interacting RNAs (piRNAs) are an emerging class of non-coding RNAs involved in tumorigenesis. Expression quantitative trait locus (eQTL) analysis has been demonstrated to help reveal the genetic mechanism of single nucleotide polymorphisms (SNPs) in cancer etiology. However, there are no databases that have been constructed to provide an eQTL analysis between SNPs and piRNA expression. In this study, we collected genotyping and piRNA expression data for 10 997 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Using linear regression cis-eQTL analysis with adjustment of appropriate covariates, we identified millions of SNP-piRNA pairs in tumor (76 924 831) and normal (24 431 061) tissues. Further, we performed differential expression and survival analyses, and linked the eQTLs to genome-wide association study (GWAS) data to comprehensively decipher the functional roles of identified cis-piRNA eQTLs. Finally, we developed a user-friendly database, piRNA-eQTL (http://njmu-edu.cn:3838/piRNA-eQTL/), to help users query, browse and download corresponding eQTL results. In summary, piRNA-eQTL could serve as an important resource to assist the research community in understanding the roles of genetic variants and piRNAs in the development of cancers.     

The impact of mtDNA mutations on proteins structure in selected types of cancer

Recently published papers report a large number of mitochondrial DNA mutations in many different cancer types, but their significance for electron transport chain proteins remains unknown. This review covers structural mutations of mitochondrial genes, choosing prostate cancer, esophageal cancer and epithelioma as research models. As all mitochondrial genes encode subunits of the electron transport chain, the review focuses on the consequences of structural mutations on cell metabolism.     

Novel potential oncogenic and druggable mutations of FGFRs recur in the kinase domain across cancer types

Fibroblast growth factor receptors (FGFRs) are recurrently altered by single nucleotide variants (SNVs) in many human cancers. The prevalence of SNVs in FGFRs depends on the cancer type. In some tumors, such as the urothelial carcinoma, mutations of FGFRs occur at very high frequency (up to 60%). Many characterized mutations occur in the extracellular or transmembrane domains, while fewer known mutations are found in the kinase domain. In this study, we performed a bioinformatics analysis to identify novel putative cancer driver or therapeutically actionable mutations of the kinase domain of FGFRs. To pinpoint those mutations that may be clinically relevant, we exploited the recurrence of alterations on analogous amino acid residues within the kinase domain (PK_Tyr_Ser-Thr) of different kinases as a predictor of functional impact. By exploiting MutationAligner and LowMACA bioinformatics resources, we highlighted novel uncharacterized mutations of FGFRs which recur in other protein kinases. By revealing unanticipated correspondence with known variants, we were able to infer their functional effects, as alterations clustering on similar residues in analogous proteins have a high probability to elicit similar effects. As FGFRs represent an important class of oncogenes and drug targets, our study opens the way for further studies to validate their driver and/or actionable nature and, in the long term, for a more efficacious application of precision oncology.     

The impact of the cardiovascular component and somatic mutations on ageing

Mechanistic insight into ageing may empower prolonging the lifespan of humans; however, a complete understanding of this process is still lacking despite a plethora of ageing theories. In order to address this, we investigated the association of lifespan with eight phenotypic traits, that is, litter size, body mass, female and male sexual maturity, somatic mutation, heart, respiratory, and metabolic rate. In support of the somatic mutation theory, we analysed 15 mammalian species and their whole-genome sequencing deriving somatic mutation rate, which displayed the strongest negative correlation with lifespan. All remaining phenotypic traits showed almost equivalent strong associations across this mammalian cohort, however, resting heart rate explained additional variance in lifespan. Integrating somatic mutation and resting heart rate boosted the prediction of lifespan, thus highlighting that resting heart rate may either directly influence lifespan, or represents an epiphenomenon for additional lower-level mechanisms, for example, metabolic rate, that are associated with lifespan.     

Upregulated KPNA2 promotes hepatocellular carcinoma progression and indicates prognostic significance across human cancer types

Hepatocellular carcinoma(HCC)is one of the most aggressive cancers worldwide.Identification of the molecular mechanisms underlying the development and progression of HCC is particularly important.Here,we demonstrated the expression pattern,clinical significance,and function of Karyopherin α2(KPNA2)in HCC.The expression of KPNA2 was upregulated in tumor tissue and negatively associated with the survival time,and a significant correlation between KPNA2 expression and aggressive clinical characteristics was established.Both in vitro and in vivo experiments demonstrated that knockdown of KPNA2 reduced migration and proliferation capacities of HCC cells,while over-expression of KPNA2 increased these malignant characteristics.The analysis of the Cancer Genome Atlas cohorts also reveals that high-KPNA2 expression is associated with poor outcome in multiple cancer types.In addition,gene sets enrichment analysis exhibited cell cycle and DNA replication as the top altered pathways in the high-KPNA2 expression group in HCC and other two cancer types.Overall,this study identified KPNA2 as a potential diagnostic and prognostic biomarker in HCC and other neoplasms,probably by regulating cell cycle and DNA replication.     

Statistical analysis of pathogenicity of somatic mutations in cancer

Recent large-scale sequencing studies have revealed that cancer genomes contain variable numbers of somatic point mutations distributed across many genes. These somatic mutations most likely include passenger mutations that are not cancer causing and pathogenic driver mutations in cancer genes. Establishing a significant presence of driver mutations in such data sets is of biological interest. Whereas current techniques from phylogeny are applicable to large data sets composed of singly mutated samples, recently exemplified with a p53 mutation database, methods for smaller data sets containing individual samples with multiple mutations need to be developed. By constructing distinct models of both the mutation process and selection pressure upon the cancer samples, exact statistical tests to examine this problem are devised. Tests to examine the significance of selection toward missense, nonsense, and splice site mutations are derived, along with tests assessing variation in selection between functional domains. Maximum-likelihood methods facilitate parameter estimation, including levels of selection pressure and minimum numbers of pathogenic mutations. These methods are illustrated with 25 breast cancers screened across the coding sequences of 518 kinase genes, revealing 90 base substitutions in 71 genes. Significant selection pressure upon truncating mutations was established. Furthermore, an estimated minimum of 29.8 mutations were pathogenic.     

A comprehensive catalogue of somatic mutations in cancer genomes

This Hot Topics contribution considers two recently published papers that demonstrate the utility of advanced DNA sequencing technologies for identifying classes of mutations other than base substitutions. Data are presented from genome analyses of immortalized cell lines derived from a malignant melanoma and a small cell carcinoma of the lung. Among other observations the studies suggest the operation of novel DNA repair mechanisms or modes.     

The role of multiple somatic point mutations in metastatic progression.

To test the hypothesis that the ability to metastasize is determined by multiple point mutations during the expansion of a neoplastic clone, a mathematical model for sequential mutations was derived. Development of the metastatic phenotype was attributed to the mutation of a specific group of genes. The average tumor size was estimated for when a cell should manifest a set number of these mutated genes. In a tumor of 10(9) cells subject to 10(-6) mutations/gene per generation, only one of these genes, on average, should have mutated. To explain the multiplicity of changes associated with the metastatic phenotype, genetic variation at rates greater than 10(-3) variations/gene per generation seems necessary. Possible mechanisms for this variation involve gene amplification, chromosomal aneuploidy, and altered gene regulation rather than point mutation.     

Strongly preserved modules between cancer tissue and cell line contribute to drug resistance analysis across multiple cancer types

The existence and emergence of drug resistance in tumor cells is the main burden of cancer treatment. Most cancer drug resistance analyses are based entirely on cell line data and ignore the discordance between human tumors and cell lines, leading to biased preclinical model transformation. Based on cancer tissue data in TCGA and cancer cell line data in CCLE, this study identified and excluded non-preserved module (NP module) between cancer tissue and cell lines. We used strongly preserved module (SP module) for clinically relevant drug resistance analysis and identified 2068 “cancer-drug-module” pairs of 7 cancer types and 212 drugs based on data in GDSC. Furthermore, we identified potentially ineffective combination therapy (PICT) from multiple perspectives. Finally, we found 1608 sets of predictors that can predict drug response. These results provide insights and clues for the clinical selection of effective chemotherapy drugs to overcome cancer resistance in a new perspective.     

A drug-inducible transgenic system for direct reprogramming of multiple somatic cell types

The study of induced pluripotency is complicated by the need for infection with high-titer retroviral vectors, which results in genetically heterogeneous cell populations. We generated genetically homogeneous 'secondary' somatic cells that carry the reprogramming factors as defined doxycycline (dox)-inducible transgenes. These cells were produced by infecting fibroblasts with dox-inducible lentiviruses, reprogramming by dox addition, selecting induced pluripotent stem cells and producing chimeric mice. Cells derived from these chimeras reprogram upon dox exposure without the need for viral infection with efficiencies 25- to 50-fold greater than those observed using direct infection and drug selection for pluripotency marker reactivation. We demonstrate that (i) various induction levels of the reprogramming factors can induce pluripotency, (ii) the duration of transgene activity directly correlates with reprogramming efficiency, (iii) cells from many somatic tissues can be reprogrammed and (iv) different cell types require different induction levels. This system facilitates the characterization of reprogramming and provides a tool for genetic or chemical screens to enhance reprogramming.     

Uncovering the profile of somatic mtDNA mutations in Chinese colorectal cancer patients

In the past decade, a high incidence of somatic mitochondrial DNA (mtDNA) mutations has been observed, mostly based on a fraction of the molecule, in various cancerous tissues; nevertheless, some of them were queried due to problems in data quality. Obviously, without a comprehensive understanding of mtDNA mutational profile in the cancerous tissue of a specific patient, it is unlikely to disclose the genuine relationship between somatic mtDNA mutations and tumorigenesis. To achieve this objective, the most straightforward way is to directly compare the whole mtDNA genome variation among three tissues (namely, cancerous tissue, para-cancerous tissue, and distant normal tissue) from the same patient. Considering the fact that most of the previous studies on the role of mtDNA in colorectal tumor focused merely on the D-loop or partial segment of the molecule, in the current study we have collected three tissues (cancerous, para-cancerous and normal tissues) respectively recruited from 20 patients with colorectal tumor and completely sequenced the mitochondrial genome of each tissue. Our results reveal a relatively lower incidence of somatic mutations in these patients; intriguingly, all somatic mutations are in heteroplasmic status. Surprisingly, the observed somatic mutations are not restricted to cancer tissues, for the para-cancer tissues and distant normal tissues also harbor somatic mtDNA mutations with a lower frequency than cancerous tissues but higher than that observed in the general population. Our results suggest that somatic mtDNA mutations in cancerous tissues could not be simply explained as a consequence of tumorigenesis; meanwhile, the somatic mtDNA mutations in normal tissues might reflect an altered physiological environment in cancer patients.