根据在癌突变谱中的共突变基因对识别癌基因

癌是一种涉及多基因变异的遗传异质性疾病,涉及多种生物学功能通路中不同基因的遗传变异。因此,识别癌基因是一项富有挑战性的工作。提出通过寻找在癌样本中突变显著共发生的基因筛选候选癌基因的方法。应用该方法,通过分析蛋白激酶基因在癌组织中的突变谱数据,发现了167个显著共发生突变的基因对,包含85个基因。分析这167个基因对发现:(1)发生共突变的基因富集已知的癌基因;(2)共突变基因对倾向于共扰动与癌症相关的通路对。以上结果提示,在癌样本中显著共发生突变的基因倾向于候选癌基因;在癌发生过程中起重要作用的基因倾向于协同扰动不同的癌相关细胞生物学过程。     

Identification of novel metastasis suppressor signaling pathways for breast cancer

Cancer lethality is mainly caused by metastasis. Therefore, understanding the nature of the genes involved in this process has become a priority. Given the heterogeneity of mutations in cancer cells, considerable focus has been directed toward characterizing metastasis genes in the context of relevant signaling pathways rather than treating genes as independent and equal entities. One signaling cascade implicated in the regulation of cell growth, invasion and metastasis is the MAP kinase pathway. Raf kinase inhibitory protein (RKIP) functions as an inhibitor of the MAP kinase pathway and is a metastasis suppressor in different cancer models. By utilizing statistical analysis of clinical data integrated with experimental validation, we recently identified components of the RKIP signaling pathway relevant to breast cancer metastasis. Using the RKIP pathway as an example, we show how prior biological knowledge can be efficiently combined with genome-wide patient data to identify gene regulatory mechanisms that control metastasis.     

Identification of novel metastasis suppressor signaling pathways for breast cancer

Cancer lethality is mainly caused by metastasis. Therefore, understanding the nature of the genes involved in this process has become a priority. Given the heterogeneity of mutations in cancer cells, considerable focus has been directed toward characterizing metastasis genes in the context of relevant signaling pathways rather than treating genes as independent and equal entities. One signaling cascade implicated in the regulation of cell growth, invasion and metastasis is the MAP kinase pathway. Raf kinase inhibitory protein (RKIP) functions as an inhibitor of the MAP kinase pathway and is a metastasis suppressor in different cancer models. By utilizing statistical analysis of clinical data integrated with experimental validation, we recently identified components of the RKIP signaling pathway relevant to breast cancer metastasis. Using the RKIP pathway as an example, we show how prior biological knowledge can be efficiently combined with genome-wide patient data to identify gene regulatory mechanisms that control metastasis.     

Integrated analysis of recurrent properties of cancer genes to identify novel drivers

The heterogeneity of cancer genomes in terms of acquired mutations complicates the identification of genes whose modification may exert a driver role in tumorigenesis. In this study, we present a novel method that integrates expression profiles, mutation effects, and systemic properties of mutated genes to identify novel cancer drivers. We applied our method to ovarian cancer samples and were able to identify putative drivers in the majority of carcinomas without mutations in known cancer genes, thus suggesting that it can be used as a complementary approach to find rare driver mutations that cannot be detected using frequency-based approaches.     

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.     

Gain and loss of phosphorylation sites in human cancer

MOTIVATION: Coding-region mutations in human genes are responsible for a diverse spectrum of diseases and phenotypes. Among lesions that have been studied extensively, there are insights into several of the biochemical functions disrupted by disease-causing mutations. Currently, there are more than 60 000 coding-region mutations associated with inherited disease catalogued in the Human Gene Mutation Database (HGMD, August 2007) and more than 70 000 polymorphic amino acid substitutions recorded in dbSNP (dbSNP, build 127). Understanding the mechanism and contribution these variants make to a clinical phenotype is a formidable problem. RESULTS: In this study, we investigate the role of phosphorylation in somatic cancer mutations and inherited diseases. Somatic cancer mutation datasets were shown to have a significant enrichment for mutations that cause gain or loss of phosphorylation when compared to our control datasets (putatively neutral nsSNPs and random amino acid substitutions). Of the somatic cancer mutations, those in kinase genes represent the most enriched set of mutations that disrupt phosphorylation sites, suggesting phosphorylation target site mutation is an active cause of phosphorylation deregulation. Overall, this evidence suggests both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancer-causing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations.     

Mouse models of DNA mismatch repair in cancer research

Germline mutations in DNA mismatch repair (MMR) genes are the cause of hereditary non-polyposis colorectal cancer/Lynch syndrome (HNPCC/LS) one of the most common cancer predisposition syndromes, and defects in MMR are also prevalent in sporadic colorectal cancers. In the past, the generation and analysis of mouse lines with knockout mutations in all of the known MMR genes has provided insight into how loss of individual MMR genes affects genome stability and contributes to cancer susceptibility. These studies also revealed essential functions for some of the MMR genes in B cell maturation and fertility. In this review, we will provide a brief overview of the cancer predisposition phenotypes of recently developed mouse models with targeted mutations in MutS and MutL homologs (Msh and Mlh, respectively) and their utility as preclinical models. The focus will be on mouse lines with conditional MMR mutations that have allowed more accurate modeling of human cancer syndromes in mice and that together with new technologies in gene targeting, hold great promise for the analysis of MMR-deficient intestinal tumors and other cancers which will drive the development of preventive and therapeutic treatment strategies.     

乳腺癌易感基因研究进展

Ｂｒｃａ１和Ｂｒｃａ２是乳腺癌易感基因,在家族性乳腺癌患者中的突变是可以遗传的,在散发性乳腺癌患者中有杂合性丢失（ＬＯＨ）,而且表达水平下降。体外实验证明,Ｂｒｃａ１能抑制乳腺癌和卵巢癌细胞的增殖。Ｂｒｃａ１和Ｂｒｃａ２基因分别定位于１７ｑ１２－２１和１３ｑ１２－１３,编码序列分别为５７１１ｂｐ和１０９８７ｂｐ,其表达有一定的组织特异性。ＢＲＣＡ１和ＢＲＣＡ２蛋白分别由１８６３个氨基酸和３４１８个氨基酸组成,这两个蛋白都具有Ｇｒａｎｉｎ蛋白的某些特征。它们的功能目前还不是很清楚,但有证据表明这两个基因为生长发育所必须,并参与细胞增殖分化和ＤＮＡ损伤修复等生命活动。     

Genome wide identification of recessive cancer genes by combinatorial mutation analysis

We devised a novel procedure to identify human cancer genes acting in a recessive manner. Our strategy was to combine the contributions of the different types of genetic alterations to loss of function: amino-acid substitutions, frame-shifts, gene deletions. We studied over 20,000 genes in 3 Gigabases of coding sequences and 700 array comparative genomic hybridizations. Recessive genes were scored according to nucleotide mismatches under positive selective pressure, frame-shifts and genomic deletions in cancer. Four different tests were combined together yielding a cancer recessive p-value for each studied gene. One hundred and fifty four candidate recessive cancer genes (p-value < 1.5 x 10(-7), FDR = 0.39) were identified. Strikingly, the prototypical cancer recessive genes TP53, PTEN and CDKN2A all ranked in the top 0.5% genes. The functions significantly affected by cancer mutations are exactly overlapping those of known cancer genes, with the critical exception for the absence of tyrosine kinases, as expected for a recessive gene-set.     

Functional impact bias reveals cancer drivers

Identifying cancer driver genes and pathways among all somatic mutations detected in a cohort of tumors is a key challenge in cancer genomics. Traditionally, this is done by prioritizing genes according to the recurrence of alterations that they bear. However, this approach has some known limitations, such as the difficulty to correctly estimate the background mutation rate, and the fact that it cannot identify lowly recurrently mutated driver genes. Here we present a novel approach, Oncodrive-fm, to detect candidate cancer drivers which does not rely on recurrence. First, we hypothesized that any bias toward the accumulation of variants with high functional impact observed in a gene or group of genes may be an indication of positive selection and can thus be used to detect candidate driver genes or gene modules. Next, we developed a method to measure this bias (FM bias) and applied it to three datasets of tumor somatic variants. As a proof of concept of our hypothesis we show that most of the highly recurrent and well-known cancer genes exhibit a clear FM bias. Moreover, this novel approach avoids some known limitations of recurrence-based approaches, and can successfully identify lowly recurrent candidate cancer drivers.     

Multiple levels of redundant processes inhibit Caenorhabditis elegans vulval cell fates

Many mutations cause obvious abnormalities only when combined with other mutations. Such synthetic interactions can be the result of redundant gene functions. In Caenorhabditis elegans, the synthetic multivulva (synMuv) genes have been grouped into multiple classes that redundantly inhibit vulval cell fates. Animals with one or more mutations of the same class undergo wild-type vulval development, whereas animals with mutations of any two classes have a multivulva phenotype. By varying temperature and genetic background, we determined that mutations in most synMuv genes within a single synMuv class enhance each other. However, in a few cases no enhancement was observed. For example, mutations that affect an Mi2 homolog and a histone methyltransferase are of the same class and do not show enhancement. We suggest that such sets of genes function together in vivo and in at least some cases encode proteins that interact physically. The approach of genetic enhancement can be applied more broadly to identify potential protein complexes as well as redundant processes or pathways. Many synMuv genes are evolutionarily conserved, and the genetic relationships we have identified might define the functions not only of synMuv genes in C. elegans but also of their homologs in other organisms.     

Structure‐energy‐based predictions and network modelling of RASopathy and cancer missense mutations

The Ras/MAPK syndromes (‘RASopathies’) are a class of developmental disorders caused by germline mutations in 15 genes encoding proteins of the Ras/mitogen‐activated protein kinase (MAPK) pathway frequently involved in cancer. Little is known about the molecular mechanisms underlying the differences in mutations of the same protein causing either cancer or RASopathies. Here, we shed light on 956 RASopathy and cancer missense mutations by combining protein network data with mutational analyses based on 3D structures. Using the protein design algorithm FoldX, we predict that most of the missense mutations with destabilising energies are in structural regions that control the activation of proteins, and only a few are predicted to compromise protein folding. We find a trend that energy changes are higher for cancer compared to RASopathy mutations. Through network modelling, we show that partly compensatory mutations in RASopathies result in only minor downstream pathway deregulation. In summary, we suggest that quantitative rather than qualitative network differences determine the phenotypic outcome of RASopathy compared to cancer mutations.     

Mutations and regulatory anomalies effecting tumor cell immune functions

The immune system is capable of interacting with tumor cells in such a way as to lead to tumor cell death, and this knowledge has inspired therapies to manipulate patient immune systems to eradicate cancer. However, tumor cells are able to mitigate the antitumor immune response, a fact that has rarely been addressed in the design of immunotherapies. There are many different tumor cell immune functions that play a role in mitigating the antitumor immune response. In some cases, these functions appear to be intimately associated with the tumor cell abnormalities that lead to loss of growth control, such as the cases where classical tumor suppressor proteins regulate tumor cell immune function genes. In other cases, tumor cell mutations appear to affect only the antitumor response, such as tumor cell mutations that eliminate MHC class I expression. Here I review the bases for tumor cell immune functions, noting in particular where tumor cell mutations, the gold standard for identifying a tumor-specific function, are known to be responsible for the tumor cell immune function. This review also discusses other known regulatory anomalies, in the absence of a known mutation, that are apparently important for tumor development and that regulate tumor cell immune functions. Surprisingly, in many cases where the tumor cell immune function is well understood in terms of its effect on the antitumor immune response, the tumor abnormality underlying the tumor cell immune function is completely uncharacterized.     

Molecular pathology of colorectal cancer.

The identification of several types of familial colorectal cancer has led to the discovery of some of the genes involved in these diseases. It was subsequently shown that somatic mutations of these genes (APC, mismatch repair genes, TP53, KRAS, and DCC) also occur in sporadic colorectal cancer. Gradually, this molecular information is being incorporated into the standard histopathological analysis of colorectal cancer and can be used for the characterization of primary tumors. Although attempts have been made to use molecular parameters to better define dysplasia grades, differentiate between adenoma and carcinoma, and subtype carcinomas, histological parameters remain the standard for the classification of primary tumors. Nonetheless, molecular parameters may help define subgroups of colorectal carcinoma differing in prognosis and requiring individualized treatment regimens. Interesting possibilities are predicting the response to chemotherapy or radiotherapy at a molecular level and the search for metastasis by looking for molecular markers in lymph nodes or circulating blood. Other pathological tests being developed include the detection of KRAS, TP53, or APC mutations in stool and plasma. Such approaches will have a significant impact on the clinical management of colorectal cancer.     

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.     

High accuracy mutation detection in leukemia on a selected panel of cancer genes

With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using Nimblegen sequence capture coupled with Roche/454 technology. First, we investigated how a maximal sensitivity and specificity of mutation detection can be achieved through a benchmark study. We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing. We found that the combination of two mapping algorithms, namely BWA-SW and SSAHA2, coupled with the variant calling algorithm Atlas-SNP2 yields the highest sensitivity (95%) and the highest specificity (93%). Next, we applied this analysis pipeline to identify mutations in a set of 58 cancer genes, in a panel of 18 T-ALL cell lines and 15 T-ALL patient samples. We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN. Interestingly, we also found mutations in several cancer genes that had not been linked to T-ALL before, including JAK3. Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4. In conclusion, we established an optimized analysis pipeline for Roche/454 data that can be applied to accurately detect gene mutations in cancer, which led to the identification of several new candidate T-ALL driver mutations.