A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers

Large-scale sequencing of cancer genomes has revealed many novel mutations and inter-tumoral heterogeneity. Therefore, prioritizing variants according to their potential deleterious effects has become essential. We constructed a disease gene network and proposed a Bayesian ensemble approach that integrates diverse sources to predict the functional effects of missense variants. We analyzed 23,336 missense disease mutations and 36,232 neutral polymorphisms of 12,039 human proteins. The results showed successful improvement of prediction accuracy in both sensitivity and specificity, and we demonstrated the utility of the method by applying it to somatic mutations obtained from colorectal and breast cancer cell lines. The candidate genes with predicted deleterious mutations as well as known cancer genes were significantly enriched in many KEGG pathways related to carcinogenesis, supporting genetic homogeneity of cancer at the pathway level. The breast cancer-specific network increased the prediction accuracy for breast cancer mutations. This study provides a ranked list of deleterious mutations and candidate cancer genes and suggests that mutations affecting cancer may occur in important pathways and should be interpreted on the phenotype-related network or pathway. A disease gene network may be of value in predicting functional effects of novel disease-specific mutations.     

Cancer driver mutations: predictions and reality

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.     

Novel molecular and computational methods improve the accuracy of insertion site analysis in Sleeping Beauty-induced tumors

The recent development of the Sleeping Beauty (SB) system has led to the development of novel mouse models of cancer. Unlike spontaneous models, SB causes cancer through the action of mutagenic transposons that are mobilized in the genomes of somatic cells to induce mutations in cancer genes. While previous methods have successfully identified many transposon-tagged mutations in SB-induced tumors, limitations in DNA sequencing technology have prevented a comprehensive analysis of large tumor cohorts. Here we describe a novel method for producing genetic profiles of SB-induced tumors using Illumina sequencing. This method has dramatically increased the number of transposon-induced mutations identified in each tumor sample to reveal a level of genetic complexity much greater than previously appreciated. In addition, Illumina sequencing has allowed us to more precisely determine the depth of sequencing required to obtain a reproducible signature of transposon-induced mutations within tumor samples. The use of Illumina sequencing to characterize SB-induced tumors should significantly reduce sampling error that undoubtedly occurs using previous sequencing methods. As a consequence, the improved accuracy and precision provided by this method will allow candidate cancer genes to be identified with greater confidence. Overall, this method will facilitate ongoing efforts to decipher the genetic complexity of the human cancer genome by providing more accurate comparative information from Sleeping Beauty models of cancer.     

DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in a wide range of biological processes and have been associated with various diseases, including cancer. In the last few years, cancer genome projects have systematically collected genetic variations underlying multiple cancer types. In parallel, the number and different types of disordered proteins characterized by experimental methods have also significantly increased. Nevertheless, the role of IDPs in various types of cancer is still not well understood. In this work, we present DisCanVis, a novel visualization tool for cancer mutations with a special focus on IDPs. In order to aid the interpretation of observed mutations, genome level information is combined with information about the structural and functional properties of proteins. The web server enables users to inspect individual proteins, collect examples with existing annotations of protein disorder and associated function or to discover currently uncharacterized examples with likely disease relevance. Through a REST API interface and precompiled tables the analysis can be extended to a group of proteins.     

Clonality and Evolutionary History of Rhabdomyosarcoma

To infer the subclonality of rhabdomyosarcoma (RMS) and predict the temporal order of genetic events for the tumorigenic process, and to identify novel drivers, we applied a systematic method that takes into account germline and somatic alterations in 44 tumor-normal RMS pairs using deep whole-genome sequencing. Intriguingly, we find that loss of heterozygosity of 11p15.5 and mutations in RAS pathway genes occur early in the evolutionary history of the PAX-fusion-negative-RMS (PFN-RMS) subtype. We discover several early mutations in non-RAS mutated samples and predict them to be drivers in PFN-RMS including recurrent mutation of PKN1. In contrast, we find that PAX-fusion-positive (PFP) subtype tumors have undergone whole-genome duplication in the late stage of cancer evolutionary history and have acquired fewer mutations and subclones than PFN-RMS. Moreover we predict that the PAX3-FOXO1 fusion event occurs earlier than the whole genome duplication. Our findings provide information critical to the understanding of tumorigenesis of RMS.     

Impact of oncogenic driver mutations on feedback between the PI3K and MEK pathways in cancer cells

Inhibition of the PI3K (phosphoinositide 3-kinase)/Akt/mTORC1 (mammalian target of rapamycin complex 1) and Ras/MEK [MAPK (mitogen-activated protein kinase)/ERK (extracellular-signal-regulated kinase) kinase]/ERK pathways for cancer therapy has been pursued for over a decade with limited success. Emerging data have indicated that only discrete subsets of cancer patients have favourable responses to these inhibitors. This is due to genetic mutations that confer drug insensitivity and compensatory mechanisms. Therefore understanding of the feedback mechanisms that occur with respect to specific genetic mutations may aid identification of novel biomarkers that predict patient response. In the present paper, we show that feedback between the PI3K/Akt/mTORC1 and Ras/MEK/ERK pathways is cell-line-specific and highly dependent on the activating mutation of K-Ras or overexpression c-Met. We found that cell lines exhibited differential signalling and apoptotic responses to PD184352, a specific MEK inhibitor, and PI103, a second-generation class I PI3K inhibitor. We reveal that feedback from the PI3K/Akt/mTORC1 to the Ras/MEK/ERK pathway is present in cancer cells harbouring either K-Ras activating mutations or amplification of c-Met but not the wild-type counterparts. Moreover, we demonstrate that inhibition of protein phosphatase activity by OA (okadaic acid) restored PI103-mediated feedback in wild-type cells. Together, our results demonstrate a novel mechanism for feedback between the PI3K/Akt/mTORC1 and the Ras/MEK/ERK pathways that only occurs in K-Ras mutant and c-Met amplified cells but not the isogenic wild-type cells through a mechanism that may involve inhibition of a specific endogenous phosphatase(s) activity. We conclude that monitoring K-Ras and c-Met status are important biomarkers for determining the efficacy of PI103 and other PI3K/Akt inhibitors in cancer therapy.     

A disequilibrium model for detecting genetic mutations for cancer

It has been recognized that genetic mutations in specific nucleotides may give rise to cancer via the alteration of signaling pathways. Thus, the detection of those cancer-causing mutations has received considerable interest in cancer genetic research. Here, we propose a statistical model for characterizing genes that lead to cancer through point mutations using genome-wide single nucleotide polymorphism (SNP) data. The basic idea of the model is that mutated genes may be in high association with their nearby SNPs because of evolutionary forces. By genotyping SNPs in both normal and cancer cells, we formulate a polynomial likelihood to estimate the population genetic parameters related to cancer, such as allele frequencies of cancer-causing alleles, mutation rates of alleles derived from maternal or paternal parents, and zygotic linkage disequilibria between different loci after the mutation occurs. We implement the EM algorithm to estimate some of these parameters because of the missing information in the likelihood construction. The model allows the elegant tests of the significant associations between mutated cancer genes and genome-wide SNPs, thus providing a way for predicting the occurrence and formation of cancer with genetic information. The model, validated through computer simulation, may help cancer geneticists design efficient experiments and formulate hypotheses for cancer gene identification.     

Prognostic value of infiltrating immune cells in clear cell renal cell carcinoma (ccRCC)

In this study, we attempted to evaluate the prognostic value of infiltrating immune/stromal cells in clear cell renal cell carcinoma (ccRCC), by using the immune scores and stromal scores based on the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” algorithm to represent the levels of infiltrating immune cells and stromal cells. We found that the infiltrating immune cells were associated with poor prognosis of ccRCC. To assess the role of infiltrating immune cells in ccRCC cells, first, we performed differentially expressed genes analysis and functional analysis for validation. The results showed that the underlying mechanism by which infiltrating immune cells promoted cancer progression involved in regulating the nuclear division, angiogenesis, and immune response. Next, we investigated the relationship between infiltrating immune cells and mutations in ccRCC cells. We found that the infiltrating immune cells have certain effects on genetic mutations. In conclusion, infiltrating immune cells within the tumor microenvironment can be used to predict prognosis in ccRCC.     

An ordinary differential equation model for the multistep transformation to cancer

Cancer is viewed as a multistep process whereby a normal cell is transformed into a cancer cell through the acquisition of mutations. We reduce the complexities of cancer progression to a simple set of underlying rules that govern the transformation of normal cells to malignant cells. In doing so, we derive an ordinary differential equation model that explores how the balance of angiogenesis, cell death rates, genetic instability, and replication rates give rise to different kinetics in the development of cancer. The key predictions of the model are that cancer develops fastest through a particular ordering of mutations and that mutations in genes that maintain genomic integrity would be the most deleterious type of mutations to inherit. In addition, we perform a sensitivity analysis on the parameters included in the model to determine the probable contribution of each. This paper presents a novel approach to viewing the genetic basis of cancer from a systems biology perspective and provides the groundwork for other models that can be directly tied to clinical and molecular data.     

Mouse embryonic stem cell-based functional assay to evaluate mutations in BRCA2

Individuals with mutations in breast cancer susceptibility genes BRCA1 and BRCA2 have up to an 80% risk of developing breast cancer by the age of 70. Sequencing-based genetic tests are now available to identify mutation carriers in an effort to reduce mortality through prevention and early diagnosis. However, lack of a suitable functional assay hinders the risk assessment of more than 1,900 BRCA1 and BRCA2 variants in the Breast Cancer Information Core database that do not clearly disrupt the gene product. We have established a simple, versatile and reliable assay to test for the functional significance of mutations in BRCA2 using mouse embryonic stem cells (ES cells) and bacterial artificial chromosomes and have used it to classify 17 sequence variants. The assay is based on the ability of human BRCA2 to complement the loss of endogenous Brca2 in mouse ES cells. This technique may also serve as a paradigm for functional analysis of mutations found in other genes linked to human diseases.     

A cross-cancer metastasis signature in the microRNA–mRNA axis of paired tissue samples

In the progression of cancer, cells acquire genetic mutations that cause uncontrolled growth. Over time, the primary tumour may undergo additional mutations that allow for the cancerous cells to spread throughout the body as metastases. Since metastatic development typically results in markedly worse patient outcomes, research into the identity and function of metastasis-associated biomarkers could eventually translate into clinical diagnostics or novel therapeutics. Although the general processes underpinning metastatic progression are understood, no clear cross-cancer biomarker profile has emerged. However, the literature suggests that some microRNAs (miRNAs) may play an important role in the metastatic progression of several cancer types. Using a subset of The Cancer Genome Atlas (TCGA) data, we performed an integrated analysis of mRNA and miRNA expression with paired metastatic and primary tumour samples to interrogate how the miRNA–mRNA regulatory axis influences metastatic progression. From this, we successfully built mRNA- and miRNA-specific classifiers that can discriminate pairs of metastatic and primary samples across 11 cancer types. In addition, we identified a number of miRNAs whose metastasis-associated dysregulation could predict mRNA metastasis-associated dysregulation. Among the most predictive miRNAs, we found several previously implicated in cancer progression, including miR-301b, miR-1296, and miR-423. Taken together, our results suggest that metastatic samples have a common cross-cancer signature when compared with their primary tumour pair, and that these miRNA biomarkers can be used to predict metastatic status as well as mRNA expression.

     

Identification of a Comprehensive Spectrum of Genetic Factors for Hereditary Breast Cancer in a Chinese Population by Next-Generation Sequencing

The genetic etiology of hereditary breast cancer has not been fully elucidated. Although germline mutations of high-penetrance genes such as BRCA1/2 are implicated in development of hereditary breast cancers, at least half of all breast cancer families are not linked to these genes. To identify a comprehensive spectrum of genetic factors for hereditary breast cancer in a Chinese population, we performed an analysis of germline mutations in 2,165 coding exons of 152 genes associated with hereditary cancer using next-generation sequencing (NGS) in 99 breast cancer patients from families of cancer patients regardless of cancer types. Forty-two deleterious germline mutations were identified in 21 genes of 34 patients, including 18 (18.2%) BRCA1 or BRCA2 mutations, 3 (3%) TP53 mutations, 5 (5.1%) DNA mismatch repair gene mutations, 1 (1%) CDH1 mutation, 6 (6.1%) Fanconi anemia pathway gene mutations, and 9 (9.1%) mutations in other genes. Of seven patients who carried mutations in more than one gene, 4 were BRCA1/2 mutation carriers, and their average onset age was much younger than patients with only BRCA1/2 mutations. Almost all identified high-penetrance gene mutations in those families fulfill the typical phenotypes of hereditary cancer syndromes listed in the National Comprehensive Cancer Network (NCCN) guidelines, except two TP53 and three mismatch repair gene mutations. Furthermore, functional studies of MSH3 germline mutations confirmed the association between MSH3 mutation and tumorigenesis, and segregation analysis suggested antagonism between BRCA1 and MSH3. We also identified a lot of low-penetrance gene mutations. Although the clinical significance of those newly identified low-penetrance gene mutations has not been fully appreciated yet, these new findings do provide valuable epidemiological information for the future studies. Together, these findings highlight the importance of genetic testing based on NCCN guidelines and a multi-gene analysis using NGS may be a supplement to traditional genetic counseling.     

Novel role of p53 in maintaining mitochondrial genetic stability through interaction with DNA Pol gamma

Mitochondrial DNA (mtDNA) mutations and deletions are frequently observed in cancer, and contribute to altered energy metabolism, increased reactive oxygen species (ROS), and attenuated apoptotic response to anticancer agents. The mechanisms by which cells maintain mitochondrial genomic integrity and the reason why cancer cells exhibit more frequent mtDNA mutations remain unclear. Here, we report that the tumor suppressor molecule p53 has a novel role in maintaining mitochondrial genetic stability through its ability to translocate to mitochondria and interact with mtDNA polymerase gamma (pol gamma) in response to mtDNA damage induced by exogenous and endogenous insults including ROS. The p53 protein physically interacts with mtDNA and pol gamma, and enhances the DNA replication function of pol gamma. Loss of p53 results in a significant increase in mtDNA vulnerability to damage, leading to increased frequency of in vivo mtDNA mutations, which are reversed by stable transfection of wild-type p53. This study provides a mechanistic explanation for the accelerating genetic instability and increased ROS stress in cancer cells associated with loss of p53.     

Genetic characterization of breast cancer and implications for clinical management

Breast cancer is a genetic disease caused by the accumulation of mutations in neoplastic cells. In the last few years, high-throughput microarray-based molecular analysis has provided increasingly more coherent information about the genetic aberrations in breast cancer. New biomarkers and molecular techniques are slowly becoming part of the diagnostic and prognostic armamentarium available for pathologists and oncologists to tailor the therapy for breast cancer patients. In this review, we will focus on the contribution of breast cancer somatic genetics to our understanding of breast cancer biology and its impact on breast cancer patient management.     

The you gene encodes an EGF-CUB protein essential for Hedgehog signaling in zebrafish

Hedgehog signaling is required for many aspects of development in vertebrates and invertebrates. Misregulation of the Hedgehog pathway causes developmental abnormalities and has been implicated in certain types of cancer. Large-scale genetic screens in zebrafish have identified a group of mutations, termed you-class mutations, that share common defects in somite shape and in most cases disrupt Hedgehog signaling. These mutant embryos exhibit U-shaped somites characteristic of defects in slow muscle development. In addition, Hedgehog pathway mutations disrupt spinal cord patterning. We report the positional cloning of you, one of the original you-class mutations, and show that it is required for Hedgehog signaling in the development of slow muscle and in the specification of ventral fates in the spinal cord. The you gene encodes a novel protein with conserved EGF and CUB domains and a secretory pathway signal sequence. Epistasis experiments support an extracellular role for You upstream of the Hedgehog response mechanism. Analysis of chimeras indicates that you mutant cells can appropriately respond to Hedgehog signaling in a wild-type environment. Additional chimera analysis indicates that wild-type you gene function is not required in axial Hedgehog-producing cells, suggesting that You is essential for transport or stability of Hedgehog signals in the extracellular environment. Our positional cloning and functional studies demonstrate that You is a novel extracellular component of the Hedgehog pathway in vertebrates.     

Molecular Profiling of Thin-Prep FNA Samples in Assisting Clinical Management of Non-Small-Cell Lung Cancer

The discovery of new target treatments for NSCLC has led to a search for new genetic and epigenetic markers able to selectively predict response to these new drugs. Somatic mutations in EGFR and KRAS genes are routinely analyzed to predict response to tyrosine kinase inhibitors (TKIs), used in the treatment of NSCLC patients, whose efficacy depend on the presence or the absence of specific mutations. MicroRNA (miRNA) expression evaluation has been recently analyzed because of the involvement of these molecules in lung cancer pathogenesis and in drug resistance. Only 30 % of NSCLC patients present a resectable stage at time of diagnosis so tissue samples cannot be the only starting material for genetic and epigenetic analysis. Therefore, the possibility to use cytological sampling already used for diagnosis also for molecular testing is emerging. The aim of this study was to evaluate for the first time in lung cancer the use of liquid-based cytology both for EGFR and KRAS mutational testing and for the expression trend of some miRNAs involved in lung cancer pathogenesis: miR-21, miR-155, miR-7, and let7a. We enrolled 20 fine-needle aspirate (FNA) samples diagnosed as NSCLC, 10 FNAs without neoplastic cells, and tissue samples coming from 5 of the 20 patients who underwent surgery after FNA NSCLC diagnosis. All Thin-Prep processed FNA samples were evaluable for DNA and RNA analysis and results were compared with those of the small group of patients whose matched tumor histology was available. The mutational status of the EGFR and KRAS genes and the expression profile of the selected miRNA showed comparable results between FNA samples and histological tissues. Our results underline that cytological samples could give the same genetic information as that obtained from histological specimens and so could be collected to create a nucleic acids bank.     

An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer

The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such “driver” mutations from innocuous “passenger” events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery.     

Whole Genome Sequencing of Newly Established Pancreatic Cancer Lines Identifies Novel Somatic Mutation (c.2587G>A) in Axon Guidance Receptor Plexin A1 as Enhancer of Proliferation and Invasion

The genetic profile of human pancreatic cancers harbors considerable heterogeneity, which suggests a possible explanation for the pronounced inefficacy of single therapies in this disease. This observation has led to a belief that custom therapies based on individual tumor profiles are necessary to more effectively treat pancreatic cancer. It has recently been discovered that axon guidance genes are affected by somatic structural variants in up to 25% of human pancreatic cancers. Thus far, however, some of these mutations have only been correlated to survival probability and no function has been assigned to these observed axon guidance gene mutations in pancreatic cancer. In this study we established three novel pancreatic cancer cell lines and performed whole genome sequencing to discover novel mutations in axon guidance genes that may contribute to the cancer phenotype of these cells. We discovered, among other novel somatic variants in axon guidance pathway genes, a novel mutation in the PLXNA1 receptor (c.2587G>A) in newly established cell line SB.06 that mediates oncogenic cues of increased invasion and proliferation in SB.06 cells and increased invasion in 293T cells upon stimulation with the receptor’s natural ligand semaphorin 3A compared to wild type PLXNA1 cells. Mutant PLXNA1 signaling was associated with increased Rho-GTPase and p42/p44 MAPK signaling activity and cytoskeletal expansion, but not changes in E-cadherin, vimentin, or metalloproteinase 9 expression levels. Pharmacologic inhibition of the Rho-GTPase family member CDC42 selectively abrogated PLXNA1 c.2587G>A-mediated increased invasion. These findings provide in-vitro confirmation that somatic mutations in axon guidance genes can provide oncogenic gain-of-function signals and may contribute to pancreatic cancer progression.