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

Chromosomal Instability in Genome Evolution: From Cancer to Macroevolution

The integrity of the genome is crucial for the survival of all living organisms. However, genomes need to adapt to survive certain pressures, and for this purpose use several mechanisms to diversify. Chromosomal instability (CIN) is one of the main mechanisms leading to the creation of genomic heterogeneity by altering the number of chromosomes and changing their structures. In this review, we will discuss the different chromosomal patterns and changes observed in speciation, in evolutional biology as well as during tumor progression. By nature, the human genome shows an induction of diversity during gametogenesis but as well during tumorigenesis that can conclude in drastic changes such as the whole genome doubling to more discrete changes as the complex chromosomal rearrangement chromothripsis. More importantly, changes observed during speciation are strikingly similar to the genomic evolution observed during tumor progression and resistance to therapy. The different origins of CIN will be treated as the importance of double-strand breaks (DSBs) or the consequences of micronuclei. We will also explain the mechanisms behind the controlled DSBs, and recombination of homologous chromosomes observed during meiosis, to explain how errors lead to similar patterns observed during tumorigenesis. Then, we will also list several diseases associated with CIN, resulting in fertility issues, miscarriage, rare genetic diseases, and cancer. Understanding better chromosomal instability as a whole is primordial for the understanding of mechanisms leading to tumor progression.

WMDS.net: a network control framework for identifying key players in transcriptome programs

MOTIVATION

Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes.

RESULTS

To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chaofen123/WMDS.net.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

Background

Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.

Results

Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.

Conclusions

Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02607-z.

Pan-cancer detection of driver genes at the single-patient resolution

BACKGROUND

Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.

RESULTS

We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.

CONCLUSIONS

sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).

Unveiling the gene regulatory landscape in diseases through the identification of DNase I-hypersensitive sites

DNase I-hypersensitive sites (DHSs) serve key roles in the regulation of gene transcription as markers of cis-regulatory elements (CREs). Recent advances in next-generation sequencing have enabled the genome-wide location and annotation of DHSs in a variety of cells. Numerous studies have confirmed that DHSs are involved in several processes in cell fate decision and development. DHSs have also been indicated in cancer and inherited diseases as driver distal regulatory elements. Here, the definition of DHSs is reviewed, in addition to high-throughput methods of DHS identification. Furthermore, the function of DHSs in gene expression is probed. The roles of DHSs in disease occurrence are also reviewed and discussed. Concomitant advances in the identification of essential roles of DHSs will assist in disclosing the underlying molecular mechanisms, supplementing gene transcription and enlarging the molecular basis of DHS-related bioprocesses, phenotypes, distinct traits and diseases.

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy.

Network-based integrated analysis of omics data reveal novel players of TGF-β1-induced EMT in human peritoneal mesothelial cells

Long-term peritoneal dialysis is associated with progressive fibrosis of the peritoneum. Epithelial-mesenchymal transition (EMT) of mesothelial cells is an important mechanism involved in peritoneal fibrosis, and TGF-β1 is considered central in this process. However, targeting currently known TGF-β1-associated pathways has not proven effective to date. Therefore, there are still gaps in understanding the mechanisms underlying TGF-β1-associated EMT and peritoneal fibrosis. We conducted network-based integrated analysis of transcriptomic and proteomic data to systemically characterize the molecular signature of TGF-β1-stimulated human peritoneal mesothelial cells (HPMCs). To increase the power of the data, multiple expression datasets of TGF-β1-stimulated human cells were employed, and extended based on a human functional gene network. Dense network sub-modules enriched with differentially expressed genes by TGF-β1 stimulation were prioritized and genes of interest were selected for functional analysis in HPMCs. Through integrated analysis, ECM constituents and oxidative stress-related genes were shown to be the top-ranked genes as expected. Among top-ranked sub-modules, TNFAIP6, ZC3H12A, and NNT were validated in HPMCs to be involved in regulation of E-cadherin, ZO-1, fibronectin, and αSMA expression. The present data shows the validity of network-based integrated analysis in discovery of novel players in TGF-β1-induced EMT in peritoneal mesothelial cells, which may serve as new prognostic markers and therapeutic targets for peritoneal dialysis patients.

Bacteria-to-Human Protein Networks Reveal Origins of Endogenous DNA Damage

DNA damage provokes mutations and cancer and results from external carcinogens or endogenous cellular processes. However, the intrinsic instigators of endogenous DNA damage are poorly understood. Here, we identify proteins that promote endogenous DNA damage when overproduced: the DNA "damage-up" proteins (DDPs). We discover a large network of DDPs in Escherichia coli and deconvolute them into six function clusters, demonstrating DDP mechanisms in three: reactive oxygen increase by transmembrane transporters, chromosome loss by replisome binding, and replication stalling by transcription factors. Their 284 human homologs are over-represented among known cancer drivers, and their RNAs in tumors predict heavy mutagenesis and a poor prognosis. Half of the tested human homologs promote DNA damage and mutation when overproduced in human cells, with DNA damage-elevating mechanisms like those in E. coli. Our work identifies networks of DDPs that provoke endogenous DNA damage and may reveal DNA damage-associated functions of many human known and newly implicated cancer-promoting proteins.

The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens

The Network of Cancer Genes (NCG) is a manually curated repository of 2372 genes whose somatic modifications have known or predicted cancer driver roles. These genes were collected from 275 publications, including two sources of known cancer genes and 273 cancer sequencing screens of more than 100 cancer types from 34,905 cancer donors and multiple primary sites. This represents a more than 1.5-fold content increase compared to the previous version. NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin, RNA and protein expression, miRNA and protein interactions, and protein function and essentiality. NCG is accessible at http://ncg.kcl.ac.uk/ .

Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer

Efforts to identify driver mutations in cancer have largely focused on genes, whereas non-coding sequences remain relatively unexplored. Here we develop a statistical method based on characteristics known to influence local mutation rate and a series of enrichment filters in order to identify distal regulatory elements harboring putative driver mutations in breast cancer. We identify ten DNase I hypersensitive sites that are significantly mutated in breast cancers and associated with the aberrant expression of neighboring genes. A pan-cancer analysis shows that three of these elements are significantly mutated across multiple cancer types and have mutation densities similar to protein-coding driver genes. Functional characterization of the most highly mutated DNase I hypersensitive sites in breast cancer (using in silico and experimental approaches) confirms that they are regulatory elements and affect the expression of cancer genes. Our study suggests that mutations of regulatory elements in tumors likely play an important role in cancer development.

Cancer driver mutations can occur within noncoding genomic sequences. Here, the authors develop a statistical approach to identify candidate noncoding driver mutations in DNase I hypersensitive sites in breast cancer and experimentally demonstrate they are regulatory elements of known cancer genes.

Computational Methods for Characterizing Cancer Mutational Heterogeneity

Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.

Synonymous Somatic Variants in Human Cancer Are Not Infamous: A Plea for Full Disclosure in Databases and Publications

Single-nucleotide variants (SNVs) are the most frequent genetic changes found in human cancer. Most driver alterations are missense and nonsense variants localized in the coding region of cancer genes. Unbiased cancer genome sequencing shows that synonymous SNVs (sSNVs) can be found clustered in the coding regions of several cancer oncogenes or tumor suppressor genes suggesting purifying selection. sSNVs are currently underestimated, as they are usually discarded during analysis. Furthermore, several public databases do not display sSNVs, which can lead to analytical bias and the false assumption that this mutational event is uncommon. Recent progress in our understanding of the deleterious consequences of these sSNVs for RNA stability and protein translation shows that they can act as strong drivers of cancer, as demonstrated for several cancer genes such as TP53 or BCL2L12. It is therefore essential that sSNVs be properly reported and analyzed in order to provide an accurate picture of the genetic landscape of the cancer genome.

The RNA-binding protein LARP4 regulates cancer cell migration and invasion

LARP4 is a La-related RNA-binding protein implicated in regulating mRNA translation, which interacts with poly(A)-binding protein (PABP). We previously identified LARP4 in an RNAi screen as one of several genes that regulate the shape of PC3 prostate cancer cells. Here we show that LARP4 depletion induces cell elongation in PC3 cells and MDA-MB-231 breast cancer cells. LARP4 depletion increases cell migration and invasion, as well as inducing invasive cell protrusions in 3D Matrigel. Conversely, LARP4 over-expression reduces cell elongation and increases cell circularity. LARP4 mutations are found in a variety of cancers. Introduction of some of these cancer-associated mutations, including a truncation mutant, into LARP4 enhances its effects on cell morphology. The truncation mutant shows enhanced interaction with PABP. We propose that LARP4 inhibits migration and invasion of cancer cells, and that some cancer-associated mutations stimulate these effects of LARP4. © 2016 The Authors. Cytoskeleton Published by Wiley Periodicals, Inc.

Long genes and genes with multiple splice variants are enriched in pathways linked to cancer and other multigenic diseases

BACKGROUND

The role of random mutations and genetic errors in defining the etiology of cancer and other multigenic diseases has recently received much attention. With the view that complex genes should be particularly vulnerable to such events, here we explore the link between the simple properties of the human genes, such as transcript length, number of splice variants, exon/intron composition, and their involvement in the pathways linked to cancer and other multigenic diseases.

RESULTS

We reveal a substantial enrichment of cancer pathways with long genes and genes that have multiple splice variants. Although the latter two factors are interdependent, we show that the overall gene length and splicing complexity increase in cancer pathways in a partially decoupled manner. Our systematic survey for the pathways enriched with top lengthy genes and with genes that have multiple splice variants reveal, along with cancer pathways, the pathways involved in various neuronal processes, cardiomyopathies and type II diabetes. We outline a correlation between the gene length and the number of somatic mutations.

CONCLUSIONS

Our work is a step forward in the assessment of the role of simple gene characteristics in cancer and a wider range of multigenic diseases. We demonstrate a significant accumulation of long genes and genes with multiple splice variants in pathways of multigenic diseases that have already been associated with de novo mutations. Unlike the cancer pathways, we note that the pathways of neuronal processes, cardiomyopathies and type II diabetes contain genes long enough for topoisomerase-dependent gene expression to also be a potential contributing factor in the emergence of pathologies, should topoisomerases become impaired.

A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.

Cancer cells undergo a mutation/selection process that resembles that of any living cell. Most mutations in cancer cell DNA occur in the so-called "non-coding" regions that represent 98.5% of the genome length. Pinning down which of these mutations contribute to the fitness of cancer cells would be important for identifying new "cancer drivers", which may in turn lead to future treatments. Unfortunately, predicting the impact of a non-coding DNA alteration remains extremely difficult. In this study, we analyze millions of non-coding cancer mutations and show cancer-specific mutational patterns can be used to predict non-coding regions that are preserved from mutations and may thus be important for cancer cell survival. Combining this information with population data, we propose a new scoring system that should help prioritize important non-coding mutations in future studies.

NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).

Systematic analysis of somatic mutations impacting gene expression in 12 tumour types

We present a novel hierarchical Bayes statistical model, xseq, to systematically quantify the impact of somatic mutations on expression profiles. We establish the theoretical framework and robust inference characteristics of the method using computational benchmarking. We then use xseq to analyse thousands of tumour data sets available through The Cancer Genome Atlas, to systematically quantify somatic mutations impacting expression profiles. We identify 30 novel cis-effect tumour suppressor gene candidates, enriched in loss-of-function mutations and biallelic inactivation. Analysis of trans-effects of mutations and copy number alterations with xseq identifies mutations in 150 genes impacting expression networks, with 89 novel predictions. We reveal two important novel characteristics of mutation impact on expression: (1) patients harbouring known driver mutations exhibit different downstream gene expression consequences; (2) expression patterns for some mutations are stable across tumour types. These results have critical implications for identification and interpretation of mutations with consequent impact on transcription in cancer.

Assessing functional impact of mutations in cancer on gene expression can improve our understanding of cancer biology and may identify potential therapeutic targets. Here, Ding et al. describe a novel statistical model named xseq for a systematic survey of how mutations impact transcriptome landscapes across 12 different tumour types.

A novel somatic mutation in ACD induces telomere lengthening and apoptosis resistance in leukemia cells

BACKGROUND

The identification of oncogenic driver mutations has largely relied on the assumption that genes that exhibit more mutations than expected by chance are more likely to play an active role in tumorigenesis. Major cancer sequencing initiatives have therefore focused on recurrent mutations that are more likely to be drivers. However, in specific genetic contexts, low frequency mutations may also be capable of participating in oncogenic processes. Reliable strategies for identifying these rare or even patient-specific (private) mutations are needed in order to elucidate more personalized approaches to cancer diagnosis and treatment.

METHODS

Here we performed whole-exome sequencing on three cases of childhood pre-B acute lymphoblastic leukemia (cALL), representing three cytogenetically-defined subgroups (high hyperdiploid, t(12;21) translocation, and cytogenetically normal). We applied a data reduction strategy to identify both common and rare/private somatic events with high functional potential. Top-ranked candidate mutations were subsequently validated at high sequencing depth on an independent platform and in vitro expression assays were performed to evaluate the impact of identified mutations on cell growth and survival.

RESULTS

We identified 6 putatively damaging non-synonymous somatic mutations among the three cALL patients. Three of these mutations were well-characterized common cALL mutations involved in constitutive activation of the mitogen-activated protein kinase pathway (FLT3 p.D835Y, NRAS p.G13D, BRAF p.G466A). The remaining three patient-specific mutations (ACD p.G223V, DOT1L p.V114F, HCFC1 p.Y103H) were novel mutations previously undescribed in public cancer databases. Cytotoxicity assays demonstrated a protective effect of the ACD p.G223V mutation against apoptosis in leukemia cells. ACD plays a key role in protecting telomeres and recruiting telomerase. Using a telomere restriction fragment assay, we also showed that this novel mutation in ACD leads to increased telomere length in leukemia cells.

CONCLUSION

This study identified ACD as a novel gene involved in cALL and points to a functional role for ACD in enhancing leukemia cell survival. These results highlight the importance of rare/private somatic mutations in understanding cALL etiology, even within well-characterized molecular subgroups.

Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers

The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distinguishing drivers from bystanders. We develop and apply an ensemble classifier (EC) machine learning method, which integrates 10 classifiers that are publically available, and apply it to breast and ovarian cancer. In particular we find the following: (1) Using both standard and non-standard metrics, EC almost always outperforms single method classifiers, often by wide margins. (2) Of the 50 highest ranked genes for breast (ovarian) cancer, 34 (30) are associated with other cancers in either the OMIM, CGC or NCG database (P < 10⁻²²). (3) Another 10, for both breast and ovarian cancer, have been identified by GWAS studies. (4) Several of the remaining genes--including a protein kinase that regulates the Fra-1 transcription factor which is overexpressed in ER negative breast cancer cells; and Fyn, which is overexpressed in pancreatic and prostate cancer, among others--are biologically plausible. Biological implications are briefly discussed. Source codes and detailed results are available at http://www.visantnet.org/misi/driver_integration.zip.

jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data

MOTIVATION

Tremendous amount of omics data being accumulated poses a pressing challenge of meta-analyzing the heterogeneous data for mining new biological knowledge. Most existing methods deal with each gene independently, thus often resulting in high false positive rates in detecting differentially expressed genes (DEG). To our knowledge, no or little effort has been devoted to methods that consider dependence structures underlying transcriptomics data for DEG identification in meta-analysis context.

RESULTS

This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA). We mathematically extend non-negative matrix factorization (NMF) to a joint version (jNMF), which is used to simultaneously decompose multiple transcriptomics data matrices into one common submatrix plus multiple individual submatrices. By the jNMF, the dependence structures underlying transcriptomics data can be interrogated and utilized, while the high-dimensional transcriptomics data are mapped into a low-dimensional space spanned by metagenes that represent hidden biological signals. jNMFMA finally identifies DEGs as genes that are associated with differentially expressed metagenes. The ability of extracting dependence structures makes jNMFMA more efficient and robust to identify DEGs in meta-analysis context. Furthermore, jNMFMA is also flexible to identify DEGs that are consistent among various types of omics data, e.g. gene expression and DNA methylation. Experimental results on both simulation data and real-world cancer data demonstrate the effectiveness of jNMFMA and its superior performance over other popular approaches.

AVAILABILITY AND IMPLEMENTATION

R code for jNMFMA is available for non-commercial use via http://micblab.iim.ac.cn/Download/.

CONTACT

hqwang@ustc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification and analysis of driver missense mutations using rotation forest with feature selection

Identifying cancer-associated mutations (driver mutations) is critical for understanding the cellular function of cancer genome that leads to activation of oncogenes or inactivation of tumor suppressor genes. Many approaches are proposed which use supervised machine learning techniques for prediction with features obtained by some databases. However, often we do not know which features are important for driver mutations prediction. In this study, we propose a novel feature selection method (called DX) from 126 candidate features' set. In order to obtain the best performance, rotation forest algorithm was adopted to perform the experiment. On the train dataset which was collected from COSMIC and Swiss-Prot databases, we are able to obtain high prediction performance with 88.03% accuracy, 93.9% precision, and 81.35% recall when the 11 top-ranked features were used. Comparison with other various techniques in the TP53, EGFR, and Cosmic2plus datasets shows the generality of our method.

DawnRank: discovering personalized driver genes in cancer

Large-scale cancer genomic studies have revealed that the genetic heterogeneity of the same type of cancer is greater than previously thought. A key question in cancer genomics is the identification of driver genes. Although existing methods have identified many common drivers, it remains challenging to predict personalized drivers to assess rare and even patient-specific mutations. We developed a new algorithm called DawnRank to directly prioritize altered genes on a single patient level. Applications to TCGA datasets demonstrated the effectiveness of our method. We believe DawnRank complements existing driver identification methods and will help us discover personalized causal mutations that would otherwise be obscured by tumor heterogeneity. Source code can be accessed at http://bioen-compbio.bioen.illinois.edu/DawnRank/.

NCG 4.0: the network of cancer genes in the era of massive mutational screenings of cancer genomes

NCG 4.0 is the latest update of the Network of Cancer Genes, a web-based repository of systems-level properties of cancer genes. In its current version, the database collects information on 537 known (i.e. experimentally supported) and 1463 candidate (i.e. inferred using statistical methods) cancer genes. Candidate cancer genes derive from the manual revision of 67 original publications describing the mutational screening of 3460 human exomes and genomes in 23 different cancer types. For all 2000 cancer genes, duplicability, evolutionary origin, expression, functional annotation, interaction network with other human proteins and with microRNAs are reported. In addition to providing a substantial update of cancer-related information, NCG 4.0 also introduces two new features. The first is the annotation of possible false-positive cancer drivers, defined as candidate cancer genes inferred from large-scale screenings whose association with cancer is likely to be spurious. The second is the description of the systems-level properties of 64 human microRNAs that are causally involved in cancer progression (oncomiRs). Owing to the manual revision of all information, NCG 4.0 constitutes a complete and reliable resource on human coding and non-coding genes whose deregulation drives cancer onset and/or progression. NCG 4.0 can also be downloaded as a free application for Android smart phones. Database URL: http://bio.ieo.eu/ncg/.