DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes

RMBase v3.0: decode the landscape, mechanisms and functions of RNA modifications

Although over 170 chemical modifications have been identified, their prevalence, mechanism and function remain largely unknown. To enable integrated analysis of diverse RNA modification profiles, we have developed RMBase v3.0 (http://bioinformaticsscience.cn/rmbase/), a comprehensive platform consisting of eight modules. These modules facilitate the exploration of transcriptome-wide landscape, biogenesis, interactome and functions of RNA modifications. By mining thousands of epitranscriptome datasets with novel pipelines, the 'RNA Modifications' module reveals the map of 73 RNA modifications of 62 species. the 'Genes' module allows to retrieve RNA modification profiles and clusters by gene and transcript. The 'Mechanisms' module explores 23 382 enzyme-catalyzed or snoRNA-guided modified sites to elucidate their biogenesis mechanisms. The 'Co-localization' module systematically formulates potential correlations between 14 histone modifications and 6 RNA modifications in various cell-lines. The 'RMP' module investigates the differential expression profiles of 146 RNA-modifying proteins (RMPs) in 18 types of cancers. The 'Interactome' integrates the interactional relationships between 73 RNA modifications with RBP binding events, miRNA targets and SNPs. The 'Motif' illuminates the enriched motifs for 11 types of RNA modifications identified from epitranscriptome datasets. The 'Tools' introduces a novel web-based 'modGeneTool' for annotating modifications. Overall, RMBase v3.0 provides various resources and tools for studying RNA modifications.

Novel ratio-metric features enable the identification of new driver genes across cancer types

An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG .

Diverse Oncogenic Fusions and Distinct Gene Expression Patterns Define the Genomic Landscape of Pediatric Papillary Thyroid Carcinoma

Pediatric papillary thyroid carcinoma (PPTC) is clinically distinct from adult-onset disease. Although there are higher rates of metastasis and recurrence in PPTC, prognosis remains highly favorable. Molecular characterization of PPTC has been lacking. Historically, only 40% to 50% of childhood papillary thyroid carcinoma (PTC) were known to be driven by genomic variants common to adult PTC; oncogenic drivers in the remainder were unknown. This contrasts with approximately 90% of adult PTC driven by a discrete number of variants. In this study, 52 PPTCs underwent candidate gene testing, followed in a subset by whole-exome and transcriptome sequencing. Within these samples, candidate gene testing identified variants in 31 (60%) tumors, while exome and transcriptome sequencing identified oncogenic variants in 19 of 21 (90%) remaining tumors. The latter were enriched for oncogenic fusions, with 11 nonrecurrent fusion transcripts, including two previously undescribed fusions, STRN-RET and TG-PBF. Most fusions were associated with 3' receptor tyrosine kinase (RTK) moieties: RET, MET, ALK, and NTRK3. For advanced (distally metastatic) tumors, a driver variant was described in 91%. Gene expression analysis defined three clusters that demonstrated distinct expression of genes involved in thyroid differentiation and MAPK signaling. Among RET-CCDC6-driven tumors, gene expression in pediatric tumors was distinguishable from that in adults. Collectively, these results show that the genomic landscape of pediatric PTC is different from adult PTC. Moreover, they identify genomic drivers in 98% of PPTCs, predominantly oncogenic fusion transcripts involving RTKs, with a pronounced impact on gene expression. Notably, most advanced tumors were driven by a variant for which targeted systemic therapy exists. SIGNIFICANCE: This study highlights important distinctions between the genomes and transcriptomes of pediatric and adult papillary thyroid carcinoma, with implications for understanding the biology, diagnosis, and treatment of advanced disease in children.

Prospective Validation of the Italian Alliance Against Cancer Lung Panel in Patients With Advanced Non-Small-Cell Lung Cancer

BACKGROUND

The deeper knowledge of non-small-cell lung cancer (NSCLC) biology and the discovery of driver molecular alterations have opened the era of precision medicine in lung oncology, thus significantly revolutionizing the diagnostic and therapeutic approach to NSCLC. In Italy, however, molecular assessment remains heterogeneous across the country, and numbers of patients accessing personalized treatments remain relatively low. Nationwide programs have demonstrated that the creation of consortia represent a successful strategy to increase the number of patients with a molecular classification.

PATIENTS AND METHODS

The Alliance Against Cancer (ACC), a network of 25 Italian Research Institutes, has developed a targeted sequencing panel for the detection of genomic alterations in 182 genes in patients with a diagnosis of NSCLC (ACC lung panel). One thousand metastatic NSCLC patients will be enrolled onto a prospective trial designed to measure the sensitivity and specificity of the ACC lung panel as a tool for molecular screening compared to standard methods.

RESULTS AND CONCLUSION

The ongoing trial is part of a nationwide strategy of ACC to develop infrastructures and improve competences to make the Italian research institutes independent for genomic profiling of cancer patients.

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.

Cancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by specific therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous sources of information into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction software.

C3: Consensus Cancer Driver Gene Caller

Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C³), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C³ is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.

Cancer driver genes: a guilty by resemblance doctrine

A major benefit of expansive cancer genome projects is the discovery of new targets for drug treatment and development. To date, cancer driver genes have been primarily identified by methods based on gene mutation frequency. This approach fails to identify culpable genes that are not mutated, rarely mutated, or contribute to the development of rare forms of cancer. Due to the complexity of the disease and the sheer volume of data, computational methods may encounter a NP-complete problem. We have developed a novel pathway and reach (PAR) method that employs a guilty by resemblance approach to identify cancer driver genes that avoids the above problems. Essentially PAR sifts through a list of genes of biological pathways to find those that are common to the same pathways and possess a similar 2-reach topology metric as a reference set of recognized driver genes. This approach leads to faster processing times and eliminates any dependency on gene mutation frequency. Out of the three pathways, signal transduction, immune system, and gene expression, a set of 50 candidate driver genes were identified, 30 of which were new. The top five were HGF, E2F1, C6, MIF, and CDK2.

QuaDMutEx: quadratic driver mutation explorer

BACKGROUND

Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations.

RESULTS

We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors.

CONCLUSIONS

Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license.

Functional consequences of the first reported mutations of the proto-oncogene PTTG1IP/PBF

Pituitary tumor-transforming gene 1-binding factor (PTTG1IP; PBF) is a multifunctional glycoprotein, which is overexpressed in a wide range of tumours, and significantly associated with poorer oncological outcomes, such as early tumour recurrence, distant metastasis, extramural vascular invasion and decreased disease-specific survival. PBF transforms NIH 3T3 fibroblasts and induces tumours in nude mice, while mice harbouring transgenic thyroidal PBF expression show hyperplasia and macrofollicular lesions. Our assumption that PBF becomes an oncogene purely through increased expression has been challenged by the recent report of mutations in PBF within the Catalogue of Somatic Mutations in Cancer (COSMIC) database. We therefore sought to determine whether the first 10 PBF missense substitutions in human cancer might be oncogenic. Anisomycin half-life studies revealed that most mutations were associated with reduced protein stability compared to wild-type (WT) PBF. Proliferation assays narrowed our interest to two mutational events which significantly altered cell turnover: C51R and R140W. C51R was mainly confined to the endoplasmic reticulum while R140W was apparent in the Golgi apparatus. Both C51R and R140W lost the capacity to induce cellular migration and significantly reduced cell invasion. Colony formation and soft agar assays demonstrated that, in contrast to WT PBF, both mutants were unable to elicit significant colony formation or anchorage-independent growth. However, C51R and R140W retained the ability to repress radioiodide uptake, a functional hallmark of PBF. Our data reveal new insight into PBF function and confirm that, rather than being oncogenic, mutations in PBF are likely to be passenger effects, with overexpression of PBF the more important aetiological event in human cancer.

Elevated PTTG and PBF predicts poor patient outcome and modulates DNA damage response genes in thyroid cancer

The proto-oncogene PTTG and its binding partner PBF have been widely studied in multiple cancer types, particularly thyroid and colorectal, but their combined role in tumourigenesis is uncharacterised. Here, we show for the first time that together PTTG and PBF significantly modulate DNA damage response (DDR) genes, including p53 target genes, required to maintain genomic integrity in thyroid cells. Critically, DDR genes were extensively repressed in primary thyrocytes from a bitransgenic murine model (Bi-Tg) of thyroid-specific PBF and PTTG overexpression. Irradiation exposure to amplify p53 levels further induced significant repression of DDR genes in Bi-Tg thyrocytes (P=2.4 × 10^-4) compared with either PBF- (P=1.5 × 10^-3) or PTTG-expressing thyrocytes (P=NS). Consistent with this, genetic instability was greatest in Bi-Tg thyrocytes with a mean genetic instability (GI) index of 35.8±2.6%, as well as significant induction of gross chromosomal aberrations in thyroidal TPC-1 cells following overexpression of PBF and PTTG. We extended our findings to human thyroid cancer using TCGA data sets (n=322) and found striking correlations with PBF and PTTG expression in well-characterised DDR gene panel RNA-seq data. In addition, genetic associations and transient transfection identified PBF as a downstream target of the receptor tyrosine kinase-BRAF signalling pathway, emphasising a role for PBF as a novel component in a pathway well described to drive neoplastic growth. We also showed that overall survival (P=1.91 × 10^-5) and disease-free survival (P=4.9 × 10^-5) was poorer for TCGA patients with elevated tumoural PBF/PTTG expression and mutationally activated BRAF. Together our findings indicate that PBF and PTTG have a critical role in promoting thyroid cancer that is predictive of poorer patient outcome.

Pro-invasive Effect of Proto-oncogene PBF Is Modulated by an Interaction with Cortactin

CONTEXT

Metastatic disease is responsible for the majority of endocrine cancer deaths. New therapeutic targets are urgently needed to improve patient survival rates.

OBJECTIVE

The proto-oncogene PTTG1-binding factor (PBF/PTTG1IP) is overexpressed in multiple endocrine cancers and circumstantially associated with tumor aggressiveness. This study aimed to understand the role of PBF in tumor cell invasion and identify possible routes to inhibit its action. Design, Setting, Patients, and Interventions: Thyroid, breast, and colorectal cells were transfected with PBF and cultured for in vitro analysis. PBF and cortactin (CTTN) expression was determined in differentiated thyroid cancer and The Cancer Genome Atlas RNA-seq data.

PRIMARY OUTCOME MEASURE

Pro-invasive effects of PBF were evaluated by 2D Boyden chamber, 3D organotypic, and proximity ligation assays.

RESULTS

Our study identified that PBF and CTTN physically interact and co-localize, and that this occurs at the cell periphery, particularly at the leading edge of migrating cancer cells. Critically, PBF induces potent cellular invasion and migration in thyroid and breast cancer cells, which is entirely abrogated in the absence of CTTN. Importantly, we found that CTTN is over-expressed in differentiated thyroid cancer, particularly in patients with regional lymph node metastasis, which significantly correlates with elevated PBF expression. Mutation of PBF (Y174A) or pharmacological intervention modulates the PBF: CTTN interaction and attenuates the invasive properties of cancer cells.

CONCLUSION

Our results demonstrate a unique role for PBF in regulating CTTN function to promote endocrine cell invasion and migration, as well as identify a new targetable interaction to block tumor cell movement.

Whole-exome sequencing of primary plasma cell leukemia discloses heterogeneous mutational patterns

Primary plasma cell leukemia (pPCL) is a rare and aggressive form of plasma cell dyscrasia and may represent a valid model for high-risk multiple myeloma (MM). To provide novel information concerning the mutational profile of this disease, we performed the whole-exome sequencing of a prospective series of 12 pPCL cases included in a Phase II multicenter clinical trial and previously characterized at clinical and molecular levels. We identified 1, 928 coding somatic non-silent variants on 1, 643 genes, with a mean of 166 variants per sample, and only few variants and genes recurrent in two or more samples. An excess of C > T transitions and the presence of two main mutational signatures (related to APOBEC over-activity and aging) occurring in different translocation groups were observed. We identified 14 candidate cancer driver genes, mainly involved in cell-matrix adhesion, cell cycle, genome stability, RNA metabolism and protein folding. Furthermore, integration of mutation data with copy number alteration profiles evidenced biallelically disrupted genes with potential tumor suppressor functions. Globally, cadherin/Wnt signaling, extracellular matrix and cell cycle checkpoint resulted the most affected functional pathways. Sequencing results were finally combined with gene expression data to better elucidate the biological relevance of mutated genes. This study represents the first whole-exome sequencing screen of pPCL and evidenced a remarkable genetic heterogeneity of mutational patterns. This may provide a contribution to the comprehension of the pathogenetic mechanisms associated with this aggressive form of PC dyscrasia and potentially with high-risk MM.

LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer

Background

The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots.

Results

We analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family.

Conclusions

LowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis.

LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0935-7) contains supplementary material, which is available to authorized users.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.