Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery

Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) has heterogeneous clinical and biological behavior. Whole-genome and -exome sequencing has contributed to the characterization of the mutational spectrum of the disease, but the underlying transcriptional profile is still poorly understood. We have performed deep RNA sequencing in different subpopulations of normal B-lymphocytes and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. We detected thousands of transcriptional elements differentially expressed between the CLL and normal B cells, including protein-coding genes, noncoding RNAs, and pseudogenes. Transposable elements are globally derepressed in CLL cells. In addition, two thousand genes-most of which are not differentially expressed-exhibit CLL-specific splicing patterns. Genes involved in metabolic pathways showed higher expression in CLL, while genes related to spliceosome, proteasome, and ribosome were among the most down-regulated in CLL. Clustering of the CLL samples according to RNA-seq derived gene expression levels unveiled two robust molecular subgroups, C1 and C2. C1/C2 subgroups and the mutational status of the immunoglobulin heavy variable (IGHV) region were the only independent variables in predicting time to treatment in a multivariate analysis with main clinico-biological features. This subdivision was validated in an independent cohort of patients monitored through DNA microarrays. Further analysis shows that B-cell receptor (BCR) activation in the microenvironment of the lymph node may be at the origin of the C1/C2 differences

Integrative pathway enrichment analysis of multivariate omics data.

Funder: BioTalent Canada Student InternshipFunder: Canadian Institutes of Health Research (CIHR) Canadian Graduate ScholarshipFunder: Ontario Institute for Cancer Research (OICR) Investigator Awards provided by the Government of Ontario; Terry Fox Research Institute (TFRI) and Canadian Institutes of Health Research (CIHR) New Investigator Awards.Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations

Ablació amb catèter de radiofreqüència de la fibril·lació auricular: Tècniques, complicacions i resultats

[cat] La Fibril·lació Auricular (FA) no només és l'arítmia més freqüent en la pràctica clínica sinó que també és una de les més complexes i amb un major impacte socio-sanitari. El tractament convencional fracassa en molts casos, en els quals aquest es limita a reduir la morbi-mortalitat associada. L'ablació de FA amb catèter de radiofreqüència ofereix l'ideal terapèutic de curació de l'arítmia, però tot i els grans avenços obtinguts des de la seva descripció inicial encara existeixen moltes qüestions a resoldre sobre aquesta prometedora teràpia. La tesi es composa de diferents treballs d'investigació desenvolupats per resoldre algunes d'aquestes qüestions, concretament:- Avaluar els resultats d'una estratègia en la que la tècnica d'ablació s'individualitza segons les característiques de cada pacient.- Comparar la incidència d'estenosis de venes pulmonars, una complicació fortament relacionada amb el procediment, entre una ablació ostial segmentària i una ablació antral.- Identificar factors basals predictors de fracàs del procediment.- Valorar l'efecte de l'ablació sobre la funció auricular.- Comprovar si incloure l'aïllament de la paret posterior de l'aurícula esquerra millora els resultats obtinguts amb un procediment estàndard.- Comprovar si la utilització d'un catèter circular multipolar per assegurar l'aïllament de les venes pulmonars millora els resultats obtinguts amb un procediment estàndard.Els resultats obtinguts en el desenvolupament de cadascun d'aquests objectius van donar lloc als pertinents articles publicats en revistes especialitzades. En resum, les principals conclusions obtingudes a partir d'aquests van ser:- En la FA paroxística, amb aurícula estructuralment normal i ectòpia d'origen esquerra freqüent i sostinguda, una ablació limitada a l'aïllament ostial de les venes pulmonars mostrant activitat elèctrica obté bons resultats.- Aquesta aproximació mostra un risc significatiu d'estenosis de venes pulmonars, sent d'especial interès la utilització d'eines que permetin localitzar el catèter d'ablació amb precisió i en temps real. En la resta de casos, la creació de lesions més extenses resulten necessàries.- La hipertensió arterial i el diàmetre auricular són predictors de fracàs del procediment, i aquestes dues variables es poden utilitzar per seleccionar els millors candidats a la teràpia valorant la relació risc/benefici en cada cas.- La contractilitat auricular es conserva o millora en la majoria de pacients amb ablació exitosa tot i la quantitat de lesions creades durant el procediment d'ablació, però aquesta continua empitjorant quan la teràpia fracassa.- L'aïllament de la paret posterior de l'aurícula esquerra afecta una gran part de teixit però no demostra millorar el resultat del procediment, i no hauria de realitzar-se de forma rutinària en aquests tipus de procediments.- La utilització d'un catèter circular multipolar per comprovar l'aïllament de les venes pulmonars millora els resultats de l'ablació i hauria d'incorporar-se en el procediment tot i augmentar-ne els seus requeriments. Finalment, destacar que tals estudis no només han donat lloc a les corresponents publicacions en revistes científiques i a múltiples comunicacions en congressos internacionals, sinó que també els seus resultats han incidit directament en la pràctica clínica de la nostra unitat millorant la comprensió dels mecanismes associats a l'arítmia, i caracteritzant el procediment realitzat per millorar-ne els seus resultats i reduir-ne el risc de complicacions

Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression

A well-established approach for detecting genes involved in tumorigenesis due to copy number alterations (CNAs) is to assess the recurrence of the alteration across multiple samples. Expression data can be used to filter this list of candidates by assessing whether the gene expression significantly differs between tumors depending on the copy number status. A drawback of this approach is that it may fail to detect low-recurrent drivers. Furthermore, this analysis does not provide information about expression changes for each gene as compared to the whole data set and does not take into consideration the expression of normal samples. Here we describe a novel method (Oncodrive-CIS) aimed at ranking genes according to the expression impact caused by the CNAs. The rationale of Oncodrive-CIS is based on the hypothesis that genes involved in cancer due to copy number changes are more biased towards misregulation than are bystanders. Moreover, to gain insight into the expression changes caused by gene dosage, the expression of samples with CNAs is compared to that of tumor samples with diploid genotype and also to that of normal samples. Oncodrive-CIS demonstrated better performance in detecting putative associations between copy-number and expression in simulated data sets as compared to other methods aimed to this purpose, and picked up genes likely to be related with tumorigenesis when applied to real cancer samples. In summary, Oncodrive-CIS provides a statistical framework to evaluate the in cis effect of CNAs that may be useful to elucidate the role of these aberrations in driving oncogenesis. An implementation of this method and the corresponding user guide are freely available at http://bg.upf.edu/oncodrivecis.The authors acknowledge funding from the Spanish Ministry of Science and Technology (grant number SAF2009-06954) and the Spanish National Institute of Bioinformatics (INB)

Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression

A well-established approach for detecting genes involved in tumorigenesis due to copy number alterations (CNAs) is to assess the recurrence of the alteration across multiple samples. Expression data can be used to filter this list of candidates by assessing whether the gene expression significantly differs between tumors depending on the copy number status. A drawback of this approach is that it may fail to detect low-recurrent drivers. Furthermore, this analysis does not provide information about expression changes for each gene as compared to the whole data set and does not take into consideration the expression of normal samples. Here we describe a novel method (Oncodrive-CIS) aimed at ranking genes according to the expression impact caused by the CNAs. The rationale of Oncodrive-CIS is based on the hypothesis that genes involved in cancer due to copy number changes are more biased towards misregulation than are bystanders. Moreover, to gain insight into the expression changes caused by gene dosage, the expression of samples with CNAs is compared to that of tumor samples with diploid genotype and also to that of normal samples. Oncodrive-CIS demonstrated better performance in detecting putative associations between copy-number and expression in simulated data sets as compared to other methods aimed to this purpose, and picked up genes likely to be related with tumorigenesis when applied to real cancer samples. In summary, Oncodrive-CIS provides a statistical framework to evaluate the in cis effect of CNAs that may be useful to elucidate the role of these aberrations in driving oncogenesis. An implementation of this method and the corresponding user guide are freely available at http://bg.upf.edu/oncodrivecis.The authors acknowledge funding from the Spanish Ministry of Science and Technology (grant number SAF2009-06954) and the Spanish National Institute of Bioinformatics (INB)

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.This work was funded by the Wellcome Trust (086375 and 102696). F.I. was supported by the European Bioinformatics Institute and Wellcome Trust Sanger Institute post-doctoral (ESPOD) program. T.A.K. was supported by the National Cancer Institute (U24CA143835) and the Netherlands Organization for Scientific Research. D.T. was supported by the People Programme (Marie Curie Actions) of the 7th Framework Programme of the European Union (FP7/2007-2013; 600388) and the Agency of Competitiveness for Companies of the Government of Catalonia (ACCIO´ ). N.L.-B. was supported by La Fundacio ´ la Marato´ de TV3. M.E. was funded by the European Research Council (268626), the Ministerio de Ciencia e Innovacion (SAF2011-22803), the Institute of Health Carlos III (ISCIII) under the Integrated Project of Excellence (PIE13/00022), the Spanish Cancer Research Network (RD12/0036/0039), the Health and Science Departments of the Catalan Government Generalitat de Catalunya 2014-SGR 633, and the Cellex Foundation. U.M. was supported by a Cancer Research UK Clinician Scientist Fellowship. We thank Aiqing He for expression data and Ilya Shmulevich for assistance with the LOBICO framework. We thank P. Campbell, M. Ranzani, J. Brammeld, M. Petljak, F. Behan, C. Alsinet Armengol, H. Francies, V. Grinkevich, and A. ‘‘Lilla’’ Mupo for useful comments. P.R.-M., H.C., and H.d.S. are employees and shareholders of Bristol-Myers Squibb. Research in the M.J.G. lab is supported in part with funding from AstraZeneca

OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action

MOTIVATION: Several computational methods have been developed to identify cancer drivers genes-genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role. RESULT: /nOncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role. AVAILABILITY AND IMPLEMENTATION: The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work was supported by the Spanish Ministry of Economy and Competitivity (grant number SAF2012-36199) and the Spanish National Institute of Bioinformatics (INB). M.P.S. and C.R.-P. are supported by FPI fellowship

Rational design of cancer gene panels with OncoPaD

BACKGROUND: Profiling the somatic mutations of genes which may inform about tumor evolution, prognostics and treatment is becoming a standard tool in clinical oncology. Commercially available cancer gene panels rely on manually gathered cancer-related genes, in a "one-size-fits-many" solution. The design of new panels requires laborious search of literature and cancer genomics resources, with their performance on cohorts of patients difficult to estimate. RESULTS: We present OncoPaD, to our knowledge the first tool aimed at the rational design of cancer gene panels. OncoPaD estimates the cost-effectiveness of the designed panel on a cohort of tumors and provides reports on the importance of individual mutations for tumorigenesis or therapy. With a friendly interface and intuitive input, OncoPaD suggests researchers relevant sets of genes to be included in the panel, because prior knowledge or analyses indicate that their mutations either drive tumorigenesis or function as biomarkers of drug response. OncoPaD also provides reports on the importance of individual mutations for tumorigenesis or therapy that support the interpretation of the results obtained with the designed panel. We demonstrate in silico that OncoPaD designed panels are more cost-effective-i.e. detect a maximum fraction of tumors in the cohort by sequencing a minimum quantity of DNA-than available panels. CONCLUSIONS: With its unique features, OncoPaD will help clinicians and researchers design tailored next-generating sequencing (NGS) panels to detect circulating tumor DNA or biopsy specimens, thereby facilitating early and accurate detection of tumors, genomics informed therapeutic decisions, patient follow-up and timely identification of resistance mechanisms to targeted agents. OncoPaD may be accessed through http://www.intogen.org/oncopad.A.G.-P. is supported by a Ramón y Cajal contract (RYC-2013-14554), which also funds the publication of this article. We also acknowledge funding from the Spanish Ministry of Economy and Competitiveness (grant no. SAF2012-36199), the Marató de TV3 Foundation, and the Spanish National Institute of Bioinformatics (INB). C.R.-P. is supported by an FPI fellowship. D.T. is supported by the People Programme (Marie Curie Actions) of the Seventh Framework Programme of the European Union (FP7/2007- 2013) under REA grant agreement no. 600388 and by the Agency of Competitiveness for Companies of the Government of Catalonia, ACCIÓ

Comparison of algorithms for the detection of cancer drivers at subgene resolution

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.E.P.-P. and A.G. acknowledge the support from the Cancer Center grants P30 CA030199 (to our institute) and R35 GM118187 (A.G.). A.K. was supported by startup funds of G.G. and by a collaboration with Bayer AG. D.T. is supported by project SAF2015-74072-JIN, which is funded by the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). N.L.-B. acknowledges funding from the European Research Council (consolidator grant 682398). A.V. and T.P. acknowledge funding by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 305444 (RD-Connect