Carlota Rubio-Perez

Non-coding recurrent mutations in chronic lymphocytic leukaemia.

Chronic lymphocytic leukaemia (CLL) is a frequent disease in which the genetic alterations determining the clinicobiological behaviour are not fully understood. Here we describe a comprehensive evaluation of the genomic landscape of 452 CLL cases and 54 patients with monoclonal B-lymphocytosis, a precursor disorder. We extend the number of CLL driver alterations, including changes in ZNF292, ZMYM3, ARID1A and PTPN11. We also identify novel recurrent mutations in non-coding regions, including the 3′ region of NOTCH1, which cause aberrant splicing events, increase NOTCH1 activity and result in a more aggressive disease. In addition, mutations in an enhancer located on chromosome 9p13 result in reduced expression of the B-cell-specific transcription factor PAX5. The accumulative number of driver alterations (0 to ≥4) discriminated between patients with differences in clinical behaviour. This study provides an integrated portrait of the CLL genomic landscape, identifies new recurrent driver mutations of the disease, and suggests clinical interventions that may improve the management of this neoplasia.

In Silico Prescription of Anticancer Drugs to Cohorts of 28 Tumor Types Reveals Targeting Opportunities

Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.

Independent Component Analysis Uncovers the Landscape of the Bladder Tumor Transcriptome and Reveals Insights into Luminal and Basal Subtypes

Extracting relevant information from large-scale data offers unprecedented opportunities in cancerology. We applied independent component analysis (ICA) to bladder cancer transcriptome data sets and interpreted the components using gene enrichment analysis and tumor-associated molecular, clinicopathological, and processing information. We identified components associated with biological processes of tumor cells or the tumor microenvironment, and other components revealed technical biases. Applying ICA to nine cancer types identified cancer-shared and bladder-cancer-specific components. We characterized the luminal and basal-like subtypes of muscle-invasive bladder cancers according to the components identified. The study of the urothelial differentiation component, specific to the luminal subtypes, showed that a molecular urothelial differentiation program was maintained even in those luminal tumors that had lost morphological differentiation. Study of the genomic alterations associated with this component coupled with functional studies revealed a protumorigenic role for PPARG in luminal tumors. Our results support the inclusion of ICA in the exploitation of multiscale data sets.

Comprehensive Characterization of Cancer Driver Genes and Mutations

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases. The resource is publicly available at http://www.cancergenomeinterpreter.org.

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: OncodriveROLE 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 Matthews 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. Contact: abel.gonzalez@upf.edu or nuria.lopez@upf.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Integrating genomic alterations in diffuse large B-cell lymphoma identifies new relevant pathways and potential therapeutic targets

Genome studies of diffuse large B-cell lymphoma (DLBCL) have revealed a large number of somatic mutations and structural alterations. However, the clinical significance of these alterations is still not well defined. In this study, we have integrated the analysis of targeted next-generation sequencing of 106 genes and genomic copy number alterations (CNA) in 150 DLBCL. The clinically significant findings were validated in an independent cohort of 111 patients. Germinal center B-cell and activated B-cell DLBCL had a differential profile of mutations, altered pathogenic pathways and CNA. Mutations in genes of the NOTCH pathway and tumor suppressor genes (TP53/CDKN2A), but not individual genes, conferred an unfavorable prognosis, confirmed in the independent validation cohort. A gene expression profiling analysis showed that tumors with NOTCH pathway mutations had a significant modulation of downstream target genes, emphasizing the relevance of this pathway in DLBCL. An in silico drug discovery analysis recognized 69 (46%) cases carrying at least one genomic alteration considered a potential target of drug response according to early clinical trials or preclinical assays in DLBCL or other lymphomas. In conclusion, this study identifies relevant pathways and mutated genes in DLBCL and recognizes potential targets for new intervention strategies.

Abstract A1-45: In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities

The development of targeted therapies against altered driver proteins holds the promise of selectively and efficiently eliminating cancer cells. However, high intertumor heterogeneity is a major obstacle to develop and apply therapeutic targeted agents to treat most cancer patients. Here, we present the first large-scale therapeutic landscape of cancer as it stands today in a 6.792 sample cohort covering 28 tumor types. To pursue this goal, we developed a three-step in silico drug prescription strategy. 1) To discover actionable driver events, we first comprehensively identified mutational cancer driver genes by detecting complementary signals of positive selection in the pattern of their mutations across the tumor cohorts. We also identified actionable copy number alteration (CNA) and fusion cancer driver genes. Second, we detected which of these driver genes would have an oncogenic role in the tumor and which ones would lose their function. With these two steps we generated the Drivers Database. 2) Next, we systematically gathered all information available on therapeutic agents; FDA approved and in clinical or pre-clinical stages. We considered three different types of targeting strategies for the cancer driver genes: direct targeting, indirect targeting and gene therapies in clinical trials. Moreover, we designed a set of rules for assigning therapeutic agents to specific genomic alterations beard for the driver genes. By doing this last step, we generated the Drivers Actionability Database. 3) Finally, by combining data of Drivers Database, Drivers Actionability Database and sample data, we developed in silico drug prescription, a novel approach to determine which of the drugs could benefit each of the tumor individuals. In all, in the Driver Database we identified 460 mutational cancer driver genes acting in one or more of the tumor types along with 39 driver genes acting via CNAs or fusions. Fifty of these cancer driver genes are targeted by FDA approved agents, 63 by molecules currently in clinical trials and 74 are bound by pre-clinical ligands. We also identified 81 therapeutically unexploited targetable cancer genes. Lastly, by applying in silico drug prescription we found that only 6.7% of the patients could be treated following clinical guidelines, and were concentrated in only 6 tumor types. Moreover, considering repurposing strategies the fraction of patients that could benefit from FDA approved drugs would increase up to 40%, increasing remarkably the fraction of targetable patients in some tumor types like glioblastoma and thyroid cancer, and up to 72% if considering targeted therapies in clinical trials. In summary, the in silico drug prescription based on Drivers and Drivers Actionability Databases was tested on one of the largest cohorts of tumor samples currently collected for research. The main result highlights the current scope of targeted anti-cancer therapies and its prospects for growth in view of the drugs that are currently in clinical trials or at pre-clinical stages. Additionally, another important output of this work is a ranked list of novel target opportunities for anticancer drug development. Continuous update of drug-target interactions information, and the application of the strategy to larger cohorts, will improve the in silico prescription rules contained within the two databases, thus enhancing its usefulness within personalized cancer medicine. Citation Format: Carlota Rubio-Perez, David Tamborero, Michael P. Schroeder, Albert A. Antolin, Jordi Deu-Pons, Christian Perez-Llamas, Jordi Mestres, Abel Gonzalez-Perez, Nuria Lopez-Bigas. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-45.

Genetic and functional characterization of disease associations explains comorbidity

Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn’s disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer’s disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations.

Rational design of cancer gene panels with OncoPaD

BackgroundProfiling 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.ResultsWe 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.ConclusionsWith 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.