A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

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Drug-perturbed RNA sequencing data can be used to identify drug targets

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Technology-based drug-target definitions often subsume literature definitions

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Literature and screening datasets provide complementary information on drug mechanisms

Bioactivity descriptors for uncharacterized chemical compounds

Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.

Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs

Until a vaccine becomes available, the current repertoire of drugs is our only therapeutic asset to fight the SARS-CoV-2 outbreak. Indeed, emergency clinical trials have been launched to assess the effectiveness of many marketed drugs, tackling the decrease of viral load through several mechanisms. Here, we present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1 million bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

A PanorOmic view of personal cancer genomes

The massive molecular profiling of thousands of cancer patients has led to the identification of many tumor type specific driver genes. However, only a few (or none) of them are present in each individual tumor and, to enable precision oncology, we need to interpret the alterations found in a single patient. Cancer PanorOmics (http://panoromics.irbbarcelona.org) is a web-based resource to contextualize genomic variations detected in a personal cancer genome within the body of clinical and scientific evidence available for 26 tumor types, offering complementary cohort- and patient-centric views. Additionally, it explores the cellular environment of mutations by mapping them on the human interactome and providing quasi-atomic structural details, whenever available. This ‘PanorOmic’ molecular view of individual tumors, together with the appropriate genetic counselling and medical advice, should contribute to the identification of actionable alterations ultimately guiding the clinical decision-making process.

Exploring the OncoGenomic Landscape of cancer

Background

The widespread incorporation of next-generation sequencing into clinical oncology has yielded an unprecedented amount of molecular data from thousands of patients. A main current challenge is to find out reliable ways to extrapolate results from one group of patients to another and to bring rationale to individual cases in the light of what is known from the cohorts.

Results

We present OncoGenomic Landscapes, a framework to analyze and display thousands of cancer genomic profiles in a 2D space. Our tool allows users to rapidly assess the heterogeneity of large cohorts, enabling the comparison to other groups of patients, and using driver genes as landmarks to aid in the interpretation of the landscapes. In our web-server, we also offer the possibility of mapping new samples and cohorts onto 22 predefined landscapes related to cancer cell line panels, organoids, patient-derived xenografts, and clinical tumor samples.

Conclusions

Contextualizing individual subjects in a more general landscape of human cancer is a valuable aid for basic researchers and clinical oncologists trying to identify treatment opportunities, maybe yet unapproved, for patients that ran out of standard therapeutic options. The web-server can be accessed at https://oglandscapes.irbbarcelona.org/.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0571-0) contains supplementary material, which is available to authorized users.

Network-based analysis of omics data: the LEAN method

Motivation

Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters.

Results

We introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments.

Evaluation on simulated and biological data suggests that LEAN generally detects dysregulated subnetworks better, and reflects biological similarity between experiments more clearly than standard approaches. A strong signal for the local subnetwork around Von Willebrand Factor (VWF), a gene which showed no change on the mRNA level, was identified by LEAN in transcriptome data in the context of the genetic disease Cerebral Cavernous Malformations (CCM). This signal was experimentally found to correspond to an unexpected strong cellular effect on the VWF protein. LEAN can be used to pinpoint statistically significant local subnetworks in any genome-scale dataset.

Availability and Implementation

The R-package LEANR implementing LEAN is supplied as supplementary material and available on CRAN (https://cran.r-project.org).

Supplementary information

Supplementary data are available at Bioinformatics online.

The Cyni framework for network inference in Cytoscape

MOTIVATION

Research on methods for the inference of networks from biological data is making significant advances, but the adoption of network inference in biomedical research practice is lagging behind. Here, we present Cyni, an open-source 'fill-in-the-algorithm' framework that provides common network inference functionality and user interface elements. Cyni allows the rapid transformation of Java-based network inference prototypes into apps of the popular open-source Cytoscape network analysis and visualization ecosystem. Merely placing the resulting app in the Cytoscape App Store makes the method accessible to a worldwide community of biomedical researchers by mouse click. In a case study, we illustrate the transformation of an ARACNE implementation into a Cytoscape app.

AVAILABILITY AND IMPLEMENTATION

Cyni, its apps, user guides, documentation and sample code are available from the Cytoscape App Store http://apps.cytoscape.org/apps/cynitoolbox

CONTACT

benno.schwikowski@pasteur.fr.