IL-4, IL-10, CCL2 and TGF-ß as potential biomarkers for severity in Plasmodium vivax malaria.

Cytokines and chemokines are immune response molecules that display diverse functions, such as inflammation and immune regulation. In Plasmodium vivax infections, the uncontrolled production of these molecules is thought to contribute to pathogenesis and has been proposed as a possible predictor for disease complications. The objective of this study was to evaluate the cytokine profile of P. vivax malaria patients with different clinical outcomes to identify possible immune biomarkers for severe P. vivax malaria. The study included patients with non-severe (n = 56), or severe (n = 50) P. vivax malaria and healthy controls (n = 50). Patient plasma concentrations of IL-4, IL-2, CXCL10, IL-1ß, TNF-α, CCL2, IL-17A, IL-6, IL-10, IFN-γ, IL-12p70, CXCL8 and active TGF-ß1 were determined through flow cytometry. The levels of several cytokines and chemokines, CXCL10, IL-10, IL-6, IL-4, CCL2 and IFN-γ were found to be significantly higher in severe, compared to non-severe P. vivax malaria patients. Severe thrombocytopenia was positively correlated with IL-4, CXCL10, IL-6, IL-10 and IFN-γ levels, renal dysfunction was related to an increase in IL-2, IL-1ß, IL-17A and IL-8, and hepatic impairment with CXCL10, MCP-1, IL-6 and IFN-γ. A Lasso regression model suggests that IL-4, IL-10, CCL2 and TGF-ß might be developed as biomarkers for severity in P. vivax malaria. Severe P. vivax malaria patients present specific cytokine and chemokine profiles that are different from non-severe patients and that could potentially be developed as biomarkers for disease severity.

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.

Identification and drug-induced reversion of molecular signatures of Alzheimer's disease onset and progression in AppNL-G-F, AppNL-F, and 3xTg-AD mouse models.

BACKGROUND: In spite of many years of research, our understanding of the molecular bases of Alzheimer's disease (AD) is still incomplete, and the medical treatments available mainly target the disease symptoms and are hardly effective. Indeed, the modulation of a single target (e.g., ß-secretase) has proven to be insufficient to significantly alter the physiopathology of the disease, and we should therefore move from gene-centric to systemic therapeutic strategies, where AD-related changes are modulated globally. METHODS: Here we present the complete characterization of three murine models of AD at different stages of the disease (i.e., onset, progression and advanced). We combined the cognitive assessment of these mice with histological analyses and full transcriptional and protein quantification profiling of the hippocampus. Additionally, we derived specific Aß-related molecular AD signatures and looked for drugs able to globally revert them. RESULTS: We found that AD models show accelerated aging and that factors specifically associated with Aß pathology are involved. We discovered a few proteins whose abundance increases with AD progression, while the corresponding transcript levels remain stable, and showed that at least two of them (i.e., lfit3 and Syt11) co-localize with Aß plaques in the brain. Finally, we found two NSAIDs (dexketoprofen and etodolac) and two anti-hypertensives (penbutolol and bendroflumethiazide) that overturn the cognitive impairment in AD mice while reducing Aß plaques in the hippocampus and partially restoring the physiological levels of AD signature genes to wild-type levels. CONCLUSIONS: The characterization of three AD mouse models at different disease stages provides an unprecedented view of AD pathology and how this differs from physiological aging. Moreover, our computational strategy to chemically revert AD signatures has shown that NSAID and anti-hypertensive drugs may still have an opportunity as anti-AD agents, challenging previous reports.

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.

Cervical cancer screening outcomes in Zambia, 2010-19: a cohort study.

BACKGROUND: Globally, cervical cancer is the fourth leading cause of cancer-related death among women. Poor uptake of screening services contributes to the high mortality. We aimed to examine screening frequency, predictors of screening results, and patterns of sensitisation strategies by age group in a large, programmatic cohort. METHODS: We did a cohort study including 11 government health facilities in Lusaka, Zambia, in which we reviewed routine programmatic data collected through the Cervical Cancer Prevention Program in Zambia (CCPPZ). Participants who underwent cervical cancer screening in one of the participating study sites were considered for study inclusion if they had a screening result. Follow-up was accomplished per national guidelines. We did descriptive analyses and mixed-effects logistic regression for cervical cancer screening results allowing random effects at the individual and clinic level. FINDINGS: Between Jan 1, 2010, and July 31, 2019, we included 183 165 women with 204 225 results for visual inspection with acetic acid and digital cervicography (VIAC) in the analysis. Of all those screened, 21 326 (10·4%) were VIAC-positive, of whom 16 244 (76·2%) received treatment. Of 204 225 screenings, 92 838 (45·5%) were in women who were HIV-negative, 76 607 (37·5%) were in women who were HIV-positive, and 34 780 (17·0%) had an unknown HIV status. Screening frequency increased 65·7% between 2010 and 2019 with most appointments being first-time screenings (n=158 940 [77·8%]). Women with HIV were more likely to test VIAC-positive than women who were HIV-negative (adjusted odds ratio 3·60, 95% CI 2·14-6·08). Younger women (≤29 years) with HIV had the highest predictive probability (18·6%, 95% CI 14·2-22·9) of screening positive. INTERPRETATION: CCPPZ has effectively increased women's engagement in screening since its inception in 2006. Customised sensitisation strategies relevant to different age groups could increase uptake and adherence to screening. The high proportion of screen positivity in women younger than 20 years with HIV requires further consideration. Our data are not able to discern if women with HIV have earlier disease onset or whether this difference reflects misclassification of disease in an age group with a higher sexually transmitted infection prevalence. These data inform scale-up efforts required to achieve WHO elimination targets. FUNDING: US President's Emergency Plan for AIDS Relief.

Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns.

Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients' progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.

Encircling the regions of the pharmacogenomic landscape that determine drug response.

BACKGROUND: The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics. METHODS: To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome. RESULTS: We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses. CONCLUSIONS: Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

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 /.

Systems analysis of intracellular pH vulnerabilities for cancer therapy.

A reverse pH gradient is a hallmark of cancer metabolism, manifested by extracellular acidosis and intracellular alkalization. While consequences of extracellular acidosis are known, the roles of intracellular alkalization are incompletely understood. By reconstructing and integrating enzymatic pH-dependent activity profiles into cell-specific genome-scale metabolic models, we develop a computational methodology that explores how intracellular pH (pHi) can modulate metabolism. We show that in silico, alkaline pHi maximizes cancer cell proliferation coupled to increased glycolysis and adaptation to hypoxia (i.e., the Warburg effect), whereas acidic pHi disables these adaptations and compromises tumor cell growth. We then systematically identify metabolic targets (GAPDH and GPI) with predicted amplified anti-cancer effects at acidic pHi, forming a novel therapeutic strategy. Experimental testing of this strategy in breast cancer cells reveals that it is particularly effective against aggressive phenotypes. Hence, this study suggests essential roles of pHi in cancer metabolism and provides a conceptual and computational framework for exploring pHi roles in other biomedical domains.

Rationalizing Drug Response in Cancer Cell Lines.

Cancer cell lines (CCLs) play an important role in the initial stages of drug discovery allowing, among others, for the screening of drug candidates. As CCL panels continue to grow in size and diversity, many polymorphisms in genes encoding drug-metabolizing enzymes, transporters and drug targets, as well as disease-related genes have been linked to altered drug sensitivity. However, identifying the correlation between this variability and pharmacological responses remains challenging due to the heterogeneity of cancer biology and the intricate interplay between cell lines and drug molecules. Here, we propose a network-based strategy that exploits information on gene expression and somatic mutations of CCLs to group cells according to their molecular similarity. We then identify genes that are characteristic of each cluster and correlate their status with drug response. We find that CCLs with similar characteristic active network regions present specific responses to certain drugs, and identify a limited set of genes that might be directly involved in drug sensitivity or resistance.

Detecting similar binding pockets to enable systems polypharmacology.

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

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.

Drug sensitivity in cancer cell lines is not tissue-specific.

BACKGROUND: Cancer cell lines have a prominent role in the initial stages of drug discovery, facilitating high-throughput screening of potential drugs. However, their clinical relevance remains controversial. FINDINGS: We assess whether drug sensitivity in cancer cell lines is able to discriminate tissue specificity. We find that cancer-specific drugs do not show higher efficacies in cell lines representing the respective tissues. Even when considering distinct cancer subtypes and targeted therapies, most drugs are evenly effective/ineffective throughout all cell lines. CONCLUSIONS: To get the most out of cell line panels, it will be necessary to look into their molecular characteristics, and integrate them into systems biology frameworks.

Systematic identification of molecular links between core and candidate genes in breast cancer.

Despite the remarkable progress achieved in the identification of specific genes involved in breast cancer (BC), our understanding of their complex functioning is still limited. In this manuscript, we systematically explore the existence of direct physical interactions between the products of BC core and associated genes. Our aim is to generate a protein interaction network of BC-associated gene products and suggest potential molecular mechanisms to unveil their role in the disease. In total, we report 599 novel high-confidence interactions among 44 BC core, 54 BC candidate/associated and 96 newly identified proteins. Our findings indicate that this network-based approach is indeed a robust inference tool to pinpoint new potential players and gain insight into the underlying mechanisms of those proteins with previously unknown roles in BC. To illustrate the power of our approach, we provide initial validation of two BC-associated proteins on the alteration of DNA damage response as a result of specific re-wiring interactions. Overall, our BC-related network may serve as a framework to integrate clinical and molecular data and foster novel global therapeutic strategies.

Charting the molecular links between driver and susceptibility genes in colorectal cancer.

Despite significant advances in the identification of specific genes and pathways important in the onset and progression of colorectal cancer (CRC), mechanistic insight into the relationship between driver and susceptibility genes is needed. In this paper, we systematically explore physical interactions between causative and putative CRC susceptibility genes to reveal the molecular mechanisms involved in tumor biology. In total, we identify 622 high-confidence protein-protein interactions between 42 CRC causative and 65 candidate susceptibility genes. Among the latter, 28 are located in the CRCS9 loci, related to the etiology of CRC, and 17 are co-expressed with well-established CRC drivers, which makes them excellent candidates for further functional studies. Moreover, we find a high degree of functional coherence between connected driver and susceptibility genes, which indicates that our network-based strategy is useful to gain insight into the underlying mechanisms of those proteins with unknown roles in CRC.