Drug repurposing and prediction of multiple interaction types via graph embedding.

BACKGROUND: Finding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore, both identifying new drug-target links, as well as delineating the type of drug interaction, are important in drug repurposing studies. RESULTS: A computational drug repurposing approach was proposed to predict novel drug-target interactions (DTIs), as well as to predict the type of interaction induced. The methodology is based on mining a heterogeneous graph that integrates drug-drug and protein-protein similarity networks, together with verified drug-disease and protein-disease associations. In order to extract appropriate features, the three-layer heterogeneous graph was mapped to low dimensional vectors using node embedding principles. The DTI prediction problem was formulated as a multi-label, multi-class classification task, aiming to determine drug modes of action. DTIs were defined by concatenating pairs of drug and target vectors extracted from graph embedding, which were used as input to classification via gradient boosted trees, where a model is trained to predict the type of interaction. After validating the prediction ability of DT2Vec+, a comprehensive analysis of all unknown DTIs was conducted to predict the degree and type of interaction. Finally, the model was applied to propose potential approved drugs to target cancer-specific biomarkers. CONCLUSION: DT2Vec+ showed promising results in predicting type of DTI, which was achieved via integrating and mapping triplet drug-target-disease association graphs into low-dimensional dense vectors. To our knowledge, this is the first approach that addresses prediction between drugs and targets across six interaction types.

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer.

BACKGROUND: Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. MATERIALS AND METHODS: Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. RESULTS: A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. CONCLUSIONS: CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

An immunologically relevant rodent model demonstrates safety of therapy using a tumour-specific IgE.

BACKGROUND: Designing biologically informative models for assessing the safety of novel agents, especially for cancer immunotherapy, carries substantial challenges. The choice of an in vivo system for studies on IgE antibodies represents a major impediment to their clinical translation, especially with respect to class-specific immunological functions and safety. Fcε receptor expression and structure are different in humans and mice, so that the murine system is not informative when studying human IgE biology. By contrast, FcεRI expression and cellular distribution in rats mirror that of humans. METHODS: We are developing MOv18 IgE, a human chimeric antibody recognizing the tumour-associated antigen folate receptor alpha. We created an immunologically congruent surrogate rat model likely to recapitulate human IgE-FcεR interactions and engineered a surrogate rat IgE equivalent to MOv18. Employing this model, we examined in vivo safety and efficacy of antitumour IgE antibodies. RESULTS: In immunocompetent rats, rodent IgE restricted growth of syngeneic tumours in the absence of clinical, histopathological or metabolic signs associated with obvious toxicity. No physiological or immunological evidence of a "cytokine storm" or allergic response was seen, even at 50 mg/kg weekly doses. IgE treatment was associated with elevated serum concentrations of TNFα, a mediator previously linked with IgE-mediated antitumour and antiparasitic functions, alongside evidence of substantially elevated tumoural immune cell infiltration and immunological pathway activation in tumour-bearing lungs. CONCLUSION: Our findings indicate safety of MOv18 IgE, in conjunction with efficacy and immune activation, supporting the translation of this therapeutic approach to the clinical arena.