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Bayesian Modelling of Synergistic Drug Combination Effects in Cancer Using Gaussian Processes

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live: https://valence-discovery.github.io/M... Also consider joining the M2D2 Slack: https://m2d2group.slack.com/join/shar... Title: Bayesian Modelling of Synergistic Drug Combination Effects in Cancer Using Gaussian Processes Abstract: High-throughput drug sensitivity experiments in cancer enable rapid in-vitro testing of various compounds on cancer cell lines, or patient-derived material, in order to determine the efficacy of a certain treatment. Accurate prediction of dose-response functions from a limited set of pre-clinical experiments is key to explore the large space of possible treatment options, or to prioritize which experiments to perform. This is particularly important when predicting the effect of drug combinations, where it is unfeasible to test all possible combinations. Drug sensitivity experiments are noisy by nature, due in part to the natural biological variability of cell growth but also technical error sources in the assays. This entails that the experimental observations of dose-response at different concentrations of a drug vary in estimation certainty both within and between experiments — a variability that has been often ignored in the literature. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). The interaction is modelled in a flexible manner, using a latent Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. We further extend the model from single-experiment to multi-experiment modelling and propose PIICM: a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian Process regression to predict dose-response surfaces in untested drug combination experiments. The permutation invariance accounts for natural symmetries in the dose-response surfaces for drug combinations, which when not accounted for can have detrimental effects on prediction performance. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, and the training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs. Speakers: Leiv Rønneberg -   / ltronneberg   Twitter Prudencio:   / tossouprudencio   Twitter Therence:   / therence_mtl   Twitter Cas:   / cas_wognum   Twitter Valence Discovery:   / valence_ai   ~ Chapters: 00:00 - Begin 00:10 - Intro & Outline 02:58 - Motivation: Drug Synergy 11:22 - Single Experiment Modelling: Bayesynergy 22:29 - Multi-experiment Modelling: PIICM 42:27 - Results on Example Data 47:08 - Q+A