ACDA: implementation of an augmented drug synergy prediction algorithm.

Motivation: Drug synergy prediction is approached with machine learning techniques using molecular and pharmacological data. The published Cancer Drug Atlas (CDA) predicts a synergy outcome in cell-line models from drug target information, gene mutations and the models’ monotherapy drug sensitivity. We observed low performance of the CDA, 0.339, measured by Pearson correlation of predicted versus measured sensitivity on DrugComb datasets. Results: We augmented the approach CDA by applying a random forest regression and optimization via cross-validation hyper-parameter tuning and named it Augmented CDA (ACDA). We benchmarked the ACDA’s performance, which is 68% higher than that of the CDA when trained and validated on the same dataset spanning 10 tissues. We compared the performance of ACDA to one of the winning methods of the DREAM Drug Combination Prediction Challenge, the performance of which was lower than ACDA in 16 out of 19 cases. We further trained the ACDA on Novartis Institutes for BioMedical Research PDX encyclopedia data and generated sensitivity predictions for PDX models. Finally, we developed a novel approach to visualize synergy-prediction data. Availability and implementation: The source code is available at https://github.com/TheJacksonLaboratory/drug-synergy and the software package at PyPI. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics Advances online

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells.

VEGF inhibitor drugs are part of standard care in oncology and ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations. We also develop and share a new software tool called DECNEO to identify them. Single-cell gene expression data are used to identify a set of co-expressed endothelial cell receptors, conserved among species (mice and humans) and enriched, within a network, of connections to up-regulated genes. This set includes several receptors previously shown to play a role in angiogenesis. Multiple statistical tests from large datasets, including an independent validation set, support the reproducibility, evolutionary conservation and role in angiogenesis of these naturally occurring combinations of receptors. We also show tissue-specific combinations and, in the case of choroid endothelial cells, consistency with both well-established and recent experimental findings, presented in a separate paper. The results and methods presented here advance the understanding of signaling to endothelial cells. The methods are generally applicable to the decoding of intercellular combinations of signals

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

<p>Processed data:</p><p>inferCNV_output_WM4237.zip, inferCNV_output_WM4007.zip - InferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_cnv_WM4237.h5ad, ad_all_human_clustered_cnv_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of the inferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_im_st_WM4237.h5ad, ad_all_human_clustered_im_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for 12 Visium samples of model WM4237 or WM4007. "_im" for imaging, "_st" for Visium.</p><p>ad_all_human_clustered_im_ad_WM4237_m.h5ad, ad_all_human_clustered_im_ad_WM4007_m.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for additional H&E slides (non-Visium) of model WM4237 or WM4007. "_im" for imaging, "_m" for Macenko normalization of the H&E slides with STQ.</p><p>ids_WM4237_AD.txt, ids_WM4007_AD.txt, ids_WM4237_ST.txt, ids_WM4237_ST.txt - list of identifiers of all samples (tissue sections).</p><p>ad_all_human_clustered_st_WM4237.h5ad, ad_all_human_clustered_st_WM4007.h5ad -  AnnData object containing integration, dimensionality reduction and clustering of RNA profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>CNV_burden_WM4237.csv, CNV_burden_WM4007.csv -  Per-spot values of InferCNV-derived CNV burden for samples of models WM4237 or WM4007.</p><p>ad_all_scaled_filtered_st_WM4237.h5ad, ad_all_scaled_filtered_st_WM4007.h5ad -  AnnData objects containing concatenated and pre-processed samples of WM4237 or WM4007.</p><p>WM4237_3_AD_m-Imaging-STQ.zip, WM4007_3_AD_m-Imaging-STQ.zip - Imaging portion of STQ pipeline output derived from additional (non-Visium) H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237_ST-Imaging-STQ.tar.gz, WM4007_ST-Imaging-STQ.tar.gz -  Imaging portion of STQ pipeline output derived from Visium H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237-ST-downstream-output.tar.gz, WM4007-ST-downstream-output.tar.gz - ST-downstream-processing pipeline output for Visium samples of model WM4237 or WM4007.</p><p>WM4237-STQ-sequencing.tar.gz, WM4007-STQ-sequencing.tar.gz - RNA sequencing portion of STQ pipeline output derived from Visium samples of model WM4237 or WM4007.</p><p>rna-pseudotime-ordered.zip -  Pseudotime ordering of RNA profiles of spots for each time point (T0, T1, T2, T3, T4, TC) for samples of models WM4237 and WM4007.</p&gt