Personalized treatment of complex diseases is an unmet medical need pushing towards drug biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach for modeling cell-centered individual-level network dynamics from high-dimensional blood data to predict infliximab response and uncover individual variation of non-response. We identified and validated that the RAC1-PAK1 axis is predictive of infliximab response in inflammatory bowel disease. Intermediate monocytes, which closely correlated with inflammation state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in Rheumatoid arthritis, validated in three public cohorts. Our findings support pan-disease drug response diagnostics from blood, implicating common mechanisms of drug response or failure across diseases.
Cells are the quanta unit of biology and their relative composition in a tissue is the prime driver of bulk tissue gene expression variation. When there is no cell information, deconvolution is an effective tool to achieve cell resolution, which provides important information for learning disease complexity and its interactions with treatments, drugs and/or the environment in a wide variety of contexts. Here we present CytoPro, a production-level tissue and condition-specific deconvolution platform, based on a large collection of human tissue-specific signatures derived from single and sorted cells. CytoPro infer per-sample multiple cell-type composition, given input bulk gene expression. CytoPro includes a rigorous QC pipeline for learning, generating and selecting signatures and performs internal automated validation using multiple QC test criteria including: Comparison to ground truth cytometry and pure sorted cells data, performance evaluation using simulated data including robustness to noise as well as agreement with biological expectations in validation datasets regarding genes and cells. We demonstrate that CytoPro outperforms existing deconvolution tools, in both accuracy and robustness. By exploring multiple datasets with predefined disease phenotypes, and analyzing a use-case of biological treatment response, we show the ability of CytoPro to flush out relevant cell biology in real pathological conditions.
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