{"id":4015,"date":"2026-07-14T03:25:22","date_gmt":"2026-07-14T01:25:22","guid":{"rendered":"https:\/\/www.upo.es\/investiga\/synergialab\/?p=4015"},"modified":"2026-07-14T03:27:20","modified_gmt":"2026-07-14T01:27:20","slug":"drug-sensitivity-prediction","status":"publish","type":"post","link":"https:\/\/www.upo.es\/investiga\/synergialab\/2026\/07\/14\/drug-sensitivity-prediction\/","title":{"rendered":"New Computational Framework from SynergIA Lab Enhances Oncology Drug Sensitivity Prediction by Controlling for &#8220;Tissue-of-Origin&#8221; Noise"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container has-pattern-background has-mask-background nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1248px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1\"><p><img decoding=\"async\" class=\"lazyload size-full wp-image-4018 alignleft\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%27150%27%20height%3D%27200%27%20viewBox%3D%270%200%20150%20200%27%3E%3Crect%20width%3D%27150%27%20height%3D%27200%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-orig-src=\"https:\/\/www.upo.es\/investiga\/synergialab\/wp-content\/uploads\/2026\/07\/1-s2.0-S1476927126X20023-cov200h.gif\" alt=\"\" width=\"150\" height=\"200\" \/><\/p>\n<div style=\"text-align: justify;\">\n<p data-path-to-node=\"6\"><strong>SIALAB researchers design a tissue-aware co-expression network analysis protocol that slashes tissue-of-origin confounding from 88.9% to 12.9%, paving the way for highly interpretable and biologically rigorous pan-cancer pharmacogenomics.<\/strong><\/p>\n<p data-path-to-node=\"6\">The <b data-path-to-node=\"6\" data-index-in-node=\"4\">SynergIA Laboratory (SIALAB)<\/b> research group at Universidad Pablo de Olavide has published a groundbreaking methodological study that establishes a new computational standard in network medicine and personalized oncology drug response prediction.<\/p>\n<p data-path-to-node=\"7\">The paper, titled <b data-path-to-node=\"7\" data-index-in-node=\"18\">&#8220;A tissue-aware computational framework for confounding-controlled co-expression network analysis: Context-dependent utility in drug sensitivity modelling&#8221;<\/b>, has been recently published in the prestigious peer-reviewed journal <b data-path-to-node=\"7\" data-index-in-node=\"244\">Computational Biology and Chemistry<\/b>. In this work, the authors propose a rigorous solution to a fundamental methodological challenge that has long hindered the translational utility of co-expression networks in pan-cancer studies.<\/p>\n<p data-path-to-node=\"8\">\ud83d\udcc4 <b data-path-to-node=\"8\" data-index-in-node=\"3\">Access the full-text article here:<\/b> <a class=\"ng-star-inserted\" href=\"https:\/\/www.google.com\/search?q=https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109235\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwiu3eOggNGVAxUAAAAAHQAAAAAQjwI\">https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109235<\/a><\/p>\n<p data-path-to-node=\"9\">\ud83d\udcbb <b data-path-to-node=\"9\" data-index-in-node=\"3\">Open-source Python repository:<\/b> <a class=\"ng-star-inserted\" href=\"https:\/\/www.google.com\/search?q=https:\/\/github.com\/SynergIA-Lab\/Within-Tissue-Co-expression-Network-Modelling-for-Robust-Oncology-Drug-Response-Prediction\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahgKEwiu3eOggNGVAxUAAAAAHQAAAAAQkAI\">GitHub &#8211; SynergIA-Lab<\/a><\/p>\n<h3 data-path-to-node=\"11\">Unlocking Bias-Free Pan-Cancer Pharmacogenomics<\/h3>\n<p data-path-to-node=\"12\">In precision oncology, gene co-expression networks are widely used to predict whether a tumor will respond to a specific therapeutic agent. However, standard pan-cancer machine learning models suffer from a crucial flaw: the topological network features often end up capturing and encoding the &#8220;identity&#8221; or tissue of origin of a cell line (e.g., lung, colon, breast) rather than the actual pharmacological sensitivity signal. This inflates the apparent predictive performance of models while obscuring the true underlying biological mechanisms.<\/p>\n<p data-path-to-node=\"13\">To overcome this confounding effect, the framework designed by the SynergIA team introduces a coordinated multi-phase protocol:<\/p>\n<ul data-path-to-node=\"14\">\n<li>\n<p data-path-to-node=\"14,0,0\"><b data-path-to-node=\"14,0,0\" data-index-in-node=\"0\">\ud83e\uddec Within-Tissue Network Reconstruction (Within-Tissue WGCNA):<\/b> Instead of building a single, biased pan-cancer reference network, the framework infers co-expression structures independently for each tissue type, employing smart kNN-based imputation to handle data-scarce tissues.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"14,1,0\"><b data-path-to-node=\"14,1,0\" data-index-in-node=\"0\">\ud83d\udd0d Deconfounding Pipeline (Intra-Tissue Z-Score):<\/b> Through precise normalization of edge disruption profiles and topological metrics per gene, the framework drastically reduces tissue identity encoding in network features from an overwhelming <b data-path-to-node=\"14,1,0\" data-index-in-node=\"242\">88.9% down to just 12.9%<\/b>.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"14,2,0\"><b data-path-to-node=\"14,2,0\" data-index-in-node=\"0\">\ud83d\ude80 Quantitative Quality Control Standard:<\/b> The authors define an auditable, reproducible quality control threshold (tissue prediction accuracy &lt; 0.15) to formally certify that variables are sufficiently decoupled from tissue-of-origin noise before being fed into predictive models.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"14,3,0\"><b data-path-to-node=\"14,3,0\" data-index-in-node=\"0\">\ud83d\udc8a Validation in Targeted Therapies:<\/b> The framework was benchmarked across a pan-cancer cohort of 660 cell lines against three clinically highly relevant targeted agents: osimertinib, crizotinib, and a KRAS G12C inhibitor. The results show that the predictive advantage of network topology is drug-context dependent, proving exceptionally robust for osimertinib.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"14,4,0\"><b data-path-to-node=\"14,4,0\" data-index-in-node=\"0\">\ud83c\udfaf Uncovering True Biological Signal (Hippo\/YAP Pathway):<\/b> By filtering out tissue confounding, the models successfully identified the central roles of <b data-path-to-node=\"14,4,0\" data-index-in-node=\"151\">YAP1<\/b> and <b data-path-to-node=\"14,4,0\" data-index-in-node=\"160\">TEAD1<\/b> as genuine, tissue-independent predictors of therapeutic resistance, showcasing the utility of this framework in discovering reliable drug targets.<\/p>\n<\/li>\n<\/ul>\n<h3 data-path-to-node=\"15\">Broader Impact on the Scientific Community<\/h3>\n<p data-path-to-node=\"16\">This research provides the biomedical and bioinformatics communities with a transparent, highly auditable, and directly applicable computational pipeline for large-scale pharmacogenomic studies, such as CCLE or GDSC. By releasing the fully reproducible codebase as open-source, the SynergIA research group reinforces its ongoing commitment to open science and the development of robust, reliable AI tools to fight cancer.<\/p>\n<p data-path-to-node=\"17\"><b data-path-to-node=\"17\" data-index-in-node=\"0\">Authors of the paper:<\/b><\/p>\n<ul data-path-to-node=\"18\">\n<li>\n<p data-path-to-node=\"18,0,0\"><b data-path-to-node=\"18,0,0\" data-index-in-node=\"0\">Marc R\u00edos-Cadenas<\/b> (Universidad Pablo de Olavide).<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"18,1,0\"><b data-path-to-node=\"18,1,0\" data-index-in-node=\"0\">Iv\u00e1n Segura-Carmona<\/b> (Universidad Pablo de Olavide).<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"18,2,0\"><b data-path-to-node=\"18,2,0\" data-index-in-node=\"0\">Aurelio L\u00f3pez-Fern\u00e1ndez<\/b> (Universidad Pablo de Olavide).<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"18,3,0\"><b data-path-to-node=\"18,3,0\" data-index-in-node=\"0\">Francisco A. G\u00f3mez-Vela<\/b> (Universidad Pablo de Olavide).<\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":4017,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"content-type":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-4015","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>New Computational Framework from SynergIA Lab Enhances Oncology Drug Sensitivity Prediction by Controlling for &quot;Tissue-of-Origin&quot; 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