By combining mouse proteomic data with human transcriptomic data, a group led by Massachusetts Institute of Technology researchers has developed a computational method for predicting inflammatory bowel disease (IBD) patient responses to anti-tumor necrosis factor (anti-TNF) therapy. The method — called translatable components regression, or TransComp R — uses principal components analysis to link IBD patient pretreatment transcriptomic data to a model of disease-relevant mouse proteins. As reported in Science Signaling, the scientists use the tool to implicate activated integrin pathway signaling in resistance to the anti-TNF drug infliximab among colonic Crohn's disease and ulcerative colitis patients. Single-cell sequencing of patient biopsies confirmed the finding and further experimentation showed that inhibiting integrin signaling enhanced the cytokine-suppressive effects of infliximab in immune cells. "We suggest that TransComp-R is a generalizable framework for addressing species, molecular, and phenotypic discrepancies between model systems and patients to translationally deliver relevant biological insights," the study's authors write.