Machine learning for medicine

Small-molecule screens aimed at identifying therapeutic candidates traditionally search for molecules that affect one to several outputs at most, limiting discovery of true disease-modifying drugs. Theodoris et al. developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell disease model of a common form of heart disease involving the aortic valve. Gene network correction by the most efficacious therapeutic candidate generalized to primary aortic valve cells derived from more than 20 patients with sporadic aortic valve disease and prevented aortic valve disease in vivo in a mouse model.

Science, this issue p. eabd0724

Determining the gene-regulatory networks that drive human disease allows the design of therapies that target the core disease mechanism rather than merely managing symptoms. However, small molecules used as therapeutic agents are traditionally screened for their effects on only one to several outputs at most, from which their predicted efficacy on the disease as a whole is extrapolated. In silico correlation of disease network dysregulation with pathways affected by molecules in surrogate cell types is limited by the relevance of the cell types used and by not directly testing compounds in patient cells.

In principle, mapping the architecture of the dysregulated network in disease-relevant cells differentiated from patient-derived induced pluripotent stem cells (iPSCs) and subsequent screening for small molecules that broadly correct the abnormal gene network could overcome this obstacle. Specifically, targeting normalization of the core regulatory elements that drive the disease process, rather than correction of peripheral downstream effectors that may not be disease modifying, would have the greatest likelihood of therapeutic success. We previously demonstrated that haploinsufficiency of NOTCH1 can cause calcific aortic valve disease (CAVD), the third most common form of heart disease, and that the underlying mechanism involves derepression of osteoblast-like gene networks in cardiac valve cells. There is no medical therapy for CAVD, and in the United States alone, >100,000 surgical valve replacements are performed annually to relieve obstruction of blood flow from the heart. Many of these occur in the setting of a congenital aortic valve anomaly present in 1 to 2% of the population in which the aortic valve has two leaflets (bicuspid) rather than the normal three leaflets (tricuspid). Bicuspid valves in humans can also be caused by NOTCH1 mutations and predispose to early and more aggressive calcification in adulthood. Given that valve calcification progresses with age, a medical therapy that could slow or even arrest progression would have tremendous impact.

We developed a machine-learning approach to identify small molecules that sufficiently corrected gene network dysregulation in NOTCH1-haploinsufficient human iPSC-derived endothelial cells (ECs) such that they classified similar to NOTCH1+/+ ECs derived from gene-corrected isogenic iPSCs. We screened 1595 small molecules for their effect on a signature of 119 genes representative of key regulatory nodes and peripheral genes from varied regions of the inferred NOTCH1-dependent network, assayed by targeted RNA sequencing (RNA-seq). Overall, eight molecules were validated to sufficiently correct the network signature such that NOTCH1+/ ECs classified as NOTCH1+/+ by the trained machine-learning algorithm. Of these, XCT790, an inverse agonist of estrogen-related receptor (ERR), had the strongest restorative effect on the key regulatory nodes SOX7 and TCF4 and on the network as a whole, as shown by full transcriptome RNA-seq.

Gene network correction by XCT790 generalized to human primary aortic valve ECs derived from explanted valves from >20 patients with nonfamilial CAVD. XCT790 was effective in broadly restoring dysregulated genes toward the normal state in both calcified tricuspid and bicuspid valves, including the key regulatory nodes SOX7 and TCF4.

Furthermore, XCT790 was sufficient to prevent as well as treat already established aortic valve disease in vivo in a mouse model of Notch1 haploinsufficiency on a telomere-shortened background. XCT790 significantly reduced aortic valve thickness, the extent of calcification, and echocardiographic signs of valve stenosis in vivo. XCT790 also reduced the percentage of aortic valve cells expressing the osteoblast transcriptional regulator RUNX2, indicating a reduction in the osteogenic cell fate switch underlying CAVD. Whole-transcriptome RNA-seq in treated aortic valves showed that XCT790 broadly corrected the genes dysregulated in Notch1-haploinsufficient mice with shortened telomeres, and that treatment of diseased aortic valves promoted clustering of the transcriptome with that of healthy aortic valves.

Network-based screening that leverages iPSC and machine-learning technologies is an effective strategy to discover molecules with broadly restorative effects on gene networks dysregulated in human disease that can be validated in vivo. XCT790 represents an entry point for developing a much-needed medical therapy for calcification of the aortic valve, which may also affect the highly related and associated calcification of blood vessels. Given the efficacy of XCT790 in limiting valve thickening, the potential for XCT790 to alter the progression of childhood, and perhaps even fetal, valve stenosis also warrants further study. Application of this strategy to other human models of disease may increase the likelihood of identifying disease-modifying candidate therapies that are successful in vivo.

A gene networkbased screening approach leveraging human disease-specific iPSCs and machine learning identified a therapeutic candidate, XCT790, which corrected the network dysregulation in genetically defined iPSC-derived endothelial cells and primary aortic valve endothelial cells from >20 patients with sporadic aortic valve disease. XCT790 was also effective in preventing and treating a mouse model of aortic valve disease.

Mapping the gene-regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve (AV). Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary AV cells and was sufficient to prevent and treat AV disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery.

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Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease - Science