Systemic sclerosis (SSc) is a rare disease, so it is often difficult to build large clinical trials to test the effectiveness of any given experimental therapy. Thus, even though whole genome gene expression is routinely gathered in these trials, trials are often statistically underpowered to detect differential expression of many important genes in improvers on the treatment. This makes it difficult to perform post hoc analysis to determine the drug’s functional role in changing pathological gene expression. Moreover, many of the molecular responses to a drug occur post-transcriptionally, e.g. affecting binding and signaling.
In collaboration with the inimitable Jaclyn Taroni, we recently performed a meta-analysis of five clinical trials in SSc. To overcome the above obstacles (low statistical power, non-transcriptional regulation), we re-analyzed gene expression in these subjects using a variant of the NetWAS strategy developed by Greene et al. [1]. Originally developed for genome-wide association studies (GWAS), NetWAS combines p-values for genes (from any statistical test) with functional genomic networks and machine learning classifiers to identify a coherent functional signature of the nominally statistically significant genes. The ethos of NetWAS is that although genome-wide statistical significance is hard to come by (except in the most high-powered settings), it is often the case that the nominally significant genes in a study are enriched for functionally relevant pathways and processes. Thus, while taking all genes that meet a statistically permissive cutoff results in many spurious gene associations, pairing statistical tests with functional information allows one to simultaneously prune the list of spurious hits and expand the list to other relevant genes that were missed by statistical criteria. The net result is that functional information about genes can be used to make an end run around statistical power issues. This opens the door for a number of genomic applications in rare diseases where samples are hard to come by (more on this in a future post, perhaps).
We co-opted NetWAS to perform an augmented form of differential expression analysis in SSc clinical trials. Instead of GWAS p-values, we used differential expression p-values. We ran NetWAS to identify the functionally similar genes among the nominally differentially expressed genes (i.e. genes that look differentially expressed by a permissive statistical cutoff). Then we extrapolated to the rest of the genome to identify genes that were similar to this signature. We systematically show that our strategy dramatically improves over differential expression analysis alone in identifying the known molecular targets of therapies, even when those targets are not even nominally differentially expressed. We show that there are significant commonalities between improver signatures across all therapies and that some therapies hit targets missed by others, suggesting the possibility of precision medicine in SSc. I’m sparing the details in this format, but please enjoy the paper [2], which just came out in JID, and get in touch with questions and comments!
- Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).
- Taroni, J. N., Martyanov, V., Mahoney, J. M. & Whitfield, M. L. A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy. (2016). http://dx.doi.org/10.1016/j.jid.2016.12.007