Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene’s proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene’s regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen’s Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance, ncCADD and ncGWAVA, and find both scores are significantly predictive of human dosage sensitive genes and appear to carry information beyond conservation, as assessed by ncGERP. These results highlight that the intolerance of noncoding sequence stretches in the human genome can provide a critical complementary tool to other genome annotation approaches to help identify the parts of the human genome increasingly likely to harbor mutations that influence risk of disease.

BACKGROUND: FGFR3-altered urothelial cancer (UC) correlates with a non-T cell-inflamed phenotype and has therefore been postulated to be less responsive to immune checkpoint blockade (ICB). Preclinical work suggests FGFR3 signalling may suppress pathways such as interferon signalling that alter immune microenvironment composition. However, correlative studies examining clinical trials have been conflicting as to whether FGFR altered tumours have equivalent response and survival to ICB in patients with metastatic UC. These findings have yet to be validated in real world data, therefore we evaluated clinical outcomes of patients with FGFR3-altered metastatic UC treated with ICB and investigate the underlying immunogenomic mechanisms of response and resistance. METHODS: 103 patients with metastatic UC treated with ICB at a single academic medical center from 2014 to 2018 were identified. Clinical annotation for demographics and cancer outcomes, as well as somatic DNA and RNA sequencing, were performed. Objective response rate to ICB, progression-free survival, and overall survival was compared between patients with FGFR3-alterations and those without. RNA expression, including molecular subtyping and T cell receptor clonality, was also compared between FGFR3-altered and non-altered patients. RESULTS: Our findings from this dataset confirm that FGFR3-altered (n = 17) and wild type (n = 86) bladder cancers are equally responsive to ICB (12 vs 19%, p = 0.73). Moreover, we demonstrate that despite being less inflamed, FGFR3-altered tumours have equivalent T cell receptor (TCR) diversity and that the balance of a CD8 T cell gene expression signature to immune suppressive features is an important determinant of ICB response. CONCLUSIONS: Our work in a real world dataset validates prior observations from clinical trials but also extends this prior work to demonstrate that FGFR3-altered and wild type tumours have equivalent TCR diversity and that the balance of effector T cell to immune suppression signals are an important determinant of ICB response.

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We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition—i.e., the parameter-space domain where it has the largest modularity relative to the input set—discarding partitions with empty domains to obtain the subset of partitions that are “admissible” candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.