Noncanonical HPV carcinogenesis drives radiosensitization of head and neck tumors.

We analyzed transcriptional data from 104 HPV+ (Human papillomavirus) HNSCC (head and neck squamous cell carcinoma) tumors together with two publicly available sources to identify highly robust transcriptional programs (modules) which could be detected consistently despite heterogeneous sequencing and quantification methodologies. Among 22 modules identified, we found a single module that naturally subclassifies HPV+ HNSCC tumors based on a bimodal pattern of gene expression, clusters all atypical features of HPV+ HNSCC biology into a single subclass, and predicts patient outcome in four independent cohorts. The subclass-defining gene set was strongly correlated with Nuclear factor kappa B (NF-κB) target expression. Tumors with high expression of this NF-κB module were rarely associated with activating PIK3CA alterations or viral integration, and also expressed higher levels of HPHPV E2 and had decreased APOBEC mutagenesis. Alternatively, they harbored inactivating alterations of key regulators of NF-κB, TNF receptor associated factor 3 (TRAF3), and cylindromatosis (CYLD), as well as retinoblastoma protein (RB1). HPV+ HNSCC cells in culture with experimental depletion of TRAF3 or CYLD displayed increased expression of the subclass-defining genes, as well as robust radio-sensitization, thus recapitulating both the tumor transcriptional state and improved treatment response observed in patient data. Across all gene sets investigated, methylation to expression correlations were the strongest for the subclass-defining, NF-κB-related genes. Increased tumor-infiltrating CD4+ T cells and increased Estrogen receptors alpha (ERα) expression were identified in NF-κB active tumors. Based on the relatively high rates of cure in HPV+ HNSCC, deintensification of therapy to reduce treatment-related morbidity is being studied at many institutions. Tumor subclassification based on oncogenic subtypes may help guide the selection of therapeutic intensity or modality for patients with HPV+ HNSCC.

Comprehensive Viral Genotyping Reveals Prognostic Viral Phylogenetic Groups in HPV16-Associated Squamous Cell Carcinoma of the Oropharynx.

Human papillomavirus-positive (HPV+) squamous cell carcinoma of the oropharynx (OPSCC) is the most prevalent HPV-associated malignancy in the United States and is primarily caused by HPV subtype 16 (HPV16). Favorable treatment outcomes have led to increasing interest in treatment deescalation to reduce treatment-related morbidity. Prognostic biomarkers are needed to identify appropriately low-risk patients for reduced treatment intensity. Targeted DNA sequencing including all HPV16 open reading frames was performed on tumors from 104 patients with HPV16+ OPSCC treated at a single center. Genotypes closely related to the HPV16-A1 reference were associated with increased numbers of somatic copy-number variants in the human genome and poor recurrence-free survival (RFS). Genotypes divergent from HPV16-A1 were associated with favorable RFS. These findings were independent of tobacco smoke exposure. Total RNA sequencing was performed on a second independent cohort of 89 HPV16+ OPSCC cases. HPV16 genotypes divergent from HPV16-A1 were again validated in this independent cohort, to be prognostic of improved RFS in patients with moderate (less than 30 pack-years) or low (no more than 10 pack-years) of tobacco smoke exposure. In summary, we show in two independent cohorts that viral sequence divergence from the HPV16-A1 reference is correlated with improved RFS in patients with moderate or low tobacco smoke exposure. IMPLICATIONS: HPV16 genotype is a potential biomarker that could be easily adopted to guide therapeutic decision-making related to deescalation therapy.

A bipartite graph-based expected networks approach identifies DDR genes not associated with TMB yet predictive of immune checkpoint blockade response.

Immune checkpoint blockade (ICB) has had remarkable success for treatment of solid tumors. However, as only a subset of patients exhibit responses, there is a continued need for biomarker development. Numerous reports have shown a link between tumor mutational burden (TMB) and ICB response, while others have identified a link between ICB response and mutation in DNA damage repair (DDR) genes. However, it remains unclear to what extent mutations in DDR genes hold predictive value above and beyond their association with TMB. Herein, we present a networks-based test and bipartite graph-based expected TMB score (BiG-BETS) with higher specificity for discriminating DDR genes and pathways that are associated with elevated TMB. Moreover, we find that mutations in certain DDR genes that are not associated with elevated TMB (low BiG-BETS) are nevertheless predictive of ICB benefit in high TMB patients, demonstrating that their inactivation contributes to ICB response in a TMB-independent manner.

Fibroblast growth factor receptor 3 alterations and response to immune checkpoint inhibition in metastatic urothelial cancer: a real world experience.

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.

Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections.

Clostridioides difficile infection (CDI) accounts for a substantial proportion of deaths attributable to antibiotic-resistant bacteria in the United States. Although C. difficile can be an asymptomatic colonizer, its pathogenic potential is most commonly manifested in patients with antibiotic-modified intestinal microbiomes. In a cohort of 186 hospitalized patients, we showed that host and microbe-associated shifts in fecal metabolomes had the potential to distinguish patients with CDI from those with non-C. difficile diarrhea and C. difficile colonization. Patients with CDI exhibited a chemical signature of Stickland amino acid fermentation that was distinct from those of uncolonized controls. This signature suggested that C. difficile preferentially catabolizes branched chain amino acids during CDI. Unexpectedly, we also identified a series of noncanonical, unsaturated bile acids that were depleted in patients with CDI. These bile acids may derive from an extended host-microbiome dehydroxylation network in uninfected patients. Bile acid composition and leucine fermentation defined a prototype metabolomic model with potential to distinguish clinical CDI from asymptomatic C. difficile colonization.

Post-Processing Partitions to Identify Domains of Modularity Optimization.

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.

The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity.

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.