Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

MOTIVATION: Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. RESULTS: Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. AVAILABILITY AND IMPLEMENTATION: An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/.

Toward personalized skin cancer care: multiple skin cancer development in five cohorts.

Importance: Many patients will develop more than one skin cancer, however most research to date has examined only case status. Objective: Describe the frequency and timing of the treatment of multiple skin cancers in individual patients over time. Design: Longitudinal claims and electronic health record-based cohort study. Setting: Vanderbilt University Medical Center database called the Synthetic Derivative, VA, Medicare, Optum Clinformatics® Data Mart Database, IBM Marketscan. Participants: All patients with a Current Procedural Terminology code for the surgical management of a skin cancer in each of five cohorts. Exposures: None. Main Outcomes and Measures: The number of CPT codes for skin cancer treatment in each individual occurring on the same day as an ICD code for skin cancer over time. Results: Our cohort included 5,508,374 patients and 13,102,123 total skin cancers treated. Conclusions and Relevance: Nearly half of patients treated for skin cancer were treated for more than one skin cancer. Patients who have not developed a second skin cancer by 2 years after the first are unlikely to develop multiple skin cancers within the following 5 years. Better data formatting will allow for improved granularity in identifying individuals at high risk for multiple skin cancers and those unlikely to benefit from continued annual surveillance. Resource planning should take into account not just the number of skin cancer cases, but the individual burden of disease.

Polygenic risk score for ulcerative colitis predicts immune checkpoint inhibitor-mediated colitis.

Immune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event of treatment with immune checkpoint inhibitors (ICI). We hypothesize that genetic susceptibility to Crohn's disease (CD) and ulcerative colitis (UC) predisposes to IMC. In this study, we first develop a polygenic risk scores for CD (PRSCD) and UC (PRSUC) in cancer-free individuals and then test these PRSs on IMC in a cohort of 1316 patients with ICI-treated non-small cell lung cancer and perform a replication in 873 ICI-treated pan-cancer patients. In a meta-analysis, the PRSUC predicts all-grade IMC (ORmeta=1.35 per standard deviation [SD], 95% CI = 1.12-1.64, P = 2×10-03) and severe IMC (ORmeta=1.49 per SD, 95% CI = 1.18-1.88, P = 9×10-04). PRSCD is not associated with IMC. Furthermore, PRSUC predicts severe IMC among patients treated with combination ICIs (ORmeta=2.20 per SD, 95% CI = 1.07-4.53, P = 0.03). Overall, PRSUC can identify patients receiving ICI at risk of developing IMC and may be useful to monitor patients and improve patient outcomes.

Deciphering the 'gut-brain axis' through microbiome diversity.

Incentivised by breakthroughs and data generated by the high-throughput sequencing technology, this paper proposes a distance-based framework to fulfil the emerging needs in elucidating insights from the high-dimensional microbiome data in psychiatric studies. By shifting focus from traditional methods that focus on the observations from each subject to the between-subject attributes that aggregate two or more subjects' entire feature vectors, the described approach revolutionises the conventional prescription for high-dimensional observations via microbiome diversity. To this end, we enrich the classical generalised linear models to articulate the multivariable regression relationship between distance-based variables. We also discuss a robust and computationally feasible semiparametric inference technique. Benefitting from the latest advances in the semiparametric efficiency theory for such attributes, the proposed estimators enjoy robustness and good asymptotic properties that guarantee sensitivity in detecting signals between clinical outcomes and microbiome diversity. It offers a readily implementable and easily interpretable solution for deciphering the gut-brain axis in mental health research.

Polygenic risk score for ulcerative colitis predicts immune checkpoint inhibitor-mediated colitis.

Immune checkpoint inhibitors (ICIs) are a remarkable advancement in cancer therapeutics; however, a substantial proportion of patients develop severe immune-related adverse events (irAEs). Understanding and predicting irAEs is a key to advancing precision immuno-oncology. Immune checkpoint inhibitor-mediated colitis (IMC) is a significant complication from ICI and can have life-threatening consequences. Based on clinical presentation, IMC mimics inflammatory bowel disease, however the link is poorly understood. We hypothesized that genetic susceptibility to Crohn's disease (CD) and ulcerative colitis (UC) may predispose to IMC. We developed and validated polygenic risk scores for CD (PRSCD) and UC (PRSUC) in cancer-free individuals and assessed the role of each of these PRSs on IMC in a cohort of 1,316 patients with non-small cell lung cancer who received ICIs. Prevalence of all-grade IMC in our cohort was 4% (55 cases), and for severe IMC, 2.5% (32 cases). The PRSUC predicted the development of all-grade IMC (HR=1.34 per standard deviation [SD], 95% CI=1.02-1.76, P=0.04) and severe IMC (HR=1.62 per SD, 95% CI=1.12-2.35, P=0.01). PRSCD was not associated with IMC or severe IMC. The association between PRSUC and IMC (all-grade and severe) was consistent in an independent pan-cancer cohort of patients treated with ICIs. Furthermore, PRSUC predicted severe IMC among patients treated with combination ICIs (OR = 2.20 per SD, 95% CI = 1.07-4.53, P=0.03). This is the first study to demonstrate the potential clinical utility of a PRS for ulcerative colitis in identifying patients receiving ICI at high risk of developing IMC, where risk reduction and close monitoring strategies could help improve overall patient outcomes.