Mapping drug biology to disease genetics to discover drug impacts on the human phenome.

Motivation: Medications can have unexpected effects on disease, including not only harmful drug side effects, but also beneficial drug repurposing. These effects on disease may result from hidden influences of drugs on disease gene networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the mechanism of latent drug effects, and can help predict new effects. Results: Here, we develop Draphnet, a model that integrates molecular data on 429 drugs and gene associations of nearly 200 common phenotypes to learn a network that explains drug effects on disease in terms of these molecular signals. We present evidence that our method can both predict drug effects, and can provide insight into the biology of unexpected drug effects on disease. Using Draphnet to map a drug's known molecular effects to downstream effects on the disease genome, we put forward disease genes impacted by drugs, and we suggest a new grouping of drugs based on shared effects on the disease genome. Our approach has multiple applications, including predicting drug uses and learning drug biology, with implications for personalized medicine. Availability and implementation: Code to reproduce the analysis is available at https://github.com/RDMelamed/drug-phenome.

Shared etiology of Mendelian and complex disease supports drug discovery.

Background: Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. Methods: In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. Results: Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. Conclusions: Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Reduction of Phosphorylated Tau in Alzheimer's Disease Induced Pluripotent Stem Cell-Derived Neuro-Spheroids by Rho-Associated Coiled-Coil Kinase Inhibitor Fasudil.

BACKGROUND: Alzheimer's disease (AD) is the most predominant form of dementia. Rho-associated coiled coil kinase (ROCK) inhibitor, fasudil, is one of the candidate drugs against the AD progression. OBJECTIVE: We aimed to investigate possible changes of AD associated markers in three-dimensional neuro-spheroids (3D neuro-spheroids) generated from induced pluripotent stem cells derived from AD patients or healthy control subjects (HC) and to determine the impact of pharmacological intervention with the ROCK inhibitor fasudil. METHODS: We treated 3D neuro-spheroids with fasudil and tested the possible effect on AD markers by ELISA, transcriptomic and proteomic analyses. RESULTS: Transcriptomic analysis revealed a reduction in the expression of AKT serine/threonine-protein kinase 1 (AKT1) in AD neuro-spheroids, compared to HC. This decrease was reverted in the presence of fasudil. Proteomic analysis showed up- and down-regulation of proteins related to AKT pathway in fasudil-treated neuro-spheroids. We found an evident increase of phosphorylated tau at four different residues (pTau181, 202, 231, and 396) in AD compared to HC-derived neuro-spheroids. This was accompanied by a decrease of secreted clusterin (clu) and an increase of intracellular clu levels in AD patient-derived neuro-spheroids. Increases of phosphorylated tau in AD patient-derived neuro-spheroids were suppressed in the presence of fasudil. CONCLUSIONS: Fasudil modulates clu protein levels and enhances AKT1 that results in the suppression of AD associated tau phosphorylation.

Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes.

Objectives: Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes. Methods: We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models. Results: From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose. Conclusions: We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.

Associations Between Natural Language Processing-Enriched Social Determinants of Health and Suicide Death Among US Veterans.

Importance: Social determinants of health (SDOHs) are known to be associated with increased risk of suicidal behaviors, but few studies use SDOHs from unstructured electronic health record notes. Objective: To investigate associations between veterans' death by suicide and recent SDOHs, identified using structured and unstructured data. Design, Setting, and Participants: This nested case-control study included veterans who received care under the US Veterans Health Administration from October 1, 2010, to September 30, 2015. A natural language processing (NLP) system was developed to extract SDOHs from unstructured clinical notes. Structured data yielded 6 SDOHs (ie, social or familial problems, employment or financial problems, housing instability, legal problems, violence, and nonspecific psychosocial needs), NLP on unstructured data yielded 8 SDOHs (social isolation, job or financial insecurity, housing instability, legal problems, barriers to care, violence, transition of care, and food insecurity), and combining them yielded 9 SDOHs. Data were analyzed in May 2022. Exposures: Occurrence of SDOHs over a maximum span of 2 years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide death were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. Suicide was ascertained by National Death Index, and patients were followed up for up to 2 years after cohort entry with a study end date of September 30, 2015. Adjusted odds ratios (aORs) and 95% CIs were estimated using conditional logistic regression. Results: Of 6 122 785 veterans, 8821 committed suicide during 23 725 382 person-years of follow-up (incidence rate 37.18 per 100 000 person-years). These 8821 veterans were matched with 35 284 control participants. The cohort was mostly male (42 540 [96.45%]) and White (34 930 [79.20%]), with 6227 (14.12%) Black veterans. The mean (SD) age was 58.64 (17.41) years. Across the 5 common SDOHs, NLP-extracted SDOH, on average, retained 49.92% of structured SDOHs and covered 80.03% of all SDOH occurrences. SDOHs, obtained by structured data and/or NLP, were significantly associated with increased risk of suicide. The 3 SDOHs with the largest effect sizes were legal problems (aOR, 2.66; 95% CI, 2.46-2.89), violence (aOR, 2.12; 95% CI, 1.98-2.27), and nonspecific psychosocial needs (aOR, 2.07; 95% CI, 1.92-2.23), when obtained by combining structured data and NLP. Conclusions and Relevance: In this study, NLP-extracted SDOHs, with and without structured SDOHs, were associated with increased risk of suicide among veterans, suggesting the potential utility of NLP in public health studies.

Using indication embeddings to represent patient health for drug safety studies.

OBJECTIVE: The electronic health record is a rising resource for quantifying medical practice, discovering the adverse effects of drugs, and studying comparative effectiveness. One of the challenges of applying these methods to health care data is the high dimensionality of the health record. Methods to discover the effects of drugs in health data must account for tens of thousands of potentially relevant confounders. Our goal in this work is to reduce the dimensionality of the health data with the aim of accelerating the application of retrospective cohort studies to this data. MATERIALS AND METHODS: Here, we develop indication embeddings, a way to reduce the dimensionality of health data while capturing information relevant to treatment decisions. We evaluate these embeddings using external data on drug indications. Then, we use the embeddings as a substitute for medical history to match patients and develop evaluation metrics for these matches. RESULTS: We demonstrate that these embeddings recover the therapeutic uses of drugs. We use embeddings as an informative representation of relationships between drugs, between health history events and drug prescriptions, and between patients at a particular time in their health history. We show that using embeddings to match cohorts improves the balance of the cohorts, even in terms of poorly measured risk factors like smoking. DISCUSSION AND CONCLUSION: Unlike other embeddings inspired by word2vec, indication embeddings are specifically designed to capture the medical history leading to the prescription of a new drug. For retrospective cohort studies, our low-dimensional representation helps in finding comparator drugs and constructing comparator cohorts.

Patchwork of contrasting medication cultures across the USA.

Health in the United States is markedly heterogeneous, with large disparities in disease incidence, treatment choices and health spending. Drug prescription is one major component of health care-reflecting the accuracy of diagnosis, the adherence to evidence-based guidelines, susceptibility to drug marketing and regulatory factors. Using medical claims data covering nearly half of the USA population, we have developed and validated a framework to compare prescription rates of 600 popular drugs in 2334 counties. Our approach uncovers geographically separated sub-Americas, where patients receive treatment for different diseases, and where physicians choose different drugs for the same disease. The geographical variation suggests influences of racial composition, state-level health care laws and wealth. Some regions consistently prefer more expensive drugs, even when they have not been proven more efficacious than cheaper alternatives. Our study underlines the benefit of aggregating massive information on medical practice into a summarized and actionable form.

Genomic Characterization of Dysplastic Nevi Unveils Implications for Diagnosis of Melanoma.

A well-defined risk factor and precursor for cutaneous melanoma is the dysplastic nevus. These benign tumors represent clonal hyperproliferation of melanocytes that are in a senescent-like state, but with occasional malignant transformation events. To portray the mutational repertoire of dysplastic nevi in patients with the dysplastic nevus syndrome and to determine the discriminatory profiles of melanocytic nevi (including dysplastic nevi) from melanoma, we sequenced exomes of melanocytic nevi including dysplastic nevi (n = 19), followed by a targeted gene panel (785 genes) characterization of melanocytic nevi (n = 46) and primary melanomas (n = 42). Exome sequencing revealed that dysplastic nevi harbored a substantially lower mutational load than melanomas (21 protein-changing mutations versus >100). Known "driver" mutations in genes for melanoma, including CDKN2A, TP53, NF1, RAC1, and PTEN, were not found among any melanocytic nevi sequenced. Additionally, melanocytic nevi including dysplastic nevi showed a significantly lower frequency and a different UV-associated mutational signature. These results show that although melanocytic nevi and dysplastic nevi harbor stable genomes with relatively few alterations, progression into melanomas requires additional mutational processes affecting key tumor suppressors. This study identifies molecular parameters that could be useful for diagnostic platforms.

An information theoretic method to identify combinations of genomic alterations that promote glioblastoma.

Tumors are the result of accumulated genomic alterations that cooperate synergistically to produce uncontrollable cell growth. Although identifying recurrent alterations among large collections of tumors provides a way to pinpoint genes that endow a selective advantage in oncogenesis and progression, it fails to address the genetic interactions behind this selection process. A non-random pattern of co-mutated genes is evidence for selective forces acting on tumor cells that harbor combinations of these genetic alterations. Although existing methods have successfully identified mutually exclusive gene sets, no current method can systematically discover more general genetic relationships. We develop Genomic Alteration Modules using Total Correlation (GAMToC), an information theoretic framework that integrates copy number and mutation data to identify gene modules with any non-random pattern of joint alteration. Additionally, we present the Seed-GAMToC procedure, which uncovers the mutational context of any putative cancer gene. The software is publicly available. Applied to glioblastoma multiforme samples, GAMToC results show distinct subsets of co-occurring mutations, suggesting distinct mutational routes to cancer and providing new insight into mutations associated with proneural, proneural/G-CIMP, and classical types of the disease. The results recapitulate known relationships such as mutual exclusive mutations, place these alterations in the context of other mutations, and find more complex relationships such as conditional mutual exclusivity.

Genetic similarity between cancers and comorbid Mendelian diseases identifies candidate driver genes.

Despite large-scale cancer genomics studies, key somatic mutations driving cancer, and their functional roles, remain elusive. Here, we propose that analysis of comorbidities of Mendelian diseases with cancers provides a novel, systematic way to discover new cancer genes. If germline genetic variation in Mendelian loci predisposes bearers to common cancers, the same loci may harbour cancer-associated somatic variation. Compilations of clinical records spanning over 100 million patients provide an unprecedented opportunity to assess clinical associations between Mendelian diseases and cancers. We systematically compare these comorbidities against recurrent somatic mutations from more than 5,000 patients across many cancers. Using multiple measures of genetic similarity, we show that a Mendelian disease and comorbid cancer indeed have genetic alterations of significant functional similarity. This result provides a basis to identify candidate drivers in cancers including melanoma and glioblastoma. Some Mendelian diseases demonstrate 'pan-cancer' comorbidity and shared genetics across cancers.

Data-driven discovery of seasonally linked diseases from an Electronic Health Records system.

BACKGROUND: Patterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patterns due to seasonal changes in environmental or infectious agents. Previous work searching for seasonal or other temporal patterns in disease diagnosis rates has been limited both in the scope of the diseases examined and in the ability to distinguish unexpected seasonal patterns. Electronic Health Records (EHR) compile extensive longitudinal clinical information, constituting a unique source for discovery of trends in occurrence of disease. However, the data suffer from inherent biases that preclude an identification of temporal trends. METHODS: Motivated by observation of the biases in this data source, we developed a method (Lomb-Scargle periodograms in detrended data, LSP-detrend) to find periodic patterns by adjusting the temporal information for broad trends in incidence, as well as seasonal changes in total hospitalizations. LSP-detrend can sensitively uncover periodic temporal patterns in the corrected data and identify the significance of the trend. We apply LSP-detrend to a compilation of records from 1.5 million patients encoded by ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), including 2,805 disorders with more than 500 occurrences across a 12 year period, recorded from 1.5 million patients. RESULTS AND CONCLUSIONS: Although EHR data, and ICD-9 coded records in particular, were not created with the intention of aggregated use for research, these data can in fact be mined for periodic patterns in incidence of disease, if confounders are properly removed. Of all diagnoses, around 10% are identified as seasonal by LSP-detrend, including many known phenomena. We robustly reproduce previous findings, even for relatively rare diseases. For instance, Kawasaki disease, a rare childhood disease that has been associated with weather patterns, is detected as strongly linked with winter months. Among the novel results, we find a bi-annual increase in exacerbations of myasthenia gravis, a potentially life threatening complication of an autoimmune disease. We dissect the causes of this seasonal incidence and propose that factors predisposing patients to this event vary through the year.

FBXW7 mutations in melanoma and a new therapeutic paradigm.

BACKGROUND: Melanoma is a heterogeneous tumor with subgroups requiring distinct therapeutic strategies. Genetic dissection of melanoma subgroups and identification of therapeutic agents are of great interest in the field. These efforts will ultimately lead to treatment strategies, likely combinatorial, based on genetic information. METHODS: To identify "driver" genes that can be targeted therapeutically, we screened metastatic melanomas for somatic mutations by exome sequencing followed by selecting those with available targeted therapies directed to the gene product or its functional partner. The FBXW7 gene and its substrate NOTCH1 were identified and further examined. Mutation profiling of FBXW7, biological relevance of these mutations and its inactivation, and pharmacological inhibition of NOTCH1 were examined using in vitro and in vivo assays. RESULTS: We found FBXW7 to be mutated in eight (8.1%) melanoma patients in our cohort (n = 103). Protein expression analysis in human tissue samples (n = 96) and melanoma cell lines (n = 20) showed FBXW7 inactivation as a common event in melanoma (40.0% of cell lines). As a result of FBXW7 loss, we observed an accumulation of its substrates, such as NOTCH1. Ectopic expression of mutant forms of FBXW7 (by 2.4-fold), as well as silencing of FBXW7 in immortalized melanocytes, accelerated tumor formation in vivo (by 3.9-fold). Its inactivation led to NOTCH1 activation, upregulation of NOTCH1 target genes (by 2.6-fold), and promotion of tumor angiogenesis and resulted in tumor shrinkage upon NOTCH1 inhibition (by fivefold). CONCLUSIONS: Our data provides evidence on FBXW7 as a critical tumor suppressor mutated and inactivated in melanoma that results in sustained NOTCH1 activation and renders NOTCH signaling inhibition as a promising therapeutic strategy in this setting.