Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates.

Background: Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drug targets, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drug repurposing, but also to highlight shared etiology explaining repurposing. Methods: We compile breast cancer's predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways, and similarly for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer. Results: We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 76 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher's exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing. Conclusions: Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known risk factors. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

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.

Multipollutant, longitudinal analysis of the association between urinary tungsten and incident diabetes in a rural population.

BACKGROUND: Cross-sectional studies suggest tungsten (W) exposure may be associated with diabetes. We assessed longitudinal associations between urinary W and fasting glucose, 2-hour glucose, insulin resistance (HOMA-IR), ß-cell function (HOMA-ß), and incident type 2 diabetes. METHODS: We used data from 1,609 Hispanic and non-Hispanic White adults with 20 to 74 years of age residing in rural Colorado and participating in the San Luis Valley Diabetes Study. Urinary metal exposure values were measured at baseline and natural log-transformed. We assessed longitudinal associations between urinary W and continuous outcome measures using linear-mixed effect models and associations with incident diabetes using Fine and Gray competing risks regression models (competing event = all-cause mortality). The main adjustment set of covariates included: age, sex, ethnicity, education, smoking status, hypertension, body mass index, caloric intake, alcohol intake, and urinary creatinine levels. Secondary models were further adjusted for arsenic, cadmium, and lead exposures. We assessed whether sex or ethnicity were effect modifiers. RESULTS: At baseline, the median W concentration was 0.22 µg/L (interquartile range = 0.20, 0.59). In the main cross-sectional analyses, lnW levels were significantly associated with 3% higher lnHOMA-IR (95% CI = 1 to 5). In the main longitudinal models, lnW was significantly associated with 1% higher natural log-transformed fasting glucose (95% CI = <1 to 1), 3% higher natural log-transformed HOMA-IR (95% CI = 2 to 5), and 28% higher incident diabetes (subdistribution hazard ratio=1.28, 95% CI = 1.09 to 1.50). Results remained significant when further adjusting for other metals. We observed evidence for effect modification by sex and ethnicity. CONCLUSION: Urinary W was longitudinally associated with adverse metabolic health indicators.

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.

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.

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.

Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.

Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell "mass cytometry" to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.