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

A mode of action protein based approach that characterizes the relationships among most major diseases

Disease classification is important for understanding disease commonalities on both the phenotypical and molecular levels. Based on predicted disease mode of action (MOA) proteins, our algorithm PICMOA (Pan-disease Classification in Mode of Action Protein Space) classifies 3526 diseases across 20 clinically classified classifications (ICD10-CM major classifications). At the top level, all diseases can be classified into "infectious" and "non-infectious" diseases. Non-infectious diseases are classified into 9 classes. To demonstrate the validity of the classifications, for common pathways predicted based on MOA proteins, 77% of the top 10 most frequent pathways have literature evidence of association to their respective disease classes/subclasses. These results indicate that PICMOA will be useful for understanding common disease mechanisms and facilitating the development of drugs for a class of diseases, rather than a single disease. The MOA proteins, molecular functions, pathways for classes, and individual diseases are available at https://sites.gatech.edu/cssb/PICMOA/ .

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 opportunities. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drugs, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drugs for 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 data 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. 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, 74 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 predisposing diseases. 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.

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.

Familial aggregation of multimorbidity in Sweden: national explorative family study

Objectives

To examine whether multimorbidity aggregates in families in Sweden.

Design

National explorative family study.

Setting

Swedish Multigeneration Register linked to the National Patient Register, 1997-2015. Multimorbidity was assessed with a modified counting method of 45 chronic non-communicable diseases according to ICD-10 (international classification of diseases, 10th revision) diagnoses.

Participants

2 694 442 Swedish born individuals (48.73% women) who could be linked to their Swedish born first, second, and third degree relatives. Twins were defined as full siblings born on the same date.

Main outcome measures

Multimorbidity was defined as two or more non-communicable diseases. Familial associations for one, two, three, four, and five or more non-communicable diseases were assessed to examine risks depending on the number of non-communicable diseases. Familial adjusted odds ratios for multimorbidity were calculated for individuals with a diagnosis of multimorbidity compared with relatives of individuals unaffected by multimorbidity (reference). An initial principal component decomposition followed by a factor analysis with a principal factor method and an oblique promax rotation was used on the correlation matrix of tetrachoric correlations between 45 diagnoses in patients to identify disease clusters.

Results

The odds ratios for multimorbidity were 2.89 in twins (95% confidence interval 2.56 to 3.25), 1.81 in full siblings (1.78 to 1.84), 1.26 in half siblings (1.24 to 1.28), and 1.13 in cousins (1.12 to 1.14) of relatives with a diagnosis of multimorbidity. The odds ratios for multimorbidity increased with the number of diseases in relatives. For example, among twins, the odds ratios for multimorbidity were 1.73, 2.84, 4.09, 4.63, and 6.66 for an increasing number of diseases in relatives, from one to five or more, respectively. Odds ratios were highest at younger ages: in twins, the odds ratio was 3.22 for those aged ≤20 years, 3.14 for those aged 21-30 years, and 2.29 for those aged >30 years at the end of follow-up. Nine disease clusters (factor clusters 1-9) were identified, of which seven aggregated in families. The first three disease clusters in the principal component decomposition were cardiometabolic disease (factor 1), mental health disorders (factor 2), and disorders of the digestive system (factor 3). Odds ratios for multimorbidity in twins, siblings, half siblings, and cousins for the factor 1 cluster were 2.79 (95% confidence interval 0.97 to 8.06), 2.62 (2.39 to 2.88), 1.52 (1.34 to 1.73), and 1.31 (1.23 to 1.39), and for the factor 2 cluster, 5.79 (4.48 to 7.48) 3.24 (3.13 to 3.36), 1.51 (1.45 to 1.57), and 1.37 (1.341.40).

Conclusions

The results of this explorative family study indicated that multimorbidity aggregated in Swedish families. The findings suggest that map clusters of diseases should be used for the genetic study of common diseases to show new genetic patterns of non-communicable diseases.

Prevalence of Periodontal Disease among Patients at the Outpatient Clinic of Internal Medicine in an Academic Hospital in The Netherlands: A Cross-Sectional Pilot Study

There is a worldwide increase in individuals suffering ≥2 chronic diseases (multimorbidity), and the cause of combinations of conditions remains largely unclear. This pilot study analysed the prevalence of periodontal disease (PD) among (multi)-morbid patients at the outpatient clinic of internal medicine. PD is an inflammatory disease of the tooth supporting tissues and has a negative impact on the overall health. Data were obtained from 345 patients, on demographics, systemic conditions and presence of PD. The possible differences in the distribution of PD status among patients with/without multimorbidity and Medical Subject Headings (MeSH) disease chapters were explored. In total, 180 (52.2%) patients suffered from multimorbidity. The prevalence of severe PD was 16.2%, while the prevalence of mild and severe PD combined (Total PD) was 53.6%. Patients with disease chapter cardiovascular diseases (CVD) had a significantly higher prevalence of severe PD (odds ratio (OR) 2.33; 95% confidence interval (CI) 1.25, 4.33) and Total PD (OR 1.61; 95% CI 1.04, 2.50) than patients without CVD. After subsequent analyses, myocardial infarction was significantly associated with severe PD (OR: 4.68 (95% CI; 1.27 to 17.25)). Those suffering from multimorbidity showed to have a non-significant increased risk for severe (OR 1.27; 95% CI 0.69, 2.34) or Total PD (OR 1.23; 95% CI 0.81, 1.88). In conclusion, PD is highly prevalent in multimorbidity patients. Furthermore, PD was significantly prevalent in patients with CVD. However, larger epidemiological studies are necessary to confirm that the prevalence of PD is significantly increased among multimorbid patients.

LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity

To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO (Large-Scale Molecular Interpretation of Disease Comorbidity), an algorithm that predicts disease comorbidities from shared mode of action proteins predicted by the artificial intelligence-based MEDICASCY algorithm. LeMeDISCO was applied to predict the occurrence of comorbid diseases for 3608 distinct diseases. Benchmarking shows that LeMeDISCO has much better comorbidity recall than the two molecular methods XD-score (44.5% vs. 6.4%) and the S_AB score (68.6% vs. 8.0%). Its performance is somewhat comparable to the phenotype method-based Symptom Similarity Score, 63.7% vs. 100%, but LeMeDISCO works for far more cases and its large comorbidity recall is attributed to shared proteins that can help provide an understanding of the molecular mechanism(s) underlying disease comorbidity. The LeMeDISCO web server is available for academic users at: http://sites.gatech.edu/cssb/LeMeDISCO .

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

A global overview of genetically interpretable multimorbidities among common diseases in the UK Biobank

BACKGROUND

Multimorbidities greatly increase the global health burdens, but the landscapes of their genetic risks have not been systematically investigated.

METHODS

We used the hospital inpatient data of 385,335 patients in the UK Biobank to investigate the multimorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify multimorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for the networks of genetically interpretable multimorbidities to detect the hub diseases and the involved molecules and functions in each module.

RESULTS

In total, 11,285 multimorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture levels. Multimorbidities affecting the same and different physiological systems displayed different patterns of the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by multimorbidities converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable multimorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the multimorbidity modules could help provide useful insights for the genetic contributors of multimorbidities.

CONCLUSIONS

Our results provide a systematic resource for understanding the genetic predispositions of multimorbidities and indicate that hub diseases and converged molecules and functions may be the key for treating multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities ( https://multimorbidity.comp-sysbio.org ).

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

Motivation

Gene prioritization at human GWAS loci is challenging due to linkage-disequilibrium and long-range gene regulatory mechanisms. However, identifying the causal gene is crucial to enable identification of potential drug targets and better understanding of molecular mechanisms. Mapping GWAS traits to known phenotypically relevant Mendelian disease genes near a locus is a promising approach to gene prioritization.

Results

We present MendelVar, a comprehensive tool that integrates knowledge from four databases on Mendelian disease genes with enrichment testing for a range of associated functional annotations such as Human Phenotype Ontology, Disease Ontology and variants from ClinVar. This open web-based platform enables users to strengthen the case for causal importance of phenotypically matched candidate genes at GWAS loci. We demonstrate the use of MendelVar in post-GWAS gene annotation for type 1 diabetes, type 2 diabetes, blood lipids and atopic dermatitis.

Availability and implementation

MendelVar is freely available at https://mendelvar.mrcieu.ac.uk

Supplementary information

Supplementary data are available at Bioinformatics online.

High incidence of FLT3 mutations in follicular thyroid cancer: potential therapeutic target in patients with advanced disease stage

Background:

Conventional treatments for follicular thyroid cancer (FTC) can be ineffective, leading to poor prognosis. The aim of this study was to identify mutations associated with FTC that would serve as novel molecular markers of the disease and its outcome and could potentially identify new therapeutic targets.

Methods:

FLT3 mutations were first detected in a 29-year-old White female diagnosed with metastasized, treatment-refractory FTC. Analyses of FLT3 mutational status through next-generation sequencing of formalin-fixed, paraffin-embedded FTC specimens were subsequently performed in 35 randomly selected patients diagnosed with FTC.

Results:

FLT3 mutations were found in 69% of patients. FLT3 mutation-positive patients were significantly older than those that were FLT3 mutation-negative [median age at diagnosis 54 (36–82) versus 45 (27–58) (p = 0.023)]. Patients over 60 years were 23 times more likely to be FLT3 mutation-positive (p = 0.006). However, the number of FLT3 mutations did not correlate with age (r-Pearson: –0.244, p-value: 0.25). A total of 26 mutations were identified in the FLT3 gene with 2–16 FLT3 mutations in each FLT3 mutation-positive patient (mean: 5.6 mutations/patient). Tyrosine kinase domain (TKD) mutations in the FLT3 gene were detected in 58% of FLT3 mutation-positive patients. All FLT3 mutation-positive patients with a disease stage of pT2N1 or worse harbored at least one mutation in the TKD of FLT3.

Conclusions:

There is a wide spectrum and high frequency of FLT3 mutations in FTC. The precise role of FLT3 mutations in the genesis of FTC, as well as its potential role as a therapeutic target, requires further investigation.

Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects

Asthma and hypertension are complex diseases coinciding more frequently than expected by chance. Unraveling the mechanisms of comorbidity of asthma and hypertension is necessary for choosing the most appropriate treatment plan for patients with this comorbidity. Since both diseases have a strong genetic component in this article we aimed to find and study genes simultaneously associated with asthma and hypertension. We identified 330 shared genes and found that they form six modules on the interaction network. A strong overlap between genes associated with asthma and hypertension was found on the level of eQTL regulated genes and between targets of drugs relevant for asthma and hypertension. This suggests that the phenomenon of comorbidity of asthma and hypertension may be explained by altered genetic regulation or result from drug side effects. In this work we also demonstrate that not only drug indications but also contraindications provide an important source of molecular evidence helpful to uncover disease mechanisms. These findings give a clue to the possible mechanisms of comorbidity and highlight the direction for future research.

Disease classification: from phenotypic similarity to integrative genomics and beyond

A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities

Background

Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets.

Methods

In this work, we integrated genome-wide association study (GWAS) associating diseases and single nucleotide polymorphisms (SNPs) with transcript regulatory activity from expression quantitative trait loci (eQTL). This allowed novel mechanistic insights for noncoding and intergenic regions. We then analyzed pairs of SNPs across diseases to identify shared molecular effectors robust to multiple test correction (False Discovery Rate FDR_eRNA < 0.05). We hypothesized that disease pairs found to be molecularly convergent would also be significantly overrepresented among comorbidities in clinical datasets. To assess our hypothesis, we used clinical claims datasets from the Healthcare Cost and Utilization Project (HCUP) and calculated significant disease comorbidities (FDR_comorbidity < 0.05). We finally verified if disease pairs resulting molecularly convergent were also statistically comorbid more than by chance using the Fisher’s Exact Test.

Results

Our approach integrates: (i) 6175 SNPs associated with 238 diseases from ~ 1000 GWAS, (ii) eQTL associations from 19 tissues, and (iii) claims data for 35 million patients from HCUP. Logistic regression (controlled for age, gender, and race) identified comorbidities in HCUP, while enrichment analyses identified cis- and trans-eQTL downstream effectors of GWAS-identified variants. Among ~ 16,000 combinations of diseases, 398 disease-pairs were prioritized by both convergent eQTL-genetics (RNA overlap enrichment, FDR_eRNA < 0.05) and clinical comorbidities (OR > 1.5, FDR_comorbidity < 0.05). Case studies of comorbidities illustrate specific convergent noncoding regulatory elements. An intergenic architecture of disease comorbidity was unveiled due to GWAS and eQTL-derived convergent mechanisms between distinct diseases being overrepresented among observed comorbidities in clinical datasets (OR = 8.6, p-value = 6.4 × 10^− 5 FET).

Conclusions

These comorbid diseases with convergent eQTL genetic mechanisms suggest clinical syndromes. While it took over a decade to confirm the genetic underpinning of the metabolic syndrome, this study is likely highlighting hundreds of new ones. Further, this knowledge may improve the clinical management of comorbidities with precision and shed light on novel approaches of drug repositioning or SNP-guided precision molecular therapy inclusive of intergenic risks.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0428-9) contains supplementary material, which is available to authorized users.

Shared ACVR1 mutations in FOP and DIPG: Opportunities and challenges in extending biological and clinical implications across rare diseases

Gain-of-function mutations in the Type I Bone Morphogenic Protein (BMP) receptor ACVR1 have been identified in two diseases: Fibrodysplasia Ossificans Progressiva (FOP), a rare autosomal dominant disorder characterized by genetically driven heterotopic ossification, and in 20-25% of Diffuse Intrinsic Pontine Gliomas (DIPGs), a pediatric brain tumor with no effective therapies and dismal median survival. While the ACVR1 mutation is causal for FOP, its role in DIPG tumor biology remains under active investigation. Here, we discuss cross-fertilization between the FOP and DIPG fields, focusing on the biological mechanisms and principles gleaned from FOP that can be applied to DIPG biology. We highlight our current knowledge of ACVR1 in both diseases, and then describe the growing opportunities and barriers to effectively investigate ACVR1 in DIPG. Importantly, learning from other seemingly unrelated diseases harboring similar mutations may uncover novel mechanisms or processes for future investigation.

Mendelian Disease Associations Reveal Novel Insights into Inflammatory Bowel Disease

Background

Monogenic diseases have been shown to contribute to complex disease risk and may hold new insights into the underlying biological mechanism of Inflammatory Bowel Disease (IBD).

Methods

We analyzed Mendelian disease associations with IBD using over 55 million patients from the Optum's deidentified electronic health records dataset database. Using the significant Mendelian diseases, we performed pathway enrichment analysis and constructed a model using gene expression datasets to differentiate Crohn's disease (CD), ulcerative colitis (UC), and healthy patient samples.

Results

We found 50 Mendelian diseases were significantly associated with IBD, with 40 being significantly associated with both CD and UC. Our results for CD replicated those from previous studies. Pathways that were enriched consisted of mainly immune and metabolic processes with a focus on tolerance and oxidative stress. Our 3-way classifier for UC, CD, and healthy samples yielded an accuracy of 72%.

Conclusions

Mendelian diseases that are significantly associated with IBD may reveal novel insights into the genetic architecture of IBD.

Quantitative mapping of genetic similarity in human heritable diseases by shared mutations

Many genetic diseases exhibit considerable epidemiological comorbidity and common symptoms, which provokes debate about the extent of their etiological overlap. The rapid growth in the number of known disease-causing mutations in the Human Gene Mutation Database (HGMD) has allowed us to characterize genetic similarities between diseases by ascertaining the extent to which identical genetic mutations are shared between diseases. Using this approach, we show that 41.6% of disease pairs in all possible pairs (42, 083) exhibit a significant sharing of mutations (P value < 0.05). These mutation-related disease pairs are in agreement with heritability-based disease-disease relations in 48 neurological and psychiatric disease pairs (Spearman's correlation coefficient = 0.50; P value = 3.4 × 10^-5 ), and share over-expressed genes significantly more often than unrelated disease pairs (1.5-1.8-fold higher; P value ≤ 1.6 × 10^-4 ). The usefulness of mutation-related disease pairs was further demonstrated for predicting novel mutations and identifying individuals susceptible to Crohn disease. Moreover, the mutation-based disease network concurs closely with that based on phenotypes.

Computational drug repositioning for rare diseases in the era of precision medicine

There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.

The ribosomal protein gene RPL5 is a haploinsufficient tumor suppressor in multiple cancer types

For many years, defects in the ribosome have been associated to cancer. Recently, somatic mutations and deletions affecting ribosomal protein genes were identified in a few leukemias and solid tumor types. However, systematic analysis of all 81 known ribosomal protein genes across cancer types is lacking. We screened mutation and copy number data of respectively 4926 and 7322 samples from 16 cancer types and identified six altered genes (RPL5, RPL11, RPL23A, RPS5, RPS20 and RPSA). RPL5 was located at a significant peak of heterozygous deletion or mutated in 11% of glioblastoma, 28% of melanoma and 34% of breast cancer samples. Moreover, patients with low RPL5 expression displayed worse overall survival in glioblastoma and in one breast cancer cohort. RPL5 knockdown in breast cancer cell lines enhanced G2/M cell cycle progression and accelerated tumor progression in a xenograft mouse model. Interestingly, our data suggest that the tumor suppressor role of RPL5 is not only mediated by its known function as TP53 or c-MYC regulator. In conclusion, RPL5 heterozygous inactivation occurs at high incidence (11-34%) in multiple tumor types, currently representing the most common somatic ribosomal protein defect in cancer, and we demonstrate a tumor suppressor role for RPL5 in breast cancer.

Identification of disease comorbidity through hidden molecular mechanisms

Despite multiple diseases co-occur, their underlying common molecular mechanisms remain elusive. Identification of comorbid diseases by considering the interactions between molecular components is a key to understand the underlying disease mechanisms. Here, we developed a novel approach utilizing both common disease-causing genes and underlying molecular pathways to identify comorbid diseases. Our approach enables the analysis of common pathologies shared by comorbid diseases through molecular interaction networks. We found that the integration of direct genetic sharing and indirect high-level molecular associations revealed significantly strong consistency with known comorbid diseases. In addition, neoplasm-related diseases showed high comorbidity patterns within themselves as well as with other diseases, indicating severe complications. This study demonstrated that molecular pathway information could be used to discover disease comorbidity and hidden biological mechanism to understand pathogenesis and provide new insight on disease pathology.

Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records

OBJECTIVES

Disease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity.

METHODS

We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present.

RESULTS

Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of disease-molecule associations, while machine learning, statistics, and information retrieval were utilized in smaller and moderate sized studies. Multiscale computations comprising shared function, network topology, and phenotypes were performed exclusively on proteins.

CONCLUSION

This review highlighted the growing methods for identifying the molecular mechanisms underpinning comorbidities that leverage multiscale molecular information and patterns from electronic health records. The survey unveiled that intergenic polymorphisms have been overlooked for similarity imputation compared to their intragenic counterparts, offering new opportunities to bridge the mechanistic and similarity gaps of comorbidity.

A model-driven methodology for exploring complex disease comorbidities applied to autism spectrum disorder and inflammatory bowel disease

We propose a model-driven methodology aimed to shed light on complex disorders. Our approach enables exploring shared etiologies of comorbid diseases at the molecular pathway level. The method, Comparative Comorbidities Simulation (CCS), uses stochastic Petri net simulation for examining the phenotypic effects of perturbation of a network known to be involved in comorbidities to predict new roles for mutations in comorbid conditions. To demonstrate the utility of our novel methodology, we investigated the molecular convergence of autism spectrum disorder (ASD) and inflammatory bowel disease (IBD) on the autophagy pathway. In addition to validation by domain experts, we used formal analyses to demonstrate the model's self-consistency. We then used CCS to compare the effects of loss of function (LoF) mutations previously implicated in either ASD or IBD on the autophagy pathway. CCS identified similar dynamic consequences of these mutations in the autophagy pathway. Our method suggests that two LoF mutations previously implicated in IBD may contribute to ASD, and one ASD-implicated LoF mutation may play a role in IBD. Future targeted genomic or functional studies could be designed to directly test these predictions.

Investigation of 7-dehydrocholesterol reductase pathway to elucidate off-target prenatal effects of pharmaceuticals: a systematic review

Mendelian diseases contain important biological information regarding developmental effects of gene mutations that can guide drug discovery and toxicity efforts. In this review, we focus on Smith–Lemli–Opitz syndrome (SLOS), a rare Mendelian disease characterized by compound heterozygous mutations in 7-dehydrocholesterol reductase (DHCR7) resulting in severe fetal deformities. We present a compilation of SLOS-inducing DHCR7 mutations and the geographic distribution of those mutations in healthy and diseased populations. We observed that several mutations thought to be disease causing occur in healthy populations, indicating an incomplete understanding of the condition and highlighting new research opportunities. We describe the functional environment around DHCR7, including pharmacological DHCR7 inhibitors and cholesterol and vitamin D synthesis. Using PubMed, we investigated the fetal outcomes following prenatal exposure to DHCR7 modulators. First-trimester exposure to DHCR7 inhibitors resulted in outcomes similar to those of known teratogens (50 vs 48% born-healthy). DHCR7 activity should be considered during drug development and prenatal toxicity assessment.

Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes

The identification of biologically significant variants in cancer genomes is critical to therapeutic discovery, but it is limited by the statistical power needed to discern driver from passenger. Independent biological data can be used to filter cancer exomes and increase statistical power. Large genetic databases for inherited diseases are uniquely suited to this task because they contain specific amino acid alterations with known pathogenicity and molecular mechanisms. However, no rigorous method to overlay this information onto the cancer exome exists. Here, we present a computational methodology that overlays any variant database onto the somatic mutations in all cancer exomes. We validate the computation experimentally and identify novel associations in a re-analysis of 7362 cancer exomes. This analysis identified activating SOS1 mutations associated with Noonan syndrome as significantly altered in melanoma and the first kinase-activating mutations in ACVR1 associated with adult tumors. Beyond a filter, significant variants found in both rare cancers and rare inherited diseases increase the unmet medical need for therapeutics that target these variants and may bootstrap drug discovery efforts in orphan indications.

PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To enable comprehensive evaluation of variants, the predictions are complemented with annotations from eight databases. The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.

Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants

Patterns of disease co-occurrence that deviate from statistical independence may represent important constraints on biological mechanism, which sometimes can be explained by shared genetics. In this work we study the relationship between disease co-occurrence and commonly shared genetic architecture of disease. Records of pairs of diseases were combined from two different electronic medical systems (Columbia, Stanford), and compared to a large database of published disease-associated genetic variants (VARIMED); data on 35 disorders were available across all three sources, which include medical records for over 1.2 million patients and variants from over 17,000 publications. Based on the sources in which they appeared, disease pairs were categorized as having predominant clinical, genetic, or both kinds of manifestations. Confounding effects of age on disease incidence were controlled for by only comparing diseases when they fall in the same cluster of similarly shaped incidence patterns. We find that disease pairs that are overrepresented in both electronic medical record systems and in VARIMED come from two main disease classes, autoimmune and neuropsychiatric. We furthermore identify specific genes that are shared within these disease groups.

Comorbidities of Psoriasis - Exploring the Links by Network Approach

Increasing epidemiological studies in patients with psoriasis report the frequent occurrence of one or more associated disorders. Psoriasis is associated with multiple comorbidities including autoimmune disease, neurological disorders, cardiometabolic diseases and inflammatory-bowel disease. An integrated system biology approach is utilized to decipher the molecular alliance of psoriasis with its comorbidities. An unbiased integrative network medicine methodology is adopted for the investigation of diseasome, biological process and pathways of five most common psoriasis associated comorbidities. A significant overlap was observed between genes acting in similar direction in psoriasis and its comorbidities proving the mandatory occurrence of either one of its comorbidities. The biological processes involved in inflammatory response and cell signaling formed a common basis between psoriasis and its associated comorbidities. The pathway analysis revealed the presence of few common pathways such as angiogenesis and few uncommon pathways which includes CCKR signaling map and gonadotrophin-realising hormone receptor pathway overlapping in all the comorbidities. The work shed light on few common genes and pathways that were previously overlooked. These fruitful targets may serve as a starting point for diagnosis and/or treatment of psoriasis comorbidities. The current research provides an evidence for the existence of shared component hypothesis between psoriasis and its comorbidities.