Stratification of Colorectal Patients Based on Survival Analysis Shows the Value of Consensus Molecular Subtypes and Reveals the CBLL1 Gene as a Biomarker of CMS2 Tumours.

The consensus molecular subtypes (CMSs) classification of colorectal cancer (CRC) is a system for patient stratification that can be potentially applied to therapeutic decisions. Hakai (CBLL1) is an E3 ubiquitin-ligase that induces the ubiquitination and degradation of E-cadherin, inducing epithelial-to-mesenchymal transition (EMT), tumour progression and metastasis. Using bioinformatic methods, we have analysed CBLL1 expression on a large integrated cohort of primary tumour samples from CRC patients. The cohort included survival data and was divided into consensus molecular subtypes. Colon cancer tumourspheres were used to analyse the expression of stem cancer cells markers via RT-PCR and Western blotting. We show that CBLL1 gene expression is specifically associated with canonical subtype CMS2. WNT target genes LGR5 and c-MYC show a similar association with CMS2 as CBLL1. These mRNA levels are highly upregulated in cancer tumourspheres, while CBLL1 silencing shows a clear reduction in tumoursphere size and in stem cell biomarkers. Importantly, CMS2 patients with high CBLL1 expression displayed worse overall survival (OS), which is similar to that associated with CMS4 tumours. Our findings reveal CBLL1 as a specific biomarker for CMS2 and the potential of using CMS2 with high CBLL1 expression to stratify patients with poor OS.

Exploring High-Throughput Immunoassays for Biomarker Validation in Rheumatic Diseases in the Context of the Human Proteome Project.

Rheumatic diseases are high prevalence pathologies with different etiology and evolution and low sensitivity in clinical diagnosis. Therefore, it is necessary to develop an early diagnosis method which allows personalized treatment, depending on the specific pathology. The biology/disease initiative, at Human Proteome Project, is an integrative approach to identify relevant proteins in the human proteome associated with pathologies. A previously reported literature data mining analysis, which identified proteins related to osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PSA) was used to establish a systematic prioritization of potential biomarkers candidates for further evaluation by functional proteomics studies. The aim was to study the protein profile of serum samples from patients with rheumatic diseases such as OA, RA, and PSA. To achieve this goal, customized antibody microarrays (containing 151 antibodies targeting 121 specific proteins) were used to identify biomarkers related to early and specific diagnosis in a screening of 960 serum samples (nondepleted) (OA, n = 480; RA, n = 192; PSA, n = 288). This functional proteomics screening has allowed the determination of a panel (30 serum proteins) as potential biomarkers for these rheumatic diseases, displaying receiver operating characteristics curves with area under the curve values of 80-90%.

Analysis of asymptomatic Drosophila models for ALS and SMA reveals convergent impact on functional protein complexes linked to neuro-muscular degeneration.

BACKGROUND: Spinal Muscular Atrophy (SMA) and Amyotrophic Lateral Sclerosis (ALS) share phenotypic and molecular commonalities, including the fact that they can be caused by mutations in ubiquitous proteins involved in RNA metabolism, namely SMN, TDP-43 and FUS. Although this suggests the existence of common disease mechanisms, there is currently no model to explain the resulting motor neuron dysfunction. In this work we generated a parallel set of Drosophila models for adult-onset RNAi and tagged neuronal expression of the fly orthologues of the three human proteins, named Smn, TBPH and Caz, respectively. We profiled nuclear and cytoplasmic bound mRNAs using a RIP-seq approach and characterized the transcriptome of the RNAi models by RNA-seq. To unravel the mechanisms underlying the common functional impact of these proteins on neuronal cells, we devised a computational approach based on the construction of a tissue-specific library of protein functional modules, selected by an overall impact score measuring the estimated extent of perturbation caused by each gene knockdown. RESULTS: Transcriptome analysis revealed that the three proteins do not bind to the same RNA molecules and that only a limited set of functionally unrelated transcripts is commonly affected by their knock-down. However, through our integrative approach we were able to identify a concerted effect on protein functional modules, albeit acting through distinct targets. Most strikingly, functional annotation revealed that these modules are involved in critical cellular pathways for motor neurons, including neuromuscular junction function. Furthermore, selected modules were found to be significantly enriched in orthologues of human neuronal disease genes. CONCLUSIONS: The results presented here show that SMA and ALS disease-associated genes linked to RNA metabolism functionally converge on neuronal protein complexes, providing a new hypothesis to explain the common motor neuron phenotype. The functional modules identified represent promising biomarkers and therapeutic targets, namely given their alteration in asymptomatic settings.

METTL1 promotes tumorigenesis through tRNA-derived fragment biogenesis in prostate cancer.

Newly growing evidence highlights the essential role that epitranscriptomic marks play in the development of many cancers; however, little is known about the role and implications of altered epitranscriptome deposition in prostate cancer. Here, we show that the transfer RNA N7-methylguanosine (m7G) transferase METTL1 is highly expressed in primary and advanced prostate tumours. Mechanistically, we find that METTL1 depletion causes the loss of m7G tRNA methylation and promotes the biogenesis of a novel class of small non-coding RNAs derived from 5'tRNA fragments. 5'tRNA-derived small RNAs steer translation control to favour the synthesis of key regulators of tumour growth suppression, interferon pathway, and immune effectors. Knockdown of Mettl1 in prostate cancer preclinical models increases intratumoural infiltration of pro-inflammatory immune cells and enhances responses to immunotherapy. Collectively, our findings reveal a therapeutically actionable role of METTL1-directed m7G tRNA methylation in cancer cell translation control and tumour biology.

Conditional expression of HGAL leads to the development of diffuse large B-cell lymphoma in mice.

Diffuse large B-cell lymphomas (DLBCLs) are clinically and genetically heterogeneous tumors. Deregulation of diverse biological processes specific to B cells, such as B-cell receptor (BCR) signaling and motility regulation, contribute to lymphomagenesis. Human germinal center associated lymphoma (HGAL) is a B-cell-specific adaptor protein controlling BCR signaling and B lymphocyte motility. In normal B cells, it is expressed in germinal center (GC) B lymphocytes and promptly downregulated upon further differentiation. The majority of DLBCL tumors, primarily GC B-cell types, but also activated types, express HGAL. To investigate the consequences of constitutive expression of HGAL in vivo, we generated mice that conditionally express human HGAL at different stages of hematopoietic development using 3 restricted Cre-mediated approaches to initiate expression of HGAL in hematopoietic stem cells, pro-B cells, or GC B cells. Following immune stimulation, we observed larger GCs in mice in which HGAL expression was initiated in GC B cells. All 3 mouse strains developed DLBCL at a frequency of 12% to 30% starting at age 13 months, leading to shorter survival. Immunohistochemical studies showed that all analyzed tumors were of the GC B-cell type. Exon sequencing revealed mutations reported in human DLBCL. Our data demonstrate that constitutive enforced expression of HGAL leads to DLBCL development.

Correction: Ten simple rules for organizing a bioinformatics training course in low- and middle-income countries.

[This corrects the article DOI: 10.1371/journal.pcbi.1009218.].

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells.

In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

Lmo2 expression defines tumor cell identity during T-cell leukemogenesis.

The impact of LMO2 expression on cell lineage decisions during T-cell leukemogenesis remains largely elusive. Using genetic lineage tracing, we have explored the potential of LMO2 in dictating a T-cell malignant phenotype. We first initiated LMO2 expression in hematopoietic stem/progenitor cells and maintained its expression in all hematopoietic cells. These mice develop exclusively aggressive human-like T-ALL In order to uncover a potential exclusive reprogramming effect of LMO2 in murine hematopoietic stem/progenitor cells, we next showed that transient LMO2 expression is sufficient for oncogenic function and induction of T-ALL The resulting T-ALLs lacked LMO2 and its target-gene expression, and histologically, transcriptionally, and genetically similar to human LMO2-driven T-ALL We next found that during T-ALL development, secondary genomic alterations take place within the thymus. However, the permissiveness for development of T-ALL seems to be associated with wider windows of differentiation than previously appreciated. Restricted Cre-mediated activation of Lmo2 at different stages of B-cell development induces systematically and unexpectedly T-ALL that closely resembled those of their natural counterparts. Together, these results provide a novel paradigm for the generation of tumor T cells through reprogramming in vivo and could be relevant to improve the response of T-ALL to current therapies.

An intact gut microbiome protects genetically predisposed mice against leukemia.

The majority of childhood leukemias are precursor B-cell acute lymphoblastic leukemias (pB-ALLs) caused by a combination of prenatal genetic predispositions and oncogenic events occurring after birth. Although genetic predispositions are frequent in children (>1% to 5%), fewer than 1% of genetically predisposed carriers will develop pB-ALL. Although infectious stimuli are believed to play a major role in leukemogenesis, the critical determinants are not well defined. Here, by using murine models of pB-ALL, we show that microbiome disturbances incurred by antibiotic treatment early in life were sufficient to induce leukemia in genetically predisposed mice, even in the absence of infectious stimuli and independent of T cells. By using V4 and full-length 16S ribosomal RNA sequencing of a series of fecal samples, we found that genetic predisposition to pB-ALL (Pax5 heterozygosity or ETV6-RUNX1 fusion) shaped a distinct gut microbiome. Machine learning accurately (96.8%) predicted genetic predisposition using 40 of 3983 amplicon sequence variants as proxies for bacterial species. Transplantation of either wild-type (WT) or Pax5+/- hematopoietic bone marrow cells into WT recipient mice revealed that the microbiome is shaped and determined in a donor genotype-specific manner. Gas chromatography-mass spectrometry (GC-MS) analyses of sera from WT and Pax5+/- mice demonstrated the presence of a genotype-specific distinct metabolomic profile. Taken together, our data indicate that it is a lack of commensal microbiota rather than the presence of specific bacteria that promotes leukemia in genetically predisposed mice. Future large-scale longitudinal studies are required to determine whether targeted microbiome modification in children predisposed to pB-ALL could become a successful prevention strategy.

Hypoxia as a driver of resistance to immunotherapy.

Hypoxia, a hallmark of solid tumors, determines the selection of invasive and aggressive malignant clones displaying resistance to radiotherapy, conventional chemotherapy or targeted therapy. The recent introduction of immunotherapy, based on immune checkpoint inhibitors (ICPIs) and chimeric antigen receptor (CAR) T-cells, has markedly transformed the prognosis in some tumors but also revealed the existence of intrinsic or acquired drug resistance. In the current review we highlight hypoxia as a culprit of immunotherapy failure. Indeed, multiple metabolic cross talks between tumor and stromal cells determine the prevalence of immunosuppressive populations within the hypoxic tumor microenvironment and confer upon tumor cells resistance to ICPIs and CAR T-cells. Notably, hypoxia-triggered angiogenesis causes immunosuppression, adding another piece to the puzzle of hypoxia-induced immunoresistance. If these factors concurrently contribute to the resistance to immunotherapy, they also unveil an unexpected Achille's heel of hypoxic tumors, providing the basis for innovative combination therapies that may rescue the efficacy of ICPIs and CAR T-cells. Although these treatments reveal both a bright side and a dark side in terms of efficacy and safety in clinical trials, they represent the future solution to enhance the efficacy of immunotherapy against hypoxic and therapy-resistant solid tumors.

Bioinformatics in Latin America and SoIBio impact, a tale of spin-off and expansion around genomes and protein structures.

Owing to the emerging impact of bioinformatics and computational biology, in this article, we present an overview of the history and current state of the research on this field in Latin America (LA). It will be difficult to cover without inequality all the efforts, initiatives and works that have happened for the past two decades in this vast region (that includes >19 million km2 and >600 million people). Despite the difficulty, we have done an analytical search looking for publications in the field made by researchers from 19 LA countries in the past 25 years. In this way, we find that research in bioinformatics in this region should develop twice to approach the average world scientific production in the field. We also found some of the pioneering scientists who initiated and led bioinformatics in the region and were promoters of this new scientific field. Our analysis also reveals that spin-off began around some specific areas within the biomolecular sciences: studies on genomes (anchored in the new generation of deep sequencing technologies, followed by developments in proteomics) and studies on protein structures (supported by three-dimensional structural determination technologies and their computational advancement). Finally, we show that the contribution to this endeavour of the Iberoamerican Society for Bioinformatics, founded in Mexico in 2009, has been significant, as it is a leading forum to join efforts of many scientists from LA interested in promoting research, training and education in bioinformatics.

Cancer drug resistance induced by EMT: novel therapeutic strategies.

Over the last decade, important clinical benefits have been achieved in cancer patients by using drug-targeting strategies. Nevertheless, drug resistance is still a major problem in most cancer therapies. Epithelial-mesenchymal plasticity (EMP) and tumour microenvironment have been described as limiting factors for effective treatment in many cancer types. Moreover, epithelial-to-mesenchymal transition (EMT) has also been associated with therapy resistance in many different preclinical models, although limited evidence has been obtained from clinical studies and clinical samples. In this review, we particularly deepen into the mechanisms of which intermediate epithelial/mesenchymal (E/M) states and its interconnection to microenvironment influence therapy resistance. We also describe how the use of bioinformatics and pharmacogenomics will help to figure out the biological impact of the EMT on drug resistance and to develop novel pharmacological approaches in the future.

Cancer immunotherapy resistance based on immune checkpoints inhibitors: Targets, biomarkers, and remedies.

Cancer is one of the main public health problems in the world. Systemic therapies such as chemotherapy and more recently target therapies as well as immunotherapy have improved the prognosis of this large group of complex malignant diseases. However, the frequent emergence of multidrug resistance (MDR) mechanisms is one of the major impediments towards curative treatment of cancer. While several mechanisms of drug chemoresistance are well defined, resistance to immunotherapy is still insufficiently unclear due to the complexity of the immune response and its dependence on the host. Expression and regulation of immune checkpoint molecules (such as PD-1, CD279; PD-L1, CD274; and CTLA-4, CD152) play a key role in the response to immunotherapy. In this regard, immunotherapy based on immune checkpoints inhibitors (ICIs) is a common clinical approach for treatment of patients with poor prognosis when other first-line therapies have failed. Unfortunately, about 70 % of patients are classified as non-responders, or they progress after initial response to these ICIs. Multiple factors can be related to immunotherapy resistance: characteristics of the tumor microenvironment (TME); presence of tumor infiltrating lymphocytes (TILs), such as CD8 + T cells associated with treatment-response; presence of tumor associated macrophages (TAMs); activation of certain regulators (like PIK3γ or PAX4) found present in non-responders; a low percentage of PD-L1 expressing cells; tumor mutational burden (TMB); gain or loss of antigen-presenting molecules; genetic and epigenetic alterations correlated with resistance. This review provides an update on the current state of immunotherapy resistance presenting targets, biomarkers and remedies to overcome such resistance.

Transcriptomic analysis of patients with immune thrombocytopenia treated with eltrombopag.

In the last years, the use of thrombopoietin receptor agonists (TPO-RA), eltrombopag and romiplostim, has improved the management of immune thrombocytopenia (ITP). Moreover, eltrombopag is also active in patients with aplastic anemia and myelodysplastic syndrome. However, their mechanisms of action and signaling pathways still remain controversial. In order to gain insight into the mechanisms underlying eltrombopag therapy, a gene expression profile (GEP) analysis in patients treated with this drug was carried out. Fourteen patients with chronic ITP were studied by means of microarrays before and during eltrombopag treatment. Median age was 78 years (range, 35-87 years); median baseline platelet count was 14 × 109/L (range, 2-68 × 109/L). Ten patients responded to the therapy, two cases relapsed after an initial response and the remaining two were refractory to the therapy. Eltrombopag induced relevant changes in the hematopoiesis, platelet activation and degranulation, as well as in megakaryocyte differentiation, with overexpression of some transcription factors and the genes PPBP, ITGB3, ITGA2B, F13A1, F13A1, MYL9 and ITGA2B. In addition, GP1BA, PF4, ITGA2B, MYL9, HIST1H4H and HIST1H2BH, genes regulated by RUNX1 were also significantly enriched after eltrombopag therapy. Furthermore, in non-responder patients, an overexpression of Bcl-X gene and genes involved in erythropoiesis, such as SLC4A1 and SLC25A39, was also observed. To conclude, overexpression in genes involved in megakaryopoiesis, platelet adhesion, degranulation and aggregation was observed in patients treated with eltrombopag. Moreover, an important role regarding heme metabolism was also present in non-responder patients.

Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling.

BACKGROUND: Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors. In this work, we identified a new set of gene markers for CRC associated to prognosis and risk using a large unified cohort of patients with transcriptomic profiles and survival information. RESULTS: We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis. The top 10 up-regulated genes found as survival markers of poor prognosis (i.e. low survival) were: DCBLD2, PTPN14, LAMP5, TM4SF1, NPR3, LEMD1, LCA5, CSGALNACT2, SLC2A3 and GADD45B. The stability and robustness of the gene survival markers was assessed by cross-validation, and the best-ranked genes were also validated with two external independent cohorts: one of microarrays with 482 samples; another of RNA-seq with 269 samples. Up-regulation of the top genes was also proved in a comparison with normal colorectal tissue samples. Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis. This risk predictor yielded an optimal separation of the individual patients of the cohort according to their survival, with a p-value of 8.25e-14 and Hazard Ratio 2.14 (95% CI: 1.75-2.61). CONCLUSIONS: The results presented in this work provide a solid rationale for the prognostic utility of a new set of genes in CRC, demonstrating their potential to predict colorectal tumor progression and evolution towards poor survival stages. Our study does not provide a fixed gene signature for prognosis and risk prediction, but instead proposes a robust set of genes ranked according to their predictive power that can be selected for additional tests with other CRC clinical cohorts.

APID interactomes: providing proteome-based interactomes with controlled quality for multiple species and derived networks.

APID (Agile Protein Interactomes DataServer) is an interactive web server that provides unified generation and delivery of protein interactomes mapped to their respective proteomes. This resource is a new, fully redesigned server that includes a comprehensive collection of protein interactomes for more than 400 organisms (25 of which include more than 500 interactions) produced by the integration of only experimentally validated protein-protein physical interactions. For each protein-protein interaction (PPI) the server includes currently reported information about its experimental validation to allow selection and filtering at different quality levels. As a whole, it provides easy access to the interactomes from specific species and includes a global uniform compendium of 90,379 distinct proteins and 678,441 singular interactions. APID integrates and unifies PPIs from major primary databases of molecular interactions, from other specific repositories and also from experimentally resolved 3D structures of protein complexes where more than two proteins were identified. For this purpose, a collection of 8,388 structures were analyzed to identify specific PPIs. APID also includes a new graph tool (based on Cytoscape.js) for visualization and interactive analyses of PPI networks. The server does not require registration and it is freely available for use at http://apid.dep.usal.es.

Screening and Validation of Novel Biomarkers in Osteoarticular Pathologies by Comprehensive Combination of Protein Array Technologies.

Osteoarthritis (OA) is one of the most prevalent articular diseases. The identification of proteins closely associated with the diagnosis, progression, prognosis, and treatment response is dramatically required for this pathology. In this work, differential serum protein profiles have been identified in OA and rheumatoid arthritis (RA) by antibody arrays containing 151 antibodies against 121 antigens in a cohort of 36 samples. Then the identified differential serum protein profiles have been validated in a larger cohort of 282 samples. The overall immunoreactivity is higher in the pathological situations in comparison with the controls. Several proteins have been identified as biomarker candidates for OA and RA. Most of these biomarker candidates are proteins related to inflammatory response, lipid metabolism, or bone and extracellular matrix formation, degradation, or remodeling.

Linking amyotrophic lateral sclerosis and spinal muscular atrophy through RNA-transcriptome homeostasis: a genomics perspective.

In this review, we present our most recent understanding of key biomolecular processes that underlie two motor neuron degenerative disorders, amyotrophic lateral sclerosis, and spinal muscular atrophy. We focus on the role of four multifunctional proteins involved in RNA metabolism (TDP-43, FUS, SMN, and Senataxin) that play a causal role in these diseases. Recent results have led to a novel scenario of intricate connections between these four proteins, bringing transcriptome homeostasis into the spotlight as a common theme in motor neuron degeneration. We review reported functional and physical interactions between these four proteins, highlighting their common association with nuclear bodies and small nuclear ribonucleoprotein particle biogenesis and function. We discuss how these interactions are turning out to be particularly relevant for the control of transcription and chromatin homeostasis, including the recent identification of an association between SMN and Senataxin required to ensure the resolution of DNA-RNA hybrid formation and proper termination by RNA polymerase II. These connections strongly support the existence of common pathways underlying the spinal muscular atrophy and amyotrophic lateral sclerosis phenotype. We also discuss the potential of genome-wide expression profiling, in particular RNA sequencing derived data, to contribute to unravelling the underlying mechanisms. We provide a review of publicly available datasets that have addressed both diseases using these approaches, and highlight the value of investing in cross-disease studies to promote our understanding of the pathways leading to neurodegeneration.

Identification of expression patterns in the progression of disease stages by integration of transcriptomic data.

BACKGROUND: In the study of complex diseases using genome-wide expression data from clinical samples, a difficult case is the identification and mapping of the gene signatures associated to the stages that occur in the progression of a disease. The stages usually correspond to different subtypes or classes of the disease, and the difficulty to identify them often comes from patient heterogeneity and sample variability that can hide the biomedical relevant changes that characterize each stage, making standard differential analysis inadequate or inefficient. RESULTS: We propose a methodology to study diseases or disease stages ordered in a sequential manner (e.g. from early stages with good prognosis to more acute or serious stages associated to poor prognosis). The methodology is applied to diseases that have been studied obtaining genome-wide expression profiling of cohorts of patients at different stages. The approach allows searching for consistent expression patterns along the progression of the disease through two major steps: (i) identifying genes with increasing or decreasing trends in the progression of the disease; (ii) clustering the increasing/decreasing gene expression patterns using an unsupervised approach to reveal whether there are consistent patterns and find genes altered at specific disease stages. The first step is carried out using Gamma rank correlation to identify genes whose expression correlates with a categorical variable that represents the stages of the disease. The second step is done using a Self Organizing Map (SOM) to cluster the genes according to their progressive profiles and identify specific patterns. Both steps are done after normalization of the genomic data to allow the integration of multiple independent datasets. In order to validate the results and evaluate their consistency and biological relevance, the methodology is applied to datasets of three different diseases: myelodysplastic syndrome, colorectal cancer and Alzheimer's disease. A software script written in R, named genediseasePatterns, is provided to allow the use and application of the methodology. CONCLUSION: The method presented allows the analysis of the progression of complex and heterogeneous diseases that can be divided in pathological stages. It identifies gene groups whose expression patterns change along the advance of the disease, and it can be applied to different types of genomic data studying cohorts of patients in different states.