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Heart failure (HF) describes a heterogenous complex spectrum of pathological conditions that results in structural and functional remodeling leading to subsequent impairment of cardiac function, including either systolic dysfunction, diastolic dysfunction, or both. Several factors chronically lead to HF, including cardiac volume and pressure overload that may result from hypertension, valvular lesions, acute, or chronic ischemic injuries. Major forms of HF include hypertrophic, dilated, and restrictive cardiomyopathy. The severity of cardiomyopathy can be impacted by other comorbidities such as diabetes or obesity and external stress factors. Age is another major contributor, and the number of patients with HF is rising worldwide in part due to an increase in the aged population. HF can occur with reduced ejection fraction (HF with reduced ejection fraction), that is, the overall cardiac function is compromised, and typically the left ventricular ejection fraction is lower than 40%. In some cases of HF, the ejection fraction is preserved (HF with preserved ejection fraction). Animal models play a critical role in facilitating the understanding of molecular mechanisms of how hearts fail. This review aims to summarize and describe the strengths, limitations, and outcomes of both small and large animal models of HF with reduced ejection fraction that are currently used in basic and translational research. The driving defect is a failure of the heart to adequately supply the tissues with blood due to impaired filling or pumping. An accurate model of HF with reduced ejection fraction would encompass the symptoms (fatigue, dyspnea, exercise intolerance, and edema) along with the pathology (collagen fibrosis, ventricular hypertrophy) and ultimately exhibit a decrease in cardiac output. Although countless experimental studies have been published, no model completely recapitulates the full human disease. Therefore, it is critical to evaluate the strength and weakness of each animal model to allow better selection of what animal models to use to address the scientific question proposed.

BACKGROUND Hospitalizations account for almost one-third of $4.1 trillion healthcare cost in the US. A substantial portion of these hospitalizations are readmissions, which led to Hospital Readmissions Reduction Program (HRRP) in 2012.15 HRRP reduces payments to hospitals with excess readmissions. In 2018, more than $700 million was withheld; this is expected to exceed $1 billion by the year 2022.1 More importantly, there is nothing more physically and emotionally taxing for readmitted patients, demoralizing hospital physicians, nurses, and administrators. OBJECTIVE Given this high uncertainty of home recovery, intelligent monitoring is needed to predict the outcome of discharged patients to reduce readmissions. Therefore, we developed a remote, low-cost, cloud-based machine learning (ML) platform to enable precision health monitoring, which may fundamentally alter the delivery of home healthcare. METHODS Our platform consists of wearable, iPhone-synced sensors connected to our cloud-based ML interface to analyze physical activity remotely and predict clinical outcomes. This system was deployed in skilled nursing facilities where we collected over 17,000 person-day data over 2 years, generating a solid training database. We employed these data to train our XGBoost-based ML environment to conduct a clinical trial, “Activity Assessment of Patients Discharged from Hospital (ACT-I Trial, Stanford University Institutional Review Board Approval #53805),” to test the hypothesis that a comprehensive profile of physical activity will predict clinical outcome. RESULTS We achieved precise prediction of the patients’ clinical outcomes with a sensitivity of 87%, specificity of 79%, and accuracy of 85%. CONCLUSIONS We present AiCare’s comprehensive technology solution, consisting of wearable sensors, Bluetooth low energy (BLE)-enabled iOS infrastructure, ML algorithm to implement artificial intelligence, and API-enabled web technology, to measure the daily activities of patients. In this study, remote data collection, robust XGBoost AI analysis and reliable prediction of clinical outcome are reported.

CMV infection is a significant complication after solid organ transplantation. We used single cell TCR αβ sequencing to determine how memory inflation impacts clonality and diversity of the CMV-responsive CD8 and CD4 T cell repertoire in the first year after transplantation in human subjects. We observed CD8 T cell inflation but no changes in clonal diversity, indicating homeostatic stability in clones. In contrast, the CD4 repertoire was diverse and stable over time, with no evidence of CMV-responsive CD4 T cell expansion. We identified shared CDR3 TCR motifs among patients but no public CMV-specific TCRs. Temporal changes in clonality in response to transplantation and in the absence of detectable viral reactivation suggest changes in the repertoire immediately after transplantation followed by an expansion with stable clonal competition that may mediate protection.

In an effort to identify disease-causing mechanisms, numerous genome-wide studies across different ancestries have discovered genetic variants influencing type 2 diabetes (T2D) risk. Amongst identified diabetes-associated loci, a PAX4 coding variant (p.Arg192His) is uniquely and reproducibly associated with altered T2D-risk in East Asian populations. PAX4 encodes a transcription factor, and in mice Pax4 is essential for beta cell development. Whether PAX4 plays a similar role in humans and how diabetes associated PAX4 variants impact beta cell development and/or function is unknown. We combined in vivo studies in human variant carriers with disease modelling in human stem cell models to address these questions. Carriers of the PAX4 p.Arg192His allele have reduced islet cell function compared to non-carriers (AIRg: p.Arg192 756.9 vs. p.His192 530.8 p In conclusion, we have demonstrated the importance of PAX4 for human endocrine cell development and provide a potential mechanism for diabetes-associated alleles through de-repression of alpha cell gene expression. Disclosure N. Krentz: None. S. Hoon: None. D. Gardner: None. S. Kao: None. E. Tai: Advisory Panel; Amgen Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk, Stock/Shareholder; Abbott, AbbVie Inc. A. L. Gloyn: Other Relationship; Genentech, Inc., Roche Pharmaceuticals. A. Teo: Stock/Shareholder; BetaLife Pte Ltd. H. Lau: None. F. Abaitua: Employee; Vertex Pharmaceuticals Incorporated. M. Perez-alcantara: None. J. Chan: None. J. N. Ajeian: None. B. Champon: None. H. Sun: None. A. Jha: None. Funding Wellcome (200837) ; National Institutes of Health (U01-DK105535, U01-DK085545, UM1DK126185, U01DK123743, U24DK098085, P30DK116074) ; Lee Foundation Grant (SHTX/LFG/002/2018) Paris-NUS 2021-06-R/UP-NUS (ANR-18-IDEX-0001)

Summary Background Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness. Methods In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing. Findings Of 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57–76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53–2·21), male sex (1·63, 1·07–2·48), smoking status (former smoker vs never smoked: 1·60, 1·03–2·47), number of comorbidities (two vs none: 4·50, 1·33–15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11–7·18), active cancer (progressing vs remission: 5·20, 2·77–9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79–4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07–0·84) or the US-Midwest (0·50, 0·28–0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality. Interpretation Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments. Funding American Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research.

BACKGROUND: Prognosis in pulmonary arterial hypertension (PAH) is closely related to indexes of right ventricular function. A better understanding of their relationship may provide important implications for risk stratification in PAH. RESEARCH QUESTION: Can clinical network graphs inform risk stratification in PAH? STUDY DESIGN AND METHODS: The study cohort consisted of 231 patients with PAH followed up for a median of 7.1 years. An undirected, correlation network was used to visualize the relationship between clinical features in PAH. This network was enriched for right heart parameters and included N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP), comprehensive echocardiographic parameters, and hemodynamics, as well as 6-min walk distance (6MWD), vital signs, laboratory data, and diffusing capacity for carbon monoxide (Dlco). Connectivity was assessed by using eigenvector and betweenness centrality to reflect global and regional connectivity, respectively. Cox proportional hazards regression was used to model event-free survival for the combined end point of death or lung transplantation. RESULTS: A network of closely intertwined features centered around NT-proBNP with 6MWD emerging as a secondary hub were identified. Less connected nodes included Dlco, systolic BP, albumin, and sodium. Over the follow-up period, death or transplantation occurred in 92 patients (39.8%). A strong prognostic model was achieved with a Harrell's C-index of 0.81 (0.77-0.85) when combining central right heart features (NT-proBNP and right ventricular end-systolic remodeling index) with 6MWD and less connected nodes (Dlco, systolic BP, albumin, sodium, sex, connective tissue disease etiology, and prostanoid therapy). When added to the baseline risk model, serial change in NT-proBNP significantly improved outcome prediction at 5 years (increase in C-statistic of 0.071 ± 0.024; P = .003). INTERPRETATION: NT-proBNP emerged as a central hub in the intertwined PAH network. Connectivity analysis provides explainability for feature selection and combination in outcome models. ispartof: CHEST vol:161 issue:5 pages:1347-1359 ispartof: location:United States status: published

Various studies have looked into the impact of the COVID-19 vaccine on large populations. However, very few studies have looked into the remote setting of hospitals where vaccination is challenging due to social structure, myths, and misconceptions. There is a consensus that elevated inflammatory markers such as CRP, ferritin, D-dimer correlate with increased severity of COVID-19 and are associated with worse outcomes. In the present study, through retrospective meta-analysis, we have looked into ∼20 months of SARS-COV2 infected patients with known mortality status and identified predictors of mortality concerning their comorbidities, various clinical parameters, inflammatory markers, superimposed infections, length of hospitalization, length of mechanical ventilation and ICU stay. Studies with larger sample sizes have covered the outcomes through epidemiological, social, and survey-based analysis—however, most studies cover larger cohorts from tertiary medical centers. In the present study, we assessed the outcome of non-vaccinated COVID 19 patients in a remote setting for 20 months from January 1, 2020, to August 30, 2021, at CHI Mercy Health in Roseburg, Oregon. We also included two vaccinated patients from September 2021 to add to the power of our cohort. The study will provide a comprehensive methodology and deep insight into multi-dimensional data in the unvaccinated group, translational biomarkers of mortality, and state-of-art to conduct such studies in various remote hospitals.

International audience; A key property of Linked Data is the representation and publication of data as interconnected labelled graphs where different resources linked to each other form a network of meaningful information. Searching these important relationships between resources – within single or distributed graphs – can be reduced to a pathfinding or navigation problem, i.e., looking for chains of intermediate nodes. SPARQL1.1, the current standard query language for RDF-based Linked Data defines a construct – called Property Paths (PPs) – to navigate between entities within a single graph. Since Linked Data technologies are naturally aimed at decentralised scenarios, there are many cases where centralising this data is not feasible or even not possible for querying purposes. To address these problems, we propose a SPARQL PP-based graph processing approach – dubbed DpcLD – where users can execute SPARQL PP queries and find paths distributed across multiple, connected graphs exposed as SPARQL endpoints. To execute the distributed path queries we implemented an index-free, cache-based query engine that communicates with a shared algorithm running on each remote endpoint, and computes the distributed paths. In this paper, we highlight the way in which this approach exploits and aggregates partial paths, within a distributed environment, to produce complete results. We perform extensive experiments to demonstrate the performance of our approach on two datasets: One representing 10 million triples from the DBPedia SPARQL benchmark, and another full benchmark dataset corresponding to 124 million triples. We also perform a scalability test of our approach using real-world genomics datasets distributed across multiple endpoints. We compare our distributed approach with other distributed and centralized pathfinding approaches, showing that it outperforms other distributed approaches by orders of magnitude, and provides a good trade-off for cases when the data cannot be centralised.

Background: Patients with hematologic malignancy (HM) are hypothesized to be at high risk of poor outcomes with coronavirus disease 2019 (COVID-19), due to disease and therapy-related immunosuppression. Despite this, there are minimal reported data describing the outcomes of HM patients with COVID-19. Methods: The COVID-19 and Cancer Consortium (CCC19) (NCT04354701) is an international registry aimed at investigating the clinical course and complications of COVID-19 in patients with cancer. The CCC19 cohort includes patients with active cancer or a history of cancer with presumed or laboratory-confirmed COVID-19. The registry contains de-identified patient demographics, information regarding cancer diagnosis and treatment history, COVID-19 treatments, and clinical outcomes. Patients greater than 18 years of age with laboratory-confirmed, symptomatic COVID-19 and HM diagnoses were included in this study. The primary outcome is a composite of severe COVID-19 illness (composed of mechanical ventilation, severe illness requiring hospitalization, intensive care unit (ICU) requirement, or death); mechanical ventilation, ICU level of care, supplemental oxygen, and 30-day mortality are reported as secondary outcomes. Baseline characteristics are reported for the entire cohort. Reported clinical outcomes are stratified by cancer type, cancer status, line of therapy received (never treated, first, second or later), receipt of cellular therapy or transplant (none, within 12 months, >12 months prior to COVID-19 diagnosis), last receipt of cytotoxic therapy (within 4 weeks, 1-3 months, 3-12 months prior to COVID-19 diagnosis), and receipt of HM therapies under investigation as repurposed treatments for COVID-19 (Bruton tyrosine kinase (BTK) inhibitors, Janus kinase (JAK) inhibitors, Bcr-Abl kinase inhibitors). Results: From March 17, 2020 to July 31, 2020, a total of 757 patients with HM and COVID-19 were enrolled and met inclusion criteria. Median follow-up was 30 days (IQR 17-70 days). Median age was 65 years (IQR 55-75), 62% (470) were over age 60, 57% were men, 45% were non-Hispanic white (22% Black, 18% Hispanic, 5% other), 39% were former or current smokers, 27% were obese, 18% had Eastern Cooperative Oncology Group (ECOG) performance status ≥2, and 51% were on active treatment within 3 months of COVID-19 diagnosis. Among patients with HM, 281 (37%) developed the primary endpoint. Five-hundred and eleven patients (67%) were hospitalized (some with non-severe disease), 188 (25%) required ICU level of care, 133 (18%) required mechanical ventilation, 409 (54%) required supplemental oxygen, and 143 (19%) died within 30 days of COVID-19 diagnosis. Stratified rates of severe outcomes are shown in the Table. The rate of severe COVID-19 was highest in patients with chronic lymphocytic leukemia (53%), and lowest in patients with Hodgkin lymphoma (23%). Patients receiving cytotoxic systemic therapy within 3 months of COVID-19 diagnosis had higher rates of severe COVID-19 (41%) and 30-day mortality (28%) than patients who completed treatment 3-12 months (26% severe COVID-19, 16% 30-day mortality) or more than 12 months (29% severe COVID-19, 13% 30-day mortality) prior to COVID-19 diagnosis. Patients receiving cellular therapy or transplant within a year prior to COVID-19 diagnosis had similar rates of severe COVID-19 (36% v. 38%) and 30-day mortality (23% v. 19%) to patients who had not received such therapies within a year prior to COVID-19 diagnosis. Patients on second-line or later therapy experienced similar rates of poor outcomes (42% severe COVID-19, 20% 30-day mortality) to patients on first-line therapy (39% severe COVID-19, 18% 30-day mortality) and untreated patients (42% severe COVID-19, 20% 30-day mortality). Outcomes for patients receiving therapies under investigation as repurposed COVID-19 treatments were similar to the cohort at large. Conclusions: This is the largest cohort study to date describing COVID-19 outcomes in patients with HM. Rates of severe COVID-19 outcomes including death were high. Unadjusted rates of severe COVID-19 outcomes were higher in patients with previously described risk factors such as advanced age, poor performance status, and progressive disease, as well as those receiving recent cytotoxic therapy. Outcomes varied widely by malignancy but were similar across treatment contexts. Additional data collection and analyses are ongoing. Disclosures Lynch: Bayer: Research Funding; Rhizen Pharmaceuticals: Research Funding; Incyte: Research Funding; TG Therapeutics: Research Funding; Takeda: Research Funding; Juno Therpeutics: Research Funding; Genentech: Research Funding; MorphoSys: Consultancy; Cyteir: Research Funding. Bakouny:BMS: Research Funding; Genentech: Research Funding. Bhutani:Sanofi: Consultancy, Research Funding. Shah:American Cancer Society and the Hope Foundation for Cancer Research: Research Funding; National Cancer Institute: Research Funding. Lyman:Mylan: Consultancy; Beyond Spring: Consultancy; Samsung: Consultancy; Sandoz: Consultancy; Invitae: Consultancy; Spectrum: Consultancy; G1 Therapeutics: Consultancy; Amgen: Research Funding. Kuderer:Janssen: Consultancy; G1 Therapeutics: Consultancy; Beyond Springs: Consultancy; Spectrum Pharmaceuticals: Consultancy; Bayer: Consultancy; Bristol-Myers Squibb: Consultancy; celldex: Consultancy; Total Health: Consultancy; Invitae: Consultancy. Warner:HemOnc.orgLLC: Other: Shareholder/Stockholder/Stock options; National Cancer Institute: Research Funding; IBM Watson Health: Consultancy; Westat: Consultancy. Mesa:Bristol Myers Squibb: Research Funding; Incyte: Research Funding; AbbVie: Research Funding; Samus Therapeutics: Research Funding; Genentech: Research Funding; Promedior: Research Funding; CTI BioPharma: Research Funding; Novartis: Consultancy; Sierra Oncology: Consultancy; LaJolla Pharmaceutical Company: Consultancy. Thompson:AIM Specialty Health, BMS, GlaxoSmithKline, Takeda, Via Oncology: Membership on an entity's Board of Directors or advisory committees; Doximity: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Synapse Precision Medical Council: Other: Travel expenses.

In vivo mechanisms of amyloid clearance and cardiac tissue damage in cardiac amyloidosis are not well understood.We aimed to define and quantify the amyloid plaque proteome in cardiac transthyretin amyloidosis (ATTR) and light chain amyloidosis (AL) and identify associations with patient characteristics and outcomes.A proteomics approach was used to identify all proteins in cardiac amyloid plaques, and to compare both normal and diseased controls. All proteins identified within amyloid plaques were defined as the expanded proteome; only proteins that were enriched in comparison to normal and disease controls were defined as the amyloid-specific proteome.Proteomic data from 292 patients with ATTR and 139 patients with AL cardiac amyloidosis were included; 160 and 161 unique proteins were identified in the expanded proteomes, respectively. In the amyloid-specific proteomes, we identified 28 proteins in ATTR, 19 in AL amyloidosis, with 13 proteins overlapping between ATTR and AL. ATTR was characterized by a higher abundance of complement and contractile proteins and AL by a higher abundance of keratins. We found that the proteome of kappa AL had higher levels of clusterin, a protective chaperone, and lower levels of light chains than lambda despite higher levels of circulating light chains. Hierarchical clustering identified a group of patients with worse survival in ATTR, characterized by high levels of PIK3C3, a protein with a central role in autophagy.Cardiac AL and ATTR have both common and distinct pathogenetic mechanisms of tissue damage. Our findings suggest that autophagy represents a pathway that may be impaired in ATTR and should be further studied.

When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments.

Prediction of metastatic sites from the primary site of origin is a impugn task in breast cancer (BRCA). Multi-dimensionality of such metastatic sites - bone, lung, kidney, and brain, using large-scale multi-dimensional Poly-Omics (Transcriptomics, Proteomics and Metabolomics) data of various type, for example, CNV (Copy number variation), GE (Gene expression), DNA methylation, path-ways, and drugs with clinical associations makes classification of metastasis a multi-faceted challenge. In this paper, we have approached the above problem in three steps; 1) Applied Linked data and semantic web to build Poly-Omics data as knowledge graphs and termed them as cancer decision network; 2) Reduced the dimensionality of data using Graph Pattern Mining and explained gene rewiring in cancer decision network by first time using Kirchhoff’s law for knowledge or any graph traversal; 3) Established ruled based modeling to understand the essential -Omics data from poly-Omics for breast cancer progression 4) Predicted the disease’s metastatic site using Kirchhoff’s knowledge graphs as a hidden layer in the graph convolution neural network(GCNN). The features (genes) extracted by applying Kirchhoff’s law on knowledge graphs are used to predict disease relapse site with 91.9% AUC (Area Under Curve) and performed detailed evaluation against the state-of-the-art approaches. The novelty of our approach is in the creation of RDF knowledge graphs from the poly-omics, such as the drug, disease, target(gene/protein), pathways and application of Kirchhoff’s law on knowledge graph to and the first approach to predict metastatic site from the primary tumor. Further, we have applied the rule-based knowledge graph using graph convolution neural network for metastasis site prediction makes the even classification novel.

Genomics technologies have generated large amounts of easily accessible biological -omics data, providing an unprecedented opportunity to study the mechanism in cancer. However, clinical research and the life sciences domain critically require to have a unified, integrated data model to facilitate the prognostic and diagnostic validation of biomarkers obtained. Knowledge graphs emerged as a promising solution based on our research for genomics and other -omics datasets. The primary reason to select a knowledge graph-based approach is that much data come from single cohorts such as TCGA, ICGC etc. that are carefully constructed to mitigate bias. Emerging datasets supporting the understanding of the complete mechanism are unstructured and in silos. The larger datasets such as TCGA and ICGC are patient cohorts and have issues ranging from patient self-selection, to confounding by indication, to limited knowledge of outcome data, and can therefore result in inadvertent bias if used alone for biomarker discovery. However the inclusion of molecular data such as CNV (copy number variation), DNA methylation, gene expression and mutation data (COSMIC, DoCM, MethylDB) adds additional and mechanistic features along with observational data from these cohorts. We applied semantic web and linked data approaches for knowledge graph embedding and federated networks for rapidly changing information in characterizing the disease, specifically cancer. The rapid change plays a critical role in disease progression and disease mechanism. Usually, these mechanisms are explained through biomarkers retrieved using comparative analysis of cancer stages with control for quantitative gene expression data. However, our knowledge graph facilitated including not only the quantitative data but also supportive molecular mechanisms to understand the change in pattern and associated factors. The cancer genomic events are layered processes and prediction models have to accommodate multi-omics data so that each molecular subtype feeds into incremental knowledge. This layered knowledge helps to improve the prediction of clinical outcomes, to elucidate the interplay between different levels and in disease modeling through layered data assembling. In our approach, we extrapolated knowledge graphs beyond the conventional knowledge enrichment and introduced a pattern mining approach to track the indicators in diseases such as cancer in a continuous way. We introduced the topological motif perturbations approach across disease stages to uncover the instances responsible for the change in the pattern, thus for disease progression, by continuous knowledge enrichment. Further, we applied a GCNN-based (graph convolution neural network) approach to identify the features required to not only track the disease mechanism but also predict survival and relapse in patients. We have customized the neural network in such a way that, while learning, we could customize the weight of each dataset or new concept added into the knowledge graph. The customized GCNN not only helped to predict the relapse accurately but also help to dichotomize the most relevant level of each dataset in cancer genomics. The above approach was tested and validated across various cancer types and contributed not only towards a novel way to integrate, understand and predict cancer but also added novel biomarker, e.g. the contribution of biomarkers in Gynecological cancers such as breast cancer, ovarian cancer, cervical cancer, and uterus cancer. The biomarkers retrieved through this approach contributed novel information about genes such as MYH7 involved in these cancers. We applied a motif-based pattern mining approach and established the relevant biomarkers to explain cancer progression mechanisms. Lastly, we developed a prediction model for breast and pancreatic cancer and developed clinical indicators. We also contributed adding COSMIC, TCGA and other RDF datasets into the linked open data (LOD) cloud with new enriched links from our knowledge graph: ``Oncology LOD''.

Multiple myeloma (MM) is characterized by the clonal expansion and metastatic spread of malignant plasma cells to multiple sites in the bone marrow (BM). Recently, we implicated the sialyltransferase ST3Gal-6, an enzyme critical to the generation of E-selectin ligands, in MM BM homing and resistance to therapy. Since E-selectin is constitutively expressed in the BM microvasculature, we wished to establish the contribution of E-selectin ligands to MM biology. We report that functional E-selectin ligands are restricted to a minor subpopulation of MM cell lines which, upon expansion, demonstrate specific and robust interaction with recombinant E-selectin in vitro. Moreover, an increase in the mRNA levels of genes involved in the generation of E-selectin ligands was associated with inferior progression-free survival in the CoMMpass study. In vivo, E-selectin ligand-enriched cells induced a more aggressive disease and were completely insensitive to Bortezomib. Importantly, this resistance could be reverted by co-administration of GMI-1271, a specific glycomimetic antagonist of E-selectin. Finally, we report that E-selectin ligand-bearing cells are present in primary MM samples from BM and peripheral blood with a higher proportion seen in relapsed patients. This study provides a rationale for targeting E-selectin receptor/ligand interactions to overcome MM metastasis and chemoresistance.

International audience; In the era of big data when a huge amount of data is continuously being generated, it is common for situations to arise where the number of samples is much smaller than the number of features (variables) per sample. This phenomenon is often found in biomedical domains, where we may have relatively few patients, compared to the amount of data per patient. For example, gene expression data typically has between 10,000 and 60,000 features per sample. A separate issue arises from the “right to explanation” found in the European General Data Protection Regulation (GDPR), which may prevent the use of black-box models in applications where explainability is required. In such situations, there is a need for robust algorithms which can identify the relevant features from experimental data by discarding irrelevant ones, yielding a simpler subset that facilitates explanation. To address these needs, we have developed a new algorithm for feature ranking and feature selection, named Ranked MSD. We have tested our proposed approach on two real-world gene expression data sets, both of which relate to respiratory viral infections. This Ranked MSD feature selection algorithm is able to reduce the feature set size from 12,023 genes (features) to 65 genes on the first data set and from 20,737 genes to 31 genes on the second data set, in both cases without any significant loss in disease prediction accuracy. In an alternative configuration, our proposed algorithm is able to identify a small subset of features that gives better accuracy than that of the full feature set. Our proposed algorithm can also identify important biomarkers (genes) with their importance score for a particular disease and the identified top-ranked biomarkers can play a vital role in drug discovery and precision medicine.

Amyloid light chain (AL) amyloidosis results from tissue deposition of clonal light chains, most commonly produced by clonal plasma cells. AL amyloidosis is closely associated with multiple myeloma (MM), another disease which arises from clonal plasma cell proliferation. Here we aim to identify gene signatures to distinguish AL cardiomyopathy (AL-CM) risk in MM patients. We utilized publicly available data sets and applied Graph Cluster Perturbation approach to cluster the co-expression networks based on pathways in MM and AL-CM utilizing 225 samples from four datasets: GSE42955 study (12 dilated CM, 12 ischemic CM and 5 control LV human heart tissues), GSE95077 study (16 amiloride drug treated/untreated myeloma cell line samples), GSE24128 study (16 newly diagnosed AL with monoclonal plasma cell samples over- or under-expressing cyclin D1), and GSE6477 study (76 primary bone marrow samples from hyper-diploid myeloma patients and 80 non-hyperdiploid MM patients). From these data sets, the networks for extracellular matrix organization, immune system, innate immune system, metabolism, and neutrophil degranulation pathways for MM and amyloid CM were extracted. Summary: Ranking of the perturbed genes based on pathways similarity index in MM and AL resulted in a panel of genes: CD44, NRAS, GRAP2, CTLA4, GSN, CCND1, NFKB1, and IRF1 (p= Figure , the high hazard ratio of this gene cluster in MM with AL-CM (3.76; p=0.021) compared to MM without CM (0.96; p=0.032) suggests the potential of this gene panel to distinguish high or low risk groups in MM based on survival.

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

The mammalian heart undergoes complex structural and functional remodeling to compensate for stresses such as pressure overload. While studies suggest that, at best, the adult mammalian heart is capable of very limited regeneration arising from the proliferation of existing cardiomyocytes, how myocardial stress affects endogenous cardiac regeneration or repair is unknown. To define the relationship between left ventricular afterload and cardiac repair, we induced left ventricle pressure overload in adult mice by constriction of the ascending aorta (AAC). One week following AAC, we normalized ventricular afterload in a subset of animals through removal of the aortic constriction (de-AAC). Subsequent monitoring of cardiomyocyte cell cycle activity via thymidine analog labeling revealed that an acute increase in ventricular afterload induced cardiomyocyte proliferation. Intriguingly, a release in ventricular overload (de-AAC) further increases cardiomyocyte proliferation. Following both AAC and de-AAC, thymidine analog-positive cardiomyocytes exhibited characteristics of newly generated cardiomyocytes, including single diploid nuclei and reduced cell size as compared to age-matched, sham-operated adult mouse myocytes. Notably, those smaller cardiomyocytes frequently resided alongside one another, consistent with local stimulation of cellular proliferation. Collectively, our data demonstrate that adult cardiomyocyte proliferation can be locally stimulated by an acute increase or decrease of ventricular pressure, and this mode of stimulation can be harnessed to promote cardiac repair.

A mishap in anti-cancer drug distribution is critical in breast cancer patients due to poor prediction model to identify the treatment regime in ER+ve and ER-ve (Estrogen Receptor (ER)) patients. The traditional method for the prediction depends on the change in expression across the normal-disease pair. However, it certainly misses the multidimensional aspect and underlying cause of relapse, such as various mutations, drug dosage side effects, methylation, etc. In this paper, we have developed a multi-layer neural network model to classify multidimensional genomics data into their similar annotation group. Further, we used this multi-layer cancer genomics perceptron for annotating differentially expressed genes (DEGs) to predict relapse based on ER status in breast cancer. This approach provides multivariate identification of genes, not just by differential expression, but, cause-effect of disease status due to drug overdosage and genomics-driven drug balancing method. The multi-layered neural network model, where each layer defines the relationship of similar databases with multidimensional knowledge. We illustrate that the use of multilayer knowledge graph with gene expression data for training the deep convolution neural network stratify the patient relapse and drug dosage along with underlying molecular properties. This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund non-peer-reviewed

Previously, we reported that Zika virus (ZIKV) causes ocular complications such as chorioretinal atrophy, by infecting cells lining the blood-retinal barrier, including the retinal pigment epithelium (RPE). To understand the molecular basis of ZIKV-induced retinal pathology, we performed a meta-analysis of transcriptome profiles of ZIKV-infected human primary RPE and other cell types infected with either ZIKV or other related flaviviruses (Japanese encephalitis, West Nile, and Dengue). This led to identification of a unique ZIKV infection signature comprising 43 genes (35 upregulated and 8 downregulated). The major biological processes perturbed include SH3/SH2 adaptor activity, lipid and ceramide metabolism, and embryonic organ development. Further, a comparative analysis of some differentially regulated genes (ABCG1, SH2B3, SIX4, and TNFSF13B) revealed that ZIKV induced their expression relatively more than dengue virus did in RPE. Importantly, the pharmacological inhibition of ABCG1, a membrane transporter of cholesterol, resulted in reduced ZIKV infectivity. Interestingly, the ZIKV infection signature revealed the downregulation of ALDH5A1 and CHML, genes implicated in neurological (cognitive impairment, expressive language deficit, and mild ataxia) and ophthalmic (choroideremia) disorders, respectively. Collectively, our study revealed that ZIKV induces differential gene expression in RPE cells, and the identified genes/pathways (e.g., ABCG1) could potentially contribute to ZIKV-associated ocular pathologies.

Background Next Generation Sequencing (NGS) is playing a key role in therapeutic decision making for the cancer prognosis and treatment. The NGS technologies are producing a massive amount of sequencing datasets. Often, these datasets are published from the isolated and different sequencing facilities. Consequently, the process of sharing and aggregating multisite sequencing datasets are thwarted by issues such as the need to discover relevant data from different sources, built scalable repositories, the automation of data linkage, the volume of the data, efficient querying mechanism, and information rich intuitive visualisation. Results We present an approach to link and query different sequencing datasets (TCGA, COSMIC, REACTOME, KEGG and GO) to indicate risks for four cancer types – Ovarian Serous Cystadenocarcinoma (OV), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) – covering the 16 healthy tissue-specific genes from Illumina Human Body Map 2.0. The differentially expressed genes from Illumina Human Body Map 2.0 are analysed together with the gene expressions reported in COSMIC and TCGA repositories leading to the discover of potential biomarkers for a tissue-specific cancer. Conclusion We analyse the tissue expression of genes, copy number variation (CNV), somatic mutation, and promoter methylation to identify associated pathways and find novel biomarkers. We discovered twenty (20) mutated genes and three (3) potential pathways causing promoter changes in different gynaecological cancer types. We propose a data-interlinked platform called BIOOPENER that glues together heterogeneous cancer and biomedical repositories. The key approach is to find correspondences (or data links) among genetic, cellular and molecular features across isolated cancer datasets giving insight into cancer progression from normal to diseased tissues. The proposed BIOOPENER platform enriches mutations by filling in missing links from TCGA, COSMIC, REACTOME, KEGG and GO datasets and provides an interlinking mechanism to understand cancer progression from normal to diseased tissues with pathway components, which in turn helped to map mutations, associated phenotypes, pathways, and mechanism.

Background: LOSS or GAIN in tumor suppressor gene RB1 play a significant role as in case of loss low penetrance where only 39% of eye at risk develops in retinoblastoma. This research covers the multiple mutation types and its effects and identification of major type of mutation involved in retinoblastoma because of HPV and RB1. Methods: First, we focus on exploring gene expression (GE) patterns for RB1 and HPV associated genes from TCGA. Second, identification of validated and non-validated standard CNV ensured using the COSMIC. Finally, the clinical profiles of filtered mutations have been validated based on ICGC pathological profiling data to infer the prognostic behavior from RB1 and HPV associated genes. In order to link and retrieve patterns of a gene from TCGA, COSMIC, and ICGC repositories, we performed following steps: transform heterogeneous data repositories and their storage formats into standard Resource Description Framework (RDF) format; to discover associations by finding specific patterns (i.e. correlations) in the GE data sets; scalable querying the large volume and frequently updating datasets covering the GE data from different repositories. Results: HPV mutations indicated in more than 127 cancer studies shows deletion and amplifications are rare mutations. Retinoblastoma: Expression profile of RB1 shows mutations such as nonsense, Missense or splice events and in GBM and gliomas the expression values in splice mutations (1500-200), nonsense mutations (200-600). In principal HPV associated retinoblastoma the higher expression of HPV genes results in splice junctions and lower in nonsense mutations. Other tissues: Pattern of RB1 where the results coming from more 123 studies show the pattern of mutations similar to the results obtained from HPV associated genes. Alteration with HPV genes study based on the alteration in Altered in 90% samples of 61 cases where TP53 is holding 90% occurrence majorly as normal mutations and SNRNP70 and BRCA1 is majorly responsible truncating mutation other highly mutated genes are AP3D1, BRD4, CCHCR1, CPSF4, CREBBP, CUL3, DDX11, EP300, EP400, FRZ1, GNB2L1, GTF2B, KDM5C, NR4A1, PRP1, PLK1, SF1, SRSF1, SRSF7, SMARCB1, SNRNP70, TAF1, TBP, TMF1, TOPBP1. Whereas RB1 is associated with 11% cased of deep deletion where the V654L is the normal mutation and all others are highly truncating mutation. Survival graph for HPV and RB1 associated genes median months(m) of survival with alteration in these query genes are 103m whereas in RB1 the median month of survival are 7.63m however the disease free survival in RB1 cases are 4.50m. The p-value are 0.76, 0.67, 0.38 respectively. To demonstrate pattern of survival gene set enrichment have been performed on both gene lists where in case of RB1 genes with highest interactions are MDM2, CDK4 and TP53. In HPV genes interacting hubs are TOP1, PARP1, TP53 and ODF2. Higher interacting genes are associated with drugs. RB1 corresponded to Insulin, p a non-cancer FDA approved drug whereas HPV genes and especially TOP1 is associated with Lucanthone, Innotecan, BTBD1 and Topotecan in case of FDA approved drugs category. Cancer drugs with HPV genes are majorly associated with TOP1, PARP1 and PLK1 namely BTBD1, AZD 2281, AG14361, BI2536 and GW84382X. In ICGC effect of RB1 is on cancers e.g. melanoma 51.91% Esophageal 45.38% ovarian 31.18% liver 27.96% Pancreatic 22.55%. Associations of RB1 with other cancer mutations are either splice as in TCGA and other associated mutation with RB1 is LPAR6 majorly these mutations are in exon-region and further understand the other mutations in TCGA ICGC reveals most of these are SNPs at chromosome-13 which defines the locus of RB1 and for HPV. Higher interacting hubs from TCGA TOP1, PAPR1 and ODF2. TOP1 is associated with melanoma two donor hubs of 42.08% and 40.91% liver cancer Hepatocellular carcinoma (Virus) with 23.08 % Esophageal (15.05%) and Ovarian (11.36%). Mutations types are SNP and Splice junctions. Citation Format: Alokkumar Jha, Yasar Khan, Dietrich Rebholz-Schumann, Ratnesh Sahay. A linked data approach to discover HPV oncoprotiens and RB1 induced mutation associations for the retinoblastoma research. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A27.

Introduction Advances in genomics have highlighted the potential for individualized prognostication and therapy in multiple myeloma (MM). Previously developed gene expression signatures have identified patients with high risk (Kuiper et al, Blood 2016) however, they provide few insights into underlying disease biology thereby limiting their use in informing treatment decisions. Glycosylation is deregulated in MM (Glavey et al), and potential consequences include altered cell adhesion, signaling, immune evasion and drug resistance. In this study we have utilized RNA sequencing data from the IA7 CoMMpass cohort to characterize the expression profile of genes involved in glycosylation. This represents a novel approach to identify a distinct molecular pathway related to outcome, which is potentially actionable. Methods A pathway based approach was adopted to evaluate genes implicated in glycosylation, including the generation of selectin ligands. A literature review and KEGG pathway analysis of pathways relating to O-glycans, N-glycans, sialic acid metabolism, glycolipid synthesis and metabolism was completed. RNA Cufflinks-gene level FPKM expression of 458 patients enrolled in the IA7 cohort of the Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) were analysed as derivation cohort. We developed expression cut-offs using a novel approach of adjusted existing linear regression model to define the gene expression cut-off by applying 3rd Quartile data (q1+q2/2-qmin). The analysis of overall survival (OS) was completed using adjusted 'kpas' R-package according to our cut-off model. Association between individual transcripts and OS was analyzed with log-rank test. Genes with p-value Results Initial analysis yielded 184 prospective genes. 147 were significant on univariate analysis. Following further prioritization of these genes, we identified thirteen genes that had significant impact upon outcomes (GiMM13). Figure 1 reveals that GiMM13 signature has a significant correlation with inferior OS (HR 4.66 p-value 0.022). The prognostic impact of stratifying GiMM13 positive (High risk) or GiMM13 negative (Low risk) by ISS stage was evaluated. In Table 1. Kaplan Meier estimates generated for GiMM13 (High) or GiMM13 (Low) stratified by ISS are compared statistically using the log rank test. The prognostic ability of GiMM13 to synthesize distinct subgroups relative to each ISS stage is shown in Figure 2. ISS1-Low is the the lowest risk group with best prognosis. Hazard ratios relative to the ISS1-Low group were 1.8, p-value 0.029 (ISS2-Low), 2.1, p-value 0.031 (ISS3-Low), 4.3, p-value 0.04 (ISS1-HR), 5.9, p-value 0.039 (ISS2-HR) and 3.1, p-value 0.001 (ISS3-HR). The GiMM13 signature enhances the prognostic ability of ISS to identify patients with inferior or superior outcomes respectively. Conclusion While the therapeutic armamentarium for MM has expanded considerably, the significant molecular heterogeneity in the disease still poses a significant challenge. Our data suggests aberrant transcription of glycosylation genes, involved predominantly in selectin ligand synthesis, is associated with inferior survival outcomes and may help identify patients likely to benefit from treatment with agents targeting aberrant glycosylation, e.g. E-selectin inhibitor. Consistent with recent findings in chemoresistant minimal residual disease (MRD) (Paiva et al, Blood 2016), it would appear that O-glycosylation, rather than N-glycosylation is most significantly implicated in this biological processes conferring inferior outcomes. In conclusion, using a novel pathway-based approach to identify a 13-gene signature (GiMM13), we have developed a robust tool that can refine patient prognosis and inform clinical decision-making. Acknowledgment These data were generated as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiatives (https://research.themmrf.org and www.themmrf.org). Disclosures O'Dwyer: Glycomimetics: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding.

Semantic Web technologies are core for the integration of disparate data resources. It can be used to exploit data from next generation sequencing (NGS) for therapeutic decisions regarding cancer. In this manuscript, we describe how different data resources, which inform on the expression of specific genes in a tissue and its variants, can be brought together to indicate a risk for tissue-specific cancer for NGS data. This approach can be used to judge patient genomic data against public reference data resources. The TCGA and COSMIC repositories are being processed to connect and query information concerning the expression of genes, copy number variants (CNV), and somatic mutations. We annotated sets of differential expression data provided from the Illumina Body map 2.0 (HBM) concerning 16 different tissue types and identify genes with an RPKM (Reads Per Kilobase of transcript per Million mapped reads) value greater than 0.5 as measure indicating an associated risk for cancer. Thus, the differential expressed genes from HBM can be associated with a tissue type and gene expressions in COSMIC and TCGA leading to a potential biomarker for that particular tissue specific cancer. In the case of ovarian cancer, we retrieved the genomic positions (loci) and the associated genes of potential biomarker candidates, and suggest that this approach and platform can serve future studies well. Altogether, the presented linked annotation platform is the first approach to represent the COSMIC data in an RDF format and to link the data with the TCGA datasets. The proposed approach enriches mutations by filling in missing links from COSMIC and TCGA datasets which in turn helped to map mutations with associated phenotypes. This publication has emanated from research supported by the research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289. non-peer-reviewed