Biology subjects

Umeton, R.

Publications and source records attributed to Umeton, R..

9 recordsLinked to original sources

bioRxiv2023

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Background Abstract Background Results Discussion Methods Data Availability Code Availability Author Contributions Competing Interests Extended Data References Renal cell carcinoma (RCC) is among the 10 most common cancers worldwide and is comprised of several histological subtypes1. The clear cell histological subtype (ccRCC) is the most common form of RCC and accounts for the vast majority (75-80%) of metastatic cases1. In addition to highly recurrent mutations in hypoxia (VHL) and chromatin regulator genes (e.g. PBRM1, BAP1, SETD2), ccRCC exhibits extensive genomic intratumoral heterogeneity (ITH)2, which wa ...

cancer biology↗

bioRxiv2022

Multi-omics biomarkers aid prostate cancer prognostication

Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the "gold standard" to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature 1: EGLN3, succinate, trans-4-hydroxyprolin; and signature 2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.

systems biology↗

bioRxiv2022

Circulating U13 small nucleolar RNA as a candidate biomarker for Huntington's disease

Background and ObjectivesFluid biomarkers are a recent field of interest in Huntington disease (HD). We focused on small circulating RNAs from plasma of subjects with prodromal (pre-HD) and overt disease by a two-stage approach: an unbiased investigation by an array method and a validation study to quantify a significant small nucleolar RNA. MethodsThrough Affymetrix Gene-Chip-miRNA-Array we performed an exploratory study on 9 HD patients, 8 healthy subjects (HS) and 5 psychiatric patients (PP; who share drugs with HD patients, to control for iatrogenic effects). Through real time PCR we validated the results in an independent population of 24 HD patients, 15 pre-HD, 24 PP, 28 Alzheimers disease (AD) patients (added to control the disease-specificity of our finding) and 23 HS. A bioinformatic analysis was also performed to interpret our finding. ResultsThe microarray results showed a significant signal for U13 small nucleolar RNA (SNORD13) that was increased in plasma of HD patients compared to controls (fold change, 1.54, p =0.003 HD vs. HS, and fold change 1.44 p = 0.0026 HD vs. PP). In the validation population the significant increase in HD patients was evident compared to both pre-HD and the three control groups (p<0.00001). The plasma levels of SNORD13 correlated with the status of mutant huntingtin carrier and the disease duration (respectively R=0.69; p<0.000001; R=0.49; p=0.015). Through receiver operating characteristic (ROC) curve analysis, we showed high accuracy of plasmatic SNORD13 in discriminating HD patients from pre-HD and control groups (AUC=0.963), outperforming values reported in another study for intrathecal or plasmatic mutant huntingtin and neurofilament light chain as biomarkers of overt HD. The bioinformatic analysis on SNORD13 interactome and pathway analysis showed enrichments for factors involved in nuclear functions beyond the ribosome biogenesis. DiscussionWe report the unprecedented finding of a potential role of small nucleolar RNAs in HD. Circulating SNORD13 seems a good biomarker for clinical purposes. It seems to be specific for HD and to peripherally report a plausible tipping point in the pathogenic cascade at neuronal level, possibly paving the way for new therapeutic targets.

neuroscience↗

bioRxiv2021

Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use-cases. PathML is publicly available at www.pathml.com.

cancer biology↗

bioRxiv2021

Examining Batch Effect in Histopathology as a Distributionally Robust Optimization Problem

Computer vision (CV) approaches applied to digital pathology have informed biological discovery and development of tools to help inform clinical decision-making. However, batch effects in the images have the potential to introduce spurious confounders and represent a major challenge to effective analysis and interpretation of these data. Standard methods to circumvent learning such confounders include (i) application of image augmentation techniques and (ii) examination of the learning process by evaluating through external validation (e.g., unseen data coming from a comparable dataset collected at another hospital). Here, we show that the source site of a histopathology slide can be learned from the image using CV algorithms in spite of image augmentation, and we explore these source site predictions using interpretability tools. A CV model trained using Empirical Risk Minimization (ERM) risks learning this source-site signal as a spurious correlate in the weak-label regime, which we abate by using a training method with abstention. We find that a patch based classifier trained using abstention outperformed a model trained using ERM by 9.9, 10 and 19.4% F1 in the binary classification tasks of identifying tumor versus normal tissue in lung adenocarcinoma, Gleason score in prostate adenocarcinoma, and tumor tissue grade in clear cell renal cell carcinoma, respectively, at the expense of up to 80% coverage (defined as the percent of tiles not abstained on by the model). Further, by examining the areas abstained by the model, we find that the model trained using abstention is more robust to heterogeneity, artifacts and spurious correlates in the tissue. Thus, a method trained with abstention may offer novel insights into relevant areas of the tissue contributing to a particular phenotype. Together, we suggest using data augmentation methods that help mitigate a digital pathology models reliance on potentially spurious visual features, as well as selecting models that can identify features truly relevant for translational discovery and clinical decision support.

cancer biology↗

bioRxiv2021

Pancreatic cancer risk predicted from disease trajectories using deep learning

Pancreatic cancer is an aggressive disease that typically presents late with poor patient outcomes. There is a pronounced medical need for early detection of pancreatic cancer, which can be addressed by identifying high-risk populations. Here we apply artificial intelligence (AI) methods to a dataset of 6 million patient records with 24,000 pancreatic cancer cases in the Danish National Patient Registry (DNPR) and, for comparison, a dataset of three million records with 3,900 pancreatic cancer cases in the United States Department of Veterans Affairs (US-VA) healthcare system. In contrast to existing methods that do not use temporal information, we explicitly train machine learning models on the time sequence of diseases in patient clinical histories and test the ability to predict cancer occurrence in time intervals of 3 to 60 months after risk assessment. For cancer occurrence within 36 months, the performance of the best model (AUROC=0.88, DNPR), trained and tested on disease trajectories, exceeds that of a model without longitudinal information (AUROC=0.85, DNPR). Performance decreases when disease events within a 3 month window before cancer diagnosis are excluded from training (AUROC[3m]=0.83). Independent training and testing on the US-VA dataset reaches comparable performance (AUROC=0.78, AUROC[3m]=0.76). These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of prediction-surveillance programs based on risk assessment in a large population followed by affordable surveillance of a relatively small number of patients at highest risk. Use of AI on real-world clinical records has the potential to shift focus from treatment of late-stage to early-stage cancer, benefiting patients by improving lifespan and quality of life.

bioinformatics↗

bioRxiv2021

Enrichment analysis of GWAS data in autoimmunity delineates the multiple sclerosis-Epstein Barr virus association

We exploited genetic information to assess non-genetic influences in autoimmunity. We isolated gene modules whose products physically interact with environmental exposures related to autoimmunity, and analyzed their nominal statistical evidence of association with autoimmune and non-autoimmune diseases in genome-wide association studies (GWAS) data. Epstein Barr virus (EBV) and other Herpesviruses interactomes emerged as specifically associated with multiple sclerosis (MS), possibly under common regulatory mechanisms. Analyses of MS blood and brain transcriptomes, cytofluorimetric studies of endogenous EBV-infected lymphoblastoid lines, and lesion immunohistochemistry, confirmed a dysregulation of MS-associated EBV interactors, suggesting their contribution to CD40 signaling alterations in MS. These interactors resulted enriched in modules from inherited axonopathies-causing genes, supporting a link between EBV and neurodegeneration in MS, in accord with the observed transcriptomic dysregulations in MS brains. They were also enriched with top-ranked pharmaceutical targets prioritized on a genetic basis. This study delineates a disease-specific influence of herpesviruses on MS biology.

neuroscience↗

bioRxiv2021

GWAS-associated variants, non-genetic factors and transient transcriptome in Multiple Sclerosis etiopathogenesis: a colocalization analysis

A clinically actionable understanding of multiple sclerosis (MS) etiology goes through GWAS interpretation, prompting research on new gene regulatory models. We previously suggested a stochastic etiologic model where small-scale random perturbations could reach a threshold for MS development. The recently described mapping of the transient transcriptome (TT), including intergenic and intronic RNAs, seems appropriate to verify this model through a rigorous colocalization analysis. We show that genomic regions coding for the TT were significantly enriched for MS-associated GWAS variants and DNA binding sites for molecular transducers mediating putative, non-genetic, etiopathogenetic factors for MS (e.g., vitamin D deficiency, Epstein Barr virus latent infection, B cell dysfunction). These results suggest a model whereby TT-coding regions are hotspots of convergence between genetic ad non-genetic factors of risk/protection for MS, and plausibly for other complex disorders. Our colocalization analysis also provides a freely available data resource (www.mscoloc.com) for future research on MS transcriptional regulation.

neuroscience↗

bioRxiv2019

Significant enrichment of Herpesvirus interactors in GWAS data suggests causal inferences for the association between Epstein Barr virus and multiple sclerosis

We exploited genetic information to assess the role of non-genetic factors in multifactorial diseases. To this aim we isolated candidate \"interactomes\" (i.e. groups of genes whose products are known to physically interact with environmental exposures and biological processes, plausibly relevant for disease pathogenesis) and analyzed nominal statistical evidence of association with genetic predisposition to multiple sclerosis (MS) and other inflammatory and non-inflammatory complex disorders. The interaction between genotype and Herpesviruses emerged as specific for MS, with Epstein Barr virus (EBV) showing higher levels of significance compared to Human Herpesvirus 8 (HHV8) and, more evidently, to cytomegalovirus (CMV). In accord with this result, when we classified the MS-associated genes contained in the interactomes into canonical pathways, the analysis converged towards biological functions of B cells, in particular the CD40 pathway. When we analyzed peripheral blood transcriptomes in persons with MS, we found a significant dysregulation of MS-associated genes belonging to the EBV interactome in primary progressive MS. This study indicates that the interaction between herpesviruses and predisposing genetic background is of causal significance in MS, and provides a mechanistic explanation for the long-recognized association between EBV and this condition.

neuroscience↗