scholarly journals A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types

2019 ◽  
Author(s):  
Melanie F. Pradier ◽  
Stephanie L. Hyland ◽  
Stefan G. Stark ◽  
Kjong Lehmann ◽  
Julia E. Vogt ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Multiple Hypothesis Testing ◽  
Environmental Data ◽  
Bayesian Nonparametric ◽  
Genetic Associations ◽  
Disease Treatment ◽  
Nonparametric Approach ◽  
Gene Level ◽  
Cancer Types ◽  
Unsupervised Approach

AbstractMotivationPersonalized medicine aims at combining genetic, clinical, and environmental data to improve medical diagnosis and disease treatment, tailored to each patient. This paper presents a Bayesian nonparametric (BNP) approach to identify genetic associations with clinical/environmental features in cancer. We propose an unsupervised approach to generate data-driven hypotheses and bring potentially novel insights about cancer biology. Our model combines somatic mutation information at gene-level with features extracted from the Electronic Health Record. We propose a hierarchical approach, the hierarchical Poisson factor analysis (H-PFA) model, to share information across patients having different types of cancer. To discover statistically significant associations, we combine Bayesian modeling with bootstrapping techniques and correct for multiple hypothesis testing.ResultsUsing our approach, we empirically demonstrate that we can recover well-known associations in cancer literature. We compare the results of H-PFA with two other classical methods in the field: case-control (CC) setups, and linear mixed models (LMMs).

scholarly journals Multi-omics analysis reveals contextual tumor suppressive and oncogenic gene modules within the acute hypoxic response

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zdenek Andrysik ◽  
Heather Bender ◽  
Matthew D. Galbraith ◽  
Joaquin M. Espinosa
Keyword(s):  
Cancer Biology ◽  
Acute Hypoxia ◽  
Transcriptional Response ◽  
Adaptation To Hypoxia ◽  
Mtor Inhibition ◽  
Collagen Remodeling ◽  
Acute Response ◽  
Relative Contribution ◽  
Transcriptional Changes ◽  
Cancer Types

AbstractCellular adaptation to hypoxia is a hallmark of cancer, but the relative contribution of hypoxia-inducible factors (HIFs) versus other oxygen sensors to tumorigenesis is unclear. We employ a multi-omics pipeline including measurements of nascent RNA to characterize transcriptional changes upon acute hypoxia. We identify an immediate early transcriptional response that is strongly dependent on HIF1A and the kinase activity of its cofactor CDK8, includes indirect repression of MYC targets, and is highly conserved across cancer types. HIF1A drives this acute response via conserved high-occupancy enhancers. Genetic screen data indicates that, in normoxia, HIF1A displays strong cell-autonomous tumor suppressive effects through a gene module mediating mTOR inhibition. Conversely, in advanced malignancies, expression of a module of HIF1A targets involved in collagen remodeling is associated with poor prognosis across diverse cancer types. In this work, we provide a valuable resource for investigating context-dependent roles of HIF1A and its targets in cancer biology.

Role of long interspersed nuclear element-1 in the regulation of chromatin landscapes and genome dynamics

2021 ◽  
pp. 153537022110312
Author(s):  
Kenneth S Ramos ◽  
Pasano Bojang ◽  
Emma Bowers
Keyword(s):  
Cancer Biology ◽  
Mobile Element ◽  
Cancer Diagnostics ◽  
Regulatory Control ◽  
Full Potential ◽  
Genome Dynamics ◽  
Human Illness ◽  
Cancer Types ◽  
Stressful Environments

LINE-1 retrotransposon, the most active mobile element of the human genome, is subject to tight regulatory control. Stressful environments and disease modify the recruitment of regulatory proteins leading to unregulated activation of LINE-1. The activation of LINE-1 influences genome dynamics through altered chromatin landscapes, insertion mutations, deletions, and modulation of cellular plasticity. To date, LINE-1 retrotransposition has been linked to various cancer types and may in fact underwrite the genetic basis of various other forms of chronic human illness. The occurrence of LINE-1 polymorphisms in the human population may define inter-individual differences in susceptibility to disease. This review is written in honor of Dr Peter Stambrook, a friend and colleague who carried out highly impactful cancer research over many years of professional practice. Dr Stambrook devoted considerable energy to helping others live up to their full potential and to navigate the complexities of professional life. He was an inspirational leader, a strong advocate, a kind mentor, a vocal supporter and cheerleader, and yes, a hard critic and tough friend when needed. His passionate stand on issues, his witty sense of humor, and his love for humanity have left a huge mark in our lives. We hope that that the knowledge summarized here will advance our understanding of the role of LINE-1 in cancer biology and expedite the development of innovative cancer diagnostics and treatments in the ways that Dr Stambrook himself had so passionately envisioned.

Bayesian Nonparametric Approach to Average Annual Daily Traffic Estimation for Bridges

2021 ◽  
pp. 036119812199459
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine
Keyword(s):  
Performance Indicators ◽  
Federal Funding ◽  
Percentage Error ◽  
Bayesian Nonparametric ◽  
Transportation Services ◽  
Nonparametric Approach ◽  
Nonparametric Models ◽  
Bayesian Nonparametric Models ◽  
Average Annual Daily Traffic ◽  
The Mean

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

scholarly journals Criticality in tumor evolution and clinical outcome

2018 ◽  
Vol 115 (47) ◽  
pp. E11101-E11110 ◽  
Author(s):  
Erez Persi ◽  
Yuri I. Wolf ◽  
Mark D. M. Leiserson ◽  
Eugene V. Koonin ◽  
Eytan Ruppin
Keyword(s):  
Clinical Outcome ◽  
Cancer Biology ◽  
Patient Survival ◽  
Fitness Landscape ◽  
Purifying Selection ◽  
Nonlinear Effects ◽  
Tumor Evolution ◽  
Evolutionary Trajectories ◽  
Cancer Types ◽  
Selection Phase

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase diagram of 6,721 tumors representing 23 cancer types by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low-ML cancers, a high number of mutations indicates poor prognosis, whereas high-ML cancers show the opposite trend, presumably due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Melanoma, with the highest ML, evolves under purifying selection, whereas in low-ML cancers, signatures of positive selection are observed, demonstrating how selection affects tumor fitness. Moreover, different cancers occupy specific positions on the ML–dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its nonlinear effects on survival.

scholarly journals Schlafens: Emerging Proteins in Cancer Cell Biology

Cells ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2238
Author(s):  
Sarmad Al-Marsoummi ◽  
Emilie E. Vomhof-DeKrey ◽  
Marc D. Basson
Keyword(s):  
Cell Proliferation ◽  
Cell Growth ◽  
Cell Biology ◽  
Cancer Biology ◽  
Immune Cell ◽  
Biological Functions ◽  
Cancer Types ◽  
Opposing Effects ◽  
High Degree ◽  
Human And Mouse

Schlafens (SLFN) are a family of genes widely expressed in mammals, including humans and rodents. These intriguing proteins play different roles in regulating cell proliferation, cell differentiation, immune cell growth and maturation, and inhibiting viral replication. The emerging evidence is implicating Schlafens in cancer biology and chemosensitivity. Although Schlafens share common domains and a high degree of homology, different Schlafens act differently. In particular, they show specific and occasionally opposing effects in some cancer types. This review will briefly summarize the history, structure, and non-malignant biological functions of Schlafens. The roles of human and mouse Schlafens in different cancer types will then be outlined. Finally, we will discuss the implication of Schlafens in the anti-tumor effect of interferons and the use of Schlafens as predictors of chemosensitivity.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

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