[P2-263]: GRAPHICAL NETWORK ANALYSES INFORMS PET Aβ-AMYLOID BIOMARKER DISCOVERY VIA QUANTIFICATION OF INDIVIDUAL PEPTIDE CONNECTIONS

Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. Discovery of novel variants in local population provides confident targets for developing biomarkers for personalized medicine. We identified the need to generate high-quality sequencing data from local colorectal cancer patients and understand the pattern of occurrence of variants. In this report, we used archived samples from Saudi Arabia and used the AmpliSeq comprehensive cancer panel to identify novel somatic variants. We report a comprehensive analysis of next-generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity, and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in the large intestine. APC, RET, and EGFR genes were most frequently mutated. A higher number of variants were identified in the left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

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Biomarker discovery in inflammatory bowel diseases using network-based feature selection

10.1101/662197 ◽

2019 ◽

Cited By ~ 1

Author(s):

Mostafa Abbas ◽

John Matta ◽

Thanh Le ◽

Halima Bensmail ◽

Tayo Obafemi-Ajayi ◽

...

Keyword(s):

Feature Selection ◽

Random Forest ◽

Biomarker Discovery ◽

Hybrid Approach ◽

Cost Effective ◽

Machine Learning Techniques ◽

Integrative Approach ◽

Selection Methods ◽

Network Analyses ◽

Metagenomics Data

ABSTRACTReliable identification of inflammatory biomarkers from metagenomics data is a promising direction for developing non-invasive, cost-effective, and rapid clinical tests for early diagnosis of IBD. We present an integrative approach to Network-Based Biomarker Discovery (NBBD) which integrates network analyses methods for prioritizing potential biomarkers and machine learning techniques for assessing the discriminative power of the prioritized biomarkers. Using a large dataset of new-onset pediatric IBD metagenomics biopsy samples, we compare the performance of Random Forest (RF) classifiers trained on features selected using a representative set of traditional feature selection methods against NBBD framework, configured using five different tools for inferring networks from metagenomics data, and nine different methods for prioritizing biomarkers as well as a hybrid approach combining best traditional and NBBD based feature selection. We also examine how the performance of the predictive models for IBD diagnosis varies as a function of the size of the data used for biomarker identification. Our results show that (i) NBBD is competitive with some of the state-of-the-art feature selection methods including Random Forest Feature Importance (RFFI) scores; and (ii) NBBD is especially effective in reliably identifying IBD biomarkers when the number of data samples available for biomarker discovery is small.

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Identification of Novel Mutations in Colorectal Cancer Patients using Ampliseq Comprehensive Cancer Panel

10.20944/preprints202104.0401.v1 ◽

2021 ◽

Author(s):

Bader Almuzzaini ◽

Jahad Alghamdi ◽

Alhanouf Alomani ◽

Saleh AlGhamdi ◽

Abdullah Ali Alsharm ◽

...

Keyword(s):

Colorectal Cancer ◽

Personalized Medicine ◽

Cancer Patients ◽

Biomarker Discovery ◽

Local Population ◽

Network Analyses ◽

Functional Perspective ◽

Novel Variants ◽

Colorectal Cancer Patients ◽

Cancer Panel

Discovery of novel variants from data derived from local population provides confident targets for developing biomarkers for personalized medicine. Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. We identified the need to generate high quality sequencing data from local population and understand the pattern of occurrence of variants in colorectal cancer patients. In this report, we used archived samples from Saudi Arabia and used Ampliseq Comprehensive Cancer panel to identify novel somatic variants. We report a comprehensive analysis of next generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in large intestine. APC, RET and EGFR genes were most frequently mutated. Higher number of variants were identified in left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

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Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection

Journal of Translational Medicine ◽

10.1186/s12967-021-03025-8 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Yuxin Lin ◽

Liangliang Wang ◽

Wenqing Ge ◽

Yu Hui ◽

Zheng Zhou ◽

...

Keyword(s):

Kidney Transplant ◽

Biomarker Discovery ◽

Transplant Rejection ◽

Mirna Gene ◽

Kidney Tissue ◽

Interaction Network ◽

Negative Effects ◽

Optimal Method ◽

Network Analyses ◽

Network Characterization

Abstract Background Kidney transplantation is an optimal method for treatment of end-stage kidney failure. However, kidney transplant rejection (KTR) is commonly observed to have negative effects on allograft function. MicroRNAs (miRNAs) are small non-coding RNAs with regulatory role in KTR genesis, the identification of miRNA biomarkers for accurate diagnosis and subtyping of KTR is therefore of clinical significance for active intervention and personalized therapy. Methods In this study, an integrative bioinformatics model was developed based on multi-omics network characterization for miRNA biomarker discovery in KTR. Compared with existed methods, the topological importance of miRNA targets was prioritized based on cross-level miRNA-mRNA and protein–protein interaction network analyses. The biomarker potential of identified miRNAs was computationally validated and explored by receiver-operating characteristic (ROC) evaluation and integrated “miRNA-gene-pathway” pathogenic survey. Results Three miRNAs, i.e., miR-145-5p, miR-155-5p, and miR-23b-3p, were screened as putative biomarkers for KTR monitoring. Among them, miR-155-5p was a previously reported signature in KTR, whereas the remaining two were novel candidates both for KTR diagnosis and subtyping. The ROC analysis convinced the power of identified miRNAs as single and combined biomarkers for KTR prediction in kidney tissue and blood samples. Functional analyses, including the latent crosstalk among HLA-related genes, immune signaling pathways and identified miRNAs, provided new insights of these miRNAs in KTR pathogenesis. Conclusions A network-based bioinformatics approach was proposed and applied to identify candidate miRNA biomarkers for KTR study. Biological and clinical validations are further needed for translational applications of the findings.

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Comprehensive Biomarker Discovery and Validation for Clinical Application

10.1039/9781849734363 ◽

2013 ◽

Cited By ~ 3

Keyword(s):

Clinical Application ◽

Biomarker Discovery

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Amyloid β-peptide and Alzheimer's disease

Essays in Biochemistry ◽

10.1042/bse0560099 ◽

2014 ◽

Vol 56 ◽

pp. 99-110 ◽

Cited By ~ 15

Author(s):

David Allsop ◽

Jennifer Mayes

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Senile Plaques ◽

Strong Support ◽

Amyloid Β ◽

Amyloid Β Peptide ◽

Amyloid Cascade Hypothesis ◽

Sequence Of Events ◽

Aβ Amyloid ◽

Amyloid Cascade

One of the hallmarks of AD (Alzheimer's disease) is the formation of senile plaques in the brain, which contain fibrils composed of Aβ (amyloid β-peptide). According to the ‘amyloid cascade’ hypothesis, the aggregation of Aβ initiates a sequence of events leading to the formation of neurofibrillary tangles, neurodegeneration, and on to the main symptom of dementia. However, emphasis has now shifted away from fibrillar forms of Aβ and towards smaller and more soluble ‘oligomers’ as the main culprit in AD. The present chapter commences with a brief introduction to the disease and its current treatment, and then focuses on the formation of Aβ from the APP (amyloid precursor protein), the genetics of early-onset AD, which has provided strong support for the amyloid cascade hypothesis, and then on the development of new drugs aimed at reducing the load of cerebral Aβ, which is still the main hope for providing a more effective treatment for AD in the future.

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Insights into amyloid disease from fly models

Essays in Biochemistry ◽

10.1042/bse0560069 ◽

2014 ◽

Vol 56 ◽

pp. 69-83 ◽

Cited By ~ 1

Author(s):

Ko-Fan Chen ◽

Damian C. Crowther

Keyword(s):

Drosophila Melanogaster ◽

Neurodegenerative Disorders ◽

Amyloid Β ◽

Amyloid Β Peptide ◽

Disease Pathogenesis ◽

Amyloid Aggregates ◽

Aβ Amyloid ◽

Polypeptide Chains ◽

Amyloid Diseases

The formation of amyloid aggregates is a feature of most, if not all, polypeptide chains. In vivo modelling of this process has been undertaken in the fruitfly Drosophila melanogaster with remarkable success. Models of both neurological and systemic amyloid diseases have been generated and have informed our understanding of disease pathogenesis in two main ways. First, the toxic amyloid species have been at least partially characterized, for example in the case of the Aβ (amyloid β-peptide) associated with Alzheimer's disease. Secondly, the genetic underpinning of model disease-linked phenotypes has been characterized for a number of neurodegenerative disorders. The current challenge is to integrate our understanding of disease-linked processes in the fly with our growing knowledge of human disease, for the benefit of patients.

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Positional Estimation Within a Latent Space Model for Networks

Methodology ◽

10.1027/1614-2241.2.1.24 ◽

2006 ◽

Vol 2 (1) ◽

pp. 24-33 ◽

Cited By ~ 17

Author(s):

Susan Shortreed ◽

Mark S. Handcock ◽

Peter Hoff

Keyword(s):

Random Effects ◽

Network Data ◽

Random Effects Models ◽

Space Model ◽

Network Analyses ◽

Recent Advances ◽

Latent Space ◽

Latent Space Model ◽

Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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