Cancer Genomics

2021 ◽  
pp. 55-66
Author(s):  
Carsten Carlberg ◽  
Eunike Velleuer
Keyword(s):  
Cancer Genomics

scholarly journals Bayesian graphical models for modern biological applications

2021 ◽  
Author(s):  
Yang Ni ◽  
Veerabhadran Baladandayuthapani ◽  
Marina Vannucci ◽  
Francesco C. Stingo
Keyword(s):  
Graphical Models ◽  
Cancer Genomics ◽  
Graph Structure ◽  
Biological Processes ◽  
Limited Sample ◽  
Large Networks ◽  
Bayesian Approaches ◽  
Complex Sampling ◽  
Complex Dependence ◽  
Bayesian Graphical Models

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

scholarly journals The Role of Network Science in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1045
Author(s):  
Marta B. Lopes ◽  
Eduarda P. Martins ◽  
Susana Vinga ◽  
Bruno M. Costa
Keyword(s):  
Personalized Medicine ◽  
Drug Development ◽  
Information Flow ◽  
Clinical Studies ◽  
Network Science ◽  
Cancer Genomics ◽  
High Dimensional ◽  
Network Discovery ◽  
Software Implementations

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

scholarly journals A Bioinformatics Analysis Identifies the Telomerase Inhibitor MST-312 for Treating High-STMN1-Expressing Hepatocellular Carcinoma

2021 ◽  
Vol 11 (5) ◽  
pp. 332
Author(s):  
Szu-Jen Wang ◽  
Pei-Ming Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Kinase Inhibitors ◽  
Cancer Genomics ◽  
Gene Signature ◽  
Migration And Invasion ◽  
Advanced Hcc ◽  
Telomerase Inhibitor ◽  
Cell Cycle Related Gene ◽  
Hcc Cells

Hepatocellular carcinoma (HCC) is a relatively chemo-resistant tumor. Several multi-kinase inhibitors have been approved for treating advanced HCC. However, most HCC patients are highly refractory to these drugs. Therefore, the development of more effective therapies for advanced HCC patients is urgently needed. Stathmin 1 (STMN1) is an oncoprotein that destabilizes microtubules and promotes cancer cell migration and invasion. In this study, cancer genomics data mining identified STMN1 as a prognosis biomarker and a therapeutic target for HCC. Co-expressed gene analysis indicated that STMN1 expression was positively associated with cell-cycle-related gene expression. Chemical sensitivity profiling of HCC cell lines suggested that High-STMN1-expressing HCC cells were the most sensitive to MST-312 (a telomerase inhibitor). Drug–gene connectivity mapping supported that MST-312 reversed the STMN1-co-expressed gene signature (especially BUB1B, MCM2/5/6, and TTK genes). In vitro experiments validated that MST-312 inhibited HCC cell viability and related protein expression (STMN1, BUB1B, and MCM5). In addition, overexpression of STMN1 enhanced the anticancer activity of MST-312 in HCC cells. Therefore, MST-312 can be used for treating STMN1-high expression HCC.

scholarly journals Pediatric Oncologists’ Experiences Returning and Incorporating Genomic Sequencing Results into Cancer Care

2021 ◽  
Vol 11 (6) ◽  
pp. 570
Author(s):  
Rebecca L. Hsu ◽  
Amanda M. Gutierrez ◽  
Sophie K. Schellhammer ◽  
Jill O. Robinson ◽  
Sarah Scollon ◽  
...  
Keyword(s):  
Cancer Care ◽  
Cancer Genomics ◽  
Emotional Responses ◽  
Genetic Counselors ◽  
Genomic Sequencing ◽  
Uncertain Information ◽  
Time Points ◽  
Genomic Results

Pediatric oncologists’ perspectives around returning and incorporating tumor and germline genomic sequencing (GS) results into cancer care are not well-described. To inform optimization of cancer genomics communication, we assessed oncologists’ experiences with return of genomic results (ROR), including their preparation/readiness for ROR, collaboration with genetic counselors (GCs) during ROR, and perceived challenges. The BASIC3 study paired pediatric oncologists with GCs to return results to patients’ families. We thematically analyzed 24 interviews with 12 oncologists at two post-ROR time points. Oncologists found pre-ROR meetings with GCs and geneticists essential to interpreting patients’ reports and communicating results to families. Most oncologists took a collaborative ROR approach where they discussed tumor findings and GCs discussed germline findings. Oncologists perceived many roles for GCs during ROR, including answering families’ questions and describing information in lay language. Challenges identified included conveying uncertain information in accessible language, limits of oncologists’ genetics expertise, and navigating families’ emotional responses. Oncologists emphasized how GCs’ and geneticists’ support was essential to ROR, especially for germline findings. GS can be successfully integrated into cancer care, but to account for the GC shortage, alternative ROR models and access to genetics resources will be needed to better support families and avoid burdening oncologists.

Establishing Adrenocortical Carcinoma Specific Prognostic Genes Signature

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S148-S149
Author(s):  
A R Patil ◽  
D S Dabrowski ◽  
J Cotelingam ◽  
D Veillon ◽  
M Ong ◽  
...  
Keyword(s):  
Median Survival ◽  
Poor Prognosis ◽  
Cancer Genomics ◽  
Disease Free Survival ◽  
Genetic Alterations ◽  
Malignant Neoplasms ◽  
Free Survival ◽  
Signature Genes ◽  
Disease Free ◽  
Disease Specific

Abstract Introduction/Objective Adrenal Cortical Carcinoma (ACC) is a rare malignant neoplasms originating from adrenal cortical tissue with an annual incidence rate of 1 to 2 cases per million individuals. These tumors have poor prognosis with 5-year disease free survival being 30% after complete resection in Stage I to Stage III patients. Hence, there is a need for identifying prognostic markers for effective management of disease in these patients. Methods We analyzed the data in The Cancer Genome Atlas of 1141 ACC individuals, using cbioportal.org, a web- based platform for analysis of large-scale cancer genomics data sets, and derived correlation between prognosis and genetic alterations in approximately 51,309 genes. Results We identified 15 signature genes (NOTCH1, TP53, ZNRF3, LRP1, KIF5A, MDM2, LETMD1, MTOR, NOTCH3, RERE, SMARCC2, LDLR, HRNR, AVPR1A and PCDH15), alterations in which indicated a poor prognosis for ACC individuals. Analysis of 15 signature genes demonstrated that disease specific median survival for the patients with ACC, was reduced to 39.5 months (p value < 8 x 10 -9 and sensitivity of 93%) when any one or more of these genes was altered. Whereas, disease specific median survival was greater than 180 months (90% survival being 180 months) with no alteration in our signature genes. In addition, our analysis of our signature genes demonstrates reduced overall survival, disease free survival and progression free survival in individuals having alterations in our signature genes. Moreover, our set of 15 genes belonged mainly to MDM2-TP53, NOTCH and mTOR pathways, and small molecule modulators of these pathways are in process of development. Conclusion Our 15 gene signature was not only able to predict poor prognosis in ACC, but also has the potential to serve as a molecular marker set for initiation of NOTCH and mTOR specific targeted therapies in these patients.

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