Deregulation of Chromosome Segregation and Cancer

Curtis, Natalie L.; Ruda, G.F.; Brennan, P.E.; Bolaños-García, Víctor M.

doi:10.1146/annurev-cancerbio-030419-033541

Cited by 17 publications

(7 citation statements)

References 161 publications

(149 reference statements)

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“…We found a signature composed of genes important for mitotic spindle assembly (HALLMARK_MITOTIC_SPINDLE) to be statistically significant and robust to sample exclusion for early-stage lung tumors with BEANIE, consistent with prior studies. 18 Another signature comprised of genes encoding proteins involved in glycolysis and gluconeogenesis (HALLMARK_GLYCOLYSIS) was also found to be statistically significant and robust to sample exclusion for the early-stage tumors with BEANIE, which is in agreement with a prior study 19 describing an association between TKI treatment and its effect on decreased activity of glycolysis. Furthermore, the top constituent genes for both of these signatures were consistently upregulated across all samples (Fig.…”

Section: Group Biology Analysis Of Distinct Clinical States In Non-small Cell Lung Carcinomasupporting

confidence: 86%

Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics

Johri

Titchen

et al. 2021

Preprint

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Given the growing number of clinically integrated cancer single-cell transcriptomic studies, robust differential enrichment methods for gene signatures to dissect tumor cellular states for discovery and translation are critical. Current analysis strategies neither adequately represent the hierarchical structure of clinical single-cell transcriptomic datasets nor account for the variability in the number of recovered cells per sample, leading to results potentially confounded by sample-driven biology instead of accurately representing true differential enrichment of group-level biology (e.g., treatment responders vs. non-responders) and a high number of false positives. This problem is even more severe for single-cell analyses of the tumor compartment due to high intra-patient similarity (as opposed to inter-patient similarity), leading to stricter hierarchical structured data for this compartment. Furthermore, to identify signatures which are truly representative of the entire group, there is a need to quantify the robustness of otherwise statistically significant signatures to sample exclusion. Here, we present a new nonparametric statistical method, BEANIE, to account for these issues, and demonstrate its utility in two cancer cohorts stratified by clinical groups to reduce biological hypotheses and guide translational investigations. Using BEANIE, we show how the consideration of sample-specific versus group biology helps decrease the false positive rate by more than 10 times and identify robust signatures which can also be corroborated across different cell type compartments.

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Section: Group Biology Analysis Of Distinct Clinical States In Non-small Cell Lung Carcinomasupporting

confidence: 86%

Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics

Johri

Titchen

et al. 2021

Preprint

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“…Mistakes in the cell division process can lead to rearrangement, loss or gain of chromosomes, genome instability and cancers. 30 Therefore, the molecular function of BUB1 and BUB1B genes in HD cancers should be further examined. Several experimental and bioinformatic studies have shown the importance of EXO1 in replication, DNA repair pathways, cell cycle checkpoints, and its association to cancer.…”

Section: Discussionmentioning

confidence: 99%

“…The SAC plays a fundamental role in maintaining genome stability by ensuring the timely segregation of the genetic material at every cell division. Mistakes in the cell division process can lead to rearrangement, loss or gain of chromosomes, genome instability and cancers 30 . Therefore, the molecular function of BUB1 and BUB1B genes in HD cancers should be further examined.…”

Section: Discussionmentioning

confidence: 99%

A supervised machine learning approach identifies gene‐regulating factor‐mediated competing endogenous RNA networks in hormone‐dependent cancers

Jayarathna

Rentería

Batra

et al. 2022

J of Cellular Biochemistry

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Competing endogenous RNAs (ceRNAs) have become an emerging topic in cancer research due to their role in gene regulatory networks. To date, traditional ceRNA bioinformatic studies have investigated microRNAs as the only factor regulating gene expression. Growing evidence suggests that genomic (e.g., copy number alteration [CNA]), transcriptomic (e.g., transcription factors [TFs]), and epigenomic (e.g., DNA methylation [DM]) factors can influence ceRNA regulatory networks. Herein, we used the Least absolute shrinkage and selection operator regression, a machine learning approach, to integrate DM, CNA, and TFs data with RNA expression to infer ceRNA networks in cancer risk. The gene‐regulating factors‐mediated ceRNA networks were identified in four hormone‐dependent (HD) cancer types: prostate, breast, colorectal, and endometrial. The shared ceRNAs across HD cancer types were further investigated using survival analysis, functional enrichment analysis, and protein–protein interaction network analysis. We found two (BUB1 and EXO1) and one (RRM2) survival‐significant ceRNA(s) shared across breast‐colorectal‐endometrial and prostate–colorectal–endometrial combinations, respectively. Both BUB1 and BUB1B genes were identified as shared ceRNAs across more than two HD cancers of interest. These genes play a critical role in cell division, spindle‐assembly checkpoint signalling, and correct chromosome alignment. Furthermore, shared ceRNAs across multiple HD cancers have been involved in essential cancer pathways such as cell cycle, p53 signalling, and chromosome segregation. Identifying ceRNAs' roles across multiple related cancers will improve our understanding of their shared disease biology. Moreover, it contributes to the knowledge of RNA‐mediated cancer pathogenesis.

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“…In this set of experiments, we define P = {x|x 7 = 1} \ U , where \ denote the set difference operator. In fact, x 7 = 1 denotes the network states with expressed Cdc20, whose overexpression has been reported in breast, lung, gastric, and pancreatic cancers [21]. Therefore, our second objective for deriving the IBR structural intervention is…”

Section: Quantifying the Multi-objective Cost Of Intervention In Uncertain Gene Regulatory Networkmentioning

confidence: 99%

Quantifying the Multi-Objective Cost of Uncertainty

2021

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Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.INDEX TERMS mean objective cost of uncertainty (MOCU), mean multi-objective cost of uncertainty (multi-objective MOCU), objective uncertainty quantification (objective-UQ), optimal experimental design (OED).

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Deregulation of Chromosome Segregation and Cancer

Cited by 17 publications

References 161 publications

Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics

Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics

A supervised machine learning approach identifies gene‐regulating factor‐mediated competing endogenous RNA networks in hormone‐dependent cancers

Quantifying the Multi-Objective Cost of Uncertainty

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