Computational prognostic indicators for breast cancer

Yang, Xinan; Ai, Xindi; Cunningham, John M.

doi:10.2147/cmar.s46483

Cited by 10 publications

(12 citation statements)

References 73 publications

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“…An obvious weakness is that a microarray represents a single snapshot of the patient. [15] But there are a large number of elements leading to disturbed gene function [16], such as large and small deletions or single base substitutions, mutations that affect promoter regions or splice-sites, as well as epigenetic silencing. Those factors may influence the result but may go undetected as well, depending on the exact type of lesion as well as its location with respect to the area hybridizing with the probe.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics in Breast Cancer Research

Yigitoglu¹,

Uctepe²,

Yiğitoğlu³

et al. 2015

A Concise Review of Molecular Pathology of Breast Cancer

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Section: Introductionmentioning

confidence: 99%

Bioinformatics in Breast Cancer Research

Yigitoglu¹,

Uctepe²,

Yiğitoğlu³

et al. 2015

A Concise Review of Molecular Pathology of Breast Cancer

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“…Treatment of NB cell lines with Ronicinib, led to downregulation of c-Myc, cell cycle arrest, and eventually cell apoptosis. Similar analyses by the same group in breast cancer models identified markers of metastasis as well as patient prognostic indicators (Yang, et al, 2014, Yang, et al, 2013). While this approach does not identify distinct markers of NB CSCs, per say, it can help us better understand the gene networks associated with aggressive cell subpopulations and identify potential CSC targets for the treatment of high-risk disease.…”

Section: Advancing Technology For Identification and Characterizatmentioning

confidence: 59%

Neuroblastoma pathogenesis: deregulation of embryonic neural crest development

2017

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Neuroblastoma (NB) is an aggressive pediatric cancer that originates from neural crest tissues of the sympathetic nervous system. NB is highly heterogeneous both from a clinical and molecular perspective. Clinically, this cancer represents a wide range of phenotypes ranging from spontaneous regression of 4S disease to unremitting treatment-refractory progression and death of high risk metastatic disease. At a cellular level, the heterogeneous behavior of NB likely arises from an arrest and deregulation of normal neural crest development. In the present review, we summarize our current knowledge of neural crest development as it relates to pathways promoting ‘stemness’ and how deregulation may contribute to the development of tumor initiating CSCs. There is an emerging consensus that such tumor subpopulations contribute to the evolution of drug resistance, metastasis, and relapse in other equally aggressive malignancies. As relapsed, refractory disease remains the primary cause of death for neuroblastoma, the identification and targeting of CSCs or other primary drivers of tumor progression remains a critical, clinically significant goal for neuroblastoma. We will critically review recent and past evidence in the literature supporting the concept of CSCs as drivers of neuroblastoma pathogenesis.

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“…Yang et al [19] highlight some of the challenges associated with developing prognostic indicators for breast cancer. Integration of information such as gene deletions, translocations, and locus amplification; biomarkers from high-throughput -omics technologies such as genomics, proteomics, and metabolomics; and long recognized outcome variables such as tumor size, histologic grade, axillary nodal status, and estrogen receptor (ER) status [19] can be used to provide a more tailored therapy. One of the key challenges is the integration of -omics data to provide a robust and effective solution.…”

Section: Challenges In Biomarker Developmentmentioning

confidence: 99%

Biomarker discovery, high performance and cloud computing: A comprehensive review

Blayney

Haberland

Lightbody

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Abstract-The analysis of biological markers (biomarkers) have the ability to improve clinical outcomes of a disease through prediction and early detection. The constant improvement of next generation sequencing (NGS) technologies coupled with falling equipment price are driving research and application especially in the medical domain. NGS technology is being applied to cancer research promising greater understanding of carcinogenesis. However, as sequence capacity grows, algorithmic speed is becoming an important bottleneck. To understand these challenges we present a review on difficulties currently faced in discovering biomarkers. The review places the spotlight on sequencing technologies and the bottlenecks encountered in these pipelines. Cloud computing and high performance computing technologies such as Grid are summarized in tackling computational challenges presented by technologies and algorithms used in biomarker research.

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Computational prognostic indicators for breast cancer

Cited by 10 publications

References 73 publications

Bioinformatics in Breast Cancer Research

Bioinformatics in Breast Cancer Research

Neuroblastoma pathogenesis: deregulation of embryonic neural crest development

Biomarker discovery, high performance and cloud computing: A comprehensive review

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