Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients

Mallmann, Michael R.; Staratschek‐Jox, Andrea; Rudlowski, Christian; Braun, Michael; Gaarz, Andrea; Wolfgarten, Matthias; Kuhn, Walther; Schultze, Joachim L.

doi:10.1007/s13167-010-0044-z

Cited by 9 publications

(2 citation statements)

References 108 publications

(143 reference statements)

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“…6. The scientific literature in gene-expression profiling in breast cancer was reviewed [6]. This review describes the research to develop a range of biomarkers to accurately describe the various types and stages of breast cancer.…”

Section: The Connection Between the Two Concepts "First Domentioning

confidence: 99%

Cancer diagnostics and treatment: are we ready to implement PPPM?

Desiderio

2010

EPMA Journal

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The scientific community is always excited whenever a new journal appears in the open scientific literature. Such was the case when the first issue of the EPMA Journal of International Reviews in Predictive, Preventive and Personalized Medicine (PPPM) appeared in 2010.This present issue focuses on PPPM in Cancer, and it contains a spectrum of topics that covers the range from basic research, via clinical research, to potential implementation. Each review focuses on one or a reasonable combination of the three main topics embodied in the EPMA journal, and highlights the predictive, preventive, and personalized aspects of cancer treatment. It is clear from the scientific literature, in general, and in each one of the reviews in this issue, in particular, that the next quantum leap in clinical science now derives explicitly from one or more of those three features of medicine: accurate prediction, efficacious prevention, and detailed personalized treatment. The objective of this journal and of this issue on cancer is to present state-of-the-art results from researchers from around the world, and to provide timely reviews of cutting-edge basic research, clinical research, and clinical results. The reviews will range from the molecular level (DNA, RNA, proteins, metabolites), the cellular level, clinical research, and clinical practice.A brief overview follows of each topic that appears in this current issue of PPPM in Cancer. These reviews cover the following areas of research: the "First Do No Harm" concept; the shift from correlative to predictive oncology; selenium and oxidative stress in cancer; BRCA1 and BRCA2 gene mutations; a proposal for non-invasive proteomics in breast cancer; gene-expression profiling in breast cancer; proteomics and nitroproteomics of pituitary adenomas; epidemiological analysis of gastric adenocarcinomas; endoscopic diagnosis for the early detection of gastrointestinal cancer; fluoropyrimidine toxicity and polymorphisms of the dihydropyrimidine dehydrogenase gene; and circulating DNA and RNA.

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Section: The Connection Between the Two Concepts "First Domentioning

confidence: 99%

Cancer diagnostics and treatment: are we ready to implement PPPM?

Desiderio

2010

EPMA Journal

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“…In this paper, we set out to identify DC patterns that are associated with cancer overall survival time. Previous studies of cancer overall survival time mainly focused on one‐dimensional gene expression levels 18‐20 . Studying dynamic joint gene co‐expression patterns would offer new insights into the mechanisms of cancer survival and provide new strategies for treatment and prognosis prediction.…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide search algorithms for identifying dynamic gene co‐expression via Bayesian variable selection

Zhang,

Ma,

Wang

et al. 2023

Statistics in Medicine

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A wealth of gene expression data generated by high‐throughput techniques provides exciting opportunities for studying gene‐gene interactions systematically. Gene‐gene interactions in a biological system are tightly regulated and are often highly dynamic. The interactions can change flexibly under various internal cellular signals or external stimuli. Previous studies have developed statistical methods to examine these dynamic changes in gene‐gene interactions. However, due to the massive number of possible gene combinations that need to be considered in a typical genomic dataset, intensive computation is a common challenge for exploring gene‐gene interactions. On the other hand, oftentimes only a small proportion of gene combinations exhibit dynamic co‐expression changes. To solve this problem, we propose Bayesian variable selection approaches based on spike‐and‐slab priors. The proposed algorithms reduce the computational intensity by focusing on identifying subsets of promising gene combinations in the search space. We also adopt a Bayesian multiple hypothesis testing procedure to identify strong dynamic gene co‐expression changes. Simulation studies are performed to compare the proposed approaches with existing exhaustive search heuristics. We demonstrate the implementation of our proposed approach to study the association between gene co‐expression patterns and overall survival using the RNA‐sequencing dataset from The Cancer Genome Atlas breast cancer BRCA‐US project.

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Introduction

Berliner¹,

Lemke²

2015

An Information Technology Framework for Predictive, Preventive and Personalised Medicine

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Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients

Cited by 9 publications

References 108 publications

Cancer diagnostics and treatment: are we ready to implement PPPM?

Cancer diagnostics and treatment: are we ready to implement PPPM?

Genome‐wide search algorithms for identifying dynamic gene co‐expression via Bayesian variable selection

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