Support vector machines coupled with proteomics approaches for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer

Han, Mingyong; Dai, Jing; Zhang, Ying; Lin, Qi; Jiang, Man; Xu, Xin; Liu, Qi; Jia, Jihui

doi:10.3892/or.2012.2037

Cited by 18 publications

(12 citation statements)

References 28 publications

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Section: Systems Biology and Cisplatin Resistancementioning

confidence: 99%

“…262, 263 The latter approach allowed for the identification of α 1-antitripsin (a circulating protease inhibitor with anti-inflammatory functions) and S100A9 (a Ca 2+ -binding protein that exerts immunomodulatory effects) as possible biomarkers of clinical CDDP resistance. 262, 263 Moreover, CDDP resistance has been associated with immunopeptidomic alterations, that is, changes in the composition and abundance of peptides presented on the cell surface in complex with MHC molecules. 260 This is particularly important in view of the facts that (i) several anticancer regimens operate by eliciting or boosting anticancer immunity, some of them by altering the immunopeptidome of malignant cells 264, 265 and that (ii) CDDP is generally considered as a non-immunogenic anticancer agent.…”

Section: Systems Biology and Cisplatin Resistancementioning

confidence: 99%

See 1 more Smart Citation

Systems biology of cisplatin resistance: past, present and future

Vitale²,

et al. 2014

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The platinum derivative cis-diamminedichloroplatinum(II), best known as cisplatin, is currently employed for the clinical management of patients affected by testicular, ovarian, head and neck, colorectal, bladder and lung cancers. For a long time, the antineoplastic effects of cisplatin have been fully ascribed to its ability to generate unrepairable DNA lesions, hence inducing either a permanent proliferative arrest known as cellular senescence or the mitochondrial pathway of apoptosis. Accumulating evidence now suggests that the cytostatic and cytotoxic activity of cisplatin involves both a nuclear and a cytoplasmic component. Despite the unresolved issues regarding its mechanism of action, the administration of cisplatin is generally associated with high rates of clinical responses. However, in the vast majority of cases, malignant cells exposed to cisplatin activate a multipronged adaptive response that renders them less susceptible to the antiproliferative and cytotoxic effects of the drug, and eventually resume proliferation. Thus, a large fraction of cisplatin-treated patients is destined to experience therapeutic failure and tumor recurrence. Throughout the last four decades great efforts have been devoted to the characterization of the molecular mechanisms whereby neoplastic cells progressively lose their sensitivity to cisplatin. The advent of high-content and high-throughput screening technologies has accelerated the discovery of cell-intrinsic and cell-extrinsic pathways that may be targeted to prevent or reverse cisplatin resistance in cancer patients. Still, the multifactorial and redundant nature of this phenomenon poses a significant barrier against the identification of effective chemosensitization strategies. Here, we discuss recent systems biology studies aimed at deconvoluting the complex circuitries that underpin cisplatin resistance, and how their findings might drive the development of rational approaches to tackle this clinically relevant problem.

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Section: Systems Biology and Cisplatin Resistancementioning

confidence: 99%

Section: Systems Biology and Cisplatin Resistancementioning

confidence: 99%

Systems biology of cisplatin resistance: past, present and future

Vitale²,

et al. 2014

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“…An SVM feature selection based on profiling of urinary RNA metabolites was applied to predict breast cancer (36). SVMs coupled with proteomics approaches were applied for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer (37).…”

Section: Biomarker/signature Discoverymentioning

confidence: 99%

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

2018

CGP

687

347

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Abstract. Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that hasMachine learning (ML) "learns" a model from past data in order to predict future data (1). The key process is the learning which is one of the artificial intelligences. Many different statistical, probabilistic, and optimization techniques can be implemented as the learning methods such as the logistic regression, artificial neural networks (ANN), K-nearest neighbor (KNN), decision trees (DT) and Naive Bayes. There are two main types of ML learning -supervised learning and unsupervised learning. The supervised learning builds a model by learning from known classes (labeled training data). In contrast, unsupervised learning methods learn the common features from unknown class data (unlabeled training data).ML algorithms have been used for key feature training and recognition and for group classification. The strength of ML methods is it could detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to complex genomic data, especially in cancer studies. For example, ANN and DT have been used in cancer detection and diagnosis for nearly 20 years (2-3). The clinical implication of cancer heterogeneity and various cancer genomic data available motivate the applications of ML for cancer classification using genomic data.SVM learning is one of many ML methods. Compared to the other ML methods SVM is very powerful at recognizing subtle patterns in complex datasets (4). SVM can be used to recognize handwriting, recognize fraudulent credit cards, identify a speaker, as well as detect face (5). Cancer is a genetic disease where the genomic feature patterns or feature function patterns may represent the cancer subtypes, the outcome prognosis, drug benefit prediction, tumorigenesis drivers, or a tumor-specific biological process. Therefore, the Artificial Intelligence of SVM can help us in recognizing these patterns in a variety of applications. SVM ModelSVM is a powerful method for building a classifier. It aims to create a decision boundary between two classes that enables the prediction of labels from one or more feature vectors (6). This decision boundary, known as the hyperplane, is orientated in such a way that it is as far as 41

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“…If systems embedded with mathematical models are trained with large sample size, the system can also predict unknown samples. Among mathematical models, artificial neural network (NNET) [18] and support vector machines (SVM) [20] are frequently employed in biological discriminations [12]. NNET is a form of machine learning and non-linear statistical data modeling tool which processes data using a connectionism approach through an interconnected group of artificial neurons [18].…”

Section: In Contrast To Gene Expression Dna Methylation Consistentlymentioning

confidence: 99%

A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells

Wang

et al. 2013

PLoS ONE

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Induced pluripotent stem cells (iPSCs) derived from somatic cells (SCs) and embryonic stem cells (ESCs) provide promising resources for regenerative medicine and medical research, leading to a daily identification of new cell lines. However, an efficient system to discriminate the different types of cell lines is lacking. Here, we develop a quantitative system to discriminate the three cell types, iPSCs, ESCs, and SCs. The system consists of DNA-methylation biomarkers and mathematical models, including an artificial neural network and support vector machines. All biomarkers were unbiasedly selected by calculating an eigengene score derived from analysis of genome-wide DNA methylations. With 30 biomarkers, or even with as few as 3 top biomarkers, this system can discriminate SCs from pluripotent cells (PCs, including ESCs and iPSCs) with almost 100% accuracy. With approximately 100 biomarkers, the system can distinguish ESCs from iPSCs with an accuracy of 95%. This robust system performs precisely with raw data without normalization as well as with converted data in which the continuous methylation levels are accounted. Strikingly, this system can even accurately predict new samples generated from different microarray platforms and the next-generation sequencing. The subtypes of cells, such as female and male iPSCs and fetal and adult SCs, can also be discriminated with this method. Thus, this novel quantitative system works as an accurate framework for discriminating the three cell types, iPSCs, ESCs, and SCs. This strategy also supports the notion that DNA-methylation generally varies among the three cell types.

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Support vector machines coupled with proteomics approaches for detecting biomarkers predicting chemotherapy resistance in small cell lung cancer

Cited by 18 publications

References 28 publications

Systems biology of cisplatin resistance: past, present and future

Systems biology of cisplatin resistance: past, present and future

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells

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