Dataset Information

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

ABSTRACT: Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

SUBMITTER: Winter C

PROVIDER: S-EPMC3355064 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Winter Christof C Kristiansen Glen G Kersting Stephan S Roy Janine J Aust Daniela D Knösel Thomas T Rümmele Petra P Jahnke Beatrix B Hentrich Vera V Rückert Felix F Niedergethmann Marco M Weichert Wilko W Bahra Marcus M Schlitt Hans J HJ Settmacher Utz U Friess Helmut H Büchler Markus M Saeger Hans-Detlev HD Schroeder Michael M Pilarsky Christian C Grützmann Robert R

PLoS computational biology 20120517 5

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational app ...[more]

PMID: 22615549

Similar Datasets

Project description:Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.

Project description:BackgroundUnderstanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.ResultsIn this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant (R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works (Ammad-Ud-Din et al., J Chem Inf Model 54:2347-9, 2014), (Haider et al., PLoS ONE 10:0144490, 2015), (Menden et al., PLoS ONE 8:61318, 2013). However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0.ConclusionsOur approach is able to predict the drug effects on cancer cell lines with high accuracy, and its performance remains stable with less but high-quality data, and with fewer features for the cancer cell lines. tCNNS can also solve the problem of outliers in other feature space. Besides achieving high scores in these statistical metrics, tCNNS also provides some insights into the phenotypic screening. However, the performance of tCNNS drops in the blind test.

Project description:BackgroundProstate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa.MethodsLeveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments.ResultsWe identified a comprehensive set of 543 ECMGs and established a strong correlation between ECMGs and both the prognostic evaluation and immune classification in PCa. Notably, ECMGPS exhibited robust predictive capability, surpassing traditional clinical features and 80 published signatures in terms of both independence and accuracy across five cohorts. Significantly, ECMGPS demonstrated significant promise in identifying potential PCa patients who might benefit from immunotherapy and personalized medicine, thereby moving us nearer to tailored therapeutic approaches for individuals. Moreover, the role of TMED3 in promoting malignant proliferation of PCa cells was validated.ConclusionsOur findings highlight ECMGPS as a powerful tool for improving PCa patient outcomes and supply a robust conceptual framework for in-depth examination of PCa complexities. Simultaneously, our study has the potential to develop a novel alternative for PCa diagnosis and prognostication.

Dataset Information

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Publications

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets