Identification of high-quality cancer prognostic markers and metastasis network modules (original) (raw)
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Corrigendum: Identification of high-quality cancer prognostic markers and metastasis network modules
Nature Communications, 2012
Cancer patients are often overtreated because of a failure to identify low-risk cancer patients. Thus far, no algorithm has been able to successfully generate cancer prognostic gene signatures with high accuracy and robustness in order to identify these patients. In this paper, we developed an algorithm that identifi es prognostic markers using tumour gene microarrays focusing on metastasis-driving gene expression signals. Application of the algorithm to breast cancer samples identifi ed prognostic gene signature sets for both estrogen receptor (ER) negative ( − ) and positive ( + ) subtypes. A combinatorial use of the signatures allowed the stratifi cation of patients into low-, intermediate-and high-risk groups in both the training set and in eight independent testing sets containing 1,375 samples. The predictive accuracy for the low-risk group reached 87 -100 % . Integrative network analysis identifi ed modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes. These modules are recurrent in many breast tumours and contribute to metastasis. P c-2-random > = P c-2-NRC1 and P c-3-random > = P c-3-NRC1 , by performing 5,000 randomization tests and calculating the P -v a l u e .
Molecular cancer therapeutics, 2004
The prognostic and treatment-predictive markers currently in use for breast cancer are commonly based on the protein levels of individual genes (e.g., steroid receptors) or aspects of the tumor phenotype, such as histological grade and percentage of cells in the DNA synthesis phase of the cell cycle. Microarrays have previously been used to classify binary classes in breast cancer such as estrogen receptor (ER)-alpha status. To test whether the properties and specific values of conventional prognostic markers are encoded within tumor gene expression profiles, we have analyzed 48 well-characterized primary tumors from lymph node-negative breast cancer patients using 6728-element cDNA microarrays. In the present study, we used artificial neural networks trained with tumor gene expression data to predict the ER protein values on a continuous scale. Furthermore, we determined a gene expression profile-directed threshold for ER protein level to redefine the cutoff between ER-positive and...
BMC Bioinformatics, 2010
Background: Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process.
Prognostic gene network modules in breast cancer hold promise
Breast Cancer Research, 2010
An outstanding problem in the clinical management of breast cancer is overtreatment. It is estimated that approxi mately 55 to 75% of breast cancer patients who receive adjuvant chemotherapy would do equally well without it [1], but identifying this low-risk population with a high enough predictive value (≥90%) is not possible using standard prognostic factors such as lymph node status or tumour size. Several recently developed gene expression classifi ers have shown promise of achieving the required predictive values.
Breast cancer prognosis risk estimation using integrated gene expression and clinical data
BioMed research international, 2014
Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group. We propose a novel algorithm called the IPRE (integrated prognosis risk estimation) algorithm. We used integrated microarray datasets from multiple studies to increase the sample sizes (∼ 2,700 samples). The IPRE algorithm consists of a virtual chromosome for the extraction of the prognostic gene signature that has 79 genes, and a mu...
A program to identify prognostic and predictive gene signatures
BMC research notes, 2014
The advent of high-throughput technologies to profile human tumors has generated unprecedented insight into our molecular understanding of cancer. However, analysis of such high dimensional data is challenging and requires significant expertise which is not routinely available to many cancer researchers. To overcome this limitation, we developed a freely accessible and user friendly Program to Identify Molecular Signatures (PIMS). Importantly, such signatures allow important insight into cancer biology, as well as provide clinical tools to identify potential biomarkers that might provide means to accurately stratify patients into different risk or treatment groups. We evaluated the performance of PIMS by identifying and testing predictive and prognostic gene signatures for breast cancer, using multiple breast tumor microarray cohorts representing hundreds of patients. Importantly, PIMS identified signatures classified patients into high and low risk groups with at least similar perf...
Identifying genomic signatures for predicting breast cancer outcomes
Identifying Genomic Signatures for predicting Breast Cancer outcomes Shruti Rathnagiriswaran Predicting the risk for recurrence in breast cancer patients is a critical task in clinics. Recent developments in DNA microarrays have fostered tremendous advances in molecular diagnosis and prognosis of breast cancer.
IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples
2014
Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with co...