Dataset Information

Individualized Survival and Treatment Response Predictions in Breast Cancer Patients: Involvements of Phospho-EGFR and Phospho-Her2/neu Proteins.

ABSTRACT: Our robust prediction system for individual breast cancer patients combines three well-known machine-learning classifiers to provide stable and accurate clinical outcome prediction (N=269). The average performance of the selected classifiers is used as the evaluation criterion in breast cancer outcome predictions. A profile (incorporating histology, lymph node status, tumor grade, tumor stage, ER, PR, Her2/neu, patient's age and smoking status) generated over 95% accuracy in individualized disease-free survival and treatment response predictions. Furthermore, our analysis demonstrated that the measurement of phospho-EGFR and phospho-Her2/neu is more powerful in breast cancer survival prediction than that of total EGFR and total Her2/neu (p < 0.05). The incorporation of hormone receptor status, Her2/neu, patient's age and smoking status into the traditional pathologic markers creates a powerful standard to perform individualized survival and treatment outcome predictions for breast cancer patients.

SUBMITTER: Guo L

PROVIDER: S-EPMC4282131 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Individualized Survival and Treatment Response Predictions in Breast Cancer Patients: Involvements of Phospho-EGFR and Phospho-Her2/neu Proteins.

Guo Lan L Abraham Jame J Flynn Daniel C DC Castranova Vincent V Shi Xianglin X Qian Yong Y

The open clinical cancer journal 20080501

Our robust prediction system for individual breast cancer patients combines three well-known machine-learning classifiers to provide stable and accurate clinical outcome prediction (<i>N</i>=269). The average performance of the selected classifiers is used as the evaluation criterion in breast cancer outcome predictions. A profile (incorporating histology, lymph node status, tumor grade, tumor stage, ER, PR, Her2/neu, patient's age and smoking status) generated over 95% accuracy in individualize ...[more]

PMID: 25558292

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Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate â¤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics. Affymetrix GeneChip Mouse Genome 430 2.0 microarrays were used to profile whole tissues from 5 different tissue types of 25 tumor-bearing and 25 control mice of the Her2/Neu breast cancer mouse model. The 5 tissues tested were from breast, liver, spleen, blood cell, and thymus. The tumor-bearing mice were bitransgenic for MMTV-rtTA/TetO-NeuNT, and the control mice were transgenic for TetO-NeuNT only. The control mice were age- and cage-matched to the tumor-bearing mice. All samples were lysed and total RNA isolated and amplified prior to hybridization.

Dataset Information

Individualized Survival and Treatment Response Predictions in Breast Cancer Patients: Involvements of Phospho-EGFR and Phospho-Her2/neu Proteins.

Publications

Individualized Survival and Treatment Response Predictions in Breast Cancer Patients: Involvements of Phospho-EGFR and Phospho-Her2/neu Proteins.

Similar Datasets

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