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CaArray_golub-00236: Multiclass cancer diagnosis using tumor gene expression signatures

ABSTRACT: The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

ORGANISM(S): Homo sapiens

PROVIDER: GSE68928 | GEO | 2015/05/16

SECONDARY ACCESSION(S): PRJNA284131

REPOSITORIES: GEO

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caArray_golub-00236: Multiclass cancer diagnosis using tumor gene expression signatures

Project description:The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics. golub-00236 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu35KsubA, Hu6800 Organism: Homo sapiens (ncbitax) Material Types: synthetic_RNA, organism_part, whole_organism, total_RNA Disease States: Colorectal Adenocarcinoma, Melanoma, Normal, pancreatic adenocarcinoma, Acute Myeloid Leukemia, Breast Adenocarcinoma, T-cell ALL, Lung Adenocarcinoma, bladder transitional cell carcinoma, Ovarian Adenocarcinoma, Mesothelioma, B-cell ALL, prostate adenocarcinoma, Medulloblastoma, Follicular Lymphoma, renal cell carcinoma, uterine adenocarcinoma, large B-cell lymphoma, Glioblastoma, bladder transitonal cell carcinoma

2015-05-16 | E-GEOD-68928 | biostudies-arrayexpress

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caArray_golub-00299: Chemosensitivity prediction by transcriptional profiling

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Development and validation of a NanoString BASE47 bladder cancer gene classifier

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2015-05-15 | GSE68895 | GEO

caArray_golub-00299: Chemosensitivity prediction by transcriptional profiling

Project description:In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells' tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P < 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible. golub-00299 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu6800 Organism: Homo sapiens (ncbitax) Tissue Sites: Kidney, Brain, Blood, Lung, Skin, Colon, Breast, Prostate, Ovary Material Types: cell, synthetic_RNA, whole_organism, total_RNA Disease States: Carcinoma, Glioblastoma, Lymphoma, Adenocarcinoma, Melanoma, Amelanotic Melanoma, Large Cell Carcinoma, Precursor Lymphoblastic Leukemia, Carcinosarcoma, Myeloid Leukemia, Plasma Cell Myeloma, Bronchogenic Carcinoma, Chronic Myelogenous Leukemia, Squamous Cell Carcinoma

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