Cancer Program Publication

Multi-Class Cancer Diagnosis Using Tumor Gene Expression Signatures
ProjectBioinformatics & Computational Biology
Abstract 
The optimal treatment of cancer patients depends on establishing accurate diagnoses using a complex combination of clinical and histopathologic data. In some instances this is difficult or impossible due to 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 multi-class 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 to their well-differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multi-class molecular cancer classification, and suggest a strategy for future clinical implementation of molecular cancer diagnostics.
AuthorsSridhar Ramaswamy, Pablo Tamayo, Ryan Rifkin, Sayan Mukherjee, Chen-Hsiang Yeang, Michael Angelo, Christine Ladd, Michael Reich, Eva Latulippe , Jill P. Mesirov, Tomaso Poggio, William Gerald , Massimo Loda, Eric S. Lander, Todd R. Golub
Publication Date12/18/2001
Contact emails sridhar@genome.wi.mit.edu
Publication URLhttp://www.pnas.org/cgi/content/abstract/98/26/15149
CitationPNAS 98: 15149-15154
KeywordsMicroarray; Computational Biology; Cancer Classification; Diagnosis; Genomics
 
Supplemental Information
URLs
NameURL
CEL files (1/11, 109MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_1_of_11.tar.gz
CEL files (2/11, 105MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_2_of_11.tar.gz
CEL files (3/11, 105MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_3_of_11.tar.gz
CEL files (4/11, 107MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_4_of_11.tar.gz
CEL files (5/11, 108MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_5_of_11.tar.gz
CEL files (6/11, 110MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_6_of_11.tar.gz
CEL files (7/11, 108MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_7_of_11.tar.gz
CEL files (8/11, 104MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_8_of_11.tar.gz
CEL files (9/11, 104MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_9_of_11.tar.gz
CEL files (10/11, 107MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_10_of_11.tar.gz
CEL files (11/11, 89MB)ftp://ftp.broad.mit.edu/pub/gcm_files/gcm_11_of_11.tar.gz
Files
DescriptionFile
Manuscript (PDF)GCM.pdf
Supplementary Information (PDF)PNAS_Supplementary_Information.pdf
SAMPLES (XLS)SAMPLES.xls
GCM_Training.resGCM_Training.res
GCM_Training.clsGCM_Training.cls
GCM_Test.resGCM_Test.res
GCM_Test.clsGCM_Test.cls
GCM_PD.resGCM_PD.res
GCM_PD.clsGCM_PD.cls
GCM_Total.resGCM_Total.res
GCM_Total.clsGCM_Total.cls
OVA MARKERS (XLS)OVA_MARKERS.xls
TUMOR NORMAL MARKERS (XLS)TUMOR_NORMAL_MARKERS.xls