TY - JOUR
T1 - Multiclass cancer diagnosis using tumor gene expression signatures
AU - Ramaswamy, Sridhar
AU - Tamayo, Pablo
AU - Rifkin, Ryan
AU - Mukherjee, Sayan
AU - Yeang, Chen Hsiang
AU - Angelo, Michael
AU - Ladd, Christine
AU - Reich, Michael
AU - Latulippe, Eva
AU - Mesirov, Jill P.
AU - Poggio, Tomaso
AU - Gerald, William
AU - Loda, Massimo
AU - Lander, Eric S.
AU - Golub, Todd R.
PY - 2001/12/18
Y1 - 2001/12/18
N2 - 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.
AB - 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.
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U2 - 10.1073/pnas.211566398
DO - 10.1073/pnas.211566398
M3 - Article
C2 - 11742071
AN - SCOPUS:0347201147
SN - 0027-8424
VL - 98
SP - 15149
EP - 15154
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 26
ER -