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Title: 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.

Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Whitehead Institute for Biomedical Research, Cambridge, MA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1188200
Grant/Contract Number:  
FG02-01ER63185
Resource Type:
Accepted Manuscript
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 98; Journal Issue: 26; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences, Washington, DC (United States)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C. -H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J. P., Poggio, T., Gerald, W., Loda, M., Lander, E. S., and Golub, T. R. Multiclass cancer diagnosis using tumor gene expression signatures. United States: N. p., 2001. Web. doi:10.1073/pnas.211566398.
Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C. -H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J. P., Poggio, T., Gerald, W., Loda, M., Lander, E. S., & Golub, T. R. Multiclass cancer diagnosis using tumor gene expression signatures. United States. doi:10.1073/pnas.211566398.
Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C. -H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J. P., Poggio, T., Gerald, W., Loda, M., Lander, E. S., and Golub, T. R. Tue . "Multiclass cancer diagnosis using tumor gene expression signatures". United States. doi:10.1073/pnas.211566398. https://www.osti.gov/servlets/purl/1188200.
@article{osti_1188200,
title = {Multiclass cancer diagnosis using tumor gene expression signatures},
author = {Ramaswamy, S. and Tamayo, P. and Rifkin, R. and Mukherjee, S. and Yeang, C. -H. and Angelo, M. and Ladd, C. and Reich, M. and Latulippe, E. and Mesirov, J. P. and Poggio, T. and Gerald, W. and Loda, M. and Lander, E. S. and Golub, T. R.},
abstractNote = {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.},
doi = {10.1073/pnas.211566398},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 26,
volume = 98,
place = {United States},
year = {2001},
month = {12}
}

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