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* Whitehead Institute/Massachusetts Institute of Technology Center
for Genome Research, Cambridge, MA 02138; Departments of
Contributed by Eric S. Lander, October 23, 2001
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.
Medical Sciences
Multiclass cancer diagnosis using tumor gene
expression signatures
,
,,,,
,,
Adult and Pediatric Oncology,
Dana-Farber Cancer Institute/Harvard Medical School, Boston, MA
02115; Department of Pathology, Brigham and Women's
Hospital, Boston, MA 02115; ¶ Department of Pathology,
Memorial Sloan-Kettering Cancer Center, New York, NY 10021; and
Departments of ** Biology, McGovern Institute, Center
for Brain and Computational Learning, and § Artificial
Intelligence Laboratory, Massachusetts Institute of Technology,
Cambridge, MA 02139
To whom reprint requests should be addressed at:
Dana-Farber Cancer Institute, 44 Binney Street, Dana 640, Boston, MA
02115. E-mail: golub{at}genome.wi.mit.edu.
www.pnas.org/cgi/doi/10.1073/pnas.211566398
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