Multiclass cancer diagnosis using tumor gene expression signatures

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.

doi:10.1073/pnas.211566398

Title: Multiclass cancer diagnosis using tumor gene expression signatures

Journal Article · 10 December 2001 · Proceedings of the National Academy of Sciences of the United States of America

DOI: https://doi.org/10.1073/pnas.211566398 · OSTI ID:1188200

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.

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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Research Organization:: Whitehead Institute for Biomedical Research, Cambridge, MA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FG02-01ER63185

OSTI ID:: 1188200

Journal Information:: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 26 Vol. 98; ISSN 0027-8424

Publisher:: National Academy of Sciences, Washington, DC (United States)Copyright Statement

Country of Publication:: United States

Language:: English

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Additional file 3 of Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study Tan, Xueyun; Li, Yuan; Wang, Sufei figshare https://doi.org/10.6084/m9.figshare.19915420.v1	image	January 2022
Additional file 4 of Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study Tan, Xueyun; Li, Yuan; Wang, Sufei figshare https://doi.org/10.6084/m9.figshare.19915426.v1	image	January 2022
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