Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
Abstract
Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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Gene expression profiling has already been used to identify genes that could serve as molecular markers of cancer classification and outcome prediction.
“In fact, this approach has already been used to identify genes that could serve as molecular markers of cancer classification and outcome prediction (10-14).”
From Prediction of the response to chemotherapy in advanced esophageal cancer by gene expression profiling of biopsy samples (2010) · cites this paper as [10]
The model was evaluated by leave-one-out cross-validation.
“This model was evaluated by leave-one-out cross-validation, whereby one sample was withheld, a gene expression based model was trained using the remaining samples, and then the model was used to predict the class of the withheld sample (10).”
From Prediction of the response to chemotherapy in advanced esophageal cancer by gene expression profiling of biopsy samples (2010) · cites this paper as [10]
HoxA9 is overexpressed in 70% of AML cases, and overexpression of this transcription factor alone is sufficient to immortalize murine bone marrow derived myeloid cells.
“The transcription factor HoxA9 is overexpressed in 70% of AML cases, and overexpression of this transcription factor alone is sufficient to immortalize murine bone marrow derived myeloid cells (Ayton and Cleary, 2003; Golub et al., 1999; Kroon et al., 1998).”
From Targeting Metabolism for Cancer Therapy (2017) · cites this paper as Golub et al., 1999
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- Authors
- Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., & Lander, E. S.
- Affiliations
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- St. Jude Children's Research Hospital (US)
- The Ohio State University (US)
- Corresponding author
- Golub, T. R.
- Slonim, D. K.
- Lander, E. S.
- ORCID
- Venue
- Science
- Published
- 1999-10-15
- Volume
- 286
- Issue
- 5439
- Pages
- 531-537
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2109363337
- Cited by
- 11,516
- References
- 51
- Citations by year
- 2026: 81 · 2025: 152 · 2024: 150 · 2023: 247 · 2022: 216 · 2021: 262 · 2020: 275 · 2019: 329 · 2018: 309 · 2017: 322 · 2016: 332 · 2015: 398 · 2014: 451 · 2013: 468 · 2012: 522
- FWCI
- 108.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Acute Lymphoblastic Leukemia research
- Keywords
- cancer classification, molecular classification, gene expression profiling, DNA microarrays, class discovery, class prediction, acute leukemia, acute myeloid leukemia, acute lymphoblastic leukemia, leukemia subtypes
- MeSH terms
- Acute Disease, Antineoplastic Combined Chemotherapy Protocols, Cell Adhesion, Cell Cycle, Humans, Leukemia, Myeloid, Neoplasm Proteins, Neoplasms, Oncogenes, Predictive Value of Tests, Reproducibility of Results, Treatment Outcome, Homeodomain Proteins, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Precursor Cell Lymphoblastic Leukemia-Lymphoma
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