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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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“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]
In the citing paper
“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]
In the citing paper
“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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- Each gene is represented by an expression vector v ( g ) = ( e 1 e 2 … e n ) where e i denotes the expression level of gene g in i th sample in the initial set S of samples. A class distinction is represented by an idealized expression pattern c = ( c 1 c 2 … c n ) where c i = +1 or 0 according to whether the i -th sample belongs to class 1 or class 2. One can measure “correlation” between a gene and a class distinction in a variety of ways. One can use the Pearson correlation coefficient or the Euclidean distance. We used a measure of correlation P ( g c ) that emphasizes the “signal-to-noise” ratio in using the gene as a predictor. Let [μ 1 ( g ) σ 1 ( g )] and [μ 2 ( g ) σ 2 ( g )] denote the means and SDs of the log of the expression levels of gene g for the samples in class 1 and class 2 respectively. Let P ( g c ) = [μ 1 ( g ) − μ 2 ( g )]/[σ 1 ( g ) + σ 2 ( g )] which reflects the difference between the classes relative to the SD within the classes. Large values of | P ( g c )| indicate a strong correlation between the gene expression and the class distinction while the sign of P ( g c ) being positive or negative corresponds to g being more highly expressed in class 1 or class 2. Unlike a standard Pearson correlation coefficient P ( g c ) is not confined to the range [–1 +1]. Neighborhoods N 1 ( c r ) and N 2 ( c r ) of radius r around class 1 and class 2 were defined to be the sets of genes such that P ( g c ) = r and P ( g c ) = − r respectively. An unusually large number of genes within the neighborhoods indicates that many genes have expression patterns closely correlated with the class vector.
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Metadata
- 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 › Hematology › 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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