CoChat
Sign Up
Cited by 11,516Gene expression and cancer classificationBreast Cancer Treatment StudiesAcute Lymphoblastic Leukemia research

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

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.·1999·Science
DOI 10.1126/science.286.5439.531
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1126/science.286.5439.531

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 14

  1. Triche T. J., et al., Prog. Clin. Biol. Res. 271, 475 (1988).
  2. Shurtleff S. A., et al., Leukemia 9, 1985 (1995);
  3. Rowley J. D., Ann. Genet. 16, 109 (1973) .
  4. 10.1038/ng1296-457 · DOI
  5. Khan J., et al., Cancer Res. 58, 5009 (1998);
  6. 10.1038/ng1296-457 · DOI
  7. Yang G. P., Ross D. T., Kuang W. W., Brown P. O., Weigel R. J., Nucleic Acids Res. 27, 1517 (1999); · DOI
  8. Khan J., et al., Cancer Res. 58, 5009 (1998);
  9. 10.1038/nbt1297-1359 · DOI
  10. 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.
  11. Ross W., Rowe T., Glisson B., Yalowich J., Liu L., Cancer Res. 44, 5857 (1984).
  12. 10.1073/pnas.96.6.2907 · DOI
  13. Turc-Carel S., et al., Cancer Genet. Cytogenet. 19, 361 (1986); · DOI
  14. Douglass E. C. et al. 45 148 (1987). · DOI

Cited by

Sorted by citations

See all 11,516

Related

See all

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
Publisher
American Association for the Advancement of Science (AAAS)
Published
1999-10-15
Volume
286
Issue
5439
Pages
531-537
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.286.5439.531
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review