The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Clinical Utility of Microarray-Based Gene Expression Profiling in the Diagnosis and Subclassification of Leukemia: Report From the International Microarray Innovations in Leukemia Study Group
Open accessAbstract
Purpose: The Microarray Innovations in Leukemia study assessed the clinical utility of gene expression profiling as a single test to subtype leukemias into conventional categories of myeloid and lymphoid malignancies. Methods: The investigation was performed in 11 laboratories across three continents and included 3,334 patients. An exploratory retrospective stage I study was designed for biomarker discovery and generated whole-genome expression profiles from 2,143 patients with leukemias and myelodysplastic syndromes. The gene expression profiling–based diagnostic accuracy was further validated in a prospective second study stage of an independent cohort of 1,191 patients. Results: On the basis of 2,096 samples, the stage I study achieved 92.2% classification accuracy for all 18 distinct classes investigated (median specificity of 99.7%). In a second cohort of 1,152 prospectively collected patients, a classification scheme reached 95.6% median sensitivity and 99.8% median specificity for 14 standard subtypes of acute leukemia (eight acute lymphoblastic leukemia and six acute myeloid leukemia classes, n = 693). In 29 (57%) of 51 discrepant cases, the microarray results had outperformed routine diagnostic methods. Conclusion: Gene expression profiling is a robust technology for the diagnosis of hematologic malignancies with high accuracy. It may complement current diagnostic algorithms and could offer a reliable platform for patients who lack access to today's state-of-the-art diagnostic work-up. Our comprehensive gene expression data set will be submitted to the public domain to foster research focusing on the molecular understanding of leukemias.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues
Swerdlow, S. H. (2017) · Medical Entomology and Zoology · Cited by 12,933
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., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
WHO Classification: Tumours of the Haematopoietic and Lymphoid Tissues (2008)
(2015) · Postgraduate Haematology · doi:10.1002/9781118853771.ch51 · Cited by 3,290
Yeoh, E.-J., Ross, M. E., Shurtleff, S. A., Williams, W. K., Patel, D., Mahfouz, R., Behm, F. G., Raimondi, S. C., Relling, M. V., Patel, A., et al. (2002) · Cancer Cell · doi:10.1016/s1535-6108(02)00032-6 · Cited by 1,917
Self-citationMLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia
Armstrong, S. A., Staunton, J. E., Silverman, L. B., Pieters, R., den Boer, M. L., Minden, M. D., Sallan, S. E., Lander, E. S., Golub, T. R., & Korsmeyer, S. J. (2002) · Nature Genetics · doi:10.1038/ng765 · Cited by 1,723
Byrd, J. C. (2002) · Blood · doi:10.1182/blood-2002-03-0772 · Cited by 1,597
Prognostically Useful Gene-Expression Profiles in Acute Myeloid Leukemia
Valk, P. J. M., Verhaak, R. G. W., Beijen, M. A., Erpelinck, C. A. J., van Doorn-Khosrovani, S. B. V. W., Boer, J. M., Beverloo, H. B., Moorhouse, M. J., van der Spek, P. J., Löwenberg, B., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040465 · Cited by 1,370
NCBI GEO: mining millions of expression profiles--database and tools
Barrett, T. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki022 · Cited by 1,206
Cited by
Paris, J., Morgan, M., Campos, J., Spencer, G. J., Shmakova, A., Ivanova, I., Mapperley, C., Lawson, H., Wotherspoon, D. A., Sepulveda, C., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.03.021 · Cited by 502
Rasmussen, K. D., Jia, G., Johansen, J. V., Pedersen, M. T., Rapin, N., Bagger, F. O., Porse, B. T., Bernard, O. A., Christensen, J., & Helin, K. (2015) · Genes & Development · doi:10.1101/gad.260174.115 · Cited by 270
Bagger, F. O., Kinalis, S., & Rapin, N. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1076 · Cited by 229
Alves, R., Gonçalves, A. C., Rutella, S., Almeida, A. M., De Las Rivas, J., Trougakos, I. P., & Sarmento Ribeiro, A. B. (2021) · Cancers · doi:10.3390/cancers13194820 · Cited by 204
TP53 mutations in myelodysplastic syndromes and secondary AML confer an immunosuppressive phenotype
Sallman, D. A., McLemore, A. F., Aldrich, A. L., Komrokji, R. S., McGraw, K. L., Dhawan, A., Geyer, S., Hou, H.-A., Eksioglu, E. A., Sullivan, A., et al. (2020) · Blood · doi:10.1182/blood.2020006158 · Cited by 196
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Metadata
- Authors
- Haferlach, T., Kohlmann, A., Wieczorek, L., Basso, G., Kronnie, G. T., Béné, M.-C., De Vos, J., Hernández, J. M., Hofmann, W.-K., Mills, K. I., Gilkes, A., Chiaretti, S., Shurtleff, S. A., Kipps, T. J., Rassenti, L. Z., Yeoh, A. E., Papenhausen, P. R., Liu, W.-M., Williams, P. M., & Foà, R.
- Venue
- Journal of Clinical Oncology
- Published
- 2010-05-20
- Volume
- 28
- Issue
- 15
- Pages
- 2529-2537
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W1994041743
- Cited by
- 654
- References
- 36
- FWCI
- 22.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Haferlach, T., Kohlmann, A., Wieczorek, L., Basso, G., Kronnie, G. T., Béné, M.-C., De Vos, J., Hernández, J. M., Hofmann, W.-K., Mills, K. I., Gilkes, A., Chiaretti, S., Shurtleff, S. A., Kipps, T. J., Rassenti, L. Z., Yeoh, A. E., Papenhausen, P. R., Liu, W.-M., Williams, P. M., & Foà, R. (2010). Clinical Utility of Microarray-Based Gene Expression Profiling in the Diagnosis and Subclassification of Leukemia: Report From the International Microarray Innovations in Leukemia Study Group. Journal of Clinical Oncology. https://doi.org/10.1200/jco.2009.23.4732
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
