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Conference Paper: Gene profiling of lung cancers with EGFR mutations

TitleGene profiling of lung cancers with EGFR mutations
Authors
Issue Date2005
PublisherAmerican Association for Cancer Research. The Journal's web site is located at http://www.aacr.org/home/scientists/meetings--workshops.aspx
Citation
AACR 96th Annual Meeting, Anaheim CA, 16–20 April 2005. In Cancer Research, 2005, v. 65 n. 9S, p. 208 Abstract no. 884 How to Cite?
AbstractResponse to gefitinib/Iressa has recently been found to be associated with somatic mutation of EGFR in lung cancers. This important discovery should lead to a better selection of patients who will most likely benefit from gefitinib treatment. One way of identifying these patients is to use a gene profiling approach, whereby testing the expression levels of a small set of genes can predict whether a patient will harbor an EGFR mutation and thus would potentially respond well to the drug. To this aim, 17 primary non-small cell lung cancers from the University of Michigan, of which half (8) contained an EGFR mutation, were analyzed with microarrays (Affymetrix GeneChips HG-U133Plus2 which contains more than 54,000 elements). Using these as ...
Response to gefitinib/Iressa has recently been found to be associated with somatic mutation of EGFR in lung cancers. This important discovery should lead to a better selection of patients who will most likely benefit from gefitinib treatment. One way of identifying these patients is to use a gene profiling approach, whereby testing the expression levels of a small set of genes can predict whether a patient will harbor an EGFR mutation and thus would potentially respond well to the drug. To this aim, 17 primary non-small cell lung cancers from the University of Michigan, of which half (8) contained an EGFR mutation, were analyzed with microarrays (Affymetrix GeneChips HG-U133Plus2 which contains more than 54,000 elements). Using these as a training set, a leave-one-out supervised classification approach identified 100 genes whose expression levels formed a good class predictor. These genes were then applied to a test set of 36 primary non-small cell lung cancers originated from Hong Kong and analyzed with Affymetrix GeneChips HG-U133A and HG-U133B (∼45,000 elements altogether) and whose EGFR mutation status had previously been determined (15 wild-type and 21 mutated). Expression levels of the 100 class predictor genes in those 36 tumors could correctly classify them with a 75% accuracy (P = 0.0006) as either EGFR mutated or wild-type. Specifically, 17 of the 21 EGFR mutants were correctly predicted (81%, P = 0.0007) as well as 10 of the 15 EGFR wild-types (67%, P = 0.06). This shows that a small set of genes can be used to screen patients likely to be responsive to gefitinib and serves as an alternative diagnostic tool to direct EGFR sequencing. Further, this prediction method worked even though the training and test sets were from different populations and were analyzed with different GeneChip platforms. Additionally, we analyzed 44 lung cancer cell lines from Dallas with Affymetrix HG-U133A and HG-U133B, of which 8 were found to be mutated in EGFR. These and the previous two sets of lung cancers were screened for differentially regulated genes in EGFR mutant versus wild-type tumor samples. Using a cutoff of 2-fold difference and filtering for named genes with a T-test p-value of less than 0.05, we identified 93, 220, and 537 genes upregulated in the EGFR-mutated subgroups from the Michigan, Hong Kong, and Dallas sets respectively, and 80, 211, and 657 genes downregulated in the same subgroups. Forty-two genes were found upregulated in at least two tumor sets (including EGFR which was upregulated in all three sets), and 55 genes were downregulated in two or more tumor sets. Study of these genes should prove valuable both for EGFR signaling research and the discovery of new drug targets. An independent analysis of the Hong Kong tumor set with additional cases is presented in the abstract by CL Lam et al. (Supported by Lung Cancer SPORE P50CA70907 & Gilson Longenbaugh Foundation)
Persistent Identifierhttp://hdl.handle.net/10722/104477
ISSN
2015 Impact Factor: 8.556
2015 SCImago Journal Rankings: 5.372

 

DC FieldValueLanguage
dc.contributor.authorGirard, Len_HK
dc.contributor.authorLam, CLen_HK
dc.contributor.authorShigematsu, Hen_HK
dc.contributor.authorWong, MPen_HK
dc.contributor.authorPeyton, Men_HK
dc.contributor.authorSheridan, Sen_HK
dc.contributor.authorBeer, DGen_HK
dc.contributor.authorGazdar, AFen_HK
dc.contributor.authorMinna, JDen_HK
dc.date.accessioned2010-09-25T21:54:41Z-
dc.date.available2010-09-25T21:54:41Z-
dc.date.issued2005en_HK
dc.identifier.citationAACR 96th Annual Meeting, Anaheim CA, 16–20 April 2005. In Cancer Research, 2005, v. 65 n. 9S, p. 208 Abstract no. 884-
dc.identifier.issn0008-5472-
dc.identifier.urihttp://hdl.handle.net/10722/104477-
dc.description.abstractResponse to gefitinib/Iressa has recently been found to be associated with somatic mutation of EGFR in lung cancers. This important discovery should lead to a better selection of patients who will most likely benefit from gefitinib treatment. One way of identifying these patients is to use a gene profiling approach, whereby testing the expression levels of a small set of genes can predict whether a patient will harbor an EGFR mutation and thus would potentially respond well to the drug. To this aim, 17 primary non-small cell lung cancers from the University of Michigan, of which half (8) contained an EGFR mutation, were analyzed with microarrays (Affymetrix GeneChips HG-U133Plus2 which contains more than 54,000 elements). Using these as ...-
dc.description.abstractResponse to gefitinib/Iressa has recently been found to be associated with somatic mutation of EGFR in lung cancers. This important discovery should lead to a better selection of patients who will most likely benefit from gefitinib treatment. One way of identifying these patients is to use a gene profiling approach, whereby testing the expression levels of a small set of genes can predict whether a patient will harbor an EGFR mutation and thus would potentially respond well to the drug. To this aim, 17 primary non-small cell lung cancers from the University of Michigan, of which half (8) contained an EGFR mutation, were analyzed with microarrays (Affymetrix GeneChips HG-U133Plus2 which contains more than 54,000 elements). Using these as a training set, a leave-one-out supervised classification approach identified 100 genes whose expression levels formed a good class predictor. These genes were then applied to a test set of 36 primary non-small cell lung cancers originated from Hong Kong and analyzed with Affymetrix GeneChips HG-U133A and HG-U133B (∼45,000 elements altogether) and whose EGFR mutation status had previously been determined (15 wild-type and 21 mutated). Expression levels of the 100 class predictor genes in those 36 tumors could correctly classify them with a 75% accuracy (P = 0.0006) as either EGFR mutated or wild-type. Specifically, 17 of the 21 EGFR mutants were correctly predicted (81%, P = 0.0007) as well as 10 of the 15 EGFR wild-types (67%, P = 0.06). This shows that a small set of genes can be used to screen patients likely to be responsive to gefitinib and serves as an alternative diagnostic tool to direct EGFR sequencing. Further, this prediction method worked even though the training and test sets were from different populations and were analyzed with different GeneChip platforms. Additionally, we analyzed 44 lung cancer cell lines from Dallas with Affymetrix HG-U133A and HG-U133B, of which 8 were found to be mutated in EGFR. These and the previous two sets of lung cancers were screened for differentially regulated genes in EGFR mutant versus wild-type tumor samples. Using a cutoff of 2-fold difference and filtering for named genes with a T-test p-value of less than 0.05, we identified 93, 220, and 537 genes upregulated in the EGFR-mutated subgroups from the Michigan, Hong Kong, and Dallas sets respectively, and 80, 211, and 657 genes downregulated in the same subgroups. Forty-two genes were found upregulated in at least two tumor sets (including EGFR which was upregulated in all three sets), and 55 genes were downregulated in two or more tumor sets. Study of these genes should prove valuable both for EGFR signaling research and the discovery of new drug targets. An independent analysis of the Hong Kong tumor set with additional cases is presented in the abstract by CL Lam et al. (Supported by Lung Cancer SPORE P50CA70907 & Gilson Longenbaugh Foundation)-
dc.languageengen_HK
dc.publisherAmerican Association for Cancer Research. The Journal's web site is located at http://www.aacr.org/home/scientists/meetings--workshops.aspx-
dc.relation.ispartofCancer Researchen_HK
dc.titleGene profiling of lung cancers with EGFR mutationsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0197-016X&volume=46, abstract no. 884&spage=&epage=&date=2005&atitle=Gene+profiling+of+lung+cancers+with+EGFR+mutations-
dc.identifier.emailWong, MP: mwpik@hkucc.hku.hken_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.hkuros108053en_HK
dc.identifier.volume46-
dc.publisher.placeUnited States-
dc.description.otherThe 96th Annual Meeting of the American Association for Cancer Research (AACR 2005), Anaheim, CA., 16-20 April 2005. In American Association for Cancer Research. Proceedings of the Annual Meeting, 2005, v. 46, abstract no. 884-

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