Data from <i>IRS2</i> Copy Number Gain, <i>KRAS</i> and <i>BRAF</i> Mutation Status as Predictive Biomarkers for Response to the IGF-1R/IR Inhibitor BMS-754807 in Colorectal Cancer Cell Lines Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.1158/1535-7163.c.6537472
Insulin-like growth factor receptor 1 (IGF-1R)–targeting therapies are currently at an important crossroad given the low clinical response rates seen in unselected patients. Predictive biomarkers for patient selection are critical for improving clinical benefit. Coupling in vitro sensitivity testing of BMS-754807, a dual IGF-1R/IR inhibitor, with genomic interrogations in 60 human colorectal cancer cell lines, we identified biomarkers correlated with response to BMS-754807. The results showed that cell lines with BRAFV600E or KRASG13D mutation were resistant, whereas cell lines with wild-type of both KRAS and BRAF were particularly sensitive to BMS-754807 if they have either higher RNA expression levels of IR-A or lower levels of IGFBP6. In addition, the cell lines with KRAS mutations, those with either insulin receptor substrate 2 (IRS2) copy number gain (CNG) or higher IGF-1R expression levels, were more sensitive to the drug. Furthermore, cell lines with IRS2 CNG had higher levels of ligand-stimulated activation of IGF-1R and AKT, suggesting that these cell lines with IGF-IR signaling pathways more actively coupled to AKT signaling are more responsive to IGF-1R/IR inhibition. IRS2 siRNA knockdown reduced IRS2 protein expression levels and decreased sensitivity to BMS-754807, providing evidence for the functional involvement of IRS2 in mediating the drug response. The prevalence of IRS2 CNG in colorectal cancer tumors as measured by qPCR-CNV is approximately 35%. In summary, we identified IRS2 CNG, IGF-1R, IR-A, and IGFBP6 RNA expression levels, and KRAS and BRAF mutational status as candidate predictive biomarkers for response to BMS-754807. This work proposed clinical development opportunities for BMS-754807 in colorectal cancer with patient selection to improve clinical benefit. Mol Cancer Ther; 14(2); 620–30. ©2014 AACR.
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Data from <i>IRS2</i> Copy Number Gain, <i>KRAS</i> and <i>BRAF</i> Mutation Status as Predictive Biomarkers for Response to the IGF-1R/IR Inhibitor BMS-754807 in Colorectal Cancer Cell LinesWork title
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preprintOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-04-03Full publication date if available
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Fei Huang, Han Chang, Ann Greer, Stephen Hillerman, Karen A. Reeves, Warren Hurlburt, John Cogswell, Dharmesh Patel, Zhenhao Qi, Craig Fairchild, Rolf-Peter Ryseck, Tai W. Wong, Friedrich Graf Finckenstein, Jeffrey R. Jackson, Joan M. CarboniList of authors in order
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sensitivity | 37, 184 |
| abstract_inverted_index.<i>Mol | 261 |
| abstract_inverted_index.Furthermore, | 137 |
| abstract_inverted_index.particularly | 87 |
| abstract_inverted_index.approximately | 214 |
| abstract_inverted_index.opportunities | 248 |
| abstract_inverted_index.interrogations | 47 |
| abstract_inverted_index.vitro</i> | 36 |
| abstract_inverted_index.ligand-stimulated | 147 |
| abstract_inverted_index.(IGF-1R)–targeting | 5 |
| abstract_inverted_index.<i>BRAF</i> | 85, 232 |
| abstract_inverted_index.<i>IRS2</i> | 141, 203, 220 |
| abstract_inverted_index.<i>KRAS</i> | 83, 112, 230 |
| abstract_inverted_index.(<i>IRS2</i>) | 121 |
| abstract_inverted_index.AACR</i>.</p></div> | 267 |
| abstract_inverted_index.<div>Abstract<p>Insulin-like | 0 |
| abstract_inverted_index.<i>KRAS</i><sup>G13D</sup> | 72 |
| abstract_inverted_index.<i>BRAF</i><sup>V600E</sup> | 70 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 15 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.55986509 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |