Therapeutic Resistance Prediction of Tyrosine Kinase Inhibitors
Study Details
Study Description
Brief Summary
The investigators propose a non-invasive prognostic tool for TKIs resistance in patients with stage IV EGFR-mutant non-small cell lung cancer (NSCLC) by computed tomography phenotypic features, which can be conveniently translated to facilitate the pre-therapy individualized management of EGFR TKIs in this disease.
Condition or Disease | Intervention/Treatment | Phase |
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Detailed Description
The investigators develop a multi-CT-phenotypic-feature-based classifier to predict TKI benefit and therapeutic resistance for stage IV EGFR-mutant non-small cell lung cancer (NSCLC). The investigators also compared its prognostic and predictive efficacy with single features and clinicopathological risk factors. An individualized nomogram integrated the classifier and three clinicopathological risk factors was built for clinical use. The prognostic accuracy of the proposed model was evaluated in two independent validation sets.
Nearly 500 patients will be enrolled in this clinical trial. Eligible patients were diagnosed with NSCLC, and stage IV according to the TNM system classification of the American Joint Committee on Cancer, presence of activating EGFR mutations, age 20 years or older, and no history of systemic anticancer therapy for advanced disease. Patients who underwent first-line or second-line EGFR TKIs were eligible for inclusion. All patients had to be capable of undergoing contrast-enhanced CT, and pretreatment CT was strictly controlled in two weeks before the EGFR TKIs starts. Patients who underwent resection for local advanced or metastatic disease were withdrawn from the study.
Therapeutic resistance was measured by PFS, as the time from the initiation of EGFR TKIs therapy to the date of confirmed disease progression or death. PFS was censored at the date of death from other causes, or the date of the last follow-up visit for progression-free patients.
The investigators will use extracted 1000 phenotypic features on the region of interest manually segmented by radiologists. The Lasso Cox regression model and Nomogram will be used to build a prognosis model for the therapeutic resistance prediction of EGFR TKIs for stage IV EGFR-mutant NSCLC. The Harrell's concordance index(C-index) of the proposed nomogram will be used to quantify the discrimination performance.
Study Design
Outcome Measures
Primary Outcome Measures
- progression-free survival [3 years]
Eligibility Criteria
Criteria
Inclusion Criteria:
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Eligible patients were diagnosed with NSCLC, and stage IV according to the TNM system classification of the American Joint Committee on Cancer.
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Presence of activating EGFR mutations.
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Age 20 years or older, and no history of systemic anticancer therapy for advanced disease.
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Patients who underwent first-line or second-line EGFR TKIs were eligible for inclusion.
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All patients had to be capable of undergoing contrast-enhanced CT, and pretreatment CT was strictly controlled in two weeks before the EGFR TKIs starts.
Exclusion Criteria:
- Based on the criteria above, patients who underwent resection for local advanced or metastatic disease were withdrawn from the study.
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | Key Laboratory of Molecular Imaging, Chinese Academy of Sciences | Beijing | Beijing | China | 100190 |
Sponsors and Collaborators
- Chinese Academy of Sciences
- Guangdong Academy of Medical Sciences
- West China Hospital
- Shanghai Pulmonary Hospital, Shanghai, China
Investigators
- Principal Investigator: Jiangdian Song, Ph.D., CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences
Study Documents (Full-Text)
None provided.More Information
Publications
- Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015 Apr;16(4):e173-80. doi: 10.1016/S1470-2045(14)71116-7. Review.
- Crystal AS, Shaw AT, Sequist LV, Friboulet L, Niederst MJ, Lockerman EL, Frias RL, Gainor JF, Amzallag A, Greninger P, Lee D, Kalsy A, Gomez-Caraballo M, Elamine L, Howe E, Hur W, Lifshits E, Robinson HE, Katayama R, Faber AC, Awad MM, Ramaswamy S, Mino-Kenudson M, Iafrate AJ, Benes CH, Engelman JA. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science. 2014 Dec 19;346(6216):1480-6. doi: 10.1126/science.1254721. Epub 2014 Nov 13.
- Lambin P, van Stiphout RG, Starmans MH, Rios-Velazquez E, Nalbantov G, Aerts HJ, Roelofs E, van Elmpt W, Boutros PC, Granone P, Valentini V, Begg AC, De Ruysscher D, Dekker A. Predicting outcomes in radiation oncology--multifactorial decision support systems. Nat Rev Clin Oncol. 2013 Jan;10(1):27-40. doi: 10.1038/nrclinonc.2012.196. Epub 2012 Nov 20. Review.
- Miller VA, Hirsh V, Cadranel J, Chen YM, Park K, Kim SW, Zhou C, Su WC, Wang M, Sun Y, Heo DS, Crino L, Tan EH, Chao TY, Shahidi M, Cong XJ, Lorence RM, Yang JC. Afatinib versus placebo for patients with advanced, metastatic non-small-cell lung cancer after failure of erlotinib, gefitinib, or both, and one or two lines of chemotherapy (LUX-Lung 1): a phase 2b/3 randomised trial. Lancet Oncol. 2012 May;13(5):528-38. doi: 10.1016/S1470-2045(12)70087-6. Epub 2012 Mar 26. Erratum in: Lancet Oncol. 2012 May;13(5):e186.
- Seto T, Kato T, Nishio M, Goto K, Atagi S, Hosomi Y, Yamamoto N, Hida T, Maemondo M, Nakagawa K, Nagase S, Okamoto I, Yamanaka T, Tajima K, Harada R, Fukuoka M, Yamamoto N. Erlotinib alone or with bevacizumab as first-line therapy in patients with advanced non-squamous non-small-cell lung cancer harbouring EGFR mutations (JO25567): an open-label, randomised, multicentre, phase 2 study. Lancet Oncol. 2014 Oct;15(11):1236-44. doi: 10.1016/S1470-2045(14)70381-X. Epub 2014 Aug 27. Erratum in: Lancet Oncol. 2014 Oct;15(11):e475.
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