Tumor Growth Rate (TGR) Predicts Clinical Outcomes for Advanced Non-small Cell Lung Cancer Undergoing Immunotherapy.
NCT ID: NCT04722406
Last Updated: 2021-03-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
350 participants
OBSERVATIONAL
2019-08-15
2021-08-01
Brief Summary
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Detailed Description
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All response and outcome evaluation were determined as per RECIST 1.1 by two senior radiologists blinded to patients'information. Discrepancy was solved by consensus.
X-tile software was used to determine cut-off values that maximumly differentiate overall survival (OS). Log-rank tests and Cox regression models were performed for survival analysis. The predictive value of TGR for clinical outcomes in ICI-treated aNSCLC patients was validated in two external cohorts, recruited form Guangdong Province Traditional Chinese Medical Hospital and Shanghai Chest Hospital.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Low TGR group
Patients with low level of TGR determined by X-tile program
This item is not applicable to our observational study.
This item is not applicable to our observational study.
High TGR group
Patients with high level of TGR determined by X-tile program
This item is not applicable to our observational study.
This item is not applicable to our observational study.
Interventions
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This item is not applicable to our observational study.
This item is not applicable to our observational study.
Eligibility Criteria
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Inclusion Criteria
* Having received anti-PD-1/PD-L1 therapy
* Must have two consecutive computed tomography (CT) scans upon early treatment (from baseline to the first imaging evaluation)
Exclusion Criteria
* Without measurable lesions at baseline CT scan
* Having received local anticancer therapy during ICI treatment, for example, radiotherapy and radiofrequency ablation
18 Years
ALL
No
Sponsors
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Guangdong Provincial Hospital of Traditional Chinese Medicine
OTHER
Shanghai Chest Hospital
OTHER
Sun Yat-sen University
OTHER
Responsible Party
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Shaodong Hong
Deputy Chief Physician, MD
Principal Investigators
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Shaodong Hong, MD
Role: PRINCIPAL_INVESTIGATOR
Sun Yat-sen University
Locations
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Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, China
Guangdong Provincial Hospital of Traditional Chinese Medicine
Guangzhou, Guangdong, China
Shaihai Chest Hospital
Shanghai, Shanghai Municipality, China
Countries
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Central Contacts
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Facility Contacts
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References
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Antonia SJ, Borghaei H, Ramalingam SS, Horn L, De Castro Carpeno J, Pluzanski A, Burgio MA, Garassino M, Chow LQM, Gettinger S, Crino L, Planchard D, Butts C, Drilon A, Wojcik-Tomaszewska J, Otterson GA, Agrawal S, Li A, Penrod JR, Brahmer J. Four-year survival with nivolumab in patients with previously treated advanced non-small-cell lung cancer: a pooled analysis. Lancet Oncol. 2019 Oct;20(10):1395-1408. doi: 10.1016/S1470-2045(19)30407-3. Epub 2019 Aug 14.
Xia L, Liu Y, Wang Y. PD-1/PD-L1 Blockade Therapy in Advanced Non-Small-Cell Lung Cancer: Current Status and Future Directions. Oncologist. 2019 Feb;24(Suppl 1):S31-S41. doi: 10.1634/theoncologist.2019-IO-S1-s05.
Mok TSK, Wu YL, Kudaba I, Kowalski DM, Cho BC, Turna HZ, Castro G Jr, Srimuninnimit V, Laktionov KK, Bondarenko I, Kubota K, Lubiniecki GM, Zhang J, Kush D, Lopes G; KEYNOTE-042 Investigators. Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet. 2019 May 4;393(10183):1819-1830. doi: 10.1016/S0140-6736(18)32409-7. Epub 2019 Apr 4.
Gettinger S, Horn L, Jackman D, Spigel D, Antonia S, Hellmann M, Powderly J, Heist R, Sequist LV, Smith DC, Leming P, Geese WJ, Yoon D, Li A, Brahmer J. Five-Year Follow-Up of Nivolumab in Previously Treated Advanced Non-Small-Cell Lung Cancer: Results From the CA209-003 Study. J Clin Oncol. 2018 Jun 10;36(17):1675-1684. doi: 10.1200/JCO.2017.77.0412. Epub 2018 Mar 23.
Ferte C, Fernandez M, Hollebecque A, Koscielny S, Levy A, Massard C, Balheda R, Bot B, Gomez-Roca C, Dromain C, Ammari S, Soria JC. Tumor growth rate is an early indicator of antitumor drug activity in phase I clinical trials. Clin Cancer Res. 2014 Jan 1;20(1):246-52. doi: 10.1158/1078-0432.CCR-13-2098. Epub 2013 Nov 15.
Lamarca A, Crona J, Ronot M, Opalinska M, Lopez Lopez C, Pezzutti D, Najran P, Carvhalo L, Franca Bezerra RO, Borg P, Vietti Violi N, Vidal Trueba H, de Mestier L, Schaefer N, Sundin A, Costa F, Pavel M, Dromain C; Knowledge Network. Value of Tumor Growth Rate (TGR) as an Early Biomarker Predictor of Patients' Outcome in Neuroendocrine Tumors (NET)-The GREPONET Study. Oncologist. 2019 Nov;24(11):e1082-e1090. doi: 10.1634/theoncologist.2018-0672. Epub 2019 Mar 25.
Stein WD, Wilkerson J, Kim ST, Huang X, Motzer RJ, Fojo AT, Bates SE. Analyzing the pivotal trial that compared sunitinib and IFN-alpha in renal cell carcinoma, using a method that assesses tumor regression and growth. Clin Cancer Res. 2012 Apr 15;18(8):2374-81. doi: 10.1158/1078-0432.CCR-11-2275. Epub 2012 Feb 17.
Gomez-Roca C, Koscielny S, Ribrag V, Dromain C, Marzouk I, Bidault F, Bahleda R, Ferte C, Massard C, Soria JC. Tumour growth rates and RECIST criteria in early drug development. Eur J Cancer. 2011 Nov;47(17):2512-6. doi: 10.1016/j.ejca.2011.06.012. Epub 2011 Jul 15.
Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004 Nov 1;10(21):7252-9. doi: 10.1158/1078-0432.CCR-04-0713.
He LN, Fu S, Ma H, Chen C, Zhang X, Li H, Du W, Chen T, Jiang Y, Wang Y, Wang Y, Zhou Y, Lin Z, Yang Y, Huang Y, Zhao H, Fang W, Zhang H, Zhang L, Hong S. Early on-treatment tumor growth rate (EOT-TGR) determines treatment outcomes of advanced non-small-cell lung cancer patients treated with programmed cell death protein 1 axis inhibitor. ESMO Open. 2022 Dec;7(6):100630. doi: 10.1016/j.esmoop.2022.100630. Epub 2022 Nov 25.
Other Identifiers
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SHong
Identifier Type: -
Identifier Source: org_study_id
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