Novel endpoints based on tumor size ratio to support early clinical decision-making in oncology drug-development

Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247

Article  CAS  PubMed  Google Scholar 

Bruno R, Chanu P, Kågedal M, Mercier F, Yoshida K, Guedj J, Li C, Beyer U, Jin JY (2023) Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models. Br J Cancer 129:1–6

Article  Google Scholar 

Michaelis LC, Ratain MJ (2006) Measuring response in a post-RECIST world: from black and white to shades of grey. Nat Rev Cancer 6(5):409–414

Article  CAS  PubMed  Google Scholar 

Wang Y, Sung C, Dartois C, Ramchandani R, Booth B, Rock E, Gobburu J (2009) Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin Pharmacol Ther 86(2):167–174

Article  CAS  PubMed  Google Scholar 

Le Tourneau C, Servois V, Diéras V, Ollivier L, Tresca P, Paoletti X (2012) Tumour growth kinetics assessment: added value to RECIST in cancer patients treated with molecularly targeted agents. Br J Cancer 106(5):854–857

Article  PubMed  PubMed Central  Google Scholar 

Claret L, Gupta M, Han K, Joshi A, Sarapa N, He J, Powell B, Bruno R (2013) Evaluation of tumor-size response metrics to predict overall survival in western and Chinese patients with first-line metastatic colorectal cancer. J Clin Oncol 31(17):2110–2114

Article  CAS  PubMed  Google Scholar 

Ter-Minassian M, Zhang S, Brooks NV, Brais LK, Chan JA, Christiani DC, Lin X, Gabriel S, Dinet J, Kulke MH (2017) Association between tumor progression endpoints and overall survival in patients with advanced neuroendocrine tumors. Oncologist 22(2):165–172

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chatterjee M, Elassaiss-Schaap J, Lindauer A, Turner D, Sostelly A, Freshwater T, Mayawala K, Ahamadi M, Stone J, De Greef R et al (2017) Population pharmacokinetic/pharmacodynamic modeling of tumor size dynamics in pembrolizumab-treated advanced melanoma. CPT 6(1):29–39

CAS  Google Scholar 

Dromain C, Pavel ME, Ruszniewski P, Langley A, Massien C, Baudin E, Caplin ME (2019) Tumor growth rate as a metric of progression, response, and prognosis in pancreatic and intestinal neuroendocrine tumors. BMC Cancer 19:1–9

Article  Google Scholar 

Gong Y, Mason J, Shen Y-L, Chang E, Kazandjian D, Blumenthal GM, Singh H, Theoret MR, Tang S, Pazdur R et al (2020) An FDA analysis of the association of tumor growth rate and overall and progression-free survival in metastatic non-small cell lung cancer (NSCLC) patients. p 9541

Krishnan SM, Friberg LE (2022) Bayesian forecasting of tumor size metrics and overall survival. CPT 11(12):1604–1613

CAS  Google Scholar 

Mansmann UR, Sartorius U, Laubender RP, Giessen CA, Esser R, Heinemann V (2013) Deepness of response: a quantitative analysis of its impact on post-progression survival time after first-line treatment in patients with mCRC. 427

Lee C-K, Kim S-S, Park S, Kim C, Heo SJ, Lim JS, Kim H, Kim HS, Rha SY, Chung HC et al (2017) Depth of response is a significant predictor for long-term outcome in advanced gastric cancer patients treated with trastuzumab. Oncotarget 8(19):31169

Article  PubMed  PubMed Central  Google Scholar 

Xie X, Li X, Yao W (2021) A narrative review: depth of response as a predictor of the long-term outcomes for solid tumors. Transl Cancer Res 10(2):1119

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kassir N, Chan P, Dang S, Bruno R (2023) External validation of a tumor growth inhibition-overall survival model in non-small-cell lung cancer based on atezolizumab studies using alectinib data. Cancer Chemother Pharmacol 92(3):205–210

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hamada I, Suryawanshi S, Osawa M, Hu C, Roy A, Kondic A (2023) Development and external validation of a tumor growth dynamic—overall survival (TGD-OS) model for metastatic melanoma. In Proceeding of the American conference on pharmacometrics (National Harbor, MD)

Ellis LM, Bernstein DS, Voest EE, Berlin JD, Sargent D, Cortazar P, Garrett-Mayer E, Herbst RS, Lilenbaum RC, Sima C et al (2014) American society of clinical oncology perspective: raising the bar for clinical trials by defining clinically meaningful outcomes. J Clin Oncol 32(12):1277–1280

Article  PubMed  Google Scholar 

Beal S, Sheiner L, Boeckmann A, Bauer R et al (2018) Nonmem 7.4. 3 users guides (1989-2018). ICON Development Solutions, Hanover

Feng Y, Wang X, Suryawanshi S, Bello A, Roy A (2019) Linking tumor growth dynamics to survival in ipilimumab-treated patients with advanced melanoma using mixture tumor growth dynamic modeling. CPT 8(11):825–834

CAS  Google Scholar 

Comments (0)

No login
gif