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Gynecol Oncol. 2014 Jun;133(3):460-6. doi: 10.1016/j.ygyno.2014.04.003. Epub 2014 Apr 12.

Dynamic modeling in ovarian cancer: an original approach linking early changes in modeled longitudinal CA-125 kinetics and survival to help decisions in early drug development.

Author information

1
EMR 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France. Electronic address: melanie.wilbaux@gmail.com.
2
EMR 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France.
3
Department of Medical Oncology and Hematology, Princess Margaret Hospital, University Health Network, Toronto, Ontario, Canada.
4
Hôpital Hôtel Dieu, Place du Parvis Notre-Dame, 75004 Paris, France.
5
EMR 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France; Service d'Oncologie Médicale, Investigational Center for Treatments in Oncology and Hematology of Lyon, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, F-69310 Pierre-Bénite, France.

Abstract

OBJECTIVE:

Early prediction of the expected benefit of treatment in recurrent ovarian cancer (ROC) patients may help in drug development decisions. The actual value of 50% CA-125 decrease is being reconsidered. The main objective of the present study was to quantify the links between longitudinal assessments of CA-125 kinetics and progression-free survival (PFS) in treated recurrent ovarian cancer (ROC) patients.

METHODS:

The CALYPSO randomized phase III trial database comparing two platinum-based regimens in ROC patients was randomly split into a "learning dataset" and a "validation dataset". A parametric survival model was developed to associate longitudinal modeled CA-125 changes (ΔCA125), predictive factors, and PFS. The predictive performance of the model was evaluated with simulations.

RESULTS:

The PFS of 534 ROC patients were properly characterized by a parametric mathematical model. The modeled ΔCA125 from baseline to week 6 was a better predictor of PFS than the modeled fractional change in tumor size. Simulations confirmed the model's predictive performance.

CONCLUSIONS:

We present the first parametric survival model quantifying the relationship between PFS and longitudinal CA-125 kinetics in treated ROC patients. The model enabled calculation of the increase in ΔCA125 required to observe a predetermined benefit in PFS to compare therapeutic strategies in populations. Therefore, ΔCA125 may be a predictive marker of the expected gain in PFS and an early predictive tool in drug development decisions.

KEYWORDS:

CA-125; Drug development; Ovarian cancer; Progression-free survival

PMID:
24726614
DOI:
10.1016/j.ygyno.2014.04.003
[Indexed for MEDLINE]

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