Early prediction of treatment resistance in low-risk gestational trophoblastic neoplasia using population kinetic modelling of hCG measurements

Abstract

Background:In low-risk gestational trophoblastic neoplasia (GTN) patients, a predictive marker for early identification of methotrexate (MTX) resistance would be useful. We previously demonstrated that kinetic modelling of human chorionic gonadotrophin (hCG) measurements could provide such a marker. Here we validate this approach in a large independent patient cohort.Methods:Serum hCG measurements of 800 low-risk GTN patients treated with MTX were analysed. The cohort was divided into Model and Test data sets. hCG kinetics were described from initial treatment day to day 50 using: ‘(hCG(time))=hCG0*exp(–k*time)+hCGres', where hCGres is the modelled residual production, hCG0 is the baseline hCG level, and k is the rate constant. HCGres-predictive value was investigated against previously reported predictors of MTX resistance.Results:Declining hCG measurements were well fitted by the model. The best discriminator of MTX resistance in the Model data set was hCGres, categorised by an optimal cut-off value of >20.44 IU l−1: receiver-operating characteristic (ROC) area under the curve (AUC)=0.87; Se=0.91; Sp=0.83. The predictive value of hCGres was reproducible using the Test data set: ROC AUC=0.87; Se=0.88; Sp=0.86. Multivariate analyses revealed hCGres as a better predictor of MTX resistance (HR=1.01, P<0.0001) and MTX failure-free survival (HR=13.25, P<0.0001) than other reported predictive factors.Conclusion:hCGres, a modelled kinetic parameter calculated after fully dosed three MTX cycles, has a reproducible value for identifying patients with MTX resistance.

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors

“…Certainly, there are several recent examples where mathematical models have been used to describe the time course of tumor markers and their link with clinical outcomes in different cancer indications. Some include human chorionic gonadotropin as an early predictor of methotrexate resistance in low-risk gestational trophoblastic neoplasia patients (43), mathematical models to personalize vaccination regimens to stabilize prostate-specific antigen (PSA) levels (42,44), soluble VEGF receptor 3 to monitor adverse events and clinical response in patients with imatinib-resistant gastrointestinal stromal tumors (45,46), a semimechanistic model involving lactate dehydrogenase (LDH) and neuron-specific enolase (NSE) dynamics to individualize disease monitoring in small cell lung cancer patients (27,47), and CA-125 as an early predictive biomarker of recurrent ovarian cancer (48).…”

Trophoblastic Disease Review for Diagnosis and Management: A Joint Report From the International Society for the Study of Trophoblastic Disease, European Organisation for the Treatment of Trophoblastic Disease, and the Gynecologic Cancer InterGroup

“…10 Once patients are on therapy, the use of hCG regression nomograms and/or hCG kinetics can predict the onset of resistance to therapy at an earlier time point. 18,19 However, none of these refinements are yet in routine practice. An ISSTD working party is now looking at this issue.…”

“…Detection of resistance may be identified earlier by using either hCG regression normograms or kinetics, although these approaches are not yet in routine practice. 18,19 Once the hCG is normal, 3 consolidation treatments for 6 weeks are required to minimize the risk for recurrence. Lybol et al 24 …”

Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology

“…In fact, NLME models have been used in the past to describe circulating biomarker dynamics and clinical outcome in different cancer indications. Some examples include the use of mathematical models in prostate cancer to personalize vaccination regimens to stabilize prostate-specific antigen levels (37,38), the use of CA125 as a predictive marker of ovarian cancer (39), the kinetic modeling of human chorionic gonadotropin as an early predictor of methotrexate resistance in low-risk gestational trophoblastic neoplasia patients (40), and the use of soluble VEGF receptor 3 to monitor adverse events and clinical response in patients with imatinib-resistant gastrointestinal stromal tumors (41,42). A key strength of NLME models is that they provide a description of individual variability in population level trends.…”