Objectives In low-risk gestational trophoblastic neoplasia, chemotherapy effect is monitored and adjusted with serum human chorionic gonadotrophin (hCG) levels. Mathematical modeling of hCG kinetics may allow prediction of methotrexate (MTX) resistance, with production parameter “hCGres.” This approach was evaluated using the GOG-174 (NRG Oncology/Gynecologic Oncology Group–174) trial database, in which weekly MTX (arm 1) was compared with dactinomycin (arm 2).
Methods Database (210 patients, including 78 with resistance) was split into 2 sets. A 126-patient training set was initially used to estimate model parameters. Patient hCG kinetics from days 7 to 45 were fit to: [hCG(time)] = hCG7 * exp(−k * time) + hCGres, where hCGres is residual hCG tumor production, hCG7 is the initial hCG level, and k is the elimination rate constant. Receiver operating characteristic (ROC) analyses defined putative hCGRes predictor of resistance. An 84-patient test set was used to assess prediction validity.
Results The hCGres was predictive of outcome in both arms, with no impact of treatment arm on unexplained variability of kinetic parameter estimates. The best hCGres cutoffs to discriminate resistant versus sensitive patients were 7.7 and 74.0 IU/L in arms 1 and 2, respectively. By combining them, 2 predictive groups were defined (ROC area under the curve, 0.82; sensitivity, 93.8%; specificity, 70.5%). The predictive value of hCGres-based groups regarding resistance was reproducible in test set (ROC area under the curve, 0.81; sensitivity, 88.9%; specificity, 73.1%). Both hCGres and treatment arm were associated with resistance by logistic regression analysis.
Conclusions The early predictive value of the modeled kinetic parameter hCGres regarding resistance seems promising in the GOG-174 study. This is the second positive evaluation of this approach. Prospective validation is warranted.
- Chorionic gonadotropin
- Drug resistance
- Gestational trophoblastic neoplasia
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This study was supported by National Cancer Institute grants to the Gynecologic Oncology Group Administrative Office (CA27469), the Gynecologic Oncology Group Statistics and Data Center (CA37517), and the NRG Oncology 1 U10CA180822.
The authors declare no conflicts of interest.
This abstract was presented at the 2012 ASCO Annual Meeting, Chicago, IL.