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Development of a Composite Score Based on Carbohydrate Antigen 19-9 Dynamics to Predict Survival in Carbohydrate
Ingmar F Rompen1,2, Elisabetta Sereni1,3, Joseph R Habib1,4
1Department of Surgery, The NYU Grossman School of Medicine and NYU Langone Health, New York, NY.
Purpose:
Dynamics of carbohydrate antigen 19-9 (CA19-9) often inform treatment decisions during and after neoadjuvant chemotherapy (NAT) of patients with pancreatic ductal adenocarcinoma (PDAC). However, considerable dispute persists regarding the clinical relevance of specific CA19-9 thresholds and dynamics. Therefore, we aimed to define optimal thresholds for CA19-9 values and create a biochemically driven composite score to predict survival in CA19-9-producing patients with PDAC after NAT.
Methods:
Patients with PDAC who underwent NAT and surgical resection from 2012 to 2022 were retrospectively identified from three high-volume centers. CA19-9 nonproducers and patients with 90-day mortality, and macroscopically incomplete resections were excluded. A composite score was created on the basis of relative CA19-9 change and newly defined optimal thresholds of pre- and postneoadjuvant values for overall survival (OS) using patients from two centers and validated using data from the third center.
Results:
A total of 492 patients met inclusion criteria in the development cohort. Optimal CA19-9 cutoff values for predicting a difference in OS were 202 U/mL for preneoadjuvant and 78 U/mL for postneoadjuvant levels. Furthermore, increase in CA19-9 during neoadjuvant treatment was associated with worse OS (median-OS, 17.5 months v 26.0 months; P = .008). Not surpassing any or only one of these thresholds (composite score of 0-1) was associated with improved OS compared with patients with 2-3 points (median-OS, 29.9 months v 15.8 months; P < .001). Major serological response (90% decrease of CA19-9) had a positive and negative predictive value of 32% and 88%, respectively.
Conclusion:
The composite score consisting of CA19-9 levels at diagnosis, after neoadjuvant treatment, and its dynamics demonstrates prognostic discrimination between low and high scores. However, better predictive biomarkers are needed to facilitate treatment decisions during neoadjuvant treatment.
