A Gene Expression-based Model to Predict Metabolic Response After Two Courses of ABVD in Hodgkin Lymphoma Patients

Abstract

Purpose: Early response to ABVD, assessed with interim FDG-PET (iPET), is prognostic for classical Hodgkin lymphoma (cHL) and supports the use of response adapted therapy. The aim of this study was to identify a gene-expression profile on diagnostic biopsy to predict iPET positivity (iPET⁺).

Experimental design: Consecutive untreated patients with stage I-IV cHL who underwent iPET after two cycles of ABVD were identified. Expression of 770 immune-related genes was analyzed by digital expression profiling (NanoString Technology). iPET was centrally reviewed according to the five-point Deauville scale (DS 1-5). An iPET⁺ predictive model was derived by multivariate regression analysis and assessed in a validation set identified using the same inclusion criteria.

Results: A training set of 121 and a validation set of 117 patients were identified, with 23 iPET⁺ cases in each group. Sixty-three (52.1%), 19 (15.7%), and 39 (32.2%) patients had stage I-II, III, and IV, respectively. Diagnostic biopsy of iPET⁺ cHLs showed transcriptional profile distinct from iPET^-. Thirteen genes were stringently associated with iPET⁺. This signature comprises two functionally stromal-related nodes. Lymphocytes/monocytes ratio (LMR) was also associated to iPET⁺. In the training cohort a 5-gene/LMR integrated score predicted iPET⁺ [AUC, 0.88; 95% confidence interval (CI), 0.80-0.96]. The score achieved a 100% sensitivity to identify DS5 cases. Model performance was confirmed in the validation set (AUC, 0.68; 95% CI, 0.52-0.84). Finally, iPET score was higher in patients with event versus those without.

Conclusions: In cHL, iPET is associated with a genetic signature and can be predicted by applying an integrated gene-based model on the diagnostic biopsy.