Anti-PD-1 monoclonal antibodies (anti PD-1 mAbs) such as nivolumab and pembrolizumab are associated with high response rates in patients with relapsed or refractory (R/R) classical Hodgkin lymphoma (HL). To date, no prognostic factor for overall survival (OS) has been established with these agents in HL. We examined whether the first early response assessment evaluated using 18F-Fluorodeoxyglucose PET/CT (ePET 1), may be associated with OS in this setting. Methods: This retrospective study included 45 patients from 34 institutions. In a blinded, centralized review, three independent radiologists classified ePET 1 obtained at a median of 2.0 months (interquartile range: 1.7-3.7 months) after nivolumab initiation using existing criteria (i.e., Lugano 2014, LYRIC 2016). Patients were classified at ePET 1 according to 4 possible response categories: complete metabolic response (CMR), partial metabolic response (PMR), no metabolic response (NMR), or progressive metabolic disease (PMD). As the OS of patients with NMR and PMR were similar, they were grouped together. OS was estimated using the Kaplan-Meier method and compared between groups using log-rank testing. Results: Eleven patients (24%) died after a median follow-up of 21.2 months. The classification at ePET 1 was identical between Lugano and LYRIC since all 16 progression events at ePET 1 classified as indeterminate response per LYRIC were confirmed on subsequent evaluations. Both Lugano and LYRIC 2016 classified patients as CMR in 13 pts (29%), PMD in 16 pts (36%), NMR in 4 pts (9%) and PMR in 12 pts (27%). The 2-year OS [95CI] probability was significantly different in patients with

No abstract

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction Patients with relapsed/refractory Hodgkin lymphoma (R/R HL) experience high response rates upon anti-PD1 therapy. In these patients, the optimal duration of treatment and the risk of relapse after anti-PD1 discontinuation are unknown. Methods We retrospectively analyzed patients with R/R HL who responded to anti-PD1 monotherapy and discontinued the treatment either because of unacceptable toxicity or prolonged remission. A machine learning algorithm based on 17 candidate variables was trained and validated to predict progression-free survival (PFS) landmarked at the time of discontinuation of anti-PD1 therapy. Results Forty patients from 14 centers were randomly assigned to training (n = 25) and validation (n = 15) sets. At the time of anti-PD1 discontinuation, patients had received treatment for a median duration of 11.2 (range, 0-time to best response was not statistically significant in discriminating patients with PFS lesser or greater than 12 months). Considering PFS status as a binary variable (alive or dead) at a specific time point (12 months) is convenient, intuitive and allows for comparing the value of potential predicting variables in these two groups of patients. Nonetheless, this approach has two drawbacks: first, it binarizes outcome; second, it excludes patients alive with a time to last follow up lesser 12 months. Therefore, it is less powerful to demonstrate statistically significant association with PFS even if it exists 5 months. Patients discontinued anti-PD1 treatment either because of prolonged remission (N = 27, 67.5%) or unacceptable toxicity (N = 13, 32.5%). Most patients were in CR (N = 35, 87.5%) at the time of anti-PD1 discontinuation. In the training set, the machine learning algorithm identified that the most important variables to predict PFS were patients' age, time to best response, and presence or absence of CR. The performance observed in the training set was validated in the validation set. Conclusion In this pilot, proof of concept study using a machine learning algorithm, we identified biomarkers capable of predicting the risk of relapse after anti-PD1 discontinuation (age, time to best response, quality of response). Once confirmed, these simple biomarkers will represent useful tools to guide the management of these patients.

No abstract