Role of Stem Cell Transplant in CD30+ PTCL Following Frontline Brentuximab Vedotin Plus CHP or CHOP in ECHELON-2.

Peripheral T-cell lymphomas (PTCLs) are a heterogeneous group of aggressive non-Hodgkin lymphomas, the majority of which have high relapse rates following standard therapy. Despite use of consolidative stem cell transplant (SCT) following frontline therapy, there remains no consensus on its utility. The double-blind randomized phase 3 ECHELON-2 study (#NCT01777152; clinicaltrials.gov) demonstrated improved progression-free survival (PFS) and overall survival with frontline brentuximab vedotin plus cyclophosphamide, doxorubicin, and prednisone (A+CHP). Herein, we conducted an exploratory subgroups analysis of the impact of consolidative SCT on PFS in patients with previously untreated CD30+ PTCL (ALK- anaplastic large cell lymphoma [ALCL] and non-ALCL) who were in complete response (CR) after frontline treatment with A+CHP or cyclophosphamide, doxorubicin, vincristine, and prednisone. Median PFS follow-up was 47.57 months. The PFS hazard ratio was 0.36, equating to a 64% reduction in the risk of a PFS event in patients who underwent SCT. The median PFS in patients who underwent SCT was not reached, vs 55.66 months in patients who did not undergo SCT. PFS results favored the use of SCT in both ALK- ALCL and non-ALCL subgroups. These data support the consideration of consolidative SCT in patients with CD30+PTCL who achieve CR following treatment with A+CHP

Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma

Purpose: Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS).Patients and methods: This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort).Results: In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis.Conclusion: The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI

Parsaclisib, a PI3Kδ inhibitor, in relapsed and refractory mantle cell lymphoma (CITADEL-205): a phase 2 studyResearch in context

Summary: Background: Parsaclisib is a potent and highly selective PI3Kδ inhibitor that has shown clinical benefit in patients with relapsed/refractory (R/R) B-cell malignancies. In this phase 2 study (CITADEL-205; NCT03235544, EudraCT 2017-003148-19), the efficacy and safety of parsaclisib was evaluated in patients with R/R mantle cell lymphoma (MCL). Methods: Patients ≥18 years old with pathologically confirmed R/R MCL and prior treatment with 1–3 systemic therapies, with (cohort 1) or without (cohort 2) previous Bruton kinase inhibitor (BTKi) treatment, received oral parsaclisib 20 mg once-daily (QD) for 8 weeks, then either parsaclisib 20 mg once-weekly (weekly dosing group [WG]) or parsaclisib 2.5 mg QD (daily dosing group [DG]). The primary endpoint was objective response rate (ORR). Findings: At the primary analysis data cutoff on January 15, 2021, 53 patients in cohort 1 (BTKi-experienced) (WG, n = 12; DG: n = 41) and 108 patients in cohort 2 (BTKi-naive) (WG, n = 31; DG: n = 77) had received parsaclisib monotherapy. The BTKi-experienced cohort was closed after an interim analysis demonstrated limited clinical benefit. In the BTKi-naive cohort, the ORR (95% CI) for DG (dosing selected for further study) was 70.1% (58.6%–80.0%), with a complete response rate (95% CI) of 15.6% (8.3%–25.6%) and a median duration of response (95% CI) of 12.1 (9.0–not evaluable) months. Treatment-emergent adverse events (TEAEs) occurred among 90.7% (98/108) of all treated patients in the BTKi-naive cohort. Grade ≥3 TEAEs occurred among 62.0% (67/108) of patients, including diarrhoea (13.9%, 15/108) and neutropenia (8.3%, 9/108). Parsaclisib interruption, reduction, or discontinuation due to TEAEs occurred among 47.2% (51/108), 8.3% (9/108), and 25.0% (27/108) of patients, respectively. Fatal TEAEs were experienced by six patients and determined to be treatment-related in one patient. Interpretation: Parsaclisib, a potent, highly selective, PI3Kδ inhibitor demonstrated meaningful clinical benefits and a manageable safety profile (25.0% discontinuation rate, low incidences of individually reported grade ≥3 or serious adverse events) in R/R MCL patients with no prior BTKi therapy. Limited clinical benefit was observed with parsaclisib monotherapy in patients who had previously received BTKi treatment. Future development of PI3K inhibitors for NHL will require further investigation of dose optimisation to improve safety and long-term survival. Funding: Incyte Corporation