Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study

Abstract

There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients often cycle through cytotoxic chemotherapy (CC), epigenetic modifiers (EM) or small molecule inhibitors (SMI) empirically. Ideal therapy at each line remains unknown. We conducted a retrospective, multiple intervention, 'target-trial' using the PETAL global cohort. Patients received front-line CC, then second and third line (2L and 3L) with either CC again, EM or SMI (12 possible treatment scenarios). Overall survival (OS; 2L or 3L to death) was compared across treatment sequences using Cox, reinforcement learning and synthetic intervention methods adjusting for age, histology, primary refractory disease, prognostic index for T-cell lymphoma (PIT) score, response to 2L, and receipt of 2L transplant consolidation. Five hundred and forty received 2L (EM = 101, SMI = 45, CC = 394), and 290 received 3L (EM = 65, SMI = 44, CC = 181). 2L SMI then 3L EM improved OS (adjusted hazard ratio [aHR]: 0.29, 95% confidence interval [CI]: 0.11-0.74; p = 0.010) versus 2L-3L CC-CC, and consistently across most other sequential strategies. In 2L stability analyses, benefit was notable with 2L SMI in angioimmunoblastic T-cell lymphoma (vs. CC: aHR: 0.23, 95% CI: 0.10-0.4; p < 0.001); vs. EM: aHR: 0.32, 95% CI: 0.12-0.82; p = 0.020), and both SMI and EM in PIT-stratified high-risk groups (SMI: aHR: 0.40, 95% CI: 0.21-0.76; p = 0.005; EM: aHR: 0.60, 95% CI: 0.39-0.92; p = 0.020) versus 2L CC. Results were consistent across all other independent stability and causal inference analyses providing a treatment selection framework.

Keywords: cytotoxic chemotherapy; epigenetic modifiers; machine learning; mature T‐cell and NK‐cell lymphomas; molecular therapeutics.