Older age, CNS leukaemic involvement and induction tumour lysis increases the risk of methotrexate (MTX)-induced neurotoxicity in childhood acute lymphoblastic leukaemia/lymphoma: Experience from a tertiary care centre in South India.

Methotrexate (MTX), although an indispensable part of contemporary treatment protocols for childhood acute lymphoblastic leukaemia (ALL)/lymphomas (LBL) in improving outcomes, can lead to serious neurotoxicity with long-term consequences. The aetiopathogenesis, predisposing factors and treatment for MTX-induced neurotoxicity are not yet well defined. The aim of our study was to detect the incidence, risk factors and to assess the overall outcomes of MTX-induced neurotoxicity among large cohort of paediatric ALL/LBL patients treated on a uniform protocol. We conducted retrospective audit of medical records of 622 consecutive children (≤14 years) diagnosed with ALL and LBL between January 2018 and December 2022 and treated on modified BFM-95 protocol at the Department of Pediatric Oncology, Regional Cancer Centre, Thiruvananthapuram. Risk factors predisposing to MTX-induced neurotoxicity were identified using binary logistic regression analysis. Forty-three children were diagnosed with MTX-induced neurotoxicity with an incidence rate of 6.9%. More than two-thirds of them had high-grade MTX-induced neurotoxicity CTCAE v5.0 with a median age of 9 years (range: 9 months to 14 years). Almost half of them developed MTX neurotoxicity during Protocol M followed by Phase-Ib consolidation (15%). Majority of these patients (84%, 36/43) were challenged again with MTX, with 11% (4/36) developing recurrence. Fifteen per cent had persistent neurological deficits at last follow-up. Univariate analysis found older age (age > 5 years) (p < 0.001), T-cell phenotype (p = 0.040), tumour lysis syndrome during induction (p < 0.001), baseline renal problems prior to MTX exposure (p < 0.001) and CNS leukaemic involvement (p < 0.003) to be significantly associated with MTX neurotoxicity. On multivariate analysis, older age (>5 years), tumour lysis during induction and CNS leukaemia retained statistical significance (p < 0.05). Methotrexate-induced neurotoxicity during paediatric acute lymphoblastic leukaemia/lymphoma therapy is a transient phenomenon in majority and re-challenge with MTX is generally safe. Older age children who develop tumour lysis during induction and CNS leukaemic involvement are at increased risk for MTX-induced neurotoxicity during ALL/LBL treatment.

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.