Case Report: Myeloid Sarcoma Development During Treatment for B Cell Lymphoblastic Lymphoma in a Boy with KRAS/NRAS Gene Mutations

A Pediatric Case of Treatment-related Myelodysplastic Syndrome While on Therapy for Pre-B Acute Lymphoblastic Leukemia

BACKGROUND

Treatment-related myelodysplastic syndrome (t-MDS) is a rare late effect of cancer therapy. After alkylating agents, this typically occurs years after completion of therapy. Treatment of t-MDS in pediatrics is an allogeneic stem cell transplant, however, the prognosis remains poor.

OBSERVATIONS

This case demonstrates t-MDS developing in a patient receiving treatment for pre-B acute lymphoblastic leukemia. This patient was treated with a combination of hematopoietic stem cell transplant and hypomethylating agents.

CONCLUSIONS

These agents should be considered for use in patients with t-MDS, before transplant to limit additional chemotherapy and as maintenance therapy post-transplant to reduce the risk of relapse.

Population pharmacokinetics of methotrexate in pediatric patients with acute lymphoblastic leukemia and malignant lymphoma

1. This study aimed to identify physiological and pharmacogenomic covariates and develop a population pharmacokinetic model of high-dose methotrexate (HD-MTX) in Chinese pediatric patients with acute lymphoblastic leukemia (ALL) and malignant lymphoma.2. A total 731 MTX courses and 1658 MTX plasm concentration from 205 pediatric patients with ALL and malignant lymphoma were analyzing using a nonlinear mixed-effects model technique. 47 SNPs in 16 MTX-related gene were genotyped and screened as covariates. A PPK model was established to determine the influence of covariates such as body surface area (BSA), age, laboratory test value, and SNPs on the pharmacokinetic process of HD-MTX.3. Two-compartmental model with allometric scaling using BSA could nicely characterize the in vivo behavior of HD-MTX. After accounting for body size, rs17004785 and rs4148416 were the covariates that influence MTX clearance (CL). The PPK model obtained was: CL =9.33 * (BSA/1.73) ^0.75 * e^{0.13*rs17004785} * e^{0.39*rs4148416} * e^ηCL, Vc =24.98 * (BSA/1.73) * e^ηvc, Q = 0.18 * (BSA/1.73) ^0.75 * e^ηQ and Vp =4.70 * (BSA/1.73) * e^ηvp.4. The established model combined with Bayesian approach could estimate individual pharmacokinetic parameters and optimize personalized HD-MTX therapy for pediatric patients with ALL and malignant lymphoma.

Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute lymphoblastic leukemia

Methotrexate (MTX), an antimetabolite for the treatment of leukemia, could cause neutropenia and subsequently fever, which might lead to treatment delay and affect prognosis. Here, we aimed to predict neutropenia and fever related to high-dose MTX using artificial intelligence. This study included 139 pediatric patients newly diagnosed with standard- or intermediate risk B-cell acute lymphoblastic leukemia. Fifty-seven SNPs of 16 genes were genotyped. Univariate and multivariate analysis were used to select SNPs and clinical covariates for model developing. Five machine learning algorithms combined with four resampling techniques were used to build optimal predictive model. The combination of random forest with adaptive synthetic appeared to be the best model for neutropenia (sensitivity = 0.935, specificity = 0.920, AUC = 0.927) and performed best for fever (sensitivity = 0.818, specificity = 0.924, AUC = 0.870). By machine learning, we have developed and validated comprehensive models to predict the risk of neutropenia and fever. Such models may be helpful for medical oncologists in quick decision-making.