Treatment outcomes of children with acute lymphoblastic leukemia in a middle‐income developing country: high mortalities, early relapses, and poor survival

A Bayesian joint model for multivariate longitudinal and time-to-event data with application to ALL maintenance studies

The most common type of cancer diagnosed among children is the Acute Lymphocytic Leukemia (ALL). A study was conducted by Tata Translational Cancer Research Center (TTCRC) Kolkata, in which 236 children (diagnosed as ALL patients) were treated for the first two years (approximately) with two standard drugs (6MP and MTx) and were then followed nearly for the next 3 years. The goal is to identify the longitudinal biomarkers that are associated with time-to-relapse, and also to assess the effectiveness of the drugs. We develop a Bayesian joint model in which a linear mixed model is used to jointly model three biomarkers (i.e. white blood cell count, neutrophil count, and platelet count) and a semi-parametric proportional hazards model is used to model the time-to-relapse. Our proposed joint model can assess the effects of different covariates on the progression of the biomarkers, and the effects of the biomarkers (and the covariates) on time-to-relapse. In addition, the proposed joint model can impute the missing longitudinal biomarkers efficiently. Our analysis shows that the white blood cell (WBC) count is not associated with time-to-relapse, but the neutrophil count and the platelet count are significantly associated with it. We also infer that a lower dose of 6MP and a higher dose of MTx jointly result in a lower relapse probability in the follow-up period. Interestingly, we find that relapse probability is the lowest for the patients classified into the "high-risk" group at presentation. The effectiveness of the proposed joint model is assessed through the extensive simulation studies.

A Bayesian joint model for multivariate longitudinal and time-to-event data with application to ALL maintenance studies

The most common type of cancer diagnosed among children is the acute lymphocytic leukemia (ALL). A study was conducted by Tata Translational Cancer Research Center (TTCRC) Kolkata, in which 236 children (diagnosed as ALL patients) were treated for the first two years (approximately) with two standard drugs (6MP and MTx) and were then followed nearly for the next three years. The goal is to identify the longitudinal biomarkers that are associated with time-to-relapse, and also to assess the effectiveness of the drugs. We develop a Bayesian joint model in which a linear mixed model is used to jointly model three biomarkers (i.e. white blood cell count, neutrophil count, and platelet count) and a semi-parametric proportional hazards model is used to model the time-to-relapse. Our proposed joint model can assess the effects of different covariates on the progression of the biomarkers, and the effects of the biomarkers (and the covariates) on time-to-relapse. In addition, the proposed joint model can impute the missing longitudinal biomarkers efficiently. Our analysis shows that the white blood cell (WBC) count is not associated with time-to-relapse, but the neutrophil count and the platelet count are significantly associated with it. We also infer that a lower dose of 6MP and a higher dose of MTx jointly result in a lower relapse probability in the follow-up period. Interestingly, we find that relapse probability is the lowest for the patients classified into the "high-risk" group at presentation. The effectiveness of the proposed joint model is assessed through the extensive simulation studies.

A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries

Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.

The Influence of Nutritional Status at Diagnosis of Childhood B-Cell Acute Lymphoblastic Leukemia on Survival Rates: Data from a Hispanic Cohort

The impact of nutritional status at diagnosis of childhood acute lymphoblastic leukemia (ALL) on survival rates was assessed in a Hispanic cohort. Children <16 years with newly diagnosed ALL-B from 2011 to 2019 were studied. Overweight and obesity were classified by body mass index (BMI) and Z-score according to WHO and CDC criteria. BMI, weight percentiles for age and Z-Score were assessed using the WHO Anthro (0-5 years) and AnthroPlus (5-19 years) programs. Cox model was used to estimate risk factors for relapse and death; differences between groups were assessed with Student's T test for parametric and Mann-Whitney U test for non-parametric variables. Disease-free survival (DFS) and overall survival (OS) were determined by the Kaplan-Meier method, calculating time, status, cumulative survival and standard error with a 95% confidence interval. Equal data distribution was estimated with the log-rank test. One-hundred and seventy-two B-ALL children were studied. The overweight-obese group had a non-significant lower DFS (CDC: 54% vs. 60%, p = 0.80; WHO: 57% vs. 64%, p = 0.89) and OS rate (CDC:76% vs. 82%, p = 0.38; WHO:65% vs. 81%, p = 0.13). An association between nutritional status determined by CDC and WHO criteria at diagnosis of B-cell ALL and survival rates was not documented.