Molecular and Pharmacogenetic Marker Evaluation in Relation to the Toxicity and Clinical Response of Acute Lymphoblastic Leukemia Treatment in Indian Children (MPGx-INDALL): Protocol for a Prospective Observational Cohort Study
Bibliographic record
Abstract
Background: Understanding interindividual variability in treatment response and toxicity is essential for optimizing outcomes in pediatric acute lymphoblastic leukemia (ALL). Molecular and pharmacogenetic markers hold promise in predicting treatment efficacy and adverse effects, particularly in genetically diverse populations. This protocol outlines the methodology for a prospective, nonrandomized observational cohort designed to evaluate molecular and pharmacogenetic factors associated with treatment response and toxicity in Indian children diagnosed with ALL. Objective: The primary objective is to identify genetic markers associated with treatment-related toxicity and therapeutic response. Secondary objectives include evaluating associations between the occurrence of early toxicities and quality of life during active ALL treatment, specific pharmacogenetic variants, and survival outcomes along with generating data to support the future implementation of personalized treatment strategies in Indian children with ALL. Methods: In this prospective, observational cohort, 556 children (≤18 years of age) with newly diagnosed ALL treated under the Indian Childhood Collaborative Leukemia-Acute Lymphoblastic Leukemia 2014 (ICiCLe-ALL-14) protocol at two Indian centers will be enrolled, aiming for a minimum of 500 evaluable children. Eligible participants will be enrolled prior to the initiation of chemotherapy and followed longitudinally throughout treatment. Clinical and laboratory data (demographics, nutritional assessment, quality of life, comorbidities, treatment regimen, toxicity graded by Common Terminology Criteria for Adverse Events v5.0, remission status, and survival) will be collected at predefined intervals up to day 100 of the maintenance phase. Germline and somatic DNA will be sampled at diagnosis and remission. The first phase will use whole-exome sequencing to discover candidate variants by implementing a candidate gene prioritization strategy. The second phase will genotype the top candidates in the full cohort using array technology. Associations with early treatment-related toxicities, steroid response, and survival will be tested by multivariable regression and Cox models. A machine learning approach with pharmacogenetic predictors as classifiers will be implemented further with cross-validation and sensitivity analyses. Results: Ethical committees approved the protocol version 1.0 in 2020: IEC-1167/06.11.2020 (All India Institute of Medical Sciences, New Delhi), JIP/IEC/2020/201 (Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry), and AO_2021-00048 (UNIGE, Geneva). Funding was received from Swiss National Science Foundation, Switzerland; Department of Biotechnology, India; and CANSEARCH Foundation, Switzerland. Recruitment began in December 2022 and is likely to conclude by 2027. A comprehensive analysis of the complete study cohort is anticipated to be completed by 2027. Conclusions: The MPGx-INDALL (Molecular and Pharmacogenetic Marker Evaluation in Relation to the Toxicity and Clinical Response of Acute Lymphoblastic Leukemia Treatment in Indian Children) study will generate actionable insights for individualized ALL therapy in India via systematically evaluating germline and somatic markers in a large ethnically distinct cohort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".