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Record W4253348189 · doi:10.1182/blood-2019-126814

Genetic Ancestry and Skeletal Toxicities Among Childhood Acute Lymphoblastic Leukemia Patients in the DFCI 05-001 Cohort

2019· article· en· W4253348189 on OpenAlexaff
Qianqian Zhu, Song Yao, Peter D. Cole, Justine M. Kahn, Marian H. Harris, Emily Schiller, Uma H. Athale, Luis A. Clavell, Caroline Laverdière, Jean‐Marie Leclerc, Bruno Michon, Marshall A. Schorin, Jennifer Welch, Stephen E. Sallan, Lewis B. Silverman, Kara M. Kelly

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsCentre hospitalier de l'Université LavalCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineCumulative incidenceProportional hazards modelCohortEthnic groupInternal medicineIncidence (geometry)Hazard ratioGenetic genealogyDemographyOncologyPopulationConfidence interval

Abstract

fetched live from OpenAlex

Background: Despite outstanding cure rates of pediatric acute lymphoblastic leukemia (ALL), Blacks and Hispanics have inferior survival than Whites. We recently reported that among 794 children from DFCI ALL Consortium Protocol 05-001 trial, Hispanic patients had significantly lower rates of fracture and osteonecrosis, but higher risk of relapse and death compared with non-Hispanic Whites (PMID: 29090520). Studies from other groups have reported inferior ALL outcomes in children with Native American ancestry, however the association between genetic ancestry and skeletal toxicities has not been explored. We examined whether genetic inheritance could provide an explanation for the reduced incidence of skeletal toxicities in Hispanic patients in DFCI 05-001. Methods: A total of 576 DNA samples extracted from bone marrow samples or blood samples obtained during remission, including 2% blind duplicates, were genotyped using the Illumina OmniExpress Beadchip array. After data QC and cleaning, 449 ALL patients were retained in the final analysis. Estimates of global genetic ancestry were derived from STRUCTURE program, which was used to re-classify individuals with discordant clinical race/ethnicity as reported by study site, and to assign individuals with unknown race/ethnicity information to an ethnic group when possible. Regression model for the subdistribution hazard of the cumulative incidence function was used to relate clinical race/ethnicity, genetically reclassified race/ethnicity, and genetic ancestry respectively with risk of fracture and osteonecrosis, with death and recurrence as competing risk factors while controlling for age, gender and baseline clinical factors. Cox proportional regression models were used to test race/ethnicity and ancestry with overall survival (OS) and event-free survival (EFS). Results: Among the 449 patients analyzed, average age was 6.7 years; 26% of patients were ≥10 years and 44% were female. The demographic and clinical characteristics of patients with genotype data were similar to those of the overall cohort, although the proportion of Hispanics was slightly lower in the genotyped sub-cohort (17% vs. 21%), whereas the rates of fracture and osteonecrosis were higher (fracture: 25% vs. 18%; osteonecrosis: 10% vs. 8%). Based on clinical race/ethnicity, 66% of patients were non-Hispanic White, 17% were Hispanic, 5% were non-Hispanic Black, 3% were Asian, and 10% were reported as Other. Genetic ancestry analyses revealed that non-Hispanic White patients had a median of 96% European ancestry, non-Hispanic Black patients had a median of 76% African ancestry, and Asian patients had a median of 58% Asian ancestry. The genetic make-up of Hispanic patients in the 05-001 cohort was more admixed, with 23% Native American and 17% African ancestry, higher than the national average (18% and 6%, respectively). After genetic reassignment, racial/ethnic groups were as follows: 68% non-Hispanic White, 17% Hispanic, 9% non-Hispanic Black, 6% Asian, and 1% unassigned. In analysis of genetically reassigned race/ethnicity with skeletal toxicities, Hispanic and Black patients had significantly lower risk of fracture compared with white patients (Hispanic: subdistribution hazard ratio [SHR]=0.42, 95% confidence interval [CI]=0.22, 0.81; Black: HR=0.28, 95%CI=0.10, 0.75). These groups also had significantly less osteonecrosis (Hispanic: SHR=0.24, 95%CI=0.08, 0.78; Black: SHR=0.12, 95%CI=0.02, 0.93). Similar results were observed when using clinical race/ethnicity. Further analyses revealed that African genetic ancestry, but not Native American ancestry was associated with lower risk of fracture and osteonecrosis in a dose-dependent manner (Table 1). In analysis of death and recurrence, those with higher proportion of Native American ancestry had significantly higher risk of death/recurrence after adjustment (OS: hazard ratio [HR]=4.00, 95%CI=1.45, 11.02; EFS: HR=2.07, 95%CI=1.13, 3.79). Analysis of single variants and polygenic risk scores with skeletal toxicities and survival outcomes is ongoing. Conclusion: Hispanic children and adolescents from the DFCI 05-001 cohort, had highly heterogenous genetic ancestral make-up. Among Hispanic patients, the observed lower risk of skeletal toxicities might be driven by African ancestry, whereas poorer survival observed might be driven by Native American ancestry. Disclosures Silverman: Servier: Consultancy, Research Funding; Takeda: Consultancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.231
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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