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Comparison of immunoglobulin high-throughput sequencing MRD in bone marrow and peripheral blood in pediatric B-ALL: A report from the Children's Oncology Group AALL1731.

2024· article· en· W4399281478 on OpenAlexaff
Rachel E. Rau, Sumit Gupta, John A. Kairalla, Cindy Wang, Lik Wee Lee, Heidi Michelle Simmons, Karen R. Rabin, Anne Angiolillo, Mary Shago, Andrew J. Carroll, Brent L. Wood, Michael J. Borowitz, Yvonne Moyer, Alexis Cameron, Christian Deardorff, Tyler Jones, Allison P. Jacob, Ilan R. Kirsch, Elizabeth A. Raetz, Mignon L. Loh

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineBone marrowPeripheral bloodOncologyAntibodyPediatric oncologyInternal medicineCancerImmunology

Abstract

fetched live from OpenAlex

10014 Background: Minimal (measurable) residual disease (MRD) at end of induction (EOI) therapy is a strong predictor of outcome in pediatric B-ALL. Currently, EOI MRD is assessed in bone marrow (BM). We hypothesized that the highly sensitive assay, high-throughput sequencing (HTS) of immunoglobulin loci, can effectively monitor MRD in peripheral blood (PB) and may provide a less invasive way to track therapy response. Methods: We conducted HTS MRD on paired EOI BM and PB samples from 808 NCI standard risk (SR) pediatric B-ALL patients enrolled on Children’s Oncology Group study AALL1731 (NCT03914625). We determined the correlation between BM and PB HTS MRD via Spearman’s rank correlations. We calculated the BM/PB MRD ratio and compared these by subgroup using Kruskal-Wallis tests. We defined subgroups by cytogenetics (cyto) ( ETV6::RUNX1, double trisomies of chromosome 4 and 10 (DT), Unfavorable (hypodiploidy, iAMP21, or KMT2A-rearranged), or Neutral (lacking ETV6::RUNX1, DT, or unfavorable)), and risk group (SR-average (AVG) and SR-High). Flow cytometry-defined EOI BM MRD was < 0.01% for all SR-AVG patients (N = 623) and ≥0.01% for selected SR-High patients (N = 185). Results: There was strong correlation between PB and BM HTS MRD with an overall correlation coefficient of 0.75 (P < 0.001). Correlation was similar by cytogenetics: ETV6::RUNX1, 0.69 (N = 63; P < 0.001), DT, 0.75 (N = 147; P < 0.001), Neutral, 0.74 (N = 580; P < 0.001), and Unfavorable, 0.66(N = 18; P = 0.003). For risk groups, correlation for SR-AVG was 0.67 (p < 0.001) and SR-High, 0.64 (p < 0.001). Of the 591 SR-AVG patients with detectable BM HTS MRD, PB HTS MRD was detectable in 474 (80.2%), undetectable in 94 (15.9%) and indeterminate (no leukemic cell detected and < 500,000 total cells in sample) in 23 (3.9%). Among 182 SR-High patients with detectable BM HTS MRD, 175 (96.2%) had detectable PB HTS MRD. Disease burden was higher in the BM than PB with a significantly higher BM/PB ratio in SR-High compared to SR-AVG patients (median 16.5 vs 2.6, P < 0.001). The median BM/PB ratio also varied by cytogenetics with those with Unfavorable cyto having the highest ratio (15.3 vs 6.3 in DT, 3.8 in ETV6::RUNX1, 3.1 in Neutral; P = 0.013). Conclusions: This is the largest analysis of paired B-ALL BM/PB HTS MRD to date. We show strong correlation between PB and BM across risk and cytogenetic groups. The ratio of BM/PB MRD varied and was highest among patients with Unfavorable cyto suggesting BM tropism. Importantly, PB MRD was detectable in nearly all patients with flow EOI BM MRD ≥0.01%, a threshold warranting therapy intensification. However, most patients with EOI BM flow MRD < 0.01% also had detectable PB HTS MRD. Thus, PB HTS MRD may provide a useful adjunct for screening and clinical management of B-ALL patients. Defining a PB HTS MRD threshold useful for risk stratification will require correlation with outcome.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.455
Teacher spread0.373 · 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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Citations1
Published2024
Admission routes1
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