MétaCan
Menu
Back to cohort
Record W3095713007 · doi:10.1182/blood-2020-136364

Cytogenetic Subgroups Drive Risk Stratification and Response to Chemotherapy and Blinatumomab in Children and Young Adults with Relapsed B-ALL: A Children's Oncology Group Study

2020· article· en· W3095713007 on OpenAlexaff
Teena Bhatla, Laura Hogan, Xinxin Xu, Lingyun Ji, Andrew J. Carroll, Nyla A. Heerema, Mary Shago, Lia Gore, James A. Whitlock, Stephen P. Hunger, Mignon L. Loh, Patrick A. Brown

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineInternal medicineBlinatumomabOncologyMinimal residual diseaseChemotherapyChemotherapy regimenLeukemiaLymphoblastic Leukemia

Abstract

fetched live from OpenAlex

Background At initial diagnosis, sentinel cytogenetic (CG) abnormalities differ based on demographic/clinical features and are predictive of relapse risk in B-lymphoblastic leukemia (B-ALL). The relevance of CG at 1st relapse is not well defined. Children's Oncology Group (COG) AALL1331 enrolled non-BCR-ABL1+, non-Down syndrome 1st relapse B-ALL patients (pts) 1-30 years (yr) old. After a common chemotherapy induction (Block 1), pts were risk assigned as: High risk (HR): Early bone marrow (BM) relapse <36 months from diagnosis or isolated extramedullary (IEM) relapse <18 months; Intermediate risk (IR): Late BM relapse (>36 months) with minimal residual disease (MRD) ≥0.1% at end Block-1 (EB-1); Low risk (LR): Late BM relapse with MRD <0.1% at EB-1; Treatment failure (TF): M3 BM or failure to clear central nervous system at EB-1. The primary objective of the trial for HR/IR pts was to randomly compare post-reinduction therapy with 2 blocks of chemotherapy vs. blinatumomab, followed by allogeneic transplant. We previously reported the superiority of blinatumomab for HR/IR pts (Brown, ASH 2019). We hypothesized that CG would correlate with clinical features at relapse and response to therapy, enhancing relapse risk stratification. Methods CG data (karyotype and FISH) at relapse were centrally reviewed. CG groups were defined as: favorable (fav) (double trisomies (DT) or ETV6-RUNX1), unfavorable (unfav) (hypodiploid, KMT2A-rearranged (KMT2A-R) or iAMP21), and neutral (all others). Of 662 eligible pts, 130 IEM relapses were excluded, since CG data were not available due to lack of BM involvement. Of 532 BM relapses, CG data were available for 394 (74%); this group was used for associations of CG with clinical features and response to Block 1. Of 188 BM relapses risk assigned HR/IR and randomized, CG data were available for 147 (78%; 72 Arm A chemotherapy, 75 Arm B blinatumomab); this group was used for associations of CG with response to randomized post-reinduction therapy. CG distributions at initial diagnosis were based on data from over 16,000 pts enrolled on COG upfront trials from 1996-2014. Statistical comparisons used Pearson's chi-squared, Fisher's exact, Wilcoxon rank sum, or logrank test as appropriate. All p values are two-sided. Results As expected, unfav CG were more common at relapse compared to diagnosis (17% vs 7% p<0.001), and the opposite for fav CG (22% vs 42% p<0.001). Also expected was the younger age at relapse for the fav (median 8 yr) and KMT2A-R (median 2 yr) CG vs. others (median 12 yr, p=0.002). WBC at relapse was higher in the KMT2A-R (median 11k/uL), but similar in all others (median 5k/uL, p=0.041). Time from diagnosis to relapse differed by CG group, with late BM relapses comprising 79% of fav vs. 61% of neutral vs. 48% of unfav (p<0.001). EB-1 MRD-negative rates were highest for fav (53%), intermediate for neutral (38%) and lowest for unfav (25%, p=0.002). Interestingly, in fav CG group, 62% of ETV6-RUNX1 vs. 40% of DT were EB-1 MRD-negative (p=0.043), suggesting that as for newly diagnosed pts, MRD clearance during relapse reinduction may be slower for DT than for ETV6-RUNX1. EB-1 risk assignments differed significantly by CG group, with rates for fav/neutral/unfav CG of 57%/35%/27% for LR, 16%/31%/34% for HR, and 3%/7%/7% for TF (p<0.01). After 1st course of randomized therapy for HR/IR pts, the rate of EB-2 MRD negativity was (as previously reported) much higher for arm B (75%) vs arm A (25%, p<0.001). Interestingly, EB-2 MRD clearance rates did not vary significantly by CG group for either randomized arm. There was, however, an influence of CG group on survival for arm B, but not for arm A. The 2 yr DFS/OS for fav CG on arm B was 77%/93%, vs. 49%/65% for neutral and 49%/49% for unfav (p=0.069 for DFS, p=0.035 for OS, univariate analysis). For arm A, DFS/OS was similar and consistently poor in all three groups (44%/52% fav vs. 49%/61% neutral and 39%/61% unfav, p=0.92 for DFS, p=0.62 for OS) (Table 1). Conclusions Pts with fav CG B-ALL relapse later and are more likely to achieve MRD negativity after reinduction chemotherapy, resulting in higher rates of LR risk assignment. Interestingly, while the superior MRD clearance with blinatumomab vs. chemotherapy is seen in all CG groups, this only translated into higher rates of DFS/OS in the fav CG group. Thus, the influence of CG on outcomes after 1st relapse may differ based on whether chemotherapy or immunotherapy is used as post-reinduction consolidation. Disclosures Gore: Amgen, Novartis, Roche: Membership on an entity's Board of Directors or advisory committees. Hunger:Novartis: Consultancy; Amgen: Current equity holder in publicly-traded company, Honoraria. Loh:Medisix Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pfizer: Other: Institutional Research Funding. Brown:Jazz: Honoraria; Servier: Honoraria; Janssen: Consultancy; Novartis: 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)0.002
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.258
Teacher spread0.251 · 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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicAcute Lymphoblastic Leukemia researchFrench-language works237,207