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Abstract B026: Infant ALL without KMT2A rearrangements harbor clinically relevant alterations and share common origins with childhood ALL

2024· article· en· W4402266922 on OpenAlexaffabout
Matthew Zatzman, Jennifer Seelisch, Federico Comitani, Fabio Fuligni, Scott Davidson, Kyoko E. Yuki, Lisa-Monique Edwards, Ledia Brunga, Erin Guest, Stephen P. Hunger, Mignon L. Loh, Elizabeth A. Raetz, John Chen, Jack Bartram, Johann Hitzler, Patrick A. Brown, Sumit Gupta, Adam Shlien

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsHospital for Sick ChildrenLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineChildhood cancerPediatricsCancerInternal medicine

Abstract

fetched live from OpenAlex

Abstract Infant acute lymphoblastic leukemia (ALL) is characterized by high frequency of rearrangements in KMT2A (KMT2Ar), associated with poor outcomes. Infants lacking KMT2Ar typically have superior outcomes, but remain understudied. Here, we use whole genome and transcriptome sequencing to define driver mutations and transcriptional phenotypes in non-KMT2Ar infant ALL. Based on two index cases, we initially suspected that some infants with high-risk clinical features harbored clinically undetected non-canonical alterations to the KMT2A gene; however, we find no such evidence in our data. Instead, we find that these infants acquire other clinically relevant features such as Ph-like expression signatures, fusions impacting ZNF384, TCF3, ETV6::RUNX1, PAX5 or NUTM1, and events in known tumor genes such as CDKN2A, NOTCH1, and others. By mapping transcriptional profiles between infant and childhood B-ALL, we find that - in the absence of KMT2Ar - infant ALL resembles well defined childhood B-ALL subtypes, sharing the same genetic drivers. NUTM1 fusions are particularly enriched in infants compared to older children, and are associated with decreased MHC-Class II expression and B-cell developmental signaling pathways. Ph-like transcriptional signatures were apparent in several infants and confirmed by machine learning driven classification. Overall, our data support a common developmental origin of ALL without KMT2Ar in infants and children. Citation Format: Matthew Zatzman, Jennifer Seelisch, Federico Comitani, Fabio Fuligni, Scott Davidson, Kyoko E. Yuki, Lisa-Monique Edwards, Ledia Brunga, Erin Guest, Stephen P. Hunger, Mignon L. Loh, Elizabeth A. Raetz, John Chen, Jack Bartram, Johann K. Hitzler, Patrick A. Brown, Sumit Gupta, Adam Shlien. Infant ALL without KMT2A rearrangements harbor clinically relevant alterations and share common origins with childhood ALL [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B026.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.072
GPT teacher head0.438
Teacher spread0.366 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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