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Record W4389246804 · doi:10.1182/blood-2023-182412

Inflammatory Signatures Define a New High-Risk T-Lineage ALL Subtype

2023· article· en· W4389246804 on OpenAlexaff
Mark Gower, Ximing Li, Alicia G. Aguilar-Navarro, Brian Lin, Minerva Fernandez, Gibran Edun, Mursal Nader, Andrea Arruda, Anne Tierens, Elvin Wagenblast, Lin Yang, Ho Seok Lee, Sanam Loghavi, John E. Dick, Mark D. Minden, Johann Hitzler, Courtney L. Jones, Gregory W. Schwartz, Igor Dolgalev, Soheil Jahangiri, Anastasia N. Tikhonova

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity of GuelphUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsImmunophenotypingMyeloidBiologyLeukemiaLineage (genetic)T cellImmunologyPhenotypeCancerCancer researchMedicineAntigenGeneticsGeneImmune system

Abstract

fetched live from OpenAlex

T-lineage acute lymphoblastic leukemia (ALL) is an aggressive cancer comprising of diverse subtypes that are challenging to stratify using conventional immunophenotyping and have historically exhibited poor treatment outcomes in response to cytotoxic chemotherapy ( Nguyen, K et al. Leukemia 2008). Early T-cell Precursor ALL (ETP-ALL) represent a distinct subtype that displays a developmental block at the earliest stages of T-cell commitment, accompanied by the aberrant expression of myeloid and stem cell markers ( Coustan-Smith, E. et al. Lancet Oncol. 2009). The classification can be further complicated by T/Myeloid Mixed phenotype acute leukemia (T/My-MPAL), a rare and aggressive malignancy characterized by blasts with both T-cell lymphoid and myeloid markers ( George, B. S. et al. Biomedicines 2022). Accurate clinical diagnosis of T-lineage ALL is hindered by phenotypic variations among its subsets. Nevertheless, the precise diagnosis holds critical importance as drug sensitivity in preclinical models of T-lineage ALL is closely linked to the differentiation state, such as the sensitivity of ETP-ALL to the BCL-2 inhibitor venetoclax ( Chonghaile, T. N. et al. Nat Cancer 2021). This highlights the critical need to identify consistent phenotypic traits associated with unique therapeutic vulnerabilities to effectively tailor therapy to individual patients. To gain insights into subset-specific therapeutic vulnerabilities, we performed an integrative multiomic analysis of bone marrow (BM) samples (n=21) from newly diagnosed T-lineage ALL patients, including T-cell ALL (T-ALL), ETP-ALL, and T/My-MPAL. Leveraging cellular indexing of transcriptomes and epitopes in conjunction with T-cell receptor sequencing, we identified a distinct subset of patients spanning all three subtypes characterized by the high frequency of leukemic cells that most closely resemble healthy hematopoietic stem and progenitor cells (HSPC) by Symphony ( Kang, J. B. et al. Nat Commun 2021) query mapping, indicative of an earlier stage of differentiation arrest ( Figure 1A bottom). Pathway analysis revealed an inflammatory signature in patients with high frequencies of stem-like cells suggestive of an inflammatory microenvironment akin to high-risk AML ( Lasry, A. et al. Nat Cancer 2023), which we refer to as inflammatory T-lineage ALL. Next, we mined our multiomic data to generate a comprehensive geneset for inflammatory T-lineage ALL scoring in single cells and bulk samples ( Figure 1A top). Interestingly, Olink cytokine array analysis uncovered upregulation of production of cytokines IL-1β, IL-18, and/or OSM in inflammatory T-lineage ALL samples. Thus, this comprehensive multiomic analysis identified a subset of T-lineage ALL patients characterized by early differentiation arrest and inflammatory signatures. To explore the relationship between inflammatory T-lineage status and clinical response, we utilized the ssGSEA algorithm to score activity of our inflammatory signature in bulk RNA-sequencing data from the TARGET T-lineage ALL dataset (n=265, 74/265 inflammatory) ( Liu, Y. et al. Nat Genet 2017) and the Tran et al. study (n=27, 17/27 inflammatory) ( Tran, T. H. et al. Blood Adv 2022). We found a significant association between high inflammatory ssGSEA score and elevated MRD. To uncover the inflammatory T-lineage ALL mutational landscape, we performed whole exome sequencing on our study cohort, and compiled the reported mutations in the TARGET dataset. Interestingly, inflammatory T-lineage ALL patients, as predicted by inflammatory signature activity, had a significantly higher prevalence of mutations affecting cytokine signaling ( NRAS, JAK3), chromatin remodeling genes ( EZH2), and the WT1 oncogene. These analyses demonstrate a significant relationship between inflammatory status, genomic lesions, and MRD. Finally, we demonstrate that inflammatory, but not non-inflammatory, T-lineage ALL cells are significantly more sensitive to BCL-2 inhibitor venetoclax ex vivo (Figure 1B).Overall, our study has identified a new subtype of T-lineage ALL defined by an early arrest in T-cell development, inflammatory signaling, a unique mutational landscape, sensitivity to venetoclax, and an association with adverse clinical parameters.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.016
GPT teacher head0.269
Teacher spread0.253 · 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
Published2023
Admission routes1
Has abstractyes

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