An inflammatory state defines a high-risk T-lineage acute lymphoblastic leukemia subgroup
Bibliographic record
Abstract
T-lineage acute lymphoblastic leukemia (ALL) is an aggressive cancer comprising diverse subtypes that are challenging to stratify using conventional immunophenotyping. To gain insights into subset-specific therapeutic vulnerabilities, we performed an integrative multiomics analysis of bone marrow samples from newly diagnosed T cell ALL, early T cell precursor ALL, and T/myeloid mixed phenotype acute leukemia. Leveraging cellular indexing of transcriptomes and epitopes in conjunction with T cell receptor sequencing, we identified a subset of patient samples characterized by activation of inflammatory and stem gene programs. These inflammatory T-lineage samples exhibited distinct biological features compared with other T-lineage ALL samples, including the production of proinflammatory cytokines, prevalence of mutations affecting cytokine signaling and chromatin remodeling, an altered immune microenvironment, and poor treatment responses. Moreover, we found that, although inflammatory T-lineage ALL samples were less sensitive to dexamethasone, they exhibited unique sensitivity to a BCL-2 inhibitor, venetoclax. To facilitate classification of patients with T-lineage ALL, we developed a computational inflammatory gene signature scoring system, which stratified patients and was associated with disease prognosis in three additional patient cohorts. By identifying a high-risk T-lineage ALL subtype on the basis of an inflammatory score, our study provides a framework for targeted therapeutic approaches for these challenging-to-treat cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".