Functional genomics and tumor microenvironment analysis reveal prognostic biological subtypes in Mantle cell lymphoma
Bibliographic record
Abstract
Abstract Mantle cell lymphoma (MCL) is a genetically and clinically heterogeneous B-cell malignancy. We studied two MCL cohorts with differing treatment patterns: one enriched for immunochemotherapy, the other for chemotherapy alone. TP53 alterations were consistently associated with poor prognosis, whereas ATM mutations correlated with improved outcomes following rituximab-based chemotherapy. Based on recurrent genetic events, six clusters were identified and refined into three prognostic groups: high-risk ( TP53 mutations and deletions at 17p13.3, 13q14.2, and 19p13.3), intermediate-risk ( ATM and epigenetic regulator mutations, or gains at 8q/17q/15q), and low-risk (lacking TP53 alterations, rare ATM mutations without 11q deletions, gains at 3q, deletions at 6q). Transcriptomic analysis revealed enrichment of proliferation, metabolism-promoting gene signatures in high-risk; angiogenesis and NOTCH signaling in intermediate-risk; and proinflammatory-related (i.e., IFNα, TNFα) in low-risk MCLs. Multi-proteomic spatial profiling using imaging mass cytometry (IMC) demonstrated enrichment of CD8⁺ T cells with high expression of exhaustion markers and dominant population of myeloid cells skewed toward an M2-like phenotype. Compared to ATM -perturbed tumors, TP53 -perturbed tumors exhibited enriched SOX11⁺ tumor cells and enhanced tumor-immune cell interactions. Functional analysis revealed that p53 represses BCR signaling through PTPN6 activation. Collectively, these findings highlight distinct molecular and immune landscapes and reveal therapeutic vulnerabilities in high-risk TP53 -altered MCL.
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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 | 0.000 |
| 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 | 0.000 | 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".