Discrete genetic subtypes and tumor microenvironment signatures correlate with peripheral T-cell lymphoma outcomes
Bibliographic record
Abstract
Peripheral T-cell lymphoma (PTCL) exhibits a diverse clinical spectrum, necessitating methods to categorize patients based on genomic abnormalities or tumor microenvironment (TME) profiles. We conducted an integrative multiomics study in 129 PTCL patients, performing whole-exome sequencing and identifying three genetic subtypes: C1, C2, and C3. C2 was characterized by loss of tumor suppressor genes and chromosomal instability, while C1 and C3 shared T follicular helper (TFH)-related genomic alterations, with C3 also showing a high incidence of IDH2 mutations and chromosome 5 gain. Compared to C1, survival was significantly worse in C2 (HR 2.52; 95% CI, 1.37-4.63) and C3 (HR 2.14; 95% CI, 1.17-3.89). We also estimated the proportions of immune cell fractions from the bulk RNA sequencing data using CIBERSORTx and classified TME signatures into the following hierarchical clusters: TME1 (characterized by increased B and TFH cells), TME2 (macrophages), and TME3 (activated mast cells). TME2 was associated with shorter survival (HR 3.4; 95% CI, 1.6-7.5) and was more frequent in C2 (64.3%) than in C1 (7.7%), whereas C1 had more TME3 signatures (80.8% vs. 28.6%). These findings highlight a significant relationship between genetic subtypes and TME signatures in PTCL, with important implications for clinical prognosis.
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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 | 0.000 |
| 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".