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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".