Genomic alterations important for the prognosis in patients with follicular lymphoma treated in SWOG study S0016
Bibliographic record
Abstract
Abstract Although recent advances in molecular genetics have enabled improved risk classification of follicular lymphoma (FL) using, for example, the m7-FLIPI score, the impact on treatment has been limited. We aimed to assess the prognostic significance of copy-number aberrations (CNAs) and copy-neutral loss of heterozygosity (cnLOH) identified by chromosome genomic-array testing (CGAT) at FL diagnosis using prospectively collected clinical trial specimens from 255 patients enrolled in the SWOG study S0016. The impact of genomic aberrations was assessed for early progression (progressed or died within 2 years after registration), progression-free survival (PFS), and overall survival (OS). We showed that increased genomic complexity (ie, the total number of aberration calls) was associated with poor outcome in FL. Certain chromosome arms were critical for clinical outcome. Prognostic CNAs/cnLOH were identified: whereas early progression was correlated with 2p gain (P = .007; odds ratio [OR] = 2.55 [1.29, 5.03]) and 2p cnLOH (P = .005; OR = 10.9 [2.08, 57.2]), 2p gain specifically encompassing VRK2 and FANCL predicted PFS (P = .01; hazard ratio = 1.80 [1.14, 2.68]) as well as OS (P = .005; 2.40 [1.30, 4.40]); CDKN2A/B (9p) deletion correlated with worse PFS (P = .004, 3.50 [1.51, 8.28]); whereas CREBBP (16p) (P < .001; 6.70 [2.52, 17.58]) and TP53 (17p) (P < .001; 3.90 [1.85, 8.31]) deletion predicted worse OS. An independent cohort from the m7-FLIPI study was explored, and the prognostic significance of aberration count, and TP53 and CDKN2A/B deletion were further validated. In conclusion, assessing genomic aberrations at FL diagnosis with CGAT improves risk stratification independent of known clinical parameters, and provides a framework for development of future rational targeted therapies.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".