Gene expression signatures in follicular lymphoma: are they ready for the clinic?
Bibliographic record
Abstract
T he lymphoma field has fully embraced the molecular era for several years, the result of a number of studies utilizing genome-wide analysis platforms including gene expression profiling, array comparative genomic hybridization (aCGH), single nucleotide polymorphisms (SNP) arrays and several more novel technologies that provide information about methylation status and the epigenome.[1][2][3][4] As these new technologies became available, lymphoid cancers were a relatively early priority, in part because of the ease of sample acquisition and the availability of retrospective fresh-frozen biopsy material. Heterogeneity of follicular lymphomaFollicular lymphoma (FL) is the most common indolent non-Hodgkin's lymphoma (NHL).It typically presents as advanced-stage disease and in this setting is considered imminently treatable, but not curable with standard chemotherapy or immuno-chemotherapy regimens used today.There is growing evidence that the current era of therapy has produced improvements in overall survival.5 Survival following diagnosis is markedly heterogeneous, with median survivals in the order of 8-10 years.Currently we have no reliable means to distinguish those patients who will die within three years of diagnosis from those who will be alive with little morbidity and requiring little if any therapy 25 years after diagnosis.These strikingly disparate outcomes in FL underlie the fury of activity surrounding the discovery of prognostic and predictive markers.The identification of molecular biomarkers that would help us better predict clinical outcome and risk of transformation would be a welcome addition, if indeed they could improve the prediction of prognosis beyond our current multi-parameter clinical indices.6 An ideal biomarker would not only identify patients with inferior
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".