Prognostic Model to Predict Post-Autologous Stem-Cell Transplantation Outcomes in Classical Hodgkin Lymphoma
Bibliographic record
Abstract
Purpose Our aim was to capture the biology of classical Hodgkin lymphoma (cHL) at the time of relapse and discover novel and robust biomarkers that predict outcomes after autologous stem-cell transplantation (ASCT). Materials and Methods We performed digital gene expression profiling on a cohort of 245 formalin-fixed, paraffin-embedded tumor specimens from 174 patients with cHL, including 71 with biopsies taken at both primary diagnosis and relapse, to investigate temporal gene expression differences and associations with post-ASCT outcomes. Relapse biopsies from a training cohort of 65 patients were used to build a gene expression-based prognostic model of post-ASCT outcomes (RHL30), and two independent cohorts were used for validation. Results Gene expression profiling revealed that 24% of patients exhibited poorly correlated expression patterns between their biopsies taken at initial diagnosis and relapse, indicating biologic divergence. Comparative analysis of the prognostic power of gene expression measurements in primary versus relapse specimens demonstrated that the biology captured at the time of relapse contained superior properties for post-ASCT outcome prediction. We developed RHL30, using relapse specimens, which identified a subset of high-risk patients with inferior post-ASCT outcomes in two independent external validation cohorts. The prognostic power of RHL30 was independent of reported clinical prognostic markers (both at initial diagnosis and at relapse) and microenvironmental components as assessed by immunohistochemistry. Conclusion We have developed and validated a novel clinically applicable prognostic assay that at the time of first relapse identifies patients with unfavorable post-ASCT outcomes. Moving forward, it will be critical to evaluate the clinical use of RHL30 in the context of positron emission tomography-guided response assessment and the evolving cHL treatment landscape.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".