Analysis of Relapse Biopsies in Classical Hodgkin Lymphoma Reveals Correlations with Outcome after Autologous Stem Cell Transplantation
Bibliographic record
Abstract
Abstract Introduction : Classical Hodgkin lymphoma (cHL) is the most common lymphoma affecting individuals under the age of 30. Despite improvements in standard treatment, 25-30% of patients still relapse after first-line treatment with ABVD. Relapsed patients are then typically treated by high dose chemotherapy and autologous stem cell transplantation (HDC/aSCT). However, this salvage therapy only cures approximately 50% of relapsed patients, and the mechanisms of second-line treatment failure are unclear. Moreover, currently no reliable biological markers are available to predict the outcome of HDC/aSCT at the time of relapse. The specific aim of this study is to compare diagnostic and relapse biopsies along with integrating clinical outcome data to uncover biology associated with resistance to first-line therapy, and identify prognostic markers for HDC/aSCT. Materials & Methods: NanoString Technologies digital gene expression profiling was used to ascertain the gene expression of 785 genes in formalin-fixed paraffin embedded tissue of 245 biopsies sampled from 174 cHL patients. This cohort included 90 patients with single biopsies performed at first diagnosis, 13 patients with single biopsies taken at relapse, and 71 patients with paired biopsies taken at both first diagnosis and at relapse. All 174 patients relapsed following uniform first line treatment with ABVD. Of these, 151 patients went on to receive salvage treatment that included HDC/aSCT. The 785 genes reflect biological signatures representative of cell types typically found in the microenvironment of cHL, and those previously reported to be associated with outcome in cHL. A tissue microarray was constructed containing all biopsies for immunohistochemistry (IHC) using CD68 antibodies (clone KP1). Spearman correlation tests were used for comparative gene expression analyses and IHC correlations. Differential expression analyses were performed using a non-parametric Wilcoxon test. Gaussian mixture models were used to cluster patients into poorly and well-correlated biopsy pairs. Outcome correlations were performed using the log-rank test and Cox regression analysis. Results : 24% of patients had a histological subtype transition between diagnostic and relapse biopsies with the most common transition (46%) from mixed cellularity (at diagnosis) to nodular sclerosis (at relapse). Comparative gene expression analysis revealed that 17 of the 71 patients (24%) had poorly correlated biopsy pairs (R2 < 0.75). Specifically, genes associated with macrophage differentiation (e.g. CD68, MARCO; FDR < 0.1) were found to be more highly expressed in the relapse biopsies compared to the matching samples at first diagnosis. CD68 IHC staining was well correlated with mRNA expression (p < 0.001) and confirmed that CD68+ cells were significantly more frequent in the relapse biopsies (p = 0.023). Patients with poorly correlated biopsy pairs had an inferior post-HDC/aSCT failure-free survival (5-y FFS: poorly correlated 39% vs. well correlated 68%; log-rank p = 0.005). Additionally, we applied our previously published 23-gene predictor (Scott, JCO, 2013), that was originally developed for pre-treatment biopsies, to the relapse biopsies. Using the published thresholds, 19% of patients were designated high-risk and had significantly inferior outcomes post-HDC/aSCT (5 year post-HDC/aSCT overall survival 40% vs. 82%, log-rank p = 0.001; 5 year post-HDC/aSCT failure-free survival 39% vs. 72%, log-rank p = 0.004). Conclusions : Our comparative analysis of cHL pre-treatment and relapse biopsies reveals differences at both the histopathological and molecular levels. We have identified novel factors, such as a poor correlation between paired biopsies and the 23-gene predictor, that are predictive of an inferior post-HDC/aSCT outcome. These findings suggest that re-biopsying patients at relapse will yield important biological insight and superior predictive power for second-line treatment failure. Disclosures Gerrie: F Hoffmann-La Roche: Other. Savage:F Hoffmann-La Roche: Other.
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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 | 0.000 |
| 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 | 0.000 | 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".