Gene Expression Model of Survival and Transformation in Follicular Lymphoma (FL): A Study by the LLMPP.
Bibliographic record
Abstract
Abstract Background: FL is a common NHL that has a broad spectrum of clinical outcomes. Over time some pts will transform to an aggressive histology (Tly) associated with inferior survival. In 2004, the LLMPP constructed a model that was predictive of overall survival (OS) based on the gene expression profiles (GEP) of 191 specimens taken from pts with untreated FL. The genes associated with survival were derived from the non-neoplastic immune response (IR) cells. However the risk of developing Tly was not addressed in this study. Thus we re-analyzed the GEP with updated clinical data. Our goal was to validate our previous model with extended follow-up and to create a model that would predict the risk of developing TLy. Methods: 170 of 191 previously untreated FL pts had updated clinical information but only 142 had transformation outcome. Transformation was defined as biopsy proven DLBCL or clinically based on the presence of at least one of the following: hypercalcemia, a sudden rise in LDH >twice baseline, unusual extranodal growth or rapid discordant nodal growth. Raw CEL files from Affymetrix U133A arrays were pre-processed and normalized using Bioconductor’s GCRMA package. Models were developed using SignS package (http://signs/bioinfo.cnio.es/), with 10 times cross-validation. All gene lists produced in these analyses were then re-tested for association with outcome using Bioconductor’s Globaltest package. Over Representation Analysis of signature components was performed using Dchip. Results: The median OS of these patients was 8 yrs. A new 7-component survival model (85 genes) was developed that was significantly associated with survival (p= 2.9×10−13). In Globaltest, these gene lists were associated with survival at a level of (p=2.6×10−5). The previous model using IR-1 and IR-2 signatures was associated with survival at a level of p=2.6×10−4. Although there is little overlap between the 2 models, the new model confirms the importance of IR genes and extracellular matrix genes as being prognostically important. Interestingly, one component containing 10 genes on chromosome 6q was associated with a superior survival (p<1×107). 27% developed Tly over a median follow-up time of 11.2 yrs (69% biopsy proven). Our transformation model included 53 genes divided into 3 components (p=0.001). The Globaltest analysis for association of these genes with transformation was significant (p=0.018). 54 genes overlapped between the survival genes and transformation genes that were present in >1 cross validation run. These were significantly enriched in genes important in immune response like T cell and macrophage activation. Conclusion: Our survival model is stable and confirms the importance of key genes involved in the immune response and lymph node remodeling. It also introduces new genes that are potentially important for survival. Our transformation model may shed light on the mechanisms involved in the progression of FL to DLBCL but it is less stable and less reliable than our survival model at predicting outcome.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".