Identification of Combined Clinical and Biomarker Prognostic Factors in Follicular Lymphoma.
Bibliographic record
Abstract
Abstract Follicular lymphoma (FL) is generally an indolent disease, progressing slowly over a period of many years. However, some patients experience a rapid progression or histological transformation to an aggressive lymphoma requiring more aggressive treatment. Identification of these patients at the time of the initial diagnosis would allow more informed decisions to be made regarding clinical management. We investigated whether the expression of specific proteins in FL cells in combination with baseline clinical data could predict for relapse, transformation and survival of patients with follicular lymphoma. A tissue microarray (TMA) was generated of 67 cases of FL diagnosed between 1974 and 2003. Clinical baseline and follow-up data were obtained (median of 58 months). Forty-one patients experienced relapse within the follow-up period and thirty-one individuals transformed to aggressive lymphoma. Median overall survival was 13.6 years. Immunohistochemistry (IHC) staining was obtained for markers routinely used in lymphoma diagnosis, as well as oncogenesis-relevant proteins including p53, bcl-2, bcl-6, mum1, p16 and p65. Evaluations of the presence of benign T-cells, infiltrating macrophages, and the follicular dendritic cell network were also made. Univariate analysis was undertaken using the Kaplan-Meier method in order to assess the prognostic power of various clinical, histological and IHC variables with respect to overall survival, time to transformation and time to relapse. Variables that had prognostic significance (p<0.05) in univariate analysis were included in multivariate models of these end-points. High tumour grade, expression in tumour cells of p16 and lack of bcl-2 predicted shorter transformation-free survival in multivariate analysis. Advanced tumour stage, presence of B symptoms, expression of p53 or p16 in tumour cells, presence of benign T-cells in the tumour environment and integrity of the benign follicular dendritic cell network had independent prognostic power for shorter relapse-free survival. Greater than 5 nodal sites of disease, presence of B symptoms at diagnosis and expression of p53 in lymphoma cells each emerged as significant and independent predictors of overall survival using a Cox regression model. These results indicate that protein-based expression profiling using TMAs in combination with clinical data is a potentially productive means of identifying a prognostic index that can be implemented in the clinical management of follicular lymphoma patients. Further study is required to compare these results to existing clinical prognostic scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".