Combined FOXP3+ and PD1+ T Cell Density and Architectural Patterns Predict Overall Survival and Risk of Transformation in Uniformly Treated Patients with Follicular Lymphoma
Bibliographic record
Abstract
Abstract Background: Follicular lymphoma (FL) is an indolent heterogeneous lymphoid neoplasm with a variable clinical course. Transformation into an aggressive lymphoma is a dominant clinical event frequently associated with inferior survival. No consistent biological markers are predictive of survival or risk of transformation (RT). Recent studies have highlighted the role of the microenvironment, including regulatory T cells (Treg) as important prognostic factors in FL. Using tissue microarrays (TMA), we previously identified FOXP3+ T cell distribution but not cell density within the tumor is an independent adverse prognostic factor for both overall survival (OS), progression-free (PFS) survival and RT in advanced-stage FL pts treated uniformly with an aggressive treatment regimen (ASH 2007). Programmed death-1(PD1) belongs to the immunoglobulin CD28 receptor family and is characteristically expressed by germinal center associated T cells. They have imunoregulatory activity and are FOXP3-negative. Using the same cohort we assessed the role of PD1+ cells alone and in combination with FOXP3+ cells in FL prognosis. Methods: Between 1987 and 1993, 126 pts were enrolled on a phase II study of BP-VACOP chemotherapy with involved region radiotherapy. All patients were treatment naive, < 61y and had advanced-stage FL. Paraffin blocks were available for 105 pts. TMAs consisted of duplicate 1.0mm cores of diagnostic biopsies and were immunostained with both FOXP3 and PD1 (NAT105) specific antibodies. Both cell content and immuno-architectural patterns were determined and correlated with OS and RT including univariate and multivariate analysis. Results: There were 101 evaluable cases. The median follow-up of the living pts was 14 years. Histologic grade included 78 grade 1, 17 grade 2 and 6 grade 3a FLs. The IPI was predictive of OS (RR = 2.5, 95% CI =1.5–4.4, p = 0.002), but not RT (p = 0.14). Cases revealed zero to 731 FOXP3+ cells per core (median of 242 cells per core) and zero to 654 PD1+ cells per core (median of 102 cells per core). There was a predominantly intrafollicular or perifollicular localization of positive cells (“follicular pattern”) in 38 and 43 pts for FOXP3 and PD1, respectively. There was no significant correlation between cell density (χ2=0.1) or pattern (χ2=0.8) between markers. Separately, cell density did not impact prognosis. Yet, when quartiles were combined and dichotomized, cases with high cell content of FOXP3+/PD1+ cells correlated with inferior OS and increased RT in univariate (p = 0.002 and p = 0.01) and multivariate Cox models (RR = 2.18, 95% CI =1.2–3.9, p = 0.01 & RR = 3.4, 95% CI =1.3–8.8, p = 0.015). In univariate analysis, a PD1 follicular pattern correlated with inferior OS (p=0.027) but did not impact RT. When combined with follicular FOXP3+ cases, a subset of 15 “double follicular” FOXP3 & PD1 cases correlated with inferior OS in univariate (p=0.0016) and multivariate Cox models (RR = 2.1, 95% CI =1–4.3, p = 0.04). This subset of cases also showed a trend for increased RT (p = 0.09). Conclusions: Combined cell density and distribution within FL of immuno-regulatory T cell subsets are important predictors of OS & RT in advanced-stage FL patients treated uniformly with an aggressive treatment regimen. Our results reinforce the importance of the microenvironment in FL biology and further suggest an active role for immuno-modulatory T cells in pro-tumoral immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".