Tumor-Associated Macrophages Predict Outcome in Follicular Lymphoma
Bibliographic record
Abstract
Abstract Abstract 682 Background: Follicular lymphoma (FL) is the most common indolent lymphoma and is clinically characterized by slow progression and responsiveness to therapy, but almost inevitable disease recurrences. While patient outcomes are generally favorable, a substantial proportion of patients are at risk of early death due to treatment resistance, early progression or transformation. CD68 positive macrophages were shown to predict poor survival in the pre-rituximab era (Farinha et al., Blood 2005 Sep 15;106(6):2169–74), but their association with poor outcome was lost or even reversed since the introduction of rituximab into the routine management of FL (Taskinen et al, Clin Cancer Res. 2007 Oct 1;13(19):5784–9 and Canioni et al, J Clin Oncol. 2008 Jan 20;26(3):440–6). Macrophages can be broadly divided into M1 and M2 subtypes with the latter associated with pro-tumoral immunity. We hypothesize that CD68 alone insufficiently captures the various phenotypes of tumor-associated macrophages and that staining for CD163 as a marker for alternatively activated macrophages (M2) provides additional prognostic information. Methods: We constructed a tissue microarray (TMA) from formalin-fixed and paraffin-embedded tissue blocks of 187 systemic treatment-naive FL patients that were selected from the Lymphoid Cancer Database of the BC Cancer Agency and that had been uniformly treated between 2004 and 2010 with rituximab, cyclophosphamide, vincristine and prednisone (R-CVP). Since 2006, institutional guidelines recommended maintenance with rituximab for patients achieving a complete or partial remission after first-line therapy. Median follow-up of living patients was 56 months. Immunohistochemistry was performed on the TMA with antibodies against CD68 (clone KP1, DAKO) and CD163 (clone 10D6, Novocastra) and was analyzed by computer image analysis (Aperio) using the Positive Pixel Count algorithm. Disease-specific survival (DSS) served as the primary end-point, and progression-free survival (PFS) and time to transformation (TTT) as secondary endpoints. The statistical software X-tile was used to determine the optimum thresholds for CD68 and CD163 as the values that maximized the Chi-square value of the log-rank test. Results: Within the cores on the TMA, the mean percentage of positivity was 4.7% for CD68 (range 0.3–22.6%) and 1.8% for CD163 (range 0–13.7%). Above a threshold of 1.8%, high CD68 expression was associated with favorable DSS (p=0.03), but not with PFS (p=0.47) or TTT (p=0.82). Increased CD163 expression on the other hand, above a threshold of 1.1%, was significantly associated with poor DSS (p<0.01), but not with PFS (p=0.68) or time to transformation (p=0.13). When adjusting for the International Prognostic Index (IPI) in a Cox regression model, the association of high CD68 expression with favorable DSS (hazard ratio=0.42, p=0.05) and high CD163 expression with poor DSS (hazard ratio=2.93, p=0.01) remained significant. The correlation between CD68 and CD163 expression was low (Pearson correlation coefficient=0.25), suggesting that both markers define different subsets of tumor-associated macrophages. To explore whether additional prognostic information could be gained from combining these markers, we grouped CD68-high and CD163-low cases into one category (n=83) and compared them against all others (n=102). High CD68 and low CD163 expression was associated with favorable DSS (p<0.01), but not PFS (p=0.40) or TTT (p=0.21). Patients with high CD68 and low CD163 expression had excellent outcomes at 7 years of follow-up (96% DSS versus 68%). The impact on DSS remained significant after adjusting for IPI in a Cox regression model (hazard ratio=0.15, p<0.01). Conclusion: In FL patients treated with uniform induction therapy, CD163 defines a subset of tumor-associated macrophages that is associated with inferior DSS, but not with early progression or transformation, whereas the combination of CD68-high and CD163-low defines a patient category that has excellent prognosis. Our results suggest that the detrimental role of alternatively activated M2 type macrophages is not reversed by rituximab and that a population of non-tumor promoting macrophages exists that is correlated with favorable outcome in FL. Our study also illustrates the power of image analysis to provide accurate scoring, capable of delineating patient populations at variable risk of death. Disclosures: Al-Tourah: F. Hoffmann-La Roche (Roche Canada): Research Funding. Moccia:F. Hoffmann-La Roche (Roche Canada): Research Funding. Connors:F. Hoffmann-La Roche (Roche Canada): Research Funding. Sehn:F. Hoffmann-La Roche (Roche Canada): Research Funding.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".