Number of Lymphoma-Associated-Macrophages (LAM) Is An Independent Predictor of Survival in Patients with Mantle Cell Lymphoma (MCL).
Bibliographic record
Abstract
Abstract Abstract 3944 Poster Board III-880 Background MCL is a clinically aggressive lymphoma characterized by frequent relapses and short survival. Clinical behavior and response to therapy vary considerably and are often unpredictable based on clinical factors. Prior gene and protein expression studies have demonstrated proliferation and TP53 expression as biological mechanisms that strongly influence survival (Rosenwald Cancer Cell 2003 & Katzenberger Blood 2006 and Louie Blood 1995). Recent studies have highlighted the role of the microenvironment in follicular lymphoma (FL) including numbers of lymphoma-associated-macrophages (LAM), as important prognostic factors (Dave NEJM 2004 & Farinha Blood 2005). Within most tumors, macrophages display an M2 phenotype that promotes tumor growth and angiogenesis. In MCL, the clinical impact of LAM is unknown. Our aim was to assess the prognostic impact of LAM in MCL patients from a single institution experience using tissue microarrays (TMA), immunohistochemistry and clinical correlates. Methods A TMA block was built with duplicate 0.6mm cores of paraffin-embedded formalin-fixed diagnostic biopsies from 185 patients treated at the BC Cancer Agency (1983-2004). A subset of cases was also analyzed for the presence of the t(11;14) by FISH. Primary and subsequent therapy included multiple regimens: single or multi-agent chemotherapy with or without rituximab, stem cell transplant, radiation and therapeutic splenectomy. Standard immunohistochemistry was performed with CD20, CCND1, CD68, CD163 (an M2 marker), CD34, TP53 and Ki67. TP53 and Ki67 IHC were scored using image analysis (VIAS). Univariate and multivariate correlates of overall survival (OS) were determined using SPSS software. Results The median follow-up of living patients was 5.75 years. The IPI predicted OS (p=0.0013). Numbers of cases with interpretable staining varied for the different biomarkers (range 157 to 180). All 185 cases were either CCND1 positive and/or t(11;14) FISH positive. Both proliferation (quartiles of Ki67+ cells) and TP53 (dichotomized based on cut-off >40%) were significant predictors of OS independent of IPI (OS, p=0.0002). Cases showing more than 5% infiltrating CD68+ cells (18/185) had a 4-y OS of 5% vs. 35% (p=0.0081). These CD68+ cases significantly correlated with 12 cases showing more than 5% infiltrating CD163+ cells (x2=0.001), which also significantly influenced OS (p=0.008). 17 cases had increased microvessel density (MVD), defined by CD34+ cells, which correlated with inferior OS (p=0.02). Interestingly, cases with increased MVD often had >5% CD163+ cells (x2=0.05), but not increased CD68+ cells. Multivariate analysis including IPI, TP53, Ki67, CD68, CD163 and CD34, showed IPI (RR=1.8, 95%CI=1.0-3.1, p=0.047), TP53 (RR=2.3, 95%CI=1.3-4.3, p=0.006), Ki67 (RR=2.2, 95%CI=1.2-4.2, p=0.012) and CD68 (RR=2.1, 95%CI=1.1-3.8, p=0.022) to be independent predictors of OS. Finally, in the 27 patients who received rituximab at some time during their disease course, increased CD68+ cells did not significantly affect OS (p=0.1), suggesting that, similar to FL, the prognostic effect of LAM may be abolished by rituximab. Conclusions The number of LAMs is an important prognostic factor in MCL, independent of clinical parameters, proliferation and TP53 expression. Expression of CD163, described as an M2 polarized macrophages, also predicts survival and correlates with increased angiogenesis. These preliminary data suggest that the use of rituximab may abrogate the prognostic impact of LAM in MCL. In summary, similar to other lymphoma subtypes, the microenvironment is important in MCL biology and prognosis and may be an important new target for therapy in this aggressive disease. Disclosures: Connors: Roche Canada (F Hoffmann-La Roche): Research Funding. Gascoyne:Roche Canada, Genentech, Lilly, Millennium : Honoraria, Membership on an entity's Board of Directors or advisory committees, 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 | 0.000 | 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) | 0.002 | 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".