The Stromal Cell Marker SPARC Predicts for Survival in Patients With Diffuse Large B-Cell Lymphoma Treated With Rituximab
Bibliographic record
Abstract
The cellular composition of the tumor microenvironment may affect survival in diffuse large B-cell lymphoma (DLBCL). We performed immunostains for 2 stromal cell markers, CD68 and SPARC (secreted protein, acidic and rich in cysteine), in 262 patients with DLBCL treated with rituximab and cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or CHOP-like therapies. Patients with any SPARC+ cells in the microenvironment had a significantly longer overall survival, and patients with high SPARC positivity in the microenvironment also had a significantly longer event-free survival. Survival differences were mainly due to the prognostic effect of SPARC+ cells in activated B-cell (ABC)-type DLBCL, with no effect found in the germinal center B-cell-type DLBCL. Of clinical features examined, only the number of extranodal sites was significantly associated with SPARC expression. Multivariate analysis revealed that SPARC expression predicted patient survival independent of the International Prognostic Index or tumor cell of origin. SPARC expression in the microenvironment of DLBCL can be used for prognostic purposes, determining a subgroup of patients with ABC DLBCL who have significantly longer survival. More aggressive chemotherapy protocols should be considered for patients with ABC DLBCL without SPARC+ stromal cells. CD68 expression by cells in the microenvironment did not predict survival.
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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) | 0.001 | 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".