Prognostic Impact of Natural Killer Cell Count in Follicular Lymphoma and Diffuse Large B-cell Lymphoma Patients Treated with Immunochemotherapy
Bibliographic record
Abstract
Abstract Purpose: Natural killer (NK) cells are key effector cells for anti-CD20 monoclonal antibodies (mAb), such as obinutuzumab and rituximab. We assessed whether low pretreatment NK-cell count (NKCC) in peripheral blood or tumor tissue was associated with worse outcome in patients receiving antibody-based therapy. Patients and Methods: Baseline peripheral blood NKCC was assessed by flow cytometry (CD3−CD56+ and/or CD16+ cells) in 1,064 of 1,202 patients with follicular lymphoma treated with obinutuzumab or rituximab plus chemotherapy in the phase III GALLIUM trial (NCT01332968) and 1,287 of 1,418 patients with diffuse large B-cell lymphoma (DLBCL) treated with obinutuzumab or rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (G-CHOP or R-CHOP) in the phase III GOYA trial (NCT01287741). The prognostic value of tumor NK-cell gene expression, as assessed by whole-transcriptome gene expression using TruSeq RNA sequencing, was also analyzed. The association of baseline variables, such as treatment arm, was evaluated using multivariate Cox regression models using a stepwise approach. Results: In this exploratory analysis, low baseline peripheral blood NKCC was associated with shorter progression-free survival (PFS) in both follicular lymphoma [hazard ratio (HR), 1.48; 95% confidence interval (CI), 1.02–2.14; P = 0.04] and DLBCL (HR, 1.36; 95% CI, 1.01–1.83; P = 0.04), and overall survival in follicular lymphoma (HR, 2.20; 95% CI, 1.26–3.86; P = 0.0058). Low tumor NK-cell gene expression was associated with shorter PFS in G-CHOP–treated patients with DLBCL (HR, 1.95; 95% CI, 1.22–3.15; P < 0.01). Conclusions: These findings indicate that the number of NK cells in peripheral blood may affect the outcome of patients with B-cell non-Hodgkin lymphoma receiving anti-CD20–based immunochemotherapy.
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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".