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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".