The KIR-HLA-CD16a Immunogenetic Profile Influences NK Cell-Mediated ADCC and Response to Rituximab Therapy in B-Cell Lymphomas
Bibliographic record
Abstract
Rituximab-based chemoimmunotherapy (R-CHOP) is the standard of care in most B-cell lymphomas, however there is marked interpatient heterogeneity in treatment response. An important mechanism of Rituximab (anti-CD20) action is through natural killer (NK) cell-mediated antibody-dependent cellular cytotoxicity (ADCC), but the factors influencing this are poorly understood. Here, we present findings from a preclinical study investigating the role of NK cell receptor polymorphisms and HLA allelic variation on ADCC against aggressive B-cell lymphoma cell lines. Genotyping was conducted using Luminex-based SSO and Sanger sequencing. Multicolor flow cytometry was used to assess the response of activated NK cells from healthy donors against 8 lymphoma cell lines. Individuals carrying the GG or GT allele in CD16a were found to have a 3-8 fold greater NK cell response compared to individuals with a TT allele and rituximab-mediated ADCC was significantly greater in individuals with a KIR-3DL1 allele when tested against B-cell lymphomas lacking the HLA-Bw4 allele. An additive effect was observed when considering both CD16a and KIR-HLA together. As expected, CD20 expression was a strong correlate of NK cell response. In summary, NK cell-mediated ADCC in response to ADCC appears to be influenced by the KIR-HLA-CD16a genotype. These findings suggest a potential role for immunogenotyping in B-cell lymphomas treated with Rituximab. Additionally, the presence of the shared F c receptor domain opens the possibility of applying this framework towards predicting NK cell responses to antibody therapies in other diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".