Inducible B effector cells (iBECs) for cancer immunotherapy (46.4)
Bibliographic record
Abstract
Abstract While cellular immunotherapy for malignances has traditionally focused on the development and manipulation of tumor targeted DCs, T and NK cells, B cells have remained vastly underutilized as a potential source for adoptive cell therapy. Here we demonstrate that B cells stimulated with FIST-2, a novel chimeric protein consisting of IL-2 fused to the ectodomain of the TGFβ receptor (type II), can adopt an effector phenotype with potent antitumor activity. Treatment with FIST-2 induces naïve splenic B cells to become B effector cells (iBECs), characterized by hyperphosphorylation of STAT3 and 5 downstream of the IL-2 receptor, upregulation of transcription factor, T-bet, and secretion of pro-inflammatory cytokines: IFNγ, TNFα and IL-6. iBECs retained their B cell identity by CD19 and PAX5 expression, but adopted an enhanced APC phenotype through upregulation of cell surface markers associated with antigen presentation and co-stimulation, including: MHC-II, CD80 and CD86. To determine whether iBECs conferred antitumor immunity, we utilized a mouse model of lymphoma expressing ovalbumin (EG.7-OVA). Syngeneic iBECs pulsed with OVA were able to activate OVA-specific OT-I and OT-II T cells in vitro, suggesting that they act as APCs. In vivo administration of OVA-pulsed iBECs protected immunocompetent C57BL/6 mice from EG.7-OVA tumor challenge, and promoted tumor regression in mice with pre-established tumors. These data support the concept of B cell-based adoptive immunotherapy.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".