The combination of chemotherapy and systemic immunotherapy with soluble B7–immunoglobulin G leads to cure of murine leukemia and lymphoma and demonstration of tumor-specific memory responses
Bibliographic record
Abstract
Major mechanisms underlying poor immune responses to autologous tumor-associated antigens are overwhelming tumor kinetics and the absence of effective T-cell costimulation by antigen-presenting cells. To address these issues, leukemia and lymphoma mice were treated with the combination of chemotherapy and systemic immunotherapy with recombinant soluble murine B7-immunoglobulin G (IgG) molecules. In this report, 3 murine models were used, a radiation-induced SJL acute myeloid leukemia, a transplantable spontaneous SJL lymphoma, and the C57BL/6 EL-4 thymic lymphoma. Various treatment modalities were evaluated: single treatments with either B7-IgG or chemotherapy as well as combination therapies. The results demonstrate the following: (1) in all tumor models, the combination of chemotherapy and soluble B7-IgGs is more potent than either therapy alone, leading to cure of tumor-bearing animals; (2) the therapeutic responses are T-cell-dependent, because combined therapy is not efficacious in severe combined immunodeficient mice; (3) the rejection of tumor cells leads to the development of tumor-specific immunity, because cured mice are immune to the rejected tumor but not to a different syngeneic tumor; and (4) (51)Cr release assays show that rejection of tumor cells leads to the development of very potent tumor-specific cytotoxic T-lymphocyte activity. On the basis of these results, it is proposed that chemotherapy-mediated tumor reduction, together with consequent augmented tumor-antigen presentation to activated T cells, are primary mechanisms leading to curative responses. The safety profile of the B7-IgG fusion proteins and their synergy with chemotherapy strongly suggest that the combination regimen is a promising strategy in cancer treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".