Quantifying Antibody Responses Induced by Antigen-Agnostic Immunotherapies
Bibliographic record
Abstract
As the development and clinical application of cancer immunotherapies continue to expand, so does the need for novel methods to dissect their mechanisms of action. Antibodies are important effector molecules in cancer therapies due to their potential to bind directly to surface-expressed antigens and facilitate Fc receptor-mediated uptake of antigens by antigen-presenting cells. Quantifying antibodies that are specific for defined antigens is straightforward. However, we describe herein a preclinical method to evaluate tumor-associated and virus-specific antibody responses to antigen-agnostic immunotherapies. This method uses autologous tumor cells as reservoirs of bulk tumor antigens, which can be bound by antibodies from the serum or plasma of tumor-bearing mice. These antibodies can then be detected and quantified using isotype-specific secondary antibodies conjugated to a fluorochrome. Alternatively, virus-infected cells can be used as a source of viral antigens. This method will enable researchers to assess antibody responses following immunotherapies without requiring pre-defined antigens. Alternatively, total virus-specific antibody responses could be studied as an alternative to more limited virus-neutralizing antibody assays. Therefore, this method can facilitate studying the role of humoral responses in the context of immunotherapies, including those that rely on the use of viral vectors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".