Multiplex flow cytometry-based assay for quantifying tumor- and virus-associated antibodies induced by immunotherapies
Bibliographic record
Abstract
Novel immunotherapies continue to be developed and tested for application against a plethora of diseases. The clinical translation of immunotherapies requires an understanding of their mechanisms. The contributions of antibodies in driving long-term responses following immunotherapies continue to be revealed given their diverse effector functions. Developing an in-depth understanding of the role of antibodies in treatment efficacy is required to optimize immunotherapies and improve the chance of successfully translating them into the clinic. However, analyses of antibody responses can be challenging in the context of antigen-agnostic immunotherapies, particularly in the context of cancers that lack pre-defined target antigens. As such, robust methods are needed to evaluate the capacity of a given immunotherapy to induce beneficial antibody responses, and to identify any therapy-limiting antibodies. We previously developed a comprehensive method for detecting antibody responses induced by antigen-agnostic immunotherapies for application in pre-clinical models of vaccinology and cancer therapy. Here, we extend this method to a high-throughput, flow cytometry-based assay able to identify and quantify isotype-specific virus- and tumor-associated antibody responses induced by immunotherapies using small sample volumes with rapid speed and high sensitivity. This method provides a valuable and flexible protocol for investigating antibody responses induced by immunotherapies, which researchers can use to expand their analyses and optimize their own treatment regimens.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".