Immune monitoring for immuno-oncology applications
Bibliographic record
Abstract
Abstract The immunotherapy revolution has spurred the development of many new drugs and drug regimens for patient treatment. A key challenge is to identify the factors that drive patient toxicities and responses to treatment, with a particularly acute need for predictive biomarkers that can discriminate patients destined to respond and fail treatment. Technologies to interrogate immune cells are now readily available, but important gaps remain in their application, which limit the full realization of the promise of precision oncology. High parameter flow cytometry is a gold-standard but is limited by difficulty of panel design and lack of standardization. We present Color Wheel, a panel design tool, which builds optimized antibody panels based on the user’s instrument. These optimized panels have been manufactured in a dried, ready to use format to drive workflow and assay standardization. Initial results demonstrate excellent concordance between the dried and liquid versions of the high parameter multicolor panel(s). Molecular cytometry represents an exciting new approach to high dimensional immune analysis because it can measure at least 102 proteins and 400 mRNA targets simultaneously per cell. An important application gap for this technology is lack of data indicating the sequencing depth needed for adequate resolution. Here, we present results from a molecular cytometry experiment sequenced deeply, and then bioinformatically sub-sampled at different levels to identify the minimum level of sequencing needed for clear identification of cells, which can serve as a reference guide for users to save time and cost in their experiments. We also present preliminary data generated using dried version of molecular cytometry panel(s).
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".