Immune Profiling Mass Cytometry Assay Harmonization: Multicenter Experience from CIMAC-CIDC
Bibliographic record
Abstract
PURPOSE: The Cancer Immune Monitoring and Analysis Centers - Cancer Immunologic Data Commons (CIMAC-CIDC) Network is supported by the NCI to identify biomarkers of response to cancer immunotherapies across clinical trials using state-of-the-art assays. A primary platform for CIMAC-CIDC studies is cytometry by time of flight (CyTOF), performed at all CIMAC laboratories. To ensure the ability to generate comparable CyTOF data across labs, a multistep cross-site harmonization effort was undertaken. EXPERIMENTAL DESIGN: We first harmonized standard operating procedures (SOPs) across the CIMAC sites. Because of a new acquisition protocol comparing original narrow- or new wide-bore injector introduced by the vendor (Fluidigm), we also tested this protocol across sites before finalizing the harmonized SOP. We then performed cross-site assay harmonization experiments using five shared cryopreserved and one lyophilized internal control peripheral blood mononuclear cell (PBMC) with a shared lyophilized antibody cocktail consisting of 14 isotype-tagged antibodies previously validated, plus additional liquid antibodies. These reagents and samples were distributed to the CIMAC sites and the data were centrally analyzed by manual gating and automated methods (Astrolabe). RESULTS: Average coefficients of variation (CV) across sites for each cell population were reported and compared with a previous multisite CyTOF study. We reached an intersite CV of under 20% for most cell subsets, very similar to a previously published study. CONCLUSIONS: These results establish the ability to reproduce CyTOF data across sites in multicenter clinical trials, and also highlight the importance of quality control procedures, such as the use of spike-in control samples, for tracking variability in this assay.
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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 | 0.124 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".