Validation of CyTOF Against Flow Cytometry for Immunological Studies and Monitoring of Human Cancer Clinical Trials
Bibliographic record
Abstract
Flow cytometry is a widely applied approach for exploratory immune profiling and biomarker discovery in cancer and other diseases. However, flow cytometry is limited by the number of parameters that can be simultaneously analyzed, severely restricting its utility. Recently, the advent of mass cytometry (CyTOF) has enabled high dimensional and unbiased examination of the immune system, allowing simultaneous interrogation of a large number of parameters. This is important for deep interrogation of immune responses and particularly when sample sizes are limited (such as in tumors). Our goal was to compare the accuracy and reproducibility of CyTOF against flow cytometry as a reliable analytic tool for human PBMC and tumor tissues for cancer clinical trials. We developed a 40+ parameter CyTOF panel and demonstrate that compared to flow cytometry, CyTOF yields analogous quantification of cell lineages in conjunction with markers of cell differentiation, function, activation, and exhaustion for use with fresh and viably frozen PBMC or tumor tissues. Further, we provide a protocol that enables reliable quantification by CyTOF down to low numbers of input human cells, an approach that is particularly important when cell numbers are limiting. Thus, we validate CyTOF as an accurate approach to perform high dimensional analysis in human tumor tissue and to utilize low cell numbers for subsequent immunologic studies and cancer clinical trials.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".