Abstract 6671: Simplifying high-parameter phenotypic and functional characterization of cancer immune cells
Bibliographic record
Abstract
Abstract Interrogating immune cell composition and function in patients with cancer is critical for making disease prognoses, monitoring clinical efficacy of tumor immunotherapies, identifying novel therapeutic targets, and discovering predictive biomarkers of disease. Both the adaptive and innate arms of the immune system play important roles in generating pro- or anti-tumor milieus. In particular, natural killer (NK) cells are gaining increasing attention as potential cell-based therapies in immuno-oncology, including in adoptive transfer of chimeric antigen receptor (CAR) and iPSC-derived NK cells or off-the-shelf NK cell lines to target multiple myeloma. Since NK cells can also indirectly impact CAR T cell or antibody-based immunotherapies, characterizing these cells using optimized and reproducible assays is critical. CyTOF® is a high-plex flow cytometry technology that exploits metal-isotope-tagged antibodies to probe cellular phenotypes and functions. In contrast to fluorescence-based conventional and spectral flow cytometry, CyTOF experimental workflows are streamlined as autofluorescence is not an issue and signal spillover is minimal, allowing rapid design and application of 40-plus-marker panels. To expand on the increasing clinical and preclinical utility of the 30-marker Maxpar® Direct™ Immune Profiling Assay™ (Maxpar Direct Assay), we developed nine add-on Expansion Panels for deeper phenotyping of specific cell types and activation states, including panels designed to characterize ex vivo and activated myeloid cells, T cells, and NK cells. Peripheral blood mononuclear cells from healthy donors and donors with multiple myeloma were stimulated in vitro, then stained in the 30-antibody Maxpar Direct Assay tube with the NK Cell Expansion Panel (CD181, NKp30, NKp46, PD-1, NKG2A, ICOS, and TIGIT) or the T Cell Panel 3 (OX40, TIGIT, CD69, PD-1, Tim-3, ICOS, and 4-1BB) as drop-in antibodies. Surface staining was followed by intracellular staining with the Basic Activation Expansion Panel antibodies (IL-2, TNFα, IFNγ, perforin, granzyme B) after cell fixation and permeabilization. Anti-CD107a was added during stimulation to measure degranulation. Samples were acquired on a CyTOF XT™ instrument in automated batch acquisition mode. Automated analysis was performed with Maxpar Pathsetter™ software to enumerate immune cell types and quantify marker expression. In addition to the 37 populations identified by Pathsetter with the base Maxpar Direct Assay, the new Expansion Panels allowed deeper profiling of NK and T cells, while the Basic Activation Panel revealed their cytokine responsiveness and cytotoxic potential. Thus, the optimized single-tube Maxpar Direct Assay is a powerful tool that can be further expanded and customized with predefined panels or antibodies to comprehensively study immune cells in health and disease. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Deeqa Mahamed, Michael Cohen, Stephen Li, Lauren Tracey, Huihui Yao, Christina Loh, Leslie Fung. Simplifying high-parameter phenotypic and functional characterization of cancer immune cells. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6671.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".