P02.10 Simplifying high-parameter phenotypic and functional characterization of cancer immune cells
Bibliographic record
Abstract
<h3>Background</h3> Interrogating immune cell composition andfunction in patients with cancer is critical for making disease prognoses,monitoring clinical efficacy of tumor immunotherapies, identifying novel therapeutic targets, and discovering pre-dictive biomarkers of disease. Both the adaptive and innate arms of the immune system play important roles in generating pro-or anti-tumor milieus. Effector cells such as NK cells and T cells can directly kill tumor cells via secretion or cell-surface expression of cytolytic proteins and modulate the immune response through costimulatory molecules. <h3>Materials and Methods</h3> In multiple myeloma, malignant plasma cells accumulate in the bone marrow through clonal expansion, crowding out other cells and leading to anemia, renal insufficiency, immunosuppression, and increasing risk of multisystem organ damage if untreated. Cellular and antibody-mediated immunotherapeutic approaches, including CAR T cells and monoclonal antibodies targeting CD38, have been developed to treat multiple myeloma. Since NK cells can also indirectly impact CAR T cell or antibody-based immuno-therapies, characterizing these cells using optimized and reproducible assays is critical. <h3>Results</h3> CyTOF®is a high-plex flow cytometry technology that uses metal-isotope-taggedantibodies to probe cellular phenotypes and functions. In contrast tofluorescence-based conventional and spectral flow cytometry, CyTOF experimental workflows are streamlined because 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 9 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. <h3>Conclusions</h3> Here we demonstrate combining the Maxpar Direct Immune Profiling Assay with the NK Cell Expansion Panel (CD181, NKp30, NKp46, PD-1, NKG2A, ICOS, and TIGIT) or the T Cell Expansion Panel 3 (OX40, TIGIT, CD69, PD-1, Tim-3, ICOS, and 4-1BB) with the Basic Activation Expansion Panel (IL-2,TNFα, IFNγ, CD107a, perforin, granzyme B) to enable deep immunoprofiling of multiple myeloma PBMC. <b>A. Thomas-Claudepierre:</b> A. Employment (full or part-time); Significant; Standard BioTools. <b>D. Mahamed:</b> A. Employment (full or part-time); Significant; Standard BioTools. <b>M. Cohen:</b> A. Employment (full or part-time); Significant; Standard BioTools. <b>S. Li:</b> A. Employment (full or part-time); Significant; Standard BioTools. <b>L. Tracey:</b> A. Employment (full or part-time); Significant; Standard BioTools. <b>H. Yao:</b> A. Employment (full or part-time); Significant; Standard BioTools. <b>C. Loh:</b> A. Employment (full or part-time); Significant; Standard BioTools. <b>L. Fung:</b> A. Employment (full or part-time); Significant; Standard BioTools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".