Abstract 4051: First step for development of checkpoint inhibitors immune monitoring panels in human whole blood and peripheral mononuclear cells
Bibliographic record
Abstract
Cancer immunotherapies represent more than half of treatments approved for oncology, as well as those currently in development. Among the key targets of these therapies are immune-checkpoint molecules (i.e. PD-1, LAG3, PD-L1, CTLA-4). Novel immunotherapies targeting these molecules are designed to impact tumor microenvironment immune-suppression. The characterization and monitoring of circulating and tumor infiltrating immune cells by flow cytometry is a critical aspect of pre-clinical and clinical drug development. In order to generated robust data to support drug development, flow cytometric methods must be fully optimized. In this presentation will discuss the critical aspects of assay optimization for flow cytometric methods with an emphasis on screening monoclonal antibody clones. The importance of the data is illustrated in the differences in assay performance when the correct clones are identified.Methods: Four different anti-PD-1 obtained from different providers were screened on activated human PBMC. Additionally, four PD-L1 and five LAG-3 monoclonal antibody clones were screened on activated or rested PBMC spiked in whole blood. All antibodies were all titrated to obtain saturating concentration and optimal titers of different clones were compared.Results: On CD4+ T cells from thawed cryopreserved peripheral blood mononuclear cells (PBMC), the best performance was observed with EH12.2H7 or EH12.1 clones which identified 30-33% of CD4+ T cells as PD-1 positive and up to 55-60% of CD8+ T cells as PD-1 positive. Clone eBioJ105 staining of the same samples on the same day identified 14% PD-1 positive in CD4+ T cell and around 40% PD-1 positive in CD8+ T cell, while clone MIH4 staining revealed low level of PD-1 expression on CD4+ T cells and CD8+ T cells. After ex vivo stimulation with staphylococcus enterotoxin B (SEB), EH12.2H7, EH12.1 and eBioJ105 clones all yielded similar results with 76-85% PD-1 positive on both CD4+ T cells and CD8+ T cells but fewer positive cells were identified with MIH4 clone.Out of the four PD-L1 antibodies tested, only two were able to distinguish a clear PD-L1 population on PBMC rested for 4 days and spiked in whole blood used as positive control for PD-L1 induction.For LAG-3 antibodies, all five were able to reveal the induction of this checkpoint molecule on cells activated by SEB and spiked in whole blood with % of LAG3+ cells in lymphocytes ranging from 13% to 25%.Conclusions: These results illustrate the importance of thorough reagent evaluation in order to achieve a sensitivity coherent with the information-rich promises of flow cytometry.Citation Format: Laila-Aicha Hanfi, Scott Sugden. First step for development of checkpoint inhibitors immune monitoring panels in human whole blood and peripheral mononuclear cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4051.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".