Public immunotherapy developer uses 40+ parameter cytometry to uncover immune cells driving response in ~200 blood samples in Phase 1 study

Client:

$5B+ public immunotherapy company

Goal

• See how the drug changed the immune cell composition in healthy participants in a Phase 1 trial at two doses and one placebo control
  • The developer sought to see if specific immune cells were driven to the right level.
  • Specifically, the developer wanted to look for changes in a myeloid cell subset, as these cells could drive response. But, the developer was aware that too many of this subset could lead to toxicity.
  • In parallel, the developer wanted to see impacts on other immune cell types that could be impacted indirectly by effects of the drug.

Method:

• Teiko Custom Panel: 42 marker panel, 4 custom markers
• Mass Cytometry
• Sample Type: PBMC
• Cohort:
  • ~200 Peripheral Blood samples
    • ~20 healthy volunteers, ~10 timepoints per patient

Run time: 6.5 weeks

Results:

• For each of the custom markers, we performed a 6-point titration from 6 µg/mL, following a two-fold dilution down to 0.1875 µg/mL. At each concentration, we calculated the archsinh ratio, and assessed for background signal and spillover into neighboring channels. 
• We ran the full panel with 38 backbone markers and 4 additional markers, using both unstimulated and stimulated PBMC control samples. We evaluated the immune population distribution (percentage of non-granulocytes or parent) and compared each marker’s median channel value (MCV) to the reference. Finally, we checked that the full panel run did not cause spillover into new marker channels or antibody aggregations leading to false positives.
• Once samples were processed on the mass cytometers, all results were loaded onto app.teiko.bio. The developer was able to access the raw .fcs files, GatingML files, a sample gating scheme, a subset hierarchy showing which markers were evaluated in which immune cell populations, and their sample and gating QC reports.
• The developer found expected changes in the myeloid cell subset they expected, and found statistically significant detailed changes in lymphoid cell subsets (and in particular T cell subsets) that were associated with dose.

Who cares?

By implementing a ~17X increase (from 52 subsets to 858) in resolution over a comparable 8 marker panel, the drug developer was able to validate expected findings that the developer previously found on conventional flow cytometry. Moreover, the developer found new details that could drive indication selection.