Abstract A029: Treatment-Specific Immune Phenotypes Identified by nELISA High-Throughput Proteomics Reveal Actionable Insights for Drug Discovery
Bibliographic record
Abstract
Abstract Understanding tumor immunology requires high content tools that can capture the complex microenvironment, as well as high-throughput cell-based assays to rapidly screen compounds, antibodies, or cell therapies. Unfortunately, proteomics tools to investigate interactions between cancer and immune cells compromise either content or cost, limiting access to phenotypic data. To overcome this issue, we developed the nELISA: a high-throughput miniaturized ELISA quantifying 191 cytokines, chemokines, proteases and growth factors, at 10x-reduced cost compared to previous tools, and applied it to cell based models to demonstrate its ability to characterize immune phenotypes in co-culture systems. We ran the largest PBMC secretome screen to date, in which ~10,000 PBMC samples were treated with various inflammatory stimuli, and were further perturbed with a selected library of 80 recombinant protein “perturbagens”. 191 secreted proteins were profiled in all samples, resulting in ~2M datapoints. The nELISA profiles were able to capture phenotypes associated with specific stimulation conditions, individual donors, and potent cytokine perturbagens. By compensating for stimulation and donor differences, we clustered perturbagens according to their effects on PBMC secretomes. As expected, perturbagens such as IFN gamma and IL-4 led to well-established Th1 or Th2 responses, respectively, and clustered with perturbagens involved in these phenotypes. Novel phenotypic effects were also identified, such as distinct responses to the near identical CXCL12 alpha and beta isoforms. Interestingly, we observed important similarities between PBMC responses to the cytokine drugs IFN beta and IL-1 Receptor antagonist, supporting the use of the latter as a replacement for the former in certain indications. These findings highlight the ability of the nELISA to capture actionable insights from high-throughput screens, and demonstrate its applicability to SAR studies and drug repurposing screens. Thus, the nELISA is a powerful tool for immunotherapy drug discovery, and we will expand upon its use for target identification, in vitro pharmacology, predicting patient response to therapy, as well as characterizing the potential of iPSC- or donor-derived material for cell therapy. Citation Format: Nathaniel Robichaud, Grant Ongo, Ivan Teahulos, Woojong Rho, Milad Dagher. Treatment-Specific Immune Phenotypes Identified by nELISA High-Throughput Proteomics Reveal Actionable Insights for Drug Discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A029.
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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 | 0.001 | 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".