Abstract B023: A scalable, proteome-wide protein profiling platform with absolute quantification of 1000 proteins
Bibliographic record
Abstract
Abstract Background: High-plex proteomics is critical to enabling cancer research through comprehensive profiling of immune and tumor-derived signals, facilitating early detection, biomarker discovery, irAE prediction, and real-time monitoring of therapeutic responses. To date, proteomics has been constrained by flexibility, high costs, and inconsistent quantification. Here, we present Omni 1000, a quantitative 1000-plex solution powered by nELISA technology; designed for broad, reproducible, and cost-effective protein measurement. Omni 1000 delivers 0.1 pg/mL sensitivity, 99.99% specificity, and dynamic range spanning 3-6 logs. Method: Omni 1000 content was developed through rigorous, data-driven strategy to achieve comprehensive proteome-wide coverage while retaining high-value markers. The foundation is built from two sources: (1) a curated set of Most Valuable Proteins (MVPs)—biomarkers selected heuristically based on prevalence in key signaling pathways, translational research, and validated endpoints; and (2) large-scale, high-plex proteomic datasets with disease-association. To optimize, we iteratively applied Minimum Redundancy Maximum Relevance (mRMR) to reduce overlap, reconstruction loss minimization to preserve signal from high-dimensional datasets, and prioritized MVPs. Each iteration was validated against key biological ontologies—achieving 92% MVP coverage, 100% Reactome level 0, >80% Reactome level 1, 100% pharma-relevant KEGG signaling pathways, and 100% MeSH disease classes. In addition, we determined the disease prediction power of Omni 1000 content at >95% equivalent to a 3000+ panel, evaluated on the UK Biobank cohort. Results: For biomarker discovery, Omni 1000 makes large-scale and clinically relevant studies achievable through rapid readout with flow cytometry. We leveraged Omni 1000’s capabilities in a high-thoughput drug screening platform structured on patient-derived tumor organoids. Use of Omni 1000 demonstrated insights on baseline donor heterogeneity and drug compound responses and resistances specific to patient tissue profiles. Of interest for immunotherapy applications were compounds inducing cell death while promoting pro-inflammatory immune environments. We observed cytotoxicity with 2 CDK9 inhibitors in organoids across donors, through increased levels of intracellular proteins in culture supernatant and broad decreases in most other protein levels. They simultaneously resulted in increased secretion of chemokines CXCL2, CXCL3, CXCL5, and maintained CCL2 and IL-8 expression, possibly promoting additional immune involvement parallel to direct cell killing. These findings underscore Omni 1000’s capacity to profile functional heterogeneity in tumor immune microenvironments and support development of precision medicine with immunotherapeutic potential. Conclusions: Together, we demonstrate a novel 1000-plex solution, Omni 1000, with content balancing critical targets and biological breadth, and demonstrated real-world utility in early detection of disease and high throughput cancer drug development. Citation Format: Narges Rashidi, Kiran Edwardson, Nathaniel Robichaud, Alyssa Rosenbloom, Grant Ongo, Milad Dagher. A scalable, proteome-wide protein profiling platform with absolute quantification of 1000 proteins [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr B023.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".