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BackgroundThe identification of novel cancer immunotherapies and the characterization of their immunomodulatory effects is complicated by the plasticity of immune cells and the wide range of phenotypes they may adopt. However, the high cost of capturing this diversity at scale limits typical drug discovery efforts to simple readouts, such as cell killing, or the expression of individual cytokines. We previously described the Nomic platform, a proteomics tool capable of quantifying thousands of proteins at high-throughput and low cost.MethodsHere, we leverage the Nomic platform to screen a library of bioactive small molecules, characterize their immunomodulatory properties, and assess potential toxicities. To achieve this, we collected supernatants from hepatocytes, cardiomyocytes, and microglia treated with 510 compounds at three concentrations. We used Nomic’s Omni 1000 to measure 1,000 proteins across 20,000 samples, generating 20 million data points.ResultsWe identified both expected and novel immune-regulating compounds, and captured in vitro signs of toxicity from therapies that failed clinical development due to dose-limiting toxicities, highlighting the power of our approach. For example, our dataset recapitulated the effects of control compounds such as corticosteroids, which reduced the expression of multiple cytokines while increasing the expression of SAA by hepatocytes, as well as TLR agonists, which dramatically increased the expression of TNFa, IL-12p40, IL-6, and several chemokines. Interestingly, 150 compounds (29%) displayed immunomodulatory properties, many of which were previously unreported. We simultaneously identified potential toxicities of the screened compounds, which was characterized by widespread decreases in protein levels in the supernatant, but increased levels of typically intracellular proteins such as CASP3, GAPDH, IRF3, TYMP, MAPK3, and eIF2a. We identified 68 cytotoxic compounds; of note, while most of these reduced cytokine expression, doxorubicin induced cytokine expression even at non-toxic doses, consistent with its reported pro-inflammatory properties. Other notable exceptions included the GSK-3 inhibitor LY2090314, and the BET inhibitor (+)-JQ1, which were toxic at higher doses, but induced the expression of distinct chemokines at lower doses. Based on the balance of toxicity and immunomodulation, we identified candidates for immunotherapy combinations. For example, the Met inhibitor PF-04217903 potently induced CXCL9-10 and IL-12 expression by hepatocytes, with no signs of toxicity. Considering that PF-04217903 was well tolerated in the clinic but was not pursued for strategic reasons, our results suggest potential for development in combination with checkpoint inhibitors for Met-driven tumors.ConclusionsOur results demonstrate the value of high-throughput proteomics to identify new immunomodulatory compounds and simultaneously characterize their safety profile.

BackgroundThe rapid and necessary expansion of cancer drug development requires accessible and scalable strategies for early toxicity screening and immune risk profiling. Liver plays a central role in drug metabolism, detoxification, and immune modulation. Robust detection of liver-specific responses to oncologic therapeutics is essential to identify and mitigate hepatotoxicity and immune-related adverse events (irAEs) which could limit clinical development. Here, we demonstrate a high-throughput, high-plex proteomic platform as a powerful approach to evaluate functional perturbations of hepatocytes across a spectrum of cancer drugs.MethodsUtilizing the novel Omni 1000 proteomic solution, based on nELISA technology, we profiled 1,000 proteins in >6500 samples of primary human hepatocytes following exposure to a diverse panel of over 550 distinct drug compou nds at 3 doses per compound. Well-chosen Omni 1000 protein target content enables simultaneous quantification of stress response markers, inflammatory cytokines, metabolic enzymes, and signaling pathway effectors. This allows for evaluation of both cytotoxic and immunomodulatory signatures, revealing potential off-target effects and mechanistic insights.ResultsPerturbation with tripolide, doxorubicin, and cobimetinib resulted in dose dependent cell toxicity, detected via increased levels of intracellular proteins such as GAPDH and Casp3 released into the supernatant, consistent with clinically observed hepatotoxicity. We also detect increased Galectin-3, Galectin-1, and VEGFA at non-toxic perturbation doses, indicative of cell stress and potential toxicity at higher doses. In addition, we concurrently observe loss of proteins associated with protection against apoptosis, such as ST6GAL1, a potent inhibitor of FAS-mediated apoptosis. Across the 510 compounds screened, 68 showed similar signs of toxicity, including compounds with dose-limiting liver toxicity in the clinic. Of particular note is PHA-793887, a potential therapeutic candidate, which failed clinically due to unexpected hepatotoxicity, which here was captured in a pre-clinical in vitro model.We further demonstrated the platform’s ability to identify likely irAEs through detection of dose dependent increases in key cytokines. Hepatocytes perturbed with Imatinib and Pomalidomide demonstrated increases in IL-6, IL-10, and IL-2, indicative of potential irAE and consistent with published literature for these drugs.ConclusionsHigh-throughput high-plex proteomic analysis of hepatocyte responses offers a significant advance in preclinical toxicology and immune safety, and enables an early stage indication of drug-induced liver injury (DILI) risk and immunotoxic potential, two major barriers in oncology drug development. As the pharmaceutical industry increasingly utilizes systems-level screening, such proteomic platforms will be instrumental in enhancing predictive toxicology, reducing clinical attrition, and accelerating the path from discovery to approval.

BackgroundHigh-plex proteomics is critical to enabling cancer research through comprehensive profiling of immune and tumor-derived signals, facilitating early detection, biomarker discovery, immune-related adverse event (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.MethodsOmni 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.ResultsFor 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.ConclusionsTogether, 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.

BackgroundProteomics holds great promise for cancer immunotherapy, with intensive efforts being exerted for early disease identification, patients selection, and adverse event prediction. Despite this potential, the high cost and low throughput of existing tools to profile circulating proteins render such studies prohibitively slow and costly, limiting their wide-spread application. As a result, proteomics studies in the field have been constrained to sample sizes in the 10s and 100s , restricting the power to discover key biomarkers. Here, we leverage a novel proteomics tool, the nELISA, to quantify ~600 circulating proteins across ~3000 samples from the RADIOHEAD (Resistance Drivers for Immuno-Oncology Patients Interrogated by Harmonized Molecular Datasets) cohort, a prospective study of 1070 immunotherapy naive pan-tumor patients on standard of care immune checkpoint inhibitor (ICI) therapy regimens from community oncology clinics.MethodsThe Nomic platform is a highly multiplexed immunoassay technology that enables the profiling of hundreds of proteins across 1536 samples per instrument daily, at significantly reduced costs. The method miniaturizes sandwich immunoassays by placing antibody pairs on the surface of color-coded microparticles, which can then be analyzed via high-throughput flow cytometry.ResultsUsing this technology, our preliminary data identified several markers of response to treatment; for example, PD-1 inhibitors result in increased circulating levels of soluble PD-1, and ICIs increase levels of several chemokines including CXCL9 and CXCL10, as seen in several other small-scale studies. We also identified several markers potentially predicting response to treatment and irAEs, which will require much larger datasets for validation. The scalable nature of the nELISA platform now allows us to validate these findings in a large longitudinal cohort, providing the power needed for such a broad biomarker discovery effort. In this presentation, we share the results of applying nELISA to the RADIOHEAD blood serum samples from pretreatment, early on-treatment, 6-month, and 12-month timepoints. For participants who experienced immune-related adverse events, additional samples were collected upon presentation and in follow-up visits - these samples were also analyzed in this study.ConclusionsPairing nELISA protein profiling of these longitudinal samples with associated demographic metadata and clinical outcomes provides an opportunity to identify clinically actionable mechanisms for ICI resistance and adverse events, discover targets for combination therapies and post-ICI treatment, and inform system biology approaches to elucidate disease pathways. Here, we highlight biomarkers and protein signatures related to patient outcomes, to reveal additional insights and further accelerate research in the field of cancer immunotherapy.

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. nELISA can measure both protein concentration and their post-translational modifications. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity nor impact on performance vs 1-plex signals, with sensitivities as low as 0.1 pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale inflammatory-secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and readouts, measuring 7,392 samples and generating ~1.4M protein data points in under a week; a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We validate nELISA for phenotypic screening, where its capacity to faithfully report hundreds of proteins make it a powerful tool across multiple stages of drug discovery.

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity or impact on performance vs 1-plex signals, with sensitivities as low as 0.1pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and read-outs, measuring 7,392 samples and generating ~1.5M protein datapoints in under a week, a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We also validated the nELISA's use in phenotypic screening, and propose its application to drug discovery.We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity or impact on performance vs 1-plex signals, with sensitivities as low as 0.1pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and read-outs, measuring 7,392 samples and generating ~1.5M protein datapoints in under a week, a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We also validated the nELISA's use in phenotypic screening, and propose its application to drug discovery.