Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies.

Introduction: Understanding changes in PPI maps from a healthy and diseased state can illuminate our understanding of biological changes and disease processes. PPI maps enable a higher order of information than a simple listing of components by providing functional context, yet existing maps grossly underrepresent the total biological information potential of PPIs. Herein, we describe Proteograph, a novel platform that leverages the nano-bio interactions of nanoparticles (NPs) for deep and unbiased proteomic sampling that can provide insights on PPI across biological samples. Proteograph leverages the protein corona that forms on the surface of NPs as a function of their distinct biophysicochemical properties. NPs reproducibly bind subsets of proteins from biofluids as a function of protein concentration, protein-NP affinity, and protein-protein interactions to form a corona on the NP surface. We have employed Proteograph to quantify known PPIs using a panel of 3 distinct NPs to capture plasma proteins and derive maps of NSCLC and control subjects in order to identify biological changes in interactions, potentially indicative of health and disease. Method and Results: We collected plasma samples from 288 subjects: healthy (n=82), comorbid (n=81) and NSCLC stages I-IV (n=125). In this initial study, we used three NPs with distinct properties and evaluated the protein corona of plasma samples by mass spectrometry (MS) to quantify 1,235 protein groups (1% FDR). A fully automated assay workflow enabled preparation of 3 NPs' corona for MS analysis across 288 subjects in approximately 6 days. We mapped the protein groups to a PPI map derived from the STRING database. Partitioning the network into clusters identified 9 interaction clusters with greater than 10 protein members. These clusters enabled us to investigate differences in the PPI networks between NSCLC patients vs. controls. Evaluating the expression of proteins in these groups, we identified interaction clusters that had significant differences between cancer vs. control (t-test, p < 0.01 Bonferroni corrected). Six of the clusters show differential behavior between NSCLC vs. healthy controls (p < 0.01). Two of these clusters show differential behavior between NSCLC vs. healthy and comorbid (p < 0.01). Investigation of these differentially expressed clusters reveals links to known cancer biology with proteins related to the immune system and endocytosis pathways. Discussion: We have used the Proteograph platform to identify PPI clusters that are differentiated between NSCLC and control individuals. We believe the efficiency of the Proteograph platform applied to sufficiently powered studies may enable comprehensive understanding of known PPIs, and potentially infer and confirm new PPIs, in health and disease. Citation Format: Asim Siddiqui, John E. Blume, William C. Manning, Gregory Troiano, Philip Ma, Robert Langer, Vivek Farias, Omid C. Farokhzad. Plasma protein-protein interactome (PPI) maps derived from the protein corona captured at the nano-bio interface of nanoparticles reveal differential networks for non-small cell lung cancer (NSCLC) and control subjects [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6571.

Introduction: Plasma proteins should be useful biomarkers for disease detection, yet few proteins (~120) are FDA approved. Productive biomarker discovery studies are resource-limited due to the complex biochemical fractionation methods used to address the inherent challenges in unbiased plasma profiling such as the large dynamic range. Herein, we describe Proteograph, a novel platform that leverages the nano-bio interactions of nanoparticles (NPs) for deep and unbiased proteomic sampling. NPs reproducibly bind subsets of proteins from biofluids as a function of protein concentration, protein-NP affinity, and protein-protein interactions to form a corona on the NP surface. The corona composition is directly a function of the NPs' biophysicochemical properties and requires no prior knowledge of the proteins that might be selected by each distinctly engineered NP. With an optimized panel of 10 NPs, we can broadly and deeply interrogate the plasma proteome and rapidly quantify potential biomarkers. The highly parallel NP workflow makes large studies practicable and should improve biomarker discovery and validation. Methods and Results: We have screened 200+ NPs with different biophysicochemical properties and selected a panel of 10 based on protein detection. A fully automated assay workflow was developed that can process the 10 NP panel across 8 plasma samples in a 7 hr assay. Evaluation of this panel across 16 individual plasma samples detected 2,009 protein groups (1% protein FDR, 84% with 2 or more peptides). Accuracy was demonstrated with spike-recovery experiments using 4 NPs in which CRP, Angiogenin and S100a8/9 were added to plasma at 2X, 5X, 10X, and 100X of measured endogenous levels. Linear model fits for NP corona MS signal vs. ELISA were created with mean slopes of 1.06 ± 0.22 and mean adjusted-r2 of 0.95 ± 0.05. Precision was demonstrated across 3 NPs using three assay replicates in which the mean of the median CVs for each NP is 24%. The depth of plasma proteome coverage for the 10 NP panel using a pooled plasma sample was determined by comparison of the NP-detected proteins to published MS intensities and spanned nearly the entire reported range. Examining protein annotations (e.g., GO Cellular Compartment and Biological Process, KEGG and Pfam) within each NP corona reveals correlations by 1D-enrichment analysis between protein annotations and NP biophysicochemical properties suggesting specific relationships at the nano-bio surface. Discussion: We have demonstrated the selection and optimization of a panel of 10 NPs for plasma proteome profiling. We have also demonstrated the breadth and depth of this panel's ability to accurately and precisely quantify proteins from plasma. We believe the robustness and scalability of the Proteograph platform could enable population-scale deep and unbiased proteomics analysis previously not feasible using existing workflows. Citation Format: John E. Blume, William C. Manning, Gregory Troiano, Asim Siddiqui, Philip Ma, Robert Langer, Vivek Farias, Omid C. Farokhzad. Analytical validation of the multi-nanoparticle Proteograph platform for rapid and deep proteomic profiling [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2848.

Introduction: Though early detection of NSCLC greatly improves prognosis, we lack useful clinical tests. Genomics approaches utilizing cell-free DNA provide suitable specificity but moderate sensitivity for early cancer detection. Plasma proteins have the potential to deliver robust panels of biomarkers for early cancer detection that may be complimentary to genomics markers. Complex workflows, which enable deep and unbiased interrogation of plasma proteins that span 10 orders of magnitude, have made it impractical to efficiently perform robust studies, and consequently, comprehensive proteomic data vastly lags other “omics”. Herein, we report a multi-NP Proteograph platform that rapidly, reproducibly, deeply, and scalably interrogates proteins from biofluids. In a study of 268 subjects, comparing on average 1779 plasma proteins of NSCLC subjects to healthy and pulmonary co-morbid controls, we identified classification panels comprising proteins with known and unknown roles in NSCLC, offering the promise of new biomarkers for early disease detection. Methods: Subject plasma samples were grouped into NSCLC stages 1,2,3 (early), NSCLC stage 4 (late), or healthy and pulmonary co-morbid controls, for a randomly selected cohort of 288 age- and gender-matched subjects, and interrogated with a panel of NPs in an efficient automated work-flow. Peptides from NP-bound proteins underwent data-independent-acquisition mass spectrometry. Subject samples were also interrogated using conventional Agilent MARS-14 immunodepletion column, which has historically yielded limited clinical value, to determine differences in depth and types protein coverage achieved as compared with panel of NPs. Results: On average 1,779 proteins were detected from each of the 268 subject samples vs. 413 from depleted plasma. The healthy vs early NSCLC random classification after depleted plasma protein removal achieved an average AUC of 0.90. Classification of healthy subjects to late NSCLC had an average AUC of 0.98. Comparison of the top features of the NSCLC classifiers to the co-morbid classifier indicated clinically significant differences. Among the former were proteins with both known and unknown roles in NSCLC (OpenTargets), underscoring the value of unbiased proteomic analysis. Conclusions: We demonstrate the utility of the multi-NP Proteograph platform to deeply profile plasma proteins as novel biomarkers. The performance of the healthy vs. early NSCLC classifier confirms the potential of proteins in early disease detection. Our platform enables deep unbiased plasma protein biomarker profiling that matches genomics workflow throughput and suggests feasibility of parallel large-scale complementary studies of proteins and nucleic acids. Citation Format: John E. Blume, William C. Manning, Gregory Troiano, Asim Siddiqui, Philip Ma, Robert Langer, Vivek Farias, Omid C. Farokhzad. Efficient and scalable profiling of an average of 1779 plasma proteins in 268 subjects with multi-nanoparticle (NP) Proteograph platform enables robust detection of early-stage non-small cell lung cancer (NSCLC) and classification vs. healthy and co-morbid subjects [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-147.