AACR Virtual Meeting II – American Association for Cancer Research –  June 2020

Authors

Asim Siddiqui¹, John E Blume¹, William C Manning¹, Gregory Troiano¹, Philip Ma¹, Robert Langer², Vivek Farias², Omid C. Farokhzad¹

¹Seer, Redwood City, CA, United States
²Massachusetts Institute of Technology, Boston, MA, United States

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.

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