An enabling approach for deep metaproteomic characterization ofmicrobial contributors to tumorigenesis in clinical samples - SUMMARY Microbes, specifically bacteria, are known to influence the host tumor microenvironment during cancer development. Bacterial proteins perform biochemical functions directly, and indirectly, interacting with host cancer cells, regulating the expression of genes and pathways associated with tumorigenesis. Mass spectrometry (MS)-based metaproteomics offers a powerful means to characterize functional proteins that contribute to microbe-host interactions in cancer. However, metaproteomic analysis is limited by the low abundance of microbes compared to host cells within the tumor microenvironment. Current approaches for enrichment (e.g., differential centrifugation) only modestly increase detection of microbes, while culturing samples to boost bacterial abundance suffers from loss of unculturable microbes and their in vivo. An approach to efficiently enrich microbes directly from clinical cancer samples, such as tissue biopsies and fluids, and enable deep metaproteomic analysis, would transform our ability to investigate the mechanisms by which non-host microorganisms influence carcinogenesis and treatment. To address this challenge, we propose to develop and optimize a novel approach for enriching microbes from clinical cancer sample (CS) specimens, by developing a novel approach based on bio-orthogonal non-canonical amino acid tagging (BONCAT), coupled with cell sorting enrichment and sensitive MS-based metaproteomics. Our interdisciplinary team, which includes clinical and translational cancer research partners, brings the necessary expertise and access to clinical samples necessary for developing, optimizing, and demonstrating effectiveness of our approach. Our work will pursue these Aims: Specific Aim 1. Optimize and evaluate CS-BONCAT in samples of known composition; Specific Aim 2. Optimize and demonstrate proof-of-concept using CS-BONCAT to enable metaproteomic analysis of clinical cancer samples. We will focus on optimization in brush biopsy samples from oral cancer patients, and further demonstrate effectiveness in evaluating bacterial metaproteomes associated with colorectal and ovarian cancers from clinical samples. We will evaluate the effectiveness of our optimized approach through quantitative performance measures, ensuring CS-BONCAT will be useful for the cancer research community. We will promote adoption of CS-BONCAT by cancer researchers as part of a complete, clinical metaproteomics workflow through accessible online and on-demand multimedia resources. Once completed, we will deliver a transformative approach to better understand microorganism contributors to tumorigenesis, impacting diagnosis, prevention, and treatment for many cancer types.
