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The colonic mucus layer, comprised of highly O-glycosylated mucins, is vital to mediating host-gut microbiota interactions, yet the impact of dietary changes on colonic mucin O-glycosylation and its associations with the gut microbiota remains unexplored. Here, we used an array of omics techniques including glycomics to examine the effect of dietary fiber consumption on the gut microbiota, colonic mucin O-glycosylation and host physiology of high-fat diet-fed C57BL/6J mice. The high-fat diet group had significantly impaired glucose tolerance and altered liver proteome, gut microbiota composition, and short-chain fatty acid production compared to normal chow diet group. While dietary fiber inclusion did not reverse all high fat-induced modifications, it resulted in specific changes, including an increase in the relative abundance of bacterial families with known fiber digesters and a higher propionate concentration. Conversely, colonic mucin O-glycosylation remained similar between the normal chow and high-fat diet groups, while dietary fiber intervention resulted in major alterations in O-glycosylation. Correlation network analysis revealed previously undescribed associations between specific bacteria and mucin glycan structures. For example, the relative abundance of the bacterium Parabacteroides distasonis positively correlated with glycan structures containing one terminal fucose and correlated negatively with glycans containing two terminal fucose residues or with both an N-acetylneuraminic acid and a sulfate residue. This is the first comprehensive report of the impact of dietary fiber on the colonic mucin O-glycosylation and associations of these mucosal glycans with specific gut bacteria.

There is growing public interest in the use of fiber supplements as a way of increasing dietary fiber intake and potentially improving the gut microbiota composition and digestive health. However, currently there is limited research into the effects of commercially available fiber supplements on the gut microbiota. Here we used an in vitro human digestive and gut microbiota model system to investigate the effect of three commercial fiber products; NutriKane™, Benefiber® and Psyllium husk (Macro) on the adult gut microbiota. The 16S rRNA gene amplicon sequencing results showed dramatic fiber-dependent changes in the gut microbiota structure and composition. Specific bacterial OTUs within the families Bacteroidaceae, Porphyromonadaceae, Ruminococcaceae, Lachnospiraceae, and Bifidobacteriaceae showed an increase in the relative abundances in the presence of one or more fiber product(s), while Enterobacteriaceae and Pseudomonadaceae showed a reduction in the relative abundances upon addition of all fiber treatments compared to the no added fiber control. Fiber-specific increases in SCFA concentrations showed correlation with the relative abundance of potential SCFA-producing gut bacteria. The chemical composition, antioxidant potential and polyphenolic content profiles of each fiber product were determined and found to be highly variable. Observed product-specific variations could be linked to differences in the chemical composition of the fiber products. The general nature of the fiber-dependent impact was relatively consistent across the individuals, which may demonstrate the potential of the products to alter the gut microbiota in a similar, and predictable direction, despite variability in the starting composition of the individual gut microbiota.

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The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

3120 Background: Accurate tumour classification based upon tissue of origin (TOO) remains important to guide treatment selection and prognosis but can be challenging in patients with poorly differentiated malignancy, cancer of unknown primary (CUP) or those with prior malignancy. Data-independent acquisition mass spectrometry (DIA-MS)-based proteomics is emerging as a potential clinical diagnostic and prognostic tool. We aimed to develop a protein-based signature to identify histological subtype and adenocarcinoma TOO using DIA-MS data obtained from a pan-cancer study of human tissue samples as an adjunct to histopathological assessment in challenging clinical scenarios. Methods: We performed DIA-MS-based proteomic profiling of 795 fresh frozen tumour and 494 tumour-adjacent normal samples from the Victorian Cancer Biobank in a clinically orientated workflow. We filtered the cohort to include tumour types relevant to CUP. Protein quantification was derived from raw peptide intensity data. Random forest classifiers to identify histological subtype and adenocarcinoma TOO were subsequently trained and tested using 70% and 30% of the data respectively. Evaluation metrics included top-k accuracy (predicting how often the correct class is among the top k predicted classes) and area under the receiver operating curve (AUROC) (one-versus-rest) ± 95% confidence interval. Results: The final tumour cohort consisted of 427 tumour samples representing eight histological subtypes (adenocarcinoma, germ cell tumour, lymphoma, melanoma, renal cell carcinoma, sarcoma, squamous cell carcinoma, thyroid carcinoma) and seven adenocarcinoma TOO (breast, colorectal, liver, lung, ovary, pancreas, prostate). From 9,051 quantified proteins, 83 were identified with potential utility at identifying histological subtype and adenocarcinoma TOO for use in machine learning models. The histological subtype model identified cancer subtype in the test set with top-1 and top-2 accuracy of 0.95 ± 0.03 and 0.98 ± 0.02 respectively. Average test AUROC over all cancer types (n=8) was 0.98 ± 0.02. The adenocarcinoma TOO model identified tumour TOO in the test set with top-1 and top 2- accuracy of 0.88 ± 0.07 and 0.95 ± 0.04 respectively. Average test AUROC over all adenocarcinoma TOO (n=7) was 0.97 ± 0.02. Conclusions: Our clinically orientated DIA-MS-based proteomic workflow and supervised machine learning can identify protein signatures that classify histological subtype and TOO in tumour samples with high accuracy. This technology may assist diagnostic classification of cancer in challenging clinical scenarios, such as CUP.

Introduction: Pancreatic cancer has a very poor prognosis, with no established prognostic biomarkers in clinical use. This project aims to identify a prognostic proteomic-based signature for pancreatic adenocarcinomas. Methods: Fresh frozen tumors and matched normal samples from 125 patients were prepared for proteomic analyses using data-independent acquisition mass spectrometry (DIA-MS). Differential expression analyses were conducted on the normalized protein matrix to identify the top differentially expressed proteins (DEP) within the tumor samples. DEP were subjected to crosstalk and pathway enrichment analysis (PEA). Survival analysis based on initial univariate and subsequent 100 runs of multivariate Cox regression with Least Absolute Shrinkage and Selection Operator (LASSO) was performed to obtain a reduced list of candidate proteins associated with Overall Survival (OS). The proteins that appeared in greater than 95% of the LASSO runs were then used in a multivariate Cox model with recursive feature selection, which yielded the final 29 proteins. A risk score was built from the final 29 proteins. Consensus clustering was performed on the median absolute deviation-based top 20% highly variable proteins in tumor samples to identify proteomic-based subtypes. Results: Proteomic analyses revealed 5614 proteins identified from 599 sample runs. Differential expression analyses revealed 398 DEP in tumor samples (FDR-adjusted p-value <0.05, and |logFC|>1). PEA showed that these proteins were related to focal adhesion, extracellular matrix interaction (ECM), angiogenesis, and PI3K signaling pathways. A total of 803 proteins were significantly associated with OS in a univariate Cox regression analysis (p<0.05). PEA on the top 200 proteins associated with poorer OS revealed pathways related to focal adhesion, PI3K signaling, ECM and hypoxia-induced factor-1. Using LASSO multivariate Cox regression modeling, a 29-protein signature was identified, from which a risk score was calculated that dichotomized patients into high- and low-risk groups in terms of OS (Hazard ratio (HR) 2.8, 95% Confidence Interval (CI) [2.3, 3.3], concordance index of 0.91). This risk score was also prognostic for recurrence and three-year survival (both p<0.0001). A multivariate Cox regression model adjusted for other clinical variables revealed a significant association of the risk score with OS (HR 2.91, 95% CI [2.4, 3.5], p<0.001) while maintaining the concordance index (0.907). Consensus clustering analyses revealed four proteomic-based clusters, with cluster 3 showing the worst OS (p<0.001), independent of other clinical variables. PEA on the DEP within cluster 3 showed upregulation of proteins related to cell adhesion, angiogenesis, and immune-related pathways. Conclusion: A 29-protein signature identified a sub-group of patients with pancreatic adenocarcinoma with a poorer prognosis independent of clinical variables. Citation Format: Adel T. Aref, AKM Azad, Asim Anees, Mohashin Pathan, Jason Grealey, Daniela-Lee Smith, Erin M. Humphries, Daniel Bucio-Noble, Jennifer M. Koh, Erin Sykes, Steven G. Williams, Ruth Lyons, Natasha Lucas, Dylan Xavier, Sumit Sahni, Anubhav Mittal, Jaswinder S. Samra, John V. Pearson, Nicola Waddell, Peter G. Hains, Phil J. Robinson, Qing Zhong, Roger R. Reddel, Anthony J. Gill. A proteomic-based prognostic signature of pancreatic adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2209.

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