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Engineered probiotics are an emerging platform for in situ delivery of therapeutics to the gut. Herein, we developed an orally administered, yeast-based therapeutic delivery system to deliver next-generation immune checkpoint inhibitor (ICI) proteins directly to gastrointestinal tumors. We engineered Saccharomyces cerevisiae var. boulardii (Sb), a probiotic yeast with high genetic tractability and innate anticancer activity, to secrete "miniature" antibody variants that target programmed death ligand 1 (Sb_haPD-1). When tested in an ICI-refractory colorectal cancer (CRC) mouse model, Sb_haPD-1 significantly reduced intestinal tumor burden and resulted in significant shifts to the immune cell profile and microbiome composition. This oral therapeutic platform is modular and highly customizable, opening new avenues of targeted drug delivery that can be applied to treat a myriad of gastrointestinal malignancies., Competing Interests: Declaration of interests G.D., B.A.H., O.N.R., M.J.W., J.P., and S.K. are inventors on a patent application related to the yeast-based ICI delivery system. G.D. and S.K. are inventors on another patent related to the use of engineered microbes as therapeutic agents (Publication #: WO/2020/061389, filed 20 September 2019). Following completion of this work, S.K. is now employed by Kookmin University (Seoul, South Korea) and B.M. is now employed by Flagship Pioneering (Cambridge, MA, USA)., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background and Aims: Diagnostic differentiation between Crohn's disease (CD) and ulcerative colitis (UC) is crucial for timely and suitable therapeutic measures. The current gold standard for differentiating between CD and UC involves endoscopy and histology, which are invasive and costly. We aimed to identify blood plasma proteomic signatures using a Protein-Wide Association Study (PWAS) approach to differentiate CD from UC and evaluate the efficacy of these signatures as features in machine learning (ML) classifiers., Methods: Among participants (n=1,106; n CD =636; n UC =470) of the Study of a Prospective Adult Research Cohort with IBD (SPARC), plasma protein (n=2,920) levels were estimated using Olink proteomics. A PWAS with Bonferroni correction for multiple testing was used to identify proteins associated with disease states after controlling for age, sex, and disease severity. ML classifiers examined the diagnostic utility of these models. Feature importance was determined via SHapley Additive exPlanations (SHAP) analysis., Results: Thirteen proteins which were significantly differentially abundant in CD vs UC (all |β|s > 0.22, all adjusted p values < 8.42E-06). Random forest models of proteins differentiated between CD and UC with models trained only on PWAS identified proteins (Average ROC-AUC 0.73) outperforming models trained of the full proteome (Average ROC-AUC 0.62). SHAP analysis revealed that Granzyme B, insulin-like peptide 5 (INSL5), and interleukin-12 subunit beta (IL-12B) were the most important features., Conclusions: Our findings demonstrate that PWAS-based feature selection approaches are a powerful method to identify features in complex, noisy datasets. Importantly, we have identified novel peptide based biomarkers such as INSL5, that can be potentially used to complement existing strategies to differentiate between CD and UC., Competing Interests: PD: has received research support under a sponsored research agreement unrelated to the data in the paper and/or consulting from AbbVie, Arena Pharmaceuticals, Boehringer Ingelheim, Bristol Myers Squibb, Janssen, Pfizer, Prometheus Biosciences, Takeda Pharmaceuticals, Roche Genentech, Scipher Medicine, Fresenius Kabi, Teva Pharmaceuticals, Landos Pharmaceuticals, Iterative scopes and CorEvitas, LLC. U.J. has received research support from Boehringer Ingelheim.