Johannes F. Fahrmann

Exercise Training Reduces the Inflammatory Response and Promotes Intestinal Mucosa-Associated Immunity in Lynch Syndrome

Abstract Purpose: Lynch syndrome (LS) is a hereditary condition with a high lifetime risk of colorectal and endometrial cancers. Exercise is a non-pharmacologic intervention to reduce cancer risk, though its impact on patients with LS has not been prospectively studied. Here, we evaluated the impact of a 12-month aerobic exercise cycling intervention in the biology of the immune system in LS carriers. Patients and Methods: To address this, we enrolled 21 patients with LS onto a non-randomized, sequential intervention assignation, clinical trial to assess the effect of a 12-month exercise program that included cycling classes 3 times weekly for 45 minutes versus usual care with a one-time exercise counseling session as control. We analyzed the effects of exercise on cardiorespiratory fitness, circulating, and colorectal-tissue biomarkers using metabolomics, gene expression by bulk mRNA sequencing, and spatial transcriptomics by NanoString GeoMx. Results: We observed a significant increase in oxygen consumption (VO2peak) as a primary outcome of the exercise and a decrease in inflammatory markers (prostaglandin E) in colon and blood as the secondary outcomes in the exercise versus usual care group. Gene expression profiling and spatial transcriptomics on available colon biopsies revealed an increase in the colonic mucosa levels of natural killer and CD8+ T cells in the exercise group that were further confirmed by IHC studies. Conclusions: Together these data have important implications for cancer interception in LS, and document for the first-time biological effects of exercise in the immune system of a target organ in patients at-risk for cancer.

A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

Abstract Purpose: To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts. Experimental Design: Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed. Results: A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76–0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84–0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84–0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone. Conclusions: A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.