Stool protein mass spectrometry identifies biomarkers for the early detection of diffuse-type gastric cancer.

There is a high unmet need for early detection approaches for diffuse gastric cancer (DGC). We examined whether the stool proteome of mouse models of GC or individuals with hereditary diffuse GC (HDGC) have utility as biomarkers for early detection. Proteomic mass spectrometry of stool from a genetically engineered mouse model driven by oncogenic KrasG12D and loss of p53 and Cdh1 in gastric parietal cells (known as TCON mice) identified differentially abundant proteins compared to littermate controls. Immunoblot assays validated a panel of proteins including actinin alpha 4 (ACTN4), N-acylsphingosine amidohydrolase 2 (ASAH2), dipeptidyl peptidase 4 (DPP4), and valosin-containing protein (VCP) as enriched in TCON stool compared to littermate control stool. Immunofluorescence analysis of these proteins in TCON stomach sections revealed increased protein expression as compared to littermate controls. Proteomic mass spectrometry of stool obtained from HDGC patients with CDH1 mutations identified increased expression of ASAH2, DPP4, VCP, lactotransferrin (LTF), and tropomyosin-2 (TPM2) relative to stool from healthy sex and age-matched donors. Chemical inhibition of ASAH2 using C6-urea ceramide was toxic to GC cell lines and patient derived-GC organoids. This toxicity was reversed by adding downstream products of the S1P synthesis pathway, suggesting a dependency on ASAH2 activity in GC. An exploratory analysis of the HDGC stool microbiome identified features which correlated with patient tumors. Here we provide evidence supporting the potential of analyzing stool biomarkers for the early detection of DGC.

Effect of Low-dose and Standard-dose Aspirin on PGE2 Biosynthesis Among Individuals with Colorectal Adenomas: A Randomized Clinical Trial.

Low-dose aspirin is recommended by the U.S. Preventive Services Task Force for primary prevention of colorectal cancer in certain individuals. However, broader implementation will require improved precision prevention approaches to identify those most likely to benefit. The major urinary metabolite of PGE2, 11α-hydroxy-9,15-dioxo-2,3,4,5-tetranor-prostane-1,20-dioic acid (PGE-M), is a biomarker for colorectal cancer risk, but it is unknown whether PGE-M is modifiable by aspirin in individuals at risk for colorectal cancer. Adults (N = 180) who recently underwent adenoma resection and did not regularly use aspirin or NSAIDs were recruited to a double-blind, placebo-controlled, randomized trial of aspirin at 81 or 325 mg/day for 8-12 weeks. The primary outcome was postintervention change in urinary PGE-M as measured by LC/MS. A total of 169 participants provided paired urine samples for analysis. Baseline PGE-M excretion was 15.9 ± 14.6 (mean ± S.D, ng/mg creatinine). Aspirin significantly reduced PGE-M excretion (-4.7 ± 14.8) compared with no decrease (0.8 ± 11.8) in the placebo group (P = 0.015; mean duration of treatment = 68.9 days). Aspirin significantly reduced PGE-M levels in participants receiving either 81 (-15%; P = 0.018) or 325 mg/day (-28%; P < 0.0001) compared with placebo. In 40% and 50% of the individuals randomized to 81 or 325 mg/day aspirin, respectively, PGE-M reduction reached a threshold expected to prevent recurrence in 10% of individuals. These results support that aspirin significantly reduces elevated levels of PGE-M in those at increased colorectal cancer risk to levels consistent with lower risk for recurrent neoplasia and underscore the potential utility of PGE-M as a precision chemoprevention biomarker. The ASPIRED trial is registered as NCT02394769.

The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action.

The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.