Society: AGA

Background: Colonic polyposis, which is a risk factor for colorectal cancer (CRC), can be driven by a pathogenic variant in a polyposis gene. However, in many cases germline genetic testing is negative, leaving the cause of the polyposis unknown. There has been increasing evidence that specific microbiome and metabolomic signatures may be associated with colon polyp development and CRC. However, at this time it remains uncertain if the microbiome plays a role in the development of colonic polyposis in the absence of a known polyposis gene mutation.

Methods: Subjects undergoing a standard-of-care colonoscopy were recruited via an IRB-approved protocol as either control subjects with 2 or fewer lifetime colon adenomas, or polyposis subjects with ≥10 total colonic adenomas and either negative polyposis multigene panel genetic testing or a known familial adenomatous polyposis (FAP) or MUTYH-associated polyposis (MAP). A stool sample was collected prior to the colonoscopy, and during the procedure mucosal biopsies were obtained from the proximal and distal colon. To measure bacterial community composition, we carried out 16S rRNA marker gene sequencing (hypervariable region V1-V2) on the Illumina MiSeq platform. Analysis was performed using QIIME2 and R.

Results: A total of 30 subjects were recruited, including 17 with polyposis (5 with FAP/MAP [gene-positive] and 12 with negative polyposis genetic testing [gene-negative]) and 13 control subjects. Global analysis of the entire microbial community showed that there is a significant decrease in alpha diversity as measured by richness in the gene-positive polyposis group compared to the gene-negative polyposis and control groups (P= 0.047, P=0.02). Faith’s PD was decreased in the gene-positive polyposis compared to the gene-negative polyposis groups (P=0.031). Bacterial communities in the gene-positive and gene-negative polyposis groups were also different by weighted and unweighted UniFrac distances (PERMANOVA test, P=0.02, P=0.01). Although we could not see any statistically significant differences in individual taxa between the groups through linear models, our analysis was limited by the sample size.

Conclusions: This study provides the first comprehensive microbiome characterization and comparison between individuals with colonic polyposis both with and without a known genetic driver, showing these distinct populations have different microbiome compositions. Elucidating microbiome signatures of the gene-negative polyposis cohort is a critical first step for future studies examining the potential causal role of the microbiome in the development of gene-negative polyposis and for the development of microbiome-targeted therapies to aid in decreasing polyp development and CRC risk.

Purpose: HCC incidence is highest in Asians and Pacific Islanders, followed by Blacks, Hispanics, and non-Hispanic Whites, and is rising at an alarming rate in the U.S. Most HCC is diagnosed at an advanced incurable stage, emphasizing the need for accurate biomarkers and early diagnosis. The predictive accuracy in available biomarkers such as alpha-fetoprotein (AFP) is sub-optimal, and these have not been systematically assessed across ethnic and racial subgroups. Here, we report on the performance of a new predictive model for HCC in patients from diverse ethnic/racial backgrounds, using clinical characteristics and biomarker proteins from the TGF-β superfamily signaling pathway that have shown relevance to hepatocarcinogenesis.
Experimental Procedures: We combined human genomic studies using The Cancer Genome Atlas and mechanistic insight from animal models to identify 108 biologically relevant proteins involved in TGF-β superfamily signaling, which were measured using SomaScan proteomic analysis, utilizing 55 µL serum samples from 248 individuals with cirrhosis, 94 of whom had HCC by imaging criteria. Patients were recruited from four participating centers with significant cohorts of ethnic minorities (11% Asian, 18% Black, 5% Hispanic, 4% Hawaiian/Pacific Islander, 54% White patients). In a derivation sample (N=129; 31 with HCC), we tested serum markers using univariable non-parametric analysis, adjusted for false discovery rate, used quintiles to eliminate skewness, and then combined the resulting serum biomarkers with clinical and demographic risk factors to develop a new model for detecting current early-stage HCC on a background of cirrhosis, and tested it in a separate validation sample (N=119; 63 with HCC). We tested heterogeneity by race in the derivation and test samples for area under the ROC curve (AUC).
Results: The new functional model included 4 clinical variables (age, serum AFP, Bilirubin level, HBV y/n) and 3 serum protein biomarkers (AKR1B10, COL15A1, and INHBB). The new model had better AUC than Doylestown or AFP in both the derivation and validation samples. In the derivation sample, AUC for our model was 0.92 in Black patients, 0.90 in White patients, and 0.81 in other race/ethnic groups, all of which were better than AFP alone or the Doylestown model AUC in those samples (Table). In the validation sample, our model outperformed the Doylestown model, but AFP alone performed slightly better in White patients.
Conclusions and Next Steps: This study demonstrates the improved performance of our novel biological markers and model to detect early-stage HCC across cirrhotic patients compared to the Doylestown model or AFP alone in a racially and ethnically diverse population. Next steps are large-scale validation of this biomarker panel in cirrhotic patients across all racial and ethnic subgroups in Phase II/III studies.

Vikas Kundra

Appearances