Most patients diagnosed with resected pancreatic adenocarcinoma (PDAC) survive less than 5 years, but a minor subset survives longer. Here, we dissect the role of the tumor microbiota and the immune system in influencing long-term survival. Using 16S rRNA gene sequencing, we analyzed the tumor microbiome composition in PDAC patients with short-term survival (STS) and long-term survival (LTS). We found higher alpha-diversity in the tumor microbiome of LTS patients and identified an intra-tumoral microbiome signature (Pseudoxanthomonas-Streptomyces-Saccharopolyspora-Bacillus clausii) highly predictive of long-term survivorship in both discovery and validation cohorts. Through human-into-mice fecal microbiota transplantation (FMT) experiments from STS, LTS, or control donors, we were able to differentially modulate the tumor microbiome and affect tumor growth as well as tumor immune infiltration. Our study demonstrates that PDAC microbiome composition, which cross-talks to the gut microbiome, influences the host immune response and natural history of the disease.

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms.Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model.Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma.Availability: http://odin.mdacc.tmc.edu/∼vbaladan/. Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

BackgroundFecal microbiota transplantation (FMT) via colonoscopy or enema has become a commonly used treatment of recurrent C. difficile infection (CDI).AimsTo compare the safety and preliminary efficacy of orally administered lyophilized microbiota product compared with frozen product by enema.MethodsIn a single center, adults with ≥ 3 episodes of recurrent CDI were randomized to receive encapsulated lyophilized fecal microbiota from 100–200 g of donor feces (n = 31) or frozen FMT from 100 g of donor feces (n = 34) by enema. Safety during the three months post FMT was the primary study objective. Prevention of CDI recurrence during the 60 days after FMT was a secondary objective. Fecal microbiome changes were examined in first 39 subjects studied.ResultsAdverse experiences were commonly seen in equal frequency in both groups and did not appear to relate to the route of delivery of FMT. CDI recurrence was prevented in 26 of 31 (84%) subjects randomized to capsules and in 30 of 34 (88%) receiving FMT by enema (p = 0.76). Both products normalized fecal microbiota diversity while the lyophilized orally administered product was less effective in repleting Bacteroidia and Verrucomicrobia classes compared to frozen product via enema.ConclusionsThe route of delivery, oral or rectal, did not influence adverse experiences in FMT. In preliminary evaluation, both routes appeared to show equivalent efficacy, although the dose may need to be higher for lyophilized product. Spore-forming bacteria appear to be the most important engrafting organisms in FMT by the oral route using lyophilized product.Trial registrationClinicalTrials.gov NCT02449174

The block bootstrap for time series consists in randomly resampling blocks of consecutive v alues of the given data and aligning these blocks into a bootstrap sample. Here we suggest improving the performance of this method by aligning with higher likelihood those blocks which match at their ends. This is achieved by resampling the blocks according to a Markov c hain whose transitions depend on the data. The matching algorithms we propose take some of the dependence structure of the data into account. They are based on a kernel estimate of the conditional lag one distribution or on a tted autoregression of small order. Numerical and theoretical analyses in the case of estimating the variance of the sample mean show that matching reduces bias and, perhaps unexpectedly, has relatively little e ect on variance. Our theory extends to the case of smooth functions of a vector mean.

IntroductionTranslation initiation is activated in cancer through increase in eukaryotic initiation factor 4E (eIF4E), eIF4G, phosphorylated eIF4E-binding protein (p4E-BP1) and phosphorylated ribosomal protein S6 (pS6), and decreased programmed cell death protein 4 (pdcd4), a translational inhibitor. Further, translation elongation is deregulated though alterations in eukaryotic elongation factor 2 (eEF2) and eEF2 kinase (eEF2K). We sought to determine the association of these translational aberrations with clinical-pathologic factors and survival outcomes in hormone receptor-positive breast cancer.MethodsPrimary tumors were collected from 190 patients with Stage I to III hormone receptor-positive breast cancer. Expression of eIF4E, eIF4G, 4E-BP1, p4E-BP1 T37/46, p4E-BP1 S65, p4E-BP1 T70, S6, pS6 S235/236, pS6 S240/244, pdcd4, eEF2 and eEF2K was assessed by reverse phase protein arrays. Univariable and multivariable analyses for recurrence-free survival (RFS) and overall survival (OS) were performed.ResultsHigh eEF2, S6, pS6 S240/244, p4E-BP1 T70, and low pdcd4 were significantly associated with node positivity. Median follow-up for living patients was 96 months.High p4E-BP1 T36/47, p4E-BP1 S65, p4E-BP1 T70 and 4E-BP1 were associated with worse RFS. High p4E-BP1 T70 and pS6 S235/236, and low pdcd4, were associated with worse OS. In multivariable analysis, in addition to positive nodes, p4E-BP1 S65 remained a significant predictor of RFS (HR = 1.62, 95% CI = 1.13-2.31; P = 0.008). In addition to age, pS6 S235/236 (HR = 1.73, 95% CI = 1.03-2.90, P = 0.039), eEF2K (HR = 2.19, 95% CI = 1.35-3.56, P = 0.002) and pdcd4 (HR = 0.42, 95% CI = 0.25-0.70, P = 0.001) were associated with OS.ConclusionsIncreased pS6, p4E-BP1, eEF2K and decreased pdcd4 are associated with poor prognosis in hormone receptor-positive breast cancer, suggesting their role as prognostic markers and therapeutic targets.