SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Enterolignan Production in a Flaxseed Intervention Study in Postmenopausal US Women of African Ancestry and European Ancestry.

Lignans are phytochemicals studied extensively as dietary factors in chronic disease etiology. Our goal was to examine associations between the gut microbiota and lignan metabolism and whether these associations differ by ethnicity. We conducted a flaxseed (FS) dietary intervention in 252 healthy, postmenopausal women of African ancestry (AA) and European ancestry (EA). Participants consumed ~10 g/d ground flaxseed for 6 weeks and provided overnight urine collections and fecal samples before and after intervention. The gut microbiota was characterized using 16S rRNA gene sequencing and differences in microbial community composition compared by ethnicity and intervention status. We observed a significant difference in the composition of the microbiota measured as beta diversity (p < 0.05) between AA and EA at baseline that was attenuated with FS consumption. Genera that were significantly associated with ENL production (e.g., Klebsiella, Lactobacillus, Slackia, Senegalimassilia) were unique to each group. Bacteria (e.g., Fusobacteria, Pyramidobacter and Odoribacter) previously associated with colorectal cancer and cardiovascular disease, both diet-related chronic diseases, were unique to either AA or EA and were significantly reduced in the FS intervention. This study suggests that ethnic variation in ENL metabolism may be linked to gut microbiota composition, and its impact on disease risk deserves future investigation.

Identification of supervised and sparse functional genomic pathways.

Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various "omics" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease. Our goal in this study is to develop methods to discover these functional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm.

An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer.

OBJECTIVES: The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. METHODS: Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. RESULTS: Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53-3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10-7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04-4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. CONCLUSIONS: A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system.

Clinicopathologic Significance of Mismatch Repair Defects in Endometrial Cancer: An NRG Oncology/Gynecologic Oncology Group Study.

PURPOSE: The clinicopathologic significance of mismatch repair (MMR) defects in endometrioid endometrial cancer (EEC) has not been definitively established. We undertook tumor typing to classify MMR defects to determine if MMR status is prognostic or predictive. METHODS: Primary EECs from NRG/GOG0210 patients were assessed for microsatellite instability (MSI), MLH1 methylation, and MMR protein expression. Each tumor was assigned to one of four MMR classes: normal, epigenetic defect, probable mutation (MMR defect not attributable to MLH1 methylation), or MSI-low. The relationships between MMR classes and clinicopathologic variables were assessed using contingency table tests and Cox proportional hazard models. RESULTS: A total of 1,024 tumors were assigned to MMR classes. Epigenetic and probable mutations in MMR were significantly associated with higher grade and more frequent lymphovascular space invasion. Epigenetic defects were more common in patients with higher International Federation of Gynecology and Obstetrics stage. Overall, there were no differences in outcomes. Progression-free survival was, however, worse for women whose tumors had epigenetic MMR defects compared with the MMR normal group (hazard ratio, 1.37; P < .05; 95% CI, 1.00 to 1.86). An exploratory analysis of interaction between MMR status and adjuvant therapy showed a trend toward improved progression-free survival for probable MMR mutation cases. CONCLUSION: MMR defects in EECs are associated with a number of well-established poor prognostic indicators. Women with tumors that had MMR defects were likely to have higher-grade cancers and more frequent lymphovascular space invasion. Surprisingly, outcomes in these patients were similar to patients with MMR normal tumors, suggesting that MMR defects may counteract the effects of negative prognostic factors. Altered immune surveillance of MMR-deficient tumors, and other host/tumor interactions, is likely to determine outcomes for patients with MMR-deficient tumors.

Identification of consistent functional genetic modules.

It is often of scientific interest to find a set of genes that may represent an independent functional module or network, such as a functional gene expression module causing a biological response, a transcription regulatory network, or a constellation of mutations jointly causing a disease. In this paper we are specifically interested in identifying modules that control a particular outcome variable such as a disease biomarker. We discuss the statistical properties that functional networks should possess and introduce the concept of network consistency which should be satisfied by real functional networks of cooperating genes, and directly use the concept in the pathway discovery method we present. Our method gives superior performance for all but the simplest functional networks.

Information metrics in genetic epidemiology.

Information-theoretic metrics have been proposed for studying gene-gene and gene-environment interactions in genetic epidemiology. Although these metrics have proven very promising, they are typically interpreted in the context of communications and information transmission, diminishing their tangibility for epidemiologists and statisticians. In this paper, we clarify the interpretation of information-theoretic metrics. In particular, we develop the methods so that their relation to the global properties of probability models is made clear and contrast them with log-linear models for multinomial data. Hopefully, a better understanding of their properties and probabilistic implications will promote their acceptance and correct usage in genetic epidemiology. Our novel development also suggests new approaches to model search and computation.