Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression

Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression

Novel Use of a Social-Media-Based Survey to Detect Regional Differences in Management of Monochorionic-Diamniotic Twins.

ObjectiveThis study aims to evaluate the utility of social media to distribute a patient survey on differences in management and outcomes of monochorionic-diamniotic (MCDA) pregnancies.Study designA cross-sectional survey was posted to an English-language MCDA twins patient-centered support group within the social media site, Facebook from April 2, 2018 to June 26, 2018. Subjects were recruited through a technique called "snowballing," whereby individuals shared the survey to assist with recruiting. Patient reported data were analyzed using Chi-square and Kruskal-Wallis's tests to explore characteristics associated with surveillance and outcomes as related to region and provider type.ResultsOver 3 months, the post "reached" 14,288 Facebook users, among which 5,653 (40%) clicked on the post. A total of 2,357 respondents with MCDA pregnancies completed the survey. Total 1,928 (82%) were from the United States (US) and 419 (18%) from other countries. Total 85% of patients had co-management with maternal-fetal medicine (MFM), more in the US compared with the rest of the world (87 vs. 74%, p < 0.01). MFM involvement led to increased adherence to biweekly ultrasounds (91 vs. 65%, p < 0.01), diagnosis of monochorionicity by 12 weeks (74 vs. 69%, p < 0.01) and better education about twin-twin transfusion syndrome (90 vs. 66%, p < 0.01). Pregnancies with MFM involvement had a higher take-home baby rate for both babies (92 vs. 89%, p < 0.01) or for at least one baby (98 vs. 93%, p < 0.01) compared with those without MFM involvement.ConclusionA survey distributed via social media can be effective in evaluating real-life management and outcomes of an uncommon obstetrical diagnosis. This survey elucidates wide international variation in adherence to guidelines, management, and outcomes

Prostate cancer support groups: Canada-based specialists\u27 perspectives

To understand prostate cancer (PCa) specialists’ views about prostate cancer support groups (PCSGs), a volunteer sample of Canada-based PCa specialists ( n = 150), including urologists ( n = 100), radiation oncologists ( n = 40), and medical oncologists ( n = 10) were surveyed. The 56-item questionnaire used in this study included six sets of attitudinal items to measure prostate cancer specialists’ beliefs about positive and negative influences of PCSGs, reasons for attending PCSGs, the attributes of effective PCSGs, and the value of face-to-face and web-based PCSGs. In addition, an open-ended question was included to invite additional input from participants. Results showed that PCSGs were positively valued, particularly for information sharing, education and psychosocial support. Inclusivity, privacy, and accessibility were identified as potential barriers, and recommendations were made for better marketing PCSGs to increase engagement. Findings suggest prostate cancer specialists highly valued the role and potential benefits of face-to-face PCSGs. Information provision and an educational role were perceived as key benefits. Some concerns were expressed about the ability of web-based PCSGs to effectively engage and educate men who experience prostate cancer

Synthetic Studies of Neoclerodane Diterpenes from Salvia divinorum: Role of the Furan in Affinity for Opioid Receptors

Further synthetic modification of the furan ring of salvinorin A (1), the major active component of Salvia divinorum, has resulted in novel neoclerodane diterpenes with opioid receptor affinity and activity. A computational study has predicted 1 to be a reproductive toxicant in mammals and is suggestive that use of 1 may be associated with adverse effects. We report in this study that piperidine 21 and thiomorpholine 23 have been identified as selective partial agonists at kappa opioid receptors. This indicates that additional structural modifications of 1 may provide ligands with good selectivity for opioid receptors but with reduced potential for toxicity

SETD2 regulates chromatin accessibility and transcription to suppress lung tumorigenesis

SETD2, a H3K36 trimethyltransferase, is the most frequently mutated epigenetic modifier in lung adenocarcinoma, with a mutation frequency of approximately 9%. However, how SETD2 loss of function promotes tumorigenesis remains unclear. Using conditional Setd2-KO mice, we demonstrated that Setd2 deficiency accelerated the initiation of KrasG12D-driven lung tumorigenesis, increased tumor burden, and significantly reduced mouse survival. An integrated chromatin accessibility and transcriptome analysis revealed a potentially novel tumor suppressor model of SETD2 in which SETD2 loss activates intronic enhancers to drive oncogenic transcriptional output, including the KRAS transcriptional signature and PRC2-repressed targets, through regulation of chromatin accessibility and histone chaperone recruitment. Importantly, SETD2 loss sensitized KRAS-mutant lung cancer to inhibition of histone chaperones, the FACT complex, or transcriptional elongation both in vitro and in vivo. Overall, our studies not only provide insight into how SETD2 loss shapes the epigenetic and transcriptional landscape to promote tumorigenesis, but they also identify potential therapeutic strategies for SETD2 mutant cancers

Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression

Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations

Background: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. Methods: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. Results: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Conclusions: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer

Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/

Higher Absolute Lymphocyte Counts Predict Lower Mortality from Early-Stage Triple-Negative Breast Cancer

Purpose: Tumor-infiltrating lymphocytes (TIL) in pretreatment biopsies are associated with improved survival in triple-negative breast cancer (TNBC). We investigated whether higher peripheral lymphocyte counts are associated with lower breast cancer–specific mortality (BCM) and overall mortality (OM) in TNBC. Experimental Design: Data on treatments and diagnostic tests from electronic medical records of two health care systems were linked with demographic, clinical, pathologic, and mortality data from the California Cancer Registry. Multivariable regression models adjusted for age, race/ethnicity, socioeconomic status, cancer stage, grade, neoadjuvant/adjuvant chemotherapy use, radiotherapy use, and germline BRCA1/2 mutations were used to evaluate associations between absolute lymphocyte count (ALC), BCM, and OM. For a subgroup with TIL data available, we explored the relationship between TILs and peripheral lymphocyte counts. Results: A total of 1,463 stage I–III TNBC patients were diagnosed from 2000 to 2014; 1,113 (76%) received neoadjuvant/adjuvant chemotherapy within 1 year of diagnosis. Of 759 patients with available ALC data, 481 (63.4%) were ever lymphopenic (minimum ALC <1.0 K/μL). On multivariable analysis, higher minimum ALC, but not absolute neutrophil count, predicted lower OM [HR = 0.23; 95% confidence interval (CI), 0.16–0.35] and BCM (HR = 0.19; CI, 0.11–0.34). Five-year probability of BCM was 15% for patients who were ever lymphopenic versus 4% for those who were not. An exploratory analysis (n = 70) showed a significant association between TILs and higher peripheral lymphocyte counts during neoadjuvant chemotherapy. Conclusions: Higher peripheral lymphocyte counts predicted lower mortality from early-stage, potentially curable TNBC, suggesting that immune function may enhance the effectiveness of early TNBC treatment