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Constitutively active estrogen receptor α (ER/ESR1) mutations have been identified in approximately one-third of ER+ metastatic breast cancers. Although these mutations are known as mediators of endocrine resistance, their potential role in promoting metastatic disease has not yet been mechanistically addressed. In this study, we show the presence of ESR1 mutations exclusively in distant but not local recurrences in five independent breast cancer cohorts. In concordance with transcriptomic profiling of ESR1-mutant tumors, genome-edited ESR1 Y537S and D538G-mutant cell models exhibited a reprogrammed cell adhesive gene network via alterations in desmosome/gap junction genes and the TIMP3/MMP axis, which functionally conferred enhanced cell–cell contacts while decreasing cell-extracellular matrix adhesion. In vivo studies showed ESR1-mutant cells were associated with larger multicellular circulating tumor cell (CTC) clusters with increased compactness compared with ESR1 wild-type CTCs. These preclinical findings translated to clinical observations, where CTC clusters were enriched in patients with ESR1-mutated metastatic breast cancer. Conversely, context-dependent migratory phenotypes revealed cotargeting of Wnt and ER as a vulnerability in a D538G cell model. Mechanistically, mutant ESR1 exhibited noncanonical regulation of several metastatic pathways, including secondary transcriptional regulation and de novo FOXA1-driven chromatin remodeling. Collectively, these data provide evidence for ESR1 mutation–modulated metastasis and suggest future therapeutic strategies for targeting ESR1-mutant breast cancer. Significance: Context- and allele-dependent transcriptome and cistrome reprogramming in mutant ESR1 cell models elicit diverse metastatic phenotypes related to cell adhesion and migration, which can be pharmacologically targeted in metastatic breast cancer.

Background Diurnal rhythms in gene expression have been detected in the human brain. Previous studies found that males and females exhibit 24-hour rhythms in known circadian genes, with earlier peak expression in females. Whether there are sex differences in large-scale transcriptional rhythms in the cortex that align with observed sex differences in physiological and behavioral rhythms is currently unknown. Methods Diurnal rhythmicity of gene expression was determined for males and females using RNA sequencing data from human postmortem dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). Sex differences among rhythmic genes were determined using significance cutoffs, threshold-free analyses, and R2 difference. Phase concordance was assessed across the DLPFC and ACC for males and females. Pathway and transcription factor analyses were also conducted on significantly rhythmic genes. Results Canonical circadian genes had diurnal rhythms in both sexes with similar amplitude and phase. When analyses were expanded to the entire transcriptome, significant sex differences in transcriptional rhythms emerged. There were nearly twice as many rhythmic transcripts in the DLPFC in males and nearly 4 times as many rhythmic transcripts in the ACC in females. Results suggest a diurnal rhythm in synaptic transmission specific to the ACC in females (e.g., GABAergic [gamma-aminobutyric acidergic] and cholinergic neurotransmission). For males, there was phase concordance between the DLPFC and ACC, while phase asynchrony was found in females. Conclusions There are robust sex differences in molecular rhythms of genes in the DLPFC and ACC, providing potential mechanistic insights into how neurotransmission and synaptic function are modulated in a circadian-dependent and sex-specific manner.

Summary Clustering with variable selection is a challenging yet critical task for modern small-n-large-p data. Existing methods based on sparse Gaussian mixture models or sparse $K$-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with a Gaussian assumption. In this article, we develop a negative binomial mixture model with lasso or fused lasso gene regularization to cluster samples (small $n$) with high-dimensional gene features (large $p$). A modified EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with existing methods using extensive simulations and two real transcriptomic applications in rat brain and breast cancer studies. The result shows the superior performance of the proposed count data model in clustering accuracy, feature selection, and biological interpretation in pathways.

Estrogen receptor alpha (ER/ESR1) is frequently mutated in endocrine resistant ER-positive (ER+) breast cancer and linked to ligand-independent growth and metastasis. Despite the distinct clinical features of ESR1 mutations, their role in intrinsic subtype switching remains largely unknown. Here we find that ESR1 mutant cells and clinical samples show a significant enrichment of basal subtype markers, and six basal cytokeratins (BCKs) are the most enriched genes. Induction of BCKs is independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated insulated neighborhood. BCK-high ER+ primary breast tumors exhibit a number of enriched immune pathways, shared with ESR1 mutant tumors. S100A8 and S100A9 are among the most induced immune mediators and involve in tumor-stroma paracrine crosstalk inferred by single-cell RNA-seq from metastatic tumors. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities.

Inactivation of Pten gene through deletions and mutations leading to excessive pro-growth signaling pathway activations frequently occurs in cancers. Here, we report a Pten derived pro-cancer growth gene fusion Pten-NOLC1 originated from a chr10 genome rearrangement and identified through a transcriptome sequencing analysis of human cancers. Pten-NOLC1 fusion is present in primary human cancer samples and cancer cell lines from different organs. The product of Pten-NOLC1 is a nuclear protein that interacts and activates promoters of EGFR, c-MET, and their signaling molecules. Pten-NOLC1 promotes cancer proliferation, growth, invasion, and metastasis, and reduces the survival of animals xenografted with Pten-NOLC1-expressing cancer cells. Genomic disruption of Pten-NOLC1 induces cancer cell death, while genomic integration of this fusion gene into the liver coupled with somatic Pten deletion produces spontaneous liver cancers in mice. Our studies indicate that Pten-NOLC1 gene fusion is a driver for human cancers.

Valproate (VPA) has been used in the treatment of bipolar disorder since the 1990s. However, the therapeutic targets of VPA have remained elusive. Here we employ a preclinical model to identify the therapeutic targets of VPA. We find compounds that inhibit histone deacetylase proteins (HDACs) are effective in normalizing manic-like behavior, and that class I HDACs (e.g., HDAC1 and HDAC2) are most important in this response. Using an RNAi approach, we find that HDAC2, but not HDAC1, inhibition in the ventral tegmental area (VTA) is sufficient to normalize behavior. Furthermore, HDAC2 overexpression in the VTA prevents the actions of VPA. We used RNA sequencing in both mice and human induced pluripotent stem cells (iPSCs) derived from bipolar patients to further identify important molecular targets. Together, these studies identify HDAC2 and downstream targets for the development of novel therapeutics for bipolar mania.

Motivation There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test. Results We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system. Availability and implementation Code is available on https://github.com/batmanlab/Semi-paired-Association-Test. Supplementary information Supplementary data are available at Bioinformatics online.

Psychiatric disorders are associated with accelerated aging and enhanced risk for neurodegenerative disorders. Brain aging is associated with molecular, cellular, and structural changes that are robust on the group level, yet show substantial inter-individual variability. Here we assessed deviations in gene expression from normal age-dependent trajectories, and tested their validity as predictors of risk for major mental illnesses and neurodegenerative disorders. We performed large-scale gene expression and genotype analyses in postmortem samples of two frontal cortical brain regions from 214 control subjects aged 20–90 years. Individual estimates of “molecular age” were derived from age-dependent genes, identified by robust regression analysis. Deviation from chronological age was defined as “delta age”. Genetic variants associated with deviations from normal gene expression patterns were identified by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic risk scores (PRScis-eQTL and PRSGWAS), and tested for predicting brain disorders or pathology in independent postmortem expression datasets and clinical cohorts. In these validation datasets, molecular ages, defined by 68 and 76 age-related genes for two brain regions respectively, were positively correlated with chronological ages (r = 0.88/0.91), elevated in bipolar disorder (BP) and schizophrenia (SCZ), and unchanged in major depressive disorder (MDD). Exploratory analyses in independent clinical datasets show that PRSs were associated with SCZ and MDD diagnostics, and with cognition in SCZ and pathology in Alzheimer’s disease (AD). These results suggest that older molecular brain aging is a common feature of severe mental illnesses and neurodegeneration.

Severe and persistent disruptions to sleep and circadian rhythms are common in people with opioid use disorder (OUD). Preclinical evidence suggests altered molecular rhythms in the brain modulate opioid reward and relapse. However, whether molecular rhythms are disrupted in the brains of people with OUD remained an open question, critical to understanding the role of circadian rhythms in opioid addiction. Using subjects' times of death as a marker of time of day, we investigated transcriptional rhythms in the brains of subjects with OUD compared to unaffected comparison subjects. We discovered rhythmic transcripts in both the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc), key brain areas involved in OUD, that were largely distinct between OUD and unaffected subjects. Fewer rhythmic transcripts were identified in DLPFC of subjects with OUD compared to unaffected subjects, whereas in the NAc, nearly double the number of rhythmic transcripts was identified in subjects with OUD. In NAc of subjects with OUD, rhythmic transcripts peaked either in the evening or near sunrise, and were associated with an opioid, dopamine, and GABAergic neurotransmission. Associations with altered neurotransmission in NAc were further supported by co-expression network analysis which identified OUD-specific modules enriched for transcripts involved in dopamine, GABA, and glutamatergic synaptic functions. Additionally, rhythmic transcripts in DLPFC and NAc of subjects with OUD were enriched for genomic loci associated with sleep-related GWAS traits, including sleep duration and insomnia. Collectively, our findings connect transcriptional rhythm changes in opioidergic, dopaminergic, GABAergic signaling in the human brain to sleep-related traits in opioid addiction.

The steroid receptor coactivator-1 (SRC-1) is a nuclear receptor co-activator, known to play key roles in both estrogen response in bone and in breast cancer metastases. We previously demonstrated that the P1272S single nucleotide polymorphism (SNP; P1272S; rs1804645) in SRC-1 decreases the activity of estrogen receptor in the presence of selective estrogen receptor modulators (SERMs) and that it is associated with a decrease in bone mineral density (BMD) after tamoxifen therapy, suggesting it may disrupt the agonist action of tamoxifen. Given such dual roles of SRC-1 in the bone microenvironment and in tumor cell-intrinsic phenotypes, we hypothesized that SRC-1 and a naturally occurring genetic variant, P1272S, may promote breast cancer bone metastases. We developed a syngeneic, knock-in mouse model to study if the SRC-1 SNP is critical for normal bone homeostasis and bone metastasis. Our data surprisingly reveal that the homozygous SRC-1 SNP knock-in increases tamoxifen-induced bone protection after ovariectomy. The presence of the SRC-1 SNP in mammary glands resulted in decreased expression levels of SRC-1 and reduced tumor burden after orthotopic injection of breast cancer cells not bearing the SRC-1 SNP, but increased metastases to the lungs in our syngeneic mouse model. Interestingly, the P1272S SNP identified in a small, exploratory cohort of bone metastases from breast cancer patients was significantly associated with earlier development of bone metastasis. This study demonstrates the importance of the P1272S SNP in both the effect of SERMs on BMD and the development of tumor in the bone.

Background Prevalence rates of opioid use disorder (OUD) have increased dramatically, accompanied by a surge of overdose deaths. While opioid dependence has been extensively studied in preclinical models, an understanding of the biological alterations that occur in the brains of people who chronically use opioids and who are diagnosed with OUD remains limited. To address this limitation, RNA sequencing was conducted on the dorsolateral prefrontal cortex and nucleus accumbens, regions heavily implicated in OUD, from postmortem brains in subjects with OUD. Methods We performed RNA sequencing on the dorsolateral prefrontal cortex and nucleus accumbens from unaffected comparison subjects (n = 20) and subjects diagnosed with OUD (n = 20). Our transcriptomic analyses identified differentially expressed transcripts and investigated the transcriptional coherence between brain regions using rank-rank hypergeometric orderlap. Weighted gene coexpression analyses identified OUD-specific modules and gene networks. Integrative analyses between differentially expressed transcripts and genome-wide association study datasets using linkage disequilibrium scores assessed the genetic liability of psychiatric-related phenotypes in OUD. Results Rank-rank hypergeometric overlap analyses revealed extensive overlap in transcripts between the dorsolateral prefrontal cortex and nucleus accumbens in OUD, related to synaptic remodeling and neuroinflammation. Identified transcripts were enriched for factors that control proinflammatory cytokine, chondroitin sulfate, and extracellular matrix signaling. Cell-type deconvolution implicated a role for microglia as a potential driver for opioid-induced neuroplasticity. Linkage disequilibrium score analysis suggested genetic liabilities for risky behavior, attention-deficit/hyperactivity disorder, and depression in subjects with OUD. Conclusions Overall, our findings suggest connections between the brain’s immune system and opioid dependence in the human brain.

ObjectivesNext-generation sequencing (NGS) has the potential to identify genetic alterations that are actionable with targeted therapy. Our objective was to identify the impact of NGS testing on advanced breast and gynecologic malignancies.MethodsA retrospective review of 108 patients who underwent NGS testing between 2015 and 2019 was performed. The NGS clinical action rate was calculated based on documentation of positive clinical action taken in cases with an actionable NGS result.ResultsThe 108 specimens tested included 35 breast cancers and 73 gynecologic malignancies, with most of the testing performed at Foundation Medicine (90%). Actionable mutation(s) were identified in 79 (73%) of 108 cases. The overall clinical action rate of NGS testing was 38% (30 of 79 cases). Overall, 47 (44%) of 108 patients died, all succumbing to disease. The average survival was 10.9 months. The survival difference between patients with actionable NGS result and targeted treatment, actionable NGS result but no targeted treatment, and patients with nonactionable NGS result was not significant (log-rank test, P = .5160).ConclusionsNGS testing for advanced breast and gynecologic cancers at our institution has a 38% clinical action rate. However, the increased clinical action rate over the years did not translate into improved survival.

Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been founded to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level, and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR rhythmicity and LR diff was demonstrated in two real data applications, including a brain aging data (gene expression microarray data of human postmortem brain) and a time-restricted feeding data (RNA sequencing data of human skeletal muscles). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

Estimating the number of clusters (K) is a critical and often difficult task in cluster analysis. Many methods have been proposed to estimate K, including some top performers using resampling approach. When performing cluster analysis in high-dimensional data, simultaneous clustering and feature selection is needed for improved interpretation and performance. To our knowledge, little has been studied for simultaneous estimation of K and feature sparsity parameter in a high-dimensional exploratory cluster analysis. In this paper, we propose a resampling method to bridge this gap and evaluate its performance under the sparse K-means clustering framework. The proposed target function balances between sensitivity and specificity of clustering evaluation of pairwise subjects from clustering of full and subsampled data. Through extensive simulations, the method performs among the best over classical methods in estimating K in low-dimensional data. For high-dimensional simulation data, it also shows superior performance to simultaneously estimate K and feature sparsity parameter. Finally, we evaluated the methods in four microarray, two RNA-seq, one SNP, and two non-omics datasets. The proposed method achieves better clustering accuracy with fewer selected predictive genes in almost all real applications.

The human striatum can be subdivided into the caudate, putamen, and nucleus accumbens (NAc). Each of these structures have some overlapping and some distinct functions related to motor control, cognitive processing, motivation, and reward. Previously, we used a "time-of-death" approach to identify diurnal rhythms in RNA transcripts in human cortical regions. Here, we identify molecular rhythms across the three striatal subregions collected from postmortem human brain tissue in subjects without psychiatric or neurological disorders. Core circadian clock genes are rhythmic across all three regions and show strong phase concordance across regions. However, the putamen contains a much larger number of significantly rhythmic transcripts than the other two regions. Moreover, there are many differences in pathways that are rhythmic across regions. Strikingly, the top rhythmic transcripts in NAc (but not the other regions) are predominantly small nucleolar RNAs and long noncoding RNAs, suggesting that a completely different mechanism might be used for the regulation of diurnal rhythms in translation and/or RNA processing in the NAc versus the other regions. Further, although the NAc and putamen are generally in phase with regard to timing of expression rhythms, the NAc and caudate, and caudate and putamen, have several clusters of discordant rhythmic transcripts, suggesting a temporal wave of specific cellular processes across the striatum. Taken together, these studies reveal distinct transcriptome rhythms across the human striatum and are an important step in helping to understand the normal function of diurnal rhythms in these regions and how disruption could lead to pathology.

Symbiotic microbial colonization through the establishment of the intestinal microbiome is critical to many intestinal functions, including nutrient metabolism, intestinal barrier integrity, and immune regulation. Recent studies suggest that education of intestinal immunity may be ongoing in utero. However, the drivers of this process are unknown. The microbiome and its byproducts are one potential source. Whether a fetal intestinal microbiome exists is controversial, and whether microbially derived metabolites are present in utero is unknown. Here, we aimed to determine whether bacterial DNA and microbially derived metabolites can be detected in second trimester human intestinal samples. Although we were unable to amplify bacterial DNA from fetal intestines, we report a fetal metabolomic intestinal profile with an abundance of bacterially derived and host-derived metabolites commonly produced in response to microbiota. Though we did not directly assess their source and function, we hypothesize that these microbial-associated metabolites either come from the maternal microbiome and are vertically transmitted to the fetus to prime the fetal immune system and prepare the gastrointestinal tract for postnatal microbial encounters or are produced locally by bacteria that were below our detection threshold., A unique human fetal metabolomic intestinal profile is reported with an abundance of bacterially derived metabolites.

Alternative polyadenylation (APA) in breast tumor samples results in the removal/addition of cis-regulatory elements such as microRNA (miRNA) target sites in the 3′-untranslated region (3′-UTRs) of genes. Although previous computational APA studies focused on a subset of genes strongly affected by APA (APA genes), we identify miRNAs of which widespread APA events collectively increase or decrease the number of target sites [probabilistic inference of microRNA target site modification through APA (PRIMATA-APA)]. Using PRIMATA-APA on the cancer genome atlas (TCGA) breast cancer data, we found that the global APA events change the number of the target sites of particular microRNAs [target sites modified miRNA (tamoMiRNA)] enriched for cancer development and treatments. We also found that when knockdown (KD) of NUDT21 in HeLa cells induces a different set of widespread 3′-UTR shortening than TCGA breast cancer data, it changes the target sites of the common tamoMiRNAs. Since the NUDT21 KD experiment previously demonstrated the tumorigenic role of APA events in a miRNA dependent fashion, this result suggests that the APA-initiated tumorigenesis is attributable to the miRNA target site changes, not the APA events themselves. Further, we found that the miRNA target site changes identify tumor cell proliferation and immune cell infiltration to the tumor microenvironment better than the miRNA expression levels or the APA events themselves. Altogether, our computational analyses provide a proof-of-concept demonstration that the miRNA target site information indicates the effect of global APA events with a potential as predictive biomarker.

BackgroundBreast cancer is the most common malignancy to spread to the orbit and periorbit, and the invasive lobular carcinoma (ILC) histologic subtype of breast cancer has been reported to form these ophthalmic metastases (OM) more frequently than invasive ductal carcinomas (IDC). We herein report our single academic institution experience with breast cancer OM with respect to anatomical presentation, histology (lobular vs. ductal), treatment, and survival.MethodsWe employed the natural language processing platform, TIES (Text Information Extraction System), to search 2.3 million de-identified patient pathology and radiology records at our institution in order to identify patients with OM secondary to breast cancer. We then compared the resultant cohort, the “OM cohort,” to two other representative metastatic breast cancer patient (MBC) databases from our institution. Histological analysis of selected patients was performed.ResultsOur TIES search and manual refinement ultimately identified 28 patients who were diagnosed with breast cancer between 1995 and 2016 that subsequently developed OM. Median age at diagnosis was 54 (range 28–77) years of age. ER, PR, and HER2 status from the 28 patients with OM did not differ from other patients with MBC from our institution. The relative proportion of patients with ILC was significantly higher in the OM cohort (32.1%) than in other MBC patients in our institution (11.3%,p = 0.007). Median time to first OM in the OM cohort was 46.7 months, and OM were the second most frequent first metastases after bony metastases. After diagnosis of the first distant metastasis of any kind, median survival of patients with ILC (21.4 months) was significantly shorter than that of patients with IDC (55.3 months,p = 0.03). Nine patients developed bilateral OM. We observed a significant co-occurrence of OM and central nervous system metastases (p = 0.0053). The histological analysis revealed an interesting case in which the primary tumor was of a mixed ILC/IDC subtype, while only ILC was present in the OM.ConclusionsOM from breast cancer are illustrative of the difference in metastatic behavior of ILC versus IDC and should be considered when treating patients with ILC, especially in those with complaints of visual acuity changes.

Necrotizing enterocolitis (NEC) is a severe gastrointestinal complication of prematurity. Using suspension and imaging mass cytometry coupled with single-cell RNA sequencing, we demonstrate severe inflammation in patients with NEC. NEC mucosa could be subtyped by an influx of three distinct neutrophil phenotypes (immature, newly emigrated, and aged). Furthermore, CD16+CD163+ monocytes/Mϕ, correlated with newly emigrated neutrophils, were specifically enriched in NEC mucosa, found adjacent to the blood vessels, and increased in circulation of infants with surgical NEC, suggesting trafficking from the periphery to areas of inflammation. NEC-specific monocytes/Mϕ transcribed inflammatory genes, including TREM1, IL1A, IL1B, and calprotectin, and neutrophil recruitment genes IL8, CXCL1, CXCL2, CXCL5 and had enrichment of gene sets in pathways involved in chemotaxis, migration, phagocytosis, and reactive oxygen species generation. In summary, we identify a novel subtype of inflammatory monocytes/Mϕ associated with NEC that should be further evaluated as a potential biomarker of surgical NEC and a target for the development of NEC-specific therapeutics.

The backbone of ovarian cancer treatment is platinum-based chemotherapy and aggressive surgical debulking. New therapeutic approaches using immunotherapy via immune checkpoint blockade, which have demonstrated clinical efficacy in other tumor types, have been less promising in ovarian cancer. To increase their clinical efficacy, checkpoint inhibitors are now being tested in clinical trials in combination with chemotherapy. Here, we evaluated the impact of cisplatin on tumor immunogenicity and its in vivo roles when used alone or in combination with anti-PD-L1, in two novel murine ovarian cancer cell models. The 2F8 and its platinum-resistant derivative 2F8cis model, display distinct inflammatory profiles and chemotherapy sensitivities, and mirror the primary and recurrent human disease, respectively. Acute and chronic exposure to cisplatin enhances tumor immunogenicity by increasing calreticulin, MHC class I, antigen presentation and T-cell infiltration. Cisplatin also upregulates PD-L1 expression in vitro and in vivo, demonstrating a dual, paradoxical immune modulatory effect and supporting the rationale for combination with immune checkpoint blockade. One of the pathways activated by cisplatin treatment is the cGAS/STING pathway. Chronic cisplatin treatment led to upregulation of cGAS and STING proteins in 2F8cis compared to parental 2F8 cells, while acute exposure to cisplatin further increases cGAS and STING levels in both 2F8 and 2F8cis cells. Overexpression of cGAS/STING modifies tumor immunogenicity by upregulating PD-L1, MHC I and calreticulin in tumor cells. Anti-PD-L1 alone in a platinum-sensitive model or with cisplatin in a platinum-resistant model increases survival. These studies have high translational potential in ovarian cancer.

Invasive lobular carcinoma (ILC) is the second most common subtype of breast cancer following invasive ductal carcinoma (IDC) and characterized by the loss of E-cadherin–mediated adherens junctions. Despite displaying unique histologic and clinical features, ILC still remains a chronically understudied disease, with limited knowledge gleaned from available laboratory research models. Here we report a comprehensive 2D and 3D phenotypic characterization of four estrogen receptor–positive human ILC cell lines: MDA-MB-134, SUM44, MDA-MB-330, and BCK4. Compared with the IDC cell lines MCF7, T47D, and MDA-MB-231, ultra-low attachment culture conditions revealed remarkable anchorage independence unique to ILC cells, a feature not evident in soft-agar gels. Three-dimensional Collagen I and Matrigel culture indicated a generally loose morphology for ILC cell lines, which exhibited differing preferences for adhesion to extracellular matrix proteins in 2D. Furthermore, ILC cells were limited in their ability to migrate and invade in wound-scratch and transwell assays, with the exception of haptotaxis to Collagen I. Transcriptional comparison of these cell lines confirmed the decreased cell proliferation and E-cadherin–mediated intercellular junctions in ILC while uncovering the induction of novel pathways related to cyclic nucleotide phosphodiesterase activity, ion channels, drug metabolism, and alternative cell adhesion molecules such as N-cadherin, some of which were differentially regulated in ILC versus IDC tumors. Altogether, these studies provide an invaluable resource for the breast cancer research community and facilitate further functional discoveries toward understanding ILC, identifying novel drug targets, and ultimately improving the outcome of patients with ILC. Significance: These findings provide the breast cancer research community with a comprehensive assessment of human invasive lobular carcinoma (ILC) cell line signaling and behavior in various culture conditions, aiding future endeavors to develop therapies and to ultimately improve survival in patients with ILC. Cancer Res; 78(21); 6209–22. ©2018 AACR.

To investigate changes in estrogen receptor alpha (ERα) signaling during progression of endometriosis to endometriosis-associated ovarian cancer (EAOC) as a driver of malignant transformation. We procured tissue samples of normal endometrium, endometriosis (benign, atypical, concurrent with EAOC), and EAOC. We evaluated expression of a 236-gene signature of estrogen signaling. ANOVA and unsupervised clustering were used to identify gene expression profiles across disease states. These profiles were compared to profiles of estrogen regulation in cancer models from the Gene Expression Omnibus (GEO). Gene Set Enrichment Analysis (GSEA) was performed to determine whether gene expression in EAOC was consistent with ERα activity. ANOVA revealed 158 differentially expressed genes (q

Motivation Integrative analysis of multi-omics data from different high-throughput experimental platforms provides valuable insight into regulatory mechanisms associated with complex diseases, and gains statistical power to detect markers that are otherwise overlooked by single-platform omics analysis. In practice, a significant portion of samples may not be measured completely due to insufficient tissues or restricted budget (e.g. gene expression profile are measured but not methylation). Current multi-omics integrative methods require complete data. A common practice is to ignore samples with any missing platform and perform complete case analysis, which leads to substantial loss of statistical power. Methods In this article, inspired by the popular Integrative Bayesian Analysis of Genomics data (iBAG), we propose a full Bayesian model that allows incorporation of samples with missing omics data. Results Simulation results show improvement of the new full Bayesian approach in terms of outcome prediction accuracy and feature selection performance when sample size is limited and proportion of missingness is large. When sample size is large or the proportion of missingness is low, incorporating samples with missingness may introduce extra inference uncertainty and generate worse prediction and feature selection performance. To determine whether and how to incorporate samples with missingness, we propose a self-learning cross-validation (CV) decision scheme. Simulations and a real application on child asthma dataset demonstrate superior performance of the CV decision scheme when various types of missing mechanisms are evaluated. Availability and implementation Freely available on the GitHub at https://github.com/CHPGenetics/FBM Supplementary information Supplementary data are available at Bioinformatics online.

Background Major depressive disorder (MDD) affects women approximately twice as often as men. Women are three times as likely to have atypical depression, with hypersomnia and weight gain. This suggests that the molecular mechanisms of MDD may differ by sex. Methods To test this hypothesis, we performed a large-scale gene expression meta-analysis across three corticolimbic brain regions: the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and basolateral amygdala (26 men, 24 women with MDD and sex-matched control subjects). Results were further analyzed using a threshold-free approach, Gene Ontology, and cell type–specific analyses. A separate dataset was used for independent validation (13 MDD subjects/sex and 22 control subjects [13 men, 9 women]). Results Of the 706 genes differentially expressed in men with MDD and 882 genes differentially expressed in women with MDD, only 21 were changed in the same direction in both sexes. Notably, 52 genes displayed expression changes in opposite directions between men and women with MDD. Similar results were obtained using a threshold-free approach, in which the overall transcriptional profile of MDD was opposite in men and women. Gene Ontology indicated that men with MDD had decreases in synapse-related genes, whereas women with MDD exhibited transcriptional increases in this pathway. Cell type–specific analysis indicated that men with MDD exhibited increases in oligodendrocyte- and microglia-related genes, while women with MDD had decreases in markers of these cell types. Conclusions The brain transcriptional profile of MDD differs greatly by sex, with multiple transcriptional changes in opposite directions between men and women with MDD.

Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare gene expression datasets using ‘threshold-free’ comparisons. Significance cut-offs to identify genes shared between datasets may be too stringent and may miss concordant patterns of gene expression with potential biological relevance. A threshold-free approach gaining popularity in several research areas, including neuroscience, is Rank–Rank Hypergeometric Overlap (RRHO). Genes are ranked by their p-value and effect size direction, and ranked lists are compared to identify significantly overlapping genes across a continuous significance gradient rather than at a single arbitrary cut-off. We have updated the previous RRHO analysis by accurately detecting overlap of genes changed in the same and opposite directions between two datasets. Here, we use simulated and real data to show the drawbacks of the previous algorithm as well as the utility of our new algorithm. For example, we show the power of detecting discordant transcriptional patterns in the postmortem brain of subjects with psychiatric disorders. The new R package, RRHO2, offers a new, more intuitive visualization of concordant and discordant gene overlap.

Motivation With the prevalent usage of microarray and massively parallel sequencing, numerous high-throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the high-dimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization. Results In this article, we combine multiple omics datasets of identical or similar biological hypothesis and introduce two variations of meta-analytic framework of PCA, namely MetaPCA. Regularization is further incorporated to facilitate sparse feature selection in MetaPCA. We apply MetaPCA and sparse MetaPCA to simulations, three transcriptomic meta-analysis studies in yeast cell cycle, prostate cancer, mouse metabolism and a TCGA pan-cancer methylation study. The result shows improved accuracy, robustness and exploratory visualization of the proposed framework. Availability and implementation An R package MetaPCA is available online. (http://tsenglab.biostat.pitt.edu/software.htm). Supplementary information Supplementary data are available at Bioinformatics online.

Schizophrenia (SZ) is associated with dysfunction of the dorsolateral prefrontal cortex (DLPFC). This dysfunction is manifest as cognitive deficits that appear to arise from disturbances in gamma frequency oscillations. These oscillations are generated in DLPFC layer 3 (L3) via reciprocal connections between pyramidal cells (PCs) and parvalbumin (PV)-containing interneurons. The density of cortical PV neurons is not altered in SZ, but expression levels of several transcripts involved in PV cell function, including PV, are lower in the disease. However, the transcriptome of PV cells has not been comprehensively assessed in a large cohort of subjects with SZ. In this study, we combined an immunohistochemical approach, laser microdissection, and microarray profiling to analyze the transcriptome of DLPFC L3 PV cells in 36 matched pairs of SZ and unaffected comparison subjects. Over 800 transcripts in PV neurons were identified as differentially expressed in SZ subjects; most of these alterations have not previously been reported. The altered transcripts were enriched for pathways involved in mitochondrial function and tight junction signaling. Comparison with the transcriptome of L3 PCs from the same subjects revealed both shared and distinct disease-related effects on gene expression between cell types. Furthermore, network structures of gene pathways differed across cell types and subject groups. These findings provide new insights into cell type-specific molecular alterations in SZ which may point toward novel strategies for identifying therapeutic targets.

The orbitofrontal cortex (OFC) is vulnerable to normal and pathologic aging. Currently, layer resolution large-scale proteomic studies describing “normal” age-related alterations at OFC are not available. Here, we performed a large-scale exploratory high-throughput mass spectrometry–based protein analysis on OFC layer 2/3 from 15 “young” (15–43 years) and 18 “old” (62–88 years) human male subjects. We detected 4193 proteins and identified 127 differentially expressed (DE) proteins (p-value ≤0.05; effect size >20%), including 65 up- and 62 downregulated proteins (e.g., GFAP, CALB1). Using a previously described categorization of biological aging based on somatic tissues, that is, peripheral “hallmarks of aging,” and considering overlap in protein function, we show the highest representation of altered cell-cell communication (54%), deregulated nutrient sensing (39%), and loss of proteostasis (35%) in the set of OFC layer 2/3 DE proteins. DE proteins also showed a significant association with several neurologic disorders; for example, Alzheimer's disease and schizophrenia. Notably, despite age-related changes in individual protein levels, protein co-expression modules were remarkably conserved across age groups, suggesting robust functional homeostasis. Collectively, these results provide biological insight into aging and associated homeostatic mechanisms that maintain normal brain function with advancing age.

Objectives Ovarian cancer leads to abdominal carcinomatosis and late stage (III/IV) diagnosis in 75% of patients. Three randomized phase III trials have demonstrated that intraperitoneal (IP) chemotherapy improves outcomes in epithelial ovarian cancer. While IP treatment is validated by clinical trials, there is a poor understanding of the mechanism(s) leading to the survival advantage other than the increased concentration of cytotoxic drugs within the tumor microenvironment. A better understanding of this process through analysis of dynamic biomarkers should promote novel approaches that may enhance tumor clearance. We propose this pilot study to confirm the feasibility of collecting serial peritoneal samples from implanted catheters in women receiving IP chemotherapy. We believe these specimens may be used for multiplex analysis to reveal unique biomarker fluctuations when compared to peripheral blood. Methods From 13 women participating on GOG 252, 30 whole blood, 12 peritoneal fluid (PF), and 20 peritoneal wash (PW) with 30mL saline were obtained. Samples were requested prior to the first three chemotherapy cycles. Samples were assessed for volume, cell populations, protein, RNA, and miRNA content changes. Results Median volume for PF was 1.6mL and 3.1mL for PW. PW is a dilution of PF capable of capturing measurable biomarkers. Peritoneal aspirates contain a unique profile of biomarkers distinct from blood. miRNA undergo earlier alteration with chemotherapy than genes. Flow cytometry does not adequately capture biomarker fluctuations. Conclusions As a proof of principle study, this trial provides evidence that sampling the peritoneal cavity can be adapted for biomarker analysis.

As the sequencing cost continued to drop in the past decade, RNA sequencing (RNA-seq) has replaced microarray to become the standard high-throughput experimental tool to analyze transcriptomic profile. As more and more datasets are generated and accumulated in the public domain, meta-analysis to combine multiple transcriptomic studies to increase statistical power has received increasing popularity. In this article, we propose a Bayesian hierarchical model to jointly integrate microarray and RNA-seq studies. Since systematic fold change differences across RNA-seq and microarray for detecting differentially expressed genes have been previously reported, we replicated this finding in several real datasets and showed that incorporation of a normalization procedure to account for the bias improves the detection accuracy and power. We compared our method with the popular two-stage Fisher's method using simulations and two real applications in a histological subtype (invasive lobular carcinoma) of breast cancer comparing PR+ versus PR− and early-stage versus late-stage patients. The result showed improved detection power and more significant and interpretable pathways enriched in the detected biomarkers from the proposed Bayesian model.

Miniature chromosome maintenance (MCM) proteins play critical roles in DNA replication licensing, initiation and elongation. MCM8, one of the MCM proteins playing a critical role in DNA repairing and recombination, was found to have over-expression and increased DNA copy number in a variety of human malignancies. The gain of MCM8 is associated with aggressive clinical features of several human cancers. Increased expression of MCM8 in prostate cancer is associated with cancer recurrence. Forced expression of MCM8 in RWPE1 cells, the immortalized but non-transformed prostate epithelial cell line, exhibited fast cell growth and transformation, while knocked down of MCM8 in PC3, DU145 and LNCaP cells induced cell growth arrest, and decreased tumor volumes and mortality of severe combined immunodeficiency mice xenografted with PC3 and DU145 cells. MCM8 bound cyclin D1 and activated Rb protein phosphorylation by cyclin-dependent kinase 4 in vitro and in vivo. The cyclin D1/MCM8 interaction is required for Rb phosphorylation and S phase entry in cancer cells. As a result, our study showed that copy number increase and overexpression of MCM8 may play critical roles in human cancer development.

Specifically targeting genomic rearrangements and mutations in tumor cells has remained an elusive goal in cancer therapy. Here, we use Cas9-based genome editing to introduce the gene for the pro-drug converting enzyme ‘herpes simplex virus type 1 thymidine kinase’ (HSV1-tk) into the genome of cancer cells that carry unique sequences resulting from genome rearrangements. Specifically, we targeted the breakpoints of TMEM135-CCDC67 and MAN2A1-FER fusions in human prostate cancer or hepatocellular carcinoma cells in vitro and in mouse xenografts. We designed one adenovirus to deliver the nickase Cas9D10A and gRNAs targeting the breakpoint sequences and another to deliver an EGFP-HSV1-tk construct flanked by sequences homologous to those surrounding the breakpoint. Infection with both viruses resulted in breakpoint-dependent expression of EGFP-tk and ganciclovir-mediated apoptosis. When mouse xenografts were treated with adenoviruses and ganciclovir, all animals showed reduction of tumor burden with no mortality over the observation period. Our results suggest that Cas9-mediated suicide gene insertion might be a viable genotype-specific therapy for cancer.

OBJECTIVE This study aims to investigate whether a systemic molecular pattern associated with aging (senescent-associated secretory phenotype [SASP]) is elevated in adults with late-life depression (LLD), compared with never-depressed elderly comparison participants. DESIGN Cross-sectional study. PARTICIPANTS We included 111 older adults (80 with LLD and 31 comparison participants) in this study. MEASUREMENT A panel of 22 SASP-related proteins was extracted from a previous multiplex protein panel performed in these participants. We conducted a principal component analysis to create the SASP index based on individual weights of each of protein. RESULTS Participants with LLD showed a significantly increased SASP index compared with comparison participants, after controlling for age, depressive symptoms, medical comorbidity (CIRS-G) scores, sex, and cognitive performance (F(1,98) = 7.3, p = 0.008). Correlation analyses revealed that the SASP index was positively correlated with age (r = 0.2, p = 0.03) and CIRS score (r = 0.27, p = 0.005), and negatively correlated with information processing speed (r = -0.34, p = 0.001), executive function (r = -0.27, p = 0.004) and global cognitive performance (r = -0.28, p = 0.007). CONCLUSIONS To the best of our knowledge, this is the first study to show that a set of proteins (i.e., SASP index) primarily associated with cellular aging is abnormally regulated and elevated in LLD. These results suggest that individuals with LLD display enhanced aging-related molecular patterns that are associated with higher medical comorbidity and worse cognitive function. Finally, we provide a set of proteins that can serve as potential therapeutic targets and biomarkers to monitor the effects of therapeutic or preventative interventions in LLD.

Background Frailty is more prevalent among black versus white older Americans. We previously identified 37 metabolites associated with the vigor to frailty spectrum using the Scale of Aging Vigor in Epidemiology (SAVE) among older black men from the Health, Aging, and Body Composition (Health ABC) study. Here, we sought to develop a metabolite composite score based on the 37 SAVE-associated metabolites and determine whether the composite score predicts mortality and whether it attenuates the association between frailty and mortality among older black men. Methods Plasma metabolites were measured using liquid chromatography–mass spectrometry. Most of the 37 metabolites were organic acids/derivatives or lipids. Metabolites were ranked into tertiles: tertiles associated with more vigorous SAVE scores were scored 0, mid-tertiles were scored 1, and tertiles associated with frailer SAVE scores were scored 2. Composite scores were the sum of metabolite tertile scores. We examined mortality associations using Cox regression. Percent attenuation estimated the extent to which metabolites attenuated the association between frailty and mortality. Results One standard deviation frailer SAVE was associated with 30% higher mortality, adjusting for age and site (p = .0002); this association was attenuated by 56% after additionally adjusting for the metabolite composite score. In this model, one standard deviation higher metabolite composite score was associated with 46% higher mortality (p < .0001). Metabolite composite scores also predicted mortality (p = .045) in a validation sample of 120 older adults (40% men, 90% white). Conclusion These metabolites may provide a deeper characterization of the higher mortality that is associated with frailty among older adults.

Background Low walking ability is highly prevalent with advancing age and predicts major health outcomes. Metabolomics may help to better characterize differences in walking ability among older adults, providing insight into potentially altered molecular processes underlying age-related decline in functioning. We sought to identify metabolites and metabolic pathways associated with high versus low walking ability among 120 participants ages 79–95 from the CHS All Stars study. Methods Using a nested case–control design, 60 randomly selected participants with low walking ability were matched one-to-one on age, gender, race, and fasting time with 60 participants with high walking ability. High versus low walking ability was defined as being in the best versus worst tertiles for both gait speed (≥0.9 vs Results Ninety-six metabolites were associated with walking ability, where 24% were triacylglycerols. Triacylglycerols that were higher among those with high walking ability consisted mostly of polyunsaturated fatty acids, whereas triacylglycerols that were lower among those with high walking ability consisted mostly of saturated or monounsaturated fatty acids. Body composition partly explained associations between some metabolites and walking ability. Proline and arginine metabolism was a top pathway associated with walking ability. Conclusion These results may partly reflect pathways of modifiable risk factors, including excess dietary lipids and lack of physical activity, contributing to obesity and further alterations in metabolic pathways that lead to age-related decline in walking ability in this older adult cohort.

Chromosome changes are one of the hallmarks of human malignancies. Chromosomal rearrangement is frequent in human cancers. One of the consequences of chromosomal rearrangement is gene fusions in the cancer genome. We have previously identified a panel of fusion genes in aggressive prostate cancers. In this study, we showed that 6 of these fusion genes are present in 7 different types of human malignancies with variable frequencies. Among them, the CCNH-C5orf30 and TRMT11-GRIK2 gene fusions were found in breast cancer, colon cancer, non-small cell lung cancer, esophageal adenocarcinoma, glioblastoma multiforme, ovarian cancer and liver cancer, with frequencies ranging from 12.9% to 85%. In contrast, four other gene fusions (mTOR-TP53BP1, TMEM135-CCDC67, KDM4-AC011523.2 and LRRC59-FLJ60017) are less frequent. Both TRMT11-GRIK2 and CCNH-C5orf30 are also frequently present in lymph node metastatic cancer samples from the breast, colon and ovary. Thus, detecting these fusion transcripts may have significant biological and clinical implications in cancer patient management.

A consistent gene set undergoes age-associated expression changes in the human cerebral cortex, and our Age-by-Disease Model posits that these changes contribute to psychiatric diseases by “pushing” the expression of disease-associated genes in disease-promoting directions. DNA methylation (DNAm) is an attractive candidate mechanism for age-associated gene expression changes. We used the Illumina HumanMethylation450 array to characterize genome-wide DNAm in the postmortem orbital frontal cortex from 20 younger (60 years) subjects. DNAm data were integrated with existing normal brain aging expression data and sets of psychiatric disease risk genes to test the hypothesis that age-associated DNAm changes contribute to age-associated gene expression changes and, by extension, susceptibility to psychiatric diseases. We found that age-associated differentially methylated regions (aDMRs) are common, robust, bidirectional, concentrated in CpG island shelves and sea, depleted in CpG islands, and enriched among genes undergoing age-associated expression changes (OR = 2.30, p = 1.69 × 10−27). We found the aDMRs are enriched among genetic association-based risk genes for schizophrenia, Alzheimer’s disease (AD), and major depressive disorder (MDD) (OR = 2.51, p = 0.00015; OR = 2.38, p = 0.036; and OR = 3.08, p = 0.018, respectively) as well as expression-based MDD-associated genes (OR = 1.48, p = 0.00012). Similar patterns of enrichment were found for aDMRs that correlate with local gene expression. These results were replicated in a large publically-available dataset, and confirmed by meta-analysis of the two datasets. Our findings suggest DNAm is a molecular mechanism for age-associated gene expression changes and support a role for DNAm in age-by-disease interactions through preferential targeting of disease-associated genes.

Schizophrenia is associated with disrupted cognitive control and sleep-wake cycles. Here we identify diurnal rhythms in gene expression in the human dorsolateral prefrontal cortex (dlPFC), in schizophrenia and control subjects. We find significant diurnal (24 h) rhythms in control subjects, however, most of these transcripts are not rhythmic in subjects with schizophrenia. Instead, subjects with schizophrenia have a different set of rhythmic transcripts. The top pathways identified in transcripts rhythmic only in subjects with schizophrenia are associated with mitochondrial function. Importantly, these rhythms drive differential expression patterns of these and several other genes that have long been implicated in schizophrenia (including BDNF and GABAergic-related transcripts). Indeed, differential expression of these transcripts is only seen in subjects that died during the night, with no change in subjects that died during the day. These data provide insights into a potential mechanism that underlies changes in gene expression in the dlPFC with schizophrenia., Sleep disturbance is common in psychiatric disease, and this may contribute to altered circadian rhythm in gene expression. Here the authors show that rhythms in gene expression in the dorsolateral prefrontal cortex in schizophrenia are different to that seen in healthy controls.

This cohort study evaluates the continued use of sentinel lymph node biopsy for axillary staging in older patients with early-stage breast cancer and greater likelihood of having more comorbidities and functional limitations., Key Points Question What is the rate of sentinel lymph node biopsy (SLNB) and adjuvant radiotherapy (RT) use in women 70 years or older with early-stage, estrogen receptor–positive, ERBB2 (formerly HER2)–negative, clinically node-negative breast cancer, and what are the recurrence outcomes? Findings In this cohort study of 2109 older women with early-stage, estrogen receptor–positive breast cancer, rates of SLNB and RT use remained high and were increasing for SLNB. Treating physicians tended to select healthier patients with fewer comorbidities for SLNB and RT, but receipt of either SLNB or RT was not associated with improvements in locoregional recurrence-free or disease-free survival. Meaning Results of this study suggest that deimplementation of both SLNB and RT interventions should be strongly considered in older patients with estrogen receptor–positive, clinically node-negative breast cancer., Importance Overtreatment of early-stage breast cancer with favorable tumor biology in older patients may be harmful without affecting recurrence and survival. Guidelines that recommend deimplementation of sentinel lymph node biopsy (SLNB) (Choosing Wisely) and radiotherapy (RT) (National Comprehensive Cancer Network) have been published. Objective To describe the use rates and association with disease recurrence of SLNB and RT in older women with breast cancer. Design, Setting, and Participants This cohort study obtained patient and clinical data from an integrated cancer registry and electronic health record of a single health care system in Pennsylvania. The cohort was composed of consecutive female patients 70 years or older who were diagnosed with early-stage, estrogen receptor–positive, ERBB2 (formerly HER2)–negative, clinically node-negative breast cancer from January 1, 2010, to December 31, 2018, who were treated at 15 community and academic hospitals within the health system. Exposures Sentinel lymph node biopsy and adjuvant RT. Main Outcomes and Measures Primary outcomes were 5-year locoregional recurrence-free survival (LRFS) rate and disease-free survival (DFS) rate after SLNB and after RT. Secondary outcomes included recurrence rate, subgroups that may benefit from SLNB or RT, and use rate of SLNB and RT over time. Propensity scores were used to create 2 cohorts to separately evaluate the association of SLNB and RT with recurrence outcomes. Cox proportional hazards regression model was used to estimate hazard ratios (HRs). Results From 2010 to 2018, a total of 3361 women 70 years or older (median [interquartile range {IQR}] age, 77.0 [73.0-82.0] years) with estrogen receptor–positive, ERBB2-negative, clinically node-negative breast cancer were included in the study. Of these women, 2195 (65.3%) received SLNB and 1828 (54.4%) received adjuvant RT. Rates of SLNB steadily increased (1.0% per year), a trend that persisted after the 2016 adoption of the Choosing Wisely guideline. Rates of RT decreased slightly (3.4% per year). To examine patient outcomes and maximize follow-up time, the analysis was limited to cases from 2010 to 2014, identifying 2109 patients with a median (IQR) follow-up time of 4.1 (2.5-5.7) years. In the propensity score–matched cohorts, no association was found between SLNB and either LRFS (HR, 1.26; 95% CI, 0.37-4.30; P = .71) or DFS (HR, 1.92; 95% CI, 0.86-4.32; P = .11). In addition, RT was not associated with LRFS (HR, 0.33; 95% CI, 0.09-1.24; P = .10) or DFS (HR, 0.99; 95% CI, 0.46-2.10; P = .97). Subgroup analysis showed that stratification by tumor grade or comorbidity was not associated with LRFS or DFS. Low absolute rates of recurrence were observed when comparing the groups that received SLNB (3.5%) and those that did not (4.5%) as well as the groups that received RT (2.7%) and those that did not (5.5%). Conclusions and Relevance This study found that receipt of SLNB or RT was not associated with improved LRFS or DFS in older patients with ER-positive, clinically node-negative breast cancer. Despite limited follow-up time and wide 95% CIs, this study supports the continued deimplementation of both SLNB and RT in accordance with the Choosing Wisely and National Comprehensive Cancer Network guidelines.

With the rapid advances in technologies of microarray and massively parallel sequencing, data of multiple omics sources from a large patient cohort are now frequently seen in many consortium studies. Effective multi-level omics data integration has brought new statistical challenges. One important biological objective of such integrative analysis is to cluster patients in order to identify clinically relevant disease subtypes, which will form basis for tailored treatment and personalized medicine. Several methods have been proposed in the literature for this purpose, including the popular iCluster method used in many cancer applications. When clustering high-dimensional omics data, effective feature selection is critical for better clustering accuracy and biological interpretation. It is also common that a portion of "scattered samples" has patterns distinct from all major clusters and should not be assigned into any cluster as they may represent a rare disease subcategory or be in transition between disease subtypes. In this paper, we firstly propose to improve feature selection of the iCluster factor model by an overlapping sparse group lasso penalty on the omics features using prior knowledge of inter-omics regulatory flows. We then perform regularization over samples to allow clustering with scattered samples and generate tight clusters. The proposed group structured tight iCluster method will be evaluated by two real breast cancer examples and simulations to demonstrate its improved clustering accuracy, biological interpretation, and ability to generate coherent tight clusters.

KALRN (KAL) is a Rho GEF that is highly involved in regulation of the actin cytoskeleton within dendrites. There are several isoforms of the protein that arise from differential splicing of KALRN's 66 exons. KAL isoforms have different functions in development. For example, overexpression of the KAL9 and KAL12 isoforms induce dendritic elongation in early development. However, in mature neurons KAL9 overexpression reduces dendritic length, a phenotype also observed in normal human ageing. We therefore hypothesized that KAL9 would have increased expression with age, and undertook to evaluate the expression of individual KALRN exons throughout the adult lifespan. Postmortem human brain grey matter from Brodmann's area (BA) 11 and BA47 derived from a cohort of 209 individuals without psychiatric or neurodegenerative disease, ranging in age from 16 to 91 years, were analysed for KALRN expression by Affymetrix exon array. Analysis of the exon array data in an isoform-specific manner, as well as confirmatory isoform-specific qPCR studies, indicated that the longer KAL9 and KAL12 isoforms demonstrated a statistically significant, but modest, increase with age. The small magnitude of the age effect suggests that inter-individual factors other than age likely contribute to a higher degree to KAL9 and KAL12 expression. In contrast to KAL9 and KAL12, global KALRN expression did not increase with age. Our work suggests that global measures of KALRN gene expression may be misleading and future studies should focus on isoform-specific quantification.

Mucin1 (MUC1) is an epithelial glycoprotein overexpressed in ovarian cancer and actively involved in tumor cell migration and metastasis. Using novel in vitro and in vivo MUC1-expressing conditional (Cre-loxP) ovarian tumor models, we focus here on MUC1 biology and the roles of Kras activation and Pten deletion during cell transformation and epithelial-to-mesenchymal transition (EMT). We generated several novel murine ovarian cancer cell lines derived from the ovarian surface epithelia (OSE) of mice with conditional mutations in Kras, Pten or both. In addition, we also generated several tumor-derived new cell lines that reproduce the original tumor phenotype in vivo and mirror late stage metastatic disease. Our results demonstrate that de novo activation of oncogenic Kras does not trigger increased proliferation, cellular transformation or EMT, and prevents MUC1 upregulation. In contrast, Pten deletion accelerates cell proliferation, triggers cellular transformation in vitro and in vivo, and stimulates MUC1 expression. Ovarian tumor-derived cell lines MKP-Liver and MKP-Lung cells reproduce in vivo EMT and represent the first immune competent mouse model for distant hematogenous spread. Whole genome microarray expression analysis using tumor and OSE-derived cell lines reveal a 121 gene signature associated with EMT and metastasis. When applied to n=542 cases from The Cancer Genome Atlas (TCGA) ovarian cancer dataset, the gene signature identifies a patient subset with decreased survival (P=0.04). Using an extensive collection of novel murine cell lines we have identified distinct roles for Kras and Pten on MUC1 and EMT in vivo and in vitro. The data has implications for future design of combination therapies targeting Kras mutations, Pten deletions and MUC1 vaccines.

Background & Aims Studies are needed to determine the mechanisms of mucosal dysregulation in patients with inflammatory bowel diseases (IBDs) and differences in inflammatory responses of patients with ulcerative colitis (UC) vs Crohn’s disease (CD). We used mass cytometry (CyTOF) to characterize and compare immune cell populations in the mucosa and blood from patients with IBD and without IBD (controls) at single-cell resolution. Methods We performed CyTOF analysis of colonic mucosa samples (n = 87) and peripheral blood mononuclear cells (n = 85) from patients with active or inactive UC or CD and controls. We also performed single-cell RNA sequencing, flow cytometry, and RNA in situ hybridization analyses to validate key findings. We used random forest modeling to identify differences in signatures across subject groups. Results Compared with controls, colonic mucosa samples from patients with IBD had increased abundances of HLA-DR+CD38+ T cells, including T-regulatory cells that produce inflammatory cytokines; CXCR3+ plasmablasts; and IL1B+ macrophages and monocytes. Colonic mucosa samples from patients with UC were characterized by expansion of IL17A+ CD161+ effector memory T cells and IL17A+ T-regulatory cells; expansion of HLA-DR+CD56+ granulocytes; and reductions in type 3 innate lymphoid cells. Mucosal samples from patients with active CD were characterized by IL1B+HLA-DR+CD38+ T cells, IL1B+TNF+IFNG+ naive B cells, IL1B+ dendritic cells (DCs), and IL1B+ plasmacytoid DCs. Peripheral blood mononuclear cells from patients with active CD differed from those of active UC in that the peripheral blood mononuclear cells from patients with CD had increased IL1B+ T-regulatory cells, IL1B+ DCs and IL1B+ plasmacytoid DCs, IL1B+ monocytes, and fewer group 1 innate lymphoid cells. Random forest modeling differentiated active UC from active CD in colonic mucosa and blood samples; top discriminating features included many of the cellular populations identified above. Conclusions We used single-cell technologies to identify immune cell populations specific to mucosa and blood samples from patients with active or inactive CD and UC and controls. This information might be used to develop therapies that target specific cell populations in patients with different types of IBD.

PURPOSE This study is aimed at developing a molecular diagnostics platform to enhance the interpretation of renal allograft biopsies using quantitative proteomic profiling of formalin-fixed and paraffin-embedded (FFPE) specimens. EXPERIMENTAL DESIGN A quantitative proteomics platform composed of 1) an optimized FFPE protein sample preparation method, 2) a tandem mass tag TMT10-plex-based proteomic workflow, and 3) a systematic statistical analysis pipeline to reveal differentially expressed proteins has been developed. This platform is then tested on a small sample set (five samples per phenotype) to reveal proteomic signatures that can differentiate T-cell mediated rejection (TCMR) and polyomavirus BK nephropathy (BKPyVN) from healthy functionally stable kidney tissue (STA). RESULTS Among 2798 quantified proteins, the expression levels of 740 BKPyVN and 638 TCMR associated proteins are significantly changed compared to STA specimens. Principal component analysis demonstrated good segregation of all three phenotypes investigated. Protein detection and quantitation are highly reproducible: replicate comparative analyses demonstrated 71-84% overlap of detected proteins, and the coefficient of variation for protein measurements is

Molecular diagnosis is being increasingly used in transplant pathology to render more objective and quantitative determinations that also provide mechanistic and prognostic insights. This study performed RNA-Seq on biopsies from kidneys with stable function (STA) and biopsies with classical findings of T-cell-mediated rejection (TCMR). Machine learning tools were used to develop prediction models for distinguishing TCMR and STA samples using the top genes identified by DSeq2. The prediction models were tested on 703 biopsies with Affymetrix chip gene expression profiles available in the public domain. Linear discriminant analysis predicted TCMR in 55 of 67 biopsies labeled TCMR, and 65 of 105 biopsies designated as antibody-mediated rejection. The random forest and support vector machine models showed comparable performance. These data illustrate the feasibility of using RNA-Seq for molecular diagnosis of TCMR in formalin-fixed tissue. Application of the derived diagnostic algorithms to publicly available data sets demonstrates frequent coexistence of TCMR in biopsies designated as antibody-mediated rejection. This underrecognition of TCMR in renal allograft biopsies has significant implications with respect to patient care.

Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer following invasive ductal carcinoma (IDC). ILC differs from IDC in a number of histological and clinical features, such as single strand growth, difficulty in detection, and frequent late recurrences. To understand the molecular pathways involved in the clinical characteristics of ILC, we compared the gene expression profiles of luminal A ILC and luminal A IDC using data from TCGA and utilized samples from METABRIC as a validation data set. Top pathways that were significantly enriched in ILC were related to immune response. ILC exhibited a higher activity of almost all types of immune cells based on cell type-specific signatures compared to IDC. Conversely, pathways that were less enriched in ILC were related to protein translation and metabolism, which we functionally validated in cell lines. The higher immune activity uncovered in our study highlights the currently unexplored potential of a response to immunotherapy in a subset of patients with ILC. Furthermore, the lower rates of protein translation and metabolism - known features of tumor dormancy - may play a role in the late recurrences of ILC and lower detection rate in mammography and PET scanning.

Fusion transcript formation is one of the fundamental mechanisms that drives the development of prostate cancer. Because of the advance of high-throughput parallel sequencing, many fusion transcripts have been discovered. However, the discovery rate of fusion transcripts specific for prostate cancer is lagging behind the discoveries made on chromosome abnormalities of prostate cancer. Recent analyses suggest that many fusion transcripts are present in both benign and cancerous tissues. Some of these fusion transcripts likely represent important components of normal gene expression in cells. It is necessary to identify the criteria and features of fusion transcripts that are specific for cancer. In this review, we discuss optimization of RNA sequencing depth for fusion transcript discovery and the characteristics of fusion transcripts in normal prostate tissues and prostate cancer. We also propose a new classification of cancer-specific fusion transcripts on the basis of their tail gene fusion protein product and the roles that these fusions may play in cancer development.

Schizophrenia is associated with alterations in working memory that reflect dysfunction of dorsolateral prefrontal cortex (DLPFC) circuitry. Working memory depends on the activity of excitatory pyramidal cells in DLPFC layer 3 and, to a lesser extent, in layer 5. Although many studies have profiled gene expression in DLPFC gray matter in schizophrenia, little is known about cell-type-specific transcript expression in these two populations of pyramidal cells. We hypothesized that interrogating gene expression, specifically in DLPFC layer 3 or 5 pyramidal cells, would reveal new and/or more robust schizophrenia-associated differences that would provide new insights into the nature of pyramidal cell dysfunction in the illness. We also sought to determine the impact of other variables, such as a diagnosis of schizoaffective disorder or medication use at the time of death, on the patterns of gene expression in pyramidal neurons. Individual pyramidal cells in DLPFC layers 3 or 5 were captured by laser microdissection from 36 subjects with schizophrenia or schizoaffective disorder and matched normal comparison subjects. The mRNA from cell collections was subjected to transcriptome profiling by microarray followed by quantitative PCR validation. Expression of genes involved in mitochondrial (MT) or ubiquitin-proteasome system (UPS) functions were markedly downregulated in the patient group (P-values for MT-related and UPS-related pathways were10(-7) and10(-5), respectively). MT-related gene alterations were more prominent in layer 3 pyramidal cells, whereas UPS-related gene alterations were more prominent in layer 5 pyramidal cells. Many of these alterations were not present, or found to a lesser degree, in samples of DLPFC gray matter from the same subjects, suggesting that they are pyramidal cell specific. Furthermore, these findings principally reflected alterations in the schizophrenia subjects were not present or present to a lesser degree in the schizoaffective disorder subjects (diagnosis of schizoaffective disorder was the most significant covariate, P10(-6)) and were not attributable to factors frequently comorbid with schizophrenia. In summary, our findings reveal expression deficits in MT- and UPS-related genes specific to layer 3 and/or layer 5 pyramidal cells in the DLPFC of schizophrenia subjects. These cell type-specific transcriptome signatures are not characteristic of schizoaffective disorder, providing a potential molecular-cellular basis of differences in clinical phenotypes.

BackgroundBreast cancer brain metastases (BrMs) are defined by complex adaptations to both adjuvant treatment regimens and the brain microenvironment. Consequences of these alterations remain poorly understood, as does their potential for clinical targeting. We utilized genome-wide molecular profiling to identify therapeutic targets acquired in metastatic disease.MethodsGene expression profiling of 21 patient-matched primary breast tumors and their associated brain metastases was performed by TrueSeq RNA-sequencing to determine clinically actionable BrM target genes. Identified targets were functionally validated using small molecule inhibitors in a cohort of resected BrM ex vivo explants (n = 4) and in a patient-derived xenograft (PDX) model of BrM. All statistical tests were two-sided.ResultsConsiderable shifts in breast cancer cell-specific gene expression profiles were observed (1314 genes upregulated in BrM; 1702 genes downregulated in BrM; DESeq; fold change > 1.5, Padj < .05). Subsequent bioinformatic analysis for readily druggable targets revealed recurrent gains in RET expression and human epidermal growth factor receptor 2 (HER2) signaling. Small molecule inhibition of RET and HER2 in ex vivo patient BrM models (n = 4) resulted in statistically significantly reduced proliferation (P < .001 in four of four models). Furthermore, RET and HER2 inhibition in a PDX model of BrM led to a statistically significant antitumor response vs control (n = 4, % tumor growth inhibition [mean difference; SD], anti-RET = 86.3% [1176; 258.3], P < .001; anti-HER2 = 91.2% [1114; 257.9], P < .01).ConclusionsRNA-seq profiling of longitudinally collected specimens uncovered recurrent gene expression acquisitions in metastatic tumors, distinct from matched primary tumors. Critically, we identify aberrations in key oncogenic pathways and provide functional evidence for their suitability as therapeutic targets. Altogether, this study establishes recurrent, acquired vulnerabilities in BrM that warrant immediate clinical investigation and suggests paired specimen expression profiling as a compelling and underutilized strategy to identify targetable dependencies in advanced cancers.