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S1. Generation and characterization of AXL knockout cells. S2. Generation and characterization of AXL overexpression cells. S3. Aggregate characterization. S4. Characterization of bone signal in AXL-on mice at time of death. Supplementary Table 1: Differentially regulated genes in doxycycline-treated Tet-Axl 2 cells compared to Tet-Axl 1.

Background: Detection of MRD after curative intent treatment may identify patients at high risk for recurrence. Most ctDNA-based MRD assays require a priori knowledge of genomic alterations from tumor tissue to achieve acceptable sensitivity and specificity. However, tissue availability may be limited in some patients with breast cancer, particularly following neoadjuvant therapy. Here we report results of a pilot study evaluating a plasma-only multiomic ctDNA MRD assay and its association with breast cancer recurrence. Methods: 47 plasma samples from 38 patients with early-stage breast cancer were collected through the BRandO BiO Registry at 12- or 36-months post-diagnosis and/or at the time of clinical recurrence. BRandO BiO is a multi-center regional registry with longitudinal biobanking from patients with newly diagnosed breast cancer at 20 affiliated network hospitals in Germany. The presence of ctDNA was evaluated using the Guardant Reveal multi-cancer assay, a next generation sequencing panel covering ~500 genes and ~4Mb of epigenomic regions that undergo differential methylation in multiple solid tumor types. The presence/absence of ctDNA is determined by a custom bioinformatics classifier that identifies tumor-derived somatic variants and methylation profiles specific to individual cancer types without the need for tumor tissue or peripheral blood mononuclear cell analysis. Samples were analyzed blinded to recurrence status.Results: 20 patients were confirmed to have clinical recurrence (7 local, 13 distant). ctDNA was detected at or prior to distant recurrence in 11/13 (85%) patients and in 1/7 (14%) patients with local recurrence. Five patients with distant recurrence had a sample available that was collected prior to distant recurrence; ctDNA was detected in 4/5 with a 3.8-18.6 month lead time. Among 15 ctDNA+ samples, 5 were positive for both methylation and somatic variant calls, 1 by somatic variants only, and 9 by methylation only. Somatic mutations were identified in AKT, RB1, KRAS, PIK3CA, ERBB2, MAP3K1, GATA3, and ESR1. The ESR1 mutation occurred in a patient with endocrine therapy resistance, confirming the ability of the assay to identify acquired treatment resistance mutations. ctDNA was not detected in samples from the 14 patients who had no confirmed clinical recurrence (100% specificity). Conclusions: This is the first study to demonstrate the feasibility of MRD detection in breast cancer using a plasma-only multiomic ctDNA-based approach. The Guardant Reveal assay demonstrated high sensitivity and specificity to detect distant breast cancer recurrence. Larger studies in this population are ongoing to further validate the clinical performance of the assay and demonstrate its applications in the management of early-stage breast cancer. Citation Format: Wolfgang Janni, Jens Huober, Tatjana Braun, Volkmar Müller, Angelina Fink, Amelie de Gregorio, Brigitte Rack, Thomas W. Friedl, Lisa Wiesmüller, Klaus Pantel, Thereasa Rich, Princy Parsana, Elena Zotenko, Shile Zhang, Sophia Huesmann. Multiomic, plasma-only circulating tumor DNA (ctDNA) assay identifies breast cancer patients with minimal residual disease (MRD) and predicts distant recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3403.

The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing. Some human genetic variants affect the amount of RNA produced and the splicing of gene transcripts, crucial steps in development and maintaining a healthy individual. However, some of these changes only occur in a small number of tissues within the body. The Genotype-Tissue Expression (GTEx) project has been expanded over time, and, looking at the final data in version 8, Aguet et al. present a deep characterization of genetic associations and gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson). This large study was able to characterize the details underlying many aspects of gene expression and provides a resource with which to better understand the fundamental molecular mechanisms of how genetic variants affect gene regulation and complex traits in humans. Science, this issue p. 1318; see also p. 1298 The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues. We thank the donors and their families for their generous gifts of organ donation for transplantation and tissue donations for the GTEx research project; the Genomics Platform at the Broad Institute for data generation; J. Struewing for support and leadership of the GTEx project; M. Khan and C. Stolte for the illustrations in Fig. 1; and R. Do, D. Jordan, and M. Verbanck for providing GWAS pleiotropy scores. Funding: This work was supported by the Common Fund of the Office of the Director, U.S. National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, NIA, NIAID, and NINDS through NIH contracts HHSN261200800001E (Leidos Prime contract with NCI: A.M.S., D.E.T., N.V.R., J.A.M., L.S., M.E.B., L.Q., T.K., D.B., K.R., and A.U.), 10XS170 (NDRI: W.F.L., J.A.T., G.K., A.M., S.S., R.H., G.Wa., M.J., M.Wa., L.E.B., C.J., J.W., B.R., M.Hu., K.M., L.A.S., H.M.G., M.Mo., and L.K.B.), 10XS171 (Roswell Park Cancer Institute: B.A.F., M.T.M., E.K., B.M.G., K.D.R., and J.B.), 10X172 (Science Care Inc.), 12ST1039 (IDOX), 10ST1035 (Van Andel Institute: S.D.J., D.C.R., and D.R.V.), HHSN268201000029C (Broad Institute: F.A., G.G., K.G.A., A.V.S., X.Li., E.T., S.G., A.G., S.A., K.H.H., D.T.N., K.H., S.R.M., and J.L.N.), 5U41HG009494 (F.A., G.G., and K.G.A.), and through NIH grants R01 DA006227-17 (University of Miami Brain Bank: D.C.M. and D.A.D.), Supplement to University of Miami grant DA006227 (D.C.M. and D.A.D.), R01 MH090941 (University of Geneva), R01 MH090951 and R01 MH090937 (University of Chicago), R01 MH090936 (University of North Carolina–Chapel Hill), R01MH101814 (M.M.-A., V.W., S.B.M., R.G., E.T.D., D.G.-M., and A.V.), U01HG007593 (S.B.M.), R01MH101822 (C.D.B.), U01HG007598 (M.O. and B.E.S.), U01MH104393 (A.P.F.), extension H002371 to 5U41HG002371 (W.J.K.), as well as other funding sources: R01MH106842 (T.L., P.M., E.F., and P.J.H.), R01HL142028 (T.L., Si.Ka., and P.J.H.), R01GM122924 (T.L. and S.E.C.), R01MH107666 (H.K.I.), P30DK020595 (H.K.I.), UM1HG008901 (T.L.), R01GM124486 (T.L.), R01HG010067 (Y.Pa.), R01HG002585 (G.Wa. and M.St.), Gordon and Betty Moore Foundation GBMF 4559 (G.Wa. and M.St.), 1K99HG009916-01 (S.E.C.), R01HG006855 (Se.Ka. and R.E.H.), BIO2015-70777-P, Ministerio de Economia y Competitividad and FEDER funds (M.M.-A., V.W., R.G., and D.G.-M.), la Caixa Foundation ID 100010434 under agreement LCF/BQ/SO15/52260001 (D.G.-M.), NIH CTSA grant UL1TR002550-01 (P.M.), Marie-Skłodowska Curie fellowship H2020 Grant 706636 (S.K.-H.), R35HG010718 (E.R.G.), FPU15/03635, Ministerio de Educación, Cultura y Deporte (M.M.-A.),R01MH109905, 1R01HG010480 (A.Ba.), Searle Scholar Program (A.Ba.), R01HG008150 (S.B.M.), 5T32HG000044-22, NHGRI Institutional Training Grant in Genome Science (N.R.G.), EU IMI program (UE7-DIRECT-115317-1) (E.T.D. and A.V.), FNS funded project RNA1 (31003A_149984) (E.T.D. and A.V.), DK110919 (F.H.), F32HG009987 (F.H.), Massachusetts Lions Eye Research Fund Grant (A.R.H.), Wellcome grant WT108749/Z/15/Z (P.F.), and European Molecular Biology Laboratory (P.F. and D.Z.). Peer Reviewed "Article signat per 1 autors/es del BSC membres del THE GTEX CONSORTIUM: Marta Mele Messeguer"

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation. INTRODUCTION: Many complex human pheno-types, including diseases, exhibit sex-differentiated characteristics. These sex differences have been variously attributed to hormones, sex chromosomes, genotype × sex effects, differences in behavior, and differences in environmental exposures; however, their mechanisms and underlying biology remain largely unknown. The Genotype-Tissue Expression (GTEx) project provides an opportunity to investigate the prevalence and genetic mechanisms of sex differences in the human transcriptome by surveying many tissues that have not previously been characterized in this manner. RATIONALE: To characterize sex differences in the human transcriptome and its regulation, and to discover how sex and genetics interact to influence complex traits and disease, we generated a catalog of sex differences in gene expression and its genetic regulation across 44 human tissue sources surveyed by the GTEx project (v8 data release), analyzing 16,245 RNA-sequencing samples and genotypes of 838 adult individuals. We report sex differences in gene expression levels, tissue cell type composition, and cis expression quantitative trait loci (cis-eQTLs). To assess their impact, we integrated these results with gene function, transcription factor binding annotation, and genome-wide association study (GWAS) summary statistics of 87 GWASs. RESULTS: Sex effects on gene expression are ubiquitous (13,294 sex-biased genes across all tissues). However, these effects are small and largely tissue-specific. Genes with sex-differentiated expression are not primarily driven by tissue-specific gene expression and are involved in a diverse set of biological functions, such as drug and hormone response, embryonic development and tissue morphogenesis, fertilization, sexual reproduction and spermatogenesis, fat metabolism, cancer, and immune response. Whereas X-linked genes with higher expression in females suggest candidates for escape from X-chromosome inactivation, sex-biased expression of autosomal genes suggests hormone-related transcription factor regulation and a role for additional transcription factors, as well as sex-differentiated distribution of epigenetic marks, particularly histone H3 Lys(27) trimethylation (H3K27me3). Sex differences in the genetic regulation of gene expression are much less common (369 sex-biased eQTLs across all tissues) and are highly tissue-specific. We identified 58 gene-trait associations driven by genetic regulation of gene expression in a single sex. These include loci where sex-differentiated cell type abundances mediate genotype-phenotype associations, as well as loci where sex may play a more direct role in the underlying molecular mechanism of the association. For example, we identified a female-specific eQTL in liver for the hexokinase HKDC1 that influences glucose metabolism in pregnant females, which is subsequently reflected in the birth weight of the offspring. CONCLUSION: By integrating sex-aware analyses of GTEx data with gene function and transcription factor binding annotations, we describe tissue-specific and tissue-shared drivers and mechanisms contributing to sex differences in the human transcriptome and eQTLs. We discovered multiple sex-differentiated genetic effects on gene expression that colocalize with complex trait genetic associations, thereby facilitating the mechanistic interpretation of GWAS signals. Because the causative tissue is unknown for many phenotypes, analysis of the diverse GTEx tissue collection can serve as a powerful resource for investigations into the basis of sex-biased traits. This work provides an extensive characterization of sex differences in the human transcriptome and its genetic regulation. ■

Prostate cancer bone metastasis remains lethal and incurable, and often arises years after elimination of the primary tumor. It is unclear what underlies the decades-long clinical latency before recurrence, but evidence points to the existence of dormant residual tumor cells that disseminated before the primary tumor was eliminated. To design therapies to prevent progression of disseminated tumor cells (DTC) into lethal metastases, it is crucial to understand the mechanism(s) underlying this dormancy. The current study functionally validated our previous observation that implicated the GAS6/AXL axis in mediating DTC dormancy in the bone marrow. AXL-null and AXL-overexpressing prostate cancer cell lines were generated to determine if AXL was necessary and/or sufficient for dormancy. Characterization of these cells in vitro and using in vivo mouse models of DTC growth demonstrated that AXL was indeed sufficient to induce dormancy, but was unable to maintain it long-term and was not absolutely required for a dormancy period. Clinically, AXL expression correlated with longer survival in prostate cancer patients, and AXL was not expressed by cancer cells in primary or metastatic tissue. These data point to a tumor-suppressive role for AXL in prostate cancer, and future work is required to determine if AXL is expressed on human bone marrow DTCs. Implications: The ability of AXL to initiate but not maintain dormancy, coupled with its dispensability, suggests that targeting AXL alone will not prevent lethal metastatic outgrowth, and likely a cooperative network of factors exists to mediate long-term cellular dormancy.

Gene networks have yielded numerous neurobiological insights, yet an integrated view across brain regions is lacking. We leverage RNA sequencing in 864 samples representing 12 brain regions to robustly identify 12 brain-wide, 50 cross-regional and 114 region-specific coexpression modules. Nearly 40% of genes fall into brain-wide modules, while 25% comprise region-specific modules reflecting regional biology, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. Schizophrenia and autism genetic risk are enriched in brain-wide and multiregional modules, indicative of broad impact; these modules implicate neuronal proliferation and activity-dependent processes, including endocytosis and splicing, in disease pathophysiology. We find that cell-type-specific long noncoding RNA and gene isoforms contribute substantially to regional synaptic diversity and that constrained, mutation-intolerant genes are primarily enriched in neurons. We leverage these data using an omnigenic-inspired network framework to characterize how coexpression and gene regulatory networks reflect neuropsychiatric disease risk, supporting polygenic models.

Gene networks have proven their utility for elucidating transcriptome structure in the brain, yielding numerous biological insights. Most analyses have focused on expression relationships within a circumspect number of regions – how these relationships vary across a broad array of brain regions is largely unknown. By leveraging RNA-sequencing in 864 samples representing 12 brain regions in a cohort of 131 phenotypically normal individuals, we identify 12 brain-wide, 114 region-specific, and 50 cross-regional co-expression modules. We replicate the majority (81%) of modules in regional microarray datasets. Nearly 40% of expressed genes fall into brain-wide modules corresponding to major cell classes and conserved biological processes. Region-specific modules comprise 25% of expressed genes and correspond to region-specific cell types and processes, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. We further leverage these modules to capture cell-type-specific lncRNA and gene isoforms, both of which contribute substantially to regional synaptic diversity. We identify enrichment of neuropsychiatric disease risk variants in brain wide and multi-regional modules, consistent with their broad impact on cell classes, and highlight specific roles in neuronal proliferation and activity-dependent processes. Finally, we examine the manner in which gene co-expression and gene regulatory networks reflect genetic risk, including the recently framed omnigenic model of disease architecture.

168 Background: Identifying molecular residual disease (MRD) using circulating tumor DNA (ctDNA) analysis after curative surgery can potentially stratify the recurrence risk and facilitate personalization of adjuvant treatment in patients with colorectal cancer (CRC). We conducted a prospective study “COSMOS-CRC-01” to evaluate the utility of a plasma-only ctDNA assay integrating genomic and epigenomic signatures. Methods: Patients with resectable clinical stage 0–III colorectal cancer were eligible. Plasma samples were collected at structured pre- and post-surgical timepoints and analyzed using Guardant Reveal, a plasma-only ctDNA assay that detects the presence of MRD by identifying somatic alterations and methylation signatures of cell-free DNA associated with colorectal cancer. Results: As of April 2021, 501 patients were enrolled in the COSMOS-CRC-01, of which 496 patients had their post-operative 4-week ctDNA status. In this analysis, we included the first 100 patients enrolled with clinical stage II or more CRC. Seven patients were excluded due to non-curative resection or pathological stage (pStage) IV. The assay was able to produce a result for all 93 samples analyzed (failure rate of 0%). MRD was detected in 23 (25%) patients 4 weeks after surgery. The MRD detection rate was 20% in pStage II disease and 29% in pStage III disease. Patients with positive MRD were older than those with negative MRD ( p = 0.0001). Across all MRD positive samples, 30%, 9%, and 61% were positive by both genomic and epigenomic, only genomic, and only epigenomic calls. Genomic signatures included APC, BRAF, KRAS, and TP53 mutations with the lowest variant allelic fraction of 0.04%. With a median follow-up time of 12.2 months (range 8-18 months), 9 of 93 patients recurred (9.7%). ctDNA was detected from a single 4-week post-surgical sample in 55% of patients who recurred (5 of 9). 1-year disease-free survival was 81.2% in patients with positive MRD and 93.9% in those with negative MRD (hazard ratio 3.49, 95% CI 0.93–13.10, p = 0.049). Multivariate analysis including baseline characteristics associated with recurrence risk showed that MRD status had the strongest association with the recurrence. Post-operative 4-week serum CEA level was not associated with risk for recurrence. Conclusions: While follow-up in this cohort is currently limited, the results suggest that a single post-surgery sample run on a plasma-only assay that integrates genomic and epigenomic signatures can more accurately stratify patients with CRC by recurrence risk than previously known clinical factors. Data from baseline and longitudinal timepoints will be reported as available along with longer term clinical follow-up. Clinical trial information: UMIN000037765.

Prostate-specific membrane antigen (PSMA) is highly expressed in prostate cancer and within the neovasculature of other solid tumors. The nonprostatic expression of PSMA has been reported exclusively within the neovasculature endothelial cells of nonprostatic cancers; however, there are few reports on PSMA expression in epithelial cells. Herein, we describe PSMA expression in nonprostatic epithelial cells and characterize the potential of PSMA-binding agents to noninvasively detect that expression. Methods: PSMA expression data were extracted from publicly available genomic databases. Genomic data were experimentally validated for PSMA expression—by quantitative reverse transcription polymerase chain reaction, flow cytometry, and Western blotting—in several nonprostatic cell lines and xenografts of melanoma and small cell lung cancer (SCLC) origin. The feasibility of PSMA detection in those tumor models was further established using PSMA-based nuclear and optical imaging agents and by biodistribution, blocking, and ex vivo molecular characterization studies. Results: We discovered that a small percentage of nonprostatic cancer cell lines and tumors express PSMA. Importantly, PSMA expression was sufficiently high to image established melanoma and SCLC xenografts using PSMA-based nuclear and optical imaging agents. Conclusion: These results indicate that PSMA expression in nonprostatic tumors may not be limited to the endothelium but may also include solid tumor tissue of nonprostatic cancers including melanoma and SCLC. Our observations indicate broader applicability of PSMA-targeted imaging and therapeutics.

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

3045 Background: Detection of minimal residual disease (MRD) by circulating tumor DNA (ctDNA) post-curative intent treatment is predictive of recurrence in many solid tumors. Due to biological challenges with low ctDNA shed in early-stage disease and potential to detect non-tumor derived alterations in plasma (e.g. CHIP), most ctDNA MRD assays are dependent on a priori knowledge of genomic alterations from tumor tissue to achieve high sensitivity and specificity. Tissue-dependent methods limit the clinical application of a MRD assay, especially in cancer types where tissue biopsy is challenging, neoadjuvant therapy (NAT) is standard of care, and/or rapid turnaround times are needed for clinical decision making. We previously validated an assay (Guardant Reveal) that combines somatic and epigenomic analysis to detect ctDNA from early-stage colorectal tumors without tumor tissue or peripheral blood cells. Here we describe the expansion of this assay to detect MRD across multiple tumor types. Methods: Cell-free DNA (cfDNA) fragments are extracted from patient plasma, partitioned based on methylation fraction, enriched using a panel to target informative genomic and epigenomic regions, barcoded, and pooled for sequencing. Methylation status is determined non-destructively and with minimal loss of molecules, allowing sensitive genomic and epigenomic analysis of the same cfDNA fragments. A single assay with a total panel size of 5.3 Mb was developed for MRD analysis in multiple cancer types, including CRC, Lung and Bladder. A “ctDNA detected” result is defined by the de novo identification of tumor-derived somatic variants and/or the observation of a tumor-specific methylation profile exceeding predefined thresholds. Results: The assay performance was tested using 163 pre-treatment clinical samples from patients with early-stage non small cell lung cancer (NSCLC) and bladder cancer and 133 self-declared healthy donors. Sensitivity for pre-treatment detection in NSCLC was 68.9% at 95% specificity (20/42 Stage I: 47.6%, 25/30 Stage II: 83.3%, 19/21 Stage III: 90.5%). Sensitivity for bladder cancer was 44.2% at 95% specificity (13/43 NMIBC: 30.2%, 18/27 MIBC: 66.7%). Additional development from larger cohorts and other tumor types is ongoing and data will be presented as available. Conclusions: Using cancer-specific genomic and epigenomic signals combined with learning-based classifiers, we developed a highly specific method for detecting the presence of ctDNA in early-stage NSCLC and bladder cancer patients from plasma without the need for tumor tissue but with comparable sensitivities to tissue-dependent approaches. A plasma-only MRD assay will allow for greater clinical impact by overcoming the challenges of tissue procurement, particularly following NAT, and enabling faster time to results.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.SummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

Supplemental methods and results. This file contains theoretical proofs, supplemental methods, results, figures, and tables. (PDF 963Â kb)

All supplemental figures. (PDF 5182 kb)

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

BackgroundGene co-expression networks capture diverse biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanisms. Functional interactions between genes have not been fully characterized for most organisms, and therefore reconstruction of gene co-expression networks has been of common interest in a variety of settings. However, methods routinely used for reconstruction of gene co-expression networks do not account for confounding artifacts known to affect high dimensional gene expression measurements.ResultsIn this study, we show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. Both theoretically and empirically, we demonstrate that removing the effects of top principal components from gene expression measurements prior to network inference can reduce false discoveries, especially when well annotated technical covariates are not available. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, we show that this latent factor residualization approach often reduces false discoveries in the reconstructed networks.ConclusionNetwork reconstruction is susceptible to confounders that affect measurements of gene expression. Even controlling for major individual known technical covariates fails to fully eliminate confounding variation from the data. In studies where a wide range of annotated technical factors are measured and available, correcting gene expression data with multiple covariates can also improve network reconstruction, but such extensive annotations are not always available. Our study shows that principal component correction, which does not depend on study design or annotation of all relevant confounders, removes patterns of artifactual variation and improves network reconstruction in both simulated data, and gene expression data from GTEx project. We have implemented our PC correction approach in the Bioconductor package sva which can be used prior to network reconstruction with a range of methods.

Background Previous approaches to defining subtypes of colorectal carcinoma (CRC) and other cancers based on transcriptomes have assumed the existence of discrete subtypes. We analyze gene expression patterns of colorectal tumors from a large number of patients to test this assumption and propose an approach to identify potentially a continuum of subtypes that are present across independent studies and cohorts. Results We examine the assumption of discrete CRC subtypes by integrating 18 published gene expression datasets and > 3700 patients, and contrary to previous reports, find no evidence to support the existence of discrete transcriptional subtypes. Using a meta-analysis approach to identify co-expression patterns present in multiple datasets, we identify and define robust, continuously varying subtype scores to represent CRC transcriptomes. The subtype scores are consistent with established subtypes (including microsatellite instability and previously proposed discrete transcriptome subtypes), but better represent overall transcriptional activity than do discrete subtypes. The scores are also better predictors of tumor location, stage, grade, and times of disease-free survival than discrete subtypes. Gene set enrichment analysis reveals that the subtype scores characterize T-cell function, inflammation response, and cyclin-dependent kinase regulation of DNA replication. Conclusions We find no evidence to support discrete subtypes of the CRC transcriptome and instead propose two validated scores to better characterize a continuity of CRC transcriptomes.

Dissemination of cancer stem cells (CSCs) serves as the basis of metastasis. Recently, we demonstrated that circulating prostate cancer targets the hematopoietic stem cell (HSCs) 'niche' in marrow during dissemination. Once in the niche, disseminated tumor cells (DTCs) may remain dormant for extended periods. As the major function of the HSC niche is to maintain stem cell functions, we hypothesized that the niche regulates CSC activities of DTCs. Here we show that DTCs recovered from marrow were significantly enriched for a CSC phenotype. Critically, the conversion of DTCs to CSCs is regulated by niche-derived GAS6 through the Mer/mTOR; molecules previously shown to regulate dormancy. The data demonstrate that the niche plays a significant role in maintaining tumor-initiating prostate cancer in marrow and suggests a functional relationship between CSCs and dormancy. Understanding how the marrow niche regulates the conversion of DTCs to CSCs is critical for the development of therapeutics specifically targeting skeletal bone metastasis and dormancy.

Zinc finger E-box binding homeobox 1 (ZEB1) is a transcription factor that plays a central role in the epithelial to mesenchymal transition (EMT) of cancer cell lines. Studies on its regulation have mostly focused on the negative 3'UTR binding of miR200c. Interestingly, it has been previously reported that androgen receptor (AR) regulates ZEB1 expression in breast and prostate cancers. In order to validate this, various ZEB1 promoter deletions were cloned into a luciferase reporter system to elucidate the contribution of two putative androgen response elements (AREs). The in vivo contribution of AR was also assessed in cell lines after R1881 treatment using qPCR with prostate specific antigen (PSA) as the positive control. We discovered that AR upregulates the levels of expression of ZEB1 10-fold on a luciferase promoter that only contains the distal ARE. However, when the proximal ARE is included, no additional activation is apparent with AR or its hormone independent variant, AR-V7. Furthermore, we demonstrate here that a promoter construct containing both AREs activates transcription of ZEB1 even in the AR-null cell lines DU145 and PC3. Incubation of the AR-positive cell line, LNCaP with R1881, failed to substantially increase the expression levels of ZEB1. Despite the presence of AREs in the promoter region, it appears that ZEB1 expression can be induced even without AR. In addition, the region around the distal ARE is a potent repressor in AR-null cell lines.

BACKGROUND Prostate cancer progression is concomitant with quantifiable nuclear structure and texture changes as compared to non-cancer tissue. Malignant progression is associated with an epithelial–mesenchymal transition (EMT) program whereby epithelial cancer cells take on a mesenchymal phenotype and dissociate from a tumor mass, invade, and disseminate to distant metastatic sites. The objective of this study was to determine if epithelial and mesenchymal prostate cancer cells have different nuclear morphology. METHODS Murine tibia injections of epithelial PC3 (PC3-Epi) and mesenchymal PC3 (PC3-EMT) prostate cancer cells were processed and stained with H&E. Cancer cell nuclear image data was obtained using commercially available image-processing software. Univariate and multivariate statistical analysis were used to compare the two phenotypes. Several non-parametric classifiers were constructed and permutation-tested at various training set fractions to ensure robustness of classification between PC3-Epi and PC3-EMT cells in vivo. RESULTS PC3-Epi and PC3-EMT prostate cancer cells were separable at the single cell level in murine tibia injections on the basis of nuclear structure and texture remodeling associated with an EMT. Support vector machine and multinomial logistic regression models based on nuclear architecture features yielded AUC–ROC curves of 0.95 and 0.96, respectively, in separating PC3-Epi and PC3-EMT prostate cancer cells in vivo. CONCLUSIONS Prostate cancer cells that have undergone an EMT demonstrated an altered nuclear structure. The association of nuclear changes and a mesenchymal phenotype demonstrates quantitative morphometric image analysis may be used to detect cancer cells that have undergone EMT. This morphometric measurement could provide valuable prognostic information in patients regarding the likelihood of [future] metastatic disease. Prostate 75:218–224, 2015. © 2014 Wiley Periodicals, Inc.

Innovative therapies are needed for advanced Non-Small Cell Lung Cancer (NSCLC). We have undertaken a genomics based, hypothesis driving, approach to query an emerging potential that epigenetic therapy may sensitize to immune checkpoint therapy targeting PD-L1/PD-1 interaction. NSCLC cell lines were treated with the DNA hypomethylating agent azacytidine (AZA – Vidaza) and genes and pathways altered were mapped by genome-wide expression and DNA methylation analyses. AZA-induced pathways were analyzed in The Cancer Genome Atlas (TCGA) project by mapping the derived gene signatures in hundreds of lung adeno (LUAD) and squamous cell carcinoma (LUSC) samples. AZA up-regulates genes and pathways related to both innate and adaptive immunity and genes related to immune evasion in a several NSCLC lines. DNA hypermethylation and low expression of IRF7, an interferon transcription factor, tracks with this signature particularly in LUSC. In concert with these events, AZA up-regulates PD-L1 transcripts and protein, a key ligand-mediator of immune tolerance. Analysis of TCGA samples demonstrates that a significant proportion of primary NSCLC have low expression of AZA-induced immune genes, including PD-L1. We hypothesize that epigenetic therapy combined with blockade of immune checkpoints – in particular the PD-1/PD-L1 pathway – may augment response of NSCLC by shifting the balance between immune activation and immune inhibition, particularly in a subset of NSCLC with low expression of these pathways. Our studies define a biomarker strategy for response in a recently initiated trial to examine the potential of epigenetic therapy to sensitize patients with NSCLC to PD-1 immune checkpoint blockade.

Hierarchical Clustering of top 200 differentially expressed genes with data corrected by quantile normalization. (PDF 429 kb)

Hierarchical Clustering of top 200 differentially expressed genes with data corrected by SVA (Surrogate Variable Analysis) normalization. (PDF 122 kb)

C1orf116 expression in clinical patient data from breast and colorectal cancer. (PDF 25 kb)

Expression of Estrogen responsive genes - (A) early and (B) late in prostate cancer cell line samples from integrated data. (PDF 115 kb)

Hierarchical Clustering of top 200 differentially expressed genes with data corrected by MCtr (Median Centered Column Scaled) normalization. (PDF 122 kb)

Expression of C1orf116 in breast, prostate and others (retinal pigment, liver, colon and esophageal) cancer cell lines from integrated data. (PDF 45 kb)

Understanding the genetics of gene regulation provides information on the cellular mechanisms through which genetic variation influences complex traits. Expression quantitative trait loci, or eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. While most analyses of human data has focused on regulation of expression by nearby variants (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue Expression (GTEx) project data1, consisting of 449 individuals with RNA-sequencing data across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs across human tissues, which contribute to an improved understanding of the tissue-specific cellular mechanisms of regulatory genetic variation.

While advances in the treatment of localized prostate cancer (PCa) have improved five-year survival to near 100%, metastatic disease remains incurable. Lethal metastases are the end-result of a cancer cell that escapes the primary tumor, travels through the vasculature, and eventually invades and colonizes a secondary site. Thus, in order for a cancer cell to metastasize from the primary tumor, it must move. In a critical early event of metastasis, a rare population of cells in the primary tumor undergoes a phenotype switch from a proliferating epithelial cell to gain mesenchymal cell characteristics, including high migratory capacity, during the process of epithelial-to-mesenchymal transition (EMT). Using a multistudy gene expression discovery analysis, we previously identified C1orf116 as a novel candidate driver of EMT (doi: 10.1186/s12885-017-3413-3). C1orf116 is an unnamed gene of unknown function that remains largely uncharacterized in any disease state, including cancer or EMT. To substantiate its potential role in EMT, we queried the Cancer Cell Line Encyclopedia dataset (GSE36133) and found that C1orf116 expression is associated with classical epithelial markers and inversely associated with mesenchymal markers. To specifically interrogate the role of C1orf116 in EMT, we overexpressed C1orf116 in otherwise C1orf116low PC3 cells (PC3-oe). PC3-oe cells demonstrated distinctive cobblestone epithelial cellular morphology. Correspondingly, PC3-oe cultures showed increased epithelial marker expression (including OVOL1, CDH1, ESRP), indicating that C1orf116 expression was sufficient to induce an epithelial transition. To determine whether C1orf116 influenced migration, we performed a scratch-closure assay. PC3-oe cultures showed reduced motility compared to control, suggesting that C1orf116 restricts motility. Cellular migration is influenced by many factors, including cell-ECM adhesion, “go vs. grow” switching, and cell-cell adhesion. We observed that PC-oe established cell-cell contacts more rapidly than cells in the control cultures (time-lapse photography immediately following culture seeding) and hypothesized that C1orf116 promoted cell-cell adhesion. We found that PC3-oe had increased expression of tight junction complexes (including ZO1, ZO2, ZO3). Likewise, siRNA-mediated knockdown of C1orf116 expression in C42b (C1orf116high) cells resulted in decreased expression of tight junction proteins. These findings demonstrate that C1orf116 is a novel driver of the epithelial phenotype. In particular, C1orf116 expression promotes cell-cell tight junctions, thereby restricting the active emigrant phenotype of potentially lethal metastatic cells. Citation Format: Sarah R. Amend, James Hernandez, Princy Parsana, Kenneth J. Pienta. C1orf116, a gene of unknown function, promotes tight junctions and epithelial cell phenotype in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A037.

The X chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. We have conducted an analysis of the impact of both sex and the X chromosome on patterns of gene expression identified through transcriptome sequencing of whole blood from 922 individuals. We identified that genes on the X chromosome are more likely to have sex-specific expression compared to the autosomal genes. Furthermore, we identified a depletion of regulatory variants on the X chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X chromosome. To resolve the molecular mechanisms underlying such effects, we generated and connected sex-specific chromatin accessibility to sex-specific expression and regulatory variation. As sex-specific regulatory variants can inform sex differences in genetic disease prevalence, we have integrated our data with genome-wide association study data for multiple immune traits and to identify traits with significant sex biases. Together, our study provides genome-wide insight into how the X chromosome and sex shape human gene regulation and disease.

// Dongya Jia 1, 2, * , Mohit Kumar Jolly 1, 3, * , Marcelo Boareto 1, 7 , Princy Parsana 8 , Steven M. Mooney 9, 10 , Kenneth J. Pienta 9, 10, 11, 12 , Herbert Levine 1, 3, 4 , Eshel Ben-Jacob 1, 5, 6 1 Center for Theoretical Biological Physics, Rice University, Houston, TX 77005-1827, USA 2 Graduate Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX 77005-1827, USA 3 Department of Bioengineering, Rice University, Houston, TX 77005-1827, USA 4 Department of Physics and Astronomy, Rice University, Houston, TX 77005-1827, USA 5 Department of Biosciences, Rice University, Houston, TX 77005-1827, USA 6 School of Physics and Astronomy and The Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv 69978, Israel 7 Institute of Physics, University of Sao Paulo, Sao Paulo 05508, Brazil 8 Department of Computer Science, Johns Hopkins University, Baltimore, MD 21287, USA 9 The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA 10 Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA 11 Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA 12 Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA * These authors have contributed equally to this work Correspondence to: Kenneth J. Pienta, e-mail: kpienta1@jhmi.edu Herbert Levine, e-mail: herbert.levine@rice.edu Eshel Ben-Jacob, e-mail: eshel@rice.edu Keywords: EMT, metastasis, OVOL, partial EMT, cancer systems biology Received: March 04, 2015 Accepted: April 10, 2015 Published: April 22, 2015 ABSTRACT Metastasis involves multiple cycles of Epithelial-to-Mesenchymal Transition (EMT) and its reverse-MET. Cells can also undergo partial transitions to attain a hybrid epithelial/mesenchymal (E/M) phenotype that has maximum cellular plasticity and allows migration of Circulating Tumor Cells (CTCs) as a cluster. Hence, deciphering the molecular players helping to maintain the hybrid E/M phenotype may inform anti-metastasis strategies. Here, we devised a mechanism-based mathematical model to couple the transcription factor OVOL with the core EMT regulatory network miR-200/ZEB that acts as a three-way switch between the E, E/M and M phenotypes. We show that OVOL can modulate cellular plasticity in multiple ways - restricting EMT, driving MET, expanding the existence of the hybrid E/M phenotype and turning both EMT and MET into two-step processes. Our theoretical framework explains the differences between the observed effects of OVOL in breast and prostate cancer, and provides a platform for investigating additional signals during metastasis.

Metastatic prostate cancer is an incurable lethal disease and is the cause of death for 28,000 men in the US annually. In order for a cancer cell to metastasize from the primary tumor, it must move. In a critical early event of metastasis, a rare population of cells in the primary tumor undergoes a phenotype switch from a proliferating epithelial cell to gaining the migratory capacity of a mesenchymal cell, a process, termed epithelial-to-mesenchymal transition (EMT). To identify previously overlooked regulators of EMT, we undertook a multi-study gene expression array analysis (15 published gene expression studies; 95 total samples; 6 cancer types) and identified C1orf116 as a candidate driver of the epithelial phenotype in EMT across multiple cancer types. Of note, C1orf116 is an unnamed gene of unknown function that is largely uncharacterized in any setting, including in cancer biology or in EMT. To substantiate its potential role in EMT, we queried the NCI-60 cell line collection gene expression dataset and found that C1orf116 shared a similar expression pattern with epithelial marker genes (ESRP1, ESRP2, OVOL1, OVOL2) and a converse expression pattern to mesenchymal marker genes (CDH2, SNAI2, VIM). Moreover, we found that C1orf116 expression is decreased in metastatic prostate cancer tumors compared to localized disease (Oncomine), suggesting the potential for clinical translation. To specifically interrogate the role of C1orf116 in EMT, we utilized the PC3 cell line, an in vitro model of prostate cancer EMT. We found that C1orf 116 expression was higher in an epithelial PC3 cell line (PC3-epi) compared to PC3 cells that had undergone a stable EMT (PC3-emt). Moreover, shRNA-mediated knockdown of C1orf116 expression in PC3-epi cells resulted in decreased gene expression of canonical epithelial markers (OVOL1, ESRP1, CDH1) and elevated expression of standard mesenchymal markers (CDH2, ZEB1, VIM), suggesting a role as a driver of the epithelial phenotype. Interestingly, the sole publication specifically studying C1orf116 describes it as Specifically Androgen Regulated Gene (SARG). The authors demonstrate the presence of an androgen response element upstream of the C1orf116 start site the in androgen-responsive prostate cancer cell line LNCaP. Our data indicate a role for C1orf116 in the androgen receptor-negative prostate cancer cell line PC3, suggesting a non-androgen-mediated role for C1orf116 in prostate cancer. In addition to elucidating its role in EMT, work is underway to characterize the RNA and protein products of C1orf116. Alternative splicing results in two transcripts of varying length that likely give rise to at least two protein product sizes. RNA sequencing and mass spectrometry will reveal transcript and protein identity and provide insight into potential isoform switching regulating EMT and other processes. Citation Format: Sarah R. Amend, James Hernandez, Princy Parsana, Kenneth J. Pienta. C1orf116, a gene with unknown function, is a novel driver of epithelial phenotype in epithelial-to-mesenchymal transition in human cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 853. doi:10.1158/1538-7445.AM2017-853

Zinc finger E-box binding homeobox 1 (ZEB1) is a transcription factor that plays a central role in the epithelial to mesenchymal transition (EMT) of cancer cell lines. Studies on its regulation have mostly focused on the negative 3’UTR binding of miR200c. Interestingly, it has been previously reported that androgen receptor (AR) regulates ZEB1 expression in breast and prostate cancers. In order to validate this, various ZEB1 promoter deletions were cloned into a luciferase reporter system to elucidate the contribution of two putative androgen response elements (AREs). The in vivo contribution of AR was also assessed in cell lines after R1881 treatment using qPCR with prostate specific antigen (PSA) as the positive control. We discovered that AR upregulates the levels of expression of ZEB1 10-fold on a luciferase promoter that only contains the distal ARE. However, when the proximal ARE is included, no additional activation is apparent with AR or its hormone independent variant, AR-V7. Furthermore, we demonstrate here that a promoter construct containing both AREs activates transcription of ZEB1 even in the AR-null cell lines DU145 and PC3. Incubation of the AR-positive cell line, LNCaP with R1881, failed to substantially increase the expression levels of ZEB1. Despite the presence of AREs in the promoter region, it appears that ZEB1 expression can be induced even without AR. In addition, the region around the distal ARE is a potent repressor in AR-null cell lines.

To emigrate from a primary tumor to a distant site, a proliferating cancer cell must acquire the phenotypic traits necessary for migration and invasion. This shift from “growing” to “going,” termed epithelial-to-mesenchymal transition (EMT), may be a genetic or epigenetic phenomenon, ultimately characterized by altered cellular function that promotes metastasis. In addition to its role in malignant progression, EMT is integral in development and for healthy wound healing. EMT has been described in all carcinomas, and, because it of its established physiologic role, we hypothesize that there are a set of central EMT regulators that are universal to all carcinoma types. To identify global regulators of EMT, we integrated data from 15 published gene expression microarray studies that include a total of 49 epithelial and 46 mesenchymal cell line samples across 6 malignant tissue types (breast, prostate, colon, esophageal, liver, retinal pigment) and with various in vitro EMT induction strategies. While accounting for batch effects and other technical variability inherent in gene expression data, we performed differential expression analysis to identify genes that vary significantly between epithelial and mesenchymal states. Importantly, we found differential expression of established EMT markers (e.g. CDH1, ZEB1) validating our approach. We also identified genes that had not previously been implicated in cancer progression, representing novel candidate drivers of EMT, for example SARG (C1orf116). We found that SARG expression is decreased in high-grade cancer and metastatic disease, and is negatively associated with chemotherapy resistance across multiple cancer types (Oncomine). In an in vitro model of prostate cancer EMT, we found that SARG had >5-fold increased RNA and protein expression in PC3-epithelial cells. Knockdown of SARG expression in PC3-epithelial cells resulted in decreased gene expression of the epithelial-marker CDH1 and elevated expression of the mesenchymal marker CDH2, suggesting a role as a driver of the epithelial phenotype. In parallel with functional in vitro experiments, we have applied a Bayesian network learning approach to identify differentially expressed genes that are likely to play regulatory roles in EMT with causal influence on other genes. Importantly, this analysis provides valuable information regarding novel downstream effectors of known key regulators of EMT such as CDH1 and ELF3. Moreover, we also identified new EMT regulators such as CEP170 that have not been previously described. Experiments are underway to further explore the functional implications of these gene networks. This integrative approach of global gene expression analysis, network learning, and functional validation may identify causal genes and regulatory interactions in EMT, both expanding upon current knowledge and identifying novel drivers of this key metastatic process. Citation Format: Sarah R. Amend, Princy Parsana, James Hernandez, Alexis Battle, Kenneth J. Pienta. Identifying novel drivers of the epithelial-to-mesenchymal transition across multiple cancer types: from bioinformatics to the bench. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1592.

Epigenetic alterations driving carcinogenesis and cancer progression can be specifically targeted by the demethylating agent azacitidine (Aza) and the histone deacetylase inhibitor entinostat. While this treatment combination has been effective in a limited number of patients (pts) with treatment-refractory non-small cell lung cancer (NSCLC), we observed clinical benefit in 5 of 5 patients who received immunotherapy with PD-1/PD-L1 pathway blockade immediately following epigenetic therapy. Three of 5 pts developed partial tumor regressions (RECIST criteria, duration 10+ to 20+ mo.) and 2 pts had stable disease ≥6 mo. This compares to the objective response + SD rates of NSCLC to monotherapy with anti-PD-1 (18% + 5%) or anti-PD-L1 (10% + 12%). To understand how epigenetic therapy may synergize with blockade of the immunosuppressive PD-1 pathway, we used genome wide methylation and expression profiling on 8 NSCLC cell lines treated with low dose Aza. We discovered complex immunomodulatory effects of Aza with up-regulation of diverse immune related pathways including Jun/Jnk, NFKB, viral defense, type I interferon signaling, the inflammasome, antigen processing and presentation and immune evasion including up-regulation of PD-L1 expression. Multiple cancer-testes antigens were also up-regulated, thereby conferring de novo antigenicity. Supporting the idea that Aza acts specifically through inhibition and degradation of DNA methyltransferase proteins, colon cancer cells genetically haplo-insufficient for DNMT1 and devoid of DNMT3b mirror the immunomodulatory effects of Aza. Upstream events potentially controlling these pathways were defined, and prominent among them was up-regulation of the transcription factor, interferon regulatory factor 7 (IRF7), a DNA hypermethylated gene. These data were used to query hundreds of primary NSCLC samples from the Cancer Genome Atlas project (TCGA). A low basal expression signature of interferon pathway related genes was significantly associated with low IRF7 expression and promoter methylation in squamous tumors. Another hypermethylated transcription factor, PITX1, which inhibits a subset of type I interferon signaling genes, tracked with non-squamous cancers. Together, these findings support a model in which epigenetic modulation activates innate and adaptive immune responses within the tumor microenvironment together with induction of counter-regulatory immune checkpoint ligands which can be therapeutically blocked with antibodies. Based on these findings, a clinical trial testing the efficacy of DNMT and HDAC inhibition combined with PD-1 pathway blockade is under development. This work will form the basis for an immune-classification of NSCLC, as well as biomarker discovery for a novel therapeutic paradigm combining epigenetic and immunotherapy with potentially synergistic activity against the world's most deadly malignancy. Supported by Stand Up to Cancer. Citation Format: John Wrangle, Wei Wang, Alexander Koch, Hariharan Easwaran, Helai Mohammad, Princy Parsana, Frank Vendetti, Kristen Rodgers, Xiaoyu Pan, Kirsten Harbom, Cynthia Zahnow, Janis Taube, Julie Brahmer, Peter Jones, Suzanne Topalian, Charles Rudin, Malcolm Brock, Drew Pardoll, Stephen Baylin. Epigenetic therapy and sensitization of lung cancer to immunotherapy. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4619. doi:10.1158/1538-7445.AM2013-4619

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Phenotypic variation and diseases are influenced by factors such as genetic variants and gene expression. Here, Barbeira et al. develop S-PrediXcan to compute PrediXcan results using summary data, and investigate the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes.