scholarly journals Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

2019 ◽  
Author(s):  
Riyue Bao ◽  
Jason J. Luke
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Hedgehog Signaling ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Type I ◽  
Tumor Type ◽  
Cancer Types

AbstractThe T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational profile may also dictate response with some oncogenes (i.e. WNT/β-catenin) known to mediate immuno-suppression. Building on these observations we performed a multi-omic analysis of human cancer correlating the T cell-inflamed gene expression signature with the somatic mutanome and transcriptome for different immune phenotypes, by tumor type and across cancers. Strong correlations were noted between mutations in oncogenes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c and genes with unknown functions. Conversely, we observe many genes associating with the T cell-inflamed phenotype including VHL and PBRM1, among others. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion broadly active across cancer types including HIF1A and MYC. Novel examples from specific tumors include sonic hedgehog signaling in ovarian cancer or hormone signaling and novel transcription factors across multiple tumors. Using network analysis, somatic and transcriptomic events were integrated, demonstrating that most non-T cell-inflamed tumors are influenced by multiple pathways. Validating these analyses, we observe significant inverse relationships between protein levels and the T cell-inflamed gene signature with examples including NRF2 in lung, ERBB2 in urothelial and choriogonadotropin in cervical cancer. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. These results nominate molecular targets and drugs potentially available for immediate translation into clinical trials for patients with cancer.

scholarly journals Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Riyue Bao ◽  
Daniel Stapor ◽  
Jason J. Luke
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Hedgehog Signaling ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Type I ◽  
Tumor Type ◽  
Sonic Hedgehog Signaling ◽  
Cancer Types

Abstract Background The T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational burden (TMB) may also dictate response, and some oncogenes (i.e., WNT/β-catenin) are known to mediate immunosuppression. Methods We performed an integrated multi-omic analysis of human cancer including 11,607 tumors across multiple databases and patients treated with anti-PD1. After adjusting for TMB, we correlated the T cell-inflamed gene expression signature with somatic mutations, transcriptional programs, and relevant proteome for different immune phenotypes, by tumor type and across cancers. Results Strong correlations were noted between mutations in oncogenes and tumor suppressor genes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c, and genes with unknown functions. Conversely, we observe genes associating with the T cell-inflamed phenotype including VHL and PBRM1. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion across cancer types (HIF1A and MYC) as well as novel examples in specific tumors such as sonic hedgehog signaling, hormone signaling and transcription factors. Using network analysis, somatic and transcriptomic events were integrated. In contrast to previous reports of individual tumor types such as melanoma, integrative pan-cancer analysis demonstrates that most non-T cell-inflamed tumors are influenced by multiple signaling pathways and that increasing numbers of co-activated pathways leads to more highly non-T cell-inflamed tumors. Validating these analyses, we observe highly consistent inverse relationships between pathway protein levels and the T cell-inflamed gene expression across cancers. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. Conclusions These results nominate molecular targets and drugs potentially available for further study and potential immediate translation into clinical trials for patients with cancer.

scholarly journals Quantitative Analysis of Differential Expression of HOX Genes in Multiple Cancers

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1572
Author(s):  
Orit Adato ◽  
Yaron Orenstein ◽  
Juri Kopolovic ◽  
Tamar Juven-Gershon ◽  
Ron Unger
Keyword(s):  
Gene Expression ◽  
Quantitative Analysis ◽  
Differential Expression ◽  
Survival Data ◽  
Hox Genes ◽  
Expression Patterns ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Hox Gene ◽  
Cancer Types

Transcription factors encoded by Homeobox (HOX) genes play numerous key functions during early embryonic development and differentiation. Multiple reports have shown that mis-regulation of HOX gene expression plays key roles in the development of cancers. Their expression levels in cancers tend to differ based on tissue and tumor type. Here, we performed a comprehensive analysis comparing HOX gene expression in different cancer types, obtained from The Cancer Genome Atlas (TCGA), with matched healthy tissues, obtained from Genotype-Tissue Expression (GTEx). We identified and quantified differential expression patterns that confirmed previously identified expression changes and highlighted new differential expression signatures. We discovered differential expression patterns that are in line with patient survival data. This comprehensive and quantitative analysis provides a global picture of HOX genes’ differential expression patterns in different cancer types.

scholarly journals Effects of TP53 mutational status on gene expression patterns across 10 human cancer types

2014 ◽  
Vol 232 (5) ◽  
pp. 522-533 ◽  
Author(s):  
Neha Parikh ◽  
Susan Hilsenbeck ◽  
Chad J Creighton ◽  
Tajhal Dayaram ◽  
Ryan Shuck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Mutational Status ◽  
Cancer Types ◽  
Tp53 Mutational Status

scholarly journals STING and cGAS gene expressions were downregulated among HIV-1-infected persons after antiretroviral therapy

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Lorena Leticia Peixoto de Lima ◽  
Allysson Quintino Tenório de Oliveira ◽  
Tuane Carolina Ferreira Moura ◽  
Ednelza da Silva Graça Amoras ◽  
Sandra Souza Lima ◽  
...  
Keyword(s):  
Gene Expression ◽  
Viral Load ◽  
T Cell ◽  
Antiretroviral Therapy ◽  
Cell Count ◽  
Enzyme Linked Immunosorbent Assay ◽  
Type I ◽  
Α Level ◽  
The Impact ◽  
Hiv 1

Abstract Background The HIV-1 epidemic is still considered a global public health problem, but great advances have been made in fighting it by antiretroviral therapy (ART). ART has a considerable impact on viral replication and host immunity. The production of type I interferon (IFN) is key to the innate immune response to viral infections. The STING and cGAS proteins have proven roles in the antiviral cascade. The present study aimed to evaluate the impact of ART on innate immunity, which was represented by STING and cGAS gene expression and plasma IFN-α level. Methods This cohort study evaluated a group of 33 individuals who were initially naïve to therapy and who were treated at a reference center and reassessed 12 months after starting ART. Gene expression levels and viral load were evaluated by real-time PCR, CD4+ and CD8+ T lymphocyte counts by flow cytometry, and IFN-α level by enzyme-linked immunosorbent assay. Results From before to after ART, the CD4+ T cell count and the CD4+/CD8+ ratio significantly increased (p < 0.0001), the CD8+ T cell count slightly decreased, and viral load decreased to undetectable levels in most of the group (84.85%). The expression of STING and cGAS significantly decreased (p = 0.0034 and p = 0.0001, respectively) after the use of ART, but IFN-α did not (p = 0.1558). Among the markers evaluated, the only markers that showed a correlation with each other were STING and CD4+ T at the time of the first collection. Conclusions ART provided immune recovery and viral suppression to the studied group and indirectly downregulated the STING and cGAS genes. In contrast, ART did not influence IFN-α. The expression of STING and cGAS was not correlated with the plasma level of IFN-α, which suggests that there is another pathway regulating this cytokine in addition to the STING–cGAS pathway.

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

scholarly journals Unique Gene Expression Patterns in Human T-cell Lines Generated from Multiple Sclerosis Patients by Stimulation with a Synthetic MOG Peptide

2005 ◽  
Vol 12 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Mathilda Mandel ◽  
Michael Gurevich ◽  
Gad Lavie ◽  
Irun R. Cohen ◽  
Anat Achiron
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cell Lines ◽  
Healthy Subjects ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
T Cell Lines ◽  
Myelin Antigens

Multiple sclerosis (MS) is an autoimmune disease where T-cells activated against myelin antigens are involved in myelin destruction. Yet, healthy subjects also harbor T-cells responsive to myelin antigens, suggesting that MS patient-derived autoimmune T-cells might bear functional differences from T-cells derived from healthy individuals. We addressed this issue by analyzing gene expression patterns of myelin oligodendrocytic glycoprotein (MOG) responsive T-cell lines generated from MS patients and healthy subjects. We identified 150 transcripts that were differentially expressed between MS patients and healthy controls. The most informative 43 genes exhibited >1.5-fold change in expression level. Eighteen genes were up-regulated including BCL2, lifeguard, IGFBP3 and VEGF. Twenty five genes were down-regulated, including apoptotic activators like TNF and heat shock protein genes. This gene expression pattern was unique to MOG specific T-cell lines and was not expressed in T-cell lines reactive to tetanus toxin (TTX). Our results indicate that activation in MS that promotes T-cell survival and expansion, has its own state and that the unique gene expression pattern that characterize autoreactive T-cells in MS represent a constellation of factors in which the chronicity, timing and accumulation of damage make the difference between health and disease.

scholarly journals 77 Prevalence of secondary immunotherapeutic targets in the absence of established immune biomarkers in solid tumors

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A86-A86
Author(s):  
Paul DePietro ◽  
Mary Nesline ◽  
Yong Hee Lee ◽  
RJ Seager ◽  
Erik Van Roey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Reference Population ◽  
List Type ◽  
Tumor Type ◽  
Genomic Profiling ◽  
Rna Seq ◽  
Immune Biomarkers ◽  
Cancer Types ◽  
Immune Related Genes

BackgroundImmune checkpoint inhibitor-based therapies have achieved impressive success in the treatment of several cancer types. Predictive immune biomarkers, including PD-L1, MSI and TMB are well established as surrogate markers for immune evasion and tumor-specific neoantigens across many tumors. Positive detection across cancer types varies, but overall ~50% of patients test negative for these primary immune markers.1 In this study, we investigated the prevalence of secondary immune biomarkers outside of PD-L1, TMB and MSI.MethodsComprehensive genomic and immune profiling, including PD-L1 IHC, TMB, MSI and gene expression of 395 immune related genes was performed on 6078 FFPE tumors representing 34 cancer types, predominantly composed of lung cancer (36.7%), colorectal cancer (11.9%) and breast cancer (8.5%). Expression levels by RNA-seq of 36 genes targeted by immunotherapies in solid tumor clinical trials, identified as secondary immune biomarkers, were ranked against a reference population. Genes with a rank value ≥75th percentile were considered high and values were associated with PD-L1 (positive ≥1%), MSI (MSI-H or MSS) and TMB (high ≥10 Mut/Mb) status. Additionally, secondary immune biomarker status was segmented by tumor type and cancer immune cycle roles.ResultsIn total, 41.0% of cases were PD-L1+, 6.4% TMB+, and 0.1% MSI-H. 12.6% of cases were positive for >2 of these markers while 39.9% were triple negative (PD-L1-/TMB-/MSS). Of the PD-L1-/TMB-/MSS cases, 89.1% were high for at least one secondary immune biomarker, with 69.3% having ≥3 markers. PD-L1-/TMB-/MSS tumor types with ≥50% prevalence of high secondary immune biomarkers included brain, prostate, kidney, sarcoma, gallbladder, breast, colorectal, and liver cancer. High expression of cancer testis antigen secondary immune biomarkers (e.g., NY-ESO-1, LAGE-1A, MAGE-A4) was most commonly observed in bladder, ovarian, sarcoma, liver, and prostate cancer (≥15%). Tumors demonstrating T-cell priming (e.g., CD40, OX40, CD137), trafficking (e.g., TGFB1, TLR9, TNF) and/or recognition (e.g., CTLA4, LAG3, TIGIT) secondary immune biomarkers were most represented by kidney, gallbladder, and sarcoma (≥40%), with melanoma, esophageal, head & neck, cervical, stomach, and lung cancer least represented (≥15%).ConclusionsOur studies show comprehensive tumor profiling that includes gene expression can detect secondary immune biomarkers targeted by investigational therapies in ~90% of PD-L1-/TMB-/MSS cases. While genomic profiling could also provide therapeutic choices for a percentage of these patients, detection of secondary immune biomarkers by RNA-seq provides additional options for patients without a clear therapeutic path as determined by PD-L1 testing and genomic profiling alone.ReferenceHuang R S P, Haberberger J, Severson E, et al. A pan-cancer analysis of PD-L1 immunohistochemistry and gene amplification, tumor mutation burden and microsatellite instability in 48,782 cases. Mod Pathol 2021;34: 252–263.

scholarly journals Social history and exposure to pathogen signals modulate social status effects on gene regulation in rhesus macaques

2019 ◽  
Vol 117 (38) ◽  
pp. 23317-23322 ◽  
Author(s):  
Joaquín Sanz ◽  
Paul L. Maurizio ◽  
Noah Snyder-Mackler ◽  
Noah D. Simons ◽  
Tawni Voyles ◽  
...  
Keyword(s):  
Gene Expression ◽  
Social Status ◽  
Social History ◽  
Immune Cell ◽  
Rhesus Macaques ◽  
Expression Patterns ◽  
Social Experience ◽  
Type I ◽  
Gene Expression Response ◽  
Expression Of Genes

Social experience is an important predictor of disease susceptibility and survival in humans and other social mammals. Chronic social stress is thought to generate a proinflammatory state characterized by elevated antibacterial defenses and reduced investment in antiviral defense. Here we manipulated long-term social status in female rhesus macaques to show that social subordination alters the gene expression response to ex vivo bacterial and viral challenge. As predicted by current models, bacterial lipopolysaccharide polarizes the immune response such that low status corresponds to higher expression of genes in NF-κB–dependent proinflammatory pathways and lower expression of genes involved in the antiviral response and type I IFN signaling. Counter to predictions, however, low status drives more exaggerated expression of both NF-κB– and IFN-associated genes after cells are exposed to the viral mimic Gardiquimod. Status-driven gene expression patterns are linked not only to social status at the time of sampling, but also to social history (i.e., past social status), especially in unstimulated cells. However, for a subset of genes, we observed interaction effects in which females who fell in rank were more strongly affected by current social status than those who climbed the social hierarchy. Taken together, our results indicate that the effects of social status on immune cell gene expression depend on pathogen exposure, pathogen type, and social history—in support of social experience-mediated biological embedding in adulthood, even in the conventionally memory-less innate immune system.

FOXA1 in breast cancer

2009 ◽  
Vol 11 ◽  
Author(s):  
Harikrishna Nakshatri ◽  
Sunil Badve
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Signature ◽  
Specific Gene ◽  
Breast Cancers ◽  
Transcription Factor Network ◽  
Histopathological Classification

Breast cancer is a heterogeneous disease and classification is important for clinical management. At least five subtypes can be identified based on unique gene expression patterns; this subtype classification is distinct from the histopathological classification. The transcription factor network(s) required for the specific gene expression signature in each of these subtypes is currently being elucidated. The transcription factor network composed of the oestrogen (estrogen) receptor α (ERα), FOXA1 and GATA3 may control the gene expression pattern in luminal subtype A breast cancers. Breast cancers that are dependent on this network correspond to well-differentiated and hormone-therapy-responsive tumours with good prognosis. In this review, we discuss the interplay between these transcription factors with a particular emphasis on FOXA1 structure and function, and its ability to control ERα function. Additionally, we discuss modulators of FOXA1 function, ERα–FOXA1–GATA3 downstream targets, and potential therapeutic agents that may increase differentiation through FOXA1.

An FLT3 gene-expression signature predicts clinical outcome in normal karyotype AML

Blood ◽  
2008 ◽  
Vol 111 (9) ◽  
pp. 4490-4495 ◽  
Author(s):  
Lars Bullinger ◽  
Konstanze Döhner ◽  
Raphael Kranz ◽  
Christoph Stirner ◽  
Stefan Fröhling ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Outcome ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Normal Karyotype ◽  
Mutation Status ◽  
Signature Genes ◽  
Starting Point

Abstract Acute myeloid leukemia with normal karyotype (NK-AML) represents a cytogenetic grouping with intermediate prognosis but substantial molecular and clinical heterogeneity. Within this subgroup, presence of FLT3 (FMS-like tyrosine kinase 3) internal tandem duplication (ITD) mutation predicts less favorable outcome. The goal of our study was to discover gene-expression patterns correlated with FLT3-ITD mutation and to evaluate the utility of a FLT3 signature for prognostication. DNA microarrays were used to profile gene expression in a training set of 65 NK-AML cases, and supervised analysis, using the Prediction Analysis of Microarrays method, was applied to build a gene expression–based predictor of FLT3-ITD mutation status. The optimal predictor, composed of 20 genes, was then evaluated by classifying expression profiles from an independent test set of 72 NK-AML cases. The predictor exhibited modest performance (73% sensitivity; 85% specificity) in classifying FLT3-ITD status. Remarkably, however, the signature outperformed FLT3-ITD mutation status in predicting clinical outcome. The signature may better define clinically relevant FLT3 signaling and/or alternative changes that phenocopy FLT3-ITD, whereas the signature genes provide a starting point to dissect these pathways. Our findings support the potential clinical utility of a gene expression–based measure of FLT3 pathway activation in AML.

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