Determinants of gastric cancer immune escape identified from non-coding immune-landscape quantitative trait loci
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE261709 | GEO | 2024/04/15
REPOSITORIES: GEO
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Project description:Understanding the function of rare non-coding genetic variants represents a significant challenge. Here, we developed MapUTR, a screen to identify rare 3’ UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated a further interrogation of 11,929 cancer somatic mutations, uncovering 3,928 (33%) functional mutations in well-established cancer driver genes, such as CDKN2A. Functional MapUTR variants were enriched in miRNA targets and protein-RNA interaction sites. Based on MapUTR, we define a new metric, untranslated tumor mutation burden (uTMB), reflecting the amount of somatic functional MapUTR variants of a tumor. We showed the potential of uTMB in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), illustrating their cancer-driving potential. Our study elucidates the function of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.
2024-02-06 | GSE232572 | GEO
Project description:Understanding the function of rare non-coding genetic variants represents a significant challenge. Here, we developed MapUTR, a screen to identify rare 3’ UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated a further interrogation of 11,929 cancer somatic mutations, uncovering 3,928 (33%) functional mutations in well-established cancer driver genes, such as CDKN2A. Functional MapUTR variants were enriched in miRNA targets and protein-RNA interaction sites. Based on MapUTR, we define a new metric, untranslated tumor mutation burden (uTMB), reflecting the amount of somatic functional MapUTR variants of a tumor. We showed the potential of uTMB in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), illustrating their cancer-driving potential. Our study elucidates the function of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.
2024-02-06 | GSE232571 | GEO
Project description:Understanding the function of rare non-coding genetic variants represents a significant challenge. Here, we developed MapUTR, a screen to identify rare 3’ UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated a further interrogation of 11,929 cancer somatic mutations, uncovering 3,928 (33%) functional mutations in well-established cancer driver genes, such as CDKN2A. Functional MapUTR variants were enriched in miRNA targets and protein-RNA interaction sites. Based on MapUTR, we define a new metric, untranslated tumor mutation burden (uTMB), reflecting the amount of somatic functional MapUTR variants of a tumor. We showed the potential of uTMB in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), illustrating their cancer-driving potential. Our study elucidates the function of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.
2024-02-06 | GSE232570 | GEO
Project description:This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication:
Creemers JHA, Lesterhuis WJ, Mehra N, et al.
"A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery."
Journal for ImmunoTherapy of Cancer 2021;9:e002032.
doi:10.1136/jitc-2020-002032
Comment:
Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used:
Reproduction of Fig. 1(C), xi = 0.0005
Reproduction of Fig. 1(D), xi = 0.00025
Abstract:
Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.
Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.
Results: Our model shows that a tipping point—a sharp state transition between immune control and immune evasion—induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.
Conclusion: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient’s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.
2024-09-02 | BIOMD0000001022 | BioModels
Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (?eQTLs?) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3?UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3?UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Illumina gene expression microarrays for 60 normal human livers. Expression quantitative trait loci were identified by genome wide association mapping.
2011-06-27 | E-GEOD-28893 | biostudies-arrayexpress
Project description:Background: Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors which have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown. Results: Using an integrated linkage and genomic analysis of a mouse model of skin cancer that produces both benign tumors and malignant carcinomas, we document major changes in germline control of gene expression during skin tumor development resulting from cell selection, somatic genetic events, and changes in the tumor microenvironment. The number of significant expression Quantitative Trait Loci (eQTLs) is progressively reduced in benign and malignant skin tumors when compared to normal skin. However, novel tumor-specific eQTLs are detected for several genes associated with tumor susceptibility, including Interleukin 18, Granzyme E, Sprouty homolog 2, and MAP kinase kinase 4. Conclusions: We conclude that the genetic architecture is substantially altered in tumors, and that eQTL analysis of tumors can identify host factors that influence the tumor microenvironment, MAP kinase signaling, and cancer susceptibility.
2010-12-23 | GSE26273 | GEO
Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (‘eQTLs’) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3’UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3’UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Agilent gene expression microarrays for 206 normal human livers (464 unique arrays corresponding to those samples). Genotypes were also derived from 224 of these samples using Illumina SNP chips. Expression quantitative trait loci were identified by genome wide association mapping. This submission represents the primary transcriptome component of study.
2011-06-27 | E-GEOD-25935 | biostudies-arrayexpress
Project description:Background: Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors which have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown. Results: Using an integrated linkage and genomic analysis of a mouse model of skin cancer that produces both benign tumors and malignant carcinomas, we document major changes in germline control of gene expression during skin tumor development resulting from cell selection, somatic genetic events, and changes in the tumor microenvironment. The number of significant expression Quantitative Trait Loci (eQTLs) is progressively reduced in benign and malignant skin tumors when compared to normal skin. However, novel tumor-specific eQTLs are detected for several genes associated with tumor susceptibility, including Interleukin 18, Granzyme E, Sprouty homolog 2, and MAP kinase kinase 4. Conclusions: We conclude that the genetic architecture is substantially altered in tumors, and that eQTL analysis of tumors can identify host factors that influence the tumor microenvironment, MAP kinase signaling, and cancer susceptibility. A backcross was generated using male Mus spretus and female FVB/N mice; female F1 hybrids were mated with male FVB/N mice. Backcross mice (8-12 weeks old) received a single dose of DMBA (25 µg per mouse in 200 µl acetone). Starting one week after the initiation tumors were promoted with TPA (200 µl of 10-4 M solution in acetone) twice weekly for 20 weeks. Initiation and promotion were performed on doral back skin. DNA from 62 Carcinomas and matched untreated tails (used for normal DNA comparison) was obtained from tissue that was snap frozen when animals were sacrificed.
2010-12-23 | E-GEOD-26273 | biostudies-arrayexpress
Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation.
2014-12-19 | PXD001406 | Pride
Project description:Metastatic, castration-resistant prostate cancer (mCRPC) is an advanced form of prostate cancer with a high mortality rate due to a current lack of treatment options. While much is already known about how mutations in protein-coding sequences across the genome affect prostate cancer, somatic mutations occurring in the 3’ untranslated regions (3’UTRs) of genes are largely unstudied. The 3’UTR is a genomic region that controls post-transcriptional gene expression through its recruitment of trans-acting factors such as RNA-binding proteins (RBPs) and microRNAs (miRNAs), which themselves are known to be oncogenes and tumor suppressors in many cases. To better understand the role of 3’UTR mutations across prostate cancer, we have created a database of 3’UTR somatic mutations in 185 patients with mCRPC, discovering 14,497 single-nucleotide mutations throughout the 3’UTRome. In order to functionally assay these variants, we have developed a novel pair of massively parallel reporter assays (MPRA) able to determine the effect of thousands of patient somatic mutations on post-transcriptional gene expression. In this two-pronged approach, we are able to measure whether each of 6,892 mutations found in recurrently mutated 3’UTRs affect mRNA stability, steady-state transcript level, and translation efficiency. This deep functional assessment of thousands of 3’UTR mutations allows us to uncover patterns in mutation functionality, including their association with RNA motifs and sequence conservation. Investigation into how the resultant gene expression changes from 3’UTR mutations affect prostate cancer pathogenesis, such as cancer growth or response to treatment, is also underway. This work represents an unprecedented view of the extent to which disease-relevant 3’UTR mutations affect mRNA stability, translation efficiency, and cancer phenotypes, expanding the boundaries of functional cancer genomics and potentially uncovering novel therapeutic targets in previously unexplored regulatory regions.
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