Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA sequencing
Background Defining the tumor immune microenvironment (TIME) of patients using transcriptome analysis is gaining more popularity. Here, we examined and discussed the pros and cons of using RNA sequencing for fresh frozen samples and targeted gene expression immune profiles (NanoString) for formalin-...
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Veröffentlicht in: | Cancer Immunology, Immunotherapy Immunotherapy, 2023-08, Vol.72 (8), p.2659-2670 |
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description | Background
Defining the tumor immune microenvironment (TIME) of patients using transcriptome analysis is gaining more popularity. Here, we examined and discussed the pros and cons of using RNA sequencing for fresh frozen samples and targeted gene expression immune profiles (NanoString) for formalin-fixed, paraffin-embedded (FFPE) samples to characterize the TIME of ependymoma samples.
Results
Our results showed a stable expression of the 40 housekeeping genes throughout all samples. The Pearson correlation of the endogenous genes was high. To define the TIME, we first checked the expression of the
PTPRC
gene, known as
CD45
, and found it was above the detection limit in all samples by both techniques. T cells were identified consistently using the two types of data. In addition, both techniques showed that the immune landscape was heterogeneous in the 6 ependymoma samples used for this study.
Conclusions
The low-abundant genes were detected in higher quantities using the NanoString technique, even when FFPE samples were used. RNA sequencing is better suited for biomarker discovery, fusion gene detection, and getting a broader overview of the TIME. The technique that was used to measure the samples had a considerable effect on the type of immune cells that were identified. The limited number of tumor-infiltrating immune cells compared to the high density of tumor cells in ependymoma can limit the sensitivity of RNA expression techniques regarding the identification of the infiltrating immune cells. |
doi_str_mv | 10.1007/s00262-023-03450-2 |
format | Article |
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Defining the tumor immune microenvironment (TIME) of patients using transcriptome analysis is gaining more popularity. Here, we examined and discussed the pros and cons of using RNA sequencing for fresh frozen samples and targeted gene expression immune profiles (NanoString) for formalin-fixed, paraffin-embedded (FFPE) samples to characterize the TIME of ependymoma samples.
Results
Our results showed a stable expression of the 40 housekeeping genes throughout all samples. The Pearson correlation of the endogenous genes was high. To define the TIME, we first checked the expression of the
PTPRC
gene, known as
CD45
, and found it was above the detection limit in all samples by both techniques. T cells were identified consistently using the two types of data. In addition, both techniques showed that the immune landscape was heterogeneous in the 6 ependymoma samples used for this study.
Conclusions
The low-abundant genes were detected in higher quantities using the NanoString technique, even when FFPE samples were used. RNA sequencing is better suited for biomarker discovery, fusion gene detection, and getting a broader overview of the TIME. The technique that was used to measure the samples had a considerable effect on the type of immune cells that were identified. The limited number of tumor-infiltrating immune cells compared to the high density of tumor cells in ependymoma can limit the sensitivity of RNA expression techniques regarding the identification of the infiltrating immune cells.</description><identifier>ISSN: 0340-7004</identifier><identifier>EISSN: 1432-0851</identifier><identifier>DOI: 10.1007/s00262-023-03450-2</identifier><identifier>PMID: 37072536</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Cancer Research ; CD45 antigen ; Fusion protein ; Gene expression ; Immunology ; Lymphocytes T ; Medicine ; Medicine & Public Health ; Microenvironments ; Oncology ; Paraffin ; Ribonucleic acid ; RNA ; Transcriptomes ; Tumor cells ; Tumor-infiltrating lymphocytes</subject><ispartof>Cancer Immunology, Immunotherapy, 2023-08, Vol.72 (8), p.2659-2670</ispartof><rights>The Author(s) 2023</rights><rights>2023. The Author(s).</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c426t-cc07387bf35cbd4b3dffc2cc5825e143e895b8832a0fa63a8e083f6045ee15113</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361846/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361846/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27923,27924,41487,42556,51318,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37072536$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>de Koning, W.</creatorcontrib><creatorcontrib>Feenstra, F. F.</creatorcontrib><creatorcontrib>Calkoen, F. G. J.</creatorcontrib><creatorcontrib>van der Lugt, J.</creatorcontrib><creatorcontrib>Kester, L. A.</creatorcontrib><creatorcontrib>Mustafa, D. A. M.</creatorcontrib><title>Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA sequencing</title><title>Cancer Immunology, Immunotherapy</title><addtitle>Cancer Immunol Immunother</addtitle><addtitle>Cancer Immunol Immunother</addtitle><description>Background
Defining the tumor immune microenvironment (TIME) of patients using transcriptome analysis is gaining more popularity. Here, we examined and discussed the pros and cons of using RNA sequencing for fresh frozen samples and targeted gene expression immune profiles (NanoString) for formalin-fixed, paraffin-embedded (FFPE) samples to characterize the TIME of ependymoma samples.
Results
Our results showed a stable expression of the 40 housekeeping genes throughout all samples. The Pearson correlation of the endogenous genes was high. To define the TIME, we first checked the expression of the
PTPRC
gene, known as
CD45
, and found it was above the detection limit in all samples by both techniques. T cells were identified consistently using the two types of data. In addition, both techniques showed that the immune landscape was heterogeneous in the 6 ependymoma samples used for this study.
Conclusions
The low-abundant genes were detected in higher quantities using the NanoString technique, even when FFPE samples were used. RNA sequencing is better suited for biomarker discovery, fusion gene detection, and getting a broader overview of the TIME. The technique that was used to measure the samples had a considerable effect on the type of immune cells that were identified. The limited number of tumor-infiltrating immune cells compared to the high density of tumor cells in ependymoma can limit the sensitivity of RNA expression techniques regarding the identification of the infiltrating immune cells.</description><subject>Cancer Research</subject><subject>CD45 antigen</subject><subject>Fusion protein</subject><subject>Gene expression</subject><subject>Immunology</subject><subject>Lymphocytes T</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Microenvironments</subject><subject>Oncology</subject><subject>Paraffin</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Transcriptomes</subject><subject>Tumor cells</subject><subject>Tumor-infiltrating lymphocytes</subject><issn>0340-7004</issn><issn>1432-0851</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU9v1DAQxS0EotvCF-CALHHhEhjbceI9oWoFBakCCcHZcpxJ1lVsBzup2n563G4pfw6cbM385nmeHyEvGLxhAO3bDMAbXgEXFYhaQsUfkQ2rRSkpyR6TTalC1QLUR-Q454ty4bDdPiVHooWWS9FsyM1ub5KxCyZ348JIlz3SZfUxUef9GpB6Z1PEcOlSDB7DQuNAccbQX_voTaZrvhszacQFezpimcGrOWHOLgY6pzi4CTM1oadfP5_SjD9WDLYMPSNPBjNlfH5_npDvH95_232szr-cfdqdnle25s1SWQutUG03CGm7vu5EPwyWWysVl1jcotrKTinBDQymEUYhKDE0UEtEJhkTJ-TdQXdeO4-9LSaSmfScnDfpWkfj9N-d4PZ6jJeagWiYqpui8PpeIcWyfV60d9niNJmAcc2aKxCCb5stL-irf9CLuKZQ_BWqhppJ2UKh-IEqf5tzwuFhGwb6Nlt9yFaXbPVdtvpW-uWfPh5GfoVZAHEAcmmFEdPvt_8j-xOuyrKD</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>de Koning, W.</creator><creator>Feenstra, F. 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F. ; Calkoen, F. G. J. ; van der Lugt, J. ; Kester, L. A. ; Mustafa, D. A. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-cc07387bf35cbd4b3dffc2cc5825e143e895b8832a0fa63a8e083f6045ee15113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Cancer Research</topic><topic>CD45 antigen</topic><topic>Fusion protein</topic><topic>Gene expression</topic><topic>Immunology</topic><topic>Lymphocytes T</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Microenvironments</topic><topic>Oncology</topic><topic>Paraffin</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Transcriptomes</topic><topic>Tumor cells</topic><topic>Tumor-infiltrating lymphocytes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Koning, W.</creatorcontrib><creatorcontrib>Feenstra, F. F.</creatorcontrib><creatorcontrib>Calkoen, F. G. J.</creatorcontrib><creatorcontrib>van der Lugt, J.</creatorcontrib><creatorcontrib>Kester, L. A.</creatorcontrib><creatorcontrib>Mustafa, D. A. 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F.</au><au>Calkoen, F. G. J.</au><au>van der Lugt, J.</au><au>Kester, L. A.</au><au>Mustafa, D. A. M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA sequencing</atitle><jtitle>Cancer Immunology, Immunotherapy</jtitle><stitle>Cancer Immunol Immunother</stitle><addtitle>Cancer Immunol Immunother</addtitle><date>2023-08-01</date><risdate>2023</risdate><volume>72</volume><issue>8</issue><spage>2659</spage><epage>2670</epage><pages>2659-2670</pages><issn>0340-7004</issn><eissn>1432-0851</eissn><abstract>Background
Defining the tumor immune microenvironment (TIME) of patients using transcriptome analysis is gaining more popularity. Here, we examined and discussed the pros and cons of using RNA sequencing for fresh frozen samples and targeted gene expression immune profiles (NanoString) for formalin-fixed, paraffin-embedded (FFPE) samples to characterize the TIME of ependymoma samples.
Results
Our results showed a stable expression of the 40 housekeeping genes throughout all samples. The Pearson correlation of the endogenous genes was high. To define the TIME, we first checked the expression of the
PTPRC
gene, known as
CD45
, and found it was above the detection limit in all samples by both techniques. T cells were identified consistently using the two types of data. In addition, both techniques showed that the immune landscape was heterogeneous in the 6 ependymoma samples used for this study.
Conclusions
The low-abundant genes were detected in higher quantities using the NanoString technique, even when FFPE samples were used. RNA sequencing is better suited for biomarker discovery, fusion gene detection, and getting a broader overview of the TIME. The technique that was used to measure the samples had a considerable effect on the type of immune cells that were identified. The limited number of tumor-infiltrating immune cells compared to the high density of tumor cells in ependymoma can limit the sensitivity of RNA expression techniques regarding the identification of the infiltrating immune cells.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37072536</pmid><doi>10.1007/s00262-023-03450-2</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Cancer Research CD45 antigen Fusion protein Gene expression Immunology Lymphocytes T Medicine Medicine & Public Health Microenvironments Oncology Paraffin Ribonucleic acid RNA Transcriptomes Tumor cells Tumor-infiltrating lymphocytes |
title | Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA sequencing |
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