Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy

Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We pe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of immunological methods 2021-07, Vol.494, p.113041-113041, Article 113041
Hauptverfasser: Zhang, Li, Cham, Jason, Cooley, James, He, Tao, Hagihara, Katsunobu, Yang, Hai, Fan, Frances, Cheung, Alexander, Thompson, Debrah, Kerns, B.J., Fong, Lawrence
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 113041
container_issue
container_start_page 113041
container_title Journal of immunological methods
container_volume 494
creator Zhang, Li
Cham, Jason
Cooley, James
He, Tao
Hagihara, Katsunobu
Yang, Hai
Fan, Frances
Cheung, Alexander
Thompson, Debrah
Kerns, B.J.
Fong, Lawrence
description Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We performed comparisons across three panels in two platforms: Nanostring nCounter® PanCancer Immune Profiling Panel (nS), HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) and Precision Immuno-Oncology Panel (HTG PIP). All tissue samples of 14 neoadjuvant GM-CSF treated, 14 neoadjuvant Provenge treated, and 12 untreated prostate cancer patients were radical prostatectomy (RP) tissues, while 6 prostatitis patients and 6 non-prostatitis subjects were biopsies. For all 52 patients, more than 90% of the common genes were significantly correlated (p  0.5) between any two panels. Co-inertia analysis also demonstrated high overall dataset structure similarity (correlation>0.84). Although both dimensionality reduction visualization analysis and unsupervised hierarchical cluster analysis for highly correlated common genes (r > 0.9) suggested a high-level of consistency across the panels, there were subsets of genes that were differentially expressed across the panels. In addition, while the effect size of the differential testing for neoadjuvant treated vs. untreated localized prostate cancer patients across the panels were significantly correlated, some genes were only differentially expressed in the HTG panels. Finally, the HTG PIP panel had the best classification performance among the 3 panels. These differences detected may be a result of the different panels or platforms due to their technical setting and focus. Thus, researchers should be aware of those potential differences when deciding which platform and panel to use.
doi_str_mv 10.1016/j.jim.2021.113041
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2504349800</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0022175921000867</els_id><sourcerecordid>2504349800</sourcerecordid><originalsourceid>FETCH-LOGICAL-c396t-9f5aa908df86091c5920d153a0353f7391b85d56fa48368a77d828831b90d6523</originalsourceid><addsrcrecordid>eNp9kMGOFCEQhonRuLOrD-DFcPTSYwFDN8STmay6ySZe9EwYKFYmTdNCt7o3H10mM3r0VKnU9_9JfYS8YrBlwPq3x-0xpi0HzraMCdixJ2TD1MC7QYN8SjYAnHdskPqKXNd6BAAGPTwnV0IMUoDuN-T3vuRau3m0S8glUZfTbEuseaI50JjSOmFXsJ3R0weckOKvuWCtsRFLprbWttA4LcUua8rlkqGNmfPUjjTkccw_4_RAnZ0cXoi8fMNi58cX5FmwY8WXl3lDvn64_bL_1N1__ni3f3_fOaH7pdNBWqtB-aB60MxJzcEzKSwIKcIgNDso6WUf7E6JXtlh8IorJdhBg-8lFzfkzbl3Lvn7inUxKVaH42gnzGs1XMJO7LQCaCg7o-7kpmAwc4nJlkfDwJzEm6Np4s1JvDmLb5nXl_r1kND_S_w13YB3ZwDbkz8iFlNdxObDx4JuMT7H_9T_ARDcldU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2504349800</pqid></control><display><type>article</type><title>Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy</title><source>ScienceDirect Journals (5 years ago - present)</source><creator>Zhang, Li ; Cham, Jason ; Cooley, James ; He, Tao ; Hagihara, Katsunobu ; Yang, Hai ; Fan, Frances ; Cheung, Alexander ; Thompson, Debrah ; Kerns, B.J. ; Fong, Lawrence</creator><creatorcontrib>Zhang, Li ; Cham, Jason ; Cooley, James ; He, Tao ; Hagihara, Katsunobu ; Yang, Hai ; Fan, Frances ; Cheung, Alexander ; Thompson, Debrah ; Kerns, B.J. ; Fong, Lawrence</creatorcontrib><description>Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We performed comparisons across three panels in two platforms: Nanostring nCounter® PanCancer Immune Profiling Panel (nS), HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) and Precision Immuno-Oncology Panel (HTG PIP). All tissue samples of 14 neoadjuvant GM-CSF treated, 14 neoadjuvant Provenge treated, and 12 untreated prostate cancer patients were radical prostatectomy (RP) tissues, while 6 prostatitis patients and 6 non-prostatitis subjects were biopsies. For all 52 patients, more than 90% of the common genes were significantly correlated (p &lt; 0.05) and more than 76% of the common genes were highly correlated (r &gt; 0.5) between any two panels. Co-inertia analysis also demonstrated high overall dataset structure similarity (correlation&gt;0.84). Although both dimensionality reduction visualization analysis and unsupervised hierarchical cluster analysis for highly correlated common genes (r &gt; 0.9) suggested a high-level of consistency across the panels, there were subsets of genes that were differentially expressed across the panels. In addition, while the effect size of the differential testing for neoadjuvant treated vs. untreated localized prostate cancer patients across the panels were significantly correlated, some genes were only differentially expressed in the HTG panels. Finally, the HTG PIP panel had the best classification performance among the 3 panels. These differences detected may be a result of the different panels or platforms due to their technical setting and focus. Thus, researchers should be aware of those potential differences when deciding which platform and panel to use.</description><identifier>ISSN: 0022-1759</identifier><identifier>EISSN: 1872-7905</identifier><identifier>DOI: 10.1016/j.jim.2021.113041</identifier><identifier>PMID: 33753096</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Classification performance ; Gene expression profiling ; HTG EdgeSeq ; Nanostring platform ; Neoadjuvant immunotherapy ; Prostate cancer</subject><ispartof>Journal of immunological methods, 2021-07, Vol.494, p.113041-113041, Article 113041</ispartof><rights>2021</rights><rights>Published by Elsevier B.V.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-9f5aa908df86091c5920d153a0353f7391b85d56fa48368a77d828831b90d6523</citedby><cites>FETCH-LOGICAL-c396t-9f5aa908df86091c5920d153a0353f7391b85d56fa48368a77d828831b90d6523</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jim.2021.113041$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,3539,27911,27912,45982</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33753096$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Cham, Jason</creatorcontrib><creatorcontrib>Cooley, James</creatorcontrib><creatorcontrib>He, Tao</creatorcontrib><creatorcontrib>Hagihara, Katsunobu</creatorcontrib><creatorcontrib>Yang, Hai</creatorcontrib><creatorcontrib>Fan, Frances</creatorcontrib><creatorcontrib>Cheung, Alexander</creatorcontrib><creatorcontrib>Thompson, Debrah</creatorcontrib><creatorcontrib>Kerns, B.J.</creatorcontrib><creatorcontrib>Fong, Lawrence</creatorcontrib><title>Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy</title><title>Journal of immunological methods</title><addtitle>J Immunol Methods</addtitle><description>Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We performed comparisons across three panels in two platforms: Nanostring nCounter® PanCancer Immune Profiling Panel (nS), HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) and Precision Immuno-Oncology Panel (HTG PIP). All tissue samples of 14 neoadjuvant GM-CSF treated, 14 neoadjuvant Provenge treated, and 12 untreated prostate cancer patients were radical prostatectomy (RP) tissues, while 6 prostatitis patients and 6 non-prostatitis subjects were biopsies. For all 52 patients, more than 90% of the common genes were significantly correlated (p &lt; 0.05) and more than 76% of the common genes were highly correlated (r &gt; 0.5) between any two panels. Co-inertia analysis also demonstrated high overall dataset structure similarity (correlation&gt;0.84). Although both dimensionality reduction visualization analysis and unsupervised hierarchical cluster analysis for highly correlated common genes (r &gt; 0.9) suggested a high-level of consistency across the panels, there were subsets of genes that were differentially expressed across the panels. In addition, while the effect size of the differential testing for neoadjuvant treated vs. untreated localized prostate cancer patients across the panels were significantly correlated, some genes were only differentially expressed in the HTG panels. Finally, the HTG PIP panel had the best classification performance among the 3 panels. These differences detected may be a result of the different panels or platforms due to their technical setting and focus. Thus, researchers should be aware of those potential differences when deciding which platform and panel to use.</description><subject>Classification performance</subject><subject>Gene expression profiling</subject><subject>HTG EdgeSeq</subject><subject>Nanostring platform</subject><subject>Neoadjuvant immunotherapy</subject><subject>Prostate cancer</subject><issn>0022-1759</issn><issn>1872-7905</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kMGOFCEQhonRuLOrD-DFcPTSYwFDN8STmay6ySZe9EwYKFYmTdNCt7o3H10mM3r0VKnU9_9JfYS8YrBlwPq3x-0xpi0HzraMCdixJ2TD1MC7QYN8SjYAnHdskPqKXNd6BAAGPTwnV0IMUoDuN-T3vuRau3m0S8glUZfTbEuseaI50JjSOmFXsJ3R0weckOKvuWCtsRFLprbWttA4LcUua8rlkqGNmfPUjjTkccw_4_RAnZ0cXoi8fMNi58cX5FmwY8WXl3lDvn64_bL_1N1__ni3f3_fOaH7pdNBWqtB-aB60MxJzcEzKSwIKcIgNDso6WUf7E6JXtlh8IorJdhBg-8lFzfkzbl3Lvn7inUxKVaH42gnzGs1XMJO7LQCaCg7o-7kpmAwc4nJlkfDwJzEm6Np4s1JvDmLb5nXl_r1kND_S_w13YB3ZwDbkz8iFlNdxObDx4JuMT7H_9T_ARDcldU</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Zhang, Li</creator><creator>Cham, Jason</creator><creator>Cooley, James</creator><creator>He, Tao</creator><creator>Hagihara, Katsunobu</creator><creator>Yang, Hai</creator><creator>Fan, Frances</creator><creator>Cheung, Alexander</creator><creator>Thompson, Debrah</creator><creator>Kerns, B.J.</creator><creator>Fong, Lawrence</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20210701</creationdate><title>Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy</title><author>Zhang, Li ; Cham, Jason ; Cooley, James ; He, Tao ; Hagihara, Katsunobu ; Yang, Hai ; Fan, Frances ; Cheung, Alexander ; Thompson, Debrah ; Kerns, B.J. ; Fong, Lawrence</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-9f5aa908df86091c5920d153a0353f7391b85d56fa48368a77d828831b90d6523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Classification performance</topic><topic>Gene expression profiling</topic><topic>HTG EdgeSeq</topic><topic>Nanostring platform</topic><topic>Neoadjuvant immunotherapy</topic><topic>Prostate cancer</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Li</creatorcontrib><creatorcontrib>Cham, Jason</creatorcontrib><creatorcontrib>Cooley, James</creatorcontrib><creatorcontrib>He, Tao</creatorcontrib><creatorcontrib>Hagihara, Katsunobu</creatorcontrib><creatorcontrib>Yang, Hai</creatorcontrib><creatorcontrib>Fan, Frances</creatorcontrib><creatorcontrib>Cheung, Alexander</creatorcontrib><creatorcontrib>Thompson, Debrah</creatorcontrib><creatorcontrib>Kerns, B.J.</creatorcontrib><creatorcontrib>Fong, Lawrence</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of immunological methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Li</au><au>Cham, Jason</au><au>Cooley, James</au><au>He, Tao</au><au>Hagihara, Katsunobu</au><au>Yang, Hai</au><au>Fan, Frances</au><au>Cheung, Alexander</au><au>Thompson, Debrah</au><au>Kerns, B.J.</au><au>Fong, Lawrence</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy</atitle><jtitle>Journal of immunological methods</jtitle><addtitle>J Immunol Methods</addtitle><date>2021-07-01</date><risdate>2021</risdate><volume>494</volume><spage>113041</spage><epage>113041</epage><pages>113041-113041</pages><artnum>113041</artnum><issn>0022-1759</issn><eissn>1872-7905</eissn><abstract>Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We performed comparisons across three panels in two platforms: Nanostring nCounter® PanCancer Immune Profiling Panel (nS), HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) and Precision Immuno-Oncology Panel (HTG PIP). All tissue samples of 14 neoadjuvant GM-CSF treated, 14 neoadjuvant Provenge treated, and 12 untreated prostate cancer patients were radical prostatectomy (RP) tissues, while 6 prostatitis patients and 6 non-prostatitis subjects were biopsies. For all 52 patients, more than 90% of the common genes were significantly correlated (p &lt; 0.05) and more than 76% of the common genes were highly correlated (r &gt; 0.5) between any two panels. Co-inertia analysis also demonstrated high overall dataset structure similarity (correlation&gt;0.84). Although both dimensionality reduction visualization analysis and unsupervised hierarchical cluster analysis for highly correlated common genes (r &gt; 0.9) suggested a high-level of consistency across the panels, there were subsets of genes that were differentially expressed across the panels. In addition, while the effect size of the differential testing for neoadjuvant treated vs. untreated localized prostate cancer patients across the panels were significantly correlated, some genes were only differentially expressed in the HTG panels. Finally, the HTG PIP panel had the best classification performance among the 3 panels. These differences detected may be a result of the different panels or platforms due to their technical setting and focus. Thus, researchers should be aware of those potential differences when deciding which platform and panel to use.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>33753096</pmid><doi>10.1016/j.jim.2021.113041</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0022-1759
ispartof Journal of immunological methods, 2021-07, Vol.494, p.113041-113041, Article 113041
issn 0022-1759
1872-7905
language eng
recordid cdi_proquest_miscellaneous_2504349800
source ScienceDirect Journals (5 years ago - present)
subjects Classification performance
Gene expression profiling
HTG EdgeSeq
Nanostring platform
Neoadjuvant immunotherapy
Prostate cancer
title Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T19%3A27%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Cross-platform%20comparison%20of%20immune-related%20gene%20expression%20to%20assess%20intratumor%20immune%20responses%20following%20cancer%20immunotherapy&rft.jtitle=Journal%20of%20immunological%20methods&rft.au=Zhang,%20Li&rft.date=2021-07-01&rft.volume=494&rft.spage=113041&rft.epage=113041&rft.pages=113041-113041&rft.artnum=113041&rft.issn=0022-1759&rft.eissn=1872-7905&rft_id=info:doi/10.1016/j.jim.2021.113041&rft_dat=%3Cproquest_cross%3E2504349800%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2504349800&rft_id=info:pmid/33753096&rft_els_id=S0022175921000867&rfr_iscdi=true