Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures
(1) C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Using Random Forest (RF) analysis, we searched for en...
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| Veröffentlicht in: | Cancers 2023-01, Vol.15 (2), p.392 |
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| creator | Marquardt, André Hartrampf, Philipp Kollmannsberger, Philip Solimando, Antonio G Meierjohann, Svenja Kübler, Hubert Bargou, Ralf Schilling, Bastian Serfling, Sebastian E Buck, Andreas Werner, Rudolf A Lapa, Constantin Krebs, Markus |
| description | (1)
C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2)
Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing
= 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in
= 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3)
We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4)
CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets. |
| doi_str_mv | 10.3390/cancers15020392 |
| format | Article |
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C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2)
Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing
= 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in
= 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3)
We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4)
CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers15020392</identifier><identifier>PMID: 36672341</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Angiogenesis ; Cancer ; CD8 antigen ; Chemokine receptors ; CXCR4 protein ; Datasets ; Endothelial cells ; Fibroblast activation protein ; Fibroblasts ; Gene set enrichment analysis ; Genes ; Genomes ; Infiltration ; Invasiveness ; Learning algorithms ; Machine learning ; Malignancy ; Metastases ; Metastasis ; Microenvironments ; MicroRNAs ; miRNA ; mRNA ; Positron emission tomography ; Prostate ; Solid tumors ; Tumor-infiltrating lymphocytes ; Tumors</subject><ispartof>Cancers, 2023-01, Vol.15 (2), p.392</ispartof><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 by the authors. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c421t-8fb833bad601c3317dc0ac0bb708861ce412c50c5773c9b5982fd75eafdde48f3</citedby><cites>FETCH-LOGICAL-c421t-8fb833bad601c3317dc0ac0bb708861ce412c50c5773c9b5982fd75eafdde48f3</cites><orcidid>0000-0002-2293-9698 ; 0000-0001-5622-9849 ; 0000-0002-1154-0964 ; 0000-0001-7536-2207 ; 0000-0002-2425-7033 ; 0000-0001-8859-4103</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9856808/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9856808/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36672341$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Marquardt, André</creatorcontrib><creatorcontrib>Hartrampf, Philipp</creatorcontrib><creatorcontrib>Kollmannsberger, Philip</creatorcontrib><creatorcontrib>Solimando, Antonio G</creatorcontrib><creatorcontrib>Meierjohann, Svenja</creatorcontrib><creatorcontrib>Kübler, Hubert</creatorcontrib><creatorcontrib>Bargou, Ralf</creatorcontrib><creatorcontrib>Schilling, Bastian</creatorcontrib><creatorcontrib>Serfling, Sebastian E</creatorcontrib><creatorcontrib>Buck, Andreas</creatorcontrib><creatorcontrib>Werner, Rudolf A</creatorcontrib><creatorcontrib>Lapa, Constantin</creatorcontrib><creatorcontrib>Krebs, Markus</creatorcontrib><title>Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures</title><title>Cancers</title><addtitle>Cancers (Basel)</addtitle><description>(1)
C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2)
Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing
= 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in
= 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3)
We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4)
CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.</description><subject>Angiogenesis</subject><subject>Cancer</subject><subject>CD8 antigen</subject><subject>Chemokine receptors</subject><subject>CXCR4 protein</subject><subject>Datasets</subject><subject>Endothelial cells</subject><subject>Fibroblast activation protein</subject><subject>Fibroblasts</subject><subject>Gene set enrichment analysis</subject><subject>Genes</subject><subject>Genomes</subject><subject>Infiltration</subject><subject>Invasiveness</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Malignancy</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Microenvironments</subject><subject>MicroRNAs</subject><subject>miRNA</subject><subject>mRNA</subject><subject>Positron emission tomography</subject><subject>Prostate</subject><subject>Solid tumors</subject><subject>Tumor-infiltrating lymphocytes</subject><subject>Tumors</subject><issn>2072-6694</issn><issn>2072-6694</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkU1v1DAQhi0EolXpmRuyxIVLqD-S2LkgrSIKSFt11S6Cm-XYk12XxG5tZxH_gR9Nln6o7VxmpHnm1bx6EXpLyUfOG3JitDcQE60II7xhL9AhI4IVdd2ULx_NB-g4pSsyF-dU1OI1OuB1LRgv6SH6u4pgncnOb_CZMzGA37kY_Ag-Y-dx-7O9KAusvcWni1WxCslltwN8GQZn8XoaQ0zFAq-0L9r_7-AzbbbOA16Cjn4v-yPEX_0QfuM-RLzeQtQ-pOwMXuu4gYwvc5xMniKkN-hVr4cEx3f9CH0__bxuvxbL8y_f2sWyMCWjuZB9JznvtK0JNXtP1hBtSNcJImVNDZSUmYqYSghumq5qJOutqED31kIpe36EPt3qXk_dCNbMXqMe1HV0o45_VNBOPd14t1WbsFONrGpJ5Czw4U4ghpsJUlajSwaGQXsIU1JM1JIxyWU1o--foVdhin62t6cEbUhF6Uyd3FJzAilF6B-eoUTtw1bPwp4v3j328MDfR8v_AWctqKw</recordid><startdate>20230106</startdate><enddate>20230106</enddate><creator>Marquardt, André</creator><creator>Hartrampf, Philipp</creator><creator>Kollmannsberger, Philip</creator><creator>Solimando, Antonio G</creator><creator>Meierjohann, Svenja</creator><creator>Kübler, Hubert</creator><creator>Bargou, Ralf</creator><creator>Schilling, Bastian</creator><creator>Serfling, Sebastian E</creator><creator>Buck, Andreas</creator><creator>Werner, Rudolf A</creator><creator>Lapa, Constantin</creator><creator>Krebs, Markus</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T5</scope><scope>7TO</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2293-9698</orcidid><orcidid>https://orcid.org/0000-0001-5622-9849</orcidid><orcidid>https://orcid.org/0000-0002-1154-0964</orcidid><orcidid>https://orcid.org/0000-0001-7536-2207</orcidid><orcidid>https://orcid.org/0000-0002-2425-7033</orcidid><orcidid>https://orcid.org/0000-0001-8859-4103</orcidid></search><sort><creationdate>20230106</creationdate><title>Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures</title><author>Marquardt, André ; Hartrampf, Philipp ; Kollmannsberger, Philip ; Solimando, Antonio G ; Meierjohann, Svenja ; Kübler, Hubert ; Bargou, Ralf ; Schilling, Bastian ; Serfling, Sebastian E ; Buck, Andreas ; Werner, Rudolf A ; Lapa, Constantin ; Krebs, Markus</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-8fb833bad601c3317dc0ac0bb708861ce412c50c5773c9b5982fd75eafdde48f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Angiogenesis</topic><topic>Cancer</topic><topic>CD8 antigen</topic><topic>Chemokine receptors</topic><topic>CXCR4 protein</topic><topic>Datasets</topic><topic>Endothelial cells</topic><topic>Fibroblast activation protein</topic><topic>Fibroblasts</topic><topic>Gene set enrichment analysis</topic><topic>Genes</topic><topic>Genomes</topic><topic>Infiltration</topic><topic>Invasiveness</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Malignancy</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Microenvironments</topic><topic>MicroRNAs</topic><topic>miRNA</topic><topic>mRNA</topic><topic>Positron emission tomography</topic><topic>Prostate</topic><topic>Solid tumors</topic><topic>Tumor-infiltrating lymphocytes</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Marquardt, André</creatorcontrib><creatorcontrib>Hartrampf, Philipp</creatorcontrib><creatorcontrib>Kollmannsberger, Philip</creatorcontrib><creatorcontrib>Solimando, Antonio G</creatorcontrib><creatorcontrib>Meierjohann, Svenja</creatorcontrib><creatorcontrib>Kübler, Hubert</creatorcontrib><creatorcontrib>Bargou, Ralf</creatorcontrib><creatorcontrib>Schilling, Bastian</creatorcontrib><creatorcontrib>Serfling, Sebastian E</creatorcontrib><creatorcontrib>Buck, Andreas</creatorcontrib><creatorcontrib>Werner, Rudolf A</creatorcontrib><creatorcontrib>Lapa, Constantin</creatorcontrib><creatorcontrib>Krebs, Markus</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Immunology Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cancers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Marquardt, André</au><au>Hartrampf, Philipp</au><au>Kollmannsberger, Philip</au><au>Solimando, Antonio G</au><au>Meierjohann, Svenja</au><au>Kübler, Hubert</au><au>Bargou, Ralf</au><au>Schilling, Bastian</au><au>Serfling, Sebastian E</au><au>Buck, Andreas</au><au>Werner, Rudolf A</au><au>Lapa, Constantin</au><au>Krebs, Markus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures</atitle><jtitle>Cancers</jtitle><addtitle>Cancers (Basel)</addtitle><date>2023-01-06</date><risdate>2023</risdate><volume>15</volume><issue>2</issue><spage>392</spage><pages>392-</pages><issn>2072-6694</issn><eissn>2072-6694</eissn><abstract>(1)
C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2)
Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing
= 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in
= 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3)
We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4)
CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>36672341</pmid><doi>10.3390/cancers15020392</doi><orcidid>https://orcid.org/0000-0002-2293-9698</orcidid><orcidid>https://orcid.org/0000-0001-5622-9849</orcidid><orcidid>https://orcid.org/0000-0002-1154-0964</orcidid><orcidid>https://orcid.org/0000-0001-7536-2207</orcidid><orcidid>https://orcid.org/0000-0002-2425-7033</orcidid><orcidid>https://orcid.org/0000-0001-8859-4103</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Cancers, 2023-01, Vol.15 (2), p.392 |
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| source | PubMed Central Open Access; MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Angiogenesis Cancer CD8 antigen Chemokine receptors CXCR4 protein Datasets Endothelial cells Fibroblast activation protein Fibroblasts Gene set enrichment analysis Genes Genomes Infiltration Invasiveness Learning algorithms Machine learning Malignancy Metastases Metastasis Microenvironments MicroRNAs miRNA mRNA Positron emission tomography Prostate Solid tumors Tumor-infiltrating lymphocytes Tumors |
| title | Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures |
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