<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Gaglia G</submitter><funding>NHLBI NIH HHS</funding><funding>NCI NIH HHS</funding><funding>NIGMS NIH HHS</funding><pagination>871-886.e10</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10193529</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>41(5)</volume><pubmed_abstract>Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1&lt;sup>+&lt;/sup> PD-1&lt;sup>+&lt;/sup> progenit</pubmed_abstract><journal>Cancer cell</journal><pubmed_title>Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma.</pubmed_title><pmcid>PMC10193529</pmcid><funding_grant_id>T32 HL007627</funding_grant_id><funding_grant_id>P30 CA006516</funding_grant_id><funding_grant_id>R01 CA194005</funding_grant_id><funding_grant_id>R41 CA224503</funding_grant_id><funding_grant_id>P30 CA014051</funding_grant_id><funding_grant_id>U54 CA225088</funding_grant_id><funding_grant_id>K99 CA256497</funding_grant_id><funding_grant_id>R50 CA274277</funding_grant_id><funding_grant_id>U2C CA233262</funding_grant_id><funding_grant_id>T32 GM007748</funding_grant_id><pubmed_authors>Sorger PK</pubmed_authors><pubmed_authors>Coy S</pubmed_authors><pubmed_authors>Crossland GE</pubmed_authors><pubmed_authors>Rammos D</pubmed_authors><pubmed_authors>Gaglia G</pubmed_authors><pubmed_authors>Warchol S</pubmed_authors><pubmed_authors>Nirmal AJ</pubmed_authors><pubmed_authors>Dai Y</pubmed_authors><pubmed_authors>Krueger R</pubmed_authors><pubmed_authors>Jaeger AM</pubmed_authors><pubmed_authors>Jacks T</pubmed_authors><pubmed_authors>Burger ML</pubmed_authors><pubmed_authors>Pfister H</pubmed_authors><pubmed_authors>Santagata S</pubmed_authors><pubmed_authors>Tavana SZ</pubmed_authors><pubmed_authors>Naranjo S</pubmed_authors><pubmed_authors>Ritch CC</pubmed_authors><pubmed_authors>Lin JR</pubmed_authors></additional><is_claimable>false</is_claimable><name>Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma.</name><description>Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1&lt;sup>+&lt;/sup> PD-1&lt;sup>+&lt;/sup> progenit</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 May</publication><modification>2025-04-06T19:18:45.296Z</modification><creation>2024-11-06T13:10:46.63Z</creation></dates><accession>S-EPMC10193529</accession><cross_references><pubmed>37059105</pubmed><doi>10.1016/j.ccell.2023.03.015</doi></cross_references></HashMap>