<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>5(4)</volume><submitter>Deng Y</submitter><funding>National Key Research and Development Program of China Stem Cell and Translational Research</funding><funding>National Natural Science Foundation of China</funding><pubmed_abstract>Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8&lt;sup>+&lt;/sup> T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8&lt;sup>+&lt;/sup> T c</pubmed_abstract><journal>Cell reports. Medicine</journal><pagination>101489</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11031428</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages.</pubmed_title><pmcid>PMC11031428</pmcid><pubmed_authors>Lai H</pubmed_authors><pubmed_authors>Zhang G</pubmed_authors><pubmed_authors>Deng S</pubmed_authors><pubmed_authors>Ma L</pubmed_authors><pubmed_authors>Hu Y</pubmed_authors><pubmed_authors>Liu L</pubmed_authors><pubmed_authors>Zhang J</pubmed_authors><pubmed_authors>Zhu Y</pubmed_authors><pubmed_authors>Gan F</pubmed_authors><pubmed_authors>Wang M</pubmed_authors><pubmed_authors>Liu Y</pubmed_authors><pubmed_authors>Liu Z</pubmed_authors><pubmed_authors>Xia L</pubmed_authors><pubmed_authors>Liao H</pubmed_authors><pubmed_authors>Cheng Q</pubmed_authors><pubmed_authors>Wei S</pubmed_authors><pubmed_authors>Chen Y</pubmed_authors><pubmed_authors>Shen S</pubmed_authors><pubmed_authors>Guo C</pubmed_authors><pubmed_authors>Deng Y</pubmed_authors><pubmed_authors>Yuan Y</pubmed_authors><pubmed_authors>Kang R</pubmed_authors><pubmed_authors>Chen L</pubmed_authors><pubmed_authors>Zha Z</pubmed_authors><pubmed_authors>Li K</pubmed_authors><pubmed_authors>Bai Y</pubmed_authors><pubmed_authors>Yuan G</pubmed_authors><pubmed_authors>Pu Q</pubmed_authors><pubmed_authors>Yang Z</pubmed_authors><pubmed_authors>Yang Y</pubmed_authors><pubmed_authors>Luo L</pubmed_authors><pubmed_authors>Mei J</pubmed_authors><pubmed_authors>Lin F</pubmed_authors><pubmed_authors>Liu C</pubmed_authors></additional><is_claimable>false</is_claimable><name>Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages.</name><description>Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8&lt;sup>+&lt;/sup> T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8&lt;sup>+&lt;/sup> T c</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Apr</publication><modification>2026-05-29T12:37:35.47Z</modification><creation>2026-04-08T04:46:16.21Z</creation></dates><accession>S-EPMC11031428</accession><cross_references><pubmed>38554705</pubmed><doi>10.1016/j.xcrm.2024.101489</doi></cross_references></HashMap>