<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Coleman S</submitter><funding>NIDCR NIH HHS</funding><funding>NCI NIH HHS</funding><funding>National Institutes of Health</funding><pagination>77-89</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9771882</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>62(1)</volume><pubmed_abstract>Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published p</pubmed_abstract><journal>Molecular carcinogenesis</journal><pubmed_title>Systematic evaluation of the predictive gene expression signatures of immune checkpoint inhibitors in metastatic melanoma.</pubmed_title><pmcid>PMC9771882</pmcid><funding_grant_id>P30 CA076292</funding_grant_id><funding_grant_id>R01 DE030508</funding_grant_id><pubmed_authors>Tan AC</pubmed_authors><pubmed_authors>Xie M</pubmed_authors><pubmed_authors>Coleman S</pubmed_authors><pubmed_authors>Tarhini AA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Systematic evaluation of the predictive gene expression signatures of immune checkpoint inhibitors in metastatic melanoma.</name><description>Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published p</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Jan</publication><modification>2025-04-04T08:43:59.185Z</modification><creation>2025-04-04T08:43:59.185Z</creation></dates><accession>S-EPMC9771882</accession><cross_references><pubmed>35781709</pubmed><doi>10.1002/mc.23442</doi></cross_references></HashMap>