Ahmad Charifa, Alfonso Lam, Hong Zhang, Andrew Ip, Andrew Pecora, Stanley Waintraub, Deena Graham, Donna McNamara, Martin Gutierrez, Andrew Jennis, Ipsa Sharma, Jeffrey Estella, Wanlong Ma, Andre Goy, Maher Albitar
{"title":"Predicting PD-L1 Status in Solid Tumors Using Transcriptomic Data and Artificial Intelligence Algorithms","authors":"Ahmad Charifa, Alfonso Lam, Hong Zhang, Andrew Ip, Andrew Pecora, Stanley Waintraub, Deena Graham, Donna McNamara, Martin Gutierrez, Andrew Jennis, Ipsa Sharma, Jeffrey Estella, Wanlong Ma, Andre Goy, Maher Albitar","doi":"10.1097/cji.0000000000000489","DOIUrl":null,"url":null,"abstract":"Programmed death ligand-1 (PD-L1) immunohistochemistry (IHC) is routinely used to predict the clinical response to immune checkpoint inhibitors (ICIs); however, multiple assays and antibodies have been used. This study aimed to evaluate the potential of targeted transcriptome and artificial intelligence (AI) to determine PD-L1 RNA expression levels and predict the ICI response compared with traditional IHC. RNA from 396 solid tumors samples was sequenced using next-generation sequencing (NGS) with a targeted 1408-gene panel. RNA expression and PD-L1 IHC were assessed across a broad range of PD-L1 expression levels. AI was used to predict the PD-L1 status. PD-L1 RNA levels assessed by NGS demonstrated robust linearity across high and low expression ranges, and those assessed using NGS and IHC (tumor proportion score and tumor-infiltrating immune cells) had a similar pattern. RNA sequencing provided in-depth information on the tumor microenvironment and immune response, including CD19, CD22, CD8A, CTLA4 , and PD-L2 expression status. Subanalyses showed a sustained correlation of mRNA expression with IHC (tumor proportion score and immune cells) across different solid tumor types. Machine learning showed high accuracy in predicting PD-L1 status, with the area under the curve varying between 0.83 and 0.91. Targeted transcriptome sequencing combined with AI is highly useful for predicting PD-L1 status. Measuring PD-L1 mRNA expression by NGS is comparable to measuring PD-L1 expression by IHC for predicting ICI response. RNA expression has the added advantages of being amenable to standardization and avoiding interpretation bias, along with an in-depth evaluation of the tumor microenvironment.","PeriodicalId":15996,"journal":{"name":"Journal of Immunotherapy","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Immunotherapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/cji.0000000000000489","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Programmed death ligand-1 (PD-L1) immunohistochemistry (IHC) is routinely used to predict the clinical response to immune checkpoint inhibitors (ICIs); however, multiple assays and antibodies have been used. This study aimed to evaluate the potential of targeted transcriptome and artificial intelligence (AI) to determine PD-L1 RNA expression levels and predict the ICI response compared with traditional IHC. RNA from 396 solid tumors samples was sequenced using next-generation sequencing (NGS) with a targeted 1408-gene panel. RNA expression and PD-L1 IHC were assessed across a broad range of PD-L1 expression levels. AI was used to predict the PD-L1 status. PD-L1 RNA levels assessed by NGS demonstrated robust linearity across high and low expression ranges, and those assessed using NGS and IHC (tumor proportion score and tumor-infiltrating immune cells) had a similar pattern. RNA sequencing provided in-depth information on the tumor microenvironment and immune response, including CD19, CD22, CD8A, CTLA4 , and PD-L2 expression status. Subanalyses showed a sustained correlation of mRNA expression with IHC (tumor proportion score and immune cells) across different solid tumor types. Machine learning showed high accuracy in predicting PD-L1 status, with the area under the curve varying between 0.83 and 0.91. Targeted transcriptome sequencing combined with AI is highly useful for predicting PD-L1 status. Measuring PD-L1 mRNA expression by NGS is comparable to measuring PD-L1 expression by IHC for predicting ICI response. RNA expression has the added advantages of being amenable to standardization and avoiding interpretation bias, along with an in-depth evaluation of the tumor microenvironment.
期刊介绍:
Journal of Immunotherapy features rapid publication of articles on immunomodulators, lymphokines, antibodies, cells, and cell products in cancer biology and therapy. Laboratory and preclinical studies, as well as investigative clinical reports, are presented. The journal emphasizes basic mechanisms and methods for the rapid transfer of technology from the laboratory to the clinic. JIT contains full-length articles, review articles, and short communications.