Predicting PD-L1 Status in Solid Tumors Using Transcriptomic Data and Artificial Intelligence Algorithms

Author:

Charifa Ahmad1,Lam Alfonso1,Zhang Hong1,Ip Andrew2,Pecora Andrew2,Waintraub Stanley2,Graham Deena2,McNamara Donna2,Gutierrez Martin2,Jennis Andrew2,Sharma Ipsa1,Estella Jeffrey1,Ma Wanlong1,Goy Andre2,Albitar Maher1

Affiliation:

1. Genomic Testing Cooperative, LCA, Irvine, CA

2. John Theurer Cancer Center at Hackensack University Medical Center, Hackensack, New Jersey, NJ

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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cancer Research,Pharmacology,Immunology,Immunology and Allergy

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