January 11, 2022
Looking Beyond PD-L1
One of the most successful biomarker-based cancer immunotherapies is Merck’s pembrolizumab, sold under the brand name Keytruda, an anti-PD-1 checkpoint inhibitor. In the drug’s latest phase 3 trial, non-small-cell lung carcinoma patients positive for the PD-L1 biomarker, which was used to predict pembrolizumab success, had a five-year survival rate twice as high as patients treated with chemotherapy.
However, a new study by Merck’s research team leveraged RNA-seq to try to better characterize the genetic signatures that determine pembrolizumab response or resistance beyond the individual biomarker of PD-L1.
RNA-Seq of Pembrolizumab Patients
Using data from 1,188 patients taking pembrolizumab as their only cancer treatment across seven tumor types, the research team identified 11 biologically relevant RNA-seq-derived gene expression signatures and their associations with pembrolizumab treatment outcomes.
One signature was the interferon gamma-mediated, 18-gene T-cell-inflamed gene expression profile, or T cellinfGEP. The classical biomarker for pembrolizumab, PD-L1, is one of the components of this T cell expression profile but the two biomarkers do not have identical predictive power with a moderate correlation around 0.60.
New Biomarkers for Pembrolizumab Response
In addition to the partially known interferon gamma-mediated T cell gene profile as a whole, Merck’s research team wanted to identify gene expression signatures that correlate to pembrolizumab response or resistance that are unrelated to interferon-gamma signaling.
Their results indicate that the T cellinfGEP signature of T cell genes regulated by interferon-gamma is positively associated with pembrolizumab response, as may have been expected due to PD-L1’s inclusion in that gene expression profile group.
However, their new findings indicate that gene expression signatures for angiogenesis (blood vessel growth), monocytic myeloid-derived suppressor cells (mMDSCs), and stroma/epithelial-to-mesenchymal transition (EMT)/TGF-Beta (promoting cancer invasiveness and migration) were negatively associated with response to pembrolizumab treatment.
These signatures are consensus signatures, meaning that like T cellinfGEP, they are comprised of RNA-seq data for multiple relevant genes to the biological feature or process of interest. The multi-part nature of these gene signatures makes them more robust than a single biomarker because they incorporate multiple genes and aspects of tumor biology.
Leveraging RNA-Seq for Future Applications
This is just one example of the vast number of potential applications of RNA-seq studies and the accompanying bioinformatics analysis. For researchers who do not have in-house bioinformaticians, outsourcing these tasks to a bioinformatics service provider like Bridge Informatics will streamline the research process and improve results.
Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].