A digital assay for programmed death-ligand 1 (22C3) quantification combined with immune cell recognition algorithms
Will Paces1, Elliott Ergon1, Elizabeth Bueche1
1Flagship Biosciences, Inc., Broomfield, CO, USA.
Abstract:
PD-L1 (22C3) checkpoint inhibitor therapy represents a mainstay of modern cancer immunotherapy for non-small cell lung cancer (NSCLC). In vitro diagnostic (IVD) PD-L1 antibody staining is widely used to predict clinical intervention efficacy. However, pathologist interpretation of this assay is cumbersome and variable, resulting in poor positive predictive value concerning patient therapy response. To address this, we developed a digital assay (DA) termed Tissue Insight (TI) 22C3 NSCLC, for the quantification of PD-L1 in NSCLC tissues, including digital recognition of macrophages and lymphocytes. We completed clinical validation of this digital image analysis solution in 66 NSCLC patient samples, followed by concordance studies (comparison of PD-L1 manual and digital scores) in an additional 99 patient samples. We then combined this DA with three distinct immune cell recognition algorithms for detecting tissue macrophages, alveolar macrophages, and lymphocytes to aid in sample interpretation. Our PD-L1 (22C3) DA was successfully validated and had a scoring agreement (digital to manual) higher than the inter-pathologist scoring. Furthermore, the number of algorithm-identified immune cells showed significant correlation when compared with those identified by immunohistochemistry in serial sections stained by double immunofluorescence. Here, we demonstrated that TI 22C3 NSCLC DA yields comparable results to pathologist interpretation while eliminating the intra- and inter-pathologist variability associated with manual scoring while providing characterization of the immune microenvironment, which can aid in clinical treatment decisions.
Insights
A new digital assay (DA) for quantifying PD-L1 in non-small cell lung cancer (NSCLC) offers accurate, reproducible scoring. This Tissue Insight (TI) 22C3 NSCLC DA improves upon manual pathologist interpretation for cancer immunotherapy decisions.
Area of Science:
- Oncology
- Immunotherapy
- Digital Pathology
Background:
- Programmed death-ligand 1 (PD-L1) (22C3) checkpoint inhibitor therapy is crucial for non-small cell lung cancer (NSCLC) treatment.
- In vitro diagnostic (IVD) PD-L1 antibody staining predicts therapy efficacy but suffers from subjective pathologist interpretation, leading to variability and reduced predictive value.
- Manual scoring of PD-L1 assays introduces inconsistencies, impacting patient treatment decisions.
Purpose of the Study:
- To develop and validate a digital assay (DA) for objective quantification of PD-L1 (22C3) in NSCLC tissues.
- To assess the concordance of the digital assay with manual pathologist scoring.
- To integrate immune cell recognition algorithms for comprehensive analysis of the tumor microenvironment.
Main Methods:
- Development of a digital image analysis solution, Tissue Insight (TI) 22C3 NSCLC, for PD-L1 quantification.
- Clinical validation in 66 NSCLC patient samples.
- Concordance studies in 99 NSCLC patient samples comparing manual and digital PD-L1 scores.
- Integration of algorithms for recognizing macrophages and lymphocytes.
Main Results:
- The TI 22C3 NSCLC digital assay demonstrated successful validation.
- Digital assay scoring agreement with manual scoring exceeded inter-pathologist agreement.
- Algorithm-identified immune cell counts correlated significantly with immunohistochemistry results from serial sections.
- The digital assay provided comparable results to manual interpretation while reducing variability.
Conclusions:
- The TI 22C3 NSCLC digital assay offers a reproducible and objective method for PD-L1 quantification in NSCLC.
- This digital approach minimizes intra- and inter-pathologist scoring variability.
- The assay's ability to characterize the immune microenvironment aids in optimizing clinical treatment decisions for NSCLC patients.
