Improving practice in PD-L1 testing of non-small cell lung cancer in the UK: current problems and potential solutions

Exploring immune checkpoint inhibitors: Focus on PD-1/PD-L1 axis and beyond

Immunotherapy emerges as a promising approach, marked by recent substantial progress in elucidating how the host immune response impacts tumor development and its sensitivity to various treatments. Immune checkpoint inhibitors have revolutionized cancer therapy by unleashing the power of the immune system to recognize and eradicate tumor cells. Among these, inhibitors targeting the programmed cell death protein 1 (PD-1) and its ligand (PD-L1) have garnered significant attention due to their remarkable clinical efficacy across various malignancies. This review delves into the mechanisms of action, clinical applications, and emerging therapeutic strategies surrounding PD-1/PD-L1 blockade. We explore the intricate interactions between PD-1/PD-L1 and other immune checkpoints, shedding light on combinatorial approaches to enhance treatment outcomes and overcome resistance mechanisms. Furthermore, we discuss the expanding landscape of immune checkpoint inhibitors beyond PD-1/PD-L1, including novel targets such as CTLA-4, LAG-3, TIM-3, and TIGIT. Through a comprehensive analysis of preclinical and clinical studies, we highlight the promise and challenges of immune checkpoint blockade in cancer immunotherapy, paving the way for future advancements in the field.

InterobServer AgreeMent in Pd-l1 evaLuatIoN on cytoloGical samples-SAMPLING project: A multi-institutional, international study

INTRODUCTION

The aim of this project is to assess interobserver agreement for programmed death-ligand 1 (PD-L1) scoring on of non-small cell lung cancer (NSCLC) on cytological specimens in a large-scale multicenter study, by exploiting the cell block-derived tissue microarray (cbTMA) approach.

METHODS

A total of 65 cell blocks (CB) diagnosed as NSCLC were retrospectively collected and selected for TMA preparation. Hematoxylin-eosin and PD-L1 stained slides were digitized and uploaded on a free web sharing platform. Participants were asked to provide PD-L1 assessment by using the clinically relevant cutoff of tumor proportion score (TPS) (<1%; 1%-49%; >50%). Interobserver agreement was calculated using Fleiss's κ.

RESULTS

Of 65 CBs, 11 were deemed not suitable; therefore, an overall number of 54 cores were used for the preparation of four TMAs. A total of 1674 evaluations were provided by 31 cytopathologists from 21 different institutions in nine countries. The statistical analysis showed a moderate overall agreement (κ = 0.49). The highest agreement was achieved in the TPS >50% category (κ = 0.57); moderate agreement was observed in TPS <1% category (κ = 0.51) and the lowest κ value was obtained for TPS 1%-49% category (k = 0.32).

CONCLUSIONS

The overall moderate agreement observed showed that there is still room for improvement in inter-pathologist agreement for PD-L1 evaluation on cytological samples, highlighting the need for standardization in sample preparation, focused training in PD-L1 evaluation on cytological material, and the integration of machine learning tools to improve interobserver consistency.

Why PD-L1 expression varies between studies of lung cancer: results from a Bayesian meta-analysis

PD-L1 expression is an important biomarker for the management of non-small cell lung cancer (NSCLC) but has been highly heterogeneous across studies. We developed a statistical model to reconcile conflicting estimates of PD-L1 prevalence by accounting for between-study variation in test sensitivity, specimen age, and laboratory count. In doing so, we obtained refined estimates for PD-L1 expression prevalence and identified differences by histological subtype, mutational status, and stage. Across 92 studies published between 2015 and 2023, the detectability of PD-L1 declined with increasing specimen age while the consistency of detection rates was greater for studies incorporating data from a higher number of laboratories. Using the 22C3 antibody as a benchmark, we predicted that 58.3% (95% CrI 49.8-66.1%) and 27.0% (95% CrI 21.2-33.1%) of NSCLC will have PD-L1 tumour proportion scores at the ≥ 1% and ≥ 50% threshold. PD-L1 expression was lower in EGFR-mutated NSCLC and higher in NSCLC with ALK, KRAS, MET, ROS1, and RET alterations. PD-L1 expression was more common with later-stage disease. Overall, this work highlights the continuing challenge of consistency in PD-L1 testing. Although the underlying prevalence of PD-L1 expression varies in the lung cancer population based on tumour-related factors, controllable differences in testing parameters also account for variations in PD-L1 prevalence.

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms

AIMS

To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.

METHODS

The study included samples from 50 early-stage NSCLCs. PD-L1 immunohistochemistry (IHC) stained slides (clone SP263) were scored manually and with two different AI tools (PathAI and Navify Digital Pathology) by three pathologists. TILs were digitally assessed on H&E and CD8 IHC stained sections with two different algorithms (PathAI and Navify Digital Pathology, respectively). The agreement between observers and methods for each biomarker was analysed. For PD-L1, the turn-around time (TAT) for manual versus AI-assisted scoring was recorded.

RESULTS

Agreement was higher in tumours with low PD-L1 expression regardless of the approach. Both AI-powered tools identified a significantly higher number of cases equal or above 1% PD-L1 tumour proportion score as compared with manual scoring (p=0.00015), a finding with potential therapeutic implications. Regarding TAT, there were significant differences between manual scoring and AI use (p value <0.0001 for all comparisons). The total TILs density with the PathAI algorithm and the total density of CD8+ cells with the Navify Digital Pathology software were significantly correlated (τ=0.49 (95% CI 0.37, 0.61), p value<0.0001).

CONCLUSIONS

This preliminary study supports the use of AI algorithms for the scoring of PD-L1 and TILs in patients with NSCLC.

Genome-Wide Methylation Analysis in Two Wild-Type Non-Small Cell Lung Cancer Subgroups with Negative and High PD-L1 Expression

We conducted a pilot study to analyze the differential methylation status of 20 primary acinar adenocarcinomas of the lungs. These adenocarcinomas had to be wild type in mutation analysis and had either high (TPS > 50%; n = 10) or negative (TPS < 1%; n = 10) PD-L1 status to be integrated into our study. To examine the methylation of 866,895 specific sites, we utilized the Illumina Infinium EPIC bead chip array. Both hypermethylation and hypomethylation play significant roles in tumor development, progression, and metastasis. They also impact the formation of the tumor microenvironment, which plays a decisive role in tumor differentiation, epigenetics, dissemination, and immune evasion. The gained methylation patterns were correlated with PD-L1 expression. Our analysis has identified distinct methylation patterns in lung adenocarcinomas with high and negative PD-L1 expression. After analyzing the correlation between the methylation results of genes and promoters with their pathobiology, we found that tumors with high expression of PD-L1 tend to exhibit oncogenic effects through hypermethylation. On the other hand, tumors with negative PD-L1 expression show loss of their suppressor functions through hypomethylation. The suppressor functions of hypermethylated genes and promoters are ineffective compared to simultaneously activated dominant oncogenic mechanisms. The tumor microenvironment supports tumor growth in both groups.

Machine-learning-based image analysis algorithms improve interpathologist concordance when scoring PD-L1 expression in non-small-cell lung cancer

Programmed death ligand 1 (PD-L1) expression on tumour cells is the only predictive biomarker of response to immuno-modulatory therapy for patients with non-small-cell lung cancer (NSCLC). Accuracy of this biomarker is hampered by its challenging interpretation. Here we explore if the use of machine-learning derived image analysis tools can improve interpathologist concordance of assessing PD-L1 expression in NSCLC.Five pathologists who routinely score PD-L1 at a major regional referral hospital for thoracic surgery participated. 13 NSCLC small diagnostic biopsies were stained for PD-L1 (SP263 clone) and digitally scanned. Each pathologist independently scored each case with and without the Roche uPath PD-L1 (SP263) image analysis NSCLC algorithm with a wash-out interim period of 6 weeks.A consistent improvement in interpathologist concordance was seen when using the image analysis tool compared with scoring without: (Fleiss' kappa 0.886 vs 0.613 (p<0.0001) and intraclass coefficient correlation 0.954 vs 0.837 (p<0.001)). Five cases (38%) were classified into clinically relevant different categories (negative/weak/strong) by multiple pathologists when not using the image analysis algorithm, whereas only two cases (15%) were classified differently when using the image analysis algorithm.The use of the image analysis algorithm improved the concordance of assessing PD-L1 expression between pathologists. Critically, there was a marked improvement in the placement of cases into more consistent clinical groupings. This small study is evidence that the use of image analysis tools may improve consistency in assessing tumours for PD-L1 expression and may therefore result in more consistent prediction to targeted treatment options.