Alexander Haragan

PD‐L1 expression for the prediction of response to immune‐checkpoint blockade remains the most broadly utilised clinically validated biomarker in a range of tumour types. In this study, we aimed to assess, in a prospectively collected matched cohort, the impact of sampling technique and both formalin and alcohol fixation on PD‐L1 expression and heterogeneity in non‐small cell lung carcinoma (NSCLC). Patients undergoing surgical resection for NSCLC were consented. Surgical specimens were received directly from theatre and sampled fresh to produce two sets of core biopsies, two fine‐needle aspirates (FNAs) and two whole‐block tissue sections from each specimen. A matched biopsy, FNA, and whole‐block were placed into formalin or an alcohol‐based fixative (Cytolyt™) prior to PD‐L1 immunohistochemistry assessment. A total of 114 specimens from 57 patients were included. All whole‐block cases (100%), 92% of core biopsies, and 88% of FNAs were adequate for PD‐L1 expression analysis. Fixation had no significant impact on adequacy, but cytology specimens fixed in alcohol showed a significant reduction in PD‐L1 expression, with 25% of cases placed into different clinically relevant categories of PD‐L1 expression. PD‐L1 expression by immunochemistry is an exemplar of the challenges of utilising a heterogeneously expressed protein‐based predictive biomarker. Regardless of sampling technique, a good quality biopsy or FNA is likely to give a statistically representative PD‐L1 expression, although expression ranges close to clinically relevant cut‐offs of 1% and 50% remain a source of potential discordance.

Objectives: Comprehensive molecular analysis for patients with non‐small‐cell lung carcinoma (NSCLC) is essential for managing modern targeted therapies. This study sought to establish the feasibility of utilising real‐time PCR to perform rapid and comprehensive profiling on minimal amounts of endobronchial ultrasound‐guided (EBUS) aspirates as a fast, tissue‐sparing route of predictive profiling.Methods: A volume of 500 μL of EBUS aspirate and fixative from patients with NSCLC was decanted, and 80 μL (KRAS mutations (37%), 5 EGFR mutations (8%) and 1 ROS fusion (2%) were identified. Average time from specimen receipt to molecular read‐out was 5 h.Conclusion: Real‐time PCR utilising the Idylla™ platform is rapid, utilises minimal amounts of tissue and provides accurate results. We propose this is a useful ancillary method to utilise alongside next‐generation sequencing (NGS) in cases of urgent clinical requirement or EBUS aspirates with inadequate quantities of tissue for NGS.

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.