Rapid molecular profiling utilising minimal quantities of endobronchial ultrasound-guided aspirates for the detection of Epidermal Growth Factor Receptor, KRAS, ALK, ROS1, RET, NTRK and MET gene alterations from patients with non-small-cell lung carcinomas on the Biocartis Idylla™ platform.

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 (<1% of total specimen received) was utilised for analysis. Biocartis Idylla™ cartridges for epidermal growth factor receptor (EGFR) mutations, KRAS mutations and a GeneFusion cartridge (ALK, ROS1, RET, NTRK1/2/3 rearrangements & MET 14 exon skipping) were analysed for each case to provide molecular data on the main clinically relevant targets as per UK guidelines. RESULTS: A total of 62 cases were included; all of which had successful DNA analysis (EGFR and KRAS cartridges). RNA analysis (GeneFusion cartridge) was successful for 42 of 51 (82%) with initial approach, with 11 of 11 (100%) achieving a successful result with modified protocol. In all, 23 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.

Accelerated instability testing reveals quantitative mass spectrometry overcomes specimen storage limitations associated with PD-L1 immunohistochemistry.

Immunohistochemistry (IHC) using formalin-fixed, paraffin embedded (FFPE) tissue is limited by epitope masking, posttranslational modification and immunoreactivity loss that occurs in stored tissue by poorly characterized mechanisms. Conformational epitopes recognized by many programmed-death-ligand-1 (PD-L1) IHC assays are particularly susceptible to degradation and provide an ideal model for understanding signal loss in stored FFPE tissue. Here we assessed 1206 tissue sections to evaluate environmental factors impacting immunoreactivity loss. PD-L1 IHC using four antibodies (22C3, 28-8, E1L3N, and SP142), raised against intracellular and extracellular epitopes, was assessed in stored FFPE tissue alongside quantitative mass spectrometry (MS). Global proteome analyses were used to assess proteome-wide oxidation across an inventory of 3041 protein groups (24,737 distinct peptides). PD-L1 quantitation correlated well with IHC expression on unaged sections (R2 = 0.744; P < 0.001), with MS demonstrating no loss of PD-L1 protein, even in sections with significant signal loss by IHC impacting diagnostic category. Clones 22C3 and 28-8 were most susceptible to signal loss, with E1L3N demonstrating the most robust signal (56%, 58%, and 33% reduction respectively; p < 0.05). Increased humidity and temperature resulted in significant acceleration of immunoreactivity loss, which was mitigated by storage with desiccant. MS demonstrated only modest oxidation of 274 methionine-containing peptides and aligned with IHC results suggesting peptide oxidation is not a major factor. These data imply immunoreactivity loss driven by humidity and temperature results in structural distortion of epitopes rendering them unsuitable for antibody binding following epitope retrieval. Limitations of IHC biomarker analysis from stored tissue sections may be mitigated by cost-effective use of desiccant when appropriate. In some scenarios, complementary MS is a preferred approach for retrospective analyses of archival FFPE tissue collections.

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.

Chondromyxoid Fibroma of the Rib: A Rare Benign Tumor With Potential for Local Recurrence.

Chondromyxoid fibroma (CMF) is a benign cartilaginous tumor that typically occurs in the long bones of young adult males, with the clinical presentation varying from asymptomatic to localized pain, swelling, and movement restriction. We report an unusual presentation of CMF involving a rib, along with a literature review of the management of CMF. Although benign, local recurrence is not uncommon, and malignant transformation has been reported on rare occasions. En bloc surgical excision, with adequate tumor-free resection margins, of radiologically suspected chondromyxoid fibroma is crucial for the treatment and confirmation of diagnosis. A high index of suspicion, adequate treatment, and follow-up are critical for the successful management of these uncommon benign chondroid tumors.

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR = 0.539, p = 0.004 and second system: HR = 0.525, p = 0.003) compared to TC scoring by pathologists (HR = 0.574, p = 0.01).

Programmed death ligand 1 expression in EBUS aspirates of non-small cell lung cancer: Is interpretation affected by type of fixation?

BACKGROUND: Much of the reluctance about using cytology specimens rather than histology specimens to assess programmed death ligand 1 (PD-L1) expression for guiding the use of immune modulating drugs in the management of non-small cell lung cancer (NSCLC) is based on the belief that the alcohol-based fixatives favored by cytopathologists might reduce the antigenicity of PD-L1 and lead to artifactually low expression levels and false-negative reporting. Therefore, this study was performed to determine whether there is any difference in PD-L1 expression between endobronchial ultrasound (EBUS)-guided aspirates of NSCLC fixed in alcohol-based fixatives and those fixed in neutral buffered formalin (NBF), the standard laboratory fixative for histology specimens. METHODS: The expression of PD-L1 was compared in 50 paired EBUS aspirates of NSCLC taken from the same lymph node during the same procedure. One aspirate of each pair was fixed in an alcohol-based fixative, and the other was fixed in NBF. RESULTS: In none of the 50 pairs was there any significant difference, qualitative or quantitative, in the strength, pattern, or extent of PD-L1 expression. In the great majority, the expression was identical, regardless of fixation. CONCLUSIONS: There is no evidence from this study showing that the use of alcohol-based fixatives has any effect on the expression of PD-L1 or its interpretation. Notwithstanding the general challenges in accurately assessing such expression in cytology specimens, pathologists should feel able to interpret them with confidence, and clinicians should feel able to rely on the results.

Analysis of Immune Checkpoint Drug Targets and Tumor Proteotypes in Non-Small Cell Lung Cancer.

New therapeutics targeting immune checkpoint proteins have significantly advanced treatment of non-small cell lung cancer (NSCLC), but protein level quantitation of drug targets presents a critical problem. We used multiplexed, targeted mass spectrometry (MS) to quantify immunotherapy target proteins PD-1, PD-L1, PD-L2, IDO1, LAG3, TIM3, ICOSLG, VISTA, GITR, and CD40 in formalin-fixed, paraffin-embedded (FFPE) NSCLC specimens. Immunohistochemistry (IHC) and MS measurements for PD-L1 were weakly correlated, but IHC did not distinguish protein abundance differences detected by MS. PD-L2 abundance exceeded PD-L1 in over half the specimens and the drug target proteins all displayed different abundance patterns. mRNA correlated with protein abundance only for PD-1, PD-L1, and IDO1 and tumor mutation burden did not predict abundance of any protein targets. Global proteome analyses identified distinct proteotypes associated with high PD-L1-expressing and high IDO1-expressing NSCLC. MS quantification of multiple drug targets and tissue proteotypes can improve clinical evaluation of immunotherapies for NSCLC.

Heterogeneity of PD-L1 expression in non-small cell lung cancer: Implications for specimen sampling in predicting treatment response.

OBJECTIVES: PD-L1 expression on tumour cells can guide the use of anti-PD-1/PD-L1 immune modulators to treat patients with non-small cell lung cancer (NSCLC). Heterogeneity of PD-L1 expression both within and between tumour sites is a well-documented phenomenon that compromises its predictive power. Our aim was to better characterise the pattern and extent of PD-L1 heterogeneity with a view to optimising tumour sampling and improve its accuracy as a biomarker. MATERIALS AND METHODS: Expression of PD-L1 was assessed by immunochemistry using the SP263 clone in 107 resected primary NSCLCs and their nodal metastases. Intra-tumoural heterogeneity, defined as 'small-scale' (mm²), 'medium-scale' (cm²) and 'large-scale' (between tumour blocks), was assessed by digital imaging using a novel 'squares method'. Inter-tumoural heterogeneity between the primary tumours and their nodal metastases and between N1 and N2 nodal stages was also assessed. RESULTS: The majority of tumours demonstrated intra-tumoural heterogeneity (small-scale 78%, medium-scale 50%, large-scale 46%). Inter-tumoural heterogeneity between the primary and nodal metastases was present in 53% of cases and, in 17%, between N1 and N2 disease. These differences were occasionally sufficient to lead to discrepancy across the ≥1%, ≥25% and ≥50% cut-offs used to guide therapy. CONCLUSION: Heterogeneity of PD-L1 expression is common, variable in scale and extent, and carries significant implications for its accuracy as a predictive biomarker. Extensive sampling reduces, but cannot eliminate, this inaccuracy.