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Four immunohistochemistry (IHC) diagnostic assays have been approved for tumour PD-L1 protein assessment in the clinic. However, mRNA detection by in situ hybridisation (ISH) could be utilised as an alternative to protein detection. Detecting spatial changes in gene expression provides vital prognostic and diagnostic information, particularly in immune oncology where the phenotype, cellular infiltration and immune activity status may be associated with patient survival. Translation of mRNA expression to a clinically relevant cut off or threshold is challenging due to variability between assays and the detection of different analytes. These studies aim to confirm the suitability of formalin fixed paraffin embedded (FFPE) tissue sections for use with RNA ISH. A comparison of mRNA expression and protein expression may inform the suitability of mRNA as a patient selection biomarker in a similar manner to IHC and provide evidence of a suitable scoring algorithm. Ninety patient samples, thirty for each indication of non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC) and urothelial carcinoma (UC), previously assessed using the VENTANA PD-L1 (SP263) Assay were chosen to represent a wide dynamic range of percentage tumour cell staining (TCIHC). Expression of mRNA was assessed by ISH using the RNAScope 2.5 assay and probe CD274/PD-L1 (Advanced Cell Diagnostics) including kit provided positive and negative control probes. Brightfield whole slide images of tissues were captured. The percentage of tumour cells with PD-L1 mRNA expression (%TCmRNA) and mean punctate dots/tumour cell were determined using image analysis. Differences in RNA expression between the IHC derived TCIHC≥25% and <25% groups were assessed using t-tests. For each indication, a receiver-operating characteristic (ROC) analysis identified thresholds for patient classification using %TCmRNA and dots/tumour cell, with reference to TCIHC≥25%. Eighty-six samples were successfully tested; 3 failed due to insufficient control probe staining, 1 due to lack of tumour. Percent TCmRNA staining using RNAScope demonstrated statistical significance (at α = 0.05) in the PD-L1 high (TCIHC ≥25%) vs the PD-L1 low (TCIHC <25%) groups for NSCLC, HNSCC, and UC. The number of punctate dots/tumour cell was significantly higher in the PD-L1 high vs the PD-L1 low groups for NSCLC and HNSCC but not UC. For %TCmRNA; ROC analysis identified thresholds of: NSCLC 18.0%, HNSCC 31.8%, UC 25.8%. For dots/tumour cell, thresholds were: NSCLC 0.26, HNSCC 0.53, UC 0.45. Routine tissue fixation and processing is suitable for RNA detection using RNAScope. PD-L1 mRNA extent and level is associated with PD-L1 status determined by IHC. Threshold optimisation for %TCmRNA and mean dots/tumour cell results in high specificity to IHC PD-L1 classification, but only moderate sensitivity. [ABSTRACT FROM AUTHOR]

The majority of Myeloproliferative Neoplasms (MPNs) are characterised by mutations in genes encoding molecules or receptors involved in cell signalling, the most common being the JAK2 V617F mutation. This mutation leads to ligand-independent activation of downstream signalling pathways by constitutive phosphorylation. The signalling pathways affected include the Janus kinase-signal transducers and activators of transcription (JAK-STAT) and phosphotidylinositide-3 kinase (PI3K) pathways, which regulate cell survival and apoptosis respectively. Monoclonal antibodies to phospho-STAT5 and phospho-Akt were generated and assessed by immunocytochemistry on bone marrow biopsies of MPN patients with JAK2 V617F, JAK2 exon 12, MPL exon 10 and KIT D816V mutations. JAK2 V617F mutation was associated with significantly increased levels of phosphorylated STAT5 and Akt in haemopoietic cells, most marked in megakaryocytes. In contrast, JAK2 exon 12 and MPL exon 10 mutations did not affect the level of phosphorylation. In systemic mastocytosis with KIT D618V mutation there was significantly increased expression of phosphorylated STAT5 and Akt in neoplastic mast cells although there was no change in the expression in other haemopoietic cells. JAK2 V617F is associated with upregulated phosphorylation of STAT5 and Akt in megakaryocytes, and to a lesser extent in other haemopoietic cells. Immunocytochemistry of bone marrow trephines for these phospho-proteins can be used as a supplementary diagnostic test with a high negative predictive value. [ABSTRACT FROM AUTHOR]

Background: Antibodies targeting the programmed cell death-1 (PD-1)/PD-ligand 1 (PD-1/PD-L1) checkpoint have shown promising clinical activity in patients with advanced urothelial carcinoma (UC). Expression of PD-L1 in UC tumors has been investigated using different antibody clones, staining protocols, and scoring algorithms. The aim was to establish the extent of concordance among PD-L1 immunohistochemistry (IHC) assays. Methods: Tumor biopsy samples (N = 335) were assessed using four commercially available PD-L1 assays: VENTANA SP263, VENTANA SP142, PD-L1 IHC 28–8 pharmDx, and PD-L1 IHC 22C3 pharmDx. PD-L1 analytical staining and classification concordance, including agreement between clinically relevant scoring algorithms, were investigated using overall/positive/negative percentage agreement (OPA/PPA/NPA). Results: Good analytical correlation was observed among the VENTANA SP263, PD-L1 IHC 22C3 pharmDx, and PD-L1 IHC 28–8 pharmDx assays for tumor cell (TC) and immune cell (IC) PD-L1 staining with Spearman rank coefficients of 0.92–0.93 for TCs and 0.88–0.91 for ICs. However, concordance (preset criterion: ≥85%) between patient PD-L1 status when applying the TC or ICICArea ≥ 25% (VENTANA SP263) cutoff was only achieved for PD-L1 IHC 22C3 pharmDx versus VENTANA SP263 (OPA 92.2%, PPA 86.4%, NPA 95.4%). Differences were observed between patient populations with UC tumors classified as PD-L1 high versus PD-L1 low/negative using combined positive score (CPS) ≥1, CPS ≥10, IC ≥5%, and TC/IC ≥25%. Conclusions: The VENTANA SP263 and PD-L1 IHC 22C3 pharmDx assays are analytically similar in UC. When the different PD-L1 assays were combined with their specified clinical scoring algorithms, differences were seen in patient classification driven by substantial differences in scoring approaches. [ABSTRACT FROM AUTHOR]

Background: Several anti-programmed cell death-1 (PD-1) and anti-programmed cell death ligand-1 (PD-L1) therapies have shown encouraging safety and clinical activity in a variety of tumor types. A potential role for PD-L1 testing in identifying patients that are more likely to respond to treatment is emerging. PD-L1 expression in clinical practice is determined by testing one tumor section per patient. Therefore, it is critical to understand the impact of tissue sampling variability on patients' PD-L1 classification. Methods: Resected non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC) and urothelial carcinoma (UC) tissue samples (five samples per tumor type) were obtained from commercial sources and two tumor blocks were taken from each. Three sections from each block (~ 100 μm apart) were stained using the VENTANA PD-L1 (SP263) assay, and scored based on the percentage of PD-L1-staining tumor cells (TCs) or tumor-infiltrating immune cells (ICs) present. Each section was categorized as PD-L1 high or low/negative using a variety of cut-off values, and intra-block and intra-case (between blocks of the same tumor) concordance (overall percentage agreement [OPA]) were evaluated. An additional 200 commercial NSCLC samples were also analyzed, and intra-block concordance determined by scoring two sections per sample (≥70 μm apart). Results: Concordance in TC PD-L1 classification was high at all applied cut-offs. Intra-block and intra-case OPA for the 15 NSCLC, HNSCC or UC samples were 100% and 80–100%, respectively, across all cut-offs; intra-block OPA for the 200 NSCLC samples was 91.0–98.5% across all cut-offs. IC PD-L1 classification was less consistent; intra-block and intra-case OPA for the 15 NSCLC, HNSCC or UC samples ranged between 70 and 100% and between 60 and 100%, respectively, with similar observations in the intra-block analysis of the 200 NSCLC samples. Conclusions: These results show the reproducibility of TC PD-L1 classification across the depth of the tumor using the VENTANA PD-L1 (SP263) assay. Practically, this means that treatment decisions based on TC PD-L1 classification can be made confidently, following analysis of one tumor section. Although more variable than TC staining, consistent IC PD-L1 classification was also observed within and between blocks and across cut-offs. [ABSTRACT FROM AUTHOR]

Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour resection samples, automated scoring showed a concordance of 83% with a pathologist. The twelve discordant cases were then independently reviewed, leading to a modification of diagnosis from initial pathologist assessment for eight cases. Diagnostic discordance was found to be largely caused by perceptual differences in assessing HER2 expression due to high HER2 staining heterogeneity. This study provides evidence that deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying cases at high risk of misdiagnosis. [ABSTRACT FROM AUTHOR]