Immunohistochemistry should undergo robust validation equivalent to that of molecular diagnostics

An Optimized and Advanced Algorithm for the Quantification of Immunohistochemical Biomarkers in Keratinocytes

Advancements in pathology have given rise to software applications intended to minimize human error and improve efficacy of image analysis. Still, the subjectivity of image quantification performed manually and the limitations of the most ubiquitous tissue stain analysis software requiring parameters tuned by the observer, reveal the need for a highly accurate, automated nuclear quantification software specific to immunohistochemistry, with improved precision and efficiency compared with the methods currently in use. We present a method for the quantification of immunohistochemical biomarkers in keratinocyte nuclei proposed to overcome these limitations, contributing sensitive shape-focused segmentation, accurate nuclear detection, and automated device-independent color assessment, without observer-dependent analysis parameters.

Belgian Recommendations for Analytical Verification and Validation of Immunohistochemical Tests in Laboratories of Anatomic Pathology

Analytical verification and validation of immunohistochemical (IHC) tests and their equipment are common practices for today's anatomic pathology laboratories. Few references or guidelines are available on how this should be performed. The study of Sciensano (the Belgian national competent authority regarding licensing of medical laboratories) performed in 2016, demonstrated a significant interlaboratory variation in validation procedures of IHC tests among Belgian laboratories. These results suggest the unavailability of practical information on the approach to the verification and validation of these tests. The existing Belgian Practice Guideline for the implementation of a quality management system in anatomic pathology laboratories has been reviewed to meet this demand and, in addition, to prepare the laboratories for the EU-IVD revised regulations (IVDR). This paper describes Belgian recommendations for the verification and validation of IHC tests before implementation, for ongoing validation, and for revalidation. For each type of test (according to the IVDR classification and the origin) and its intended use (purpose), it addresses how to perform analytical verification/validation by recommending: (1) the number of cases in the validation set, (2) the performance characteristics to be evaluated, (3) the objective acceptance criteria, (4) the evaluation method for the obtained results, and (5) how and when to revalidate. A literature study and a risk analysis taking into account the majority of variables regarding verification/validation of methods have been performed, resulting in an expert consensus recommendation that is a compromise among achievability, affordability, and patient safety. This new consensus recommendation has been incorporated in the aforementioned ISO 15189:2012-based Practice Guideline.

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption

Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.

Detecting Human Epidermal Growth Factor Receptor 2 (HER2) Amplification: Proof of Concept of an Alternative Approach

BACKGROUND

There are multiple genes that are co-amplified along with human epidermal growth factor receptor 2 (HER2) in chromosome 17. GRB7 and PGAP3 are two such genes. We hypothesize that the protein products of these genes may serve as immunohistochemistry (IHC) markers for detecting HER2 amplification in breast cancer.

METHODS

Tissue sections from one hundred and thirty-five primary breast carcinoma cases were subjected to immunohistochemical staining for antibodies against HER2, GRB7, and PGAP3 and graded on a scale of 1 to 3. Both membranous staining and cytoplasmic staining were assessed for GRB7 and PGAP3. For equivocal HER2 IHC positivity, fluorescent in situ hybridization was performed to get the final HER2 status.

RESULTS

IHC staining for GRB7 and PGAP 3 was a moderate to strong predictor for HER2 status (area under the curve (AUC) of 0.768, 0.868,0.754, and 0.790 for GRB7 membranous staining, GRB7 cytoplasmic staining, PGAP3 membranous staining, and PGAP3 cytoplasmic staining respectively). A combination of GRB7 cytoplasmic and PGAP3 membranous staining resulted in an AUC of 0.905 (95% CI 0.855-0.954), while a combination of GRB7 and PGAP3 cytoplasmic staining resulted in an AUC of 0.902 (95% CI 0.851-0.953).

CONCLUSION

The point estimates for the AUC of GRB7 and combined GRB7 and PGAP3 in predicting the AUC suggest a strong predictive ability of these markers to predict HER2. With further refinement in technique, cytoplasmic staining and membranous IHC staining for GRB7 and PGAP3 have potential to serve as surrogate markers for HER2 status. The strategy of using protein products of co-amplified genes of HER2 is likely to be successful in technical validation.

Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology

Introduction

We sought to develop a novel method for a fully automated, robust quantification of protein biomarker expression within the epithelial component of high-grade serous ovarian tumors (HGSOC). Rather than defining thresholds for a given biomarker, the objective of this study in a small cohort of patients was to develop a method applicable to the many clinical situations in which immunomarkers need to be quantified. We aimed to quantify biomarker expression by correlating it with the heterogeneity of staining, using a non-subjective choice of scoring thresholds based on classical mathematical approaches. This could lead to a universal method for quantifying other immunohistochemical markers to guide pathologists in therapeutic decision-making.

Methods

We studied a cohort of 25 cases of HGSOC for which three biomarkers predictive of the response observed ex vivo to the BH3 mimetic molecule ABT-737 had been previously validated by a pathologist. We calibrated our algorithms using Stereology analyses performed by two experts to detect immunohistochemical staining and epithelial/stromal compartments. Immunostaining quantification within Stereology grids of hexagons was then performed for each histological slice. To define thresholds from the staining distribution histograms and to classify staining within each hexagon as low, medium, or high, we used the Gaussian Mixture Model (GMM).

Results

Stereology analysis of this calibration process produced a good correlation between the experts for both epithelium and immunostaining detection. There was also a good correlation between the experts and image processing. Image processing clearly revealed the respective proportions of low, medium, and high areas in a single tumor and showed that this parameter of heterogeneity could be included in a composite score, thus decreasing the level of discrepancy. Therefore, agreement with the pathologist was increased by taking heterogeneity into account.

Conclusion and discussion

This simple, robust, calibrated method using basic tools and known parameters can be used to quantify and characterize the expression of protein biomarkers within the different tumor compartments. It is based on known mathematical thresholds and takes the intratumoral heterogeneity of staining into account. Although some discrepancies need to be diminished, correlation with the pathologist's classification was satisfactory. The method is replicable and can be used to analyze other biological and medical issues. This non-subjective technique for assessing protein biomarker expression uses a fully automated choice of thresholds (GMM) and defined composite scores that take the intra-tumor heterogeneity of immunostaining into account. It could help to avoid the misclassification of patients and its subsequent negative impact on therapeutic care.

Digital quantitative tissue image analysis of hypoxia in resected pancreatic ductal adenocarcinomas

Background

Tumor hypoxia is theorized to contribute to the aggressive biology of pancreatic ductal adenocarcinoma (PDAC). We previously reported that hypoxia correlated with rapid tumor growth and metastasis in patient-derived xenografts. Anticipating a prognostic relevance of hypoxia in patient tumors, we developed protocols for automated semi-quantitative image analysis to provide an objective, observer-independent measure of hypoxia. We further validated this method which can reproducibly estimate pimonidazole-detectable hypoxia in a high-through put manner.

Methods

We studied the performance of three automated image analysis platforms in scoring pimonidazole-detectable hypoxia in resected PDAC (n = 10) in a cohort of patients enrolled in PIMO-PANC. Multiple stained tumor sections were analyzed on three independent image-analysis platforms, Aperio Genie (AG), Definiens Tissue Studio (TS), and Definiens Developer (DD), which comprised of a customized rule set.

Results

The output from Aperio Genie (AG) had good concordance with manual scoring, but the workflow was resource-intensive and not suited for high-throughput analysis. TS analysis had high levels of variability related to misclassification of cells class, while the customized rule set of DD had a high level of reliability with an intraclass coefficient of more than 85%.

Discussion

This work demonstrates the feasibility of developing a robust, high-performance pipeline for an automated, quantitative scoring of pimonidazole-detectable hypoxia in patient tumors.

p53 Immunohistochemistry and Mutation Types Mismatching in High-Grade Serous Ovarian Cancer

High-grade serous carcinoma (HGSCa) of the ovary is featured by TP53 gene mutation. Missense or nonsense mutation types accompany most cases of HGSCa that correlate well with immunohistochemical (IHC) staining results—an all (missense) or none (nonsense) pattern. However, some IHCs produce subclonal or mosaic patterns from which TP53 mutation types, including the wild type of the gene, cannot be clearly deduced. We analyzed a total of 236 cases of ovarian HGSCa and tumors of other histology by matching the results of p53 IHC staining and targeted next-generation sequencing (TruSight Tumor 170 panel). Ambiguous IHCs that do not belong to the conventional “all or none” groups were reviewed to distinguish the true wild type (WT) from potentially pathogenic subclonal or mosaic patterns. There were about 9% of sequencing-IHC mismatching cases, which were enriched by the p53 c-terminal encoding nuclear localization signal and oligomerization domain, in which the subcellular locations of p53 protein were affected. Indeed, mutations in the oligomerization domain of the p53 protein frequently revealed an unmatched signal or cytosolic staining (L289Ffs*57 (Ins), and R342*). We conclude that both mutation types and IHC patterns of p53 are important sources of information to provide a precise diagnosis of HGSCa.

Prognostic markers in invasive bladder cancer: FGFR3 mutation status versus P53 and KI-67 expression: a multi-center, multi-laboratory analysis in 1058 radical cystectomy patients

OBJECTIVES

To determine the association between the FGFR3 mutation status and immuno-histochemistry (IHC) markers (p53 and Ki-67) in invasive bladder cancer (BC), and to analyze their prognostic value in a multicenter, multi-laboratory radical cystectomy (RC) cohort.

PATIENTS AND METHODS

We included 1058 cN0M0, chemotherapy-naive BC patients who underwent RC with pelvic lymph-node dissection at 8 hospitals. The specimens were reviewed by uro-pathologists. Mutations in the FGFR3 gene were examined using PCR-SNaPshot; p53 and Ki-67 expression were determined by standard IHC. FGFR3 mutation status as well as p53 (cut-off>10%) and Ki-67 (cut-off>20%) expression were correlated to clinicopathological parameters and disease specific survival (DSS).

RESULTS

pT-stage was <pT2 in 80, pT2 in 266, pT3 in 513 and pT4 in 199 patients, respectively. Cancer-positive nodes were found in 410 (39%) patients. An FGFR3 mutation was detected in 107 (10%) and aberrant p53 and Ki-67 expression in 718 (68%) and 581(55%) tumors, respectively. The FGFR3 mutation was associated with lower pT-stage (P<0.001), lower grade (P<0.001), pN0 (P=0.001) and prolonged DSS (P<0.001). Aberrant Ki-67 and p53 expression were associated with higher pT-stage and G3-tumors, but not with pN-stage or worse DSS, even if these IHC-biomarkers were combined (P=0.81). Significant predictors for DSS in multivariable analysis were pT-stage (HR1.5, 95%CI:1.3-1.6; P<0.001), lympho-vascular invasion (LVI) (HR1.4, 95%CI:1.2-1.7; P=0.001), pN-stage (HR1.9, 95%CI:1.6-2.4; P<0.001) and FGFR3 mutation status (HR1.6, 95%CI:1.1-2.2; P=0.011).

CONCLUSION

The FGFR3 mutation selectively identified patients with favorable BC at RC while p53 and Ki-67 were only associated with adverse tumor characteristics. Our results suggest that, besides tumor-stage, nodal-status and LVI, the oncogenic FGFR3 mutation may represent a valuable tool to guide adjuvant treatment and follow-up strategies after RC.

Programmed death ligand-1 (PD-L1) as a predictive marker for immunotherapy in solid tumours: a guide to immunohistochemistry implementation and interpretation

Immunotherapy with checkpoint inhibitors is well established as an effective treatment for non-small cell lung cancer and melanoma. The list of approved indications for treatment with PD-1/PD-L1 checkpoint inhibitors is growing rapidly as clinical trials continue to show their efficacy in patients with a wide range of solid tumours. Clinical trials have used a variety of PD-L1 immunohistochemical assays to evaluate PD-L1 expression on tumour cells, immune cells or both as a potential biomarker to predict response to immunotherapy. Requests to pathologists for PD-L1 testing to guide choice of therapy are rapidly becoming commonplace. Thus, pathologists need to be aware of the different PD-L1 assays, methods of evaluation in different tumour types and the impact of the results on therapeutic decisions. This review discusses the key practical issues relating to the implementation of PD-L1 testing for solid tumours in a pathology laboratory, including evidence for PD-L1 testing, different assay types, the potential interchangeability of PD-L1 antibody clones and staining platforms, scoring criteria for PD-L1, validation, quality assurance, and pitfalls in PD-L1 assessment. This review also explores PD-L1 IHC in solid tumours including non-small cell lung carcinoma, head and neck carcinoma, triple negative breast carcinoma, melanoma, renal cell carcinoma, urothelial carcinoma, gastric and gastroesophageal carcinoma, colorectal carcinoma, hepatocellular carcinoma, and endometrial carcinoma. The review aims to provide pathologists with a practical guide to the implementation and interpretation of PD-L1 testing by immunohistochemistry.

PD-L1 Multiplex and Quantitative Image Analysis for Molecular Diagnostics

Multiplex immunofluorescence (mIF) and digital image analysis (DIA) have transformed the ability to analyse multiple biomarkers. We aimed to validate a clinical workflow for quantifying PD-L1 in non-small cell lung cancer (NSCLC). NSCLC samples were stained with a validated mIF panel. Immunohistochemistry (IHC) was conducted and mIF slides were scanned on an Akoya Vectra Polaris. Scans underwent DIA using QuPath. Single channel immunofluorescence was concordant with single-plex IHC. DIA facilitated quantification of cell types expressing single or multiple phenotypic markers. Considerations for analysis included classifier accuracy, macrophage infiltration, spurious staining, threshold sensitivity by DIA, sensitivity of cell identification in the mIF. Alternative sequential detection of biomarkers by DIA potentially impacted final score. Strong concordance was observed between 3,3'-Diaminobenzidine (DAB) IHC slides and mIF slides (R2 = 0.7323). Comparatively, DIA on DAB IHC was seen to overestimate the PD-L1 score more frequently than on mIF slides. Overall, concordance between DIA on DAB IHC slides and mIF slides was 95%. DIA of mIF slides is rapid, highly comparable to DIA on DAB IHC slides, and enables comprehensive extraction of phenotypic data and specific microenvironmental detail intrinsic to the sample. Exploration of the clinical relevance of mIF in the context of immunotherapy treated cases is warranted.

Fit-For-Purpose PD-L1 Biomarker Testing For Patient Selection in Immuno-Oncology: Guidelines For Clinical Laboratories From the Canadian Association of Pathologists-Association Canadienne Des Pathologistes (CAP-ACP)

Since 2014, programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) checkpoint inhibitors have been approved by various regulatory agencies for the treatment of multiple cancers including melanoma, lung cancer, urothelial carcinoma, renal cell carcinoma, head and neck cancer, classical Hodgkin lymphoma, colorectal cancer, gastroesophageal cancer, hepatocellular cancer, and other solid tumors. Of these approved drug/disease combinations, a subset also has regulatory agency-approved, commercially available companion/complementary diagnostic assays that were clinically validated using data from their corresponding clinical trials. The objective of this document is to provide evidence-based guidance to assist clinical laboratories in establishing fit-for-purpose PD-L1 biomarker assays that can accurately identify patients with specific tumor types who may respond to specific approved immuno-oncology therapies targeting the PD-1/PD-L1 checkpoint. These recommendations are issued as 38 Guideline Statements that address (i) assay development for surgical pathology and cytopathology specimens, (ii) reporting elements, and (iii) quality assurance (including validation/verification, internal quality assurance, and external quality assurance). The intent of this work is to provide recommendations that are relevant to any tumor type, are universally applicable and can be implemented by any clinical immunohistochemistry laboratory performing predictive PD-L1 immunohistochemistry testing.

Artificial intelligence as the next step towards precision pathology

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy requires accurate biomarker assessment. The appearance of digital image analysis holds promise to improve both the volume and precision of histomorphological evaluation. Recently, machine learning, and particularly deep learning, has enabled rapid advances in computational pathology. The integration of machine learning into routine care will be a milestone for the healthcare sector in the next decade, and histopathology is right at the centre of this revolution. Examples of potential high-value machine learning applications include both model-based assessment of routine diagnostic features in pathology, and the ability to extract and identify novel features that provide insights into a disease. Recent groundbreaking results have demonstrated that applications of machine learning methods in pathology significantly improves metastases detection in lymph nodes, Ki67 scoring in breast cancer, Gleason grading in prostate cancer and tumour-infiltrating lymphocyte (TIL) scoring in melanoma. Furthermore, deep learning models have also been demonstrated to be able to predict status of some molecular markers in lung, prostate, gastric and colorectal cancer based on standard HE slides. Moreover, prognostic (survival outcomes) deep neural network models based on digitized HE slides have been demonstrated in several diseases, including lung cancer, melanoma and glioma. In this review, we aim to present and summarize the latest developments in digital image analysis and in the application of artificial intelligence in diagnostic pathology.

Theranostic application of miR-429 in HER2+ breast cancer

Human epidermal growth factor receptor 2 (HER2) is overexpressed/amplified in one third of breast cancers (BCs), and is associated with the poorer prognosis and the higher metastatic potential in BC. Emerging evidences highlight the role of microRNAs (miRNAs) in the regulation of several cellular processes, including BC.

Methods: Here we identified, by in silico approach, a group of three miRNAs with central biological role (high degree centrality) in HER2+ BC. We validated their dysregulation in HER2+ BC and we analysed their functional role by in vitro approaches on selected cell lines and by in vivo experiments in an animal model.

Results: We found that their expression is dysregulated in both HER2+ BC cell lines and human samples. Focusing our study on the only upregulated miRNA, miR-429, we discovered that it acts as an oncogene and its upregulation is required for HER2+ cell proliferation. It controls the metastatic potential of HER2+ BC subtype by regulating migration and invasion of the cell.

Conclusions: In HER2+ BC oncogenic miR-429 is able to regulate HIF1α pathway by directly targeting VHL mRNA, a molecule important for the degradation of HIF1α. The overexpression of miR-429, observed in HER2+ BC, causes increased proliferation and migration of the BC cells. More important, silencing miR-429 succeeds in delaying tumor growth, thus miR-429 could be proposed as a therapeutic probe in HER2+ BC tumors.

PD-L1 Testing for Lung Cancer in 2019: Perspective From the IASLC Pathology Committee

The recent development of immune checkpoint inhibitors (ICIs) has led to promising advances in the treatment of patients with NSCLC and SCLC with advanced or metastatic disease. Most ICIs target programmed cell death protein 1 (PD-1) or programmed death ligand 1 (PD-L1) axis with the aim of restoring antitumor immunity. Multiple clinical trials for ICIs have evaluated a predictive value of PD-L1 protein expression in tumor cells and tumor-infiltrating immune cells (ICs) by immunohistochemistry (IHC), for which different assays with specific IHC platforms were applied. Of those, some PD-L1 IHC assays have been validated for the prescription of the corresponding agent for first- or second-line treatment. However, not all laboratories are equipped with the dedicated platforms, and many laboratories have set up in-house or laboratory-developed tests that are more affordable than the generally expensive clinical trial-validated assays. Although PD-L1 IHC test is now deployed in most pathology laboratories, its appropriate implementation and interpretation are critical as a predictive biomarker and can be challenging owing to the multiple antibody clones and platforms or assays available and given the typically small size of samples provided. Because many articles have been published since the issue of the IASLC Atlas of PD-L1 Immunohistochemistry Testing in Lung Cancer, this review by the IASLC Pathology Committee provides updates on the indications of ICIs for lung cancer in 2019 and discusses important considerations on preanalytical, analytical, and postanalytical aspects of PD-L1 IHC testing, including specimen type, validation of assays, external quality assurance, and training.

Revisiting the scientific method to improve rigor and reproducibility of immunohistochemistry in reproductive science

Immunohistochemistry (IHC) is a robust scientific tool whereby cellular components are visualized within a tissue, and this method has been and continues to be a mainstay for many reproductive biologists. IHC is highly informative if performed and interpreted correctly, but studies have shown that the general use and reporting of appropriate controls in IHC experiments is low. This omission of the scientific method can result in data that lack rigor and reproducibility. In this editorial, we highlight key concepts in IHC controls and describe an opportunity for our field to partner with the Histochemical Society to adopt their IHC guidelines broadly as researchers, authors, ad hoc reviewers, editorial board members, and editors-in-chief. Such cross-professional society interactions will ensure that we produce the highest quality data as new technologies emerge that still rely upon the foundations of classic histological and immunohistochemical principles.

Quality assurance guidance for scoring and reporting for pathologists and laboratories undertaking clinical trial work

While pathologists have always played a pivotal role in clinical trials ensuring accurate diagnosis and staging, pathology data from prognostic and predictive tests are increasingly being used to enrol, stratify and randomise patients to experimental treatments. The use of pathological parameters as primary and secondary outcome measures, either as standalone classifiers or in combination with clinical data, is also becoming more common. Moreover, reporting of estimates of residual disease, termed 'pathological complete response', have been incorporated into neoadjuvant clinical trials. Pathologists have the expertise to deliver this essential information and they also understand the requirements and limitations of laboratory testing. Quality assurance of pathology-derived data builds confidence around trial-specific findings and is necessarily focused on the reproducibility of pathological data, including 'estimates of uncertainty of measurement', emphasising the importance of pathologist education, training, calibration and demonstration of satisfactory inter-observer agreement. There are also opportunities to validate objective image analysis tools alongside conventional histological assessments. The ever-expanding portfolio of clinical trials will demand more pathologist engagement to deliver the reliable evidence-base required for new treatments. We provide guidance for quality assurance of pathology scoring and reporting in clinical trials.

Fit-for-Purpose Immunohistochemical Biomarkers

There are two aspects of immunohistochemistry (IHC) that are relevant to practicing pathologist: (1) understanding of IHC biomarker panels that are suitable for diagnostic, prognostic and predictive testing, and (2) understanding of IHC quality assurance (QA), which makes sure that the tests in these panels work as they should. The two aspects are closely linked together and call for collaborative approach between pathologists and IHC laboratory technologists as both need to be involved in developing and maintaining IHC biomarkers that are "fit-for-purpose". This article reviews the most current IHC QA concepts that are imminently material to practicing pathologists with emphasis on challenges that are specific to endocrine pathology.

Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer

Digital image analysis (DIA) is becoming central to the quantitative evaluation of tissue biomarkers for discovery, diagnosis and therapeutic selection for the delivery of precision medicine. In this study, automated DIA using a new purpose-built software platform (QuPath) is applied to a cohort of 293 breast cancer patients to score five biomarkers in tissue microarrays (TMAs): ER, PR, HER2, Ki67 and p53. This software is able to measure IHC expression following fully automated tumor recognition in the same immunohistochemical (IHC)-stained tissue section, as part of a rapid workflow to ensure objectivity and accelerate biomarker analysis. The digital scores produced by QuPath were compared with manual scores by a pathologist and shown to have a good level of concordance in all cases (Cohen's κ>0.6), and almost perfect agreement for the clinically relevant biomarkers ER, PR and HER2 (κ>0.86). To assess prognostic value, cutoff thresholds could be applied to both manual and automated scores using the QuPath software, and survival analysis performed for 5-year overall survival. DIA was shown to be capable of replicating the statistically significant stratification of patients achieved using manual scoring across all biomarkers (P<0.01, log-rank test). Furthermore, the image analysis scores were shown to consistently lead to statistical significance across a wide range of potential cutoff thresholds, indicating the robustness of the method, and identify sub-populations of cases exhibiting different expression patterns within the p53 and Ki67 data sets that warrant further investigation. These findings have demonstrated QuPath's suitability for fast, reproducible, high-throughput TMA analysis across a range of important biomarkers. This was achieved using our tumor recognition algorithms for IHC-stained sections, trained interactively without the need for any additional tumor recognition markers, for example, cytokeratin, to obtain greater insight into the relationship between biomarker expression and clinical outcome applicable to a range of cancer types.

Training in molecular cytopathology testing

Training in molecular cytopathology testing is essential in developing and maintaining skills in modern molecular technologies as they are introduced to a universal health care system such as extant in the UK and elsewhere. We review the system in place in Northern Ireland (NI) for molecular testing of solid tumours, as an example to train staff of all grades, including pathologists, clinical scientists, biomedical scientists and equivalent technical grades. We describe training of pathologists as part of the NI Deanery medical curriculum, the NI training programme for scientists and laboratory rotation for Biomedical Scientists. Collectively, the aims of our training are two-fold: to provide a means by which individuals may extend their experience and skills; and to provide and maintain a skilled workforce for service delivery. Through training and competency, we introduce new technologies and tests in response to personalised medicine therapies with a competent workforce. We advocate modifying programmes to suit individual needs for skill development, with formalised courses in pre-analytical, analytical and postanalytical demands of modern molecular pathology. This is of particular relevance for cytopathology in small samples such those from formalin-fixed paraffin-embedded cell blocks. We finally introduce how university courses can augment training and develop a skilled workforce to benefit the delivery of services to our patients.

PD-L1 immunohistochemistry for non-small cell lung carcinoma: which strategy should be adopted?

INTRODUCTION

PD-L1 detection with immunohistochemistry (IHC) is the only predictive biomarker available to date for PD-L1/PD1 immunotherapy in thoracic oncology. While many studies have been published on this biomarker, they raise a number of questions concerning mainly, (i) the type of antibody for use and its condition of utilization, (ii) the threshold to be used, (iii) the message and information to communicate to the thoracic oncologist and, (iv) the adoption of this methodology as part of the daily practices of a pathology laboratory. Areas covered: This review provides an update on the use of the different PD-L1 antibodies for IHC in the context of metastatic non-small cell lung cancer (NSCLC) and discusses their use as companion or complementary diagnostic tests. The limits of PD-L1 IHC as a predictive test, the precautions to be adopted as well as some perspectives will then be considered. Expert commentary: IHC for PD-L1 can be considered as a theranostic test, which implies providing an extremely reliable result that avoids any false positive and negative results. PD-L1 IHC requires considerable expertise and specific training of pathologists. PD-L1 IHC can be a companion or complementary diagnostic test depending on the clone employed, the molecular therapy prescribed and the indication of use.

Detection approaches for multidrug resistance genes of leukemia

Leukemia is a clonal malignant hematopoietic stem cell disease. It is the sixth most lethal cancer and accounts for 4% of all cancers. The main form of treatment for leukemia is chemotherapy. While some cancer types with a higher incidence than leukemia, such as lung and gastric cancer, have shown a sharp decline in mortality rates in recent years, leukemia has not followed this trend. Drug resistance is often regarded as the main clinical obstacle to effective chemotherapy in patients diagnosed with leukemia. Many resistance mechanisms have now been identified, and multidrug resistance (MDR) is considered the most important and prevalent mechanism involved in the failure of chemotherapy in leukemia. In order to reverse MDR and improve leukemia prognosis, effective detection methods are needed to identify drug resistance genes at initial diagnosis. This article provides a comprehensive overview of published approaches for the detection of MDR in leukemia. Identification of relevant MDR genes and methods for early detection of these genes will be needed in order to treat leukemia more effectively.

Development of RNA-FISH Assay for Detection of Oncogenic FGFR3-TACC3 Fusion Genes in FFPE Samples

Introduction and Objectives

Oncogenic FGFR3-TACC3 fusions and FGFR3 mutations are target candidates for small molecule inhibitors in bladder cancer (BC). Because FGFR3 and TACC3 genes are located very closely on chromosome 4p16.3, detection of the fusion by DNA-FISH (fluorescent in situ hybridization) is not a feasible option. In this study, we developed a novel RNA-FISH assay using branched DNA probe to detect FGFR3-TACC3 fusions in formaldehyde-fixed paraffin-embedded (FFPE) human BC samples.

Materials and Methods

The RNA-FISH assay was developed and validated using a mouse xenograft model with human BC cell lines. Next, we assessed the consistency of the RNA-FISH assay using 104 human BC samples. In this study, primary BC tissues were stored as frozen and FFPE tissues. FGFR3-TACC3 fusions were independently detected in FFPE sections by the RNA-FISH assay and in frozen tissues by RT-PCR. We also analyzed the presence of FGFR3 mutations by targeted sequencing of genomic DNA extracted from deparaffinized FFPE sections.

Results

FGFR3-TACC3 fusion transcripts were identified by RNA-FISH and RT-PCR in mouse xenograft FFPE tissues using the human BC cell lines RT112 and RT4. These cell lines have been reported to be fusion-positive. Signals for FGFR3-TACC3 fusions by RNA-FISH were positive in 2/60 (3%) of non-muscle-invasive BC (NMIBC) and 2/44 (5%) muscle-invasive BC (MIBC) patients. The results of RT-PCR of all 104 patients were identical to those of RNA-FISH. FGFR3 mutations were detected in 27/60 (45%) NMIBC and 8/44 (18%) MIBC patients. Except for one NMIBC patient, FGFR3 mutation and FGFR3-TACC3 fusion were mutually exclusive.

Conclusions

We developed an RNA-FISH assay for detection of the FGFR3-TACC3 fusion in FFPE samples of human BC tissues. Screening for not only FGFR3 mutations, but also for FGFR3-TACC3 fusion transcripts has the potential to identify additional patients that can be treated with FGFR inhibitors.

Considerations for standardizing predictive molecular pathology for cancer prognosis

INTRODUCTION

Molecular tests that were once ancillary to the core business of cyto-histopathology are becoming the most relevant workload in pathology departments after histopathology/cytopathology and before autopsies. This has resulted from innovations in molecular biology techniques, which have developed at an incredibly fast pace. Areas covered: Most of the current widely used techniques in molecular pathology such as FISH, direct sequencing, pyrosequencing, and allele-specific PCR will be replaced by massive parallel sequencing that will not be considered next generation, but rather, will be considered to be current generation sequencing. The pre-analytical steps of molecular techniques such as DNA extraction or sample preparation will be largely automated. Moreover, all the molecular pathology instruments will be part of an integrated workflow that traces the sample from extraction to the analytical steps until the results are reported; these steps will be guided by expert laboratory information systems. In situ hybridization and immunohistochemistry for quantification will be largely digitalized as much as histology will be mostly digitalized rather than viewed using microscopy. Expert commentary: This review summarizes the technical and regulatory issues concerning the standardization of molecular tests in pathology. A vision of the future perspectives of technological changes is also provided.

Objective analysis of cancer stem cell marker expression using immunohistochemistry

Analysis of immunohistochemical expression is often a subjective and semiquantitative process that can lead to the inconsistent reporting of results. To assess the effect that region selection and quantification method have on results, five different cancer stem cell markers were used in this study to compare tissue scoring with digital analysis methods that used three different tissue annotation methods. Samples of tumour and normal mucosa were used from 10 consecutive stage II colon cancer patients and stained for the putative cancer stem cell markers ALDH1, CD44v6, CD133, Lgr5 and SOX2. Tissue scoring was found to have considerably different results to digital analysis with the three different digital methods harbouring concordant results overall. However, SOX2 on normal tissue and CD133 on tumour and normal tissue produced discordant results which could be attributed to the different regions of tissue that were analysed. It is important that quantification method and selection of analysis areas are considered as part of study design to ensure that reproducible and consistent results are reported in the literature.

PTEN mRNA detection by chromogenic, RNA in situ technologies: a reliable alternative to PTEN immunohistochemistry

Immunohistochemical staining for phosphatase and tensin homolog (PTEN) does not have either an acceptable standard protocol or concordance of scoring between pathologists. Evaluation of PTEN mRNA with a unique and verified sequence probe may offer a realistic alternative providing a robust and reproducible protocol. In this study, we have evaluated an in situ hybridization (ISH) protocol for PTEN mRNA using RNAScope technology and compared it with a standard protocol for PTEN immunohistochemistry (IHC). PTEN mRNA expression by ISH was consistently more sensitive than PTEN IHC, with 56% of samples on a mixed-tumor tissue microarray (TMA) showing high expression by ISH compared with 42% by IHC. On a prostate TMA, 49% of cases showed high expression by ISH compared with 43% by IHC. Variations in PTEN mRNA expression within malignant epithelium were quantifiable using image analysis on the prostate TMAs. Within tumors, clear overexpression of PTEN mRNA on malignant epithelium compared with benign epithelium was frequently observed and quantified. The use of SpotStudio software in the mixed-tumor TMA allowed for clear demonstration of varying levels of PTEN mRNA between tumor samples by the mRNA methodology. This was evident by the quantifiable differences between distinct oropharyngeal tumors (up to 3-fold increase in average number of spots per cell between 2 cases). mRNA detection of PTEN or other biomarkers, for which optimal or standardized immunohistochemical techniques are not available, represents a means by which heterogeneity of expression within focal regions of tumor can be explored with more confidence.