PD-L1 expression in gastric cancer determined by digital image analyses: pitfalls and correlation with pathologist interpretation

Considerable interlaboratory variation in PD-L1 positivity for head and neck squamous cell carcinoma in the Netherlands- A nationwide evaluation study

AIMS

Patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC) are eligible for first-line immune checkpoint inhibition if their tumour is positive for programmed death ligand 1 (PD-L1) determined by the combined positive score (CPS). This nationwide study, using real-world data, investigated the developing PD-L1 testing landscape in the first 3 years after introduction of the test in HNSCC and examined interlaboratory variation in PD-L1 positivity rates.

METHODS

Pathology reports of HNSCC patients mentioning PD-L1 were extracted from the Dutch Pathology Registry (Palga). Tumour and PD-L1 testing characteristics were analysed per year and interlaboratory variation in PD-L1 positivity rates was assessed using funnel plots with 95% confidence limits around the overall mean.

RESULTS

A total of 817 PD-L1 tests were reported in 702 patients among 19 laboratories; 85.2% of the tests on histological material were stated to be positive. The national PD-L1 positivity rate differed significantly per year during the study period (79.7-89.9%). The use of the recommended 22C3 antibody increased from 59.9 to 74.3%. A total of 673 PD-L1 tests on histological material from 12 laboratories were analysed to investigate interlaboratory variation. Four (33%) deviated significantly from the national mean of PD-L1-positive cases using CPS ≥ 1 cut-off, while two (17%) deviated significantly for CPS ≥ 20 cut-off.

CONCLUSION

In the first 3 years of PD-L1 assessment in HNSCC, the testing landscape became more uniform. However, interlaboratory variation in PD-L1 positivity rates between Dutch laboratories was substantial. This implies that there is a need for further test standardisation to reduce this variation.

Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections

Background: One of the most frequently used methods for quantifying PD-L1 (programmed cell death-ligand 1) expression in tumor tissue is IHC (immunohistochemistry). This may predict the patient's response to anti-PD1/PD-L1 therapy in cancer. Methods: ImageJ software was used to score IHC-stained sections for PD-L1 and compare the results with the conventional manual method. Results: In diffuse large B cell lymphoma, no significant difference between the scores obtained by the conventional method and ImageJ scores obtained using the option "RGB" or "Brightness/Contrast." On the other hand, a significant difference was found between the conventional and HSB scoring methods. ImageJ faced some challenges in analyzing head and neck squamous cell carcinoma tissues because of tissue heterogenicity. A significant difference was found between the conventional and ImageJ scores using HSB or RGB but not with the "Brightness/Contrast" option. Scores obtained by ImageJ analysis after taking images using 20 × objective lens gave significantly higher readings compared to 40 × magnification. A significant difference between camera-captured images' scores and scanner whole slide images' scores was observed. Conclusion: ImageJ can be used to score homogeneous tissues. In the case of highly heterogeneous tissues, it is advised to use the conventional method rather than ImageJ scoring.

Mesonephric-like Adenocarcinoma of the Uterine Corpus: Genomic and Immunohistochemical Profiling with Comprehensive Clinicopathological Analysis of 17 Consecutive Cases from a Single Institution

Data on genetic and immunophenotypical characteristics of uterine mesonephric-like adenocarcinoma (MLA) remain limited. Therefore, we aimed to investigate the clinicopathological, immunohistochemical, and molecular features of uterine MLA. We performed targeted sequencing, array comparative genomic hybridization, and immunostaining in 17, 13, and 17 uterine MLA cases, respectively. Nine patients developed lung metastases. Eleven patients experienced disease recurrences. The most frequently mutated gene was Kirsten rat sarcoma viral oncogene homolog (KRAS; 13/17). Both the primary and matched metastatic tumors harbored identical KRAS (3/4) and phosphatase and tensin homolog deleted on chromosome 10 (1/4) mutations, and did not harbor any additional mutations. A total of 2 of the 17 cases harbored tumor protein 53 (TP53) frameshift insertion and deletion, respectively. Chromosomal gains were detected in 1q (13/13), 10 (13/13), 20 (10/13), 2 (9/13), and 12 (6/13). Programmed cell death-ligand 1 overexpression or mismatch repair deficiency was not observed in any of the cases. Initial serosal extension and lung metastasis independently predicted recurrence-free survival with hazard ratios of 6.30 and 7.31, respectively. Our observations consolidated the clinicopathological and molecular characteristics of uterine MLA. Both clinicians and pathologists should consider these features to make an accurate diagnosis of uterine MLA and to ensure appropriate therapeutic management of this rare entity.

Artificial Intelligence in the Pathology of Gastric Cancer

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

Next generation immuno-oncology biomarkers in gastrointestinal cancer: what does the future hold?

INTRODUCTION

Gastrointestinal (GI) cancers pose a significant health burden worldwide, necessitating advancements in diagnostic and treatment approaches. One promising avenue is the utilization of next-generation biomarkers, which hold the potential to revolutionize GI cancer management.

AREAS COVERED

This review explores the latest breakthroughs and expert opinions surrounding the application of next-generation immunotherapy biomarkers. It encompasses various aspects of the currently utilized biomarkers of immunotherapy in the context of GI cancers focusing on microsatellite stable cancers. It explores the promising research on the next generation of biomarkers addressing the challenges associated with integrating them into clinical practice and the need for standardized protocols and regulatory guidelines.

EXPERT OPINION

Immune profiling, multiplex immunohistochemistry, analysis of immune cell subsets, and novel genomic and epigenomic markers integrated with machine-learning approaches offer new avenues for identifying robust biomarkers. Liquid biopsy-based approaches, such as circulating tumor DNA (ctDNA) and exosome-based analyses, hold promise for real-time monitoring and early detection of treatment response.

Indirect Clinical Validation of a Programmed Death-Ligand 1 Laboratory-Developed Test for Gastric/Gastroesophageal Junction Adenocarcinoma with 22C3 Antibody Concentrate

BACKGROUND

The PD-L1 IHC 22C3 pharmDx used on the Dako Autostainer Link 48 (ASL48) staining platform is an established method for assessing programmed death-ligand 1 (PD-L1) expression in tumor tissue and determining patient eligibility for pembrolizumab treatment; however, the availability of this platform is limited in Europe and Asia.

OBJECTIVES

The aims of this study were to develop and optimize protocols for the PD-L1 22C3 antibody concentrate with multiple immunohistochemistry staining platforms and to validate these protocols using PD-L1 combined positive score (CPS) with a cut-off of ≥ 1 in gastric or gastroesophageal junction adenocarcinoma.

DESIGN

The 22C3 antibody concentrate was tested and optimized protocols were developed for use with three staining platforms: Dako ASL48, Ventana BenchMark ULTRA, and Leica BOND-MAX. Tumor specimens (N = 120) from patients with gastric or gastroesophageal junction adenocarcinoma were used for the validation study; these specimens were evaluated independently by three pathologists for PD-L1 CPS as a continuous variable and using a cut-off of ≥ 1. PD-L1 IHC 22C3 pharmDx used on the Dako ASL48 platform served as the reference or gold standard.

RESULTS

The intraclass correlation coefficient of CPS as a continuous variable between the gold standard and each staining platform assessed was 0.910-0.989. When CPS was dichotomized based on a cut-off of ≥ 1, depending on the pathologist and the platform used, positive percentage agreement was 81-99% and negative percentage agreement was 90-100%. Interobserver agreement using the gold standard showed substantial agreement (κ = 0.779).

CONCLUSION

The PD-L1 22C3 antibody concentrate can potentially be used with the laboratory-developed test on three commercially available immunohistochemistry staining platforms to determine PD-L1 expression in tumor samples from patients with gastric or gastroesophageal junction adenocarcinoma.

The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy

Simple Summary

Immunotherapy has become the filar of modern oncological treatment, and programmed death-ligand 1 expression is one of the primary immune markers assessed by pathologists. However, there are still some issues concerning the evaluation of the marker and limited information about the interaction between the tumour and associated immune cells. Recent studies have focused on cancer immunology to try to understand the complex tumour microenvironment, and multiplex imaging methods are more widely used for this purpose. The presented article aims to provide an overall review of a different multiplex in situ method using spectral imaging, supported by automated image-acquisition and software-assisted marker visualisation and interpretation. Multiplex imaging methods could improve the current understanding of complex tumour-microenvironment immunology and could probably help to better match patients to appropriate treatment regimens.

Abstract

Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex labelling enables the detection of multiple markers simultaneously and the exploration of their spatial organisation. Evaluating a variety of immune cell phenotypes and differentiating their subpopulations is possible while preserving tissue histology in most cases. Multiplexing supported by digital pathology could allow pathologists to visualise and understand every cell in a single tissue slide and provide meaning in a complex tumour-microenvironment contexture. This review aims to provide an overview of the different multiplex imaging methods and their application in PD-L1 biomarker assessment. Moreover, we discuss digital imaging techniques, with a focus on slide scanners and software.

PD-L1 immunohistochemistry: Clones, cutoffs, and controversies

Cancer immunotherapy has become a major component of oncologic treatment for a growing number of malignancies. Of particular interest to pathology has been monoclonal antibody therapy targeting immune checkpoints, notably programmed cell death (PD-1) and programmed cell death ligand (PD-L1). Targeting of these checkpoints attempt to overcome tumor evasion of the immune system. While PD-L1 testing is currently implemented as a predictive biomarker in multiple indications with the PD-L1 axis blockade, PD-L1 immunohistochemistry has been a complex issue for the pathology laboratory as it requires an understanding of multiple clones, on multiple testing platforms for multiple different malignancies, each with variable scoring criteria and thresholds. This review attempts to summarize the important PD-L1 testing algorithms and test performance for the practicing pathologist who actively reviews PD-L1 immunohistochemistry.

Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1-positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1-positive compared with PD-L1-negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1-positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.

Digital pathology and artificial intelligence in translational medicine and clinical practice

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma

Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 scores. However, manual scoring shows variability between different investigators and is influenced by cognitive and visual traps and could therefore negatively influence treatment decisions. Automated PD-L1 scoring could facilitate reliable and reproducible results. Our novel approach uses three neural networks sequentially applied for fully automated PD-L1 scoring of all three established PD-L1 scores: tumor proportion score (TPS), combined positive score (CPS) and tumor-infiltrating immune cell score (ICS). Our approach was validated using WSIs of HNSCC cases and compared with manual PD-L1 scoring by human investigators. The inter-rater correlation (ICC) between human and machine was very similar to the human-human correlation. The ICC was slightly higher between human-machine compared to human-human for the CPS and ICS, but a slightly lower for the TPS. Our study provides deeper insights into automated PD-L1 scoring by neural networks and its limitations. This may serve as a basis to improve ICI patient selection in the future.

Challenges facing pathologists evaluating PD-L1 in head & neck squamous cell carcinoma

BACKGROUND

Programmed death-ligand 1 (PD-L1) expression with combined positive score (CPS) ≥1 is required for administration of checkpoint inhibitor therapy in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC). The 22C3 pharmDx Dako immunohistochemical assay is the one approved as companion diagnostic for pembrolizumab, but many laboratories work on other platforms and/or with other clones, and studies exploring the potential interchangeability of assays have appeared.

EVIDENCE FROM THE LITERATURE

After review of the literature, it emerges that the concordance among assays ranges from fair to moderate, with a tendence of assay SP263 to yield a higher quota of positivity and of assay SP142 to stain better immune cells. Moreover, pathologists achieve very good concordance in assessing PD-L1 CPS, particularly with SP263.

CONCLUSIONS

Differences in terms of platforms, procedures, and study design still preclude a quantitative synthesis of evidence and clearly further work is needed to draw stronger conclusions on the interchangeability of PD-L1 assays in HNSCC.

Next-Generation Digital Histopathology of the Tumor Microenvironment

Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology-which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.

PD-L1 expression in paired biopsies and surgical specimens in gastric adenocarcinoma: A digital image analysis study

Programmed death-ligand 1 (PD-L1) expression in biopsies of gastric carcinoma may predict the results in corresponding surgical specimens. We compared PD-L1 immunohistochemistry (IHC) 22C3 pharmDx expression in paired biopsy and resection specimens. We also characterized the validity of a new PD-L1 assay using digital image analysis. PD-L1 IHC with 22C3 pharmDx and clone 73-10 was performed in 224 gastric cancer tissues (112 biopsies and paired surgical tissues) and the specimens were analyzed with the Leica Aperio Imagescope. For statistical analyses, the area under the receiver operating characteristic curve and R package were used. With 22C3 pharmDx, a PD-L1 combined positive score of ≥1 was found in 36 biopsied (32.14 %) and 53 surgical (47.32 %) samples. PD-L1 expression results were concordant in 71 cases (63.4 %) and discordant in 41 (36.6 %). The overall discordance rate was 36.61 % (95 % confidence interval 2.101-8.983) and the κ value was 0.254 with fair agreement. The sensitivity and specificity of biopsy PD-L1 to predict the results of the surgical specimen was 62 % and 73 %, respectively. The correlation of 22C3 pharmDx and clone 73-10 was high (correlation coefficient = 0.88). When only tumor cell staining was compared, this correlation was increased (correlation coefficient = 0.95). Our results indicated moderate association of PD-L1 expression between gastric biopsies and corresponding resected tumors. Results of PD-L1 assay with 73-10 are comparable to 22C3 pharmDx results.

Development and applications of computer image analysis algorithms for scoring of PD-L1 immunohistochemistry

Immune checkpoint inhibitors targeting programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) have rapidly become integral to standard-of-care therapy for non-small cell lung cancer and other cancers. Immunohistochemical (IHC) staining of PD-L1 is currently the accepted and approved diagnostic assay for selecting patients for PD-L1/PD-1 axis therapies in certain indications. However, the inherent biological complexity of PD-L1 and the availability of several PD-L1 assays – each with different detection systems, platforms, scoring algorithms and cut-offs – have created challenges to ensure reliable and reproducible results based on subjective visual assessment by pathologists. The increasing adoption of computer technologies into the daily workflow of pathology provides an opportunity to leverage these tools towards improving the clinical value of PD-L1 IHC assays. This review describes several image analysis software programs of computer-aided PD-L1 scoring in the hope of driving further discussion and technological advancement in digital pathology and artificial intelligence approaches, particularly as precision medicine evolves to encompass accurate simultaneous assessment of multiple features of cancer cells and their interactions with the tumor microenvironment.