Improving the Diagnostic Accuracy of the PD-L1 Test with Image Analysis and Multiplex Hybridization

Topographic analysis of pancreatic cancer by TMA and digital spatial profiling reveals biological complexity with potential therapeutic implications

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

Programmed cell death ligand 1 expression associated with subtypes of post-transplant lymphoproliferative disorder among pediatric kidney transplant recipients

BACKGROUND

Programmed cell death ligand 1 (PD-L1) expression on tumor cells engages the PD-1 receptor on T cells, inhibiting anti-tumor responses. PD-L1 has been detected in cases of post-transplant lymphoproliferative disorder (PTLD) but reports are limited. Here we examine PD-L1 expression and evaluate for clinical correlations.

METHODS

Twenty-one cases of PTLD were identified among pediatric kidney transplant recipients at our institution from February 1996 to April 2017. Using paraffin-embedded tissue biopsies, we examined 21 primary tumors for expression using PD-L1 monoclonal antibody performed with PAX5 as a double stain. We scored expression of PD-L1 on lesional B-cells as a percentage of positive cells. Clinical course and outcome were obtained from retrospective chart review.

RESULTS

Applying revised 2017 WHO PTLD classification showed five non-destructive, nine polymorphic, and seven monomorphic cases. Average PD-L1 expression based upon PTLD subtype was: non-destructive 11%, polymorphic 43%, and monomorphic 73% (p = .01). Two patients transferred shortly after diagnosis, five received chemotherapy, and three died from PTLD. Among the fatalities, all showed monomorphic PTLD and 90% of lesional B-cells expressed PD-L1.

CONCLUSION

In this case series, significant differences in PD-L1 expression were seen among different subtypes, and monomorphic PTLD demonstrated the highest expression. Study of a larger cohort is needed, and if the correlation of PD-L1 expression and PTLD subtype is confirmed, this may highlight the potential utility of checkpoint inhibitor therapy in cases of severe or refractory disease among kidney transplant recipient in whom the risk of allograft loss is acceptable given the option of chronic dialysis.

Exploring the immune microenvironment in small bowel adenocarcinoma using digital image analysis

BACKGROUND

Small bowel adenocarcinoma (SBA) is a rare malignancy of the small intestine associated with late stage diagnosis and poor survival outcome. High expression of immune cells and immune checkpoint biomarkers especially programmed cell death ligand-1 (PD-L1) have been shown to significantly impact disease progression. We have analysed the expression of a subset of immune cell and immune checkpoint biomarkers in a cohort of SBA patients and assessed their impact on progression-free survival (PFS) and overall survival (OS).

METHODS

25 patient samples in the form of formalin fixed, paraffin embedded (FFPE) tissue were obtained in tissue microarray (TMAs) format. Automated immunohistochemistry (IHC) staining was performed using validated antibodies for CD3, CD4, CD8, CD68, PD-L1, ICOS, IDO1 and LAG3. Slides were scanned digitally and assessed in QuPath, an open source image analysis software, for biomarker density and percentage positivity. Survival analyses were carried out using the Kaplan Meier method.

RESULTS

Varying expressions of biomarkers were recorded. High expressions of CD3, CD4 and IDO1 were significant for PFS (p = 0.043, 0.020 and 0.018 respectively). High expression of ICOS was significant for both PFS (p = 0.040) and OS (p = 0.041), while high PD-L1 expression in tumour cells was significant for OS (p = 0.033). High correlation was observed between PD-L1 and IDO1 expressions (Pearson correlation co-efficient = 1) and subsequently high IDO1 expression in tumour cells was found to be significant for PFS (p = 0.006) and OS (p = 0.034).

CONCLUSIONS

High levels of immune cells and immune checkpoint proteins have a significant impact on patient survival in SBA. These data could provide an insight into the immunotherapeutic management of patients with SBA.

Technical note on the exploration of COVID-19 in autopsy material

Interrogation of immune response in autopsy material from patients with SARS-CoV-2 is potentially significant. We aim to describe a validated protocol for the exploration of the molecular physiopathology of SARS-CoV-2 pulmonary disease using multiplex immunofluorescence (mIF).The application of validated assays for the detection of SARS-CoV-2 in tissues, originally developed in our laboratory in the context of oncology, was used to map the topography and complexity of the adaptive immune response at protein and mRNA levels.SARS-CoV-2 is detectable in situ by protein or mRNA, with a sensitivity that could be in part related to disease stage. In formalin-fixed, paraffin-embedded pneumonia material, multiplex immunofluorescent panels are robust, reliable and quantifiable and can detect topographic variations in inflammation related to pathological processes.Clinical autopsies have relevance in understanding diseases of unknown/complex pathophysiology. In particular, autopsy materials are suitable for the detection of SARS-CoV-2 and for the topographic description of the complex tissue-based immune response using mIF.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

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.

DNA Methylation Analysis to predict Regression of high-grade anal Intraepithelial Neoplasia in HIV+ men (MARINE): a cohort study protocol

INTRODUCTION

Anal cancer precursors, or high-grade anal intraepithelial neoplasia (HGAIN), are highly prevalent in HIV-seropositive (HIV+) men who have sex with men (MSM). Around 30% of lesions regress within 1 year, but current histopathological assessment is unable to distinguish between HGAIN likely to regress and HGAIN likely to persist or progress to cancer. We aim to assess if host cell DNA methylation markers can predict regression of HGAIN, thus determining the need for immediate treatment or active surveillance. This could reduce overtreatment and the associated anal and psycho-sexual morbidity.

METHODS AND ANALYSIS

This is an active surveillance cohort study in three centres located in Amsterdam, the Netherlands, in 200 HIV+ MSM diagnosed with HGAIN. Participants will not be treated, but closely monitored during 24 months of follow-up with 6 monthly visits including cytology, and high-resolution anoscopy with biopsies. The primary study endpoint is histopathological regression of each baseline HGAIN lesion at the end of the study. Regression is defined as ≤low grade anal intraepithelial neoplasia in the exit biopsy at 24 months. Regression proportions in lesions with low versus high methylation levels (ASCL1, ZNF582), other biomarkers (HPV genotype, HPV-E4, p16^INK4A, Ki-67) and immunological markers at baseline will be compared. Main secondary endpoints are the histological and clinical outcome (ie, the number of octants affected by HGAIN) of each baseline HGAIN lesion and overall HGAIN disease (i.e., all lesions combined) after each visit. The health-related quality of life of the study group will be compared with that of a control group of 50 HIV+ MSM receiving regular HGAIN treatment.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Institutional Review Board of the Academic Medical Center (Amsterdam, The Netherlands; reference no. 2021_099). Participants are required to provide written informed consent. Findings will be disseminated through publication in peer-reviewed scientific journals and presentations at international scientific conferences; dissemination to policy makers and the target patient group will be achieved through our (inter-)national network, professional associations and collaboration with a patient representative organisation.

TRIAL REGISTRATION NUMBER

NL9664.

A new AI-assisted scoring system for PD-L1 expression in NSCLC

BACKGROUND

Artificial intelligence (AI) analysis may serve as a scoring tool for programmed cell death ligand-1 (PD-L1) expression. In this study, a new AI-assisted scoring system for pathologists was tested for PD-L1 expression assessment in non-small cell lung cancer (NSCLC).

METHODS

PD-L1 expression was evaluated using the tumor proportion score (TPS) categorized into three levels: negative (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%). In order to train, validate, and test the Aitrox AI segmentation model at the whole slide image (WSI) level, 54, 53, and 115 cases were used as training, validation, and test datasets, respectively. TPS reading results from five experienced pathologists, six inexperienced and the Aitrox AI model were analyzed on 115 PD-L1 stained WSIs. The Gold Standard for TPS was derived from the review of three expert pathologists. Spearman's correlation coefficient was calculated and compared between the results.

RESULTS

Aitrox AI Model correlated strongly with the TPS Gold Standard and was comparable with the results of three of the five experienced pathologists. In contrast, the results of four of the six inexperienced pathologists correlated only moderately with the TPS Gold Standard. Aitrox AI Model performed better than the inexperienced pathologists and was comparable to experienced pathologists in both negative and low TPS groups. Despite the fact that the low TPS group showed 5.09% of cases with large fluctuations, the Aitrox AI Model still showed a higher correlation than the inexperienced pathologists. However, the AI model showed unsatisfactory performance in the high TPS groups, especially lower values than the Gold Standard in images with large regions of false-positive cells.

CONCLUSION

The Aitrox AI Model demonstrates potential in assisting routine diagnosis of NSCLC by pathologists through scoring of PD-L1 expression.

Correlation and colocalization of HIF-1α and pimonidazole staining for hypoxia in laryngeal squamous cell carcinomas: A digital, single-cell-based analysis

OBJECTIVE

Tumor hypoxia results in worse local control and patient survival. We performed a digital, single-cell-based analysis to compare two biomarkers for hypoxia (hypoxia-inducible factor 1-alpha [HIF-1α] and pimonidazole [PIMO]) and their effect on outcome in laryngeal cancer patients treated with accelerated radiotherapy with or without carbogen breathing and nicotinamide (AR versus ARCON).

MATERIALS AND METHODS

Immunohistochemical staining was performed for HIF-1α and PIMO in consecutive sections of 44 laryngeal cancer patients randomized between AR and ARCON. HIF-1α expression and PIMO-binding were correlated using digital image analysis in QuPath. High-density areas for each biomarker were automatically annotated and staining overlap was analyzed. Kaplan-Meier survival analyses for local control, regional control and disease-free survival were performed to predict a response benefit of ARCON over AR alone for each biomarker.

RESULTS

106 Tissue fragments of 44 patients were analyzed. A weak, significant positive correlation was observed between HIF-1α and PIMO positivity on fragment level, but not on patient level. A moderate strength correlation (r = 0.705, p < 0.001) was observed between the number of high-density staining areas for both biomarkers. Staining overlap was poor. HIF-1α expression, PIMO-binding or a combination could not predict a response benefit of ARCON over AR.

CONCLUSION

Digital image analysis to compare positive cell fractions and staining overlap between two hypoxia biomarkers using open-source software is feasible. Our results highlight that there are distinct differences between HIF-1α and PIMO as hypoxia biomarkers and therefore suggest co-existence of different forms of hypoxia within a single tumor.

Activation of a cGAS-STING-mediated immune response predicts response to neoadjuvant chemotherapy in early breast cancer

BACKGROUND

The DNA-damage immune-response (DDIR) signature is an immune-driven gene expression signature retrospectively validated as predicting response to anthracycline-based therapy. This feasibility study prospectively evaluates the use of this assay to predict neoadjuvant chemotherapy response in early breast cancer.

METHODS

This feasibility study assessed the integration of a novel biomarker into clinical workflows. Tumour samples were collected from patients receiving standard of care neoadjuvant chemotherapy (FEC + /-taxane and anti-HER2 therapy as appropriate) at baseline, mid- and post-chemotherapy. Baseline DDIR signature scores were correlated with pathological treatment response. RNA sequencing was used to assess chemotherapy/response-related changes in biologically linked gene signatures.

RESULTS

DDIR signature reports were available within 14 days for 97.8% of 46 patients (13 TNBC, 16 HER2 + ve, 27 ER + HER2-ve). Positive scores predicted response to treatment (odds ratio 4.67 for RCB 0-1 disease (95% CI 1.13-15.09, P = 0.032)). DDIR positivity correlated with immune infiltration and upregulated immune-checkpoint gene expression.

CONCLUSIONS

This study validates the DDIR signature as predictive of response to neoadjuvant chemotherapy which can be integrated into clinical workflows, potentially identifying a subgroup with high sensitivity to anthracycline chemotherapy. Transcriptomic data suggest induction with anthracycline-containing regimens in immune restricted, "cold" tumours may be effective for immune priming.

TRIAL REGISTRATION

Not applicable (non-interventional study). CRUK Internal Database Number 14232.

Choice of PD-L1 immunohistochemistry assay influences clinical eligibility for gastric cancer immunotherapy

BACKGROUND

Immune checkpoint inhibitors (ICI) are now standard-of-care treatment for patients with metastatic gastric cancer (GC). To guide patient selection for ICI therapy, programmed death ligand-1 (PD-L1) biomarker expression is routinely assessed via immunohistochemistry (IHC). However, with an increasing number of approved ICIs, each paired with a different PD-L1 antibody IHC assay used in their respective landmark trials, there is an unmet clinical and logistical need for harmonization. We investigated the interchangeability between the Dako 22C3, Dako 28-8 and Ventana SP-142 assays in GC PD-L1 IHC.

METHODS

In this cross-sectional study, we scored 362 GC samples for PD-L1 combined positive score (CPS), tumor proportion score (TPS) and immune cells (IC) using a multiplex immunohistochemistry/immunofluorescence technique. Samples were obtained via biopsy or resection of gastric cancer.

RESULTS

The percentage of PD-L1-positive samples at clinically relevant CPS ≥ 1, ≥ 5 and ≥ 10 cut-offs for the 28-8 assay were approximately two-fold higher than that of the 22C3 (CPS ≥ 1: 70.3 vs 49.4%, p < 0.001; CPS ≥ 5: 29.1 vs 13.4%, p < 0.001; CPS ≥ 10: 13.7 vs 7.0%, p = 0.004). The mean CPS score on 28-8 assay was nearly double that of the 22C3 (6.39 ± 14.5 vs 3.46 ± 8.98, p < 0.001). At the clinically important CPS ≥ 5 cut-off, there was only moderate concordance between the 22C3 and 28-8 assays.

CONCLUSION

Our findings suggest that scoring PD-L1 CPS with the 28-8 assay may result in higher PD-L1 scores and higher proportion of PD-L1 positivity compared to 22C3 and other assays. Until stronger evidence of inter-assay concordance is found, we urge caution in treating the assays as equivalent.

Biomarkers of response to checkpoint inhibitors beyond PD-L1 in lung cancer

Immunotherapy, including use of checkpoint inhibitors against PD-1, PD-L1, and CTLA-4, forms the backbone of oncologic management for the majority of non-small cell lung carcinoma patients. However, response to these therapies varies widely, from patients who have complete resolution of metastatic disease and long-term remission, to those who rapidly progress and succumb to their cancer despite use of the newest checkpoint inhibitors. While PD-L1 protein expression by immunohistochemistry serves as the principle predictive biomarker for immunotherapy response, neither the sensitivity nor the specificity of this approach is optimal, and clinical PD-L1 testing is plagued by concerns around result reproducibility and confusion born from the proliferation of different companion diagnostic assays. At the same time, insights into tumor and host immune-specific factors that inform both prognosis and response prediction are beginning to define better immunotherapy biomarkers. Beyond immune checkpoint expression status, common themes in analyses of immunotherapy response prediction include cancer neoantigen production, the state of the antigen presentation pathway in both tumor and antigen presenting cells, the admixture of effector and suppressor immune cells in the tumor microenvironment, and the genomic drivers and comutations that can influence the all of these variables. This review will address the state of PD-L1 testing in lung cancer, the role for tumor mutation burden as a predictive biomarker, the evolving status of human leukocyte antigen/major histocompatibility complex expression as a marker of antigen presentation, approaches to tumor immune cell quantitation including by multiplex immunofluorescence, and the importance of tumor genomic profiling to ascertain oncogenic driver (EGFR, ALK, KRAS, MET, etc.) and co-mutation (STK11, KEAP1, SMARCA4) status.

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.

Prognostic features of the tumour microenvironment in oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is a disease with an incredibly poor survival rate and a complex makeup. The growth and spread of OAC tumours are profoundly influenced by their surrounding microenvironment and the properties of the tumour itself. Constant crosstalk between the tumour and its microenvironment is key to the survival of the tumour and ultimately the death of the patient. The tumour microenvironment (TME) is composed of a complex milieu of cell types including cancer associated fibroblasts (CAFs) which make up the tumour stroma, endothelial cells which line blood and lymphatic vessels and infiltrating immune cell populations. These various cell types and the tumour constantly communicate through environmental cues including fluctuations in pH, hypoxia and the release of mitogens such as cytokines, chemokines and growth factors, many of which help promote malignant progression. Eventually clusters of tumour cells such as tumour buds break away and spread through the lymphatic system to nearby lymph nodes or enter the circulation forming secondary metastasis. Collectively, these factors need to be considered when assessing and treating patients clinically. This review aims to summarise the ways in which these various factors are currently assessed and how they relate to patient treatment and outcome at an individual level.

Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation

Background: Multiplex tissue analysis has revolutionized our understanding of the tumor microenvironment (TME) with implications for biomarker development and diagnostic testing. Multiplex labeling is used for specific clinical situations, but there remain barriers to expanded use in anatomic pathology practice.

Methods: We review immunohistochemistry (IHC) and related assays used to localize molecules in tissues, with reference to United States regulatory and practice landscapes. We review multiplex methods and strategies used in clinical diagnosis and in research, particularly in immuno-oncology. Within the framework of assay design and testing phases, we examine the suitability of multiplex immunofluorescence (mIF) for clinical diagnostic workflows, considering its advantages and challenges to implementation.

Results: Multiplex labeling is poised to radically transform pathologic diagnosis because it can answer questions about tissue-level biology and single-cell phenotypes that cannot be addressed with traditional IHC biomarker panels. Widespread implementation will require improved detection chemistry, illustrated by InSituPlex technology (Ultivue, Inc., Cambridge, MA) that allows coregistration of hematoxylin and eosin (H&E) and mIF images, greater standardization and interoperability of workflow and data pipelines to facilitate consistent interpretation by pathologists, and integration of multichannel images into digital pathology whole slide imaging (WSI) systems, including interpretation aided by artificial intelligence (AI). Adoption will also be facilitated by evidence that justifies incorporation into clinical practice, an ability to navigate regulatory pathways, and adequate health care budgets and reimbursement. We expand the brightfield WSI system “pixel pathway” concept to multiplex workflows, suggesting that adoption might be accelerated by data standardization centered on cell phenotypes defined by coexpression of multiple molecules.

Conclusion: Multiplex labeling has the potential to complement next generation sequencing in cancer diagnosis by allowing pathologists to visualize and understand every cell in a tissue biopsy slide. Until mIF reagents, digital pathology systems including fluorescence scanners, and data pipelines are standardized, we propose that diagnostic labs will play a crucial role in driving adoption of multiplex tissue diagnostics by using retrospective data from tissue collections as a foundation for laboratory-developed test (LDT) implementation and use in prospective trials as companion diagnostics (CDx).

The Pandora's box of novel technologies that may revolutionize lung cancer

Non-small cell lung cancer (NSCLC) is one of the most common cancers globally and has a 5-year survival rate ~20%. Immunotherapies have demonstrated long-term and durable responses in NSCLC patients, although they appear to be effective in only a subset of patients. A more comprehensive understanding of the underlying tumour biology may contribute to identifying those patients likely to achieve optimal outcomes. Profiling the tumour microenvironment (TME) has shown to be beneficial in addressing fundamental tumour-immune cell interactions. Advances in multiplexing immunohistochemistry and molecular barcoding has led to recent advances in profiling genes and proteins in NSCLC. Here, we review the recent advancements in spatial profiling technologies for the analysis of NSCLC tissue samples to gain new insights and therapeutic options for NSCLC. The combination of spatial transcriptomics combined with advanced imaging is likely to lead to deep insights into NSCLC tissue biology, which can be a powerful tool to predict likelihood of response to therapy.

The clinical and molecular significance associated with STING signaling in breast cancer

STING signaling in cancer is a crucial component of response to immunotherapy and other anti-cancer treatments. Currently, there is no robust method of measuring STING activation in cancer. Here, we describe an immunohistochemistry-based assay with digital pathology assessment of STING in tumor cells. Using this novel approach in estrogen receptor-positive (ER+) and ER- breast cancer, we identify perinuclear-localized expression of STING (pnSTING) in ER+ cases as an independent predictor of good prognosis, associated with immune cell infiltration and upregulation of immune checkpoints. Tumors with low pnSTING are immunosuppressed with increased infiltration of "M2"-polarized macrophages. In ER- disease, pnSTING does not appear to have a significant prognostic role with STING uncoupled from interferon responses. Importantly, a gene signature defining low pnSTING expression is predictive of poor prognosis in independent ER+ datasets. Low pnSTING is associated with chromosomal instability, MYC amplification and mTOR signaling, suggesting novel therapeutic approaches for this subgroup.

Clinical and research applications of multiplexed immunohistochemistry and in situ hybridization

Over the past decade, invention and adoption of novel multiplexing technologies for tissues have made increasing impacts in basic and translational research and, to a lesser degree, clinical medicine. Platforms capable of highly multiplexed immunohistochemistry or in situ RNA measurements promise evaluation of protein or RNA targets at levels of plex and sensitivity logs above traditional methods - all with preservation of spatial context. These methods promise objective biomarker quantification, markedly increased sensitivity, and single-cell resolution. Increasingly, development of novel technologies is enabling multi-omic interrogations with spatial correlation of RNA and protein expression profiles in the same sample. Such sophisticated methods will provide unprecedented insights into tissue biology, biomarker science, and, ultimately, patient health. However, this sophistication comes at significant cost, requiring extensive time, practical knowledge, and resources to implement. This review will describe the technical features, advantages, and limitations of currently available multiplexed immunohistochemistry and spatial transcriptomic platforms. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Delineating the breast cancer immune microenvironment in the era of multiplex immunohistochemistry/immunofluorescence

Breast cancer is the most common malignancy and the leading cause of cancer death in females worldwide. Treatment is challenging, especially for those who are triple-negative. Increasing evidence suggests that diverse immune populations are present in the breast tumour microenvironment, which opens up avenues for personalised drug targets. Historically, our investigations into the immune constitution of breast tumours have been restricted to analyses of one or two markers at a given time. Recent technological advances have allowed simultaneous labelling of more than 35 markers and detailed profiling of tumour-immune infiltrates at the single-cell level, as well as determining the cellular composition and spatial analysis of the entire tumour architecture. In this review, we describe emerging technologies that have contributed to the field of breast cancer diagnosis, and discuss how to interpret the vast data sets obtained in order to effectively translate them for clinically relevant use.

PD-L1 and beyond: Immuno-oncology in cytopathology

Over the past decade, immunotherapy has emerged as one of the most promising cancer treatments. Several monoclonal antibodies targeting the programmed death 1 (PD-1)/ programmed death ligand-1 (PD-L1) pathway have been integrated into standard-of-care treatments for a wide range of cancer types. Although all the available PD-L1 immunohistochemistry (IHC) assays have been developed on formalin-fixed histological specimens, a growing body of research has recently suggested the feasibility of PD-L1 testing on cytological samples. Although promising results have been reported, several important issues still need to be addressed. Among these are pre-analytical issues, cyto-hystological correlation, and inter-observer agreement. This review will briefly summarise the knowledge gaps and future directions of cytopathology in the immuno-oncology scenario.

The Potential of Digital Image Analysis to Determine Tumor Cell Content in Biobanked Formalin-Fixed, Paraffin-Embedded Tissue Samples

Introduction: Best practices dictate that biobanks ensure accurate determination of tumor content before supplying formalin-fixed, paraffin-embedded (FFPE) tissue samples to researchers for nucleic acid extraction and downstream molecular testing. It is advisable that trained and competent individuals, who understand the requirements of the downstream molecular tests, perform the microscopic morphological examination. However, the special skills, time, and costs associated with these assessments can be prohibitive, especially in large case cohorts requiring extensive pathological review. Determination of tumor content reliably by digital image analysis (DIA) could represent a significant advantage if validated, utilized, and deployed by biobanks. Materials and Methods: Whole slide digital scanned images of colorectal, lung, and breast cancer specimens were created. The scanned images were imported into the DIA software QuPath and digital annotations were completed by biobank technicians, under the direction of trained histopathology senior scientists. Automated cell detection was conducted and tumor epithelial cells were classified and quantified. Results: DIA scores were highly concordant with the manual assessment for 376 of 435 samples (86%). A detailed review of discordant cases indicated digital scores had a higher accuracy than the manual estimation. Conclusion: Automated digital quantification has the potential to replace visual estimations with reduced subjectivity and increased reliability compared with manual tumor estimations. We recommend the use of DIA by biobanks involved in provision of FFPE tissue samples, especially in large research studies requiring high volumes of cases to be analyzed.

Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software

PD-L1 expression in non-small cell lung cancer (NSCLC) is predictive of response to immunotherapy, but scoring of PD-L1 immunohistochemistry shows considerable interobserver variability. Automated methods may allow more consistent and expedient PD-L1 scoring. We aimed to assess the technical concordance of PD-L1 scores produced using free open source QuPath software with the manual scores of three pathologists. A classifier for PD-L1 scoring was trained using 30 NSCLC image patches. A separate test set of 207 image patches from 69 NSCLC resection cases was used for comparison of automated and manual scores. Automated and average manual scores showed excellent correlation (concordance correlation coeffecient = 0.925), though automated scoring resulted in significantly more 1–49% scores than manual scoring (p = 0.012). At both 1% and 50% thresholds, automated scores showed a level of concordance with our ‘gold standard’ (the average of three pathologists’ manual scores) similar to that of individual pathologists. Automated scoring showed high sensitivity (95%) but lower specificity (84%) at a 1% threshold, and excellent specificity (100%) but lower sensitivity (71%) at a 50% threshold. We conclude that our automated PD-L1 scoring system for NSCLC has an accuracy similar to that of individual pathologists. The detailed protocol we provide for free open source scoring software and our discussion of the limitations of this technology may facilitate more effective integration of automated scoring into clinical workflows.

QuPath: The global impact of an open source digital pathology system

QuPath, originally created at the Centre for Cancer Research & Cell Biology at Queen's University Belfast as part of a research programme in digital pathology (DP) funded by Invest Northern Ireland and Cancer Research UK, is arguably the most wildly used image analysis software program in the world. On the back of the explosion of DP and a need to comprehensively visualise and analyse whole slides images (WSI), QuPath was developed to address the many needs associated with tissue based image analysis; these were several fold and, predominantly, translational in nature: from the requirement to visualise images containing billions of pixels from files several GBs in size, to the demand for high-throughput reproducible analysis, which the paradigm of routine visual pathological assessment continues to struggle to deliver. Resultantly, large-scale biomarker quantification must increasingly be augmented with DP. Here we highlight the impact of the open source Quantitative Pathology & Bioimage Analysis DP system since its inception, by discussing the scope of scientific research in which QuPath has been cited, as the system of choice for researchers.

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.

Hyperspectral multiphoton microscopy for in vivo visualization of multiple, spectrally overlapped fluorescent labels

The insensitivity of multiphoton microscopy to optical scattering enables high-resolution, high-contrast imaging deep into tissue, including in live animals. Scattering does, however, severely limit the use of spectral dispersion techniques to improve spectral resolution. In practice, this limited spectral resolution together with the need for multiple excitation wavelengths to excite different fluorophores limits multiphoton microscopy to imaging a few, spectrally-distinct fluorescent labels at a time, restricting the complexity of biological processes that can be studied. Here, we demonstrate a hyperspectral multiphoton microscope that utilizes three different wavelength excitation sources together with multiplexed fluorescence emission detection using angle-tuned bandpass filters. This microscope maintains scattering insensitivity, while providing high enough spectral resolution on the emitted fluorescence and capitalizing on the wavelength-dependent nonlinear excitation of fluorescent dyes to enable clean separation of multiple, spectrally overlapping labels, in vivo. We demonstrated the utility of this instrument for spectral separation of closely-overlapped fluorophores in samples containing ten different colors of fluorescent beads, live cells expressing up to seven different fluorescent protein fusion constructs, and in multiple in vivo preparations in mouse cortex and inflamed skin with up to eight different cell types or tissue structures distinguished.

TILs Immunophenotype in Breast Cancer Predicts Local Failure and Overall Survival: Analysis in a Large Radiotherapy Trial with Long-Term Follow-Up

AIM

To determine the prognostic significance of the immunophenotype of tumour-infiltrating lymphocytes (TILs) within a cohort of breast cancer patients with long-term follow-up.

METHODS

Multiplexed immunofluorescence and automated image analysis were used to assess the expression of CD3, CD8, CD20, CD68, Fox P3, PD-1 and PD-L1 in a clinical trial of local excision and radiotherapy randomised to a cavity boost or not (n = 485, median follow-up 16 years). Kaplan-Meier and Cox multivariate analysis (MVA) methodology were used to ascertain relationships with local recurrence (LR), overall survival (OS) and disease-free survival (DFS). NanoString BC360 gene expression panel was applied to a subset of luminal patients to identify pathways associated with LR.

RESULTS

LR was predicted by low CD8 in MVA in the whole cohort (HR 2.34, CI 1.4-4.02, p = 0.002) and luminal tumours (HR 2.19, CI 1.23-3.92, p = 0.008) with associations with increased stromal components, decreased Tregs (FoxP3), inflammatory chemokines and SOX2. Poor OS was associated with low CD20 in the whole cohort (HR 1.73, CI 1.2-2.4, p = 0.002) and luminal tumours on MVA and low PD-L1 in triple-negative cancer (HR 3.44, CI 1.5-7, p = 0.003).

CONCLUSIONS

Immunophenotype adds further prognostic data to help further stratify risk of LR and OS even in TILs low-luminal tumours.