Automated Tumour Recognition and Digital Pathology Scoring Unravels New Role for PD-L1 in Predicting Good Outcome in ER-/HER2+ Breast Cancer

The search for a TNBC vaccine: the guardian vaccine

Nearly 20 million people are diagnosed with cancer each year with breast cancer being the most common among women. Triple negative breast cancer (TNBC), defined by its no/low expression of ER and PR and lack of amplification of HER2, makes up 15-20% of all breast cancer cases. While patients overall have a higher response to chemotherapy, this subgroup is associated with the lowest survival rate indicating significant clinical and molecular heterogeneity demanding alternate treatment options. Therefore, new therapies have been explored, with a large focus on utilizing the immune system. A whole host of immunotherapies have been studied including immune checkpoint inhibitors, now standard of care for eligible patients, and possibly the most exciting and promising is that of a TNBC vaccine. While currently there are no approved TNBC vaccines, this review highlights many promising studies and points to an antigen, p53, which we believe is highly relevant for TNBC.

PD-L1 as a Biomarker in Gastric Cancer Immunotherapy

Combining chemotherapy with immune checkpoint inhibitors (ICIs) that target the programmed death-1 (PD-1) protein has been shown to be a clinically effective first-line treatment for human epidermal growth factor receptor 2 (HER2)-negative and -positive advanced or metastatic gastric cancer (GC). Currently, PD-1 inhibitors combined with chemotherapy are the standard treatment for patients with HER2-negative/positive locally advanced or metastatic GC. Programmed death-ligand 1 (PD-L1) expression, as assessed using immunohistochemistry (IHC), is a crucial biomarker for predicting response to anti-PD-1/PD-L1 agents in various solid tumors, including GC. In GC, the PD-L1 IHC test serves as a companion or complementary diagnostic test for immunotherapy, and an accurate interpretation of PD-L1 status is essential for selecting patients who may benefit from immunotherapy. However, PD-L1 IHC testing presents several challenges that limit its reliability as a biomarker for immunotherapy. In this review, we provide an overview of the current practices of immunotherapy and PD-L1 testing in GC. In addition, we discuss the clinical challenges associated with PD-L1 testing and its future use as a biomarker for immunotherapy. Finally, we present prospective biomarkers currently under investigation as alternative predictors of immunotherapy response in GC.

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.

Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients.

Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review

Anti-programmed cell death ligand 1 (anti-PD-L1)  immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.

Implementation of Digital Image Analysis in Assessment of Ki67 Index in Breast Cancer

The clinical utility of the proliferation marker Ki67 in breast cancer treatment and prognosis is an active area of research. Studies have suggested that differences in pre-analytic and analytic factors contribute to low analytical validity of the assay, with scoring methods accounting for a large proportion of this variability. Use of standard scoring methods is limited, in part due to the time intensive nature of such reporting protocols. Therefore, use of digital image analysis tools may help to both standardize reporting and improve workflow. In this study, digital image analysis was utilized to quantify Ki67 indices in 280 breast biopsy and resection specimens during routine clinical practice. The supervised Ki67 indices were then assessed for agreement with a manual count of 500 tumor cells. Agreement was excellent, with an intraclass correlation coefficient of 0.96 for the pathologist-supervised analysis. This study illustrates an example of a rapid, accurate workflow for implementation of digital image analysis in Ki67 scoring in breast cancer.

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

The Cell-Autonomous Pro-Metastatic Activities of PD-L1 in Breast Cancer Are Regulated by N-Linked Glycosylation-Dependent Activation of STAT3 and STAT1

PD-L1 has been characterized as an inhibitory immune checkpoint, leading to the suppression of potential anti-tumor immune activities in many cancer types. In view of the relatively limited efficacy of immune checkpoint blockades against PD-L1 in breast cancer, our recent study addressed the possibility that in addition to its immune-inhibitory functions, PD-L1 promotes the pro-metastatic potential of the cancer cells themselves. Indeed, our published findings demonstrated that PD-L1 promoted pro-metastatic functions of breast cancer cells in a cell-autonomous manner, both in vitro and in vivo. These functions fully depended on the integrity of the S283 intracellular residue of PD-L1. Here, using siRNAs and the S283A-PD-L1 variant, we demonstrate that the cell-autonomous pro-metastatic functions of PD-L1-tumor cell proliferation and invasion, and release of the pro-metastatic chemokine CXCL8-required the activation of STAT3 and STAT1 in luminal A and triple-negative breast cancer cells. The cell-autonomous pro-metastatic functions of PD-L1 were potently impaired upon inhibition of N-linked glycosylation (kifunensine). Site-specific mutants at each of the N-linked glycosylation sites of PD-L1 (N35, N192, N200, and N219) revealed that they were all required for PD-L1-induced pro-metastatic functions to occur; the N219 site was the main regulator of STAT3 and STAT1 activation, with accompanying roles for N192 and N200 (depending on the cell type). Using a T cell-independent mouse system, we found that cells expressing N35A-PD-L1 and N219A-PD-L1 had a significantly lower tumorigenic and metastatic potential than cells expressing WT-PD-L1. TCGA analyses revealed significant associations between reduced survival and high levels of α-mannosidase II (inferring on N-linked glycosylation) in breast cancer patients. These findings suggest that N-linked glycosylation of PD-L1 may be used to screen for patients who are at greater risk of disease progression, and that modalities targeting N-linked glycosylated PD-L1 may lead to the inhibition of its cell-autonomous pro-metastatic functions and to lower tumor progression in breast cancer.

Applications of Artificial Intelligence in Breast Pathology

CONTEXT.—

Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.

OBJECTIVE.—

To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.

DATA SOURCES.—

We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.

CONCLUSIONS.—

With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.

Fusobacterium nucleatum: a novel immune modulator in breast cancer?

Breast cancer was the most commonly diagnosed cancer worldwide in 2020. Greater understanding of the factors which promote tumour progression, metastatic development and therapeutic resistance is needed. In recent years, a distinct microbiome has been detected in the breast, a site previously thought to be sterile. Here, we review the clinical and molecular relevance of the oral anaerobic bacterium Fusobacterium nucleatum in breast cancer. F. nucleatum is enriched in breast tumour tissue compared with matched healthy tissue and has been shown to promote mammary tumour growth and metastatic progression in mouse models. Current literature suggests that F. nucleatum modulates immune escape and inflammation within the tissue microenvironment, two well-defined hallmarks of cancer. Furthermore, the microbiome, and F. nucleatum specifically, has been shown to affect patient response to therapy including immune checkpoint inhibitors. These findings highlight areas of future research needed to better understand the influence of F. nucleatum in the development and treatment of breast cancer.

Computer-assisted tumor grading, validation of PD-L1 scoring, and quantification of CD8-positive immune cell density in urothelial carcinoma, a visual guide for pathologists using QuPath

Background

Advances in digital imaging in pathology and the new capacity to scan high-quality images have change the way to practice and research in surgical pathology. QuPath is an open-source pathology software that offers a reproducible way to analyze quantified variables. We aimed to present the functionality of biomarker scoring using QuPath and provide a guide for the validation of pathologic grading using a series of cases of urothelial carcinomas.

Methods

Tissue microarrays of urothelial carcinomas were constructed and scanned. The images stained with HE, CD8 and PD-L1 immunohistochemistry were imported into QuPath and dearrayed. Training images were used to build a grade classifier and applied to all cases. Quantification of CD8 and PD-L1 was undertaken for each core using cytoplasmic and membrane color segmentation and output measurement and compared with pathologists semi-quantitative assessments.

Results

There was a good correlation between tumor grade by the pathologist and by QuPath software (Kappa agreement 0.73). For low-grade carcinomas (by the report and pathologist), the concordance was not as high. Of the 32 low-grade tumors, 22 were correctly classified as low-grade, but 11 (34%) were diagnosed as high-grade, with the high-grade to the low-grade ratio in these misclassified cases ranging from 0.41 to 0.58. The median ratio for bona fide high-grade carcinomas was 0.59. Some of the reasons the authors list as potential mimickers for high-grade cases are fulguration artifact, nuclear hyperchromasia, folded tissues, and inconsistency in staining. The correlation analysis between the software and the pathologist showed that the CD8 marker showed a moderate (r = 0.595) and statistically significant (p < 0.001) correlation. The internal consistency of this parameter showed an index of 0.470. The correlation analysis between the software and the pathologist showed that the PDL1 marker showed a robust (r = 0.834) and significant (p < 0.001) correlation. The internal consistency of this parameter showed a CCI of 0.851.

Conclusions

We were able to demonstrate the utility of QuPath in identifying and scoring tumor cells and IHC quantification of two biomarkers. The protocol we present uses a free open-source platform to help researchers deal with imaging and data processing in the surgical pathology field.

Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer

Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxylin and eosin (H&E) is a robust staining used routinely for cancer diagnosis. Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques. With the help of two expert pathologists and a designed annotation software, we construct a dataset to assess the feasibility of PD-L1 prediction from H&E in breast cancer. In a cohort of 3,376 patients, our system predicts the PD-L1 status in a high area under the curve (AUC) of 0.91 - 0.93. Our system is validated on two external datasets, including an independent clinical trial cohort, showing consistent prediction performance. Furthermore, the proposed system predicts which cases are prone to pathologists miss-interpretation, showing it can serve as a decision support and quality assurance system in clinical practice.

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.

Programmed Death-Ligand 1 (PD-L1) as Immunotherapy Biomarker in Breast Cancer

Breast cancer constitutes the most common malignant neoplasm in women around the world. Approximately 12% of patients are diagnosed with metastatic stage, and between 5 and 30% of early or locally advanced BC patients will relapse, making it an incurable disease. PD-L1 ligation is an immune inhibitory molecule of the activation of T cells, playing a relevant role in numerous types of malignant tumors, including BC. The objective of the present review is to analyze the role of PD-L1 as a biomarker in the different BC subtypes, adding clinical trials with immune checkpoint inhibitors and their applicable results. Diverse trials using immunotherapy with anti-PD-1/PD-L1 in BC, as well as prospective or retrospective cohort studies about PD-L1 in BC, were included. Despite divergent results in the reviewed studies, PD-L1 seems to be correlated with worse prognosis in the hormone receptor positive subtype. Immune checkpoints inhibitors targeting the PD-1/PD-L1 axis have achieved great response rates in TNBC patients, especially in combination with chemotherapy, making immunotherapy a new treatment option in this scenario. However, the utility of PD-L1 as a predictive biomarker in the rest of BC subtypes remains unclear. In addition, predictive differences have been found in response to immunotherapy depending on the stage of the tumor disease. Therefore, a better understanding of tumor microenvironment, as well as identifying new potential biomarkers or combined index scores, is necessary in order to make a better selection of the subgroups of BC patients who will derive benefit from immune checkpoint inhibitors.

Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images

Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019-9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.

QuPath Digital Immunohistochemical Analysis of Placental Tissue

Background:

QuPath is an open-source digital image analyzer notable for its user-friendly design, cross-platform compatibility, and customizable functionality. Since it was first released in 2016, at least 624 publications have reported its use, and it has been applied in a wide spectrum of settings. However, there are currently limited reports of its use in placental tissue. Here, we present the use of QuPath to quantify staining of G-protein coupled receptor 18 (GPR18), the receptor for the pro-resolving lipid mediator Resolvin D2, in placental tissue.

Methods:

Whole slide images of vascular smooth muscle (VSM) and extravillous trophoblast (EVT) cells stained for GPR18 were annotated for areas of interest. Visual scoring was performed on these images by trained and in-training pathologists, while QuPath scoring was performed with the methodology described herein.

Results:

Bland–Altman analyses showed that, for the VSM category, the two methods were comparable across all staining levels. For EVT cells, the high-intensity staining level was comparable across methods, but the medium and low staining levels were not comparable.

Conclusions:

Digital image analysis programs offer great potential to revolutionize pathology practice and research by increasing accuracy and decreasing the time and cost of analysis. Careful study is needed to optimize this methodology further.

An Automated Approach to Improve the Quantification of Pericytes and Microglia in Whole Mouse Brain Sections

Whole slide scanning technology has enabled the generation of high-resolution images from complete tissue sections. However, commonly used analysis software is often unable to handle the large data files produced. Here, we present a method using the open-source software QuPath to detect, classify and quantify fluorescently-labeled cells (microglia and pericytes) in whole coronal brain tissue sections. Whole-brain sections from both male and female NG2DsRed x CX₃CR1^+/GFP mice were analyzed. Small regions of interest were selected and manual counts were compared with counts generated from an automated approach, across a range of detection parameters. The optimal parameters for detecting cells and classifying them as microglia or pericytes in each brain region were determined and applied to annotations corresponding to the entire somatosensory and motor cortices, hippocampus, thalamus, and hypothalamus in each section. 3.74% of all detected cells were classified as pericytes; however, this proportion was significantly higher in the thalamus (6.20%) than in other regions. In contrast, microglia (4.51% of total cells) were more abundant in the cortex (5.54%). No differences were detected between male and female mice. In conclusion, QuPath offers a user-friendly solution to whole-slide image analysis which could lead to important new discoveries in both health and disease.

A Pyramid Architecture-Based Deep Learning Framework for Breast Cancer Detection

Breast cancer diagnosis is a critical step in clinical decision making, and this is achieved by making a pathological slide and gives a decision by the doctors, which is the method of final decision making for cancer diagnosis. Traditionally, the doctors usually check the pathological images by visual inspection under the microscope. Whole-slide images (WSIs) have supported the state-of-the-art diagnosis results and have been admitted as the gold standard clinically. However, this task is time-consuming and labour-intensive, and all of these limitations make low efficiency in decision making. Medical image processing protocols have been used for this task during the last decades and have obtained satisfactory results under some conditions; especially in the deep learning era, it has exhibited the advantages than those in the shallow learning period. In this paper, we proposed a novel breast cancer region mining framework based on deep pyramid architecture from multilevel and multiscale breast pathological WSIs. We incorporate the tissue- and cell-level information together and integrate these into a LSTM model for the final sequence modelling, which successfully keeps the WSIs' integration and is not mentioned by the prevalence frameworks. The experiment results demonstrated that our proposed framework greatly improved the detection accuracy than that only using tissue-level information.

Discordance of PD-L1 Expression at the Protein and RNA Levels in Early Breast Cancer

Simple Summary

Despite the increasing use of checkpoint inhibitors for early and metastatic breast cancer, Programmed Death Ligand 1 (PD-L1) remains the only validated albeit imperfect predictive biomarker. Significant discordance in PD-L1 protein expression depending on the antibody used has been demonstrated, while the weak correlation and discordant prognostic information between protein and gene expression underscore its biologic heterogeneity. In this study, we use material from two patient cohorts of early breast cancer and multiple methodologies (immunohistochemistry, RNA fluorescent in situ hybridization, immunofluorescence, bulk gene expression, and multiplex fluorescent immunohistochemistry) to demonstrate the significant discordance in PD-L1 expression among various methods and between different areas of the same tumor, which hints toward the presence of spatial, intratumoral and biological heterogeneity.

Abstract

We aimed to assess if the discrepant prognostic information between Programmed Death Ligand 1 (PD-L1) protein versus mRNA expression in early breast cancer (BC) could be attributed to heterogeneity in its expression. PD-L1 protein and mRNA expression in BC tissue microarrays from two clinical patient cohorts were evaluated (105 patients; cohort 1: untreated; cohort 2: neoadjuvant chemotherapy-treated). Immunohistochemistry (IHC) with SP142, SP263 was performed. PD-L1 mRNA was evaluated using bulk gene expression and RNA-FISH RNAscope^®, the latter scored in a semi-quantitative manner and combined with immunofluorescence (IF) staining for the simultaneous detection of PD-L1 protein expression. PD-L1 expression was assessed in cores as a whole and in two regions of interest (ROI) from the same core. The cell origin of PD-L1 expression was evaluated using multiplex fluorescent IHC. IHC PD-L1 expression between SP142 and SP263 was concordant in 86.7% of cores (p < 0.001). PD-L1 IF/IHC was weakly correlated with spatial mRNA expression (concordance 54.6–71.2%). PD-L1 was mostly expressed by lymphocytes intra-tumorally, while its stromal expression was mostly observed in macrophages. Our results demonstrate only moderate concordance between the various methods of assessing PD-L1 expression at the protein and mRNA levels, which may be attributed to both analytical performance and spatial heterogeneity.

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.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

Digital Pathology Scoring of Immunohistochemical Staining Reliably Identifies Prognostic Markers and Anatomical Associations in a Large Cohort of Oral Cancers

Utilizing digital pathology algorithms for the objective quantification of immunohistochemical staining, this study aimed to identify robust prognostic biomarkers for oral cancer. Tissue microarrays with specimens of a large cohort of oral squamous cell carcinoma (n=222) were immunohistochemically stained to determine the expression of PD-L1, EGFR, and COX-2 and the amount of infiltrating NK cells and CD8-positive T cells. Immunoreactivity scores were assessed using both a classical manual scoring procedure and a digital semi-automatic approach using QuPath. Digital scoring was successful in quantifying the expression levels of different prognostic biomarkers (CD8: p<0.001; NK cells: p=0.002, PD-L1: p=0.026) and high levels of concordance with manual scoring results were observed. A combined score integrating EGFR expression, neck node status and immune cell signatures with a significant impact on overall and progression-free survival was identified (p<0.001). These data may contribute to the ongoing research on the identification of reliable and clinically relevant biomarkers for the individualization of primary and adjuvant treatment in oral cancer.

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.

Detection of Human Cytomegalovirus Proteins in Paraffin-Embedded Breast Cancer Tissue Specimens-A Novel, Automated Immunohistochemical Staining Protocol

Emerging evidence supports a significant association between human cytomegalovirus (HCMV) and human malignancies, suggesting HCMV as a human oncomodulatory virus. HCMV gene products are found in >90% of breast cancer tumors and seem to be correlated with more aggressive disease. The definitive diagnosis of HCMV relies on identification of virus inclusions and/or viral proteins by different techniques including immunohistochemical staining. In order to reduce biases and improve clinical value of HCMV diagnostics in oncological pathology, automation of the procedure is needed and this was the purpose of this study. Tumor specimens from 115 patients treated for primary breast cancer at Akershus University Hospital in Norway were available for the validation of the staining method in this retrospective study. We demonstrate that our method is highly sensitive and delivers excellent reproducibility for staining of HCMV late antigen (LA), which makes this method useful for future routine diagnostics and scientific applications.

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.

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.

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.

Comparison of different anti-Ki67 antibody clones and hot-spot sizes for assessing proliferative index and grading in pancreatic neuroendocrine tumours using manual and image analysis

AIMS

Ki67 proliferative index (PI) is essential for grading gastroenteric and pancreatic neuroendocrine tumours (GEP NETs). Analytical and preanalytical variables can affect Ki67 PI. In contrast to counting methodology, until now little attention has focused on the question of clone equivalence and the effect of hot-spot size on Ki67 PI in GEP NETs. Using manual counting and image analysis, this study compared the Ki67 PI achieved using MM1, K2 and 30-9 to MIB1, a clone which has been validated for, and is referenced in, guidelines relating to assessment of Ki67 PI in GEP NETs.

METHODS AND RESULTS

Forty-two pancreatic NETs were each immunohistochemically stained for the anti-Ki67 clones MIB1, MM1, K2 and 30-9. Ki67 PI was calculated manually and by image analysis, the latter using three different hot-spot sizes. In manual comparisons using single hot-spot high-power fields, non-MIB1 clones overestimated Ki67 PI compared to MIB1, resulting in grading discordances. Image analysis shows good agreement with manual Ki67 PI but a tendency to overestimate absolute Ki67 PI. Increasing the size of tumour hot-spot from 500 to 2000 cells resulted in a decrease in Ki67 PI.

CONCLUSION

Different anti-Ki67 clones do not produce equivalent PIs in GEP NETs, and clone selection may therefore affect patient care. Increasing the hot-spot size decreases the Ki67 PI. Greater standardisation in terms of antibody clone selection and hot-spot size is required for grading GEP NETs. Image analysis is an effective tool for assisting Ki67 assessment and allows easier standardisation of the size of the tumour hot-spot.

Comparison of three scoring methods using the FDA-approved 22C3 immunohistochemistry assay to evaluate PD-L1 expression in breast cancer and their association with clinicopathologic factors

BACKGROUND

In the evaluation of PD-L1 expression to select patients for anti-PD-1/PD-L1 treatment, uniform guidelines that account for different immunohistochemistry assays, different cell types and different cutoff values across tumor types are lacking. Data on how different scoring methods compare in breast cancer are scant.

METHODS

Using FDA-approved 22C3 diagnostic immunohistochemistry assay, we retrospectively evaluated PD-L1 expression in 496 primary invasive breast tumors that were not exposed to anti-PD-1/PD-L1 treatment and compared three scoring methods (TC: invasive tumor cells; IC: tumor-infiltrating immune cells; TCIC: a combination of tumor cells and immune cells) in expression frequency and association with clinicopathologic factors.

RESULTS

In the entire cohort, positive PD-L1 expression was observed in 20% of patients by TCIC, 16% by IC, and 10% by TC, with a concordance of 87% between the three methods. In the triple-negative breast cancer patients, positive PD-L1 expression was observed in 35% by TCIC, 31% by IC, and 16% by TC, with a concordance of 76%. Associations between PD-L1 and clinicopathologic factors were investigated according to receptor groups and whether the patients had received neoadjuvant chemotherapy. The three scoring methods showed differences in their associations with clinicopathologic factors in all subgroups studied. Positive PD-L1 expression by IC was significantly associated with worse overall survival in patients with neoadjuvant chemotherapy and showed a trend for worse overall survival and distant metastasis-free survival in triple-negative patients with neoadjuvant chemotherapy. Positive PD-L1 expression by TCIC and TC also showed trends for worse survival in different subgroups.

CONCLUSIONS

Our findings indicate that the three scoring methods with a 1% cutoff are different in their sensitivity for PD-L1 expression and their associations with clinicopathologic factors. Scoring by TCIC is the most sensitive way to identify PD-L1-positive breast cancer by immunohistochemistry. As a prognostic marker, our study suggests that PD-L1 is associated with worse clinical outcome, most often shown by the IC score; however, the other scores may also have clinical implications in some subgroups. Large clinical trials are needed to test the similarities and differences of these scoring methods for their predictive values in anti-PD-1/PD-L1 therapy.

The adaptive immune and immune checkpoint landscape of neoadjuvant treated esophageal adenocarcinoma using digital pathology quantitation

BACKGROUND

Limited studies examine the immune landscape in Esophageal Adenocarcinoma (EAC). We aim to identify novel associations, which may inform immunotherapy treatment stratification.

METHODS

Three hundred twenty-nine EAC cases were available in Tissue Microarrays (TMA) format. A discovery cohort of 166 EAC cases were stained immunohistochemically for range of adaptive immune (CD3, CD4, CD8 and CD45RO) and immune checkpoint biomarkers (ICOS, IDO-1, PD-L1, PD-1). A validation cohort of 163 EAC cases was also accessed. A digital pathology analysis approach was used to quantify biomarker density.

RESULTS

CD3, CD4, CD8, CD45RO, ICOS and PD-1 were individually predictive of better overall survival (OS) (Log rank p = < 0.001; p = 0.014; p = 0.001; p = < 0.001; p = 0.008 and p = 0.026 respectively). Correlation and multivariate analysis identified high CD45RO/ICOS patients with significantly improved OS which was independently prognostic (HR = 0.445, (0.223-0.886), p = 0.021). Assessment of CD45RO and ICOS high cases in the validation cohort revealed an associated with improved OS (HR = 0.601 (0.363-0.996), p = 0.048). Multiplex IHC identified cellular co-expression of high CD45RO/ICOS. High CD45RO/ICOS patients have significantly improved OS.

CONCLUSIONS

Multiplexing identifies true cellular co-expression. These data demonstrate that co-expression of immune biomarkers are associated with better outcome in EAC and may provide evidence for immunotherapy treatment stratification.

Glucocorticoid Receptor Expression Predicts Good Outcome in response to Taxane-Free, Anthracycline-Based Therapy in Triple Negative Breast Cancer

Triple negative breast cancer (TNBC) is a poor outcome subset of breast cancers characterised by the lack of expression of ER α, PR, and HER2 amplification. It is a heterogeneous group of cancers which fail to derive benefit from modern, more targeted treatments such as Tamoxifen and Herceptin. Current standard of care (SoC) is cytotoxic chemotherapy, which is effective for some patients, with other patients deriving little/no benefit and lacking alternative treatments. This study has identified the glucocorticoid receptor (GR) as a potential predictive biomarker of response to anthracycline-based chemotherapy in triple negative breast cancer (TNBC). GR gene expression levels in patient samples were analysed through publicly available microarray datasets as well as protein expression through immunohistochemistry (IHC) and correlated with clinical/pathological outcomes, including survival. While the results confirmed previous observations that high GR expression is associated with poor outcome in response to taxane-based chemotherapy, this study shows for the first time that high GR expression is associated with improved outcomes in the context of anthracycline-based chemotherapy. GR therefore has the potential to be used as a predictive biomarker to guide treatment choices and ensure that patients derive the greatest benefit from first line treatment, avoiding unnecessary costs, side effects, and disease progression.

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

Targeting of the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis with checkpoint inhibitors has changed clinical practice in non-small cell lung cancer (NSCLC). However, clinical assessment remains complex and ambiguous. We aim to assess whether digital image analysis (DIA) and multiplex immunofluorescence can improve the accuracy of PD-L1 diagnostic testing. A clinical cohort of routine NSCLC patients reflex tested for PD-L1 (SP263) immunohistochemistry (IHC), was assessed using DIA. Samples of varying assessment difficulty were assessed by multiplex immunofluorescence. Sensitivity, specificity, and concordance was evaluated between manual diagnostic evaluation and DIA for chromogenic and multiplex IHC. PD-L1 expression by DIA showed significant concordance (R² = 0.8248) to manual assessment. Sensitivity and specificity was 86.8% and 91.4%, respectively. Evaluation of DIA scores revealed 96.8% concordance to manual assessment. Multiplexing enabled PD-L1+/CD68+ macrophages to be readily identified within PD-L1+/cytokeratin+ or PD-L1-/cytokeratin+ tumor nests. Assessment of multiplex vs. chromogenic IHC had a sensitivity and specificity of 97.8% and 91.8%, respectively. Deployment of DIA for PD-L1 diagnostic assessment is an accurate process of case triage. Multiplex immunofluorescence provided higher confidence in PD-L1 assessment and could be offered for challenging cases by centers with appropriate expertise and specialist equipment.

The path to a better biomarker: application of a risk management framework for the implementation of PD-L1 and TILs as immuno-oncology biomarkers in breast cancer clinical trials and daily practice

Immune checkpoint inhibitor therapies targeting PD-1/PD-L1 are now the standard of care in oncology across several hematologic and solid tumor types, including triple negative breast cancer (TNBC). Patients with metastatic or locally advanced TNBC with PD-L1 expression on immune cells occupying ≥1% of tumor area demonstrated survival benefit with the addition of atezolizumab to nab-paclitaxel. However, concerns regarding variability between immunohistochemical PD-L1 assay performance and inter-reader reproducibility have been raised. High tumor-infiltrating lymphocytes (TILs) have also been associated with response to PD-1/PD-L1 inhibitors in patients with breast cancer (BC). TILs can be easily assessed on hematoxylin and eosin-stained slides and have shown reliable inter-reader reproducibility. As an established prognostic factor in early stage TNBC, TILs are soon anticipated to be reported in daily practice in many pathology laboratories worldwide. Because TILs and PD-L1 are parts of an immunological spectrum in BC, we propose the systematic implementation of combined PD-L1 and TIL analyses as a more comprehensive immuno-oncological biomarker for patient selection for PD-1/PD-L1 inhibition-based therapy in patients with BC. Although practical and regulatory considerations differ by jurisdiction, the pathology community has the responsibility to patients to implement assays that lead to optimal patient selection. We propose herewith a risk-management framework that may help mitigate the risks of suboptimal patient selection for immuno-therapeutic approaches in clinical trials and daily practice based on combined TILs/PD-L1 assessment in BC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Programmed Cell Death Ligand 1 in Breast Cancer: Technical Aspects, Prognostic Implications, and Predictive Value

In the light of recent advances in the immunotherapy field for breast cancer (BC) treatment, especially in the triple-negative subtype, the identification of reliable biomarkers capable of improving patient selection is paramount, because only a portion of patients seem to derive benefit from this appealing treatment strategy. In this context, the role of programmed cell death ligand 1 (PD-L1) as a potential prognostic and/or predictive biomarker has been intensively explored, with controversial results. The aim of the present review is to collect available evidence on the biological relevance and clinical utility of PD-L1 expression in BC, with particular emphasis on technical aspects, prognostic implications, and predictive value of this promising biomarker. IMPLICATIONS FOR PRACTICE: In the light of the promising results coming from trials of immune checkpoint inhibitors for breast cancer treatment, the potential predictive and/or prognostic role of programmed cell death ligand 1 (PD-L1) in breast cancer has gained increasing interest. This review provides clinicians with an overview of the available clinical evidence regarding PD-L1 as a biomarker in breast cancer, focusing on both data with a possible direct impact on clinic and methodological pitfalls that need to be addressed in order to optimize PD-L1 implementation as a clinically useful tool for breast cancer management.

Translational AI and Deep Learning in Diagnostic Pathology

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

Blockade of ErbB2 and PD-L1 using a bispecific antibody to improve targeted anti-ErbB2 therapy

A significant proportion of human epidermal growth factor receptor 2 (Her2/ErbB2)-positive metastatic breast cancer patients are refractory to Her2-targeted trastuzumab-like therapy. Some of this resistance has been attributed to the upregulation of immune checkpoints such as programmed cell death-1 (PD-1) and its ligand, PD-L1 in Her2-positive breast cancer patients. Therefore, therapies targeting both the PD-1/PD-L1 interaction and oncogenic Her2 signaling are of significant clinical interest. Here, we constructed a mouse bispecific antibody targeting PD-L1 and rat Her2 (referred to as BsPD-L1xrErbB2) aiming to redirect the anti-PD-L1 response toward Her2-expressing tumor cells. BsPD-L1xrErbB2 demonstrated additive binding to interferon (IFN)-γ treated Her2⁺ TUBO tumor cells, but it did not affect the proliferation of tumor cells in-vitro. BsPD-L1xrErbB2 also blocked the PD-1/PD-L1 interaction. This bispecific antibody was constructed with a mouse IgG2a Fc backbone and interacted with Fcγ receptors and resulted in complement deposition (C3). ADCC and complement action could be potential mechanisms of action of this molecule. BsPD-L1xrErbB2 successfully reduced TUBO tumor growth and increased tumor rejection rate compared to the monovalent anti-PD-L1, monovalent anti-ErbB2 or the combination of anti-PD-L1 and anti-ErbB2 monotherapies. The enhanced anti-tumor effect of BsPD-L1xrErbB2 was dependent on CD8⁺ T lymphocytes and IFN-γ, as depletion of CD8⁺ T lymphocytes and neutralization of IFN-γ completely abolished the antitumor activity of the bispecific antibody. Consistently, BsPD-L1xrErbB2 treatment also increased the frequency of intratumor CD8⁺ T lymphocytes. Taken together, our data support a bispecific antibody approach to enhance the anti-tumor efficacy of PD-1/PD-L1 checkpoint blockade in Her2-positive metastatic breast cancers.

A Novel Role for Cathepsin S as a Potential Biomarker in Triple Negative Breast Cancer

Cathepsin S (CTSS) has previously been implicated in a number of cancer types, where it is associated with poor clinical features and outcome. To date, patient outcome in breast cancer has not been examined with respect to this protease. Here, we carried out immunohistochemical (IHC) staining of CTSS using a breast cancer tissue microarray in patients who received adjuvant therapy. We scored CTSS expression in the epithelial and stromal compartments and evaluated the association of CTSS expression with matched clinical outcome data. We observed differences in outcome based on CTSS expression, with stromal-derived CTSS expression correlating with a poor outcome and epithelial CTSS expression associated with an improved outcome. Further subtype characterisation revealed high epithelial CTSS expression in TNBC patients with improved outcome, which remained consistent across two independent TMA cohorts. Further in silico gene expression analysis, using both in-house and publicly available datasets, confirmed these observations and suggested high CTSS expression may also be beneficial to outcome in ER-/HER2+ cancer. Furthermore, high CTSS expression was associated with the BL1 Lehmann subgroup, which is characterised by defects in DNA damage repair pathways and correlates with improved outcome. Finally, analysis of matching IHC analysis reveals an increased M1 (tumour destructive) polarisation in macrophage in patients exhibiting high epithelial CTSS expression. In conclusion, our observations suggest epithelial CTSS expression may be prognostic of improved outcome in TNBC. Improved outcome observed with HER2+ at the gene expression level furthermore suggests CTSS may be prognostic of improved outcome in ER- cancers as a whole. Lastly, from the context of these patients receiving adjuvant therapy and as a result of its association with BL1 subgroup CTSS may be elevated in patients with defects in DNA damage repair pathways, indicating it may be predictive of tumour sensitivity to DNA damaging agents.