Digital quantitative assessment of PD-L1 using digital spatial profiling

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms

AIMS

To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.

METHODS

The study included samples from 50 early-stage NSCLCs. PD-L1 immunohistochemistry (IHC) stained slides (clone SP263) were scored manually and with two different AI tools (PathAI and Navify Digital Pathology) by three pathologists. TILs were digitally assessed on H&E and CD8 IHC stained sections with two different algorithms (PathAI and Navify Digital Pathology, respectively). The agreement between observers and methods for each biomarker was analysed. For PD-L1, the turn-around time (TAT) for manual versus AI-assisted scoring was recorded.

RESULTS

Agreement was higher in tumours with low PD-L1 expression regardless of the approach. Both AI-powered tools identified a significantly higher number of cases equal or above 1% PD-L1 tumour proportion score as compared with manual scoring (p=0.00015), a finding with potential therapeutic implications. Regarding TAT, there were significant differences between manual scoring and AI use (p value <0.0001 for all comparisons). The total TILs density with the PathAI algorithm and the total density of CD8+ cells with the Navify Digital Pathology software were significantly correlated (τ=0.49 (95% CI 0.37, 0.61), p value<0.0001).

CONCLUSIONS

This preliminary study supports the use of AI algorithms for the scoring of PD-L1 and TILs in patients with NSCLC.

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

Development of small-molecular-based radiotracers for PET imaging of PD-L1 expression and guiding the PD-L1 therapeutics

PURPOSE

Programmed cell death protein ligand 1 (PD-L1) is a crucial biomarker for immunotherapy. However, nearly 70% of patients do not respond to PD-L1 immune checkpoint therapy. Accurate monitoring of PD-L1 expression and quantification of target binding during treatment are essential. In this study, a series of small-molecule radiotracers were developed to assess PD-L1 expression and direct immunotherapy.

METHODS

Radiotracers of [68Ga]Ga-D-PMED, [68Ga]Ga-D-PEG-PMED, and [68Ga]Ga-D-pep-PMED were designed based on a 2-methyl-3-biphenyl methanol scaffold and successfully synthesized. Cellular experiments and molecular docking assays were performed to determine their specificity for PD-L1. PD-L1 status was investigated via positron emission tomography (PET) imaging in MC38 tumor models. PET imaging of [68Ga]Ga-D-pep-PMED was performed to noninvasively quantify PD-L1 blocking using an anti-mouse PD-L1 antibody (PD-L1 mAb).

RESULTS

The radiosyntheses of [68Ga]Ga-D-PMED, [68Ga]Ga-D-PEG-PMED, and [68Ga]Ga-D-pep-PMED were achieved with radiochemical yields of 87 ± 6%, 82 ± 4%, and 79 ± 9%, respectively. In vitro competition assays demonstrated their high affinities (the IC50 values of [68Ga]Ga-D-PMED, [68Ga]Ga-D-PEG-PMED, and [68Ga]Ga-D-pep-PMED were 90.66 ± 1.24, 160.8 ± 1.35, and 51.6 ± 1.32 nM, respectively). At 120 min postinjection (p.i.) of the radiotracers, MC38 tumors displayed optimized tumor-to-muscle ratios for all radioligands. Owing to its hydrophilic modification, [68Ga]Ga-D-pep-PMED had the highest target-to-nontarget (T/NT) ratio of approximately 6.2 ± 1.2. Interestingly, the tumor/liver ratio was hardly affected by different concentrations of the inhibitor BMS202. We then evaluated the impacts of dose and time on accessible PD-L1 levels in the tumor during anti-mouse PD-L1 antibody treatment. The tumor uptake of [68Ga]Ga-D-pep-PMED significantly decreased with increasing PD-L1 mAb dose. Moreover, after 8 days of treatment with a single antibody, the uptake of [68Ga]Ga-D-pep-PMED in the tumor significantly increased but remained lower than that in the saline group.

CONCLUSION

PET imaging with [68Ga]Ga-D-pep-PMED, a small-molecule radiotracer, is a promising tool for evaluating PD-L1 expression and quantifying the target blockade of PD-L1 to assist in the development of effective therapeutic regimens.

Non-electrophysiological techniques targeting transient receptor potential (TRP) gene of gastrointestinal tract

Transient receptor potential (TRP) channels are cation channels related to a wide range of physical and chemical stimuli, they are expressed all along the gastrointestinal system, and a myriad of diseases are often associated with aberrant expression or mutation of the TRP gene, suggesting that TRPs are promising targets for drug therapy. Therefore, a better understanding of the information of TRPs in health and disease could facilitate the development of effective drugs for the treatment of gastrointestinal diseases like IBD. But there are very few generalizations about the experimental techniques studied in this field. In view of the promise of TRP as a therapeutic target, we discuss experimental methods that can be used for TRPs including their distribution, function and interaction with other proteins, as well as some promising emerging technologies to provide experimental methods for future studies.

Spatial Relationships in the Tumor Microenvironment Demonstrate Association with Pathologic Response to Neoadjuvant Chemoimmunotherapy in Muscle-invasive Bladder Cancer

BACKGROUND

Platinum-based neoadjuvant chemotherapy (NAC) is standard for patients with muscle-invasive bladder cancer (MIBC). Pathologic response (complete: ypT0N0 and partial:

OBJECTIVE

Using the NanoString GeoMx platform, we performed proteomic digital spatial profiling (DSP) on transurethral resections of bladder tumors from 18 responders (

DESIGN, SETTING, AND PARTICIPANTS

Pretreatment tumor samples were stained by hematoxylin and eosin and immunofluorescence (panCK and CD45) to select four regions of interest (ROIs): tumor enriched (TE), immune enriched (IE), tumor/immune interface (tumor interface = TX and immune interface = IX).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

DSP was performed with 52 protein markers from immune cell profiling, immunotherapy drug target, immune activation status, immune cell typing, and pan-tumor panels.

RESULTS AND LIMITATIONS

Protein marker expression patterns were analyzed to determine their association with pathologic response, incorporating or agnostic of their ROI designation (TE/IE/TX/IX). Overall, DSP-based marker expression showed high intratumoral heterogeneity; however, response was associated with markers including PD-L1 (ROI agnostic), Ki-67 (ROI agnostic, TE, IE, and TX), HLA-DR (TX), and HER2 (TE). An elastic net model of response with ROI-inclusive markers demonstrated better validation set performance (area under the curve [AUC] = 0.827) than an ROI-agnostic model (AUC = 0.432). A model including DSP, tumor mutational burden, and clinical data performed no better (AUC = 0.821) than the DSP-only model.

CONCLUSIONS

Despite high intratumoral heterogeneity of DSP-based marker expression, we observed associations between pathologic response and specific DSP-based markers in a spatially dependent context. Further exploration of tumor region-specific biomarkers may help predict response to neoadjuvant chemoimmunotherapy in MIBC.

PATIENT SUMMARY

In this study, we used the GeoMx platform to perform proteomic digital spatial profiling on transurethral resections of bladder tumors from 18 responders and 18 nonresponders from two studies of neoadjuvant chemotherapy (gemcitabine and cisplatin) plus immune checkpoint inhibitor therapy (LCCC1520 [pembrolizumab] and BLASST-1 [nivolumab]). We found that assessing protein marker expression in the context of tumor architecture improved response prediction.

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Key Words

• BLASST-1
• Bladder cancer
• Digital spatial profiling
• Elastic net regression
• GeoMx
• LCCC1520
• Neoadjuvant chemoimmunotherapy

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MESH Headings

• Humans
• Cisplatin
• Neoadjuvant Therapy/methods
• Gemcitabine
• Nivolumab/therapeutic use
• Proteomics
• Tumor Microenvironment
• Urinary Bladder Neoplasms/drug therapy
• Urinary Bladder Neoplasms/pathology
• Biomarkers, Tumor
• Muscles/pathology
• Immunotherapy
• Neoplasm Invasiveness
• Cystectomy

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Grants

• F30 CA278317 NCI NIH HHS
• P30 CA016086 NCI NIH HHS
• R01 CA241810 NCI NIH HHS
• T32 CA244125 NCI NIH HHS

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Affiliation(s)

Wolfgang Beckabir Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Sara E Wobker Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Pathology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jeffrey S Damrauer Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Bentley Midkiff Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Gabriela De la Cruz Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Vladmir Makarov Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
Leah Flick Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Mark G Woodcock Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Petros Grivas Department of Medicine, Division of Medical Oncology, University of Washington, Fred Hutchinson Cancer Center, Seattle, WA, USA
Marc A Bjurlin Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Michael R Harrison Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC, USA
Benjamin G Vincent Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA; Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA; Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, USA; Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA
Tracy L Rose Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Shilpa Gupta Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
William Y Kim Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Matthew I Milowsky Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

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Detection and quantitative analysis of tumor-associated tertiary lymphoid structures

Tumor-associated tertiary lymphoid structures (TLSs) are ectopic lymphoid formations within tumor tissue, with mainly B and T cell populations forming the organic aggregates. The presence of TLSs in tumors has been strongly associated with patient responsiveness to immunotherapy regimens and improving tumor prognosis. Researchers have been motivated to actively explore TLSs due to their bright clinical application prospects. Various studies have attempted to decipher TLSs regarding their formation mechanism, structural composition, induction generation, predictive markers, and clinical utilization. Meanwhile, the scientific approaches to qualitative and quantitative descriptions are crucial for TLS studies. In terms of detection, hematoxylin and eosin (H&E), multiplex immunohistochemistry (mIHC), multiplex immunofluorescence (mIF), and 12-chemokine gene signature have been the top approved methods. However, no standard methods exist for the quantitative analysis of TLSs, such as absolute TLS count, analysis of TLS constituent cells, structural features, TLS spatial location, density, and maturity. This study reviews the latest research progress on TLS detection and quantification, proposes new directions for TLS assessment, and addresses issues for the quantitative application of TLSs in the clinic.

Lack of correlation between MET and PD-L1 expression in non-small cell lung cancer revealed by comparative study of matched biopsies and surgical resection samples

INTRODUCTION

Both MET expression and the PD-L1 tumor proportion score (TPS) are companion diagnostics for treatment of advanced non-small cell lung carcinoma (aNSCLC) patients. We evaluated the rate of correlation between MET expression and the PD-L1 TPS in matched biopsies and surgically resected specimens from NSCLC patients.

PATIENTS AND METHODS

This retrospective analysis assessed the prevalence and correlation between MET expression (SP44 clone) and the PD-L1 TPS (22C3 clone) by immunohistochemistry together with molecular alterations determined by targeted next-generation sequencing in matched lung biopsy and surgically lung resected specimens from 70 patients with NSCLC.

RESULTS

The study found a significant correlation between the MET H-score in surgical samples and matched biopsies (P-value < 0.0001), as well as between the PD-L1 TPS in paired biopsies and surgical samples (P-value < 0.0001). However, there was no significant correlation between the MET H-score or expression subgroups and the PD-L1 TPS in both types of paired samples (P-value = 0.47, and P-value = 0.90). The MET H-score was significantly higher in adenocarcinoma compared to squamous cell carcinoma (P-value < 0.0001). A mutational analysis showed that the MET H-score was significantly higher in NSCLC cases with targetable molecular alterations (P-value = 0.0095), while no significant correlation was found for the PD-L1 TPS.

CONCLUSIONS

Our study found no significant correlation between PD-L1 and MET expression in samples from NSCLC patients, highlighting the importance of personalized treatment strategies based on individual expression profiles. These findings provide valuable insight into the development of effective immunotherapy and targeted therapy for NSCLC patients.

Ultra High-plex Spatial Proteogenomic Investigation of Giant Cell Glioblastoma Multiforme Immune Infiltrates Reveals Distinct Protein and RNA Expression Profiles

A deeper understanding of complex biological processes, including tumor development and immune response, requires ultra high-plex, spatial interrogation of multiple "omes". Here we present the development and implementation of a novel spatial proteogenomic (SPG) assay on the GeoMx Digital Spatial Profiler platform with next-generation sequencing readout that enables ultra high-plex digital quantitation of proteins (>100-plex) and RNA (whole transcriptome, >18,000-plex) from a single formalin-fixed paraffin-embedded (FFPE) sample. This study highlighted the high concordance, R > 0.85 and <15% change in sensitivity between the SPG assay and the single-analyte assays on various cell lines and tissues from human and mouse. Furthermore, we demonstrate that the SPG assay was reproducible across multiple users. When used in conjunction with advanced cellular neighborhood segmentation, distinct immune or tumor RNA and protein targets were spatially resolved within individual cell subpopulations in human colorectal cancer and non-small cell lung cancer. We used the SPG assay to interrogate 23 different glioblastoma multiforme (GBM) samples across four pathologies. The study revealed distinct clustering of both RNA and protein based on pathology and anatomic location. The in-depth investigation of giant cell glioblastoma multiforme (gcGBM) revealed distinct protein and RNA expression profiles compared with that of the more common GBM. More importantly, the use of spatial proteogenomics allowed simultaneous interrogation of critical protein posttranslational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods.

Significance

We describe ultra high-plex spatial proteogenomics; profiling whole transcriptome and high-plex proteomics on a single FFPE tissue section with spatial resolution. Investigation of gcGBM versus GBM revealed distinct protein and RNA expression profiles.

Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer

The utility of spatial immunobiomarker quantitation in prognostication and therapeutic prediction is actively being investigated in triple-negative breast cancer (TNBC). Here, with high-plex quantitative digital spatial profiling, we map and quantitate intraepithelial and adjacent stromal tumor immune protein microenvironments in systemic treatment-naïve (female only) TNBC to assess the spatial context in immunobiomarker-based prediction of outcome. Immune protein profiles of CD45-rich and CD68-rich stromal microenvironments differ significantly. While they typically mirror adjacent, intraepithelial microenvironments, this is not uniformly true. In two TNBC cohorts, intraepithelial CD40 or HLA-DR enrichment associates with better outcomes, independently of stromal immune protein profiles or stromal TILs and other established prognostic variables. In contrast, intraepithelial or stromal microenvironment enrichment with IDO1 associates with improved survival irrespective of its spatial location. Antigen-presenting and T-cell activation states are inferred from eigenprotein scores. Such scores within the intraepithelial compartment interact with PD-L1 and IDO1 in ways that suggest prognostic and/or therapeutic potential. This characterization of the intrinsic spatial immunobiology of treatment-naïve TNBC highlights the importance of spatial microenvironments for biomarker quantitation to resolve intrinsic prognostic and predictive immune features and ultimately inform therapeutic strategies for clinically actionable immune biomarkers.

Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody

BACKGROUND

Immunotherapy for malignant tumors has made great progress, but many patients do not benefit from it. The complex intratumoral heterogeneity (ITH) hindered the in-depth exploration of immunotherapy. Conventional bulk sequencing has masked intratumor complexity, preventing a more detailed discovery of the impact of ITH on treatment efficacy. Hence, we initiated this study to explore ITH at the multi-omics spatial level and to seek prognostic biomarkers of immunotherapy efficacy considering the presence of ITH.

METHODS

Using the segmentation strategy of digital spatial profiling (DSP), we obtained differential information on tumor and stromal regions at the proteomic and transcriptomic levels. Based on the consideration of ITH, signatures constructed by candidate proteins in different regions were used to predict the efficacy of immunotherapy.

RESULTS

Eighteen patients treated with a bispecific antibody (bsAb)-KN046 were enrolled in this study. The tumor and stromal areas of the same samples exhibited distinct features. Signatures consisting of 11 and 18 differentially expressed DSP markers from the tumor and stromal areas, respectively, were associated with treatment response. Furthermore, the spatially resolved signature identified from the stromal areas showed greater predictive power for bsAb immunotherapy response (area under the curve=0.838). Subsequently, our stromal signature was validated in an independent cohort of patients with non-small cell lung cancer undergoing immunotherapy.

CONCLUSION

We deciphered ITH at the spatial level and demonstrated for the first time that genetic information in the stromal region can better predict the efficacy of bsAb treatment.

TRIAL REGISTRATION NUMBER

NCT03838848.

High-Plex Spatial Profiling of RNA and Protein Using Digital Spatial Profiler

The rapid emergence of spatial multi-omics technologies in recent years has revolutionized biomedical research. Among these, the Digital Spatial Profiler (DSP, commercialized by nanoString) has become one of the dominant technologies in spatial transcriptomics and proteomics and has assisted in deconvoluting complex biological questions. Based on our practical experience in the past 3 years with DSP, we share here a detailed hands-on protocol and key handling notes that will allow the broader community to optimize their work procedure.

Spatial Analysis of Neural Cell Proteomic Profiles Following Ischemic Stroke in Mice Using High-Plex Digital Spatial Profiling

Stroke is ranked as the fifth leading cause of death and the leading cause of adult disability in the USA. The progression of neuronal damage after stroke is recognized to be a complex integration of glia, neurons, and the surrounding extracellular matrix, therefore potential treatments must target the detrimental effects created by these interactions. In this study, we examined the spatial cellular and neuroinflammatory mechanisms occurring early after ischemic stroke utilizing Nanostring Digital Spatial Profiling (DSP) technology. Male C57bl/6 mice were subjected to photothrombotic middle cerebral artery occlusion (MCAO) and sacrificed at 3 days post-ischemia. Spatial distinction of the ipsilateral hemisphere was studied according to the regions of interest: the ischemic core, peri-infarct tissues, and peri-infarct normal tissue (PiNT) in comparison to the contralateral hemisphere. We demonstrated that the ipsilateral hemisphere initiates distinct spatial regulatory proteomic profiles with DSP technology that can be identified consistently with the immunohistochemical markers, FJB, GFAP, and Iba-1. The core border profile demonstrated an induction of neuronal death, apoptosis, autophagy, immunoreactivity, and early degenerative proteins. Most notably, the core border resulted in a decrease of the neuronal proteins Map2 and NeuN; an increase in the autophagy proteins BAG3 and CTSD; an increase in the microglial and peripheral immune invasion proteins Iba1, CD45, CD11b, and CD39; and an increase in the neurodegenerative proteins BACE1, APP, amyloid β 1-42, ApoE, and hyperphosphorylated tau protein S-199. The peri-infarct region demonstrated increased astrocytic, immunoreactivity, apoptotic, and neurodegenerative proteomic profiles, with an increase in BAG3, GFAP, and hyperphosphorylated tau protein S-199. The PiNT region displayed minimal changes compared to the contralateral cortex with only an increase in GFAP. In this study, we showed that mechanisms known to be associated with stroke, such as apoptosis and inflammation, occur in distinct spatial domains of the injured brain following ischemia. We also demonstrated the dysregulation of specific autophagic pathways that may lead to neurodegeneration in peri-infarct brain tissues. Taken together, these data suggest that identifying post-ischemic mechanisms occurring in a spatiotemporal manner may lead to more precise targets for successful therapeutic interventions to treat stroke.

A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology

Interpreting the mechanisms and principles that govern gene activity and how these genes work according to -their cellular distribution in organisms has profound implications for cancer research. The latest technological advancements, such as imaging-based approaches and next-generation single-cell sequencing technologies, have established a platform for spatial transcriptomics to systematically quantify the expression of all or most genes in the entire tumor microenvironment and explore an array of disease milieus, particularly in tumors. Spatial profiling technologies permit the study of transcriptional activity at the spatial or single-cell level. This multidimensional classification of the transcriptomic and proteomic signatures of tumors, especially the associated immune and stromal cells, facilitates evaluation of tumor heterogeneity, details of the evolutionary trajectory of each tumor, and multifaceted interactions between each tumor cell and its microenvironment. Therefore, spatial profiling technologies may provide abundant and high-resolution information required for the description of clinical-related features in immuno-oncology. From this perspective, the present review will highlight the importance of spatial transcriptomic and spatial proteomics analysis along with the joint use of other sequencing technologies and their implications in cancers and immune-oncology. In the near future, advances in spatial profiling technologies will undoubtedly expand our understanding of tumor biology and highlight possible precision therapeutic targets for cancer patients.

Spatial Profiling of the Prostate Cancer Tumor Microenvironment Reveals Multiple Differences in Gene Expression and Correlation with Recurrence Risk

The tumor microenvironment plays a crucial role in both the development and progression of prostate cancer. Furthermore, identifying protein and gene expression differences between different regions is valuable for treatment development. We applied Digital Spatial Profiling multiplex analysis to formalin-fixed paraffin embedded prostatectomy tissue blocks to investigate protein and transcriptome differences between tumor, tumor-adjacent stroma (TAS), CD45+ tumor, and CD45+ TAS tissue. Differential expression of an immunology/oncology protein panel (n = 58) was measured. OX40L and CTLA4 were expressed at higher levels while 22 other proteins, including CD11c, were expressed at lower levels (FDR < 0.2 and p-value < 0.05) in TAS as compared to tumor epithelia. A tissue microarray analysis of 97 patients with 1547 cores found positive correlations between high expression of CD11c and increased time to recurrence in tumor and TAS, and inverse relationships for CTLA4 and OX40L, where higher expression in tumor correlated with lower time to recurrence, but higher time to recurrence in TAS. Spatial transcriptomic analysis using a Cancer Transcriptome Atlas panel (n = 1825 genes) identified 162 genes downregulated and 69 upregulated in TAS versus tumor, 26 downregulated and 6 upregulated in CD45+ TAS versus CD45+ tumor. We utilized CIBERSORTx to estimate the relative immune cell fractions using CD45+ gene expression and found higher average fractions for memory B, naïve B, and T cells in TAS. In summary, the combination of protein expression differences, immune cell fractions, and correlations of protein expression with time to recurrence suggest that closely examining the tumor microenvironment provides valuable data that can improve prognostication and treatment techniques.

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.

From rough to precise: PD-L1 evaluation for predicting the efficacy of PD-1/PD-L1 blockades

Developing biomarkers for accurately predicting the efficacy of immune checkpoint inhibitor (ICI) therapies is conducive to avoiding unwanted side effects and economic burden. At the moment, the quantification of programmed cell death ligand 1 (PD-L1) in tumor tissues is clinically used as one of the combined diagnostic assays of response to anti-PD-1/PD-L1 therapy. However, the current assays for evaluating PD-L1 remain imperfect. Recent studies are promoting the methodologies of PD-L1 evaluation from rough to precise. Standardization of PD-L1 immunohistochemistry tests is being promoted by using optimized reagents, platforms, and cutoff values. Combining novel in vivo probes with PET or SPECT will probably be of benefit to map the spatio-temporal heterogeneity of PD-L1 expression. The dynamic change of PD-L1 in the circulatory system can also be realized by liquid biopsy. Consider PD-L1 expressed on non-tumor (immune and non-immune) cells, and optimized combination detection indexes are further improving the accuracy of PD-L1 in predicting the efficacy of ICIs. The combinations of artificial intelligence with novel technologies are conducive to the intelligence of PD-L1 as a predictive biomarker. In this review, we will provide an overview of the recent progress in this rapidly growing area and discuss the clinical and technical challenges.

Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx® Digital Spatial Profiler

Characterization of the tumor microenvironment through immunoprofiling has become an essential resource for the understanding of the complex immune cell interactions and the assessment of biomarkers for prognosis and prediction of immunotherapy response; however, these studies are often limited by tissue heterogeneity and sample size. The nanoString GeoMx^® Digital Spatial Profiler (DSP) is a platform that allows high-plex profiling at the protein and RNA level, providing spatial and temporal assessment of tumors in frozen or formalin-fixed paraffin-embedded limited tissue sample. Recently, high-impact studies have shown the feasibility of using this technology to identify biomarkers in different settings, including predictive biomarkers for immunotherapy in different tumor types. These studies showed that compared to other multiplex and high-plex platforms, the DSP can interrogate a higher number of biomarkers with higher throughput; however, it does not provide single-cell resolution, including co-expression of biomarker or spatial information at the single-cell level. In this review, we will describe the technical overview of the platform, present current evidence of the advantages and limitations of the applications of this technology, and provide important considerations for the experimental design for translational immune-oncology research using this tissue-based high-plex profiling approach.

Postmortem high-dimensional immune profiling of severe COVID-19 patients reveals distinct patterns of immunosuppression and immunoactivation

A complete diagnostic autopsy is the gold-standard to gain insight into Coronavirus disease 2019 (COVID-19) pathogenesis. To delineate the in situ immune responses to SARS-CoV-2 viral infection, here we perform comprehensive high-dimensional transcriptional and spatial immune profiling in 22 COVID-19 decedents from Wuhan, China. We find TIM-3-mediated and PD-1-mediated immunosuppression as a hallmark of severe COVID-19, particularly in men, with PD-1+ cells being proximal rather than distal to TIM-3+ cells. Concurrently, lymphocytes are distal, while activated myeloid cells are proximal, to SARS-CoV-2 viral antigens, consistent with prevalent SARS-CoV-2 infection of myeloid cells in multiple organs. Finally, viral load positively correlates with specific immunosuppression and dendritic cell markers. In summary, our data show that SARS-CoV-2 viral infection induces lymphocyte suppression yet myeloid activation in severe COVID-19, so these two cell types likely have distinct functions in severe COVID-19 disease progression, and should be targeted differently for therapy.

Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future

Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.

Characteristics and Spatially Defined Immune (micro)landscapes of Early-stage PD-L1-positive Triple-negative Breast Cancer

PURPOSE

Programmed death ligand 1 [PD-(L)1]-targeted therapies have shown modest survival benefit in triple-negative breast cancer (TNBC). PD-L1+ microenvironments in TNBC are not well characterized and may inform combinatorial immune therapies. Herein, we characterized clinicopathologic features, RNA-based immune signatures, and spatially defined protein-based tumor-immune microenvironments (TIME) in early-stage PD-L1+ and PD-L1- TNBC.

EXPERIMENTAL DESIGN

From a large cohort of chemotherapy-naïve TNBC, clinicopathologic features, deconvoluted RNA immune signatures, and intraepithelial and stromal TIME (Nanostring GeoMX) were identified in subsets of PD-L1+ and PD-L1- TNBC, as defined by FDA-approved PD-L1 companion assays.

RESULTS

228 of 499 (46%) TNBC were PD-L1+ (SP142: ≥1% immune cells-positive). Using PD-L1 22C3, 46% had combined positive score (CPS) ≥ 1 and 16% had CPS ≥10. PD-L1+ TNBC were higher grade with higher tumor-infiltrating lymphocytes (TIL; P < 0.05). PD-L1 was not associated with improved survival following adjustment for TILs and other variables. RNA profiles of PD-L1+ TNBC had increased dendritic cell, macrophage, and T/B cell subset features; and decreased myeloid-derived suppressor cells. PD-L1+ stromal and intraepithelial TIMEs were highly enriched in IDO-1, HLA-DR, CD40, and CD163 compared with PD-L1-TIME, with spatially specific alterations in CTLA-4, Stimulator of Interferon Genes (STING), and fibronectin. Macrophage- and antigen presentation-related proteins correlated most strongly with PD-L1 protein.

CONCLUSIONS

In this early-stage TNBC cohort, nearly 50% were PD-L1+ (SP142 companion assay) while 16% were PD-L1+ with the 22C3 companion assay. PD-L1+ TNBC had specific myeloid-derived and lymphoid features. Spatially defined PD-L1+ TIME were enriched in several clinically actionable immune proteins. These data may inform future studies on combinatorial immunotherapies for patients with PD-L1+ TNBC.See related commentary by Symmans, p. 5446.

Selecting the optimal immunotherapy regimen in driver-negative metastatic NSCLC

The treatment landscape of driver-negative non-small-cell lung cancer (NSCLC) is rapidly evolving. Immune-checkpoint inhibitors, specifically those targeting PD-1 or PD-L1, have demonstrated durable efficacy in a subset of patients with NSCLC, and these agents have become the cornerstone of first-line therapy. Approved immunotherapeutic strategies for treatment-naive patients now include monotherapy, immunotherapy-exclusive regimens or chemotherapy-immunotherapy combinations. Decision making in this space is complex given the absence of head-to-head prospective comparisons, although a thorough analysis of long-term efficacy and safety data from pivotal clinical trials can provide insight into the optimal management of each subset of patients. Indeed, histological subtype and the extent of tumour cell PD-L1 expression are paramount to regimen selection, although other clinicopathological factors and patient preferences might also be relevant in certain scenarios. Finally, several emerging biomarkers and novel therapeutic strategies are currently under investigation, and these might further refine the current treatment paradigm. In this Review, we discuss the current treatment landscape and detail our approach to first-line immunotherapy regimen selection for patients with advanced-stage, driver-negative NSCLC.

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.

Transcriptional Spatial Profiling of Cancer Tissues in the Era of Immunotherapy: The Potential and Promise

Intratumoral heterogeneity poses a major challenge to making an accurate diagnosis and establishing personalized treatment strategies for cancer patients. Moreover, this heterogeneity might underlie treatment resistance, disease progression, and cancer relapse. For example, while immunotherapies can confer a high success rate, selective pressures coupled with dynamic evolution within a tumour can drive the emergence of drug-resistant clones that allow tumours to persist in certain patients. To improve immunotherapy efficacy, researchers have used transcriptional spatial profiling techniques to identify and subsequently block the source of tumour heterogeneity. In this review, we describe and assess the different technologies available for such profiling within a cancer tissue. We first outline two well-known approaches, in situ hybridization and digital spatial profiling. Then, we highlight the features of an emerging technology known as Visium Spatial Gene Expression Solution. Visium generates quantitative gene expression data and maps them to the tissue architecture. By retaining spatial information, we are well positioned to identify novel biomarkers and perform computational analyses that might inform on novel combinatorial immunotherapies.