Single Slide Research Articles

Can multiplexing Immunohistochemistry for PD-L1, CD68, and CD3 improve scoring for “Keytruda®” Indication?

Abstract Introduction/Objective FDA-approved companion diagnostics immunohistochemistry scoring for PD-L1 is essential for Keytruda ® treatment in patients with NSCLC. The challenge with PD-L1 testing alone is false positive PD-L1 expression score due to alveolar macrophage infiltration. Multiplexing with PD-L1, CD68, and CD3 could help alleviate this issue. By viewing PD-L1 expression on tumor cells, CD68 expression on macrophages, and the presence of CD3+ T cells on a single slide, pathologists will be able to see the presence of an immune microenvironment (high PD-L1 expression with abundant macrophages) and the potential for response to other types of Immunotherapies (presence of T cells). Methods/Case Report Positive NSCLC FFPE blocks were sectioned and stained using Agilent Dako Omnis. Two combinations of multiplex staining were prepared for each block by staining PD-L1 (brown) alone first followed by CD3 and CD68. First slide: CD3(Vina Green), CD68(Magenta); Second slide: CD3(Magenta), CD68(Vina Green). To prevent wash out, dehydration and clearing occurred with two changes of 100% alcohol and three changes of xylene for manual cover slipping. Results (if a Case Study enter NA) Manual scoring of PD-L1 alone vs two different multiplex combinations was compared to Agilent Proscia digital AI scoring using a preliminary study set (9 positives, 2 negatives). CD68-green and CD3-magenta showed better discrimination than CD68-magenta and CD3-green at a glance. This could be due to the staining intensity and nonspecific background staining of CD68-magenta. Manual scoring was higher compared to digital scoring, which could be due to the human error of positive bias. Digital scoring was significantly lower for PD-L1 alone vs both multiplex stains. The digital scoring for the multiplex stains showed a higher scoring percentage by removing false positive macrophages. Conclusion Reflex scoring workflow is being developed to focus on difficult cases using the multiplex triple stain, i.e. clear negative and clear >50% with 3+ intensity. More cases will be collected and studied to further investigate the improved PD-L1 scoring with digital imaging assisted by artificial intelligence and to compare the multiplex color combinations.

A best practices framework for spatial biology studies in drug discovery and development: enabling successful cohort studies using digital spatial profiling

ABSTRACT The discovery of biomarkers, essential for successful drug development, is often hindered by the limited availability of tissue samples, typically obtained through core needle biopsies. Standard ‘omics platforms can consume significant amounts of tissue, forcing scientist to trade off spatial context for high-plex assays, such as genome-wide assays. While bulk gene expression approaches and standard single-cell transcriptomics have been valuable in defining various molecular and cellular mechanisms, they do not retain spatial context. As such, they have limited power in resolving tissue heterogeneity and cell–cell interactions. Current spatial transcriptomics platforms offer limited transcriptome coverage and have low throughput, restricting the number of samples that can be analyzed daily or even weekly. While the Digital Spatial Profiling (DSP) method does not provide single-cell resolution, it presents a significant advancement by enabling scalable whole transcriptome and ultrahigh-plex protein analysis from distinct tissue compartments and structures using a single tissue slide. These capabilities overcome significant constraints in biomarker analysis in solid tissue specimens. These advancements in tissue profiling play a crucial role in deepening our understanding of disease biology and in identifying potential therapeutic targets and biomarkers. To enhance the use of spatial biology tools in drug discovery and development, the DSP Scientific Consortium has created best practices guidelines. These guidelines, built on digital spatial profiling data and expertise, offer a practical framework for designing spatial studies and using current and future spatial biology platforms. The aim is to improve tissue analysis in all research areas supporting drug discovery and development.

DNA-directed immobilization fluorescent immunoarray for multiplexed antibiotic residue determination in milk.

The presence of antibiotic residues in cow's milk entails high risk for consumers, the dairy industry, and the environment. Therefore, the development of highly specific and sensitive screening tools for the rapid and cost-effective identification of traces of these compounds is urgently needed. A multiplexed screening platform utilizing DNA-directed immobilization (DDI) was developed aiming to detect three classes of antibiotic residues (fluoroquinolones, sulfonamides, and tylosin) prevalently found in milk. Throughout this work, each oligonucleotide sequence was conjugated to a different hapten molecule, while the three complementary strands were immobilized in 24 independent microarray chips on a single glass slide. First, the array was incubated with the pool of hapten-oligonucleotide conjugate site encoded the signal through DNA hybridization. Next, commercial milk samples were incubated with the cocktail of monoclonal antibodies following a secondary fluorophore-labeled antibody which was required for fluorescent readout. Direct sample detection was achieved in milk diluting 20 times in assay buffer. The limits of detection (LODs) reached were 1.43µgkg-1, 1.67µgkg-1, and 0.89µgkg-1 for TYLA, STZ, and CIP, respectively, which represented in raw milk 7.15µgkg-1, 8.35µgkg-1, and 4.45µgkg-1 for TYLA, STZ, and CIP, respectively, that are below the EU regulatory limits. Cross-reactivity profiles were evaluated against the family of structurally related antibiotics in order to demonstrate the capability to detect antibiotics from the same family of compounds. A pre-validation study was performed by spiking 20 blind samples above and below the maximum residue limits established by the EU guidelines. The system was successfully implemented towards randomized sample classification as compliant or non-compliant. The proposed DDI-based immunoarray provides a fast and cost-effective alternative to obtain semi-quantitative information about the presence of three veterinary residues simultaneously in milk samples.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks.

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

Effects of SARS-COV-2 on molecules involved in vascularization and autophagy in placenta tissues

SARS-CoV-2 infection is considered as a multi-organ disease, and several studies highlighted the relevance of the virus infection in the induction of vascular injury and tissue morphological alterations, including placenta. In this study, immunohistochemical analyses were carried out on placenta samples derived from women with COVID-19 infection at delivery (SARS-CoV-2 PCR+) or women healed from a COVID-19 infection (SARS-CoV-2 negative at delivery, SARS-CoV-2 PCR-) or women who gave birth before 2019 (Control). Angiotensin Converting Enzyme 2 (ACE2) receptor, Cluster of differentiation 147 (CD147), endothelial CD34 marker, Vascular Endothelial Growth Factor (VEGF) and total Microtubule-associated protein 1 Light Chain 3B marker (LC3B) were investigated in parallel with SPIKE protein by standard IHC. Multiplexed Immunohistochemical Consecutive Staining on Single Slide (MICSSS) was used to examine antigen co-expression in the same specimen. SPIKE protein was detected in villi and decidua from women with ongoing infection, with no significant differences in SPIKE staining between both biopsy sites. VEGF was significantly increased in SARS-CoV-2 PCR + biopsies compared to control and SARS-CoV-2 PCR- samples, and MICSSS method showed the co-localization of SPIKE with VEGF and CD34. The induction of autophagy, as suggested by the LC3B increase in SARS-CoV-2 PCR + biopsies and the co-expression of LC3B with SPIKE protein, may explain one of the different mechanisms by which placenta may react to infection. These data could provide important information on the impact that SARS-CoV-2 may have on the placenta and mother-to-fetus transmission.

Genotype Phenotype Correlation of Renal Tumors in the Cancer Genome Atlas Database.

Morphological features are critical in evaluation of renal tumors and directing molecular workup. The objective of this study was to review histomorphology of renal tumors with molecular alterations of known subtypes. Renal tumors in The Cancer Genome Atlas were reviewed to identify tumors with defining molecular alterations. Single representative digital slides and pathology reports were reviewed and morphologic features recorded. Sixty tumors were identified with molecular alterations in genes characteristic of defined renal cell carcinoma (RCC) subtypes. Findings included the presence of both low- and high-grade histology in TFE3 rearranged RCCs, TFEB amplified RCCs, succinate dehydrogenase (SDH) mutated RCCs and RCCs with mutations in mismatch repair genes. Three ELOC mutated RCCs were identified, one of which demonstrated infiltrative features. Pseudostratification of nuclei in fumarate hydratase mutated RCCs and nuclear grooves in SDH mutated RCCs were intriguing findings not previously reported. Mucinous features were noted in NF2, KRAS, and SDH mutated and ALK rearranged tumors. Significant morphologic overlap was noted across most categories with limited clues for subclassification. Whereas the number of diagnostic entities for kidney tumors continues to increase, many of these have overlapping features, highlighting the significant role molecular characterization currently plays and will continue to play in the future.

IDO+ Endothelial Cells in Glomeruli of Kidney Transplantation Patients With Glomerulitis.

Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. However, long-term graft survival remains a challenge. The enzyme indoleamine 2,3 dioxygenase (IDO) has been reported to have immunomodulatory effects with IDO transcripts being elevated in both antibody-mediated rejection and T cell-mediated rejection. A metal-conjugated antibody panel for the staining of kidney biopsies was developed, allowing the visualization of 41 structural and immune markers on a single tissue slide to gain in-depth insight into the composition and localization of the immune cell compartment. Staining was applied to week 4 and 24 protocol biopsies of 49 patients as well as on 15 indication biopsies of the TRITON study and 4 additional transplantation biopsies with glomerulitis. A highly distinctive and specific glomerular IDO expression was observed in biopsies from 3 of 49 patients in imaging mass cytometry. Immunohistochemistry confirmed IDO expression in glomeruli of 10 of 10 cases with glomerulitis. IDO was found to be expressed by CD31+ glomerular endothelial cells, accompanied by the presence of granzyme-B+Tbet+CD7+CD45RA+ natural killer cells and CD68+ macrophages. Furthermore, a proportion of both the immune cells and endothelial cells expressed Ki-67, indicative of cell proliferation, which was not observed in control glomeruli. Our results show glomerular IDO expression in transplanted kidneys with glomerulitis, which is accompanied by increased numbers of natural killer cells and macrophages and likely reflects local immune activation.

A versatile tissue-rolling technique for spatial-omics analyses of the entire murine gastrointestinal tract.

Tissues are dynamic and complex biological systems composed of specialized cell types that interact with each other for proper biological function. To comprehensively characterize and understand the cell circuitry underlying biological processes within tissues, it is crucial to preserve their spatial information. Here we report a simple mounting technique to maximize the area of the tissue to be analyzed, encompassing the whole length of the murine gastrointestinal (GI) tract, from mouth to rectum. Using this method, analysis of the whole murine GI tract can be performed in a single slide not only by means of histological staining, immunohistochemistry and in situ hybridization but also by multiplexed antibody staining and spatial transcriptomic approaches. We demonstrate the utility of our method in generating a comprehensive gene and protein expression profile of the whole GI tract by combining the versatile tissue-rolling technique with a cutting-edge transcriptomics method (Visium) and two cutting-edge proteomics methods (ChipCytometry and CODEX-PhenoCycler) in a systematic and easy-to-follow step-by-step procedure. The entire process, including tissue rolling, processing and sectioning, can be achieved within 2-3 d for all three methods. For Visium spatial transcriptomics, an additional 2 d are needed, whereas for spatial proteomics assays (ChipCytometry and CODEX-PhenoCycler), another 3-4 d might be considered. The whole process can be accomplished by researchers with skills in performing murine surgery, and standard histological and molecular biology methods.

DIPG-66. MULTIPLEXED IMMUNOFLUORESCENCE MAPPING AND AI MODEL PREDICTION OF TUMOR MICROENVIRONMENT IN DIFFUSE MIDLINE GLIOMAS

Abstract BACKGROUND Diffuse midline glioma (DMG) is a fatal childhood brain cancer with a survival rate of less than one year from diagnosis. Pharmacological approaches and immunotherapy have failed to make a clinical impact. Incomplete understanding of the tumor microenvironment (TME) and tumor associated antigens have contributed to the observed poor prognosis. Therefore, mapping TME is urgently needed. METHODS Whole brains were collected at autopsy from 80 pediatric subjects, including patients diagnosed with DMG (n=50), GBM (n=9), non-malignant controls (n=10), ependymoma (n=5), ATRT (n=2). Up to four brain anatomical sites were selected: primary tumor, metastatic and adjacent healthy control sites, and processed for staining with hematoxylin and eosin (H&E). After reviewing the tumor and healthy regions, three punch cores were obtained resulting in a total of 918 core punches and two TMA blocks. Multiplexed immunofluorescence (MxIF) technology was used to probe 43 biomarkers on a single TMA slide, focusing on immune cell types and activation thereof. Clinical data including genomics alterations were obtained from each patient for downstream analyses. RESULTS Highest expressed immune markers across all patients were CD8 and CD3 (T-cells), CD68, Iba1 and CD163 (microglia). In DMGs, T-cells were mostly detected and increased in ONC201 and immunotherapy treated patients compared to only immunotherapy treated patients. Analysis of Iba1 confirmed a higher difference in activation in primary tumor compared to metastatic and adjacent healthy tissue, whereas in contrast, CD68 was significantly increased in metastatic sites. Patient plasma and RNA transcriptome analysis confirmed biomarkers and immune related pathways. Further, immune competent murine models were used to validate immune cell infiltration. AI deep learning technology modeling is ongoing now to, in future, be able to predict cell type composition and neighborhoods mapping. CONCLUSION We posit that this comprehensive data and characterization of the DMG TME will help to identify biology-informed targeted treatments.

Numerical analysis of downward progressive landslides in long natural slopes with sensitive clay

Landslides occurring in sensitive clay often result in widespread destruction, posing a significant risk to human lives and property due to the substantial decrease in undrained shear strength during deformation. Assessing the consequences of these landslides is challenging and necessitates robust numerical methods to comprehensively investigate their failure mechanisms. While studies have extensively explored upward progressive landslides in sensitive clays, understanding downward progressive cases remains limited. In this study, we utilised the nodal integration-based particle finite element method (N-PFEM) with a nonlinear strain-softening model to analyse downward progressive landslides in sensitive clay on elongated slopes, induced by surcharge loads near the crest. We focused on elucidating the underlying failure mechanisms and evaluating the effects of different soil parameters and strain-softening characteristics. The simulation results revealed the typical pattern for downward landslides, which typically start with a localised failure in proximity to the surcharge loads, followed by a combination of different types of failure mechanisms, including single flow slides, translational progressive landslides, progressive flow slides, and spread failures. Additionally, inclined shear bands occur within spread failures, often adopting distinctive ploughing patterns characterised by triangular shapes. The sensitive clay thickness at the base, the clay strength gradient, the sensitivity, and the softening rate significantly influence the failure mechanisms and the extent of diffused displacement. Remarkably, some of these effects mirror those observed in upward progressive landslides, underscoring the interconnectedness of these phenomena. This study contributes valuable insights into the complex dynamics of sensitive clay landslides, shedding light on the intricate interplay of factors governing their behaviour and progression.

A universal pipeline to combine spatial transcriptomics, proteomics, and diagnostic H&E assays on a single tissue section to study tissue microenvironment.

e14657 Background: Spatial multi-omics approaches, including spatial transcriptomics (ST) and spatial proteomics (SP), that elucidate the complex tissue microenvironment (TME) are gaining traction in cancer research studies. However, combining these approaches onto one tissue section remains challenging. ST methods visualize, locate and quantify RNA targets, evaluating the transcriptome of the TME at the subcellular level while SP methods stain and visualize protein biomarkers at the cellular level. Here we show a pipeline that combines these ST and SP methods with Hematoxylin and Eosin (H&E) approach on a single tissue section to achieve single slide multi-omics data with high resolution tissue morphology images. Methods: 5µm formalin-fixed paraffin-embedded sections of human lung cancer samples were placed on a Xenium (10x Genomics, Pleasanton, CA) slides and treated to expose RNA molecules. Probe hybridization was performed with a human lung panel kit containing 289 barcoded DNA padlock probes. Downstream ligation and amplification generated copies of the barcode and thereafter, the Xenium Analyzer performed imaging and data collection. The same tissue section was then stained and imaged in COMET (Lunaphore Technologies SA, Switzerland) using sequential immunofluorescence (seqIF) method to detect 40 protein targets in situ. Serial sections of the specimens were used for direct H&E and seqIF staining for comparative validation. Results: Multi-omics images generated from Xenium and COMET were overlayed, jointly analyzed and interpreted. Staining specificity of the protein targets for the post-Xenium slides remain comparable with the direct COMET-stained slide. 40-plex COMET staining intensity in post-Xenium slide was minimally weaker compared to direct COMET-stained slide. Additionally, H&E staining of the post-Xenium/COMET slide and direct COMET-stained slide were comparable to the direct H&E-stained section, demonstrating discernable tissue landscape features despite a fainter hematoxylin stain. Overall, tissue integrity and cell morphology were well-preserved across the different workflows performed on each section, and the protein targets remain relatively stable for labelling after in situ RNA detection process. Conclusions: We present a pipeline that combines two spatial omics approaches with the diagnostic standard H&E staining on a single tissue section, allowing the same cells to be assessed for their histological, proteomic, and transcriptomic information. A visual comparison of images showed minimal compromise to cellular morphology and protein stability at the end of the pipeline compared to direct staining of serial sections. This pipeline could be potentially generalized to most of the ST and SP approaches, thus advancing multi-omics cancer research and potentially clinical diagnostics in the future.

BAPCP: A comprehensive and user-friendly web tool for identifying biomarkers from protein microarray technologies

Background and Objective:Proteome microarrays are one of the popular high-throughput screening methods for large-scale investigation of protein interactions in cells. These interactions can be measured on protein chips when coupled with fluorescence-labeled probes, helping indicate potential biomarkers or discover drugs. Several computational tools were developed to help analyze the protein chip results. However, existing tools fail to provide a user-friendly interface for biologists and present only one or two data analysis methods suitable for limited experimental designs, restricting the use cases. Methods:In order to facilitate the biomarker examination using protein chips, we implemented a user-friendly and comprehensive web tool called BAPCP (Biomarker Analysis tool for Protein Chip Platforms) in this research to deal with diverse chip data distributions. Results:BAPCP is well integrated with standard chip result files and includes 7 data normalization methods and 7 custom-designed quality control/differential analysis filters for biomarker extraction among experiment groups. Moreover, it can handle cost-efficient chip designs that repeat several blocks/samples within one single slide. Using experiments of the human coronavirus (HCoV) protein microarray and the E. coli proteome chip that helps study the immune response of Kawasaki disease as examples, we demonstrated that BAPCP can accelerate the time-consuming week-long manual biomarker identification process to merely 3 min. Conclusions:The developed BAPCP tool provides substantial analysis support for protein interaction studies and conforms to the necessity of expanding computer usage and exchanging information in bioscience and medicine. The web service of BAPCP is available at https://cosbi.ee.ncku.edu.tw/BAPCP/.

Bioinspired integrated triboelectric electronic tongue

An electronic tongue (E-tongue) comprises a series of sensors that simulate human perception of taste and embedded artificial intelligence (AI) for data analysis and recognition. Traditional E-tongues based on electrochemical methods suffer from a bulky size and require larger sample volumes and extra power sources, limiting their applications in in vivo medical diagnosis and analytical chemistry. Inspired by the mechanics of the human tongue, triboelectric components have been incorporated into E-tongue platforms to overcome these limitations. In this study, an integrated multichannel triboelectric bioinspired E-tongue (TBIET) device was developed on a single glass slide chip to improve the device’s taste classification accuracy by utilizing numerous sensory signals. The detection capability of the TBIET was further validated using various test samples, including representative human body, environmental, and beverage samples. The TBIET achieved a remarkably high classification accuracy. For instance, chemical solutions showed 100% identification accuracy, environmental samples reached 98.3% accuracy, and four typical teas demonstrated 97.0% accuracy. Additionally, the classification accuracy of NaCl solutions with five different concentrations reached 96.9%. The innovative TBIET exhibits a remarkable capacity to detect and analyze droplets with ultrahigh sensitivity to their electrical properties. Moreover, it offers a high degree of reliability in accurately detecting and analyzing various liquid samples within a short timeframe. The development of a self-powered portable triboelectric E-tongue prototype is a notable advancement in the field and is one that can greatly enhance the feasibility of rapid on-site detection of liquid samples in various settings.

Continuous production machine for separating and shaping Taiping Houkui tea.

In response to the challenges of low automation and a lack of a continuous processing system for Taiping Houkui tea, this study proposed a design scheme for a continuous processing line and built a continuous processing prototype for testing by combining the production requirements of Taiping Houkui tea, the characteristics of withered leaves, and the existing relevant production equipment. First, the physical properties of Taiping Houkui tea were determined. A simulation was performed using the Hertz-Mindlin model, and the motion states of the tea leaves were obtained under different conditions to define the parameter design range of the experimental platform and verify its structural rationality. Then, the response surface methodology was used to optimize the working parameter ranges and obtain the best working parameters for the feeding and kneading mechanisms. Finally, a continuous production prototype was constructed for further production verification. The experimental results show that the success rate of continuous production on this platform was 70.68%, with an average output of approximately 0.4kg/h for Taiping Houkui dry tea on a single slide track, and the produced tea was similar to manually made tea. This demonstrates that the continuous production technique has high feasibility and provides a reference for continuous production of Taiping Houkui tea.

Abstract PO1-15-01: Digital pathology models reveal case-specific characteristics of the tumor microenvironment

Abstract Background Phenotypic characteristics and genetic content have been observed to vary within individual tumors. However, translational research and some clinical practices often rely on extrapolating single histopathology slides to be representative of whole-tumor biology, even when the extent of intra-tumor biological heterogeneity is unknown. In addition, technical factors in slide preparation and digitization can also contribute to variability. Here, we use digital pathology models to identify cell and tissue types in whole-slide images of breast cancer tumors, and quantify intra- and inter-case heterogeneity in cell and tissue features of the tumor microenvironment. Methods We developed PathExplore, a suite of convolutional neural network models trained on pathologist annotations of cell and tissue types in hematoxylin and eosin-stained slides. The models were deployed on the TCGA breast cancer dataset (n=1083 primary solid tumors). Quantitative human-interpretable features (HIFs) relating to cancer, stromal, and necrotic tissue areas, as well as the abundance and distribution of cancer cells, fibroblasts, and immune cells within these tissues, were extracted for each slide. Similarity between slides on sets of HIFs was assessed using Pearson correlation. Intra- and inter-case variability of individual HIFs was measured using the normalized percent difference (range divided by mean) for each slide pair. Case-specificity was quantified by the AUC of correlation or difference distributions for intra-case vs inter-case slide pairs. Only pairs of slides with matching metadata, including tissue source site, cancer stage, and scanner model, were considered for inter-case analysis. Results A total of 55 cases, comprising 114 slides, were identified as having more than one slide per case. Considering all 123 proportional HIFs, intra-case slide pairs showed high correlation compared to inter-case pairs (r=0.97 vs r=0.90, AUC=0.88), with 29% of slides coming from a multi-slide case most closely correlated with another slide from the same case out of the entire TCGA breast dataset. Subsets of these proportional HIFs relating to each identified cell type were also case-specific, with AUC’s ranging from 0.79 for macrophage HIFs to 0.87 for cancer cell HIFs. All proportion, density, and ratio HIFs (n=297) individually showed case specificity (AUC >0.5), with median intra-case differences ranging from 0.6% to 69%. Area proportion of necrosis was more variable than cancer or stroma tissue, while cancer cell count proportions were less variable than fibroblast or immune cell proportions. Cell count proportions were in general less variable and more specific than their corresponding density HIFs; for instance, the frequency of fibroblasts out of all cells in stroma had a median intra-case difference of 11% compared to 20% for the density of fibroblasts in stroma. Conclusions We quantified heterogeneity of key features of the tumor microenvironment both within and across tumors. These results reveal biological and technical variability that can inform selection and interpretation of biomarkers derived from single slides. The ability to uniquely identify slides from the same case additionally demonstrates the technical robustness of digital pathology models for yielding quantitative insights into tumor biology. Citation Format: Ylaine Gerardin, Christian Kirkup, Archit Khosla, Laura Chambre, Michael Drage, Amaro Taylor-Weiner. Digital pathology models reveal case-specific characteristics of the tumor microenvironment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-01.

Abstract PO4-01-08: Co-expression of estrogen receptor (ER), progesterone receptor (PR), and Ki67 in a single breast cancer cell indicates a favorable prognosis in ER-positive breast cancer

Abstract Background: ER-positive breast cancer is biologically and clinically divided into two subtypes: luminal-A and luminal-B. This classification is mainly based on the status of cellular proliferation. At least two different pathways drive cellular proliferation in ER-positive breast cancer. One is a classical pathway where ER binds to estrogen responsive element (ERE), which leads to the expression of downstream molecules, including PR. The other is a non-classical pathway where a complex of ER and relevant factors bind to different sites than ERE. Growth factor signaling has been suggested to potentiate a non-classical pathway. We hypothesized that examining PR status in ER-positive proliferating cells could tell which pathway is more dominant in ER-positive breast cancer. Methods: To test the hypothesis, we used a newly developed triplex immunohistochemistry (IHC) that detects three molecules simultaneously under bright-field microscopy. Postmenopausal patients who were treated with neoadjuvant endocrine therapy with aromatase inhibitors from January 2007 to September 2016 at Saitama Cancer Center were included in this study. ER, PR, and Ki67 expressions were assessed in a single slide using the triplex IHC assay with anti-ER antibody (clone SP1), anti-PR antibody (clone 1E2), and anti-Ki67 antibody (clone 30-9). ER, PR and Ki67 expression was assessed in a single cell nucleus of cancer cells (567 to 4871 cells) from multiple areas in each case. An ER-positive proliferating cell was defined as an ER-positive and Ki67-positive cell. PR status in ER-positive proliferating cells was assessed. When PR was expressed in more than 50% of ER-positive proliferating cells in a clinical case, the tumor was categorized as the PR-positive group. Luminal A and luminal B breast cancers were defined based on the pre-treatment Ki67 labeling index with a cut-off of 14 %. Statistical analyses included the Mann-Whitney test, the log-rank test, and the Cox proportional hazard model. Results: Pre-treatment tissues from 55 patients were evaluated. The median age was 62 (range: 54 to 80) years. The patients were grouped into PR-positive and PR-negative groups. The two groups had no differences in age and pre-treatment T and N stages. The median pre-treatment Ki67 labeling index was 5.9 % in the PR-positive group and 9.9 % in the PR-negative group, which showed a statistically significant difference (P = 0.01). Clinical response to neoadjuvant endocrine therapy was compared, and no difference was observed. The median post-treatment Ki67 labeling index was 3.6 % in the PR-positive group and 13.1 % in the PR-negative group with a significant difference (P = 0.035). The survival was compared between the two groups. The PR-positive group showed a significantly more favorable disease-free survival (DFS) than the PR-negative group (P = 0.0079). To adjust for the background differences including Ki67 and PR status, a multivariate analysis was performed and showed that the PR-positive group had a significantly better DFS than the PR-negative group independent of clinical stage, Ki67 labeling index, and PR status (P = 0.042). Breast cancer-specific survival (BCSS) was also better in the PR-positive group than in the PR-negative group after adjusting for clinical stage, Ki67 labeling index, and PR status (P = 0.043). Interestingly, among patients with luminal A tumors, those in the PR-positive group showed a better DFS than those in the PR-negative group (P = 0.022). Conclusion: PR status in ER-positive proliferating cells was an independent prognostic factor in DFS and BCSS and divided patients with luminal A tumors further into two prognostic groups. Citation Format: Takayuki Ueno, Rie Horii, Hiroshi Matsumoto, Makiko Ono, Yurina Maeshima, Hiroaki Nitta. Co-expression of estrogen receptor (ER), progesterone receptor (PR), and Ki67 in a single breast cancer cell indicates a favorable prognosis in ER-positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-01-08.

Abstract PO2-13-06: Analysis of Antibody Response to SV-BR-1-GM Therapeutic Vaccine in Breast Cancer Patients Using Human Protein Microarrays: Potential Correlations with Therapy Response

Abstract Background: Therapeutic cancer vaccines aim to stimulate the immune system by utilizing tumor antigens to trigger an antitumor response. In our clinical trials, we have focused on evaluating the efficacy of the breast cancer cell line secreting GM-CSF, SV-BR-1-GM as a therapeutic vaccine, and we have observed encouraging clinical outcomes. The SV-BR-1-GM regimen has been used alone (“monotherapy”, ClinicalTrials.gov NCT03066947 – study completed) and in combination with checkpoint inhibitors (“combination”, ClinicalTrials.gov NCT03328026 – study ongoing). To further improve the therapeutic efficacy of this vaccine we have initiated a study to characterize patient’s immune response to this treatment. It has been demonstrated that cancer vaccines can generate both humoral and cellular immune responses. However, predicting immune responses to cancer vaccines, especially when whole cells are employed as immunogens, presents significant challenges. We present here a preliminary analysis of the antibody response to SV-BR-1-GM in breast cancer patients using antigen arrays. Methods: Large-scale protein arrays are versatile and sensitive platforms for antibody specificity evaluation. The HuProt™ Human Proteome Microarray (CDI Laboratories, Inc., Baltimore, Maryland, United States) provides the largest number of unique, full-length, individually purified human proteins on a single microscope slide. This allows thousands of interactions to be profiled in a high-throughput manner. We utilized here an unbiased human protein microarray platform encompassing >21,000 proteins and isoforms from ~19,000 unique genes to identify IgG and IgM responses against self-antigens elicited by treatment. Serum samples from SV-BR-GM-treated patients were analyzed, comparing pre- and post-treatment. The human serum samples were probed at 1:1000 dilution. After sample processing and data collection the raw signal intensities on all arrays were quantile normalized using CDI software. Heatmaps were generated using MetaboAnalyst 5.0, which utilizes the R pheatmap package (version 0.7.7). For paired analysis, fold changes (FCs) were calculated by determining the ratio between paired pre- and post-treatment samples, resulting in one FC per pair. The means of these FCs (pair means) were then computed. ANOVA tests were performed using the RStatix R package, which automatically determines the appropriate Type I, II, or III errors for the analysis. Results: By using this approach, we were able to evaluate a broader range of antigens compared to previous investigations. Using a variety of statistical approaches, potential correlations with patient survival on SV-BR-1-GM were evaluated. Antibody responses to galectin antigens demonstrated the most consistent relationships with survival. Further confirmation with additional patients and prospective analysis are needed to fully understand relationships with clinical benefit and survival. Citation Format: Miguel Lopez-Lago, Mingjin Chang, Giuseppe Del Priore, Vikas Bhardwaj, Patience Cournoo, Pedro Ramos, Tyler Hulett, Charles Wiseman, William Williams. Analysis of Antibody Response to SV-BR-1-GM Therapeutic Vaccine in Breast Cancer Patients Using Human Protein Microarrays: Potential Correlations with Therapy Response [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-06.

Abstract LB331: Integrating multiple spatial transcriptomic and proteomic technologies to generate a cancer atlas to understand the tumor microenvironment

Abstract Deep single-cell genomics has provided a monumental leap in our understanding and categorical analysis of cancer cells and their unique molecular properties. With spatial platforms that preserve the tissue architecture, an emerging understanding of complex cell interactions and local environment influences is providing new perspectives for targeted therapies. Here we describe a comprehensive approach at profiling the tumor immune microenvironment across multiple solid malignancies to provide an atlas of tumor biology. To generate this atlas, we created a MERFISH panel of 500 genes representing over 40 cell phenotyping pathways and dozens of signaling pathways including chemokines, cytokines, interferon, and ligand-receptor interactions. This panel was used across 15 patients representing multiple solid malignancies. We also utilized the Immuno-Oncology focused Xenium panel to profile 16 different patients across the same indications. With the Xenium data we simultaneously assessed proteomic expression using a 16 plex panel focused on immuno-oncology targets and subsequently did H&E staining, allowing multiple -omic data collections within a single slide. After initial filtering and quality control this projects produced a total dataset with over 15 million cells and billions of transcripts. The 500 gene MERFISH panel revealed complex relationships between diverse cell types within the tumor microenvironment that was dependent on cellular location and surrounding area. While the presence of major immune cell types was observed in all cancer tissues, the ratio and spatial relationship to cancer cells differed between each tumor. Accordingly, we observed that differences in cellular composition of the microenvironment corresponded with associated changes in cellular signaling. Similarly, with Xenium we identified new spatial relationships between cancer and immune cells with their environment while confirming part of these relationships with subsequent immunofluorescence data. The H&E data provided an opportunity to overlay this data with standard pathological annotation. We further investigated how each cellular microneighborhood varied throughout the tumor and varied from patient. This comprehensive map of the tumor immune microenvironment is a deep cellular dive into cellular architecture. As this Cancer Atlas evolves this will drive the discovery of several new potential biological targets and cell types of interest. This will also be critical for the assessment of current cellular and drug therapies in preclinical and clinical models. This effort will continue with more patient samples across a greater number of disease indications and will include more technologies. We will also expand the development and implementation of analytical tools to maximum the knowledge and insights gained from this Atlas. Together, this will be a critical tool in the continuing fight against cancer. Citation Format: Colles Price, Kazuho Nishimura, Som Bandyopadhyay, Darrell Borger, Russell Weiner. Integrating multiple spatial transcriptomic and proteomic technologies to generate a cancer atlas to understand the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB331.

Abstract 6193: Exploring interplay between tumor intrinsic features and immune cell phenotypes in multi-tumor tissue microarrays by a single step 15-plex fluorescence immunohistochemistry and imaging on the Orion platform

Abstract Purpose: Deeper understanding of the intricacies between tumor microenvironments and immune cell infiltrates is critical for creation of next generation therapies. However, current immunohistochemistry protocols in clinical practice are restricted to one to six markers (cell types) and rely on multiple rounds of antigen retrieval and sequential staining over two days. This current workflow has high risk for tissue and signal loss. Hence, we explored utility of a single step staining workflow using 15 distinct antibodies conjugated novel flours (Argo Dye System) of formalin fixed paraffin embedded (FFPE) tumor microarrays and imaged on the Orion microscope (Rarecyte). Fidelity of the staining was assessed by comparing data from the Orion platform to internally developed clinical grade assays. The implementation of higher plex imaging will enable greater insight into immune surveillance, mechanisms of resistance and patient stratification while minimizing patient tissue required for analysis. Study Design: We utilized a custom panel developed by RareCyte (Hoescht, CD3e, CD4, CD8a, CD20, CD68, CD163, FOXP3, Ki67, Pan-CK, PD1, PDL1, CD45, Granzyme B, LAG3) to evaluate expression of immune markers on a pan-tumor and melanoma TMA including Hodgkin’s lymphoma, ovarian cancer, esophageal carcinoma, hepatic cell carcinoma, head and neck cancer. Serial sections were stained with 6-plex clinical grade assays, and correlation scores were determined between the 15-plex Orion and 6-plex assays utilizing TSA-based amplification. Results: In this study, the performance of Orion platform was assessed by quantitative and qualitative measures. Linear regression between the 6-plex clinical grade assays and 15-plex Orion workflow were calculated for a panel of biomarkers including CD3, CD4, CD8, CD68, CD163, CK, FOXP3, GZMB, Ki67, and PD1. Our results demonstrated that the Orion performed similarly to clinical grade assays both in terms of proportion of cells and fluorescence intensity (R2 ~ 0.7-0.98, Slope ~ 0.7-1.1). Conclusion: Our results suggest that the Orion platform offers a reliable and efficient solution for comprehensive biomarker analysis. Our results demonstrate that this platform can successfully capture the expression of 15 biomarkers on a single FFPE slide which offers workflow advantages over traditional TSA-based fIHC multiplexing that is limited to detection of 6 biomarkers on a single slide. The ability to analyze a wider range of biomarkers through a single platform has the potential to enhance the efficiency of biomarker analysis, leading to improved diagnostic and therapeutic outcomes in clinical practice. Citation Format: Richard Van Krieken, Jehovana O. Bender, Sophia Kekchidou, Michelle Burgin, Kristen Ruma, Naveen Dakappagari, Jennifer Bordeaux. Exploring interplay between tumor intrinsic features and immune cell phenotypes in multi-tumor tissue microarrays by a single step 15-plex fluorescence immunohistochemistry and imaging on the Orion platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6193.

Abstract 3800: Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes

Abstract Gaining spatial insights into the cellular composition of tumor tissue has tremendous potential to inform clinical and translational researchers about mechanisms behind spatial predictors of immunotherapy success, disease etiology and progression. Imaging Mass Cytometry™ (IMC™) is a high-plex spatial biology imaging technique that enables deep characterization of the diversity and complexity of the tumor microenvironment (TME). IMC supports detailed assessment of cell phenotype and function using 40-plus metal-tagged antibodies simultaneously on a single slide without issues associated with fluorescence-based spectral overlap, tissue autofluorescence or implementation of multiple washing and acquisition cycles. Currently, IMC enables user-defined regions of interest (ROI) in tissues to evaluate cellular and structural composition. To enhance the IMC user experience, we developed two new whole slide imaging (WSI) modes which enable streamlined workflows using ultrafast preview mode (PM) and high-throughput tissue mode (TM). PM samples the entire tissue at predefined spacing to rapidly capture a low-resolution image of all expressed markers in the antibody panel. PM generates an image in minutes to enable informed ROI placements while leaving the stained tissue intact for higher-resolution imaging. PM and TM are designed so acquisitions can easily occur on the same slide without additional processing steps. TM rapidly acquires images of the whole tissue at lower resolution (7 μm pixel size) at a quality that can be used for quantitative analysis of the tissue spatial biology. Specifically designed for high-throughput applications, TM in combination with a newly available 40-slide loader for the Hyperion XTi™ Imaging System permits automated and continuous imaging of more than 40 large tissue samples (400 mm2 per tissue) per week. We showcase the application of WSI modes using the newly developed Maxpar® Human Immuno-Oncology IMC Panel Kit. The 31-marker panel was combined with catalog antibodies to create a 40-plus-marker panel that expands the ability to conduct comprehensive high-plex tumor and immune cell profiling. Tumor tissue microarrays (TMA) and whole tumor tissue sections were stained with the expanded panel. Single-cell analysis of selected ROIs, on whole tumor sections and TMAs, guided by PM data successfully provided quantitative analyses of spatial biology at single-cell resolution. In parallel, TM on whole tumor sections followed by pixel-based analysis provided a spatially resolved quantitative assessment of specific tumor and immune components of the TME. This work demonstrates the expanded capabilities of IMC and establishes it as a reliable high-plex spatial biology imaging platform with high-throughput imaging capabilities ideally suited for translational and clinical applications. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Thomas D. Pfister, Jyh Yun Chwee, Liang Lim, David Howell, Nikesh Parsotam, David King, Christina Loh. Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3800.