Cancer Tissue Sections Research Articles

Covalent assembly-based two-photon fluorescent probes for in situ visualizing nitroreductase activities: From cancer cells to human cancer tissues

Nitroreductase (NTR) is widely regarded as a biomarker whose enzymatic activity correlates with the degree of hypoxia in solid malignant tumors. Herein, we utilized 2-dimethylamino-7-hydroxynaphthalene as fluorophore linked diverse nitroaromatic groups to obtain four NTR-activatable two-photon fluorescent probes based on covalent assembly strategy. With the help of computer docking simulation and in vitro assay, the sulfonate-based probe XN3 was proved to be able to identify NTR activity with best performances in rapid response, outstanding specificity, and sensitivity in comparison with the other three probes. Furthermore, XN3 could detect the degree of hypoxia by monitoring NTR activity in kinds of cancer cells with remarkable signal-to-noise ratios. In cancer tissue sections of the breast and liver in mice, XN3 had the ability to differentiate between healthy and tumorous tissues, and possessed excellent fluorescence stability, high tissue penetration and low tissue autofluorescence. Finally, XN3 was successfully utilized for in situ visualizing NTR activities in human transverse colon and rectal cancer tissues, respectively. The findings suggested that XN3 could directly identify the boundary between cancer and normal tissues by monitoring NTR activities, which provides a new method for imaging diagnosis and intraoperative navigation of tumor tissue.

Siglec-7 and Siglec-9 expression in primary triple negative and oestrogen receptor positive breast cancer and in vitro signalling.

PD-1/PD-L1 immune checkpoint blockade can be an effective treatment for advanced breast cancer patients. However, patients with oestrogen receptor positive (ER+) tumors often display only low lymphocyte infiltration, while a large part of triple negative (TN) breast tumors does not generate an effective immunotherapy response. Therefore, new treatment strategies have to be developed. Here, we investigate Siglec-7 and Siglec-9 as novel ITIM-bearing inhibitory immune checkpoint receptors similar to PD-1, but expressed on a broader range of immune cells. We assessed Siglec-7 and Siglec-9 (ligand) expression in TN and ER+ breast cancer tumors and their breast cancer cell line-induced signalling. We report that Siglec-7 and Siglec-9 are highly expressed in TN tumors, and to a low extent in ER+ tumors. Siglec-7 was observed on myeloid cells, Tcells, and NKcells and Siglec-9 preferentially on myeloid cells. Expression of sialoglycans, including Siglec-7 and Siglec-9 ligands, was observed in both TN and ER+ breast cancer tissue sections. Expression levels of Siglec-7 and Siglec-9 ligands were higher on in vitro cultured TN cell lines than ER+ cell lines. Importantly, by applying chimeric Siglec-7 reporter cells, we showed the induction of Siglec-7 signalling by multiple TN cell lines, but only by one ER+ cell line. Moreover, Siglec-7 signalling is directly related to Siglec-7 ligand expression levels of breast cancer cell lines. These data imply that immunotherapy targeting Siglec receptors may be particularly interesting for TN breast cancer patients not responding to current treatment strategies with tumors displaying high immune cell infiltration.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections.

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

Prognostic Value of "Basal-like" Morphology, Tumor-Infiltrating Lymphocytes and Multi-MAGE-A Expression in Triple-Negative Breast Cancer.

"Basal-like" (BL) morphology and the expression of cancer testis antigens (CTA) in breast cancer still have unclear prognostic significance. The aim of our research was to explore correlations of the morphological characteristics and tumor microenvironment in triple-negative breast carcinomas (TNBCs) with multi-MAGE-A CTA expression and to determine their prognostic significance. Clinical records of breast cancer patients who underwent surgery between January 2017 and December 2018 in four major Croatian clinical centers were analyzed. A total of 97 non-metastatic TNBCs with available tissue samples and treatment information were identified. Cancer tissue sections were additionally stained with programmed death-ligand 1 (PD-L1) Ventana (SP142) and multi-MAGE-A (mAb 57B). BL morphology was detected in 47 (49%) TNBCs and was associated with a higher Ki-67 proliferation index and histologic grade. Expression of multi-MAGE-A was observed in 77 (79%) TNBCs and was significantly associated with BL morphology. Lymphocyte-predominant breast cancer (LPBC) status was detected in 11 cases (11.3%) and significantly correlated with the Ki-67 proliferation index, increased number of intratumoral lymphocytes (itTIL), and PD-L1 expression. No impact of BL morphology, multi-MAGE-A expression, histologic type, or LPBC status on disease-free survival was observed. Our data suggest that tumor morphology could help identify patients with potential benefits from CTA-targeting immunotherapy.

Abstract 2352: Development of an AI-based algorithm to quantify eosinophils in H&E images from colorectal cancer (CRC) tissue sections guided by biomarker staining using multiplex immunofluorescence imaging

Abstract Background: Eosinophils are innate immune granulocytes that migrate to areas of inflammation to combat against infection and disease. Best known for their detrimental role in asthma and allergic disorders, there is growing interest in the involvement of eosinophils in cancer. Eosinophils are routinely observed in the tumor microenvironment (TME) and, depending on the cancer type, have been shown to drive other immune cells to either suppress or promote tumor growth - In colorectal cancer (CRC), eosinophil infiltration into the TME has been linked to a favorable prognosis. However, the behavior of eosinophils and their effect on associated immune mediators in the TME remains poorly understood. Currently, eosinophils are primarily identified from H&E stained tissue sections based on morphological features by a pathologist, but it can be challenging to reliably and efficiently identify all eosinophils based on morphology alone. Here, we present a novel image analysis workflow that established an AI-based cell classifier which can accurately quantify eosinophils in H&E stained CRC tissue sections by leveraging biomarker staining of eosinophils using multiplex immunofluorescence (mIF) imaging in conjunction with the morphological characteristics of eosinophils observed by H&E. Methods: CRC tissue sections were labeled with a 5plex panel of eosinophil-specific markers and imaged by mIF using the PhenoImager HT platform (Akoya). Biomarker fluorescence was then quenched, sections stained by H&E, and reimaged using the PhenoImager. mIF and H&E images were then imported into the HALO® platform for algorithm development. Halo AI cell classification of eosinophils was trained on the morphological features of eosinophil staining in H&E images, guided by eosinophil-specific labeling in the 5plex mIF images. H&E staining was also performed on a serial section of each specimen. Morphological identification of eosinophils in H&E images was performed by a pathologist. Results: Correlation analysis was performed to evaluate the relationship, per high power field (HPF), between manual eosinophil counts by a pathologist and AI algorithm derived eosinophils counts. The results showed the two methods are highly correlated, demonstrating reliable algorithm performance. Conclusion: The AI-based eosinophil detection algorithm established here enables high-throughput analysis and quantification of eosinophils from H&E stained CRC tissue specimens and facilitates morphology diagnosis. Citation Format: Arezoo Hanifi, Elizabeth Blain, James Hargrove, Jeff Lock, Nam Tran, Vladislav Chizhevsky, Qingyan Au. Development of an AI-based algorithm to quantify eosinophils in H&E images from colorectal cancer (CRC) tissue sections guided by biomarker staining using multiplex immunofluorescence imaging [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 2352.

Abstract 3769: Spatial biology in situ hybridization and multiplexed immunohistochemistry detection compatibility using a modified protocol

Abstract In the field of spatial-omics imaging, combining mRNA in situ hybridization (ISH) detection with multiplexed protein immunohistochemistry detection (IHC) is desirable. We present a simplified workflow in FFPE tissue sections where ISH is detected using branched ssDNA signal amplification visible by brightfield imaging combined with multiplexed IHC using directly labeled antibodies detected by fluorescence. In multiple studies mRNA expression of PCNA (Proliferating Cell Nuclear Antigen) shows moderate to high nuclear positivity observed in most cancer cells, with gliomas and lymphomas being the few negative exceptions. In contrast, expression of PCNA in adult non-cancerous tissue has low to no expression, with tonsil and bone marrow being the exceptions. We first show the relationship between proliferation as measured by EdU detection, PCNA antibody and PCNA mRNA expression in the embryonic mouse model where PCNA expression is prevalent in developing tissue. Then we demonstrate a simplified working protocol for combining ISH and IHC in FFPE human cancer tissue sections for the co-detection of PCNA mRNA together with IHC using a panel of six protein markers relevant to the tumor microenvironment. For IHC, we directly labeled validated unconjugated antibodies with fluorophores using amine-reactive chemistry and select the degree of labeling (DOL) and titer for optimal signal/background most useful for multiplexing on FFPE tissue. We found important protocol steps to consider for compatibility between IHC and ISH detection method. Loss of IHC protein epitope due to ISH protocol use of proteases and loss of ISH signal due to presence of RNase commonly present in IHC antibody relevant reagents can prevent combining IHC with ISH. Stabilizing IHC through refixation prior to ISH workflow is also required. Modifying the protocols to avoid these incompatible treatments and using directly labeled antibodies provides useful strategies for multiplexing IHC with ISH. Citation Format: Scott T. Clarke, Mae Voeun, Leticia Montoya, Chris Vonnegut, Bhaskar Mandavilli. Spatial biology in situ hybridization and multiplexed immunohistochemistry detection compatibility using a modified protocol [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 3769.

Abstract A107: MAPK signaling is a conserved mechanism explaining phenotypic heterogeneity across diverse drivers of epithelial-mesenchymal transition in pancreas cancer

Abstract Epithelial-mesenchymal transition (EMT) is a developmental process that is aberrantly activated in cancers such as pancreatic ductal adenocarcinoma (PDAC) to promote disease progression and chemoresistance. EMT occurs heterogeneously among PDAC ductal cells, which frustrates efforts to identify the underlying signaling regulatory mechanisms. We hypothesize that cell-to-cell variation in the activities of specific signaling pathways explains EMT heterogeneity. To test this, we developed a workflow integrating: 1) iterative indirect immunofluorescence imaging (4i) to quantify the activities of up to seven purported EMT-regulating pathways with single-cell resolution and 2) a multivariate mutual information computational model to predict the pathways that are most informative of the EMT phenotype. In cultured PDAC cells, EMT was induced by treating cells with growth factors present in the tumor microenvironment (hepatocyte growth factor, epidermal growth factor, transforming growth factor β1, or two combinations) or with chemotherapeutics (gemcitabine or 5-fluorouracil). EMT phenotypic markers and signaling pathway proxies were assessed daily for up to 96 hr. A mutual information analysis of the aggregated data identified ERK as the most important signaling node explaining EMT heterogeneity across the diverse set of agonists investigated. Kinase inhibition and siRNA studies confirmed the implied role of ERK but also revealed a compensatory, EMT-driving JNK activation in the context of MEK inhibition that explains a cooperative effect of simultaneous MEK and JNK inhibition on EMT antagonism. Importantly, a model based on cell population-level measurements nominated JNK as more informative than ERK for determining EMT state in response to growth factors. We are implementing this approach in vivo using pancreas cancer tissue sections to determine generalizability of these findings in different microenvironmental contexts. Overall, our results demonstrate the pervasive importance of ERK for diverse drivers of EMT and the need to account for the effects of heterogeneous signaling pathway activation in the determination of EMT. Citation Format: Michelle C. Barbeau, Matthew J. Lazzara. MAPK signaling is a conserved mechanism explaining phenotypic heterogeneity across diverse drivers of epithelial-mesenchymal transition in pancreas cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A107.

OR22-05 Progesterone Receptor-Grb2 Interaction Is Associated With Better Outcomes In Breast Cancer

Abstract Disclosure: N. Wittayavimol: None. E. Iwabuchi: None. P. Pateetin: None. Y. Miki: None. Y. Onodera: None. H. Sasano: None. V. Boonyaratanakornkit: None. The presence of progesterone receptor (PR) is usually associated with better clinical outcomes for several endocrine-related cancers. However, the molecular mechanism of how PR mediates growth inhibition remains unclear. In addition to its role as a nuclear transcription, PR also possesses an extranuclear function, mediated mainly through PR polyproline domain (PPD) interaction with the SH3 domains of cytoplasmic signaling molecules. Our previous study suggested a possible role of PR-PPD in mediating SH3-domain interaction. We showed that PR-PPD and SH3 domain interaction inhibited EGF-mediated signaling and decreased lung cancer cell proliferation. An essential adapter protein that helps transduce growth factor signaling is Grb2 which has an SH2 domain flanked by two SH3 domains. In this study, we hypothesized that PR interferes with cytoplasmic signaling through interaction between PR-PPD and Grb2-SH3. We used several protein-protein interaction assays in vitro and in cells to demonstrate PR-Grb2 interaction. GST-pulldown analysis showed that PR-PPD interacts specifically with SH3 domains of Grb2. No interaction was detected when key prolines in the PR-PPD to alanines. Immunofluorescence staining showed colocalization of PR and Grb2 in both the nucleus and cytoplasm of BT474 breast cancer cells. Using Bimolecular Fluorescence Complementation (BiFC) analysis, we showed that PR and Grb2 interacted in breast cancer cells, specifically through the Grb2-SH3 domain. No BiFC signal was detected when Grb2-SH3 domains were deleted. Furthermore, we used Proximity Ligation Assay (PLA) analysis to examine protein-protein interaction of a distance less than 40nm in cells. We found PR-Grb2 interaction in PR-positive breast cancer tissue sections. No PLA signal was detected in PR-negative breast cancer cells. Importantly, we examined 43 PR-positive breast cancer specimens and showed that PR-Grb2 interaction was associated with low histological stage and negatively correlated with lymph node invasion and metastasis of breast cancer. Our results provide a molecular mechanism for how PR can inhibit growth factor signaling by interfering with Grb2 signaling and a basis for developing novel therapeutic strategies for future cancer treatment. Presentation: Saturday, June 17, 2023

Evaluation of erastin synergized cisplatin anti-nasopharyngeal carcinoma effect with a glutathione-activated near-infrared fluorescent probe

Nasopharyngeal carcinoma (NPC), a malignant tumor originating from the nasopharynx, is one of the common malignant tumors of the head and neck. There are significant geographical differences in the incidence of nasopharyngeal carcinoma, with a high incidence in China and Southeast Asian countries. Herein, we designed and synthesized a novel near-infrared fluorescent (NIRF) probe to detect glutathione (GSH) in cellular and tumor environments using semi-naphthofluorescein (SNAFL) as the fluorescent molecular backbone and 2-fluoro-4-nitrobenzenesulfonate as the recognition moiety. Upon reaction with GSH, SNAFL-GSH emitted a fluorescence signal, and its emission wavelength at 650 nm was remarkably enhanced. The results of selectivity experiments indicated that SNAFL-GSH was able to discriminate GSH from Cys, Hcy, and H2S. Moreover, SNAFL-GSH could image both endogenous and exogenous GSH and distinguish normal and cancer cells by fluorescence signal difference. At the cellular level, cisplatin (DDP)-induced ferroptosis and inhibition of proliferation of various NPC cell lines (CNE2, CNE1, 5–8F cells) by erastin combined with DDP were visualized with the help of SNAFL-GSH. In a mouse tumor xenograft model, we successfully employed SNAFL-GSH for the evaluation of the efficacy of erastin combined with DDP in the treatment of NPC. More importantly, the probe could image cancerous tissue sections from NPC patients with an imaging depth of approximately 80 µm. It was foreseen that SNAFL-GSH offered great potential for application in the diagnosis and evaluation of the therapeutic efficacy of NPC, and these results would also provide new ideas for the clinical treatment of NPC.

Development of a method to diagnose endometrial cancer based on the AuNRs-AntiVimentin optical probe.

Endometrial cancer is one of the common gynecological malignancies, and its incidence has been increasing year by year in recent years, raising higher requirements for its rapid diagnosis. In this article, gold nanorods (AuNRs) with localized surface plasmon resonance properties (LSPR) has used to prepare AuNRs-antibody to waveform protein (AuNRs-AntiVimentin) optical probes, and a new method has been constructed that could rapidly detect and identify endometrial cancer tissue sections by polarized light microscopy. AuNRs were prepared by seed growth method using gold chloride as raw material, and the morphology of AuNRs and the optical characteristics of AuNRs-AntiVimentin has characterized by transmission electron microscopy (TEM), ultraviolet-visible spectroscopy (UV-Vis), and zeta potential; immunohistochemistry (IHC) and AuNRs-AntiVimentin optical probes have used to detect clinical endometrial cancer, respectively. The AuNRs-AntiVimentin optical probe has been used to detect endometrial cancer tissue sections and found to have good bio-specificity, with no significant difference in the detection of AuNRs-AntiVimentin compared with conventional IHC techniques (p > .05). An optical probe generated by coupling AuNRs with Vimentin antibodies has been obtained to detect and identify endometrial cancer with simple operation and comparable effect to conventional IHC, providing a new method and idea for the rapid detection of endometrial cancer.

Abstract 5408: AI powered-platform to predict gene modifications from prostate and breast cancer whole slide images

Abstract There is little published evidence of predicting cancer genotypes directly from tissue histology, especially for breast and prostate cancers. Artificial intelligence (AI) enables discriminating and extracting morphological features reflective of the underlying genomic alterations at visual and subvisual levels. We have built a morphology-based and AI-powered platform for cancer genotyping, risk stratification and outcome prediction that addresses the needs for treatment decision-making in a cost effective and timely fashion. A cohort of 390 prostate and 742 invasive breast cancer patients with known molecular status of key genes, such as TP53, PIK3CA, MYC, ERBB2, TMPRSS2-ERG and PTEN from The Cancer Genome Atlas were included in this study. Hematoxylin and eosin (H&E) stained whole slide images (WSI) of the cancer tissue sections were available at 20x or 40x. The WSI from the two cancer cohorts were split 2:1 into a training and test dataset, respectively. Our platform involved two different deep Convolutional Neural Network (DCNN) architectures. The platform first divided each WSI into multiple tiles. Each tile was then analyzed using a DCNN that graded the tile and generated a high dimensional vector to provide a mathematical representation of the morphology. The combination of high dimensional vectors across the WSI was then fed into a second DCNN that generated a morphological score, which predicted whether the gene under consideration was wild type or modified. Our platform has achieved 70 - 80% accuracy as defined by the Area under the Curve for the receiver operating characteristics curves for the genetic markers on the test cohorts (Table 1). Our platform can predict genotypes/molecular alterations directly from H&E stained WSI with high accuracy. This technology presents a novel, practical and cost-effective approach for cancer molecular classification and risk stratification, enabling timely and optimal treatment decision-making for positive clinical outcomes. Genotype Prediction for Breast Cancer (BrCa) and Prostate Cancer (PCa) from WSI Cohort Molecular Biomarker Training Dataset (n) Test Dataset (n) Test ROC AUC Score (%) Gene Modification/Loss Intact/Wild type Gene Modification/Loss Intact/Wild type BrCa TP53 196 302 96 149 80 PIK3CA 153 344 75 170 74 MYC 68 414 33 204 77 ERBB2 66 416 32 205 78 PCa TMPRSS2-ERG 104 157 51 78 70 PTEN 57 201 28 99 73 Citation Format: Wei Huang, Parag Jain, Chensu Xie, Hassan Muhammad, Hirak Basu, George Wilding, Rajat Roy. AI powered-platform to predict gene modifications from prostate and breast cancer whole slide images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5408.

Abstract 5406: A deep learning enabled digital pathology platform for characterization of cancer-associated thrombosis of ovarian cancer

Abstract Purpose: Cancer-associated thrombosis (CAT), characterized by activation, extravasation, and aggregation of platelets in tumor microenvironment, is a common complication observed in ovarian cancer patients. A third of ovarian cancer patients have elevated platelet counts that are associated with poor prognosis. However, the exact mechanism by which platelets preferentially extravasate into tumor microenvironment is unknown, partly owing to lack of tools that can accurately segment and measure platelet counts found in tumor tissues. In this study, we hypothesize that tumor secreted SDF-1 interacts with CXCR4 receptors on platelets and initiates platelet chemotaxis towards tumor microenvironment. We developed a digital pathology (DP) platform with automated whole tissue confocal imaging and deep learning (DL) model that can be used to automatically segment out platelets and calculate platelet density with high precision and consistency in the mouse ovarian cancer tissue sections for supporting hypothesis evaluation. Methods: A DL algorithm to accurately segment out platelets was developed based on the U-Net structure using our original datasets from the murine ovarian cancer model. Tumor-bearing mice were injected with 5 mg/kg of Plerixafor, a CXCR4 inhibitor, daily for 4 weeks. Then, tumor nodules were resected, stained with CD41a antibody, and imaged using the whole slide confocal microscopy. Using these high-resolution images, we performed manual annotation of platelets to generate the mask dataset and performed further data augmentation to increase the diversity of datasets. We trained our model using 85% of these images and performed validation using the remaining 15%. Results: Using our DLDP platform, the validation dataset achieved pixel-wise accuracy of 98.7%. A receiver operating characteristic (ROC) curve demonstrated an area under the ROC curve (AUC) of 0.99 with high sensitivity and specificity. Intersection over union (IoU) score of 0.724 was obtained. A statistically significant (p=0.0467), 7.03% reduction in the platelet density was shown in the Plerixafor-treated mouse ovarian tumor tissues (n=8) compared to the control tumor tissues (n=9). Note that the traditional threshold method was not able to find the statistical significance (p=0.0966) in the same dataset. Conclusions: We developed a DLDP platform to segment and quantitate tens of thousands of platelets from whole slide mouse ovarian cancer tissues automatically with high accuracy and consistency. Applying the DLDP platform, we demonstrated that treatment with Plerixafor leads to overall reduction in platelet infiltration within ovarian murine tumors. With high-throughput, accurate, and automated detection of platelets, our DLDP platform can serve as a powerful tool for characterization of CAT by evaluation of platelet density as a potential prognostic biomarker for ovarian cancer. Citation Format: Ju Young Ahn, Wendolyn Carlos-Alcalde, Matthew Vasquez, Min Soon Cho, Vahid Afshar-Kharghan, Stephen T. Wong. A deep learning enabled digital pathology platform for characterization of cancer-associated thrombosis of ovarian cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5406.

Abstract 5637: Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue

Abstract Cancer research across drug development, molecular biomarkers, and patient response depends on understanding biology that is dependent on complex interactions between malignant, immune, and stromal cells. To survive clearance mechanisms, a tumor can rely on a myriad of escape strategies, and the microenvironment is architected around the current path of escape. To enable a more comprehensive picture of tumor biology, we have developed the CosMx™ Spatial Molecular Imager (SMI) technology to capture a snapshot of thousands of RNA species resolved subcellularly from a single, standard histopathology slide. Building upon the previously released panels, this study tests a new 6,000-plex panel, the highest RNA plex measured in situ within human tissue, allowing the imputation of a spatial whole transcriptome in the tissue. We performed an ultra-high-plex RNA assay to detect 6,000 targets simultaneously in situ on an FFPE human liver cancer tissue (~1 cm2 area) using the CosMx SMI. This RNA panel covers broad biological areas with special emphasis on oncology, immunology, and signal transduction, such that all cancer researchers can benefit from the direct detection of targets of interest (sans imputation) in intact tissue. Analysis algorithms were developed to allow robust assessments of cell types, cell states, cell-cell interactions, and pathway activation. Imputation based on reference profiles from HCA, TCGA, and other public repositories allows estimation of non-measured transcripts at a ratio of approximately 1:3, compared to the approximate 1:20-1:70 imputations performed previously for spatial data.Thousands of transcripts were simultaneously detected with high sensitivity and specificity on the FFPE liver cancer tissue section at single-cell subcellular resolution. We were able to accurately map known reference profiles from scRNA-seq into this sample while identifying cancer-specific malignant, immune, and stromal cells in this tissue sample using this ultra-high plex RNA panel. In addition, we constructed sample-specific spatial neighborhoods, defined by cell types, cell states, and nearly unlimited sets of biological pathways through the imputed whole transcriptome. Finally, we measured >1,000 ligand-receptor interactions between key cell types of adjacent cells in the tissue, identifying mechanisms for tumor-mediated escape as well as reactive re-architecting of the native stroma which defines the trajectory of cancer’s evolution. Single-cell spatial measurements of gene expression at 6,000 plex from a single FFPE slide has the potential to transform our understanding of tumor biology and facilitate the next advances in cancer research by extracting the highest data density possible from rare specimens collected during patient treatment. Citation Format: Shanshan He, Michael Patrick, Jason W. Reeves, Patrick Danaher, Julian Preciado, Joseph Phan, Erin Piazza, Zachary Reitz, Lidan Wu, Rustem Khafizov, Haiyan Zhai, Michael Rhodes, David Ruff, Joseph Beechem. Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5637.

Abstract 2091: Open-source single-cell spatial image analysis pipeline for ten-plex immunofluorescence of histological prostate cancer tissue sections

Abstract Introduction: Multiplexed fluorescence immunohistochemistry (mfIHC) allows the detection of multiple protein markers on the same histological tissue section, generating valuable information on specific cell subsets and cellular states in their spatial tissue context. Here, we demonstrate a proof-of-concept study for profiling different tumor microenvironment cell subsets in formalin-fixed paraffin-embedded prostate cancer tissue microarrays. Materials and methods: We applied automated mfIHC simultaneous staining of 31 slides with 3.5-µm sections using an Autostainer (Epredia, Kalamazoo, USA). The protocol included four rounds of cyclic staining and scanning of the slides with AxioScanZ.1 (Zeiss, Germany). Each cycle included DAPI for nuclear staining. Here, we applied this pipeline to detect the following targets: D2-40 (lymphatics), CD3 (T lymphocytes), CD56 (NK cells/neural cells/neuroendocrine cells), CD11b+ myeloid cells, PDGFRB (fibroblasts/pericytes), CD20 (B lymphocytes), CD31 (endothelium), CD45 (leukocytes), HLADR+ antigene-presenting cells, as well as a cocktail with PanCK and ECADH to detect epithelial cells. Three different prostate cancer TMA cohorts with n=907 patient samples were analyzed here using this approach.The scanned images were exported as Big TIFF individual channel whole-slide images. Individual TMA spot images were marked with Fiji ROI1 1-Click Tool macro (ImageJ 1.53t), and separate rounds were registered with Python (version 3.11.0, Python Software Foundation) using DAPI channel as reference. Nuclear segmentation was created with nucleAIzer trained modified Mask R-CNN model (https://github.com/lopaavol/nuclei_segmentation). All TMA spot images were run through image curation, where artefacts are excluded by machine learning using ilastik software (version 1.3.3post3). Then, CellProfiler (version 4.2.1) was applied to combine image curated tissue masks and segmented nuclei for cell-based channel intensity measurements. Finally, Jupyter Notebook was applied to classify cell subsets into specific phenotypes using intensity-based thresholding for each channel. Results and Conclusions: We present a detailed pipeline for 10-plex mfIHC with open-source single-cell image analysis tools. The approach can be applied to histological laboratories when combined with a standard five fluorescent channel scanner or imager and with basic computing skills. As the system applies secondary antibodies for the detection, it is flexible for optimizing new antibody panels, and the user is not limited to pre-labelled antibody panels often connected with commercial staining and imaging systems. The approach is especially suited for large-scale high-throughput tissue profiling studies when high n-values are needed for linking associated patient clinical data with spatial phenotype data. Citation Format: Jenni Säilä, Annabrita Schoonenberg, Katja Välimäki, Timo-Pekka Lehto, Carolin Stürenberg, Andrew Erickson, Antti Rannikko, Olli Kallioniemi, Lassi Paavolainen, Tuomas Mirtti, Teijo Pellinen. Open-source single-cell spatial image analysis pipeline for ten-plex immunofluorescence of histological prostate cancer tissue sections [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2091.

Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer.

New breast cancer biomarkers have been sought for better tumor characterization and treatment. Among these putative markers, there is Biglycan (BGN). BGN is a class I small leucine-rich proteoglycan family of proteins characterized by a protein core with leucine-rich repeats. The objective of this study is to compare the protein expression of BGN in breast tissue with and without cancer, using immunohistochemical technique associated with digital histological score (D-HScore) and supervised deep learning neural networks (SDLNN). In this case-control study, 24 formalin-fixed, paraffin-embedded tissues were obtained for analysis. Normal (n = 9) and cancerous (n = 15) tissue sections were analyzed by immunohistochemistry using BGN monoclonal antibody (M01-Abnova) and 3,3'-Diaminobenzidine (DAB) as the chromogen. Photomicrographs of the slides were analysed with D-HScore, using arbitrary DAB units. Another set (n = 129) with higher magnification without ROI selection, was submitted to the inceptionV3 deep neural network image embedding recognition model. Next, supervised neural network analysis, using stratified 20 fold cross validation, with 200 hidden layers, ReLu activation, and regularization at α = 0.0001 were applied for SDLNN. The sample size was calculated for a minimum of 7 cases and 7 controls, having a power = 90%, an α error = 5%, and a standard deviation of 20, to identify a decrease from the average of 40 DAB units (control) to 4 DAB units in cancer. BGN expression in DAB units [median (range)] was 6.2 (0.8 to 12.4) and 27.31 (5.3 to 81.7) in cancer and normal breast tissue, respectively, using D-HScore (p = 0.0017, Mann-Whitney test). SDLNN classification accuracy was 85.3% (110 out of 129; 95%CI = 78.1% to 90.3%). BGN protein expression is reduced in breast cancer tissue, compared to normal tissue.

Jumonji domain-containing protein RIOX2 is overexpressed and associated with worse survival outcomes in prostate cancers.

Histone demethylase RIOX2 was cloned as a c-Myc downstream gene involved in cell proliferation and has been implicated as an oncogenic factor in multiple tumor types. Its expression profiles and correlation with disease progression in prostate cancers are unknown. Transcriptomic profiles of Jumanji domain-containing protein genes were assessed using multiple public expression datasets generated from RNA-seq and cDNA microarray assays. RIOX2 protein expression was assessed using an immunohistochemistry approach on a tissue section array from benign and malignant prostate tissues. Gene expression profiles were analyzed using the bioinformatics software R package. Western blot assay examined androgen stimulation on RIOX2 protein expression in LNCaP cells. Among 35 Jumanji domain-containing protein genes, 12 genes were significantly upregulated in prostate cancers compared to benign compartments. COX regression analysis identified that the ribosomal oxygenase 2 (RIOX2) gene was the only one significantly associated with disease-specific survival outcomes in prostate cancer patients. RIOX2 upregulation was confirmed at the protein levels using immunohistochemical assays on prostate cancer tissue sections. Meanwhile, RIOX2 upregulation was associated with clinicopathological features, including late-stage diseases, adverse Gleason scores, TP53 gene mutation, and disease-free status. In castration-resistant prostate cancers (CRPC), RIOX2 expression was positively correlated with AR signaling index but negatively correlated with the neuroendocrinal progression index. However, androgen treatment had no significant stimulatory effect on RIOX2 expression, indicating a parallel but not a causative effect of androgen signaling on RIOX2 gene expression. Further analysis discovered that RIOX2 expression was tightly correlated with its promoter hypomethylation and MYC gene expression, consistent with the notion that RIOX2 was a c-Myc target gene. The Jumanji domain-containing protein RIOX2 was significantly overexpressed in prostate cancer, possibly due to c-Myc upregulation. RIOX2 upregulation was identified as an independent prognostic factor for disease-specific survival.

Tandem Mass Spectrometry Imaging Enables High Definition for Mapping Lipids in Tissues

AbstractMass spectrometry imaging (MSI) of lipids in biological tissues is useful for correlating molecular distribution with pathological results, which could provide useful information for both biological research and disease diagnosis. It is well understood that the lipidome could not be clearly deciphered without tandem mass spectrometry analysis, but this is challenging to achieve in MSI due to the limitation in sample amount at each image spot. Here we develop a multiplexed MS2 imaging (MS2I) method that can provide MS2 images for 10 lipid species or more for each sampling spot, providing spatial structural lipidomic information. Coupling with on‐tissue photochemical derivatization, imaging of 20 phospholipid C=C location isomers is also realized, showing enhanced molecular images with high definition in structure for mouse brain and human liver cancer tissue sections. Spatially mapped t‐distributed stochastic neighbor embedding has also been adopted to visualize the tumor margin with enhancement by structural lipidomic information.

Tandem Mass Spectrometry Imaging Enables High Definition for Mapping Lipids in Tissues.

Mass spectrometry imaging (MSI) of lipids in biological tissues is useful for correlating molecular distribution with pathological results, which could provide useful information for both biological research and disease diagnosis. It is well understood that the lipidome could not be clearly deciphered without tandem mass spectrometry analysis, but this is challenging to achieve in MSI due to the limitation in sample amount at each image spot. Here we develop a multiplexed MS2 imaging (MS2 I) method that can provide MS2 images for 10 lipid species or more for each sampling spot, providing spatial structural lipidomic information. Coupling with on-tissue photochemical derivatization, imaging of 20 phospholipid C=C location isomers is also realized, showing enhanced molecular images with high definition in structure for mouse brain and human liver cancer tissue sections. Spatially mapped t-distributed stochastic neighbor embedding has also been adopted to visualize the tumor margin with enhancement by structural lipidomic information.

MALDI-IHC-Guided In-Depth Spatial Proteomics: Targeted and Untargeted MSI Combined.

Recently, a novel technology was published, utilizing the strengths of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) and immunohistochemistry (IHC), achieving highly multiplexed, targeted imaging of biomolecules in tissue. This new technique, called MALDI-IHC, opened up workflows to target molecules of interest using MALDI-MSI that are usually targeted by standard IHC. In this paper, the utility of targeted MALDI-IHC and its complementarity with untargeted on-tissue bottom-up spatial proteomics is explored using breast cancer tissue. Furthermore, the MALDI-2 effect was investigated and demonstrated to improve MALDI-IHC. Formalin-fixed paraffin-embedded (FFPE) human breast cancer tissue sections were stained for multiplex MALDI-IHC with six photocleavable mass-tagged (PC-MT) antibodies constituting a breast cancer antibody panel (CD20, actin-αSM, HER2, CD68, vimentin, and panCK). K-means spatial clusters were created based on the MALDI-IHC images and cut out using laser-capture microdissection (LMD) for further untargeted LC-MS-based bottom-up proteomics analyses. Numerous peptides could be tentatively assigned to multiple proteins, of which three proteins were also part of the antibody panel (vimentin, keratins, and actin). Post-ionization with MALDI-2 showed an increased intensity of the PC-MTs and suggests options for the development of new mass-tags. Although the on-tissue digestion covered a wider range of proteins, the MALDI-IHC allowed for easy and straightforward identification of proteins that were not detected in untargeted approaches. The combination of the multiplexed MALDI-IHC with image-guided proteomics showed great potential to further investigate diseases by providing complementary information from the same tissue section and without the need for customized instrumentation.

In Situ Hybridization (ISH) Combined with Immunohistochemistry (IHC) for Co-detection of EGFR RNA and Phosphorylated EGFR Protein in Lung Cancer Tissue.

Detection of phosphorylated proteins in tissue sections using immunohistochemistry (IHC) is a challenging task. The absence of tissue staining may be caused by either a lack of protein expression or a lack of protein activation via its phosphorylation. To address this problem, we employed Integrated Co-detection Workflow (ICW) protocol to analyze lung cancer tissue sections by combining in situ hybridization (ISH) with IHC. The target protein of interest was epidermal growth factor receptor (EGFR, also known as ErbB1 and HER1) which is the founding member of the ErbB family of receptor tyrosine kinases. Using phospho-specific antibodies specific for a phosphorylated site Y1173 of EGFR molecule allowed us to analyze IHC and ISH staining at a single cell level in lung cancer tissue. We have observed both a co-localization of IHC with ISH signals and ISH-positive cells lacking IHC labeling for phosphorylated EGFR. ICW appears to be a very powerful spatial biology technique for accurate localization of cancer cells with phosphorylated/activated and non-phosphorylated/nonactivated proteins.