Search

Nuclear membrane blebbing in amoeboid cells. Time-lapse analysis of DU145 DIAPH3-depleted cells treated with EGF and IL6 (exacerbates amoeboid features (13)) and stained with lipid fluorescent dye CellMask Orange.

3D image of nuclear membrane blebbing and emerin mislocalization in BT-549 DIAPH3-depleted cells. DNA (Hoechst, blue), emerin (FITC, green) and membrane (1,1'-Dioctadecyl-3,3,3',3'-Tetramethylindocarbocyanine Perchlorate, Dil, red) staining are shown.

Abnormalities in nuclear shape are a well-known feature of cancer, but their contribution to malignant progression remains poorly understood. Here, we show that depletion of the cytoskeletal regulator, Diaphanous-related formin 3 (DIAPH3), or the nuclear membrane–associated proteins, lamin A/C, in prostate and breast cancer cells, induces nuclear shape instability, with a corresponding gain in malignant properties, including secretion of extracellular vesicles that contain genomic material. This transformation is characterized by a reduction and/or mislocalization of the inner nuclear membrane protein, emerin. Consistent with this, depletion of emerin evokes nuclear shape instability and promotes metastasis. By visualizing emerin localization, evidence for nuclear shape instability was observed in cultured tumor cells, in experimental models of prostate cancer, in human prostate cancer tissues, and in circulating tumor cells from patients with metastatic disease. Quantitation of emerin mislocalization discriminated cancer from benign tissue and correlated with disease progression in a prostate cancer cohort. Taken together, these results identify emerin as a mediator of nuclear shape stability in cancer and show that destabilization of emerin can promote metastasis.Significance: This study identifies a novel mechanism integrating the control of nuclear structure with the metastatic phenotype, and our inclusion of two types of human specimens (cancer tissues and circulating tumor cells) demonstrates direct relevance to human cancer.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/21/6086/F1.large.jpg. Cancer Res; 78(21); 6086–97. ©2018 AACR.

Analysis all available cancer types in TCGA data sets. Correlation analysis of METABRIC data between stages or aggressiveness of cancer vs. ECM proteins expression.

Emerin deregulation links nuclear shape instability to metastatic potential

Emerin deregulation links nuclear shape instability to metastatic potential

Tumor microenvironment (TME) represents an integrated system that affects cancer cell behavior and contributes directly to disease outcome. Systemic approach to analysis of TME should uncover its complexity and facilitate discovery of mechanisms orchestrating tumor development and metastasis. Multiplex fluorescence tissue staining followed by spatial analysis of tumor tissue architecture can provide insights to pivotal interactions of cellular and acellular components of TME. Extracellular matrix (ECM is represented mainly by collagen deposition. Number of reports indicates that ECM contribution to TME state not only depends upon amount of accumulated collagen but its geometrical features and spatial orientation of fibers. These characteristics of collagen fibers contribute directly to physical and mechanical properties of tissue and can change tumor growth and metastasis. Current methods of computational image analysis of tissue implement assessment of cellular or acellular components separately. The goal of current work was to develop a new computational tool to perform integrated analysis of fibrous and cellular components of tumor tissue in spatial dependent manner to achieve detailed tumor tissue mapping and structural patterns recognition. To pursue this goal, we generated images of human lung adenocarcinoma tissue characterized by indolent and aggressive behavior. We performed multiplex immunofluorescence staining for following markers: CD3 - marker of T-lymphocytes, PanCytokeratin - marker of epithelial/tumor cells, collagen hybridizing peptide (3Helix) - marker of collagen, DAPI - nuclear counterstain. To develop image analysis pipeline, we utilized an open source graphical interface analytical platform KNIME, where we generated modular workflow. For ECM analysis, we integrated Python written code into KNIME node. Segmentation of collagen fibers was performed using skeletonization with subsequent calculation of geometrical properties (length, alignment, widths) and orientation of each fiber. Data, collected from single cell analysis and ECM architecture assessment, were combined and forwarded to downstream spatial analysis, where distances from cell to cell or cell to ECM were computed and neighborhood analysis was performed. We demonstrated that tumor cells in aggressive adenocarcinoma samples were co-localized with a smaller number of collagen fibers. In addition, length of that fibers was less in comparison to indolent group. Correlation analysis revealed positive correlation between length of collagen fibers and number of tumor cells in indolent group, but we did not observe this phenomenon in indolent group. Developed computational method provides additional dimensionality to tissue image analysis and can reveal underrecognized structural patterns of the tumor microenvironment. Citation Format: Georgii Vasiukov, Tatiana Novitskaya, Maria-Fernanda Senosain, Anna Menshikh, Andries Zijlstra, Sergey Novitskiy, Pierre Massion. Integrated computational image analysis of cellular and acellular tissue components as a method for detailed tumor tissue mapping and structural patterns recognition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 239.

Lung adenocarcinoma (ADC) is a heterogeneous group of tumors associated with dramatically different survival rates, even when detected at an early stage. We hypothesized that a single cell proteomic approach would allow the dissection of cellular determinants of early lung ADC behavior. We developed a mass cytometry panel of 34 antibodies and validated their performance in four ADC cell lines (A459, H23, PC9 and H3211) and immune cells. We tested our panel in a set of 10 early stage lung ADCs, classified into long- (LPS) (n=4) and short-predicted survival (SPS) (n=6) based on radiomics features. Tumors were disaggregated and cryopreserved until mass cytometry analysis. We identified cellular subpopulations by clustering and analyzed differences in their distribution both within the tumor microenvironment and the epithelial compartment. To validate our results a tissue micro array was generated from lung tissue blocks from patients with LPS and SPS lung adenocarcinoma. Fluorescent staining was performed for PanCK, CD45, CD3, HLA-DR, DAPI. Cell nuclei were segmented using deep learning algorithm and were further processed in KNIME analytical platform where cell segmentation, feature extraction and cell classification were performed. The antibody panel captured the phenotypical differences in ADCs cell lines and PBMCs. When tumors were analyzed long-predicted survival tumors had a higher proportion of immune cells, whereas some short-predicted survival tumors had a higher proportion of fibroblasts/mesenchymal cells. Additionally, tumors show high degree of heterogeneity with distinct protein expression profiles among epithelial subpopulations, and some subsets with high HLA-DR expression were positively correlated with CD4+ and CD8+ T cells with LPS samples being enriched for such subsets. These results were further validated by Fluorescent staining on TMA slides. We found a positive correlation between HLA-DR expression on tumor cells and T cell number (r = 0.25, p = 2.2e-05). For this, in neighborhoods of 100 um diameter for each tumor cell, HLA-DR median signal intensity on neighboring tumor cells and number of T cells were calculated in Python and used as inputs for correlative analysis. Spatial analysis was performed in KNIME by calculation of distances from each T cell to nearest 1st and 2nd tumor cell, for which LPS tumors showed smaller distances for both 1st and 2nd tumor cell compared to SPS tumors (p = 0.039, p = 0.21). Our results demonstrate a distinct cellular profile of epithelial and stromal cells among indolent vs aggressive ADCs with higher HLA-DR expression in indolent tumors, which is associated with greater T cell infiltration. Our results illustrate the heterogeneity of T cell responses and HLA DR expression in lung adenocarcinoma and should further our understanding of mechanisms related to tumorigenesis. This work deserves further validation at the cellular and molecular level to gain further insights into tumor behavior. The work was supported by CA196405 to PPM. Citation Format: Maria-Fernanda Senosain, Tatiana Novitskaya, Georgii Vasiukov, Yong Zou, Aneri Balar, Deon B. Doxie, Jonathan M. Lehman, Rosana Eisenberg, Fabien Maldonado, Andries Zijlstra, Sergey V. Novitskiy, Jonathan M. Irish, Pierre P. Massion. Single cell proteomic analysis of lung adenocarcinoma identifies high HLA-DR expression to be associated with indolent tumor behavior [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-045.

TGF-beta plays a crucial role in tumor microenvironment by regulating cell-cell and cell-stroma interactions. We previously demonstrated that TGF-beta signaling on myeloid cells regulates expression of CD73, a key enzyme for production of adenosine, a pro-tumorigenic metabolite implicated in regulation of tumor cell behaviors, immune response and angiogenesis. Using MMTV-PyMT mouse mammary tumor model, we discovered that deletion of TGF-beta signaling on myeloid cells (PyMT/TGFbRII-LysM) affects ECM formation in tumor tissue, specifically increases collagen and decreases fibronectin deposition, and these changes associate with mitigated tumor growth and reduced metastases. Using multiplexed five-color immunofluorescent staining and spatial analysis on a single-cell level, we discovered that reduced TGF-beta signaling on fibroblasts associates with their proximity to CD73 positive myeloid cells in tumor tissue. Consistent with these findings, in vitro gel contraction assay and Western blotting for Collagen I and pSMAD proteins confirmed that adenosine significantly downregulates TGF-beta signaling on fibroblasts. Using in vitro pharmacological approach, we found that this effect is regulated by A2a and A2b adenosine receptors. TCGA data base analysis revealed that patients with triple negative breast cancer and basal type have similar signature of adenosine and ECM profiles: high expression of A2b adenosine receptors correlates with decreased expression of Col1 and is associates with poor outcome. Taken together, our studies reveal a new role for TGF-beta signaling on myeloid cells in tumorigenesis. Discovered crosstalk between TGF-beta/CD73 on myeloid cells and TGF-beta signaling on fibroblasts can contribute to ECM remodeling and pro-tumorigenic actions of CAFs. Citation Format: Georgii Vasiukov, Tatiana Novitskaya, Samantha Schwager, Harold Moses, Timothy Blackwell, Igor Feoktistov, Sergey Novitskiy. TGF-beta signaling on myeloid cells regulates ECM deposition in mammary carcinoma via adenosine dependent mechanisms [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1714A.

Tumor microenvironment (TME) represents a dynamic niche that regulates cancer cell behavior contributing directly to disease outcome. Systemic approach to analysis of TME should uncover its complexity and facilitate discovery of mechanisms orchestrating tumor development and metastasis. Multiplexed fluorescent tissue stain followed by spatial analysis of tumor tissue architecture can provide insights to pivotal interactions of cellular and acellular components of TME. Extracellular matrix (ECM), one of constitutive of TME, is represented mainly by collagen deposition. Recently it is became increasingly recognized that ECM contribution to TME dynamics not only depend upon amount of accumulated collagen but its geometrical features and spatial orientation of fibers. These characteristics of collagen contribute directly to physical and mechanical properties of tissue and can contribute to tumor growth and metastasis. Current algorithms of tissue stain-based methods include estimation of ECM deposition by measuring percent of positive area and separate to it cell classification and cell count. The goal of current work was to develop a new computational tool to perform spatial distribution analysis based on geometrical features and orientation of collagen fibers in combination with cell class assessment aimed to achieve detailed tumor tissue mapping. To pursue this goal, we generated fluorescent images of human breast tumor tissue, stained for following markers: CD3 - marker of T-lymphocytes, PanCytokeratin - marker of epithelial/tumor cells, collagen hybridizing peptide (3Helix) - marker of collagen, DAPI - nuclear counterstain. To develop image analysis pipeline, we utilized open source graphical interface analytical platform KNIME, where we generated modular workflow. For ECM analysis, we integrated Python written code into KNIME node. Segmentation of collagen fibers was performed using skeletonization with subsequent calculation of geometrical properties (length, alignment, widths) and orientation of each fiber. Data, collected from single cell analysis and ECM architecture assessment, were combined and forwarded to downstream spatial analysis, where distances from cell to cell or cell to ECM were computed and neighborhood analysis was performed. We demonstrate different patterns in tumor microenvironment organization that correlate with cancer outcome. Developed image analysis algorithm provides additional dimensionality to fluorescent tissue stains and can reveal underrecognized patterns of tumor microenvironment that can contribute to better understanding of tumorigenesis and metastasis. Citation Format: GEORGII VASIUKOV, Tatiana Novitskaya, Sergey Gutor, Anna Menshikh, Andries Zijlstra, Sergey Novitskiy. Extracellular matrix segmentation combined with cell classification as a novel method for detailed tumor tissue mapping [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4425.

Purpose: Machine-learning assisted histopathology using markers of basal and luminal differentiation was employed to profile the intratumoral heterogeneity of bladder cancer from cystectomy patients and predict disease-free survival in this high-risk patient population. Methods: Urothelial carcinomas are biologically heterogeneous and vary greatly in clinical progression as well as treatment response. Delineation of molecular subtypes by gene expression analysis of luminal and basal markers has indicated differential outcomes associated with basal and luminal subtypes. However, histologic validation of this classification using protein markers (basal = KRT5/6, P63; luminal = KRT20/GATA3) has been challenging. While using multiplex-immunofluorescence to subtype a retrospective cystectomy cohort (a TMA of 380 patients), we determined that nearly 50% of tumors did not exhibit cytokeratin markers. Subtyping was further confounded by frequent loss of basal-to-luminal stratification and the emergence of intratumoral spatial heterogeneity with the basal and luminal subtypes being completely intermixed throughout the tumor. These observations caused us to hypothesize that previously undefined but clinically relevant subtypes might exist. To address this challenge we developed a single-cell image analysis pipeline that leveraged machine learning to classify molecular subtype and spatial heterogeneity within each tumor. Using the informatics software KNIME we achieved single-cell segmentation and extracted 285 features for 5 protein markers (P63, GATA3, collagen, nuclear stain, and pan-cytokeratin) from each ~20,000 cells contained in 2 cores of tumor and adjacent benign for each patient. Under guidance from a pathologist, definitive urothelial cells (luminal, intermediate, and basal cells) as well as stromal cells were selected from 25 cores normal urothelium to form the ground truth for XGboost-based machine-learning. Summary Findings: Single-cell profiling with machine learning on transcription factors could classify basal and luminal subtypes with greater than 97% accuracy according to validation in normal urothelium using keratin markers. While we were able to recapitulate differential survival associated with a pure basal subtype, it was the intratumoral heterogeneity of basal and luminal cells that was the predominant driver of disease-free survival. Conclusion: A newly identified bladder cancer subtype defined by intratumoral heterogeneity is a clinically relevant driver of disease-free survival. This abstract is also being presented as Poster B22. Citation Format: Adel Eskaros, Tatiana Novitskaya, Andries Zijlstra. Profiling intratumoral heterogeneity of bladder cancer subtypes at the single-cell level using machine-learning assisted histopathology [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr PR02.

Introduction: The manifestation of cancer malignancy occurs at the cellular level where individual cells escape tissue confinements and disseminate. Identifying this malignant behavior at a cellular level is necessary in order to deconvolve the tumor heterogeneity, identify disease progression and predict cancer-related morbidity and mortality. To achieve that goal, we established a unique computer-assisted single-cell histopathology analysis of prostate tissue that evaluates nuclear-membrane instability and the associated extracellular vesicle (EV) biogenesis to identify malignant potential and assess future disease progression. In recent years cancer EVs have been identified as important mediators of intercellular communication. The potential for using EVs as a liquid biopsy has promoted research on profiling EVs in biofluids. However, the direct analysis of EV biogenesis in tumor tissue has been largely omitted, even thought identifying such cells is likely to be a specific and sensitive measure of disease malignancy. We have previously demonstrated that highly migratory and metastatic cancer cells shed atypically large EVs, known as large oncosomes (LO, Di Vizio et. al., 2012). LO play distinct functions and contain a specific repertoire of molecules that can be used for detection of tumor-derived cargo in plasma (Minciacchi et. al., 2015). The recent discovery that the biogenesis of large oncosomes (LO) in prostate cancer is associated with nuclear membrane instability (Reis-Sobreiro et. al., 2018) offered an opportunity to investigate such biogenesis in patient specimens. We have developed a machine-learning assisted histopathology (HistoMAP) that quantitatively identifies nuclear-membrane instability in prostate cancer tissue and demonstrate a direct correlation between this molecular biogenesis of vesicles and biochemical recurrence after prostatectomy. Experimental procedures: We developed a multiplex immunofluorescent technique to detect and quantify nuclear-derived LO in formalin fixed paraffin embedded prostatectomy tissues. Using computer-assisted image segmentation and machine-learning we distinguished these LO from all other intracellular compartments and assess malignancy in a cohort of Vanderbilt prostate cancer patients. Results: LO production was significantly elevated in prostate cancer in comparison to benign tissue and particularly evident in lymph node metastases. Moreover, LO production was associated with the risk of future metastatic disease which re-enforced its relevance in cancer progression. Conclusions: Our findings demonstrate that, given knowledge of the EV machinery, EV biogenesis can be detected in patient tissues and provide critical information related to patient disease status and assist with the prediction of future clinical performance. Citation Format: Andries Zijlstra, Tatiana Novitskaya, Dolores Di Vizio, Mariana Reis- Sobreiro, Michael Freeman. Machine-learning assisted histopathology (HistoMAP) links nuclear membrane instability to disease progression in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4448.