Abstract P5-04-07: Multispectral imaging allows visualization and quantification of multiple immunologic cell types in breast tumor tissues

2015 ◽  
Author(s):  
Elizabeth A Mittendorf ◽  
Chichung Wang ◽  
Peng Yau ◽  
Kristin Roman ◽  
Yun Wu ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Multispectral Imaging ◽  
Cell Types ◽  
Tumor Tissues

scholarly journals Characterizing the Tumor Immune Microenvironment with Tyramide-Based Multiplex Immunofluorescence

2020 ◽  
Vol 25 (4) ◽  
pp. 417-432
Author(s):  
Hidetoshi Mori ◽  
Jennifer Bolen ◽  
Louis Schuetter ◽  
Pierre Massion ◽  
Clifford C. Hoyt ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Predictive Biomarkers ◽  
Cell Types ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Manual Processing ◽  
Cell Densities ◽  
Immune Profiling

AbstractMultiplex immunofluorescence (mIF) allows simultaneous antibody-based detection of multiple markers with a nuclear counterstain on a single tissue section. Recent studies have demonstrated that mIF is becoming an important tool for immune profiling the tumor microenvironment, further advancing our understanding of the interplay between cancer and the immune system, and identifying predictive biomarkers of response to immunotherapy. Expediting mIF discoveries is leading to improved diagnostic panels, whereas it is important that mIF protocols be standardized to facilitate their transition into clinical use. Manual processing of sections for mIF is time consuming and a potential source of variability across numerous samples. To increase reproducibility and throughput we demonstrate the use of an automated slide stainer for mIF incorporating tyramide signal amplification (TSA). We describe two panels aimed at characterizing the tumor immune microenvironment. Panel 1 included CD3, CD20, CD117, FOXP3, Ki67, pancytokeratins (CK), and DAPI, and Panel 2 included CD3, CD8, CD68, PD-1, PD-L1, CK, and DAPI. Primary antibodies were first tested by standard immunohistochemistry and single-plex IF, then multiplex panels were developed and images were obtained using a Vectra 3.0 multispectral imaging system. Various methods for image analysis (identifying cell types, determining cell densities, characterizing cell-cell associations) are outlined. These mIF protocols will be invaluable tools for immune profiling the tumor microenvironment.

scholarly journals Spermine oxidase (SMO) activity in breast tumor tissues and biochemical analysis of the anticancer spermine analogues BENSpm and CPENSpm

BMC Cancer ◽  
2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuela Cervelli ◽  
Gabriella Bellavia ◽  
Emiliano Fratini ◽  
Roberto Amendola ◽  
Fabio Polticelli ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Biochemical Analysis ◽  
Spermine Oxidase ◽  
Tumor Tissues ◽  
Spermine Analogues

Estrogen Content and Metabolism in Human Breast Tumor Tissues and Cells

1996 ◽  
Vol 784 (1 Challenges an) ◽  
pp. 314-324 ◽  
Author(s):  
L. A. CASTAGNETTA ◽  
M. LO CASTO ◽  
O. M. GRANATA ◽  
L. POLITO ◽  
M. CALABRÒ ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Human Breast ◽  
Human Breast Tumor ◽  
Tumor Tissues

Characterization of cell types in human breast tumor primary cultures

1987 ◽  
Vol 61 (1-2) ◽  
pp. 91-99 ◽  
Author(s):  
D. Muller ◽  
J. P. Fricker ◽  
R. Millon-Collard ◽  
J. Abecassis ◽  
J. Pusel ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Primary Cultures ◽  
Cell Types ◽  
Human Breast ◽  
Human Breast Tumor

scholarly journals Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Wei Lin ◽  
Pawan Noel ◽  
Erkut H. Borazanci ◽  
Jeeyun Lee ◽  
Albert Amini ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Cell Types ◽  
Ductal Adenocarcinoma ◽  
Cellular Composition ◽  
Primary Tumors ◽  
Metastatic Lesions ◽  
Tumor Tissues ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract Background Solid tumors such as pancreatic ductal adenocarcinoma (PDAC) comprise not just tumor cells but also a microenvironment with which the tumor cells constantly interact. Detailed characterization of the cellular composition of the tumor microenvironment is critical to the understanding of the disease and treatment of the patient. Single-cell transcriptomics has been used to study the cellular composition of different solid tumor types including PDAC. However, almost all of those studies used primary tumor tissues. Methods In this study, we employed a single-cell RNA sequencing technology to profile the transcriptomes of individual cells from dissociated primary tumors or metastatic biopsies obtained from patients with PDAC. Unsupervised clustering analysis as well as a new supervised classification algorithm, SuperCT, was used to identify the different cell types within the tumor tissues. The expression signatures of the different cell types were then compared between primary tumors and metastatic biopsies. The expressions of the cell type-specific signature genes were also correlated with patient survival using public datasets. Results Our single-cell RNA sequencing analysis revealed distinct cell types in primary and metastatic PDAC tissues including tumor cells, endothelial cells, cancer-associated fibroblasts (CAFs), and immune cells. The cancer cells showed high inter-patient heterogeneity, whereas the stromal cells were more homogenous across patients. Immune infiltration varies significantly from patient to patient with majority of the immune cells being macrophages and exhausted lymphocytes. We found that the tumor cellular composition was an important factor in defining the PDAC subtypes. Furthermore, the expression levels of cell type-specific markers for EMT+ cancer cells, activated CAFs, and endothelial cells significantly associated with patient survival. Conclusions Taken together, our work identifies significant heterogeneity in cellular compositions of PDAC tumors and between primary tumors and metastatic lesions. Furthermore, the cellular composition was an important factor in defining PDAC subtypes and significantly correlated with patient outcome. These findings provide valuable insights on the PDAC microenvironment and could potentially inform the management of PDAC patients.

Protein inhibitor of activated STAT genes are differentially expressed in breast tumor tissues

2019 ◽  
Vol 16 (4) ◽  
pp. 277-285 ◽  
Author(s):  
Mohammad Taheri ◽  
Vahid K Oskooei ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Breast Tumor ◽  
Differentially Expressed ◽  
Protein Inhibitor ◽  
Tumor Tissues

scholarly journals Mixture theory modeling for characterizing solute transport in breast tumor tissues

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Sreyashi Chakraborty ◽  
Alican Ozkan ◽  
Marissa Nichole Rylander ◽  
Wendy A. Woodward ◽  
Pavlos Vlachos
Keyword(s):  
Solute Transport ◽  
Breast Tumor ◽  
Mixture Theory ◽  
Tumor Tissues

Correlation between 99m Tc-pertechnetate uptakes and expressions of human sodium iodide symporter gene in breast tumor tissues

2001 ◽  
Vol 28 (7) ◽  
pp. 829-834 ◽  
Author(s):  
Dae Hyuk Moon ◽  
Sung Jin Lee ◽  
Ki Young Park ◽  
Kun Ku Park ◽  
Se Hyun Ahn ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Sodium Iodide ◽  
Sodium Iodide Symporter ◽  
Tumor Tissues

scholarly journals Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade

Science ◽  
2021 ◽  
Vol 372 (6547) ◽  
pp. eaba2609
Author(s):  
Sneha Berry ◽  
Nicolas A. Giraldo ◽  
Benjamin F. Green ◽  
Tricia R. Cottrell ◽  
Julie E. Stein ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Expression Patterns ◽  
Cell Types ◽  
Term Survival ◽  
Multiple Parameters ◽  
Tissue Sections ◽  
Programmed Cell Death 1 ◽  
Multiple Cell ◽  
Independent Cohort

Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this “AstroPath” whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti–programmed cell death-1 (PD-1)–based therapy, including CD163+PD-L1– myeloid cells and CD8+FoxP3+PD-1low/mid T cells. These features were combined to stratify long-term survival after anti–PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution.

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