Download Tumor Cell Subpopulation Analysis in Immunohistochemistry

CellMap : Tumor Cell Subpopulation Analysis
in Immunohistochemistry Stained Tumor Tissue
TM
Joseph S. Krueger1, Steven J. Potts,1 Mohamed Salama2, G. David Young1, and Holger Lange1
1Flagship
Introduction
Biosciences, Westminster, CO, United States: [email protected], [email protected], [email protected], [email protected]
2University of Utah, Salt Lake City, Utah: [email protected]
CellMapTM Evaluation of Biomarker Associations in Tissue Sections
How Does CellMapTM Work?
Background:
● All potential factors within individual patients which contribute to a lack of response to a given therapy are not known, but cancer biologists have
long hypothesized that distinct and disparate populations within the tumor can be selected for by therapy to outgrow and emerge as a resistant tumor.
Identifying and Isolating the Cells of Interest with CellMapTM
CellMapTM quantifies the content from each color separately
poster #2683
Biomarker Mapping Using CellMapTM
CellMapTM Modeling of Biomarker Density and Correlation
Differentiating epithelial cells from stromal cells without the assistance of a specific stain
● As more targeted therapies are being developed, the understanding of these subpopulations of cells within a tumor has become very important to
clinical strategy. Thus, there is a need to effectively distinguish and evaluate different populations of cells in a tumor within formalin fixed tissue.
Whole slide images are transformed into a cell and biomarker maps
Whole slide image
Tissue component isolation
Map of tissue components
● Immunohistochemistry (IHC) remains the most direct approach to evaluating biomarkers within tissue context, but requires a pathologist to
subjectively separate the complex components of tumor tissue and the compartments of the tumor cells themselves to deliver a numerical score that is
based on the staining intensity of a cell and the percentage of cells which stain.
Study Context:
DAB
● IHC is not considered qualitative, due to pathologist subjectivity in scoring sample regions, the inability to effectively discriminate minor differences
in staining intensities for a biomarker, and the inability to deliver a dataset with sufficient sample size to overcome bias deficiencies. Furthermore, a
significant amount of information content is lost in a typical IHC score, eliminating the potential to identify and analyze discrete cell populations within
a tumor that may be leading to refractory to therapy.
●Image analysis (IA) approaches can deliver a more quantitative IHC score by objectively distinguishing tumor components and cellular compartments,
detecting minor differences in staining intensity, and by performing this function across the whole tumor section. However, most current IA approaches
are designed only to report an average of staining across the analyzed region, without reporting the cell-by-cell statistics required to identify discrete
cell populations within a tumor.
Double biomarker staining
Hematoxylin
Fast Red
Figure 2: Separation of different stains in a tissue section, each which contains specific information content about a cell or biomarker.
CellMapTM quantifies the biomarker content from each cell compartment individually
Nucleus IHC
Cytoplasm IHC
Membrane IHC
CISH Applications
Figure 5: CellMapTM can distinguish tumor epithelial cells from surrounding stroma with hematoxylin staining alone. CellMapTM can differentiate between
epithelial cells and stromal cells based on topographic characteristics. Left: Image of breast cancer tissue with organized ductal epithelial cells and
surrounding stroma. Middle: visual markup after CellMapTM cell selection, showing included tumor cells in dark blue and excluded stromal cells in light blue.
Right: 20X magnification of image markup showing the included (dark blue) and excluded (light blue) cells.
Identifying cells at the tumor/stroma interface without the assistance of a specific stain
Study Objective:
● To deliver a cell-by-cell output, we have invented CellMapTM, which can analyze an IHC stained tumor tissue section which has been digitally imaged,
and make multiparametric measurements about cell morphology, tissue topography, and biomarker staining in every cell individually. This information
can be reported within a specific component of the tumor, and/or within a compartment of the cell simultaneously.
Figure 8: A graphical representation of the data output using CellMapTM. CellMapTM is used to distinguish and isolate tumor cells, stroma, and immune cell infiltrates.
Each tissue component is analyzed individually, and the expression of a biomarker within each component can be quantitated separately. The left and middle images
represents the transformation of a whole slide image into the map on the right. The colors in the map on the right represent the location of tumor epithelial cells
(blue), stroma (green), and immune infiltrates (red). Co-localization of these cells and correlation with a biomarker in any cell or tissue compartment can be
quantitatively evaluated to capture the dynamic biology which results in the interplay of these tissue components.
Using biomarker association maps to interrogate biological endpoints
Biomarker Staining
Biomarker High
Biomarker Low
Cell Density
Proliferation
Apoptosis
Biomarker Negative
● CellMapTM can be used to make quantitative measurements which identify specific cells with specific signaling processes, and determine their
location within a tumor section. This information can be used to identify and quantify discrete cell populations relevant to a disease hypothesis which
are associated with a specific tumor microenvironment.
Study Design:
● We utilized CellMapTM in various demonstrative scenarios to discriminate discrete tumor components (such as tumor cells, stroma, or infiltrating
lymphocytes) from each other, and made measurements of a biomarker in a cell-compartment specific (nucleus, cytoplasm, membrane) manner.
● The population profile and statistical output of CellMap
meaning in the sample set.
TM
Figure 6: CellMapTM can identify cells in a particular microenvironment regardless of staining. CellMapTM can identify contextual relationships which identify
the microenvironment of a cell, such as the tumor-stroma boundary. Left: A disorganized breast tumor with infiltrating stroma. Right: Identification of
tumor cells on the stromal boundary (marked in dark blue) based on topographical characteristics, independent of a stain.
was used to demonstrate staining patterns consistent with an inferred or known biological
● These examples of CellMapTM cell selection, cell compartment quantification, and biomarker expression mapping demonstrate how CellMapTM may
be used to make pharmacodynamic, surrogate efficacy, or patient selection decisions.
Figure 3: CellMapTM separation of nuclear, cytoplasm, and membrane cell compartments, and special applications for CISH, for quantitation.
Differentiating immune cells without the assistance of a specific stain
Immune cells
CellMapTM associates the data with single cells and provides a population output
Why Use Cell Based Measurements?
Tissue context is key to biomarker evaluation
● Different tumor architectures reflect different disease states
● Target expression and drug response are altered in different cells
● Hetereogenity can be accounted for and measured
Examples of tumor heterogeneity in a biopsy
Figure 4: A visual representation of how CellMapTM distinguishes cells and cell compartments, tabulates and differentiates the data, and ultimately maps
individual cells and their associated biomarker information to enable multiparametric analysis. CellMapTM defines cells and captures their attributes according
to multiple mathematical analyses. These outputs can be used to differentiate cell staining patterns in distinct populations of cells, and the information from
any population of selected cells can be subjected to population analysis approaches and correlated with other populations within the same tissue.
Figure 7: CellMapTM can distinguish immune cells from tumor cells and surrounding stroma independent of staining. CellMapTM can recognize moncytes (red
and orange) and macrophages (light blue) based on morphological characteristics. Above: A region of tumor with extensive monocyte infiltration between
nests of tumor epithelial cells. The image markup identifies immune cells, with upper left region of the image excluded from the analysis.
Figure 9: Making biomarker associations using CellMapTM. CellMapTM was used to correlate biomarker expression (high, low, negative) with markers for cell
proliferation and apoptosis, as well as the presence of immune cells. The biomarker of interest was membranous, the proliferation marker was nuclear, the apoptotic
marker was cytoplasmic, and the immune cells were identified as described earlier. Notably, the presence of apoptosis and immune infiltrates correlated well; but
were anti-correlated with high expression of the biomarker. Low , but not negative, biomarker expression correlated well with proliferation. These comparisons
suggest that biomarker expression and apoptosis may be influenced by immune cells , and the biomarker may have an anti-apoptotic effect.
Conclusions
Examples of several different tissue architectures a single breast tumor
CellMapTM analysis permits the measurement of cell-specific biomarkers in the context of tumor complexity and heterogeneity for research, drug discovery, clinical trials , and companion diagnostic approaches.
● Contextual evaluations are important for understanding the biology of a target, evaluating pharmacodynamic or surrogate efficacy markers, or identifying and evaluating biomarkers for a patient selection approach.
● CellMapTM enables a comprehensive, correlative analysis which extracts the full content of information in a tissue. CellMapTM allows the researcher to derive specific conclusions about the role of a biomarker of interest in a specific cell type in the context of several tumor components and processes.
● Only cell-based measurements such as CellMapTM can capture the full content of information which a tissue type, cell type, and cell-compartment specific analysis approach in a heterogenous tissue.
lobular
syncytial
papillary
infiltrative
● CellMapTM is a means to isolate cells of interest from a complex tissue, measure their expression of a biomarker of interest, and make correlations with other biomarkers and cell types. This technique is critical for determining patient stratification approaches for companion diagnostic development.
Figure 1: Variability in tissue content and architecture embodies the heterogenous disease state which confounds robust analysis of whole tissue sections.
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