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The local immunological microenvironment in colorectal cancer as a prognostic factor for treatment decisions in the clinic: The way ahead.

Halama N, Zoernig I, Grabe N, Jaeger D - Oncoimmunology (2012)

Bottom Line: Analysis of the local immunological microenvironment in colorectal cancer lesions yielded prognostic markers.Harnessing these insights for clinical application however requires the use of sophisticated technology and algorithms, especially the robust and reproducible quantification of immune cells.These technologies are available and will allow individualized treatment decisions beyond the current standard.

View Article: PubMed Central - PubMed

Affiliation: National Center for Tumor Diseases; Department of Medical Oncology; University of Heidelberg; Heidelberg, Germany ; Hamamatsu Tissue Imaging and Analysis Center; University of Heidelberg; Heidelberg, Germany.

ABSTRACT
Analysis of the local immunological microenvironment in colorectal cancer lesions yielded prognostic markers. Harnessing these insights for clinical application however requires the use of sophisticated technology and algorithms, especially the robust and reproducible quantification of immune cells. These technologies are available and will allow individualized treatment decisions beyond the current standard.

No MeSH data available.


Related in: MedlinePlus

Figure 2. Individualized analysis of the immune cell profiles for the stratification of patients.
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Figure 2: Figure 2. Individualized analysis of the immune cell profiles for the stratification of patients.

Mentions: Following the early observations more sophisticated and systematic analyses on large cohorts of patients were conducted.12 These elegant studies then could convincingly identify the prognostic role of T cell infiltrates in the center and at the invasive margin of the primary tumor.13 Further analysis of specific stages of colorectal cancer brought more insight into the effects of different immune cell populations.14-16 The role of regulatory T cells within the colorectal cancer microenvironment remains controversial. These cells are mainly identified by FOXP3 expression and their presence is either attributed to a favorable prognosis or a worse prognosis. Further delineation of regulatory T cell and regulatory immune cell subpopulations and better ways to identify these will most likely yield clarifying insights. This matter however highlights a fundamental problem in the analysis of cells on histology sections, especially immune cells. Quantification of immune cells with robust and reproducible results is problematic for human observers. This problem of quantification is long known and leads to difficulties in reproducibility and robustness. Human observers are especially good at discerning extremes: high densities vs. low densities. But gradients beyond “black and white” are very problematic, an issue that is prominently present in HER2/neu quantification. Even for direct counting of low cell numbers, the reproducibility for the same observer is low.17 It is impossible for a human observer to reliably count cells in conglomerates and as such a semi-quantitative estimation is the common solution. However, all approaches with one ore more human observers are extremely time consuming and can only analyze a minute fraction of the actual tumor tissue. A solution to this is the use of computational image analyses, where the quantification is based directly on morphological and spectral information of detected cells.18 Using thin sections, one can ascertain that no overlapping cells in conglomerates are present. Conglomerates are analyzed based on their area and conformation and a statistical dataset then allows the reproducible deduction of the immune cells present within. Robustness and reproducibility are however only one side of the benefits of an automated quantification algorithm. Coupling this methodology to whole slide scanning, the question of immune cell heterogeneity can be adressed systematically. Using an artifical grid as overlay, regions of 1 mm2 area can be quantified and the whole lattice then can be visualized (Fig. 1). The observed heterogeneity for primary colorectal cancer showed enormous variability and in the end leads to a technical question: can we reliably measure the cell numbers based on a single selected area of approximately 1 mm2? A typical core from a tissue microarray has a surface area of 0.3 mm2. The answer for individual patients is: no, the analyzed area has to be much larger for robustness.19 The average number of CD3+ T cells converges if more then five fields of 1 mm2 are analyzed for primary colorectal cancer. So it is necessary to analyse a tissue surface area large enough to achieve reliability for a single patient. This immune cell heterogeneity within the tumor tissue is evident, even when corrected for e.g., necrotic areas.19 It is important to see, that for each tumor entity and for each marker analyzed (e.g., CD3, FOXP3, CD8, CD45RO, etc.), this minimum surface tissue area has to be calculated for robustness. Combining whole slide imaging with a powerful image analysis algorithm allows the robustness and reproducibility needed for personalized diagnostics. Coupling these technologies to a dedicated tissue preparation workflow is going to deliver a basis for treatment decisions (Fig. 2). Colorectal cancer prognosis is influenced by the presence of T cells in the stroma, within the invasive front, and in the parenchyma, in an intraepithelial localization. So what is the best region for quantification: the invasive margin or the center of the tumor, or the stroma? Heterogeneity is present in all regions and one of the obvious aspects is the accumulation of immune cells in distinct regions or compartments. The stroma, either peritumoral or within the tumor lesion, harbors the vast majority of immune cells, only small percentages of T cells are in close contact with the tumor epithelium.20 In contrast, macrophage populations are in direct contact with a high percentage of infiltrating T cells.3 Furthermore, the presence or absence of broad peritumoral stroma does not automatically dictate the quantity of immune cells present in the tumor or around the tumor.20


The local immunological microenvironment in colorectal cancer as a prognostic factor for treatment decisions in the clinic: The way ahead.

Halama N, Zoernig I, Grabe N, Jaeger D - Oncoimmunology (2012)

Figure 2. Individualized analysis of the immune cell profiles for the stratification of patients.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376959&req=5

Figure 2: Figure 2. Individualized analysis of the immune cell profiles for the stratification of patients.
Mentions: Following the early observations more sophisticated and systematic analyses on large cohorts of patients were conducted.12 These elegant studies then could convincingly identify the prognostic role of T cell infiltrates in the center and at the invasive margin of the primary tumor.13 Further analysis of specific stages of colorectal cancer brought more insight into the effects of different immune cell populations.14-16 The role of regulatory T cells within the colorectal cancer microenvironment remains controversial. These cells are mainly identified by FOXP3 expression and their presence is either attributed to a favorable prognosis or a worse prognosis. Further delineation of regulatory T cell and regulatory immune cell subpopulations and better ways to identify these will most likely yield clarifying insights. This matter however highlights a fundamental problem in the analysis of cells on histology sections, especially immune cells. Quantification of immune cells with robust and reproducible results is problematic for human observers. This problem of quantification is long known and leads to difficulties in reproducibility and robustness. Human observers are especially good at discerning extremes: high densities vs. low densities. But gradients beyond “black and white” are very problematic, an issue that is prominently present in HER2/neu quantification. Even for direct counting of low cell numbers, the reproducibility for the same observer is low.17 It is impossible for a human observer to reliably count cells in conglomerates and as such a semi-quantitative estimation is the common solution. However, all approaches with one ore more human observers are extremely time consuming and can only analyze a minute fraction of the actual tumor tissue. A solution to this is the use of computational image analyses, where the quantification is based directly on morphological and spectral information of detected cells.18 Using thin sections, one can ascertain that no overlapping cells in conglomerates are present. Conglomerates are analyzed based on their area and conformation and a statistical dataset then allows the reproducible deduction of the immune cells present within. Robustness and reproducibility are however only one side of the benefits of an automated quantification algorithm. Coupling this methodology to whole slide scanning, the question of immune cell heterogeneity can be adressed systematically. Using an artifical grid as overlay, regions of 1 mm2 area can be quantified and the whole lattice then can be visualized (Fig. 1). The observed heterogeneity for primary colorectal cancer showed enormous variability and in the end leads to a technical question: can we reliably measure the cell numbers based on a single selected area of approximately 1 mm2? A typical core from a tissue microarray has a surface area of 0.3 mm2. The answer for individual patients is: no, the analyzed area has to be much larger for robustness.19 The average number of CD3+ T cells converges if more then five fields of 1 mm2 are analyzed for primary colorectal cancer. So it is necessary to analyse a tissue surface area large enough to achieve reliability for a single patient. This immune cell heterogeneity within the tumor tissue is evident, even when corrected for e.g., necrotic areas.19 It is important to see, that for each tumor entity and for each marker analyzed (e.g., CD3, FOXP3, CD8, CD45RO, etc.), this minimum surface tissue area has to be calculated for robustness. Combining whole slide imaging with a powerful image analysis algorithm allows the robustness and reproducibility needed for personalized diagnostics. Coupling these technologies to a dedicated tissue preparation workflow is going to deliver a basis for treatment decisions (Fig. 2). Colorectal cancer prognosis is influenced by the presence of T cells in the stroma, within the invasive front, and in the parenchyma, in an intraepithelial localization. So what is the best region for quantification: the invasive margin or the center of the tumor, or the stroma? Heterogeneity is present in all regions and one of the obvious aspects is the accumulation of immune cells in distinct regions or compartments. The stroma, either peritumoral or within the tumor lesion, harbors the vast majority of immune cells, only small percentages of T cells are in close contact with the tumor epithelium.20 In contrast, macrophage populations are in direct contact with a high percentage of infiltrating T cells.3 Furthermore, the presence or absence of broad peritumoral stroma does not automatically dictate the quantity of immune cells present in the tumor or around the tumor.20

Bottom Line: Analysis of the local immunological microenvironment in colorectal cancer lesions yielded prognostic markers.Harnessing these insights for clinical application however requires the use of sophisticated technology and algorithms, especially the robust and reproducible quantification of immune cells.These technologies are available and will allow individualized treatment decisions beyond the current standard.

View Article: PubMed Central - PubMed

Affiliation: National Center for Tumor Diseases; Department of Medical Oncology; University of Heidelberg; Heidelberg, Germany ; Hamamatsu Tissue Imaging and Analysis Center; University of Heidelberg; Heidelberg, Germany.

ABSTRACT
Analysis of the local immunological microenvironment in colorectal cancer lesions yielded prognostic markers. Harnessing these insights for clinical application however requires the use of sophisticated technology and algorithms, especially the robust and reproducible quantification of immune cells. These technologies are available and will allow individualized treatment decisions beyond the current standard.

No MeSH data available.


Related in: MedlinePlus