**3.2. Stainings**

thereby indicating the need for robust morphometric techniques which may standardize the

The immune system is composed of different lineages of immune cells with different functions that protect every organism against infection. Among its tasks, immunosurveillance impedes transformed cells to become neoplastic cells. Indeed, increasing literature support the hypoth‐ esis that cancer development is influenced by the host immune system [66]. In fact, specialized blood vessels, the high endothelial venules (HEV), allowing the extravasation of lymphocytes, are present in primary human solid cancers [67, 68]. Therefore, it has been postulated that analyzing the composition, distribution, and architecture of the immune infiltrate for each

Immune infiltrates are heterogeneous between tumor types, and are diverse from patient to patient. All immune cell types may be found in a tumor, including macrophages, dendritic cells (DC), mast cells, natural killer (NK) cells, naïve and memory lymphocytes, B cells, and T lymphocytes (which include various subsets of T cell: TH1, TH2, TH17, regulatory T cells (TREGS), T follicular helper cells (TFH), and cytotoxic T cells). The analysis of the location, density, and functional orientation of different immune cell populations (termed the immune contexture) in large collections of annotated human tumors has allowed the identification of components that are beneficial for patients and those that are deleterious [70–72]. The prog‐ nostic impact of immune cells such as B cells, NK cells, myeloid derived suppressor cells (MDSC), macrophages, and subset of Thelper populations (TH2, TH17, TREG cells) may differ depending on the type of cancer, and on the cancer stage [72]. In contrast, cytotoxic T cells, TH1 cells, and memory T cells were strongly associated with good clinical outcome for all cancer types [66, 72]. Notably, two large studies have shown that tumor immune infiltrate patterns and subsets in colorectal cancer are significant prognostic biomarkers [73, 74]. A potential clinical translation of these observations is the establishment of an Immunoscore, based on the numeration of two lymphocyte populations (CD3/CD45RO, CD3/CD8 or CD8/ CD45RO), both in the core and in the invasive margin of tumors, as a clinically useful prog‐ nostic marker [75]. This immunoscore sheds light into the prognostic role of the tumoral immune infiltrate, but still needs to be validated in colorectal cancer. Its utility in other

**3. Methods for the quantification of ECM elements above mentioned**

The methods explained are based on the experience of the group in the study of the ECM in

The study of tissue microarrays (TMAs) have emerged as a tool for rapid analysis for diagnostic and prognostic studies because several markers can be tested in huge amounts of samples [76–

tumor type, will offer new prognostic or predictive biomarkers [68, 69].

measurement of angiogenesis.

266 Composition and Function of the Extracellular Matrix in the Human Body

malignancies should be tested as well.

neuroblastic tumor samples.

**3.1. Samples**

**2.4. Immune system**

Several serial sections of 3μm can be made and stained with histochemistry (HC) for GAGs, reticulin fibers and collagen type I fibers, and with immunohistochemistry (IHC) anti‐CD31 for blood vessels.

Alcian blue pH 2.5 Stain Kit (Artisan™, Dako) stains acid GAGs (HS, CS, DS, and HA), sulfomucins and sialomucins in blue, the nucleus in red and the cytoplasm in pale pink (**Figure 2**).

Reticulin stains are silver stains based on the argyrophilic properties of reticulin fibers and a slightly modified Gomori can be used, which stains reticulin fibers in black (**Figure 3**). The first step in the staining procedure consists of oxidation of the hexose sugars in reticulin fibers to yield aldehydes. The second step is called sensitization in which a metallic compound such as ammonium sulfate is deposited around the reticulin fibers, followed by silver impregnation in which an ammonical or diamine silver solution is reduced by the exposed aldehyde groups to metallic silver. Further reduction of the diamine silver is achieved by transferring the sections to formaldehyde; this step is called revealing. The last step consists of toning by gold chloride in which the silver is replaced by metallic gold and the color of the reticulin fibers changes from brown to black. Masson's trichrome stain consists in the sequential staining with Harris hematoxylin which stains nuclei dark red, aniline blue which stains collagen and reticulin blue, and molybdic orange G which stains erythrocytes dark orange (**Figure 4**). Orcein is a natural dye obtained from lichens which are found to stain elastic fibers dark brown (**Figure 5**).

CD31 is a single chain type 1 transmembrane protein with a molecular mass of approximately 135 kDa, belonging to the immunoglobulin superfamily. CD34 can also be applied to a subgroup, but this marker also stains cells other than endothelial. CD31 is expressed on endothelial cells of epithelial origin (all continuous endothelia, including those of arteries, arterioles, venules, veins, and capillaries, but it is not completely expressed on discontinuous endothelium in, for example, splenic red pulp). In addition, CD31 is expressed diffusely on the surfaces of megakaryocytes, platelets, myeloid cells, natural killer cells, and some subsets of T cells, as well as on B‐cell precursors. Cells labeled by the antibody predominantly display membrane staining with weaker cytoplasmic staining (**Figure 6**).

Finally, IHC is used to detect diverse subpopulations of lymphocytes in tumoral tissues. For example, CD45 is a transmembrane glycoprotein expressed on most nucleated cells of hematopoetic origin, *i.e.*, all human leucocytes; CD20 reacts with an epitope located on the surface of B cells and appears early during B‐cell maturation; CD3 is a pan‐T cell marker for identification of T cells. It is well‐suited for labeling reactive T cells in tissue with lymphoid infiltrates, and for classification of T‐cell neoplasms; CD7 is expressed by the majority of peripheral blood T cells, NK cells, and all thymocytes. It is one of the earliest surface antigens on T and NK‐cell lineages; CD4 is a transmembrane glycoprotein, expressed on normal thymocytes, T‐helper cells, majority of mature peripheral T cells, and a subset of suppressor or cytotoxic T cells; CD8 is a 68 kDa transmembrane glycoprotein expressed as a heterodimer by a majority of thymocytes, and by class I major histocompatibility complex restricted, mature, suppressor/cytotoxic T cells; CD68 labels human monocytes and macrophages, but not myeloid cells; CD163 has been shown to mark cells of monocyte/macrophage lineage; CD11b is expressed on the surface of many leukocytes including monocytes, neutrophils, natural killer cells, granulocytes and macrophages, as well as on 8% of spleen cells and 44% of bone marrow cells; and CD11c is expressed prominently on the plasma membranes of monocytes, tissue macrophages, NK cells, and most dendritic cells (**Figure 7**).

#### **3.3. Evaluation of the samples**

#### *3.3.1. Subjective assessment*

The samples contained in TMAs or in whole slides can subjectively be analyzed by a pathol‐ ogist to assess the amount of each ECM element as non‐informative: artefact, scant material, lost cylinder; negative: no expression or <5% of stained area is detected; positive 1+: mild staining, 5–10% of the area; positive 2+: moderate staining, 10–50% of the area.; and positive 3+: strong staining, >50% of the area.

#### *3.3.2. Automated quantification*

The growing size and number of medical diagnostic images requires the use of computer‐ automated segmentation algorithms for the delineation of ECM structures of interest, which play a vital role in the research of new biomedical‐imaging markers [81]. This is critical to analyzing whole sets of HC and IHC images to identify, describe, and quantify tissue altera‐ tions of the ECM, and require digitation of the samples. Morphometric techniques based on computer‐automated segmentation algorithms attempt to decrease human error, increase efficiency, assess large areas or huge amounts of tumor samples, create reproducible results, and help to standardize the measurements [81].
