Lymphocyte Detection

The immune system is of critical importance in preventing cancer development. The immune system can kill cells that are dividing uncontrollably, resulting in prevention of cancerous growth. However, cancers can have specific mutations that help it evade this immune destruction, which is one of the hallmarks of cancer development [1]. As such, understanding the ability of immune cells to prevent cancer development or kill cancer cells is an active topic in cancer research. In particular, recent advances in immunotherapy strategies [2-4] have further increased the interest in understanding the mechanism of immune response to cancer. One of the current hypotheses states that the balance between lymphocytes, a subset of immune cells, with pro- and anti-inflammatory function is important for disease progression [5]. Specifically, lymphocytes that occur within the tumor area and with the tumor-associated stroma are of interest. These lymphocytes are called tumor infiltrating lymphocytes (TILs). Studies have shown that the presence of TILs is related to patient prognosis after undergoing surgery or immunotherapy [6-8]. Therefore, detection and quantification of lymphocytes has the potential to provide biomarkers with strong prognostic and predictive power for cancer progression and therapeutic efficacy [9].

Quantification of Immune Cells 

An important tool to detect and quantify specific cell populations in histopathology is immunohistochemistry (IHC). IHC  is a technique that allows to stain specific cell types, including lymphocytes, by attaching a colored label to a specific antigen that is expressed by a cell, making it distinguishable from other types of cells. In the context of TILs, widely used immune cell markers are CD3 (general T-cell markers) and CD8 (cytotoxic T-cell marker). Both CD3 and CD8 are membrane markers, meaning that they target an antigen in the cells' membrane, resulting in a colored ring in positive cells.


Figure 1. Examples of image regions, containing areas with a regular lymphocyte distribution, areas with lymphocyte clusters and  areas with artifacts or damaged tissue. Patches containing a single lymphocyte are depicted as well, in order to show the difference in appearance.

To quantify the immune cells in immmunohistochemistry, visual assessment via light microscopy is the standard approach in research. This procedure requires training by pathologists and suffers from inter- and intra-observer variability [10].

The rise of digital pathology has fostered the development of computer algorithms based on machine learning for the analysis of histopathology whole-slide images (WSI). These methods have the potential to make the transition from subjective visual estimation to reproducible accurate quantification of cells via automatic detection. Furthermore, moving from overall quantification to detection of each lymphocyte in the slide allows analysis of complex spatial patterns such as cell density distributions and cell-to-cell interactions, which are currently not assessed due to a lack of standardized methodology, time and difficulty in making such assessment [10].

It is easy to show that a large variety of challenges are present in tissue samples stained with lymphocyte markers, making detection a non-trivial task. In this study, we define three different types of areas containing T-cells that can be distinguished in CD3 and CD8 stained slides (see Figure 1(a-c)), namely (a) regular tissue areas, which are areas with a regular lymphocyte distribution without artifacts, damaged or large areas of cell clusters; (b) lymphocyte cluster areas, which contain significant number of clustered T-cells with vague cell boundaries; (c) artifact areas, which include various types of staining artifacts, i.e., areas with a range of non-specific stain, damaged regions or ink. Quantification of T-cells is relatively straightforward in regions of category (a), whereas in categories (b) and (c) detection and accurate quantification of lymphocytes can be very challenging. Such regions  are often not considered or discussed in scientific literature but are highly relevant for procedures that aim to fully automatically analyze immunohistochemistry.

References

  1. Hanahan, D., Weinberg, R.A., 2011. Hallmarks of cancer: the nextgeneration. Cell 144, 646–674
  2. Khalil, D.N., Smith, E.L., Brentjens, R.J., Wolchok, J.D., 2016.The future of cancer treatment: immunomodulation, cars andcombination immunotherapy. Nature reviews. Clinical oncology13, 394
  3. Rosenberg, S.A., Restifo, N.P., 2015. Adoptive cell transfer as per-sonalized immunotherapy for human cancer. Science (New York,N.Y.) 348, 62–68
  4. Xie, S., Chen, J., Zhang, M., Wu, Z., 2017. Allogenic natural killercell immunotherapy of sizeable ovarian cancer: A case report.Molecular and clinical oncology 6, 903–906
  5. Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B.,Lagorce-Pagès, C., Tosolini, M., Camus, M., Berger, A., Wind,P., Zinzindohoué, F., Bruneval, P., Cugnenc, P.H., Trajanoski,Z., Fridman, W.H., Pagès, F., 2006. Type, density, and locationof immune cells within human colorectal tumors predict clinicaloutcome. Science (New York, N.Y.) 313, 1960–1964
  6. Coussens, L.M., Zitvogel, L., Palucka, A.K., 2013.  Neutralizingtumor-promoting chronic inflammation: a magic bullet? Science(New York, N.Y.) 339, 286–291
  7. Syn, N.L., Teng, M.W.L., Mok, T.S.K., Soo, R.A., 2017. De-novo andacquired resistance to immune checkpoint targeting. The Lancet.Oncology 18, e731–e741
  8. Vánky, F., Klein, E., Willems, J., Böök, K., Ivert, T., Péterffy,A., Nilsonne, U., Kreicbergs, A., Aparisi, T., 1986.  Lysis ofautologous tumor cells by blood lymphocytes tested at the time ofsurgery. correlation with the postsurgical clinical course. Cancerimmunology, immunotherapy : CII 21, 69–76
  9. Varn, F.S., Wang, Y., Mullins, D.W., Fiering, S., Cheng, C., 2017.Systematic pan-cancer analysis reveals immune cell interactions inthe tumor microenvironment. Cancer research 77, 1271–1282
  10. Klauschen, F., Müller, K.R., Binder, A., Bockmayr, M., Hägele, M.,Seegerer, P., Wienert, S., Pruneri, G., de Maria, S., Badve, S.,Michiels, S., Nielsen, T.O., Adams, S., Savas, P., Symmans, F.,Willis, S., Gruosso, T., Park, M., Haibe-Kains, B., Gallas, B.,Thompson, A.M., Cree, I., Sotiriou, C., Solinas, C., Preusser, M.,Hewitt, S.M., Rimm, D., Viale, G., Loi, S., Loibl, S., Salgado, R.,Denkert, C., Group, I.I.O.B.W., 2018. Scoring of tumor-infiltratinglymphocytes: From visual estimation to machine learning. Semi-nars in cancer biology 52, 151–157