| Title |
Topography of cancer-associated immune cells in human solid tumors
|
|---|---|
| Published in |
eLife, September 2018
|
| DOI | 10.7554/elife.36967 |
| Pubmed ID | |
| Authors |
Jakob Nikolas Kather, Meggy Suarez-Carmona, Pornpimol Charoentong, Cleo-Aron Weis, Daniela Hirsch, Peter Bankhead, Marcel Horning, Dyke Ferber, Ivan Kel, Esther Herpel, Sarah Schott, Inka Zörnig, Jochen Utikal, Alexander Marx, Timo Gaiser, Herrmann Brenner, Jenny Chang-Claude, Michael Hoffmeister, Dirk Jäger, Niels Halama |
| Abstract |
Lymphoid and myeloid cells are abundant in the tumor microenvironment, can be quantified by immunohistochemistry and shape the disease course of human solid tumors. Yet, there is no comprehensive understanding of spatial immune infiltration patterns ('topography') across cancer entities and across various immune cell types. In this study, we systematically measure the topography of multiple immune cell types in 965 histological tissue slides from N=177 patients in a pan-cancer cohort. We provide a definition of inflamed ('hot'), non-inflamed ('cold') and immune excluded patterns and investigate how these patterns differ between immune cell types and between cancer types. In an independent cohort of N=287 colorectal cancer patients, we show that hot, cold and excluded topographies for effector lymphocytes (CD8) and tumor-associated macrophages (CD163) alone are not prognostic, but that a bivariate classification system can stratify patients. Our study adds evidence to consider immune topographies as biomarkers for patients with solid tumors. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 27% |
| Germany | 4 | 15% |
| France | 1 | 4% |
| Japan | 1 | 4% |
| Brazil | 1 | 4% |
| United Kingdom | 1 | 4% |
| Spain | 1 | 4% |
| Poland | 1 | 4% |
| Sweden | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 8 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 69% |
| Scientists | 8 | 31% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 346 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 58 | 17% |
| Researcher | 58 | 17% |
| Student > Master | 40 | 12% |
| Other | 20 | 6% |
| Student > Doctoral Student | 17 | 5% |
| Other | 56 | 16% |
| Unknown | 97 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 72 | 21% |
| Medicine and Dentistry | 47 | 14% |
| Immunology and Microbiology | 39 | 11% |
| Agricultural and Biological Sciences | 30 | 9% |
| Engineering | 14 | 4% |
| Other | 30 | 9% |
| Unknown | 114 | 33% |