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The immune contexture in cancer prognosis and treatment

Key Points

  • Most cancers have evaded immune control, or immunosurveillance, at the time of presentation; however, residual signs of an active anticancer immune response indicate a positive prognosis

  • Positive immune-related prognostic features include the presence of specific T-lymphocyte subsets, the absence of immunosuppressive elements, the localization of the immune infiltrate and specific features of its organization

  • Successful anticancer therapies, including cytotoxic chemotherapies and targeted agents, improve the local immune contexture and mediate at least part of their long-term effects by reinstating immunosurveillance

  • The presence of either a pre-existing or induced immune response indicates a more favourable prognosis than that of patients whose tumours lack either of these features

  • Immune-checkpoint inhibitors have a profound effect on the local immune infiltrate, and a variety of biomarkers have the potential to indicate a pre-existing or developing anticancer immune response

  • The discovery of immunological biomarkers in oncology has been facilitated by the advent of ever more sophisticated technologies, posing new challenges to both data integration and bioinformatic analysis

Abstract

Immunotherapy is currently the most rapidly advancing area of clinical oncology, and provides the unprecedented opportunity to effectively treat, and even cure, several previously untreatable malignancies. A growing awareness exists of the fact that the success of chemotherapy and radiotherapy, in which the patient's disease can be stabilized well beyond discontinuation of treatment (and occasionally is cured), also relies on the induction of a durable anticancer immune response. Indeed, the local immune infiltrate undergoes dynamic changes that accompany a shift from a pre-existing immune response to a therapy-induced immune response. As a result, the immune contexture, which is determined by the density, composition, functional state and organization of the leukocyte infiltrate of the tumour, can yield information that is relevant to prognosis, prediction of a treatment response and various other pharmacodynamic parameters. Several complementary technologies can be used to explore the immune contexture of tumours, and to derive biomarkers that could enable the adaptation of individual treatment approaches for each patient, as well as monitoring a response to anticancer therapies.

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Figure 1: Key elements of cancer-related inflammation.
Figure 2: The association between CD8+ T-cell density of the tumour infiltrate and overall survival among patients with primary, or metastatic solid tumours.
Figure 3: Effects of the immune infiltrate on the prognosis of patients with cancer.

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Acknowledgements

We thank N. Giraldo and F. Petitprez for their help in finalizing the manuscript and the figures. The work of W.H.F. and C.S.F. is supported by the Institut National de la santé et de la Recherche Medicale (INSERM); University Paris-Descartes; University Pierre and Marie Curie; the Site de Recherche Integrée sur le Cancer (SIRIC) Cancer Research for Personalized Medicine (CARPEM) programme; the LabEx Immuno-Oncology; the Institut National Du Cancer (INCa); the Cancéropôle Ile-de-France; O. Lecomte; and the Association pour la recherche sur le cancer (ARC). The work of L.Z. and G.K. is supported by the INCa, the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR) – Projets blancs; ANR under the frame of E-Rare-2, the ERA-Net for Research on Rare Diseases; ARC; Cancéropôle Ile-de-France; INSERM (HTE); INCa; Institut Universitaire de France; Fondation pour la Recherche Médicale (FRM); the European Commission (ArtForce); the European Research Council (ERC); the LabEx Immuno-Oncology; the SIRIC Stratified Oncology Cell DNA Repair and Tumour Immune Elimination (SOCRATE); CARPEM; and the Paris Alliance of Cancer Research Institutes (PACRI). The work of L.Z. is also supported by the Swiss Institute for Experimental Cancer Research (ISREC), by the Swiss Bridge Foundation, and by IMMUNTRAIN-H2020.

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All authors contributed to researching the data for the article and to discussion of the content. All authors reviewed/edited the manuscript before submission.

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Correspondence to Wolf H. Fridman or Guido Kroemer.

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Supplementary information

Supplementary information S1 (table)

Influence of the density of tumour-infiltrating immune cell types on the prognosis of patients with cancer (Figs 2 and 3) (PDF 263 kb)

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Glossary

Tumour microenvironment

The area immediately surrounding the tumour that is typically composed of nonmalignant lymphoid and/or myeloid cells as well as fibroblasts, vascular cells and lymphatic vessels. Specific characteristics of the tumour microenvironment can have either positive or negative implications for patient outcomes.

M1 macrophages

Macrophages that produce predominantly proinflammatory factors. M1 macrophages and are generally associated with a good prognosis when present at high densities in the tumour microenvironment.

Erysipelas

An acute bacterial infection, usually by Streptococcus pyogenes, characterized by the appearance of large, raised red patches on the skin.

M2 macrophages

Macrophages producing proangiogenic and immunosuppressive factors. M2 macrophages are generally associated with a poor prognosis when present at high densities in the tumour microenvironment.

Type 1 T helper cells

(TH1 cells). A subset of CD4+ T-helper cells that produce IFNγ and IL-2.

Tc1 cells

A subset of CD8+ cytotoxic T cells that produce IFNγ.

Immune contexture

A term describing the integration of knowledge of the density, functional orientation and spatial organization of the immune infiltrate.

Natural killer cells

(NK cells). Lymphocytes devoid of antigen receptors that are capable of cell-mediated cytotoxicity, including the destruction of tumour cells lacking MHC class I molecules.

Myeloid-derived suppressor cells

(MDSCs). A heterogeneous population composed of cells of a granulocytic or macrophagic origin that produce immunosuppressive factors. MDSCs are generally associated with a poor prognosis when present at high densities in the tumour microenvironment.

Regulatory T cells

(Treg cells). Regulatory T cells, which can be identified based on cell-surface antigen expression (CD4+/CD25+/FOXP3+). Such cells are generally immunosuppressive and associated with a poor prognosis.

CIBERSORT

(Cell-type identification by estimating relative subsets of RNA transcripts). A metagene-based analytical method of weighting the contribution of different leukocyte subpopulations to the overall immune infiltrate during the analysis of transcriptomic data by measuring the expression of genes associated with specific immune cell types relative to those expressed in all haematological cell types.

Microenvironment cell populations–counter

(MCP-Counter). A method based on metagenes highly expressed in one and only one cellular population present in the tumour microenvironment, which enables intersample quantification of infiltrating cells based on transcriptomic data.

Immunome

A method that curates metagenes preferentially expressed in immune cells, thus enabling the quantification of these cells based on their transcriptome.

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Fridman, W., Zitvogel, L., Sautès–Fridman, C. et al. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 14, 717–734 (2017). https://doi.org/10.1038/nrclinonc.2017.101

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