Key Points
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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
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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
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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
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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
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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
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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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References
Sharma, P. & Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161, 205–214 (2015).
Palucka, A. K. & Coussens, L. M. The basis of oncoimmunology. Cell 164, 1233–1247 (2016).
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Peng, D. et al. Epigenetic silencing of TH1-type chemokines shapes tumour immunity and immunotherapy. Nature 527, 249–253 (2015).
Slaney, C. Y., Rautela, J. & Parker, B. S. The emerging role of immunosurveillance in dictating metastatic spread in breast cancer. Cancer Res. 73, 5852–5857 (2013).
Elliott, M. R. & Ravichandran, K. S. The dynamics of apoptotic cell clearance. Dev. Cell 38, 147–160 (2016).
Coussens, L. M., Zitvogel, L. & Palucka, A. K. Neutralizing tumor-promoting chronic inflammation: a magic bullet? Science 339, 286–291 (2013).
Zhong, Z., Sanchez-Lopez, E. & Karin, M. Autophagy, inflammation, and immunity: a troika governing cancer and its treatment. Cell 166, 288–298 (2016).
Josephs, D. H., Bax, H. J. & Karagiannis, S. N. Tumour-associated macrophage polarisation and re-education with immunotherapy. Front. Biosci. (Elite Ed.) 7, 293–308 (2015).
Ruffell, B. & Coussens, L. M. Macrophages and therapeutic resistance in cancer. Cancer Cell 27, 462–472 (2015).
Nauts, H. C., Swift, W. E. & Coley, B. L. The treatment of malignant tumors by bacterial toxins as developed by the late William B. Coley, M. D., reviewed in the light of modern research. Cancer Res. 6, 205–216 (1946).
Kamat, A. M. et al. Bladder cancer. Lancet 388, 2796–2810 (2016).
Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Mlecnik, B. et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 44, 698–711 (2016).
Dieu-Nosjean, M. C. et al. Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. J. Clin. Oncol. 26, 4410–4417 (2008).
Dieu-Nosjean, M. C. et al. Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol. Rev. 271, 260–275 (2016).
Becht, E. et al. Immune contexture, immunoscore, and malignant cell molecular subgroups for prognostic and theranostic classifications of cancers. Adv. Immunol. 130, 95–190 (2016).
Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014).
Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).
Fridman, W. H. et al. Prognostic and predictive impact of intra- and peritumoral immune infiltrates. Cancer Res. 71, 5601–5605 (2011).
Fridman, W. H., Pages, F., Sautes-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat. Rev. Cancer 12, 298–306 (2012).
Zhang, L. et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N. Engl. J. Med. 348, 203–213 (2003).
Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).
Kirilovsky, A. et al. Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients. Int. Immunol. 28, 373–382 (2016).
Giraldo, N. A. et al. Tumor-Infiltrating and peripheral blood T-cell immunophenotypes predict early relapse in localized clear cell renal cell carcinoma. Clin. Cancer Res. http://dx.doi.org/10.1158/1078-0432.CCR-16-2848 (2017).
Becht, E. et al. Prognostic and theranostic impact of molecular subtypes and immune classifications in renal cell cancer (RCC) and colorectal cancer (CRC). Oncoimmunology 4, e1049804 (2015).
Petitprez, F. et al. PD-L1 Expression and CD8+ T cell infiltrate are associated with clinical progression in patients with node positive prostate cancer. Eur. Urol. Focus http://dx.doi.org/10.1016/j.euf.2017.05.013 (2017).
Muris, J. J. et al. Prognostic significance of activated cytotoxic T-lymphocytes in primary nodal diffuse large B-cell lymphomas. Leukemia 18, 589–596 (2004).
Scott, D. W. et al. Gene expression-based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma. J. Clin. Oncol. 31, 692–700 (2013).
Giraldo, N. A. et al. Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin. Cancer Res. 21, 3031–3040 (2015).
Meng, Y., Harlin, H., O'Keefe, J. P. & Gajewski, T. F. Induction of cytotoxic granules in human memory CD8+ T cell subsets requires cell cycle progression. J. Immunol. 177, 1981–1987 (2006).
Bronte, V. et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat. Commun. 7, 12150 (2016).
Di Caro, G. et al. Tertiary lymphoid tissue in the tumor microenvironment: from its occurrence to immunotherapeutic implications. Int. Rev. Immunol. 34, 123–133 (2015).
Sautes-Fridman, C. et al. Tertiary lymphoid structures in cancers: prognostic value, regulation, and manipulation for therapeutic intervention. Front. Immunol. 7, 407 (2016).
Goc, J. et al. Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res. 74, 705–715 (2014).
Di Caro, G. et al. Occurrence of tertiary lymphoid tissue is associated with T-cell infiltration and predicts better prognosis in early-stage colorectal cancers. Clin. Cancer Res. 20, 2147–2158 (2014).
Kroeger, D. R., Milne, K. & Nelson, B. H. Tumor-infiltrating plasma cells are associated with tertiary lymphoid structures, cytolytic T-cell responses, and superior prognosis in ovarian cancer. Clin. Cancer Res. 22, 3005–3015 (2016).
Finkin, S. et al. Ectopic lymphoid structures function as microniches for tumor progenitor cells in hepatocellular carcinoma. Nat. Immunol. 16, 1235–1244 (2015).
Shalapour, S. et al. Immunosuppressive plasma cells impede T-cell-dependent immunogenic chemotherapy. Nature 521, 94–98 (2015).
Sautes-Fridman, C. & Fridman, W. H. TLS in tumors: what lies within. Trends Immunol. 37, 1–2 (2016).
Xu, B. et al. Prognostic value of tumor infiltrating NK cells and macrophages in stage II+III esophageal cancer patients. Oncotarget 7, 74904–74916 (2016).
Thommen, D. S. et al. Progression of lung cancer is associated with increased dysfunction of T cells defined by coexpression of multiple inhibitory receptors. Cancer Immunol. Res. 3, 1344–1355 (2015).
Halama, N. et al. Localization and density of immune cells in the invasive margin of human colorectal cancer liver metastases are prognostic for response to chemotherapy. Cancer Res. 71, 5670–5677 (2011).
Remark, R. et al. Characteristics and clinical impacts of the immune environments in colorectal and renal cell carcinoma lung metastases: influence of tumor origin. Clin. Cancer Res. 19, 4079–4091 (2013).
Naxerova, K. & Jain, R. K. Using tumour phylogenetics to identify the roots of metastasis in humans. Nat. Rev. Clin. Oncol. 12, 258–272 (2015).
Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309–322 (2012).
Bruggen, M. C. et al. In situ mapping of innate lymphoid cells in human skin: evidence for remarkable differences between normal and inflamed skin. J. Invest. Dermatol. 136, 2396–2405 (2016).
Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).
Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).
Stoll, G. et al. Immune-related gene signatures predict the outcome of neoadjuvant chemotherapy. Oncoimmunology 3, e27884 (2014).
Stoll, G. et al. Meta-analysis of organ-specific differences in the structure of the immune infiltrate in major malignancies. Oncotarget 6, 11894–11909 (2015).
Kroemer, G., Senovilla, L., Galluzzi, L., Andre, F. & Zitvogel, L. Natural and therapy-induced immunosurveillance in breast cancer. Nat. Med. 21, 1128–1138 (2015).
Ali, H. R., Chlon, L., Pharoah, P. D., Markowetz, F. & Caldas, C. Patterns of immune infiltration in breast cancer and their clinical implications: a gene-expression-based retrospective study. PLoS Med. 13, e1002194 (2016).
Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).
Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).
Spranger, S. & Gajewski, T. F. Tumor-intrinsic oncogene pathways mediating immune avoidance. Oncoimmunology 5, e1086862 (2016).
Corrales, L., McWhirter, S. M., Dubensky, T. W. Jr. & Gajewski, T. F. The host STING pathway at the interface of cancer and immunity. J. Clin. Invest. 126, 2404–2411 (2016).
Wang, T. et al. Regulation of the innate and adaptive immune responses by Stat-3 signaling in tumor cells. Nat. Med. 10, 48–54 (2004).
Salerno, E. P. et al. Human melanomas and ovarian cancers overexpressing mechanical barrier molecule genes lack immune signatures and have increased patient mortality risk. Oncoimmunology 5, e1240857 (2016).
Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).
Dudley, J. C., Lin, M. T., Le, D. T. & Eshleman, J. R. Microsatellite instability as a biomarker for PD-1 blockade. Clin. Cancer Res. 22, 813–820 (2016).
Becht, E. et al. Immune and stromal classification of colorectal cancer is associated with molecular subtypes and relevant for precision immunotherapy. Clin. Cancer Res. 22, 4057–4066 (2016).
Beuselinck, B. et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin. Cancer Res. 21, 1329–1339 (2015).
Cirenajwis, H. et al. Molecular stratification of metastatic melanoma using gene expression profiling: prediction of survival outcome and benefit from molecular targeted therapy. Oncotarget 6, 12297–12309 (2015).
Burstein, M. D. et al. Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin. Cancer Res. 21, 1688–1698 (2015).
Lehmann, B. D. et al. Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS ONE 11, e0157368 (2016).
Bonsang-Kitzis, H. et al. Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis. Oncoimmunology 5, e1061176 (2016).
Bense, R. D. et al. Relevance of tumor-infiltrating immune cell composition and functionality for disease outcome in breast cancer. J. Natl Cancer Inst. 109, djw192 (2017).
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).
Zitvogel, L., Kepp, O. & Kroemer, G. Decoding cell death signals in inflammation and immunity. Cell 140, 798–804 (2010).
Brea, E. J. et al. Kinase regulation of human MHC class I molecule expression on cancer cells. Cancer Immunol. Res. 4, 936–947 (2016).
Reits, E. A. et al. Radiation modulates the peptide repertoire, enhances MHC class I expression, and induces successful antitumor immunotherapy. J. Exp. Med. 203, 1259–1271 (2006).
McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
Krysko, D. V. et al. Immunogenic cell death and DAMPs in cancer therapy. Nat. Rev. Cancer 12, 860–875 (2012).
Kroemer, G., Galluzzi, L., Kepp, O. & Zitvogel, L. Immunogenic cell death in cancer therapy. Annu. Rev. Immunol. 31, 51–72 (2013).
Galluzzi, L., Buque, A., Kepp, O., Zitvogel, L. & Kroemer, G. Immunogenic cell death in cancer and infectious disease. Nat. Rev. Immunol. 17, 97–111 (2017).
Michaud, M. et al. Autophagy-dependent anticancer immune responses induced by chemotherapeutic agents in mice. Science 334, 1573–1577 (2011).
Martins, I. et al. Molecular mechanisms of ATP secretion during immunogenic cell death. Cell Death Differ. 21, 79–91 (2014).
Obeid, M. et al. Calreticulin exposure dictates the immunogenicity of cancer cell death. Nat. Med. 13, 54–61 (2007).
Apetoh, L. et al. Toll-like receptor 4-dependent contribution of the immune system to anticancer chemotherapy and radiotherapy. Nat. Med. 13, 1050–1059 (2007).
Sistigu, A. et al. Cancer cell-autonomous contribution of type I interferon signaling to the efficacy of chemotherapy. Nat. Med. 20, 1301–1309 (2014).
Vacchelli, E. et al. Chemotherapy-induced antitumor immunity requires formyl peptide receptor 1. Science 350, 972–978 (2015).
Ruffell, B. et al. Leukocyte composition of human breast cancer. Proc. Natl Acad. Sci. USA 109, 2796–2801 (2012).
Senovilla, L. et al. An immunosurveillance mechanism controls cancer cell ploidy. Science 337, 1678–1684 (2012).
Park, J. H. et al. Clonal expansion of antitumor T cells in breast cancer correlates with response to neoadjuvant chemotherapy. Int. J. Oncol. 49, 471–478 (2016).
Nardone, V. et al. Tumor infiltrating T lymphocytes expressing FoxP3, CCR7 or PD-1 predict the outcome of prostate cancer patients subjected to salvage radiotherapy after biochemical relapse. Cancer Biol. Ther. 17, 1213–1220 (2016).
Tavare, R. et al. An effective immuno-PET imaging method to monitor CD8-dependent responses to immunotherapy. Cancer Res. 76, 73–82 (2016).
Ladoire, S. et al. The presence of LC3B puncta and HMGB1 expression in malignant cells correlate with the immune infiltrate in breast cancer. Autophagy 12, 864–875 (2016).
Ladoire, S. et al. Combined evaluation of LC3B puncta and HMGB1 expression predicts residual risk of relapse after adjuvant chemotherapy in breast cancer. Autophagy 11, 1878–1890 (2015).
Allard, B., Beavis, P. A., Darcy, P. K. & Stagg, J. Immunosuppressive activities of adenosine in cancer. Curr. Opin. Pharmacol. 29, 7–16 (2016).
Wemeau, M. et al. Calreticulin exposure on malignant blasts predicts a cellular anticancer immune response in patients with acute myeloid leukemia. Cell Death Dis. 1, e104 (2010).
Fucikova, J. et al. Calreticulin expression in human non-small cell lung cancers correlates with increased accumulation of antitumor immune cells and favorable prognosis. Cancer Res. 76, 1746–1756 (2016).
Fucikova, J. et al. Calreticulin exposure by malignant blasts correlates with robust anticancer immunity and improved clinical outcome in AML patients. Blood 128, 3113–3124 (2016).
Stoll, G. et al. Calreticulin expression: interaction with the immune infiltrate and impact on survival in patients with ovarian and non-small cell lung cancer. Oncoimmunology 5, e1177692 (2016).
Tesniere, A. et al. Immunogenic death of colon cancer cells treated with oxaliplatin. Oncogene 29, 482–491 (2010).
Vacchelli, E., Enot, D. P., Pietrocola, F., Zitvogel, L. & Kroemer, G. Impact of pattern recognition receptors on the prognosis of breast cancer patients undergoing adjuvant chemotherapy. Cancer Res. 76, 3122–3126 (2016).
Yang, H. et al. Contribution of RIP3 and MLKL to immunogenic cell death signaling in cancer chemotherapy. Oncoimmunology 5, e1149673 (2016).
Stoll, G. et al. Pro-necrotic molecules impact local immunosurveillance in human breast cancer. Oncoimmunology 6, e1299302 (2017).
Galluzzi, L., Buque, A., Kepp, O., Zitvogel, L. & Kroemer, G. Immunological effects of conventional chemotherapy and targeted anticancer agents. Cancer Cell 28, 690–714 (2015).
Vincent, J. et al. 5-Fluorouracil selectively kills tumor-associated myeloid-derived suppressor cells resulting in enhanced T cell-dependent antitumor immunity. Cancer Res. 70, 3052–3061 (2010).
Ghiringhelli, F. et al. Metronomic cyclophosphamide regimen selectively depletes CD4+CD25+ regulatory T cells and restores T and NK effector functions in end stage cancer patients. Cancer Immunol. Immunother. 56, 641–648 (2007).
Viaud, S. et al. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342, 971–976 (2013).
Wang, W. et al. Effector T cells abrogate stroma-mediated chemoresistance in ovarian cancer. Cell 165, 1092–1105 (2016).
Galluzzi, L., Senovilla, L., Zitvogel, L. & Kroemer, G. The secret ally: immunostimulation by anticancer drugs. Nat. Rev. Drug Discov. 11, 215–233 (2012).
Zitvogel, L., Rusakiewicz, S., Routy, B., Ayyoub, M. & Kroemer, G. Immunological off-target effects of imatinib. Nat. Rev. Clin. Oncol. 13, 431–446 (2016).
Rusakiewicz, S. et al. Immune infiltrates are prognostic factors in localized gastrointestinal stromal tumors. Cancer Res. 73, 3499–3510 (2013).
Delahaye, N. F. et al. Alternatively spliced NKp30 isoforms affect the prognosis of gastrointestinal stromal tumors. Nat. Med. 17, 700–707 (2011).
Mizoguchi, I. et al. Sustained upregulation of effector natural killer cells in chronic myeloid leukemia after discontinuation of imatinib. Cancer Sci. 104, 1146–1153 (2013).
Riva, G. et al. Long-term molecular remission with persistence of BCR–ABL1-specific cytotoxic T cells following imatinib withdrawal in an elderly patient with Philadelphia-positive ALL. Br. J. Haematol. 164, 299–302 (2014).
Hugo, W. et al. Non-genomic and immune evolution of melanoma acquiring MAPKi resistance. Cell 162, 1271–1285 (2015).
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
Frederick, D. T. et al. BRAF inhibition is associated with enhanced melanoma antigen expression and a more favorable tumor microenvironment in patients with metastatic melanoma. Clin. Cancer Res. 19, 1225–1231 (2013).
Knight, D. A. et al. Host immunity contributes to the anti-melanoma activity of BRAF inhibitors. J. Clin. Invest. 123, 1371–1381 (2013).
Pozzi, C. et al. The EGFR-specific antibody cetuximab combined with chemotherapy triggers immunogenic cell death. Nat. Med. 22, 624–631 (2016).
Muller, P. et al. Trastuzumab emtansine (T-DM1) renders HER2+ breast cancer highly susceptible to CTLA-4/PD-1 blockade. Sci. Transl Med. 7, 315ra188 (2015).
Guislain, A. et al. Sunitinib pretreatment improves tumor-infiltrating lymphocyte expansion by reduction in intratumoral content of myeloid-derived suppressor cells in human renal cell carcinoma. Cancer Immunol. Immunother. 64, 1241–1250 (2015).
Sliwkowski, M. X. & Mellman, I. Antibody therapeutics in cancer. Science 341, 1192–1198 (2013).
Gibney, G. T., Weiner, L. M. & Atkins, M. B. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 17, e542–e551 (2016).
Pitt, J. M. et al. Resistance mechanisms to immune-checkpoint blockade in cancer: tumor-intrinsic and -extrinsic factors. Immunity 44, 1255–1269 (2016).
Weide, B. et al. Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab. Clin. Cancer Res. 22, 5487–5496 (2016).
Brand, A. et al. LDHA-associated lactic acid production blunts tumor immunosurveillance by T and NK Cells. Cell Metab. 24, 657–671 (2016).
Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).
Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).
Bald, T. et al. Immune cell-poor melanomas benefit from PD-1 blockade after targeted type I IFN activation. Cancer Discov. 4, 674–687 (2014).
Spranger, S. et al. Up-regulation of PD-L1, IDO, and Tregs in the melanoma tumor microenvironment is driven by CD8+ T cells. Sci. Transl Med. 5, 200ra116 (2013).
Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).
van Rooij, N. et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 31, e439–442 (2013).
Rizvi, N. A. et al. Cancer immunology. mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).
Ansell, S. M. et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin's lymphoma. N. Engl. J. Med. 372, 311–319 (2015).
Vitale, I. et al. Illicit survival of cancer cells during polyploidization and depolyploidization. Cell Death Differ. 18, 1403–1413 (2011).
Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).
Roh, W. et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci. Transl Med. 9, eaah3560 (2017).
Li, B. et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. Nat. Genet. 48, 725–732 (2016).
Inoue, H. et al. Intratumoral expression levels of PD-L1, GZMA, and HLA-A along with oligoclonal T cell expansion associate with response to nivolumab in metastatic melanoma. Oncoimmunology 5, e1204507 (2016).
O'Day, S. J. et al. Phase II multicenter trial of maintenance biotherapy after induction concurrent biochemotherapy for patients with metastatic melanoma. J. Clin. Oncol. 27, 6207–6212 (2009).
Oh, D. Y. et al. Immune toxicities elicted by CTLA-4 blockade in cancer patients are associated with early diversification of the T-cell repertoire. Cancer Res. 77, 1322–1330 (2017).
Gros, A. et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat. Med. 22, 433–438 (2016).
Paulsen, E. E. et al. Assessing PDL-1 and PD-1 in non-small cell lung cancer: a novel immunoscore approach. Clin. Lung Cancer 18, 220–233 e8 (2017).
Darb-Esfahani, S. et al. Prognostic impact of programmed cell death-1 (PD-1) and PD-ligand 1 (PD-L1) expression in cancer cells and tumor-infiltrating lymphocytes in ovarian high grade serous carcinoma. Oncotarget 7, 1486–1499 (2016).
Duchnowska, R. et al. Immune response in breast cancer brain metastases and their microenvironment: the role of the PD-1/PD-L axis. Breast Cancer Res. 18, 43 (2016).
Cimino-Mathews, A. et al. PD-L1 (B7-H1) expression and the immune tumor microenvironment in primary and metastatic breast carcinomas. Hum. Pathol. 47, 52–63 (2016).
Denkert, C. et al. Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. J. Clin. Oncol. 33, 983–991 (2015).
Dunne, P. D. et al. Immune-derived PD-L1 gene expression defines a subgroup of stage II/III colorectal cancer patients with favorable prognosis who may be harmed by adjuvant chemotherapy. Cancer Immunol. Res. 4, 582–591 (2016).
Taube, J. M. et al. Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci. Transl Med. 4, 127ra37 (2012).
Blank, C. U., Haanen, J. B., Ribas, A. & Schumacher, T. N. CANCER IMMUNOLOGY. The “cancer immunogram”. Science 352, 658–660 (2016).
Ock, C. Y. et al. Pan-cancer immunogenomic perspective on the tumor microenvironment based on PD-L1 and CD8 T-cell infiltration. Clin. Cancer Res. 22, 2261–2270 (2016).
Koyama, S. et al. STK11/LKB1 deficiency promotes neutrophil recruitment and proinflammatory cytokine production to suppress T-cell activity in the lung tumor microenvironment. Cancer Res. 76, 999–1008 (2016).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Shin, D. S. et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 7, 188–201 (2017).
Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554.e12 (2016).
Koyama, S. et al. Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat. Commun. 7, 10501 (2016).
Orvedahl, A. et al. HSV-1 ICP34.5 confers neurovirulence by targeting the Beclin 1 autophagy protein. Cell Host Microbe 1, 23–35 (2007).
Li, Y. et al. ICP34.5 protein of herpes simplex virus facilitates the initiation of protein translation by bridging eukaryotic initiation factor 2α (eIF2α) and protein phosphatase 1. J. Biol. Chem. 286, 24785–24792 (2011).
Puzanov, I. et al. Talimogene laherparepvec in combination with ipilimumab in previously untreated, unresectable stage IIIB-IV melanoma. J. Clin. Oncol. 34, 2619–2626 (2016).
Natarajan, A. et al. Novel radiotracer for immunoPET imaging of PD-1 checkpoint expression on tumor infiltrating lymphocytes. Bioconjug. Chem. 26, 2062–2069 (2015).
Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e17 (2017).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
Dadi, S. et al. Cancer immunosurveillance by tissue-resident innate lymphoid cells and innate-like T cells. Cell 164, 365–377 (2016).
Fan, X. & Rudensky, A. Y. Hallmarks of tissue-resident lymphocytes. Cell 164, 1198–1211 (2016).
Zitvogel, L., Pitt, J. M., Daillere, R., Smyth, M. J. & Kroemer, G. Mouse models in oncoimmunology. Nat. Rev. Cancer 16, 759–773 (2016).
Moschella, F. et al. Cyclophosphamide induces a type I interferon-associated sterile inflammatory response signature in cancer patients' blood cells: implications for cancer chemoimmunotherapy. Clin. Cancer Res. 19, 4249–4261 (2013).
Lo, C. S. et al. Neoadjuvant chemotherapy of ovarian cancer results in three patterns of tumor-infiltrating lymphocyte response with distinct implications for immunotherapy. Clin. Cancer Res. 23, 925–934 (2017).
Limagne, E. et al. Accumulation of MDSC and Th17 cells in patients with metastatic colorectal cancer predicts the efficacy of a FOLFOX–bevacizumab drug treatment regimen. Cancer Res. 76, 5241–5252 (2016).
Teng, F. et al. Tumor-infiltrating lymphocytes, forkhead box P3, programmed death ligand-1, and cytotoxic T lymphocyte-associated antigen-4 expressions before and after neoadjuvant chemoradiation in rectal cancer. Transl Res. 166, 721–732.e1 (2015).
Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).
Mahoney, K. M. & Atkins, M. B. Prognostic and predictive markers for the new immunotherapies. Oncol. (Williston Park) 28 (Suppl. 3), 39–48 (2014).
Borghaei, H. et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N. Engl. J. Med. 373, 1627–1639 (2015).
Garon, E. B. et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 372, 2018–2028 (2015).
Larkin, J. et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N. Engl. J. Med. 373, 23–34 (2015).
Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
Johnson, D. B. et al. Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy. Nat. Commun. 7, 10582 (2016).
Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 162, 974–986 (2015).
Cha, E. et al. Improved survival with T cell clonotype stability after anti-CTLA-4 treatment in cancer patients. Sci. Transl Med. 6, 238ra70 (2014).
Twyman-Saint Victor, C. et al. Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer. Nature 520, 373–377 (2015).
Hamid, O. et al. A prospective phase II trial exploring the association between tumor microenvironment biomarkers and clinical activity of ipilimumab in advanced melanoma. J. Transl Med. 9, 204 (2011).
Chen, P. L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).
Martens, A. et al. Baseline peripheral blood biomarkers associated with clinical outcome of advanced melanoma patients treated with ipilimumab. Clin. Cancer Res. 22, 2908–2918 (2016).
Simeone, E. et al. Immunological and biological changes during ipilimumab treatment and their potential correlation with clinical response and survival in patients with advanced melanoma. Cancer Immunol. Immunother. 63, 675–683 (2014).
Weber, J. S. et al. Safety, efficacy, and biomarkers of nivolumab with vaccine in ipilimumab-refractory or -naive melanoma. J. Clin. Oncol. 31, 4311–4318 (2013).
Jacquelot, N. et al. Chemokine receptor patterns in lymphocytes mirror metastatic spreading in melanoma. J. Clin. Invest. 126, 921–937 (2016).
Hannani, D. et al. Anticancer immunotherapy by CTLA-4 blockade: obligatory contribution of IL-2 receptors and negative prognostic impact of soluble CD25. Cell Res. 25, 208–224 (2015).
Ghiringhelli, F. et al. Activation of the NLRP3 inflammasome in dendritic cells induces IL-1β-dependent adaptive immunity against tumors. Nat. Med. 15, 1170–1178 (2009).
Loi, S. et al. CD73 promotes anthracycline resistance and poor prognosis in triple negative breast cancer. Proc. Natl Acad. Sci. USA 110, 11091–11096 (2013).
Ren, Z. H. et al. CD73 is associated with poor prognosis in HNSCC. Oncotarget 7, 61690–61702 (2016).
Turcotte, M. et al. CD73 is associated with poor prognosis in high-grade serous ovarian cancer. Cancer Res. 75, 4494–4503 (2015).
Wang, X., Du, Z., Li, L., Shi, M. & Yu, Y. Beclin 1 and p62 expression in non-small cell lung cancer: relation with malignant behaviors and clinical outcome. Int. J. Clin. Exp. Pathol. 8, 10644–10652 (2015).
Schlafli, A. M. et al. Prognostic value of the autophagy markers LC3 and p62/SQSTM1 in early-stage non-small cell lung cancer. Oncotarget 7, 39544–39555 (2016).
Baccelli, I. et al. Co-expression of MET and CD47 is a novel prognosticator for survival of luminal breast cancer patients. Oncotarget 5, 8147–8160 (2014).
Yoshida, K. et al. CD47 is an adverse prognostic factor and a therapeutic target in gastric cancer. Cancer Med. 4, 1322–1333 (2015).
Willingham, S. B. et al. The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Proc. Natl Acad. Sci. USA 109, 6662–6667 (2012).
Chao, M. P. et al. Therapeutic antibody targeting of CD47 eliminates human acute lymphoblastic leukemia. Cancer Res. 71, 1374–1384 (2011).
Bidwell, B. N. et al. Silencing of Irf7 pathways in breast cancer cells promotes bone metastasis through immune escape. Nat. Med. 18, 1224–1231 (2012).
Yamazaki, T. et al. Defective immunogenic cell death of HMGB1-deficient tumors: compensatory therapy with TLR4 agonists. Cell Death Differ. 21, 69–78 (2014).
Exner, R. et al. Prognostic value of HMGB1 in early breast cancer patients under neoadjuvant chemotherapy. Cancer Med. 5, 2350–2358 (2016).
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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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)
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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DOI: https://doi.org/10.1038/nrclinonc.2017.101
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