In less than a decade, half a dozen immune checkpoint inhibitors have been approved and are currently revolutionising the treatment of many cancer (sub)types. With the clinical evaluation of novel delivery approaches (e.g. oncolytic viruses, cancer vaccines, natural killer cell-mediated cytotoxicity) and combination therapies (e.g. chemo/radio-immunotherapy) as well as the emergence of novel promising targets (e.g. TIGIT, LAG-3, TIM-3), the ‘immunotherapy tsunami’ is not about to end anytime soon. However, this enthusiasm in the field is somewhat tempered by both the relatively low percentage (<15%) of patients who display an effective anti-cancer immune response and the inability to accurately identify them. Recently, several existing or acquired features/parameters have been shown to impact the efficacy of immune checkpoint inhibitors. In the present review, we critically discuss current knowledge regarding predictive biomarkers for checkpoint inhibitor-based immunotherapy, highlight the missing/unclear links and emphasise the importance of characterising each neoplasm and its microenvironment in order to better guide the course of treatment.
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Dobosz, P. & Dzieciatkowski, T. The intriguing history of cancer immunotherapy. Front. Immunol. 10, 2965 (2019).
Hodi, F. S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).
Mazzarella, L. et al. The evolving landscape of ‘next-generation’ immune checkpoint inhibitors: a review. Eur. J. Cancer 117, 14–31 (2019).
Haslam, A. & Prasad, V. Estimation of the percentage of US patients with cancer who are eligible for and respond to checkpoint inhibitor immunotherapy drugs. JAMA Netw. Open 2, e192535 (2019).
Liu, X. et al. Association of PD-L1 expression status with the efficacy of PD-1/PD-L1 inhibitors and overall survival in solid tumours: a systematic review and meta-analysis. Int. J. Cancer 147, 116–127 (2020).
Weber, J. S. et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol. 16, 375–384 (2015).
Hodi, F. S. et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 19, 1480–1492 (2018).
Horn, L. et al. Nivolumab versus docetaxel in previously treated patients with advanced non-small-cell lung cancer: two-year outcomes from two randomized, open-label, phase iii trials (CheckMate 017 and CheckMate 057). J. Clin. Oncol. 35, 3924–3933 (2017).
Wu, Y. L. et al. Nivolumab versus docetaxel in a predominantly chinese patient population with previously treated advanced NSCLC: CheckMate 078 randomized phase III clinical trial. J. Thorac. Oncol. 14, 867–875 (2019).
Ferris, R. L. et al. Nivolumab vs investigator’s choice in recurrent or metastatic squamous cell carcinoma of the head and neck: 2-year long-term survival update of CheckMate 141 with analyses by tumor PD-L1 expression. Oral Oncol. 81, 45–51 (2018).
Davis, A. A. & Patel, V. G. The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J, Immunother, Cancer 7, 278 (2019).
Jiang, Y. & Zhan, H. Communication between EMT and PD-L1 signaling: new insights into tumor immune evasion. Cancer Lett. 468, 72–81 (2020).
Patel, S. P. & Kurzrock, R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol. Cancer Ther. 14, 847–856 (2015).
Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).
Teng, M. W., Ngiow, S. F., Ribas, A. & Smyth, M. J. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res. 75, 2139–2145 (2015).
Passiglia, F. et al. PD-L1 expression as predictive biomarker in patients with NSCLC: a pooled analysis. Oncotarget 7, 19738–19747 (2016).
Xu, Y. et al. The association of PD-L1 expression with the efficacy of anti-PD-1/PD-L1 immunotherapy and survival of non-small cell lung cancer patients: a meta-analysis of randomized controlled trials. Transl. Lung Cancer Res. 8, 413–428 (2019).
Jreige, M. et al. (18)F-FDG PET metabolic-to-morphological volume ratio predicts PD-L1 tumour expression and response to PD-1 blockade in non-small-cell lung cancer. Eur. J. Nucl. Med. Mol. Imaging 46, 1859–1868 (2019).
Gonzalez-Ericsson, P. I. et al. The path to a better biomarker: application of a risk management framework for the implementation of PD-L1 and TILs as immuno-oncology biomarkers in breast cancer clinical trials and daily practice. J. Pathol. 250, 667–684 (2020).
Allen, P. M. et al. Identification of the T-cell and Ia contact residues of a T-cell antigenic epitope. Nature 327, 713–715 (1987).
Ward, J. P., Gubin, M. M. & Schreiber, R. D. The role of neoantigens in naturally occurring and therapeutically induced immune responses to cancer. Adv. Immunol. 130, 25–74 (2016).
Smith, C. C. et al. Alternative tumour-specific antigens. Nat. Rev. Cancer 19, 465–478 (2019).
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).
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).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Hellmann, M. D. et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N. Engl. J. Med. 378, 2093–2104 (2018).
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).
Wu, Y. et al. The predictive value of tumor mutation burden on efficacy of immune checkpoint inhibitors in cancers: a systematic review and meta-analysis. Front. Oncol. 9, 1161 (2019).
Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).
Marabelle, A. et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 21, 1353–1365 (2020).
Yarchoan, M., Hopkins, A. & Jaffee, E. M. Tumor mutational burden and response rate to PD-1 inhibition. N. Engl. J. Med. 377, 2500–2501 (2017).
Chan, T. A. et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann. Oncol. 30, 44–56 (2019).
Gandara, D. R. et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat. Med. 24, 1441–1448 (2018).
Yu, H. et al. Correlation of PD-L1 expression with tumor mutation burden and gene signatures for prognosis in early-stage squamous cell lung carcinoma. J. Thorac. Oncol. 14, 25–36 (2019).
Rizvi, H. et al. Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing. J. Clin. Oncol. 36, 633–641 (2018).
Sholl, L. M. et al. The promises and challenges of tumor mutation burden as an immunotherapy biomarker: a perspective from the international association for the study of lung cancer pathology committee. J. Thorac. Oncol. 15, 1409–1424 (2020).
Jardim, D. L., Goodman, A., de Melo Gagliato, D., Kurzrock, R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell 39, 154–173 (2021).
Li, G. M. Mechanisms and functions of DNA mismatch repair. Cell Res. 18, 85–98 (2008).
Buhard, O. et al. Multipopulation analysis of polymorphisms in five mononucleotide repeats used to determine the microsatellite instability status of human tumors. J. Clin. Oncol. 24, 241–251 (2006).
Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).
Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
Cortes-Ciriano, I., Lee, S., Park, W. Y., Kim, T. M. & Park, P. J. A molecular portrait of microsatellite instability across multiple cancers. Nat. Commun. 8, 15180 (2017).
Chalmers, Z. R. et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 9, 34 (2017).
Mehnert, J. M. et al. Immune activation and response to pembrolizumab in POLE-mutant endometrial cancer. J. Clin. Invest. 126, 2334–2340 (2016).
Gong, J., Wang, C., Lee, P. P., Chu, P. & Fakih, M. Response to PD-1 blockade in microsatellite stable metastatic colorectal cancer harboring a POLE mutation. J. Natl. Compr. Cancer Netw. 15, 142–147 (2017).
Goodman, A. M., Sokol, E. S., Frampton, G. M., Lippman, S. M. & Kurzrock, R. Microsatellite-stable tumors with high mutational burden benefit from immunotherapy. Cancer Immunol. Res. 7, 1570–1573 (2019).
Gettinger, S. et al. Impaired HLA class I antigen processing and presentation as a mechanism of acquired resistance to immune checkpoint inhibitors in lung cancer. Cancer Discov. 7, 1420–1435 (2017).
Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).
McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271.e1211 (2017).
Chowell, D. et al. Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat. Med. 25, 1715–1720 (2019).
Rodig, S. J. et al. MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma. Sci. Transl. Med. 10, eaar3342 (2018).
Negrao, M. V. et al. PD-L1 expression, tumor mutational burden, and cancer gene mutations are stronger predictors of benefit from immune checkpoint blockade than HLA class I genotype in non-small cell lung cancer. J. Thorac. Oncol. 14, 1021–1031 (2019).
Spranger, S. et al. Up-regulation of PD-L1, IDO, and T(regs) in the melanoma tumor microenvironment is driven by CD8(+) T cells. Sci. Transl. Med. 5, 200ra116 (2013).
Garcia-Diaz, A. et al. Interferon receptor signaling pathways regulating PD-L1 and PD-L2 expression. Cell Rep. 19, 1189–1201 (2017).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Gao, J. et al. Loss of IFN-gamma pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 e399 (2016).
Shin, D. S. et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 7, 188–201 (2017).
Pai, C. S. et al. Clonal deletion of tumor-specific T cells by interferon-gamma confers therapeutic resistance to combination immune checkpoint blockade. Immunity 50, 477–492 e478 (2019).
Ni, L. & Lu, J. Interferon gamma in cancer immunotherapy. Cancer Med. 7, 4509–4516 (2018).
Brown, Z. J. et al. Indoleamine 2,3-dioxygenase provides adaptive resistance to immune checkpoint inhibitors in hepatocellular carcinoma. Cancer Immunol. Immunother. 67, 1305–1315 (2018).
Long, G. V. et al. Epacadostat plus pembrolizumab versus placebo plus pembrolizumab in patients with unresectable or metastatic melanoma (ECHO-301/KEYNOTE-252): a phase 3, randomised, double-blind study. Lancet Oncol. 20, 1083–1097 (2019).
Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).
Mariathasan, S. et al. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
Uryvaev, A., Passhak, M., Hershkovits, D., Sabo, E. & Bar-Sela, G. The role of tumor-infiltrating lymphocytes (TILs) as a predictive biomarker of response to anti-PD1 therapy in patients with metastatic non-small cell lung cancer or metastatic melanoma. Med. Oncol. 35, 25 (2018).
Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).
Mlecnik, B. et al. Comprehensive intrametastatic immune quantification and major impact of immunoscore on survival. J. Natl Cancer Inst. 110, https://doi.org/10.1093/jnci/djx123 (2018).
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).
Galon, J. et al. Cancer classification using the Immunoscore: a worldwide task force. J. Transl. Med. 10, 205 (2012).
Angell, H. & Galon, J. From the immune contexture to the Immunoscore: the role of prognostic and predictive immune markers in cancer. Curr. Opin. Immunol. 25, 261–267 (2013).
Galon, J. et al. Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours. J. Pathol. 232, 199–209 (2014).
Emens, L. A. & Middleton, G. The interplay of immunotherapy and chemotherapy: harnessing potential synergies. Cancer Immunol. Res. 3, 436–443 (2015).
Park, B., Yee, C. & Lee, K. M. The effect of radiation on the immune response to cancers. Int. J. Mol. Sci. 15, 927–943 (2014).
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).
Suzuki, E., Kapoor, V., Jassar, A. S., Kaiser, L. R. & Albelda, S. M. Gemcitabine selectively eliminates splenic Gr-1+/CD11b+ myeloid suppressor cells in tumor-bearing animals and enhances antitumor immune activity. Clin. Cancer Res. 11, 6713–6721 (2005).
Murciano-Goroff, Y. R., Warner, A. B. & Wolchok, J. D. The future of cancer immunotherapy: microenvironment-targeting combinations. Cell Res. 30, 507–519 (2020).
Van Der Kraak, L. et al. 5-Fluorouracil upregulates cell surface B7-H1 (PD-L1) expression in gastrointestinal cancers. J. Immunother. Cancer 4, 65 (2016).
McDaniel, A. S. et al. Expression of PDL1 (B7-H1) before and after neoadjuvant chemotherapy in urothelial carcinoma. Eur. Urol. Focus 1, 265–268 (2016).
Zemek, R. M. et al. Sensitizing the tumor microenvironment to immune checkpoint therapy. Front. Immunol. 11, 223 (2020).
Ghoneim, H. E. et al. De novo epigenetic programs inhibit PD-1 blockade-mediated T cell rejuvenation. Cell 170, 142–157 e119 (2017).
Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).
Peng, D. et al. Epigenetic silencing of TH1-type chemokines shapes tumour immunity and immunotherapy. Nature 527, 249–253 (2015).
Goswami, S. et al. Modulation of EZH2 expression in T cells improves efficacy of anti-CTLA-4 therapy. J. Clin. Invest. 128, 3813–3818 (2018).
Xiao, G. et al. EZH2 negatively regulates PD-L1 expression in hepatocellular carcinoma. J. Immunother. Cancer 7, 300 (2019).
Wu, H. X. et al. Alteration in TET1 as potential biomarker for immune checkpoint blockade in multiple cancers. J. Immunother. Cancer 7, 264 (2019).
Okamura, R. et al. ARID1A alterations function as a biomarker for longer progression-free survival after anti-PD-1/PD-L1 immunotherapy. J. Immunother. Cancer 8, e000438 (2020).
Chiappinelli, K. B., Zahnow, C. A., Ahuja, N. & Baylin, S. B. Combining epigenetic and immunotherapy to combat cancer. Cancer Res. 76, 1683–1689 (2016).
Terranova-Barberio, M. et al. HDAC inhibition potentiates immunotherapy in triple negative breast cancer. Oncotarget 8, 114156–114172 (2017).
Geva-Zatorsky, N. et al. Mining the human gut microbiota for immunomodulatory organisms. Cell 168, 928–943 e911 (2017).
Vetizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 1079–1084 (2015).
Sivan, A. et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350, 1084–1089 (2015).
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
Baruch, E. N. et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science 371, 602–609 (2021).
Chaput, N. et al. Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab. Ann. Oncol. 28, 1368–1379 (2017).
Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).
Jin, Y. et al. The diversity of gut microbiome is associated with favorable responses to anti-programmed death 1 immunotherapy in Chinese patients with NSCLC. J. Thorac. Oncol. 14, 1378–1389 (2019).
Zitvogel, L., Ma, Y., Raoult, D., Kroemer, G. & Gajewski, T. F. The microbiome in cancer immunotherapy: diagnostic tools and therapeutic strategies. Science 359, 1366–1370 (2018).
Inamura, K. Roles of microbiota in response to cancer immunotherapy. Semin. Cancer Biol. 65, 164–175 (2020).
Weide, B. et al. Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab. Clin. Cancer Res. 22, 5487–5496 (2016).
Heppt, M. V. et al. Prognostic factors and outcomes in metastatic uveal melanoma treated with programmed cell death-1 or combined PD-1/cytotoxic T-lymphocyte antigen-4 inhibition. Eur. J. Cancer 82, 56–65 (2017).
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).
Shao, Y. et al. Prognostic value of pretreatment neutrophil-to-lymphocyte ratio in renal cell carcinoma: a systematic review and meta-analysis. BMC Urol. 20, 90 (2020).
Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).
Wu, X. et al. Angiopoietin-2 as a biomarker and target for immune checkpoint therapy. Cancer Immunol. Res. 5, 17–28 (2017).
Turiello, R. et al. Serum CD73 is a prognostic factor in patients with metastatic melanoma and is associated with response to anti-PD-1 therapy. J. Immunother. Cancer 8, e001689 (2020).
Morello, S. et al. Soluble CD73 as biomarker in patients with metastatic melanoma patients treated with nivolumab. J. Transl. Med. 15, 244 (2017).
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).
Maccalli, C. et al. Soluble NKG2D ligands are biomarkers associated with the clinical outcome to immune checkpoint blockade therapy of metastatic melanoma patients. Oncoimmunology 6, e1323618 (2017).
Maccalli, C. et al. Immunological markers and clinical outcome of advanced melanoma patients receiving ipilimumab plus fotemustine in the NIBIT-M1 study. Oncoimmunology 5, e1071007 (2016).
Daassi, D., Mahoney, K. M. & Freeman, G. J. The importance of exosomal PDL1 in tumour immune evasion. Nat. Rev. Immunol. 20, 209–215 (2020).
Yin, Z. et al. Mechanisms underlying low-clinical responses to PD-1/PD-L1 blocking antibodies in immunotherapy of cancer: a key role of exosomal PD-L1. J. Immunother. Cancer 9, e001698 (2021).
Chen, G. et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature 560, 382–386 (2018).
Lee, J. H. et al. Circulating tumour DNA predicts response to anti-PD1 antibodies in metastatic melanoma. Ann. Oncol. 28, 1130–1136 (2017).
Prelaj, A. et al. EPSILoN: a prognostic score using clinical and blood biomarkers in advanced non-small-cell lung cancer treated with immunotherapy. Clin. Lung Cancer 21, 365–377 e365 (2020).
Nowicki, T. S., Hu-Lieskovan, S. & Ribas, A. Mechanisms of resistance to PD-1 and PD-L1 blockade. Cancer J. 24, 47–53 (2018).
Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).
Groth, C. et al. Immunosuppression mediated by myeloid-derived suppressor cells (MDSCs) during tumour progression. Br. J. Cancer 120, 16–25 (2019).
Highfill, S. L. et al. Disruption of CXCR2-mediated MDSC tumor trafficking enhances anti-PD1 efficacy. Sci. Transl. Med. 6, 237ra267 (2014).
Arce Vargas, F. et al. Fc-optimized anti-CD25 depletes tumor-infiltrating regulatory T cells and synergizes with PD-1 blockade to eradicate established tumors. Immunity 46, 577–586 (2017).
Taylor, N. A. et al. Treg depletion potentiates checkpoint inhibition in claudin-low breast cancer. J. Clin. Invest. 127, 3472–3483 (2017).
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic beta-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).
Loi, S. et al. RAS/MAPK activation is associated with reduced tumor-infiltrating lymphocytes in triple-negative breast cancer: therapeutic cooperation between MEK and PD-1/PD-L1 immune checkpoint inhibitors. Clin. Cancer Res. 22, 1499–1509 (2016).
Liu, C. et al. BRAF inhibition increases tumor infiltration by T cells and enhances the antitumor activity of adoptive immunotherapy in mice. Clin. Cancer Res. 19, 393–403 (2013).
Koyama, S. et al. Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat. Commun. 7, 10501 (2016).
Gao, J. et al. VISTA is an inhibitory immune checkpoint that is increased after ipilimumab therapy in patients with prostate cancer. Nat. Med. 23, 551–555 (2017).
Kakavand, H. et al. Negative immune checkpoint regulation by VISTA: a mechanism of acquired resistance to anti-PD-1 therapy in metastatic melanoma patients. Mod. Pathol. 30, 1666–1676 (2017).
Lu, S. et al. Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis. JAMA Oncol. 5, 1195–1204 (2019).
Vaidya, P. et al. Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. J. Immunother. Cancer 8, e001343 (2020).
Mo, Q., Li, R., Adeegbe, D. O., Peng, G. & Chan, K. S. Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy. Commun. Biol. 3, 784 (2020).
Linette, G. P. & Carreno, B. M. Tumor-infiltrating lymphocytes in the checkpoint inhibitor era. Curr. Hematol. Malig. Rep. 14, 286–291 (2019).
The authors thank their colleagues at the University of Liege for their helpful discussions. C.P. and M.A. are Televie fellows.
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C.P., M.A., P.D., P.H. and M.H. have no conflict of interest to declare. G.J. reports grants, personal fees and/or non-financial support from Novartis, Roche, Pfizer, Lilly, Amgen, Bristol-Myers Squibb, Astra-Zeneca, Daiichi Sankyo, Abbvie, Medimmune and MerckKGaA (not directly related to the submitted work).
M.H. is a Research Associate at the Belgian Fund for Scientific Research (FNRS). This work was supported in part by the FNRS (MIS F.4520.20; CDR/OL J.0111.20), the University of Liege (Crédits Sectoriels de Recherche en Sciences de la Santé 2018-2020), the Télévie (PDR Televie 7.8507.19) and the Leon Fredericq Foundation.
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Pilard, C., Ancion, M., Delvenne, P. et al. Cancer immunotherapy: it’s time to better predict patients’ response. Br J Cancer (2021). https://doi.org/10.1038/s41416-021-01413-x