Solving the puzzle of what makes immunotherapies work
Tài liệu tham khảo
Haslam, 2019, Estimation of the percentage of US patients with cancer who are eligible for and respond to checkpoint inhibitor immunotherapy drugs, JAMA Netw. Open, 2, 10.1001/jamanetworkopen.2019.2535
Yarchoan, 2017, Tumor mutational burden and response rate to PD-1 inhibition, N. Engl. J. Med., 377, 2500, 10.1056/NEJMc1713444
2010
Lee, 2019, Multiomics prediction of response rates to therapies to inhibit programmed cell death 1 and programmed cell death 1 ligand 1, JAMA Oncol., 5, 1614, 10.1001/jamaoncol.2019.2311
Whitson, 1912
Garon, 2015, Pembrolizumab for the treatment of non-small-cell lung cancer, N. Engl. J. Med., 372, 2018, 10.1056/NEJMoa1501824
Davis, 2019, 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, 10.1186/s40425-019-0768-9
Doroshow, 2021, PD-L1 as a biomarker of response to immune-checkpoint inhibitors, Nat. Rev. Clin. Oncol., 18, 345, 10.1038/s41571-021-00473-5
Snyder, 2014, Genetic basis for clinical response to CTLA-4 blockade in melanoma, N. Engl. J. Med., 371, 2189, 10.1056/NEJMoa1406498
Rizvi, 2015, Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer, Science, 348, 124, 10.1126/science.aaa1348
Samstein, 2019, Tumor mutational load predicts survival after immunotherapy across multiple cancer types, Nat. Genet., 51, 202, 10.1038/s41588-018-0312-8
Marabelle, 2020, 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, 10.1016/S1470-2045(20)30445-9
Garber, 2005, Hereditary cancer predisposition syndromes, J. Clin. Oncol., 23, 276, 10.1200/JCO.2005.10.042
Rahman, 2014, Realizing the promise of cancer predisposition genes, Nature, 505, 302, 10.1038/nature12981
Orru, 2013, Genetic variants regulating immune cell levels in health and disease, Cell, 155, 242, 10.1016/j.cell.2013.08.041
Yang, 2017, Transplant genetics and genomics, Nat. Rev. Genet., 18, 309, 10.1038/nrg.2017.12
International HIV Controllers Study, 2010, The major genetic determinants of HIV-1 control affect HLA class I peptide presentation, Science, 330, 1551, 10.1126/science.1195271
Rock, 2016, Present yourself! By MHC class I and MHC class II molecules, Trends Immunol., 37, 724, 10.1016/j.it.2016.08.010
Marty, 2017, MHC-I genotype restricts the oncogenic mutational landscape, Cell, 171, 1272, 10.1016/j.cell.2017.09.050
Arora, 2020, HLA heterozygote advantage against HIV-1 is driven by quantitative and qualitative differences in HLA allele-specific peptide presentation, Mol. Biol. Evol., 37, 639, 10.1093/molbev/msz249
Ferreiro-Iglesias, 2018, Fine mapping of MHC region in lung cancer highlights independent susceptibility loci by ethnicity, Nat. Commun., 9, 3927, 10.1038/s41467-018-05890-2
Chowell, 2019, Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy, Nat. Med., 25, 1715, 10.1038/s41591-019-0639-4
Lu, 2021, Germline HLA-B evolutionary divergence influences the efficacy of immune checkpoint blockade therapy in gastrointestinal cancer, Genome Med., 13, 175, 10.1186/s13073-021-00997-6
Lee, 2021, High response rate and durability driven by HLA genetic diversity in patients with kidney cancer treated with lenvatinib and pembrolizumab, Mol. Cancer Res., 19, 1510, 10.1158/1541-7786.MCR-21-0053
Litchfield, 2021, Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition, Cell, 184, 596, 10.1016/j.cell.2021.01.002
Chowell, 2018, Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy, Science, 359, 582, 10.1126/science.aao4572
Cummings, 2020, Mutational landscape influences immunotherapy outcomes among patients with non-small-cell lung cancer with human leukocyte antigen supertype B44, Nat. Cancer, 1, 1167, 10.1038/s43018-020-00140-1
Naranbhai, 2022, HLA-A*03 and response to immune checkpoint blockade in cancer: an epidemiological biomarker study, Lancet Oncol., 23, 172, 10.1016/S1470-2045(21)00582-9
Carrot-Zhang, 2020, Comprehensive analysis of genetic ancestry and its molecular correlates in cancer, Cancer Cell, 37, 639, 10.1016/j.ccell.2020.04.012
Lim, 2018, Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration, Proc. Natl. Acad. Sci. U. S. A., 115, E11701, 10.1073/pnas.1804506115
Sayaman, 2021, Germline genetic contribution to the immune landscape of cancer, Immunity, 54, 367, 10.1016/j.immuni.2021.01.011
Shahamatdar, 2020, Germline features associated with immune infiltration in solid tumors, Cell Rep., 30, 2900, 10.1016/j.celrep.2020.02.039
Kirchhoff, 2020, Germline genetics in immuno-oncology: from genome-wide to targeted biomarker strategies, Methods Mol. Biol., 2055, 93, 10.1007/978-1-4939-9773-2_4
Srivastava, 2020, Diverse neoantigens and the development of cancer therapies, Semin. Radiat. Oncol., 30, 113, 10.1016/j.semradonc.2019.12.001
Blass, 2021, Advances in the development of personalized neoantigen-based therapeutic cancer vaccines, Nat. Rev. Clin. Oncol., 18, 215, 10.1038/s41571-020-00460-2
Fotakis, 2021, Computational cancer neoantigen prediction: current status and recent advances, Immuno-Oncol. Technol., 12
Borden, 2022, Cancer neoantigens: challenges and future directions for prediction, prioritization, and validation, Front. Oncol., 12, 10.3389/fonc.2022.836821
Wells, 2020, Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction, Cell, 183, 818, 10.1016/j.cell.2020.09.015
Bakhoum, 2018, The multifaceted role of chromosomal instability in cancer and its microenvironment, Cell, 174, 1347, 10.1016/j.cell.2018.08.027
Davoli, 2017, Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy, Science, 355, eaaf8399, 10.1126/science.aaf8399
Havel, 2019, The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy, Nat. Rev. Cancer, 19, 133, 10.1038/s41568-019-0116-x
William, 2021, Immune evasion in HPV– head and neck precancer-cancer transition is driven by an aneuploid switch involving chromosome 9p loss, Proc. Natl. Acad. Sci. U. S. A., 118, 10.1073/pnas.2022655118
Han, 2021, 9p21 loss confers a cold tumor immune microenvironment and primary resistance to immune checkpoint therapy, Nat. Commun., 12, 5606, 10.1038/s41467-021-25894-9
Wu, 2022, Extrachromosomal DNA: an emerging hallmark in human cancer, Annu. Rev. Pathol., 17, 367, 10.1146/annurev-pathmechdis-051821-114223
Aldea, 2021, Overcoming resistance to tumor-targeted and immune-targeted therapies, Cancer Discov., 11, 874, 10.1158/2159-8290.CD-20-1638
Calles, 2020, Checkpoint blockade in lung cancer with driver mutation: choose the road wisely, Am. Soc. Clin. Oncol. Educ. Book, 40, 372, 10.1200/EDBK_280795
Ricciuti, 2022, Diminished efficacy of programmed death-(ligand)1 inhibition in STK11- and KEAP1-mutant lung adenocarcinoma is affected by KRAS mutation status, J. Thorac. Oncol., 17, 399, 10.1016/j.jtho.2021.10.013
Zaretsky, 2016, Mutations associated with acquired resistance to PD-1 blockade in melanoma, N. Engl. J. Med., 375, 819, 10.1056/NEJMoa1604958
Gurjao, 2019, Intrinsic resistance to immune checkpoint blockade in a mismatch repair-deficient colorectal cancer, Cancer Immunol. Res., 7, 1230, 10.1158/2326-6066.CIR-18-0683
Dhatchinamoorthy, 2021, Cancer immune evasion through loss of MHC class I antigen presentation, Front. Immunol., 12, 10.3389/fimmu.2021.636568
Mouw, 2017, DNA damage and repair biomarkers of immunotherapy response, Cancer Discov., 7, 675, 10.1158/2159-8290.CD-17-0226
Samstein, 2020, Mutations in BRCA1 and BRCA2 differentially affect the tumor microenvironment and response to checkpoint blockade immunotherapy, Nat. Cancer, 1, 1188, 10.1038/s43018-020-00139-8
Li, 2020, Epigenetic driver mutations in ARID1A shape cancer immune phenotype and immunotherapy, J. Clin. Invest., 130, 2712, 10.1172/JCI134402
Griffin, 2021, Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity, Nature, 595, 309, 10.1038/s41586-021-03520-4
Zhang, 2020, Role of DNA repair defects in predicting immunotherapy response, Biomark. Res., 8, 23, 10.1186/s40364-020-00202-7
Jones, 2019, Epigenetic therapy in immune-oncology, Nat. Rev. Cancer, 19, 151, 10.1038/s41568-019-0109-9
Binnewies, 2018, Understanding the tumor immune microenvironment (TIME) for effective therapy, Nat. Med., 24, 541, 10.1038/s41591-018-0014-x
Yost, 2021, Recruiting T cells in cancer immunotherapy, Science, 372, 130, 10.1126/science.abd1329
Jerby-Arnon, 2018, A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade, Cell, 175, 984, 10.1016/j.cell.2018.09.006
Mariathasan, 2018, TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells, Nature, 554, 544, 10.1038/nature25501
Horn, 2020, Tumor plasticity and resistance to immunotherapy, Trends Cancer, 6, 432, 10.1016/j.trecan.2020.02.001
Sparano, 2008, Development of the 21-gene assay and its application in clinical practice and clinical trials, J. Clin. Oncol., 26, 721, 10.1200/JCO.2007.15.1068
Pfister, 2021, NASH limits anti-tumour surveillance in immunotherapy-treated HCC, Nature, 592, 450, 10.1038/s41586-021-03362-0
Eroglu, 2018, High response rate to PD-1 blockade in desmoplastic melanomas, Nature, 553, 347, 10.1038/nature25187
Riaz, 2016, Recurrent SERPINB3 and SERPINB4 mutations in patients who respond to anti-CTLA4 immunotherapy, Nat. Genet., 48, 1327, 10.1038/ng.3677
Yu, 2021, Liver metastasis restrains immunotherapy efficacy via macrophage-mediated T cell elimination, Nat. Med., 27, 152, 10.1038/s41591-020-1131-x
Bera, 2019, Artificial intelligence in digital pathology – new tools for diagnosis and precision oncology, Nat. Rev. Clin. Oncol., 16, 703, 10.1038/s41571-019-0252-y
Saltz, 2018, Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images, Cell Rep., 23, 181, 10.1016/j.celrep.2018.03.086
Helmink, 2020, B cells and tertiary lymphoid structures promote immunotherapy response, Nature, 577, 549, 10.1038/s41586-019-1922-8
Kather, 2019, Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer, Nat. Med., 25, 1054, 10.1038/s41591-019-0462-y
Coudray, 2018, Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning, Nat. Med., 24, 1559, 10.1038/s41591-018-0177-5
He, 2020, Integrating spatial gene expression and breast tumour morphology via deep learning, Nat. Biomed. Eng., 4, 827, 10.1038/s41551-020-0578-x
Johannet, 2021, Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma, Clin. Cancer Res., 27, 131, 10.1158/1078-0432.CCR-20-2415
Park, 2022, Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer, J. Clin. Oncol., 40, 1916, 10.1200/JCO.21.02010
Colen, 2021, Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers, J. Immunother. Cancer, 9, 10.1136/jitc-2020-001752
van de Donk, 2020, Molecular imaging biomarkers for immune checkpoint inhibitor therapy, Theranostics, 10, 1708, 10.7150/thno.38339
Valero, 2021, Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors, Nat. Commun., 12, 729, 10.1038/s41467-021-20935-9
Chowell, 2021, Improved prediction of immune checkpoint blockade efficacy across multiple cancer types, Nat. Biotechnol., 40, 499, 10.1038/s41587-021-01070-8
Nabet, 2020, Noninvasive early identification of therapeutic benefit from immune checkpoint inhibition, Cell, 183, 363, 10.1016/j.cell.2020.09.001
Li, 2020, Emerging blood-based biomarkers for predicting response to checkpoint immunotherapy in non-small-cell lung cancer, Front. Immunol., 11, 10.3389/fimmu.2020.603157
Wang, 2021, The role of cytokines in predicting the response and adverse events related to immune checkpoint inhibitors, Front. Immunol., 12
Gandhi, 2018, Pembrolizumab plus chemotherapy in lung cancer, N. Engl. J. Med., 379
Wang, 2021, Comparative efficacy and safety of immunotherapy alone and in combination with chemotherapy for advanced non-small cell lung cancer, Front. Oncol., 11
Akinboro, 2021, Outcomes of anti-PD-(L1) therapy in combination with chemotherapy versus immunotherapy (IO) alone for first-line (1L) treatment of advanced non-small cell lung cancer (NSCLC) with PD-L1 score 1-49%: FDA pooled analysis, J. Clin. Oncol., 39, 9001, 10.1200/JCO.2021.39.15_suppl.9001
Salas-Benito, 2021, Paradigms on immunotherapy combinations with chemotherapy, Cancer Discov., 11, 1353, 10.1158/2159-8290.CD-20-1312
Haas, 2021, Acquired resistance to anti-MAPK targeted therapy confers an immune-evasive tumor microenvironment and cross-resistance to immunotherapy in melanoma, Nat. Cancer, 2, 693, 10.1038/s43018-021-00221-9
Yoo, 2022, Outcomes among patients with or without obesity and with cancer following treatment with immune checkpoint blockade, JAMA Netw. Open, 5, 10.1001/jamanetworkopen.2022.0448
Ye, 2020, Sex-associated molecular differences for cancer immunotherapy, Nat. Commun., 11, 1779, 10.1038/s41467-020-15679-x
DePeaux, 2021, Metabolic barriers to cancer immunotherapy, Nat. Rev. Immunol., 21, 785, 10.1038/s41577-021-00541-y
Diem, 2016, Serum lactate dehydrogenase as an early marker for outcome in patients treated with anti-PD-1 therapy in metastatic melanoma, Br. J. Cancer, 114, 256, 10.1038/bjc.2015.467
Hu-Lieskovan, 2020, SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery, J. Immunother. Cancer, 8, 10.1136/jitc-2020-000705
Chen, 2021, The viral expression and immune status in human cancers and insights into novel biomarkers of immunotherapy, BMC Cancer, 21, 1183, 10.1186/s12885-021-08871-9
Tray, 2018, Predictive biomarkers for checkpoint immunotherapy: current status and challenges for clinical application, Cancer Immunol. Res., 6, 1122, 10.1158/2326-6066.CIR-18-0214
Kim, 2018, Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer, Nat. Med., 24, 1449, 10.1038/s41591-018-0101-z
Zheng, 2020, Interaction between microbiota and immunity in health and disease, Cell Res., 30, 492, 10.1038/s41422-020-0332-7
Zhou, 2021, Gut microbiota in cancer immune response and immunotherapy, Trends Cancer, 7, 647, 10.1016/j.trecan.2021.01.010
McCulloch, 2022, Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1, Nat. Med., 28, 545, 10.1038/s41591-022-01698-2
Dubin, 2016, Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis, Nat. Commun., 7, 10391, 10.1038/ncomms10391
Shi, 2020, Intratumoral accumulation of gut microbiota facilitates CD47-based immunotherapy via STING signaling, J. Exp. Med., 217, 10.1084/jem.20192282
Cogdill, 2018, The impact of intratumoral and gastrointestinal microbiota on systemic cancer therapy, Trends Immunol., 39, 900, 10.1016/j.it.2018.09.007
Boesch, 2021, Tumour neoantigen mimicry by microbial species in cancer immunotherapy, Br. J. Cancer, 125, 313, 10.1038/s41416-021-01365-2
Balachandran, 2017, Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer, Nature, 551, 512, 10.1038/nature24462
Sioud, 2018, T-cell cross-reactivity may explain the large variation in how cancer patients respond to checkpoint inhibitors, Scand. J. Immunol., 87, 10.1111/sji.12643
Leng, 2020, Pre-existing heterologous T-cell immunity and neoantigen immunogenicity, Clin. Transl. Immunol., 9, 10.1002/cti2.1111
Bessell, 2020, Commensal bacteria stimulate antitumor responses via T cell cross-reactivity, JCI Insight, 5, 10.1172/jci.insight.135597
Fluckiger, 2020, Cross-reactivity between tumor MHC class I-restricted antigens and an enterococcal bacteriophage, Science, 369, 936, 10.1126/science.aax0701
Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat. Med., 25, 44, 10.1038/s41591-018-0300-7
Leclercq, 2019, Large-scale automatic feature selection for biomarker discovery in high-dimensional OMICs data, Front. Genet., 10, 452, 10.3389/fgene.2019.00452
Cindy Yang, 2021, Pan-cancer analysis of longitudinal metastatic tumors reveals genomic alterations and immune landscape dynamics associated with pembrolizumab sensitivity, Nat. Commun., 12, 5137, 10.1038/s41467-021-25432-7
Nisar, 2020, Non-invasive biomarkers for monitoring the immunotherapeutic response to cancer, J. Transl. Med., 18, 471, 10.1186/s12967-020-02656-7
Valero, 2021, Response rates to anti-PD-1 immunotherapy in microsatellite-stable solid tumors with 10 or more mutations per megabase, JAMA Oncol., 7, 739, 10.1001/jamaoncol.2020.7684
Sturgill, 2022, Discordance in tumor mutation burden from blood and tissue affects association with response to immune checkpoint inhibition in real-world settings, Oncologist, 27, 175, 10.1093/oncolo/oyab064
Vega, 2021, Aligning tumor mutational burden (TMB) quantification across diagnostic platforms: phase II of the Friends of Cancer Research TMB Harmonization Project, Ann. Oncol., 32, 1626, 10.1016/j.annonc.2021.09.016