trying...
3267088620240730
2234-943X102020Frontiers in oncologyFront OncolToward Systems Biomarkers of Response to Immune Checkpoint Blockers.10271027102710.3389/fonc.2020.01027Immunotherapy with checkpoint blockers (ICBs), aimed at unleashing the immune response toward tumor cells, has shown a great improvement in overall patient survival compared to standard therapy, but only in a subset of patients. While a number of recent studies have significantly improved our understanding of mechanisms playing an important role in the tumor microenvironment (TME), we still have an incomplete view of how the TME works as a whole. This hampers our ability to effectively predict the large heterogeneity of patients' response to ICBs. Systems approaches could overcome this limitation by adopting a holistic perspective to analyze the complexity of tumors. In this Mini Review, we focus on how an integrative view of the increasingly available multi-omics experimental data and computational approaches enables the definition of new systems-based predictive biomarkers. In particular, we will focus on three facets of the TME toward the definition of new systems biomarkers. First, we will review how different types of immune cells influence the efficacy of ICBs, not only in terms of their quantification, but also considering their localization and functional state. Second, we will focus on how different cells in the TME interact, analyzing how inter- and intra-cellular networks play an important role in shaping the immune response and are responsible for resistance to immunotherapy. Finally, we will describe the potential of looking at these networks as dynamic systems and how mathematical models can be used to study the rewiring of the complex interactions taking place in the TME.Copyright © 2020 Lapuente-Santana and Eduati.Lapuente-SantanaÓscarÓDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.EduatiFedericaFDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands.engJournal ArticleReview20200624
SwitzerlandFront Oncol1015688672234-943Xcancer signaling networksimmune checkpoint blockersmulti-omics profilingprecision immuno-oncologypredictive biomarkerssystems biologytumor microenvironment20201302020522202071760202071760202071761202011epublish32670886PMC732681310.3389/fonc.2020.01027Hargadon KM, Johnson CE, Williams CJ. Immune checkpoint blockade therapy for cancer: an overview of FDA-approved immune checkpoint inhibitors. Int Immunopharmacol. (2018) 62:29–39. 10.1016/j.intimp.2018.06.00110.1016/j.intimp.2018.06.00129990692Sun C, Mezzadra R, Schumacher TN. Regulation and function of the PD-L1 checkpoint. Immunity. (2018) 48:434–52. 10.1016/j.immuni.2018.03.01410.1016/j.immuni.2018.03.014PMC711650729562194Boutros C, Tarhini A, Routier E, Lambotte O, Ladurie FL, Carbonnel F, et al. . Safety profiles of anti-CTLA-4 and anti-PD-1 antibodies alone and in combination. Nat Rev Clin Oncol. (2016) 13:473–86. 10.1038/nrclinonc.2016.5810.1038/nrclinonc.2016.5827141885Postow MA, Sidlow R, Hellmann MD. Immune-related adverse events associated with immune checkpoint blockade. New Engl J Med. (2018) 378:158–68. 10.1056/NEJMra170348110.1056/NEJMra170348129320654Schmidt C. The benefits of immunotherapy combinations. Nature. (2017) 552:S67–9. 10.1038/d41586-017-08702-710.1038/d41586-017-08702-729293245Arora S, Velichinskii R, Lesh RW, Ali U, Kubiak M, Bansal P, et al. . Existing and emerging biomarkers for immune checkpoint immunotherapy in solid tumors. Adv Ther. (2019) 36:2638–78. 10.1007/s12325-019-01051-z10.1007/s12325-019-01051-zPMC677854531410780Le DT, Uram JN, Wang H, Bartlett B, Kemberling H, Eyring A, et al. . PD-1 blockade in tumors with mismatch repair deficiency. N Engl J Med. (2015) 372:2509–20. 10.1056/NEJMoa150059610.1056/NEJMoa1500596PMC448113626028255Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, et al. . Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. (2017) 357:409–13. 10.1126/science.aan673310.1126/science.aan6733PMC557614228596308Legrand FA, Gandara DR, Mariathasan S, Powles T, He X, Zhang W, et al. Association of high tissue TMB and atezolizumab efficacy across multiple tumor types. J Clin Oncol. (2018) 36(Suppl. 15):12000 10.1200/JCO.2018.36.15_suppl.1200010.1200/JCO.2018.36.15_suppl.12000Ott PA, Bang Y-J, Piha-Paul SA, Abdul Razak AR, Bennouna J, Soria J-C, et al. . T-cell–inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J Clin Oncol. (2019) 37:318–27. 10.1200/JCO.2018.78.227610.1200/JCO.2018.78.227630557521Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, et al. . Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science. (2018) 362:eaar3593. 10.1126/science.aar359310.1126/science.aar3593PMC671816230309915Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. . Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science. (2015) 348:124–8. 10.1126/science.aaa134810.1126/science.aaa1348PMC499315425765070Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. . Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. (2015) 350:207–11. 10.1126/science.aad009510.1126/science.aad0095PMC505451726359337Lu T, Wang S, Xu L, Zhou Q, Singla N, Gao J, et al. . Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes. Sci Immunol. (2020) 5:aaz3199. 10.1126/sciimmunol.aaz319910.1126/sciimmunol.aaz3199PMC723932732086382Page DB, Yuan J, Redmond D, Wen YH, Durack JC, Emerson R, et al. . Deep sequencing of T-cell receptor DNA as a biomarker of clonally expanded TILs in breast cancer after immunotherapy. Cancer Immunol Res. (2016) 4:835–44. 10.1158/2326-6066.CIR-16-001310.1158/2326-6066.CIR-16-0013PMC506483927587469Sims JS, Grinshpun B, Feng Y, Ung TH, Neira JA, Samanamud JL, et al. . Diversity and divergence of the glioma-infiltrating T-cell receptor repertoire. Proc Natl Acad Sci USA. (2016) 113:E3529–37. 10.1073/pnas.160101211310.1073/pnas.1601012113PMC492217727261081Cui JH, Lin KR, Yuan SH, Jin YB, Chen XP, Su XK, et al. . TCR Repertoire as a novel indicator for immune monitoring and prognosis assessment of patients with cervical cancer. Front Immunol. (2018) 9:2729. 10.3389/fimmu.2018.0272910.3389/fimmu.2018.02729PMC626207030524447Farmanbar A, Kneller R, Firouzi S. RNA sequencing identifies clonal structure of T-cell repertoires in patients with adult T-cell leukemia/lymphoma. NPJ Genom Med. (2019) 4:10. 10.1038/s41525-019-0084-910.1038/s41525-019-0084-9PMC650285731069115Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. (2019) 19:133–50. 10.1038/s41568-019-0116-x10.1038/s41568-019-0116-xPMC670539630755690Topalian SL, Taube JM, Anders RA, Pardoll DM. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. (2016) 16:275–87. 10.1038/nrc.2016.3610.1038/nrc.2016.36PMC538193827079802Camidge DR, Doebele RC, Kerr KM. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol. (2019) 16:341–55. 10.1038/s41571-019-0173-910.1038/s41571-019-0173-930718843Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al. . Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. (2014) 371:2189–99. 10.1056/NEJMoa140649810.1056/NEJMoa1406498PMC431531925409260Keenan TE, Burke KP, Van Allen EM. Genomic correlates of response to immune checkpoint blockade. Nat Med. (2019) 25:389–402. 10.1038/s41591-019-0382-x10.1038/s41591-019-0382-xPMC659971030842677Finotello F, Rieder D, Hackl H, Trajanoski Z. Next-generation computational tools for interrogating cancer immunity. Nat Rev Genet. (2019) 20:724–46. 10.1038/s41576-019-0166-710.1038/s41576-019-0166-731515541Duffy MJ, Crown J. Biomarkers for predicting response to immunotherapy with immune checkpoint inhibitors in cancer patients. Clin Chem. (2019) 65:1228–38. 10.1373/clinchem.2019.30364410.1373/clinchem.2019.30364431315901Finotello F, Eduati F. Multi-omics profiling of the tumor microenvironment: paving the way to precision immuno-oncology. Front Oncol. (2018) 8:430. 10.3389/fonc.2018.0043010.3389/fonc.2018.00430PMC618207530345255Fridman WH, Zitvogel L, Sautès-Fridman C, Kroemer G. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol. (2017) 14:717–34. 10.1038/nrclinonc.2017.10110.1038/nrclinonc.2017.10128741618Finotello F, Trajanoski Z. Quantifying tumor-infiltrating immune cells from transcriptomics data. Cancer Immunol Immunother. (2018) 67:1031–40. 10.1007/s00262-018-2150-z10.1007/s00262-018-2150-zPMC600623729541787Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov. (2019) 18:197–218. 10.1038/s41573-018-0007-y10.1038/s41573-018-0007-y30610226Giraldo NA, Becht E, Pagès F, Skliris G, Verkarre V, Vano Y, et al. . Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin Cancer Res. (2015) 21:3031–40. 10.1158/1078-0432.CCR-14-292610.1158/1078-0432.CCR-14-292625688160Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pagès C, et al. . Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. (2006) 313:1960–64. 10.1126/science.112913910.1126/science.112913917008531Pagès F, Mlecnik B, Marliot F, Bindea G, Ou F-S, Bifulco C, et al. . International validation of the consensus immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. (2018) 391:2128–39. 10.1016/S0140-6736(18)30789-X10.1016/S0140-6736(18)30789-X29754777Gruosso T, Gigoux M, Manem VSK, Bertos N, Zuo D, Perlitch I, et al. . Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J Clin Invest. (2019) 129:1785–1800. 10.1172/JCI9631310.1172/JCI96313PMC643688430753167Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, et al. . Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. (2018) 23:181–93.e7. 10.1016/j.celrep.2018.03.08610.1016/j.celrep.2018.03.086PMC594371429617659Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang T-H, et al. . The immune landscape of cancer. Immunity. (2019) 51:411–12. 10.1016/j.immuni.2019.08.00410.1016/j.immuni.2019.08.00431433971Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG, Jenkins RW, et al. . Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell. (2018) 175:998–1013.e20. 10.1016/j.cell.2018.10.03810.1016/j.cell.2018.10.038PMC664198430388456Zheng C, Zheng L, Yoo J-K, Guo H, Zhang Y, Guo X, et al. . Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell. (2017) 169:1342–56.e16. 10.1016/j.cell.2017.05.03510.1016/j.cell.2017.05.03528622514Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. . Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. (2018) 24:1550–58. 10.1038/s41591-018-0136-110.1038/s41591-018-0136-1PMC648750230127393Pauken KE, Sammons MA, Odorizzi PM, Manne S, Godec J, Khan O, et al. . Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science. (2016) 354:1160–65. 10.1126/science.aaf280710.1126/science.aaf2807PMC548479527789795Sen DR, Kaminski J, Barnitz RA, Kurachi M, Gerdemann U, Yates KB, et al. The epigenetic landscape of T cell exhaustion. Science. (2016) 354:1165–9. 10.1126/science.aae049110.1126/science.aae0491PMC549758927789799Philip M, Fairchild L, Sun L, Horste EL, Camara S, Shakiba M, et al. . Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature. (2017) 545:452–6. 10.1038/nature2236710.1038/nature22367PMC569321928514453Quaranta V, Rainer C, Nielsen SR, Raymant ML, Ahmed MS, Engle DD, et al. . Macrophage-derived granulin drives resistance to immune checkpoint inhibition in metastatic pancreatic cancer. Cancer Res. (2018) 78:4253–69. 10.1158/0008-5472.CAN-17-387610.1158/0008-5472.CAN-17-3876PMC607644029789416Klug F, Prakash H, Huber PE, Seibel T, Bender N, Halama N, et al. . Low-dose irradiation programs macrophage differentiation to an iNOS /M1 phenotype that orchestrates effective T cell immunotherapy. Cancer Cell. (2013) 24:589–602. 10.1016/j.ccr.2013.09.01410.1016/j.ccr.2013.09.01424209604Lindner S, Dahlke K, Sontheimer K, Hagn M, Kaltenmeier C, Barth TFE, et al. . Interleukin 21-induced granzyme B-expressing B cells infiltrate tumors and regulate T cells. Cancer Res. (2013) 73:2468–79. 10.1158/0008-5472.CAN-12-345010.1158/0008-5472.CAN-12-345023384943Selitsky SR, Mose LE, Smith CC, Chai S, Hoadley KA, Dittmer DP, et al. . Prognostic value of B cells in cutaneous melanoma. Genome Med. (2019) 11:36. 10.1186/s13073-019-0647-510.1186/s13073-019-0647-5PMC654052631138334Tesone AJ, Rutkowski MR, Brencicova E, Svoronos N, Perales-Puchalt A, Stephen TL, et al. . Satb1 overexpression drives tumor-promoting activities in cancer-associated dendritic cells. Cell Rep. (2016) 14:1774–86. 10.1016/j.celrep.2016.01.05610.1016/j.celrep.2016.01.056PMC476761826876172Fuertes MB, Kacha AK, Kline J, Woo S-R, Kranz DM, Murphy KM, et al. . Host type I IFN signals are required for antitumor CD8 T cell responses through CD8α dendritic cells. J Exp Med. (2011) 208:2005–16. 10.1084/jem.2010115910.1084/jem.20101159PMC318206421930765Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. . Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. (2019) 37:773–82. 10.1038/s41587-019-0114-210.1038/s41587-019-0114-2PMC661071431061481Zaitsev K, Bambouskova M, Swain A, Artyomov MN. Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures. Nat Commun. (2019) 10:2209. 10.1038/s41467-019-09990-510.1038/s41467-019-09990-5PMC652525931101809Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. . Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. (2014) 11:417–22. 10.1038/nmeth.286910.1038/nmeth.286924584193Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C, Borowsky AD, et al. . Multiplexed ion beam imaging of human breast tumors. Nat Med. (2014) 20:436–42. 10.1038/nm.348810.1038/nm.3488PMC411090524584119Galluzzi L, Chan TA, Kroemer G, Wolchok JD, López-Soto A. The hallmarks of successful anticancer immunotherapy. Sci Transl Med. (2018) 10:eaat7807. 10.1126/scitranslmed.aat780710.1126/scitranslmed.aat780730232229Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, et al. . Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. (2018) 24:541–50. 10.1038/s41591-018-0014-x10.1038/s41591-018-0014-xPMC599882229686425Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, et al. . Publisher Correction: Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med. (2018) 24:1942. 10.1038/s41591-018-0247-810.1038/s41591-018-0247-8PMC963923130333558Davoli T, Uno H, Wooten EC, Elledge SJ. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science. (2017) 355:eaaf8399. 10.1126/science.aaf839910.1126/science.aaf8399PMC559279428104840Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J, et al. . Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet. (2016) 387:1837–46. 10.1016/S0140-6736(16)00587-010.1016/S0140-6736(16)00587-026970723Huang AC, Orlowski RJ, Xu X, Mick R, George SM, Yan PK, et al. . A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat Med. (2019) 25:454–61. 10.1038/s41591-019-0357-y10.1038/s41591-019-0357-yPMC669962630804515Messina JL, Fenstermacher DA, Eschrich S, Qu X, Berglund AE, Lloyd MC, et al. . 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep. (2012) 2:765. 10.1038/srep0076510.1038/srep00765PMC347944923097687Roh W, Chen P-L, Reuben A, Spencer CN, Prieto PA, Miller JP, 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. (2017) 9:eaah3560. 10.1126/scitranslmed.aah356010.1126/scitranslmed.aah3560PMC581960728251903Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. (2015) 160:48–61. 10.1016/j.cell.2014.12.03310.1016/j.cell.2014.12.033PMC485647425594174Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. . IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. (2017) 127:2930–40. 10.1172/JCI9119010.1172/JCI91190PMC553141928650338Ock C-Y, Hwang J-E, Keam B, Kim S-B, Shim J-J, Jang H-J, et al. . Genomic landscape associated with potential response to anti-CTLA-4 treatment in cancers. Nat Commun. (2017) 8:1050. 10.1038/s41467-017-01018-010.1038/s41467-017-01018-0PMC564880129051489Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. . Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. (2017) 18:248–62. 10.1016/j.celrep.2016.12.01910.1016/j.celrep.2016.12.01928052254Szeto GL, Finley SD. Integrative approaches to cancer immunotherapy. Trends Cancer Res. (2019) 5:400–10. 10.1016/j.trecan.2019.05.01010.1016/j.trecan.2019.05.010PMC746785431311655Wellenstein MD, de Visser KE. Cancer-cell-intrinsic mechanisms shaping the tumor immune landscape. Immunity. (2018) 48:399–416. 10.1016/j.immuni.2018.03.00410.1016/j.immuni.2018.03.00429562192Spranger S, Gajewski TF. Impact of oncogenic pathways on evasion of antitumour immune responses. Nat Rev Cancer. (2018) 18:139–47. 10.1038/nrc.2017.11710.1038/nrc.2017.117PMC668507129326431Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. (2017) 168:707–23. 10.1016/j.cell.2017.01.01710.1016/j.cell.2017.01.017PMC539169228187290Peng W, Chen JQ, Liu C, Malu S, Creasy C, Tetzlaff MT, et al. . Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. (2016) 6:202–16. 10.1158/1538-7445.AM2016-436310.1158/1538-7445.AM2016-4363PMC474449926645196Akbay EA, Koyama S, Carretero J, Altabef A, Tchaicha JH, Christensen CL, et al. . Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors. Cancer Discov. (2013) 3:1355–63. 10.1158/2159-8290.CD-13-031010.1158/2159-8290.CD-13-0310PMC386413524078774Escors D, Gato-Cañas M, Zuazo M, Arasanz H, García-Granda MJ, Vera R, et al. . The intracellular signalosome of PD-L1 in cancer cells. Signal Transduct Target Ther. (2018) 3:26. 10.1038/s41392-018-0022-910.1038/s41392-018-0022-9PMC616048830275987Quigley D, Silwal-Pandit L, Dannenfelser R, Langerød A, Vollan HKM, Vaske C, et al. . Lymphocyte invasion in IC10/basal-like breast tumors is associated with wild-type TP53. Mol Cancer Res. (2015) 13:493–501. 10.1158/1541-7786.MCR-14-038710.1158/1541-7786.MCR-14-0387PMC446557925351767Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. (2015) 523:231–5. 10.1038/nature1440410.1038/nature1440425970248Maman S, Witz IP. A history of exploring cancer in context. Nat Rev Cancer. (2018) 18:359–76. 10.1038/s41568-018-0006-710.1038/s41568-018-0006-729700396Altan-Bonnet G, Mukherjee R. Cytokine-mediated communication: a quantitative appraisal of immune complexity. Nat Rev Immunol. (2019) 19:205–17. 10.1038/s41577-019-0131-x10.1038/s41577-019-0131-xPMC812614630770905Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W, Hu-Lieskovan S, et al. . Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med. (2016) 375:819–29. 10.1056/NEJMoa160495810.1056/NEJMoa1604958PMC500720627433843Bertrand F, Montfort A, Marcheteau E, Imbert C, Gilhodes J, Filleron T, et al. . TNFα blockade overcomes resistance to anti-PD-1 in experimental melanoma. Nat Commun. (2017) 8:2256. 10.1038/s41467-017-02358-710.1038/s41467-017-02358-7PMC574162829273790Jacquelot N, Yamazaki T, Roberti MP, Duong CPM, Andrews MC, Verlingue L, et al. . Sustained type I interferon signaling as a mechanism of resistance to PD-1 blockade. Cell Res. (2019) 29:846–61. 10.1038/s41422-019-0224-x10.1038/s41422-019-0224-xPMC679694231481761Türei D, Korcsmáros T, Saez-Rodriguez J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat Methods. (2016) 13:966–67. 10.1038/nmeth.407710.1038/nmeth.407727898060Ramilowski JA, Goldberg T, Harshbarger J, Kloppmann E, Lizio M, Satagopam VP, et al. . A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun. (2015) 6:7866. 10.1038/ncomms886610.1038/ncomms8866PMC452517826198319Choi H, Sheng J, Gao D, Li F, Durrans A, Ryu S, et al. . Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Rep. (2015) 10:1187–201. 10.1016/j.celrep.2015.01.04010.1016/j.celrep.2015.01.04025704820Browaeys R, Saelens W, Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods. (2020) 17:159–62. 10.1038/s41592-019-0667-510.1038/s41592-019-0667-531819264Wang S, Karikomi M, MacLean AL, Nie Q. Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res. (2019) 47:e66. 10.1093/nar/gky88210.1093/nar/gky882PMC658241130923815Ghoshdastider U, Naeini MM, Rohatgi N, Revkov E. Data-driven inference of crosstalk in the tumor microenvironment. BioRxiv. (2019). 10.1101/83551210.1101/835512Kondratova M, Czerwinska U, Sompairac N, Amigorena SD, Soumelis V, Barillot E, et al. . A multiscale signalling network map of innate immune response in cancer reveals cell heterogeneity signatures. Nat Commun. (2019) 10:4808. 10.1038/s41467-019-12270-x10.1038/s41467-019-12270-xPMC680589531641119Worzfeld T, Finkernagel F, Reinartz S, Konzer A, Adhikary T, Nist A, et al. . Proteotranscriptomics reveal signaling networks in the ovarian cancer microenvironment. Mol Cell Proteomics. (2018) 17:270–89. 10.1074/mcp.RA117.00040010.1074/mcp.RA117.000400PMC579539129141914Modugno FD, Di Modugno F, Colosi C, Trono P, Antonacci G, Ruocco G, et al. . 3D models in the new era of immune oncology: focus on T cells, CAF and ECM. J Exp Clin Cancer Res. (2019) 38:117. 10.1186/s13046-019-1086-210.1186/s13046-019-1086-2PMC642976330898166Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. (2004) 5:101–13. 10.1038/nrg127210.1038/nrg127214735121Lesterhuis WJ, Bosco A, Millward MJ, Small M, Nowak AK, Lake RA. Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity. Nat Rev Drug Discov. (2017) 16:264–72. 10.1038/nrd.2016.23310.1038/nrd.2016.23328057932Letai A. Functional precision cancer medicine-moving beyond pure genomics. Nat Med. (2017) 23:1028–35. 10.1038/nm.438910.1038/nm.438928886003Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. . The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. (2012) 483:603–7. 10.1038/nature1100310.1038/nature11003PMC332002722460905Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. . A landscape of pharmacogenomic interactions in cancer. Cell. (2016) 166:740–54. 10.1016/j.cell.2016.06.01710.1016/j.cell.2016.06.017PMC496746927397505Bar-Ephraim YE, Kretzschmar K, Clevers H. Organoids in immunological research. Nat Rev Immunol. (2020) 20:279–93. 10.1038/s41577-019-0248-y10.1038/s41577-019-0248-y31853049Wong AHH, Li H, Jia Y, Mak PI, Martins RP, da S, Liu Y, et al. . Drug screening of cancer cell lines and human primary tumors using droplet microfluidics. Sci Rep. (2017) 7:9109. 10.1038/s41598-017-08831-z10.1038/s41598-017-08831-zPMC556731528831060Eduati F, Utharala R, Madhavan D, Neumann UP, Longerich T, Cramer T, et al. . A microfluidics platform for combinatorial drug screening on cancer biopsies. Nat Commun. (2018) 9:2434. 10.1038/s41467-018-04919-w10.1038/s41467-018-04919-wPMC601504529934552Montero J, Sarosiek KA, DeAngelo JD, Maertens O, Ryan J, Ercan D, et al. . Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy. Cell. (2015) 160:977–89. 10.1016/j.cell.2015.01.04210.1016/j.cell.2015.01.042PMC439119725723171Rohrs JA, Wang P, Finley SD. Understanding the dynamics of T-cell activation in health and disease through the lens of computational modeling. JCO Clin Cancer Inform. (2019) 3:1–8. 10.1200/CCI.18.0005710.1200/CCI.18.00057PMC659312530689404Tognetti M, Gabor A, Yang M, Cappelletti V, Windhager J, Charmpi K, et al. Deciphering the signaling network landscape of breast cancer improves drug sensitivity prediction. biorxiv. (2020). 10.1101/2020.01.21.90769110.1101/2020.01.21.90769133932331Eduati F, Doldàn-Martelli V, Klinger B, Cokelaer T, Sieber A, Kogera F, et al. . Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models. Cancer Res. (2017) 77:3364–75. 10.1158/0008-5472.CAN-17-007810.1158/0008-5472.CAN-17-0078PMC643328228381545Fey D, Halasz M, Dreidax D, Kennedy SP, Hastings JF, Rauch N, et al. . Signaling pathway models as biomarkers: patient-specific simulations of JNK activity predict the survival of neuroblastoma patients. Sci Signal. (2015) 8:ra130. 10.1126/scisignal.aab099010.1126/scisignal.aab099026696630Béal J, Montagud A, Traynard P, Barillot E, Calzone L. Personalization of logical models with multi-omics data allows clinical stratification of patients. Front Physiol. (2018) 9:1965. 10.3389/fphys.2018.0196510.3389/fphys.2018.01965PMC635384430733688Eduati F, Jaaks P, Wappler J, Cramer T, Merten CA, Garnett MJ, et al. . Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Mol Syst Biol. (2020) 16:e8664. 10.15252/msb.2018866410.15252/msb.20188664PMC702972432073727Arulraj T, Barik D. Mathematical modeling identifies Lck as a potential mediator for PD-1 induced inhibition of early TCR signaling. PLoS ONE. (2018) 13:e0206232. 10.1371/journal.pone.020623210.1371/journal.pone.0206232PMC620028030356330Bolouri H, Young M, Beilke J, Johnson R, Fox B, Huang L, et al. . Integrative network modeling reveals mechanisms underlying T cell exhaustion. Sci Rep. (2020) 10:1915. 10.1038/s41598-020-58600-810.1038/s41598-020-58600-8PMC700244532024856Norton K-A, Gong C, Jamalian S, Popel AS. Multiscale agent-based and hybrid modeling of the tumor immune microenvironment. Processes. (2019) 7:37. 10.3390/pr701003710.3390/pr7010037PMC634923930701168Kather JN, Poleszczuk J, Suarez-Carmona M, Krisam J, Charoentong P, Valous NA, et al. . Modeling of immunotherapy and stroma-targeting therapies in human colorectal cancer. Cancer Res. (2017) 77:6442–52. 10.1158/0008-5472.CAN-17-200610.1158/0008-5472.CAN-17-200628923860Kather JN, Charoentong P, Suarez-Carmona M, Herpel E, Klupp F, Ulrich A, et al. . High-throughput screening of combinatorial immunotherapies with patient-specific models of metastatic colorectal cancer. Cancer Res. (2018) 78:5155–63. 10.1158/0008-5472.CAN-18-112610.1158/0008-5472.CAN-18-112629967263Thurley K, Wu LF, Altschuler SJ. Modeling cell-to-cell communication networks using response-time distributions. Cell Syst. (2018) 6:355–67.e5. 10.1016/j.cels.2018.01.01610.1016/j.cels.2018.01.016PMC591375729525203Grandclaudon M, Perrot-Dockès M, Trichot C, Karpf L, Abouzid O, Chauvin C, et al. . A quantitative multivariate model of human dendritic cell-T helper cell communication. Cell. (2019) 179:432–47.e21. 10.1016/j.cell.2019.09.01210.1016/j.cell.2019.09.01231585082Wang H, Milberg O, Bartelink IH, Vicini P, Wang B, Narwal R, et al. . In silico simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model. R Soc Open Sci. (2019) 6:190366. 10.1098/rsos.19036610.1098/rsos.190366PMC654996231218069Sontag ED. A dynamic model of immune responses to antigen presentation predicts different regions of tumor or pathogen elimination. Cell Syst. (2017) 4:231–41.e11. 10.1016/j.cels.2016.12.00310.1016/j.cels.2016.12.003PMC532336528131824Sorribes IC, Basu A, Brady R, Enriquez-Navas PM, Feng X, Kather JN, et al. Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma – Learning to adapt. biorxiv. (2019). 10.1101/56313010.1101/563130Perlstein D, Shlagman O, Kogan Y, Halevi-Tobias K, Yakobson A, Lazarev I, et al. . Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug. PLoS ONE. (2019) 14:e0226869. 10.1371/journal.pone.022686910.1371/journal.pone.0226869PMC693280331877168Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, et al. . Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. (2017) 171:934–49.e16. 10.1016/j.cell.2017.09.02810.1016/j.cell.2017.09.028PMC568555029033130Chen PL, Roh W, Reuben A, Cooper ZA, Spencer CN, Prieto PA, 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. (2016) 6:827–37. 10.1158/2159-8290.CD-15-154510.1158/2159-8290.CD-15-1545PMC508298427301722Hwang S, Kwon AY, Jeong JY, Kim S, Kang H, Park J, et al. . Immune gene signatures for predicting durable clinical benefit of anti-PD-1 immunotherapy in patients with non-small cell lung cancer. Sci Rep. (2020) 10:643. 10.1038/s41598-019-57218-910.1038/s41598-019-57218-9PMC697130131959763Peskov K, Azarov I, Chu L, Voronova V, Kosinsky Y, Helmlinger G. Quantitative mechanistic modeling in support of pharmacological therapeutics development in immuno-oncology. Front Immunol. (2019) 10:924. 10.3389/fimmu.2019.0092410.3389/fimmu.2019.00924PMC652473131134058Eisenstein M. Making cancer immunotherapy a surer bet. Nature. (2017) 552:S72–S73. 10.1038/d41586-017-08704-510.1038/d41586-017-08704-532094674trying2...
trying...
trying2...
Toward Systems Biomarkers of Response to Immune Checkpoint Blockers. | LitMetric
Browse Categories

Toward Systems Biomarkers of Response to Immune Checkpoint Blockers.

Óscar Lapuente-Santana Federica Eduati

Front Oncol

Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.

Published: June 2020

Immunotherapy with checkpoint blockers (ICBs), aimed at unleashing the immune response toward tumor cells, has shown a great improvement in overall patient survival compared to standard therapy, but only in a subset of patients. While a number of recent studies have significantly improved our understanding of mechanisms playing an important role in the tumor microenvironment (TME), we still have an incomplete view of how the TME works as a whole. This hampers our ability to effectively predict the large heterogeneity of patients' response to ICBs. Systems approaches could overcome this limitation by adopting a holistic perspective to analyze the complexity of tumors. In this Mini Review, we focus on how an integrative view of the increasingly available multi-omics experimental data and computational approaches enables the definition of new systems-based predictive biomarkers. In particular, we will focus on three facets of the TME toward the definition of new systems biomarkers. First, we will review how different types of immune cells influence the efficacy of ICBs, not only in terms of their quantification, but also considering their localization and functional state. Second, we will focus on how different cells in the TME interact, analyzing how inter- and intra-cellular networks play an important role in shaping the immune response and are responsible for resistance to immunotherapy. Finally, we will describe the potential of looking at these networks as dynamic systems and how mathematical models can be used to study the rewiring of the complex interactions taking place in the TME.

Download full-text PDF

 Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326813PMC
http://dx.doi.org/10.3389/fonc.2020.01027DOI Listing

Publication Analysis

Top Keywords

systems biomarkers
8
checkpoint blockers
8
immune response
8
biomarkers will
8
will focus
8
tme
5
systems
4
response
4
biomarkers response
4
immune
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!

Privacy Policy Site Map

© LitMetric 2024. All rights reserved.