Improved prediction of immune checkpoint blockade efficacy across multiple cancer types.

Biomarkers, Tumor / genetics Humans Immune Checkpoint Inhibitors / therapeutic use Immunotherapy / methods Mutation Neoplasms / drug therapy Retrospective Studies

Journal

Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648

Informations de publication

Date de publication:
04 2022
Historique:
received: 03 12 2020
accepted: 23 08 2021
pubmed: 3 11 2021
medline: 15 4 2022
entrez: 2 11 2021
Statut: ppublish

Résumé

Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose

Identifiants

DOI: 10.1038/s41587-021-01070-8 PMID: 34725502 PMC: PMC9363980
pubmed: 34725502
doi: 10.1038/s41587-021-01070-8
pii: 10.1038/s41587-021-01070-8
pmc: PMC9363980
mid: NIHMS1819096
doi:

Substances chimiques

Biomarkers, Tumor 0
Immune Checkpoint Inhibitors 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

499-506

Subventions

Organisme : NCI NIH HHS
ID : R01 CA205426
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NCI NIH HHS
ID : R35 CA232097
Pays : United States
Organisme : NIH HHS
ID : DP5 OD028171
Pays : United States
Organisme : NIDCR NIH HHS
ID : R01 DE027738
Pays : United States
Organisme : NIDCR NIH HHS
ID : K08 DE024774
Pays : United States

Informations de copyright

© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.

Références

Subbiah, V., Solit, D. B., Chan, T. A. & Kurzrock, R. The FDA approval of pembrolizumab for adult and pediatric patients with tumor mutational burden (TMB) ≥10: a decision centered on empowering patients and their physicians. Ann. Oncol. 31, 1115–1118 (2020).
doi: 10.1016/j.annonc.2020.07.002 pubmed: 32771306
Topalian, S. L., Taube, J. M., Anders, R. A. & Pardoll, D. M. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat. Rev. Cancer 16, 275–287 (2016).
doi: 10.1038/nrc.2016.36 pubmed: 27079802 pmcid: 5381938
Bendell, J. et al. Efficacy and safety results from IMblaze370, a randomised phase III study comparing atezolizumab plus cobimetinib and atezolizumab monotherapy vs regorafenib in chemotherapy-refractory metastatic colorectal cancer. Ann. Oncol. 29, 123–123 (2018).
doi: 10.1093/annonc/mdy208.003
Carbone, D. P. et al. First-line nivolumab in stage IV or recurrent non-small-cell lung cancer. N. Engl. J. Med. 376, 2415–2426 (2017).
doi: 10.1056/NEJMoa1613493 pubmed: 28636851 pmcid: 6487310
Cohen, E. E. et al. Pembrolizumab (pembro) vs standard of care (SOC) for recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC): phase 3 KEYNOTE-040 trial. Ann. Oncol. 28 https://oncologypro.esmo.org/meeting-resources/esmo-2017-congress/Pembrolizumab-pembro-vs-standard-of-care-SOC-for-recurrent-or-metastatic-head-and-neck-squamous-cell-carcinoma-R-M-HNSCC-Phase-3-KEYNOTE-040-trial (2017).
Powles, T. et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet 391, 748–757 (2018).
doi: 10.1016/S0140-6736(17)33297-X pubmed: 29268948
Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).
doi: 10.1038/s41568-019-0116-x pubmed: 30755690 pmcid: 6705396
Keenan, T. E., Burke, K. P. & Van Allen, E. M. Genomic correlates of response to immune checkpoint blockade. Nat. Med. 25, 389–402 (2019).
doi: 10.1038/s41591-019-0382-x pubmed: 30842677 pmcid: 6599710
Anagnostou, V. et al. Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer. Nat. Cancer 1, 99–111 (2020).
doi: 10.1038/s43018-019-0008-8 pubmed: 32984843 pmcid: 7514475
Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
doi: 10.1038/s41591-018-0300-7 pubmed: 30617339
Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).
doi: 10.1056/NEJMra1814259 pubmed: 30943338
Eisenhauer, E. A. et al. New response evaluation criteria in solid tumors: RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).
doi: 10.1016/j.ejca.2008.10.026 pubmed: 19097774
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
doi: 10.1038/nm.4333 pubmed: 28481359 pmcid: 5461196
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
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).
doi: 10.1126/science.aaa1348 pubmed: 25765070 pmcid: 4993154
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
doi: 10.1056/NEJMoa1406498 pubmed: 25409260 pmcid: 4315319
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
doi: 10.1126/science.aad0095 pubmed: 26359337 pmcid: 5054517
Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949 (2017).
Goodman, A. M. et al. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol. Cancer Ther. 16, 2598–2608 (2017).
doi: 10.1158/1535-7163.MCT-17-0386 pubmed: 28835386 pmcid: 5670009
Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–20 (2019).
doi: 10.1038/s41588-018-0312-8 pubmed: 30643254 pmcid: 6365097
Luksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
doi: 10.1038/nature24473 pubmed: 29132144 pmcid: 6137806
Valero, C. et al. The association between tumor mutational burden and prognosis is dependent on treatment context. Nat. Genet. 53, 11–15 (2021).
doi: 10.1038/s41588-020-00752-4 pubmed: 33398197 pmcid: 7796993
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).
Chowell, D. et al. Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat. Med. 25, 1715–1720 (2019).
doi: 10.1038/s41591-019-0639-4 pubmed: 31700181 pmcid: 7938381
Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).
doi: 10.1126/science.aao4572 pubmed: 29217585
Mandal, R. et al. Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response. Science 364, 485–491 (2019).
doi: 10.1126/science.aau0447 pubmed: 31048490 pmcid: 6685207
Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).
Wang, Z. et al. Paradoxical effects of obesity on T cell function during tumor progression and PD-1 checkpoint blockade. Nat. Med. 25, 141–151 (2019).
doi: 10.1038/s41591-018-0221-5 pubmed: 30420753
Sanchez, A. et al. Transcriptomic signatures related to the obesity paradox in patients with clear cell renal cell carcinoma: a cohort study. Lancet Oncol. 21, 283–293 (2020).
doi: 10.1016/S1470-2045(19)30797-1 pubmed: 31870811
Conforti, F. et al. Cancer immunotherapy efficacy and patients’ sex: a systematic review and meta-analysis. Lancet Oncol. 19, 737–746 (2018).
doi: 10.1016/S1470-2045(18)30261-4 pubmed: 29778737
Jaillon, S. et al. Neutrophil diversity and plasticity in tumour progression and therapy. Nat. Rev. Cancer 20, 485–503 (2020).
doi: 10.1038/s41568-020-0281-y pubmed: 32694624
Li, M. J. et al. Change in neutrophil to lymphocyte ratio during immunotherapy treatment is a non-linear predictor of patient outcomes in advanced cancers. J. Cancer Res. Clin. 145, 2541–2546 (2019).
doi: 10.1007/s00432-019-02982-4
Valero, C. et al. Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors. Nat. Commun. 12, 729 (2021).
doi: 10.1038/s41467-021-20935-9 pubmed: 33526794 pmcid: 7851155
Kuai, J., Yang, F., Li, G. J., Fang, X. J. & Gao, B. Q. In vitro-activated tumor-specific T lymphocytes prolong the survival of patients with advanced gastric cancer: a retrospective cohort study. Onco Targets Ther. 9, 3763–3770 (2016).
pubmed: 27382313 pmcid: 4922768
Ikeguchi, A., Machiorlatti, M. & Vesely, S. K. Disparity in outcomes of melanoma adjuvant immunotherapy by demographic profile. Melanoma Manag 7, MMT43 (2020).
doi: 10.2217/mmt-2020-0002 pubmed: 32821375 pmcid: 7426750
Jurasz, P., Alonso-Escolano, D. & Radomski, M. W. Platelet–cancer interactions: mechanisms and pharmacology of tumour cell-induced platelet aggregation. Br. J. Pharmacol. 143, 819–826 (2004).
doi: 10.1038/sj.bjp.0706013 pubmed: 15492016 pmcid: 1575943
Gupta, D. & Lis, C. G. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr. J. 9, 69 (2010).
Caro, J. J., Salas, M., Ward, A. & Goss, G. Anemia as an independent prognostic factor for survival in patients with cancer—a systematic, quantitative review. Cancer 91, 2214–2221 (2001).
doi: 10.1002/1097-0142(20010615)91:12<2214::AID-CNCR1251>3.0.CO;2-P pubmed: 11413508
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
doi: 10.1016/j.cell.2011.02.013 pubmed: 21376230
Peng, D. et al. Prognostic significance of HALP (hemoglobin, albumin, lymphocyte and platelet) in patients with bladder cancer after radical cystectomy. Sci Rep. 8, 794 (2018).
doi: 10.1038/s41598-018-19146-y pubmed: 29335609 pmcid: 5768698
Bindea, G., Mlecnik, B., Fridman, W. H., Pages, F. & Galon, J. Natural immunity to cancer in humans. Curr. Opin. Immunol. 22, 215–222 (2010).
doi: 10.1016/j.coi.2010.02.006 pubmed: 20207124
Harrell, F. E. Jr., Lee, K. L. & Mark, D. B. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15, 361–387 (1996).
doi: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 pubmed: 8668867
Steyerberg, E. W. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128–138 (2010).
doi: 10.1097/EDE.0b013e3181c30fb2 pubmed: 20010215 pmcid: 3575184
Gurjao, C., Tsukrov, D., Imakaev, M., Luquette, L. J. & Mirny, L. A. Limited evidence of tumour mutational burden as a biomarker of response to immunotherapy. Preprint at https://www.biorxiv.org/content/10.1101/2020.09.03.260265v2 (2020).
Binnewies, M. et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat. Med. 24, 541–550 (2018).
doi: 10.1038/s41591-018-0014-x pubmed: 29686425 pmcid: 5998822
Krishna, C. et al. Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy. Cancer Cell 39, 662–677 (2021).
doi: 10.1016/j.ccell.2021.03.007 pubmed: 33861994 pmcid: 8268947
Krishna, S. et al. Stem-like CD8 T cells mediate response of adoptive cell immunotherapy against human cancer. Science 370, 1328–1334 (2020).
doi: 10.1126/science.abb9847 pubmed: 33303615 pmcid: 8883579
Maier, B. et al. A conserved dendritic-cell regulatory program limits antitumour immunity. Nature 580, 257–262 (2020).
doi: 10.1038/s41586-020-2134-y pubmed: 32269339 pmcid: 7787191
Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).
doi: 10.1016/j.cell.2018.10.038 pubmed: 30388456 pmcid: 6641984
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).
doi: 10.1126/science.aan4236 pubmed: 29097493
Krishna, C., Chowell, D., Gonen, M., Elhanati, Y. & Chan, T. A. Genetic and environmental determinants of human TCR repertoire diversity. Immun. Ageing 17, 26 (2020).
doi: 10.1186/s12979-020-00195-9 pubmed: 32944053 pmcid: 7487954
Braun, D. A. et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat. Med. 26, 909–918 (2020).
doi: 10.1038/s41591-020-0839-y pubmed: 32472114 pmcid: 7499153
Miao, D. et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat. Genet. 50, 1271–1281 (2018).
doi: 10.1038/s41588-018-0200-2 pubmed: 30150660 pmcid: 6119118
Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).
doi: 10.1038/nature23477 pubmed: 28783722 pmcid: 5870757
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
doi: 10.1056/NEJMoa1604958 pubmed: 27433843 pmcid: 5007206
Skoulidis, F. et al. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discov. 8, 822–835 (2018).
doi: 10.1158/2159-8290.CD-18-0099 pubmed: 29773717 pmcid: 6030433
Samstein, R. M. et al. Mutations in BRCA1 and BRCA2 differentially affect the tumor microenvironment and response to checkpoint blockade immunotherapy. Nat. Cancer 1, 1188–1203 (2020).
doi: 10.1038/s43018-020-00139-8 pubmed: 33834176 pmcid: 8023400
Wang, F. et al. Evaluation of POLE and POLD1 mutations as biomarkers for immunotherapy outcomes across multiple cancer types. JAMA Oncol. 5, 1504–1506 (2019).
doi: 10.1001/jamaoncol.2019.2963 pubmed: 31415061 pmcid: 6696731
Amin, M. B. et al. The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more ‘personalized’ approach to cancer staging. CA Cancer J. Clin. 67, 93–99 (2017).
doi: 10.3322/caac.21388 pubmed: 28094848
Zhou, J. et al. Analysis of tumor genomic pathway alterations using broad-panel next-generation sequencing in surgically resected lung adenocarcinoma. Clin. Cancer Res. 25, 7475–7484 (2019).
doi: 10.1158/1078-0432.CCR-19-1651 pubmed: 31455678 pmcid: 6911636
Shen, R. L. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131 (2016).
Niu, B. F. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).
doi: 10.1093/bioinformatics/btt755 pubmed: 24371154
Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).
doi: 10.1038/nbt.3344 pubmed: 26372948 pmcid: 4747795
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
doi: 10.1093/bioinformatics/btq033 pubmed: 20110278 pmcid: 2832824
Pierini, F. & Lenz, T. L. Divergent allele advantage at human MHC genes: signatures of past and ongoing selection. Mol. Biol. Evol. 35, 2145–2158 (2018).
Robinson, J. et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 43, D423–D431 (2015).
doi: 10.1093/nar/gku1161 pubmed: 25414341
Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).
doi: 10.1093/nar/gkx1098 pubmed: 29155950
Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185, 862–864 (1974).
doi: 10.1126/science.185.4154.862 pubmed: 4843792
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
doi: 10.1093/nar/gkh340 pubmed: 15034147 pmcid: 390337
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Saito, T. & Rehmsmeier, M. Precrec: fast and accurate precision-recall and ROC curve calculations in R. Bioinformatics 33, 145–147 (2017).
doi: 10.1093/bioinformatics/btw570 pubmed: 27591081
Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics.12, 77 (2011).
doi: 10.1186/1471-2105-12-77 pubmed: 21414208 pmcid: 3068975
Schroder, M. S., Culhane, A. C., Quackenbush, J. & Haibe-Kains, B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics 27, 3206–3208 (2011).
doi: 10.1093/bioinformatics/btr511 pubmed: 21903630 pmcid: 3208391
Mogensen, U. B., Ishwaran, H. & Gerds, T. A. Evaluating random forests for survival analysis using prediction error curves. J. Stat. Softw. 50, 1–23 (2012).
doi: 10.18637/jss.v050.i11 pubmed: 25317082 pmcid: 4194196

Auteurs

Diego Chowell (D)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.
Department of Oncological Sciences, The Precision Immunology Institute, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Seong-Keun Yoo (SK)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.
ORCID: 0000-0002-8749-7050

Cristina Valero (C)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0003-3397-2888

Alessandro Pastore (A)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0002-3621-4807

Chirag Krishna (C)

Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Mark Lee (M)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Douglas Hoen (D)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.

Hongyu Shi (H)

Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Daniel W Kelly (DW)

Information Systems, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Neal Patel (N)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0002-4352-4723

Vladimir Makarov (V)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.

Xiaoxiao Ma (X)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.

Lynda Vuong (L)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Erich Y Sabio (EY)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Kate Weiss (K)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Fengshen Kuo (F)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0003-1797-2896

Tobias L Lenz (TL)

Research Unit for Evolutionary Immunogenomics, Department of Biology, Universität Hamburg, Hamburg, Germany.
ORCID: 0000-0002-7203-0044

Robert M Samstein (RM)

Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Nadeem Riaz (N)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0001-9873-5862

Prasad S Adusumilli (PS)

Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0002-1699-2046

Vinod P Balachandran (VP)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0002-2956-223X

George Plitas (G)

Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

A Ari Hakimi (A)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Omar Abdel-Wahab (O)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0002-3907-6171

Alexander N Shoushtari (AN)

Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Michael A Postow (MA)

Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Robert J Motzer (RJ)

Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Marc Ladanyi (M)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Ahmet Zehir (A)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0001-5406-4104

Michael F Berger (MF)

Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
ORCID: 0000-0003-3882-5000

Mithat Gönen (M)

Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Luc G T Morris (LGT)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. morrisl@mskcc.org.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA. morrisl@mskcc.org.
Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. morrisl@mskcc.org.
ORCID: 0000-0002-4417-2280

Nils Weinhold (N)

Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA. weinholn@mskcc.org.
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. weinholn@mskcc.org.
ORCID: 0000-0003-3290-6192

Timothy A Chan (TA)

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. chant2@ccf.org.
Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA. chant2@ccf.org.
Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA. chant2@ccf.org.
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. chant2@ccf.org.
Weill Cornell School of Medicine, New York, NY, USA. chant2@ccf.org.
ORCID: 0000-0002-9265-0283

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

Recherchemedicale.com › Facteurs biologiques › Marqueurs biologiques › Marqueurs biologiques tumoraux › Improved prediction of immune checkpoint...
Recherchemedicale.com › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Improved prediction of immune checkpoint...
Recherchemedicale.com › Actions chimiques et utilisations › Actions pharmacologiques › Utilisations thérapeutiques › Antinéoplasiques › Antinéoplasiques immunologiques › Inhibiteurs de points de contrôle immunitaires › Improved prediction of immune checkpoint...
Recherchemedicale.com › Thérapeutique › Biothérapie › Immunothérapie › Improved prediction of immune checkpoint...
Recherchemedicale.com › Phénomènes génétiques › Variation génétique › Mutation › Improved prediction of immune checkpoint...
Recherchemedicale.com › Tumeurs › Improved prediction of immune checkpoint...
Recherchemedicale.com › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études rétrospectives › Improved prediction of immune checkpoint...