Literature DB >> 31855576

Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy.

Ryusuke Hatae1, Kenji Chamoto1, Young Hak Kim2, Kazuhiro Sonomura3,4, Kei Taneishi5,6, Shuji Kawaguchi3, Hironori Yoshida2, Hiroaki Ozasa2, Yuichi Sakamori2, Maryam Akrami1, Sidonia Fagarasan7, Izuru Masuda8, Yasushi Okuno5, Fumihiko Matsuda3, Toyohiro Hirai2, Tasuku Honjo1.   

Abstract

BACKGROUNDCurrent clinical biomarkers for the programmed cell death 1 (PD-1) blockade therapy are insufficient because they rely only on the tumor properties, such as programmed cell death ligand 1 expression frequency and tumor mutation burden. Identifying reliable, responsive biomarkers based on the host immunity is necessary to improve the predictive values.METHODSWe investigated levels of plasma metabolites and T cell properties, including energy metabolism markers, in the blood of patients with non-small cell lung cancer before and after treatment with nivolumab (n = 55). Predictive values of combination markers statistically selected were evaluated by cross-validation and linear discriminant analysis on discovery and validation cohorts, respectively. Correlation between plasma metabolites and T cell markers was investigated.RESULTSThe 4 metabolites derived from the microbiome (hippuric acid), fatty acid oxidation (butyrylcarnitine), and redox (cystine and glutathione disulfide) provided high response probability (AUC = 0.91). Similarly, a combination of 4 T cell markers, those related to mitochondrial activation (PPARγ coactivator 1 expression and ROS), and the frequencies of CD8+PD-1hi and CD4+ T cells demonstrated even higher prediction value (AUC = 0.96). Among the pool of selected markers, the 4 T cell markers were exclusively selected as the highest predictive combination, probably because of their linkage to the abovementioned metabolite markers. In a prospective validation set (n = 24), these 4 cellular markers showed a high accuracy rate for clinical responses of patients (AUC = 0.92).CONCLUSIONCombination of biomarkers reflecting host immune activity is quite valuable for responder prediction.FUNDINGAMED under grant numbers 18cm0106302h0003, 18gm0710012h0105, and 18lk1403006h0002; the Tang Prize Foundation; and JSPS KAKENHI grant numbers JP16H06149, 17K19593, and 19K17673.

Entities:  

Keywords:  Cancer immunotherapy; Immunology; Oncology; T cells

Mesh:

Substances:

Year:  2020        PMID: 31855576      PMCID: PMC7098729          DOI: 10.1172/jci.insight.133501

Source DB:  PubMed          Journal:  JCI Insight        ISSN: 2379-3708


  47 in total

1.  The inhibitory receptor PD-1 regulates IgA selection and bacterial composition in the gut.

Authors:  Shimpei Kawamoto; Thinh H Tran; Mikako Maruya; Keiichiro Suzuki; Yasuko Doi; Yumi Tsutsui; Lucia M Kato; Sidonia Fagarasan
Journal:  Science       Date:  2012-04-27       Impact factor: 47.728

2.  Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow.

Authors:  J A Kirwan; D I Broadhurst; R L Davidson; M R Viant
Journal:  Anal Bioanal Chem       Date:  2013-03-01       Impact factor: 4.142

3.  Mitochondrial activation chemicals synergize with surface receptor PD-1 blockade for T cell-dependent antitumor activity.

Authors:  Kenji Chamoto; Partha S Chowdhury; Alok Kumar; Kazuhiro Sonomura; Fumihiko Matsuda; Sidonia Fagarasan; Tasuku Honjo
Journal:  Proc Natl Acad Sci U S A       Date:  2017-01-17       Impact factor: 11.205

Review 4.  Immune checkpoint blockade: a common denominator approach to cancer therapy.

Authors:  Suzanne L Topalian; Charles G Drake; Drew M Pardoll
Journal:  Cancer Cell       Date:  2015-04-06       Impact factor: 31.743

5.  Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients.

Authors:  V Gopalakrishnan; C N Spencer; L Nezi; A Reuben; M C Andrews; T V Karpinets; P A Prieto; D Vicente; K Hoffman; S C Wei; A P Cogdill; L Zhao; C W Hudgens; D S Hutchinson; T Manzo; M Petaccia de Macedo; T Cotechini; T Kumar; W S Chen; S M Reddy; R Szczepaniak Sloane; J Galloway-Pena; H Jiang; P L Chen; E J Shpall; K Rezvani; A M Alousi; R F Chemaly; S Shelburne; L M Vence; P C Okhuysen; V B Jensen; A G Swennes; F McAllister; E Marcelo Riquelme Sanchez; Y Zhang; E Le Chatelier; L Zitvogel; N Pons; J L Austin-Breneman; L E Haydu; E M Burton; J M Gardner; E Sirmans; J Hu; A J Lazar; T Tsujikawa; A Diab; H Tawbi; I C Glitza; W J Hwu; S P Patel; S E Woodman; R N Amaria; M A Davies; J E Gershenwald; P Hwu; J E Lee; J Zhang; L M Coussens; Z A Cooper; P A Futreal; C R Daniel; N J Ajami; J F Petrosino; M T Tetzlaff; P Sharma; J P Allison; R R Jenq; J A Wargo
Journal:  Science       Date:  2017-11-02       Impact factor: 47.728

6.  Bioconductor: open software development for computational biology and bioinformatics.

Authors:  Robert C Gentleman; Vincent J Carey; Douglas M Bates; Ben Bolstad; Marcel Dettling; Sandrine Dudoit; Byron Ellis; Laurent Gautier; Yongchao Ge; Jeff Gentry; Kurt Hornik; Torsten Hothorn; Wolfgang Huber; Stefano Iacus; Rafael Irizarry; Friedrich Leisch; Cheng Li; Martin Maechler; Anthony J Rossini; Gunther Sawitzki; Colin Smith; Gordon Smyth; Luke Tierney; Jean Y H Yang; Jianhua Zhang
Journal:  Genome Biol       Date:  2004-09-15       Impact factor: 13.583

Review 7.  Survival of the fittest: Cancer challenges T cell metabolism.

Authors:  Davide G Franchina; Feng He; Dirk Brenner
Journal:  Cancer Lett       Date:  2017-10-24       Impact factor: 8.679

Review 8.  Cancer immunotherapies targeting the PD-1 signaling pathway.

Authors:  Yoshiko Iwai; Junzo Hamanishi; Kenji Chamoto; Tasuku Honjo
Journal:  J Biomed Sci       Date:  2017-04-04       Impact factor: 8.410

9.  A batch correction method for liquid chromatography-mass spectrometry data that does not depend on quality control samples.

Authors:  Martin Rusilowicz; Michael Dickinson; Adrian Charlton; Simon O'Keefe; Julie Wilson
Journal:  Metabolomics       Date:  2016-02-18       Impact factor: 4.290

10.  Hippurate as a metabolomic marker of gut microbiome diversity: Modulation by diet and relationship to metabolic syndrome.

Authors:  Tess Pallister; Matthew A Jackson; Tiphaine C Martin; Jonas Zierer; Amy Jennings; Robert P Mohney; Alexander MacGregor; Claire J Steves; Aedin Cassidy; Tim D Spector; Cristina Menni
Journal:  Sci Rep       Date:  2017-10-20       Impact factor: 4.379

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  22 in total

1.  Active learning effectively identifies a minimal set of maximally informative and asymptotically performant cytotoxic structure-activity patterns in NCI-60 cell lines.

Authors:  Takumi Nakano; Shunichi Takeda; J B Brown
Journal:  RSC Med Chem       Date:  2020-07-20

Review 2.  Tumor immune microenvironment and systemic response in breast cancer.

Authors:  Kosuke Kawaguchi; Yurina Maeshima; Masakazu Toi
Journal:  Med Oncol       Date:  2022-09-29       Impact factor: 3.738

3.  Inhibition of stearoyl-CoA desaturase 1 (SCD1) enhances the antitumor T cell response through regulating β-catenin signaling in cancer cells and ER stress in T cells and synergizes with anti-PD-1 antibody.

Authors:  Yuki Katoh; Tomonori Yaguchi; Akiko Kubo; Takashi Iwata; Kenji Morii; Daiki Kato; Shigeki Ohta; Ryosuke Satomi; Yasuhiro Yamamoto; Yoshitaka Oyamada; Kota Ouchi; Shin Takahashi; Chikashi Ishioka; Ryo Matoba; Makoto Suematsu; Yutaka Kawakami
Journal:  J Immunother Cancer       Date:  2022-07       Impact factor: 12.469

4.  Immuno-reactive cancer organoid model to assess effects of the microbiome on cancer immunotherapy.

Authors:  Ethan Shelkey; David Oommen; Elizabeth R Stirling; David R Soto-Pantoja; Katherine L Cook; Yong Lu; Konstantinos I Votanopoulos; Shay Soker
Journal:  Sci Rep       Date:  2022-06-15       Impact factor: 4.996

5.  Serum Metabolite Biomarkers Predictive of Response to PD-1 Blockade Therapy in Non-Small Cell Lung Cancer.

Authors:  Xiaoqun Nie; Liliang Xia; Fang Gao; Lixia Liu; Yi Yang; Yingying Chen; Huangqi Duan; Yaxian Yao; Zhiwei Chen; Shun Lu; Ying Wang; Chen Yang
Journal:  Front Mol Biosci       Date:  2021-05-21

6.  Can the assessment of lymphocyte exhaustion serve as a prognostic predictor after lung cancer surgery?

Authors:  Kenji Morimoto; Yoshie Morimoto; Junji Uchino
Journal:  Transl Lung Cancer Res       Date:  2020-04

Review 7.  Metabolic programming of tumor associated macrophages in the context of cancer treatment.

Authors:  Thomas Crezee; Katrin Rabold; Lisanne de Jong; Martin Jaeger; Romana T Netea-Maier
Journal:  Ann Transl Med       Date:  2020-08

Review 8.  Exosomal PD-L1: New Insights Into Tumor Immune Escape Mechanisms and Therapeutic Strategies.

Authors:  Kaijian Zhou; Shu Guo; Fei Li; Qiang Sun; Guoxin Liang
Journal:  Front Cell Dev Biol       Date:  2020-10-15

Review 9.  Cysteine metabolic circuitries: druggable targets in cancer.

Authors:  Vasco D B Bonifácio; Sofia A Pereira; Jacinta Serpa; João B Vicente
Journal:  Br J Cancer       Date:  2020-11-23       Impact factor: 7.640

Review 10.  Immune metabolism in PD-1 blockade-based cancer immunotherapy.

Authors:  Alok Kumar; Kenji Chamoto
Journal:  Int Immunol       Date:  2021-01-01       Impact factor: 4.823

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