Literature DB >> 30279188

Predictive Biomarkers for Checkpoint Immunotherapy: Current Status and Challenges for Clinical Application.

Nancy Tray1, Jeffrey S Weber1, Sylvia Adams2.   

Abstract

Immune-checkpoint blockade (ICB), in particular PD-1 inhibition, has rapidly changed the treatment landscape and altered therapeutic paradigms across many tumor types, with unprecedented rates of durable clinical responses in a number of cancers. Despite this success, only a subset of patients responds to ICB and, as a result, predictive biomarkers would be useful to guide the selection of patients for these therapies. This article highlights currently used biomarkers, as well as several promising novel candidates, and also discusses the challenges involved in establishing their analytic validity and clinical utility. Progress is being evaluated in melanoma and non-small cell lung cancer, for which PD-1 ± CTLA-4 inhibitors have become standard therapy, to other malignancies for which PD-L1 inhibitors remain investigational. Although single biomarkers have substantial limitations, a combination of biomarkers that reflect the interaction of host and tumor will likely be needed to provide a reproducible surrogate for the benefit of checkpoint modulation. Cancer Immunol Res; 6(10); 1122-8. ©2018 AACR. ©2018 American Association for Cancer Research.

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Year:  2018        PMID: 30279188     DOI: 10.1158/2326-6066.CIR-18-0214

Source DB:  PubMed          Journal:  Cancer Immunol Res        ISSN: 2326-6066            Impact factor:   11.151


  29 in total

Review 1.  Circulating biomarkers predictive of tumor response to cancer immunotherapy.

Authors:  Ernest Y Lee; Rajan P Kulkarni
Journal:  Expert Rev Mol Diagn       Date:  2019-09-10       Impact factor: 5.225

Review 2.  Nanomedicine and Onco-Immunotherapy: From the Bench to Bedside to Biomarkers.

Authors:  Vanessa Acebes-Fernández; Alicia Landeria-Viñuela; Pablo Juanes-Velasco; Angela-Patricia Hernández; Andrea Otazo-Perez; Raúl Manzano-Román; Rafael Gongora; Manuel Fuentes
Journal:  Nanomaterials (Basel)       Date:  2020-06-29       Impact factor: 5.076

3.  ALDH2 polymorphism rs671 is a predictor of PD-1/PD-L1 inhibitor efficacy against thoracic malignancies.

Authors:  Akiko Matsumoto; Chiho Nakashima; Shinya Kimura; Eizaburo Sueoka; Naoko Aragane
Journal:  BMC Cancer       Date:  2021-05-22       Impact factor: 4.430

4.  Mass Spectrometry Imaging Reveals Neutrophil Defensins as Additional Biomarkers for Anti-PD-(L)1 Immunotherapy Response in NSCLC Patients.

Authors:  Eline Berghmans; Julie Jacobs; Christophe Deben; Christophe Hermans; Glenn Broeckx; Evelien Smits; Evelyne Maes; Jo Raskin; Patrick Pauwels; Geert Baggerman
Journal:  Cancers (Basel)       Date:  2020-04-02       Impact factor: 6.639

5.  Establishment of a novel gene panel as a biomarker of immune checkpoint inhibitor response.

Authors:  Yi-Ru Pan; Chiao-En Wu; Yu-Chao Wang; Yi-Chen Yeh; Meng-Lun Lu; Yi-Ping Hung; Yee Chao; Da-Wei Yeh; Chien-Hsing Lin; Jason Chia-Hsun Hsieh; Ming-Huang Chen; Chun-Nan Yeh
Journal:  Clin Transl Immunology       Date:  2020-06-30

6.  Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution.

Authors:  Evan M Fernandez; Kenneth Eng; Shaham Beg; Himisha Beltran; Bishoy M Faltas; Juan Miguel Mosquera; David M Nanus; David J Pisapia; Rema A Rao; Brian D Robinson; Mark A Rubin; Olivier Elemento; Andrea Sboner; Manish A Shah; Wei Song
Journal:  JCO Precis Oncol       Date:  2019-07-31

7.  Comprehensive analysis of blood-based biomarkers for predicting immunotherapy benefits in patients with advanced non-small cell lung cancer.

Authors:  Cheol-Kyu Park; Hyung-Joo Oh; Min-Seok Kim; Bo-Gun Koh; Hyun-Ju Cho; Young-Chul Kim; Hyung-Jeong Yang; Ji-Young Lee; Sung-Min Chun; In-Jae Oh
Journal:  Transl Lung Cancer Res       Date:  2021-05

8.  Early memory differentiation and cell death resistance in T cells predicts melanoma response to sequential anti-CTLA4 and anti-PD1 immunotherapy.

Authors:  Isaure Vanmeerbeek; Daniel M Borras; Jenny Sprooten; Oliver Bechter; Sabine Tejpar; Abhishek D Garg
Journal:  Genes Immun       Date:  2021-06-02       Impact factor: 2.676

9.  Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

Authors:  Paul Johannet; Nicolas Coudray; Douglas M Donnelly; George Jour; Irineu Illa-Bochaca; Yuhe Xia; Douglas B Johnson; Lee Wheless; James R Patrinely; Sofia Nomikou; David L Rimm; Anna C Pavlick; Jeffrey S Weber; Judy Zhong; Aristotelis Tsirigos; Iman Osman
Journal:  Clin Cancer Res       Date:  2020-11-18       Impact factor: 13.801

Review 10.  Surface-Enhanced Raman Spectroscopy for Cancer Immunotherapy Applications: Opportunities, Challenges, and Current Progress in Nanomaterial Strategies.

Authors:  Shuvashis Dey; Matt Trau; Kevin M Koo
Journal:  Nanomaterials (Basel)       Date:  2020-06-11       Impact factor: 5.076

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