Literature DB >> 29155348

Progress and challenges of predictive biomarkers of anti PD-1/PD-L1 immunotherapy: A systematic review.

Feifei Teng1, Xiangjiao Meng1, Li Kong2, Jinming Yu3.   

Abstract

Despite the marked success of applications of PD-1/PD-L1 checkpoint blockades in clinical, the efficacy and responsiveness of these agents varies greatly among different tumor types and across individual patients. Therefore, establishment of predictive biomarkers for checkpoint blockades is of the most importance to maximize the therapeutic benefits. In this review, we discuss the current progress and challenges of developing predictive biomarkers of immunotherapy responsiveness, aiming to provide some directions for future studies. PD-L1 expression is a logical biomarker for the prediction of response to anti-PD-(L)1 immunotherapies. However, the predictive values of PD-L1 expressions for immunotherapy are currently debating and challenging. Multiplex detecting methods and combined biomarkers may provide new strategies. For example, tumor mutation and neoantigens burden, some oncogene mutations, like EGFR, ALK, KRAS and STK11. In addition, with development of new probes and tracers, immuno-PET provide a new, non-invasive and quantitative strategy to monitor treatment response. As current evidence of those potential predictors, a consensus and standardization is needed to establish to widely applied in future studies. Multiplex detecting methods and combined biomarkers may provide new strategies.
Copyright © 2017. Published by Elsevier B.V.

Entities:  

Keywords:  Biomarker; Immune-checkpoint; Immunotherapy; Predictive

Mesh:

Substances:

Year:  2017        PMID: 29155348     DOI: 10.1016/j.canlet.2017.11.014

Source DB:  PubMed          Journal:  Cancer Lett        ISSN: 0304-3835            Impact factor:   8.679


  61 in total

Review 1.  Tumor mutational burden assessment as a predictive biomarker for immunotherapy in lung cancer patients: getting ready for prime-time or not?

Authors:  Simon Heeke; Paul Hofman
Journal:  Transl Lung Cancer Res       Date:  2018-12

Review 2.  The value of 18F-FDG PET/CT for predicting or monitoring immunotherapy response in patients with metastatic melanoma: a systematic review and meta-analysis.

Authors:  Narjess Ayati; Ramin Sadeghi; Zahra Kiamanesh; Sze Ting Lee; S Rasoul Zakavi; Andrew M Scott
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-07-29       Impact factor: 9.236

Review 3.  Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence.

Authors:  Samuel J Klempner; David Fabrizio; Shalmali Bane; Marcia Reinhart; Tim Peoples; Siraj M Ali; Ethan S Sokol; Garrett Frampton; Alexa B Schrock; Rachel Anhorn; Prasanth Reddy
Journal:  Oncologist       Date:  2019-10-02

4.  Expression of STING and PD-L1 in colorectal cancer and their correlation with clinical prognosis.

Authors:  Genshen Zhong; Chengcheng Peng; Yanan Chen; Jingya Li; Ru Yang; Minna Wu; Ping Lu
Journal:  Int J Clin Exp Pathol       Date:  2018-03-01

5.  Immune checkpoint inhibition for pediatric patients with recurrent/refractory CNS tumors: a single institution experience.

Authors:  Susan Chi; Kee Kiat Yeo; Chantel Cacciotti; Jungwhan Choi; Sanda Alexandrescu; Mary Ann Zimmerman; Tabitha M Cooney; Christine Chordas; Jessica Clymer
Journal:  J Neurooncol       Date:  2020-07-05       Impact factor: 4.130

6.  89Zr-Labeled Anti-PD-L1 Antibody Fragment for Evaluating In Vivo PD-L1 Levels in Melanoma Mouse Model.

Authors:  Caleb Bridgwater; Anne Geller; Xiaoling Hu; Joe A Burlison; Huang-Ge Zhang; Jun Yan; Haixun Guo
Journal:  Cancer Biother Radiopharm       Date:  2020-04-21       Impact factor: 3.099

Review 7.  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 8.  Imaging-based Biomarkers for Predicting and Evaluating Cancer Immunotherapy Response.

Authors:  Minghao Wu; Yanyan Zhang; Yuwei Zhang; Ying Liu; Mingjie Wu; Zhaoxiang Ye
Journal:  Radiol Imaging Cancer       Date:  2019-11-29

9.  Analyzing the characteristics of immune cell infiltration in lung adenocarcinoma via bioinformatics to predict the effect of immunotherapy.

Authors:  Yi Liao; Dingxiu He; Fuqiang Wen
Journal:  Immunogenetics       Date:  2021-07-24       Impact factor: 2.846

10.  Down-regulation of UL16-binding protein 3 mediated by interferon-gamma impairs immune killing in nasopharyngeal carcinoma.

Authors:  Lingling Guo; Yu Chen; Jing Wang; Chuanben Chen
Journal:  Am J Transl Res       Date:  2020-10-15       Impact factor: 4.060

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