Literature DB >> 31315901

Biomarkers for Predicting Response to Immunotherapy with Immune Checkpoint Inhibitors in Cancer Patients.

Michael J Duffy1,2, John Crown3.   

Abstract

BACKGROUND: Immunotherapy, especially the use of immune checkpoint inhibitors, has revolutionized the management of several different cancer types in recent years. However, for most types of cancer, only a minority of patients experience a durable response. Furthermore, administration of immunotherapy can result in serious adverse reactions. Thus, for the most efficient and effective use of immunotherapy, accurate predictive biomarkers that have undergone analytical and clinical validation are necessary. CONTENT: Among the most widely investigated predictive biomarkers for immunotherapy are programmed death-ligand 1 (PD-L1), microsatellite instability/defective mismatch repair (MSI/dMMR), and tumor mutational burden (TMB). MSI/dMMR is approved for clinical use irrespective of the tumor type, whereas PD-L1 is approved only for use in certain cancer types (e.g., for predicting response to first-line pembrolizumab monotherapy in non-small cell lung cancer). Although not yet approved for clinical use, TMB has been shown to predict response to several different forms of immunotherapy and across multiple cancer types. Less widely investigated predictive biomarkers for immunotherapy include tumor-infiltrating CD8+ lymphocytes and specific gene signatures. Despite being widely investigated, assays for MSI/dMMR, PD-L1, and TMB lack standardization and are still evolving. An urgent focus of future research should be the optimization and standardization of method for determining these biomarkers.
SUMMARY: Biomarkers for predicting response to immunotherapy are paving the way for personalized treatment for patients with diverse cancer types. However, standardization of the available biomarker assays is an urgent requirement.
© 2019 American Association for Clinical Chemistry.

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Year:  2019        PMID: 31315901     DOI: 10.1373/clinchem.2019.303644

Source DB:  PubMed          Journal:  Clin Chem        ISSN: 0009-9147            Impact factor:   8.327


  65 in total

Review 1.  Investigational Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Event Prediction and Diagnosis.

Authors:  Mitchell S von Itzstein; Shaheen Khan; David E Gerber
Journal:  Clin Chem       Date:  2020-06-01       Impact factor: 8.327

2.  Tumor mutation score is more powerful than tumor mutation burden in predicting response to immunotherapy in non-small cell lung cancer.

Authors:  Yuan Li; Zuhua Chen; Weiping Tao; Nan Sun; Jie He
Journal:  Cancer Immunol Immunother       Date:  2021-02-03       Impact factor: 6.968

Review 3.  Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?

Authors:  Geoffrey Alan Watson; Jeffrey Doi; Aaron Richard Hansen; Anna Spreafico
Journal:  Br J Clin Pharmacol       Date:  2020-06-13       Impact factor: 4.335

4.  Association of MUC19 Mutation With Clinical Benefits of Anti-PD-1 Inhibitors in Non-small Cell Lung Cancer.

Authors:  Li Zhou; Litang Huang; Qiuli Xu; Yanling Lv; Zimu Wang; Ping Zhan; Hedong Han; Yang Shao; Dang Lin; Tangfeng Lv; Yong Song
Journal:  Front Oncol       Date:  2021-03-22       Impact factor: 6.244

Review 5.  Germline Variants That Affect Tumor Progression.

Authors:  Ajay Chatrath; Aakrosh Ratan; Anindya Dutta
Journal:  Trends Genet       Date:  2020-11-14       Impact factor: 11.639

6.  A hypoxia risk signature for the tumor immune microenvironment evaluation and prognosis prediction in acute myeloid leukemia.

Authors:  Feng Jiang; Yan Mao; Binbin Lu; Guoping Zhou; Jimei Wang
Journal:  Sci Rep       Date:  2021-07-19       Impact factor: 4.379

7.  An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients.

Authors:  Shanshan Tang; Yiyi Zhuge
Journal:  Biomed Eng Online       Date:  2021-06-30       Impact factor: 2.819

8.  Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer.

Authors:  Bin Yang; Li Zhou; Jing Zhong; Tangfeng Lv; Ang Li; Lu Ma; Jian Zhong; Saisai Yin; Litang Huang; Changsheng Zhou; Xinyu Li; Ying Qian Ge; Xinwei Tao; Longjiang Zhang; Yong Son; Guangming Lu
Journal:  Respir Res       Date:  2021-06-28

Review 9.  FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients.

Authors:  Ye Wang; Zhuang Tong; Wenhua Zhang; Weizhen Zhang; Anton Buzdin; Xiaofeng Mu; Qing Yan; Xiaowen Zhao; Hui-Hua Chang; Mark Duhon; Xin Zhou; Gexin Zhao; Hong Chen; Xinmin Li
Journal:  Front Oncol       Date:  2021-06-07       Impact factor: 6.244

10.  [Expression of RASGRP2 in Lung Adenocarcinoma and Its Effect 
on Immune Microenvironment].

Authors:  Shikang Zhao; Xin Jin; Song Xu
Journal:  Zhongguo Fei Ai Za Zhi       Date:  2021-06-20
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