Literature DB >> 33315149

A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC.

Camillo Porta1,2,3, Romano Danesi4, Marzia Del Re5, Federico Cucchiara5, Eleonora Rofi5, Lorenzo Fontanelli5, Iacopo Petrini6, Nicole Gri1, Giulia Pasquini6, Mimma Rizzo1, Michela Gabelloni7, Lorenzo Belluomini8, Stefania Crucitta5, Raffaele Ciampi9, Antonio Frassoldati8, Emanuele Neri7.   

Abstract

BACKGROUND: It is still unclear how to combine biomarkers to identify patients who will truly benefit from anti-PD-1 agents in NSCLC. This study investigates exosomal mRNA expression of PD-L1 and IFN-γ, PD-L1 polymorphisms, tumor mutational load (TML) in circulating cell-free DNA (cfDNA) and radiomic features as possible predictive markers of response to nivolumab and pembrolizumab in metastatic NSCLC patients.
METHODS: Patients were enrolled and blood (12 ml) was collected at baseline before receiving anti-PD-1 therapy. Exosome-derived mRNA and cfDNA were extracted to analyse PD-L1 and IFN-γ expression and tumor mutational load (TML) by digital droplet PCR (ddPCR) and next-generation sequencing (NGS), respectively. The PD-L1 single nucleotide polymorphisms (SNPs) c.-14-368 T > C and c.*395G > C, were analysed on genomic DNA by Real-Time PCR. A radiomic analysis was performed on the QUIBIM Precision® V3.0 platform.
RESULTS: Thirty-eight patients were enrolled. High baseline IFN-γ was independently associated with shorter median PFS (5.6 months vs. not reached p = 0.0057), and levels of PD-L1 showed an increase at 3 months vs. baseline in patients who progressed (p = 0.01). PD-L1 baseline levels showed significant direct and inverse relationships with radiomic features. Radiomic features also inversely correlated with PD-L1 expression in tumor tissue. In subjects receiving nivolumab, median PFS was shorter in carriers of c.*395GG vs. c.*395GC/CC genotype (2.3 months vs. not reached, p = 0.041). Lastly, responders had higher non-synonymous mutations and more links between co-occurring genetic somatic mutations and ARID1A alterations as well.
CONCLUSIONS: A combined multiparametric approach may provide a better understanding of the molecular determinants of response to immunotherapy.

Entities:  

Keywords:  Biomarkers; Immunotherapy; Liquid biopsy; NSCLC; Radiomics

Mesh:

Substances:

Year:  2020        PMID: 33315149     DOI: 10.1007/s00262-020-02810-6

Source DB:  PubMed          Journal:  Cancer Immunol Immunother        ISSN: 0340-7004            Impact factor:   6.968


  56 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

2.  Changes in programmed death ligand 1 expression in non-small cell lung cancer patients who received anticancer treatments.

Authors:  Shota Omori; Hirotsugu Kenmotsu; Masato Abe; Reiko Watanabe; Takashi Sugino; Haruki Kobayashi; Kazuhisa Nakashima; Kazushige Wakuda; Akira Ono; Tetsuhiko Taira; Tateaki Naito; Haruyasu Murakami; Yasuhisa Ohde; Masahiro Endo; Yasuto Akiyama; Takashi Nakajima; Toshiaki Takahashi
Journal:  Int J Clin Oncol       Date:  2018-06-15       Impact factor: 3.402

3.  Dual Faces of IFNγ in Cancer Progression: A Role of PD-L1 Induction in the Determination of Pro- and Antitumor Immunity.

Authors:  Masaki Mandai; Junzo Hamanishi; Kaoru Abiko; Noriomi Matsumura; Tsukasa Baba; Ikuo Konishi
Journal:  Clin Cancer Res       Date:  2016-03-25       Impact factor: 12.531

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Journal:  Mol Carcinog       Date:  2018-08-28       Impact factor: 4.784

Review 5.  Microsatellite Instability as a Biomarker for PD-1 Blockade.

Authors:  Jonathan C Dudley; Ming-Tseh Lin; Dung T Le; James R Eshleman
Journal:  Clin Cancer Res       Date:  2016-02-15       Impact factor: 12.531

6.  PD-L1 expression is mainly regulated by interferon gamma associated with JAK-STAT pathway in gastric cancer.

Authors:  Kousaku Mimura; Jun Liang Teh; Hirokazu Okayama; Kensuke Shiraishi; Ley-Fang Kua; Vivien Koh; Duane T Smoot; Hassan Ashktorab; Takahiro Oike; Yoshiyuki Suzuki; Zul Fazreen; Bernadette R Asuncion; Asim Shabbir; Wei-Peng Yong; Jimmy So; Richie Soong; Koji Kono
Journal:  Cancer Sci       Date:  2017-11-18       Impact factor: 6.716

7.  Clinical Impact of Single Nucleotide Polymorphism in PD-L1 on Response to Nivolumab for Advanced Non-Small-Cell Lung Cancer Patients.

Authors:  Takashi Nomizo; Hiroaki Ozasa; Takahiro Tsuji; Tomoko Funazo; Yuto Yasuda; Hironori Yoshida; Yoshitaka Yagi; Yuichi Sakamori; Hiroki Nagai; Toyohiro Hirai; Young Hak Kim
Journal:  Sci Rep       Date:  2017-03-23       Impact factor: 4.379

8.  PD-L1 mRNA expression in plasma-derived exosomes is associated with response to anti-PD-1 antibodies in melanoma and NSCLC.

Authors:  Marzia Del Re; Riccardo Marconcini; Giulia Pasquini; Eleonora Rofi; Caterina Vivaldi; Francesco Bloise; Giuliana Restante; Elena Arrigoni; Chiara Caparello; Maria Grazia Bianco; Stefania Crucitta; Iacopo Petrini; Enrico Vasile; Alfredo Falcone; Romano Danesi
Journal:  Br J Cancer       Date:  2018-03-06       Impact factor: 7.640

9.  Intratumoral heterogeneity of programmed cell death ligand-1 expression is common in lung cancer.

Authors:  Sayuri Nakamura; Kentaro Hayashi; Yuki Imaoka; Yuka Kitamura; Yuko Akazawa; Kazuhiro Tabata; Ruben Groen; Tomoshi Tsuchiya; Naoya Yamasaki; Takeshi Nagayasu; Junya Fukuoka
Journal:  PLoS One       Date:  2017-10-19       Impact factor: 3.240

Review 10.  Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape.

Authors:  Xianjie Jiang; Jie Wang; Xiangying Deng; Fang Xiong; Junshang Ge; Bo Xiang; Xu Wu; Jian Ma; Ming Zhou; Xiaoling Li; Yong Li; Guiyuan Li; Wei Xiong; Can Guo; Zhaoyang Zeng
Journal:  Mol Cancer       Date:  2019-01-15       Impact factor: 27.401

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Review 4.  Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

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5.  A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning.

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7.  Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma.

Authors:  Bino Varghese; Steven Cen; Haris Zahoor; Imran Siddiqui; Manju Aron; Akash Sali; Suhn Rhie; Xiaomeng Lei; Marielena Rivas; Derek Liu; Darryl Hwang; David Quinn; Mihir Desai; Ulka Vaishampayan; Inderbir Gill; Vinay Duddalwar
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  8 in total

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