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      Assessment of Blood Tumor Mutational Burden as a Potential Biomarker for Immunotherapy in Patients With Non–Small Cell Lung Cancer With Use of a Next-Generation Sequencing Cancer Gene Panel

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          Abstract

          This study explores whether blood tumor mutational burden estimated by a next-generation sequencing gene panel is associated with clinical outcomes of patients with non–small cell lung cancer treated with anti–programmed cell death 1 and anti–programmed cell death ligand 1 agents.

          Key Points

          Question

          Is blood tumor mutational burden estimated by a next-generation gene sequencing panel with an optimized panel size and algorithm associated with clinical outcomes in patients with non–small cell lung cancer treated with anti–programmed cell death 1 (anti–PD-1) and anti–programmed cell death ligand 1 (anti–PD-L1) agents?

          Findings

          This study of 2 independent cohorts of patients (48 in cohort 1 and 50 in cohort 2) found that NCC-GP150 was a cost-effective panel for tumor mutational burden estimation with satisfactory performance. Blood tumor mutational burden estimated by NCC-GP150 correlated well with tissue tumor mutational burden calculated by whole-exome sequencing, and a blood tumor mutational burden of 6 or higher was positively associated with clinical benefits of anti–PD-1 and anti–PD-L1 therapy in patients with advanced non–small cell lung cancer.

          Meaning

          The findings suggest that blood tumor mutational burden measured by NCC-GP150 is a potential biomarker to identify patients with non–small cell lung cancer who could benefit from anti–PD-1 and anti–PD-L1 therapy.

          Abstract

          Importance

          Tumor mutational burden (TMB), as measured by whole-exome sequencing (WES) or a cancer gene panel (CGP), is associated with immunotherapy responses. However, whether TMB estimated by circulating tumor DNA in blood (bTMB) is associated with clinical outcomes of immunotherapy remains to be explored.

          Objectives

          To explore the optimal gene panel size and algorithm to design a CGP for TMB estimation, evaluate the panel reliability, and further validate the feasibility of bTMB as a clinical actionable biomarker for immunotherapy.

          Design, Setting, and Participants

          In this cohort study, a CGP named NCC-GP150 was designed and virtually validated using The Cancer Genome Atlas database. The correlation between bTMB estimated by NCC-GP150 and tissue TMB (tTMB) measured by WES was evaluated in matched blood and tissue samples from 48 patients with advanced NSCLC. An independent cohort of 50 patients with advanced NSCLC was used to identify the utility of bTMB estimated by NCC-GP150 in distinguishing patients who would benefit from anti–programmed cell death 1 (anti–PD-1) and anti–programmed cell death ligand 1 (anti–PD-L1) therapy. The study was performed from July 19, 2016, to April 20, 2018.

          Main Outcomes and Measures

          Assessment of the Spearman correlation coefficient between bTMB estimated by NCC-GP150 and tTMB calculated by WES. Evaluation of the association of bTMB level with progression-free survival and response to anti–PD-1 and anti–PD-L1 therapy.

          Results

          This study used 2 independent cohorts of patients with NSCLC (cohort 1: 48 patients; mean [SD] age, 60 [13] years; 15 [31.2%] female; cohort 2: 50 patients; mean [SD] age, 58 [8] years; 15 [30.0%] female). A CGP, including 150 genes, demonstrated stable correlations with WES for TMB estimation (median r 2 = 0.91; interquartile range, 0.89-0.92), especially when synonymous mutations were included (median r 2 = 0.92; interquartile range, 0.91-0.93), whereas TMB estimated by the NCC-GP150 panel found higher correlations with TMB estimated by WES than most of the randomly sampled 150-gene panels. Blood TMB estimated by NCC-GP150 correlated well with the matched tTMB calculated by WES (Spearman correlation = 0.62). In the anti–PD-1 and anti–PD-L1 treatment cohort, a bTMB of 6 or higher was associated with superior progression-free survival (hazard ratio, 0.39; 95% CI, 0.18-0.84; log-rank P = .01) and objective response rates (bTMB ≥6: 39.3%; 95% CI, 23.9%-56.5%; bTMB <6: 9.1%; 95% CI, 1.6%-25.9%; P = .02).

          Conclusions and Relevance

          The findings suggest that established NCC-GP150 with an optimized gene panel size and algorithm is feasible for bTMB estimation, which may serve as a potential biomarker of clinical benefit in patients with NSCLC treated with anti–PD-1 and anti–PD-L1 agents.

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          Most cited references7

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          Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non–Small Cell Lung Cancer

          Question Are pretreatment derived neutrophils/(leukocytes minus neutrophils) ratio (dNLR) and lactate dehydrogenase (LDH) level associated with resistance to immunotherapy in patients with advanced non–small cell lung cancer (NSCLC)? Findings In this cohort study evaluating 466 patients with advanced NSCLC, the Lung Immune Prognostic Index (LIPI), combining baseline dNLR and LDH, was associated with the outcomes of immunotherapy but not chemotherapy. Meaning Poor baseline LIPI, combining dNLR greater than 3 and LDH greater than upper limit of normal, was correlated with worse outcomes for immune checkpoint inhibitor treatment in patients with NSCLC, but not with chemotherapy. This cohort study investigates whether the pretreatment derived neutrophils/(leukocytes minus neutrophils) ratio and lactate dehydrogenase level are associated with resistance to immunotherapy in patients with advanced non–small cell lung cancer. Importance Derived neutrophils/(leukocytes minus neutrophils) ratio (dNLR) and lactate dehydrogenase (LDH) level have been correlated with immune checkpoint inhibitor (ICI) outcomes in patients with melanoma. Objective To determine whether pretreatment dNLR and LDH are associated with resistance to ICIs in patients with advanced non–small cell lung cancer (NSCLC). Design, Setting, and Participants Multicenter retrospective study with a test (n = 161) and a validation set (n = 305) treated with programmed death 1/programmed death ligand 1 (PD-1/PD-L1) inhibitors in 8 European centers, and a control cohort (n = 162) treated with chemotherapy only. Complete blood cell counts, LDH, and albumin levels were measured before ICI treatment. A lung immune prognostic index (LIPI) based on dNLR greater than 3 and LDH greater than upper limit of normal (ULN) was developed, characterizing 3 groups (good, 0 factors; intermediate, 1 factor; poor, 2 factors). Main Outcomes and Measures The primary end point was overall survival (OS). Secondary end points were progression-free survival (PFS) and disease control rate (DCR). Results In the pooled ICI cohort (N = 466), 301 patients (65%) were male, 422 (90%) were current or former smokers, and 401 (87%) had performance status of 1 or less; median age at diagnosis was 62 (range, 29-86) years; 270 (58%) had adenocarcinoma and 159 (34%) had squamous histologic subtype. Among 129 patients with PD-L1 data, 96 (74%) had PD-L1 of at least 1% by immunohistochemical analysis, and 33 (26%) had negative results. In the test cohort, median PFS and OS were 3 (95% CI, 2-4) and 10 (95% CI, 8-13) months, respectively. A dNLR greater than 3 and LDH greater than ULN were independently associated with OS (hazard ratio [HR] 2.22; 95% CI, 1.23-4.01 and HR, 2.51; 95% CI, 1.32-4.76, respectively). Median OS for poor, intermediate, and good LIPI was 3 months (95% CI, 1 month to not reached [NR]), 10 months (95% CI, 8 months to NR), and 34 months (95% CI, 17 months to NR), respectively, and median PFS was 2.0 (95% CI, 1.7-4.0), 3.7 (95% CI, 3.0-4.8), and 6.3 (95% CI, 5.0-8.0) months (both P  < .001). Disease control rate was also correlated with dNLR greater than 3 and LDH greater than ULN. Results were reproducible in the ICI validation cohort for OS, PFS, and DCR, but were nonsignificant in the chemotherapy cohort. Conclusions and Relevance Pretreatment LIPI, combining dNLR greater than 3 and LDH greater than ULN, was correlated with worse outcomes for ICI, but not for chemotherapy, suggesting that LIPI can serve as a potentially useful tool when selecting ICI treatment, raising the hypothesis that the LIPI might be useful for identifying patients unlikely to benefit from treatment with an ICI.
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            Gefitinib versus docetaxel in previously treated non-small-cell lung cancer (INTEREST): a randomised phase III trial.

            Two phase II trials in patients with previously-treated advanced non-small-cell lung cancer suggested that gefitinib was efficacious and less toxic than was chemotherapy. We compared gefitinib with docetaxel in patients with locally advanced or metastatic non-small-cell lung cancer who had been pretreated with platinum-based chemotherapy. We undertook an open-label phase III study with recruitment between March 1, 2004, and Feb 17, 2006, at 149 centres in 24 countries. 1466 patients with pretreated (>/=one platinum-based regimen) advanced non-small-cell lung cancer were randomly assigned with dynamic balancing to receive gefitinib (250 mg per day orally; n=733) or docetaxel (75 mg/m(2) intravenously in 1-h infusion every 3 weeks; n=733). The primary objective was to compare overall survival between the groups with co-primary analyses to assess non-inferiority in the overall per-protocol population and superiority in patients with high epidermal growth factor receptor (EGFR)-gene-copy number in the intention-to-treat population. This study is registered with ClinicalTrials.gov, number NCT00076388. 1433 patients were analysed per protocol (723 in gefitinib group and 710 in docetaxel group). Non-inferiority of gefitinib compared with docetaxel was confirmed for overall survival (593 vs 576 events; hazard ratio [HR] 1.020, 96% CI 0.905-1.150, meeting the predefined non-inferiority criterion; median survival 7.6 vs 8.0 months). Superiority of gefitinib in patients with high EGFR-gene-copy number (85 vs 89 patients) was not proven (72 vs 71 events; HR 1.09, 95% CI 0.78-1.51; p=0.62; median survival 8.4 vs 7.5 months). In the gefitinib group, the most common adverse events were rash or acne (360 [49%] vs 73 [10%]) and diarrhoea (255 [35%] vs 177 [25%]); whereas in the docetaxel group, neutropenia (35 [5%] vs 514 [74%]), asthenic disorders (182 [25%] vs 334 [47%]), and alopecia (23 [3%] vs 254 [36%]) were most common. INTEREST established non-inferior survival of gefitinib compared with docetaxel, suggesting that gefitinib is a valid treatment for pretreated patients with advanced non-small-cell lung cancer.
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              Hypermutated Circulating Tumor DNA: Correlation with Response to Checkpoint Inhibitor-Based Immunotherapy.

              Purpose: Tumor mutational burden detected by tissue next-generation sequencing (NGS) correlates with checkpoint inhibitor response. However, tissue biopsy may be costly and invasive. We sought to investigate the association between hypermutated blood-derived circulating tumor DNA (ctDNA) and checkpoint inhibitor response.Experimental Design: We assessed 69 patients with diverse malignancies who received checkpoint inhibitor-based immunotherapy and blood-derived ctDNA NGS testing (54-70 genes). Rates of stable disease (SD) ≥6 months, partial and complete response (PR, CR), progression-free survival (PFS), and overall survival (OS) were assessed based on total and VUS alterations.Results: Statistically significant improvement in PFS was associated with high versus low alteration number in variants of unknown significance (VUS, >3 alterations versus VUS ≤3 alterations), SD ≥6 months/PR/CR 45% versus 15%, respectively; P = 0.014. Similar results were seen with high versus low total alteration number (characterized plus VUS, ≥6 vs. 3 had a median PFS of 23 versus 2.3 months (P = 0.0004).Conclusions: Given the association of alteration number on liquid biopsy and checkpoint inhibitor-based immunotherapy outcomes, further investigation of hypermutated ctDNA as a predictive biomarker is warranted. Clin Cancer Res; 23(19); 5729-36. ©2017 AACR.
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                Author and article information

                Journal
                JAMA Oncol
                JAMA Oncol
                JAMA Oncol
                JAMA Oncology
                American Medical Association
                2374-2437
                2374-2445
                28 February 2019
                May 2019
                28 February 2019
                : 5
                : 5
                : 696-702
                Affiliations
                [1 ]State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
                [2 ]The Medical Department, 3D Medicines Inc, Shanghai, China
                [3 ]The Bioinformatics Department, R&D Center of Precision Medicine, 3D Medicines Inc, Shanghai, China
                [4 ]Department of Thoracic Medical Oncology, Peking University School of Oncology, Beijing Cancer Hospital & Institute, Beijing, China
                [5 ]Cancer Institute, Xinqiao Hospital, Army Medical University, Chongqing, China
                [6 ]GCP Center, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
                [7 ]Department of Internal Medicine, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
                [8 ]The 3DMed Clinical Laboratory, 3D Medicines Inc, Shanghai, China
                Author notes
                Article Information
                Accepted for Publication: December 6, 2018.
                Published Online: February 28, 2019. doi:10.1001/jamaoncol.2018.7098
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Wang Z et al. JAMA Oncology.
                Corresponding Authors: Jie Wang, MD, PhD, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 17 Pan-jia-yuan South Ln, Chaoyang District, Beijing 100021, China ( zlhuxi@ 123456163.com ).
                Author Contributions: Drs Z. Wang, Duan, Cai, and M. Han contributed equally to this work. Drs Z. Wang, Duan, Cai, and M. Han serve as co–first authors, each with equal contribution to the manuscript. Drs J. Wang and Z. Wang had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Z. Wang, Cai, Dong, J. Wang.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: All authors.
                Critical revision of the manuscript for important intellectual content: Z. Wang, Cai, J. Wang.
                Statistical analysis: M. Han, Dong, Lu, Cao, J. Zhao.
                Obtained funding: Z. Wang, J. Wang.
                Administrative, technical, or material support: Z. Wang, Duan, Cai, Dong, H. Bai, J. Han, D. Wang, Chen, J. Wang.
                Supervision: J. Wang.
                Conflict of Interest Disclosures: Drs M. Han, Dong, Lu, Cao, Li, D. Wang, Chen, Xu, X. Zhao, G. Wang, Y. Bai, J. Zhao, Z. Zhao, Zhang, Xiong, and Cai are employees of 3D Medicines Inc. No other disclosures were reported.
                Funding/Support: This work was supported by grants 81630071 (Dr J. Wang) and 81330062 from the National Natural Sciences Foundation Key Program (Dr J. Wang), grant 2016YFC0902300 from the National Key R&D Program of China (Dr J. Wang), grant CIFMS 2016-I2M-3-008 from the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) (Dr J. Wang), grant 81871889 from the China National Natural Sciences Foundation (Dr Z. Wang), grant 7172045 from the Beijing Natural Science Foundation and Beijing Novel Program (Dr Z. Wang), cross-cooperation grant Z181100006218130 (Dr J. Wang), grant IRT-17R10 from the Ministry of Education Innovation Team development project (Dr J. Wang), CAMS Key Lab of Translational Research on Lung Cancer, grant IRT13003 from the Education Ministry Innovative Research Team Program (Dr J. Wang), the CAMS Innovation Fund for Medical Sciences (Dr J. Wang), and grant KY201701 from the Aiyou Foundation (Dr J. Wang).
                Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Article
                coi180124
                10.1001/jamaoncol.2018.7098
                6512308
                30816954
                f41930c4-c80e-417b-8a0b-a464979afd9a
                Copyright 2019 Wang Z et al. JAMA Oncology.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 13 July 2018
                : 3 December 2018
                : 6 December 2018
                Categories
                Research
                Research
                Original Investigation
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