Search

Your search keyword '"Yu, Yongfeng"' showing total 292 results
Start Over Author "Yu, Yongfeng"
292 results on '"Yu, Yongfeng"'

2. Commutated-Phase Resonance Suppression in Synchronous Rectification Mode for Bidirectional Bridgeless PFC Converters

Author

Chen, Cheng, Tan, Linlin, Yu, Yongfeng, Li, Xinguo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Cai, Chunwei, editor, Qu, Xiaohui, editor, Mai, Ruikun, editor, Zhang, Pengcheng, editor, Chai, Wenping, editor, and Wu, Shuai, editor

Published
2024
Full Text
View/download PDF

5. Advances in immunotherapy of advanced non-small cell lung cancer with EGFR mutation

Author

HUANG Huayan, XU-ZHANG Wendi, XIA Liliang, YU Yongfeng, and LU Shun

Subjects
non-small cell lung cancer (nsclc), epidermal growth factor receptor (egfr), immune checkpoint inhibitor (ici), mutation subtype, Medicine
Abstract

The incidence of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC) in Asians is significantly higher than that in Westerners. For the past few years, immune checkpoint inhibitors (ICIs) that target the programmed cell death 1 (PD-1) /programmed cell death ligand 1 (PD-L1) axis have become a part of the treatment paradigm for advanced NSCLC, opening a new era of immunotherapy for lung cancer. However, previous clinical trials reported that advanced NSCLC patients with EGFR mutation could not benefit from ICIs monotherapy. The immunotherapy outcomes of different EGFR mutant subtypes showed diverse. The interim results of the latest clinical trial ORIENT-31 showed that immunotherapy combined with chemotherapy and anti-angiogenesis significantly improved the progression-free survival of EGFR tyrosine kinase inhibitors (TKIs) resistant advanced NSCLC patients, providing a new therapeutic strategy for those EGFR mutant patients. The tumor microenvironment of EGFR-mutated NSCLC is immunosuppressed. Targeting the key immunomodulatory factors that play important roles in the immunosuppression may promote the response of EGFR-mutated tumors to immunotherapy and provide a new synergistic immune combination therapy strategy, which will enrich the clinical treatment options and improve the survival prognosis of EGFR-TKIs-resistant NSCLC patients. This article summarizes the latest clinical progression of immunotherapy in advanced NSCLC with EGFR mutation, the differences of immunotherapy efficacy among different EGFR mutation subtypes, the synergistic mechanism of combined immunotherapy and the potential molecular target combining with immunotherapy in EGFR-mutated NSCLC.

Published
2023
Full Text
View/download PDF

6. Gumarontinib in patients with non-small-cell lung cancer harbouring MET exon 14 skipping mutations: a multicentre, single-arm, open-label, phase 1b/2 trial

Author

Yu, Yongfeng, Zhou, Jianya, Li, Xingya, Goto, Koichi, Min, Xuhong, Nishino, Kazumi, Cui, Jiuwei, Wu, Lin, Sakakibara, Jun, Shu, Yongqian, Dong, Xiaorong, Li, Lu, Yoneshima, Yasuto, Zhou, Chengzhi, Li, Xiaoling, Zhang, Yiping, Huang, Dingzhi, Zang, Aimin, Zhang, Wei, Wang, Xiuwen, Zhang, Li, Bai, Chong, Fang, Jian, Cao, Lejie, Zhao, Yanqiu, Yu, Yan, Shi, Meiqi, Zhong, Diansheng, Li, Fugen, Li, Meng, Wu, Qiuxia, Zhou, Jun, Sun, Minghui, and Lu, Shun

Published
2023
Full Text
View/download PDF

8. Homologous recombination deficiency (HRD) can predict the therapeutic outcomes of immuno-neoadjuvant therapy in NSCLC patients

Author

Zhou, Zhen, Ding, Zhengping, Yuan, Jie, Shen, Shengping, Jian, Hong, Tan, Qiang, Yang, Yunhai, Chen, Zhiwei, Luo, Qingquan, Cheng, Xinghua, Yu, Yongfeng, Niu, Xiaomin, Qian, Liqiang, Chen, Xiaoke, Gu, Linping, Liu, Ruijun, Ma, Shenglin, Huang, Jia, Chen, Tianxiang, Li, Ziming, Ji, Wenxiang, Song, Liwei, Shen, Lan, Jiang, Long, Yu, Zicheng, Zhang, Chao, Tai, Zaixian, Wang, Changxi, Chen, Rongrong, Carbone, David P., Xia, Xuefeng, and Lu, Shun

Published
2022
Full Text
View/download PDF

11. A pooled analysis of clinical outcome in driver-gene negative non-small cell lung cancer patients with MET overexpression treated with gumarontinib.

Author

Yu, Yongfeng, Dong, Wen, Shi, Yanxia, Wu, Rong, Yu, Qitao, Ye, Feng, Zhou, Chengzhi, Dong, Xiaorong, Li, Xingya, Li, Yongsheng, Li, Zhen, Wu, Lin, Pan, Yueyin, Shen, Hong, Wu, Dehua, Xu, Zhongyuan, Wu, Jinsheng, Xu, Nong, Qin, Yanru, and Zang, Aimin

Abstract

Background: MET overexpression represents the most MET aberration in advanced non-small-cell lung cancer (NSCLC). However, except MET exon 14 (MET ex14) skipping mutation was recognized as a clinical biomarker, the role of MET overexpression as a predictive factor to MET inhibitor is not clear. Objectives: The purpose of the pooled analysis is to explore the safety and efficiency of gumarontinib, a highly selective oral MET inhibitor, in drive-gene negative NSCLC patients with MET overexpression. Design and methods: NSCLC patients with MET overexpression [immunohistochemistry (IHC) ⩾3+ as determined by central laboratory] not carrying epidermal growth factor receptor mutation, MET ex14 skipping mutation or other known drive gene alternations who received Gumarontinib 300 mg QD from two single arm studies were selected and pooled for the analysis. The efficacy [objective response rate (ORR), disease control rate (DCR), duration of response, progression-free survival (PFS) and overall survival (OS)] and safety [treatment emergent adverse event (TEAE), treatment related AE (TRAE) and serious AE (SAE) were assessed. Results: A total of 32 patients with MET overexpression were included in the analysis, including 12 treatment naïve patients who refused or were unsuitable for chemotherapy, and 20 pre-treated patients who received ⩾1 lines of prior systemic anti-tumour therapies. Overall, the ORR was 37.5% [95% confidence interval (CI): 21.1–56.3%], the DCR was 81.3% (95% CI: 63.6–92.8%), median PFS (mPFS) and median OS (mOS) were 6.9 month (95% CI: 3.6–9.7) and 17.0 month (95% CI: 10.3–not evaluable), respectively. The most common AEs were oedema (59.4%), hypoalbuminaemia (40.6%), alanine aminotransferase increased (31.3%). Conclusion: Gumarontinib showed promising antitumour activity in driver-gene negative locally advanced or metastatic NSCLC patients with MET overexpression, which warranted a further clinical trial. Trial registration: ClinicalTrials.gov identifier: NCT03457532; NCT04270591. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

12. EGFR mutations induce the suppression of CD8+ T cell and anti-PD-1 resistance via ERK1/2-p90RSK-TGF-β axis in non-small cell lung cancer.

Author

Huang, Huayan, Zhu, Xiaokuan, Yu, Yongfeng, Li, Ziming, Yang, Yi, Xia, Liliang, and Lu, Shun

Subjects
NON-small-cell lung carcinoma, T cells, EPIDERMAL growth factor receptors, IMMUNE checkpoint inhibitors
Abstract

Background: Non-small cell lung cancer (NSCLC) patients with EGFR mutations exhibit an unfavorable response to immune checkpoint inhibitor (ICI) monotherapy, and their tumor microenvironment (TME) is usually immunosuppressed. TGF-β plays an important role in immunosuppression; however, the effects of TGF-β on the TME and the efficacy of anti-PD-1 immunotherapy against EGFR-mutated tumors remain unclear. Methods: Corresponding in vitro studies used the TCGA database, clinical specimens, and self-constructed mouse cell lines with EGFR mutations. We utilized C57BL/6N and humanized M-NSG mouse models bearing EGFR-mutated NSCLC to investigate the effects of TGF-β on the TME and the combined efficacy of TGF-β blockade and anti-PD-1 therapy. The changes in immune cells were monitored by flow cytometry. The correlation between TGF-β and immunotherapy outcomes of EGFR-mutated NSCLC was verified by clinical samples. Results: We identified that TGF-β was upregulated in EGFR-mutated NSCLC by EGFR activation and subsequent ERK1/2-p90RSK phosphorylation. TGF-β directly inhibited CD8+ T cell infiltration, proliferation, and cytotoxicity both in vitro and in vivo, but blocking TGF-β did not suppress the growth of EGFR-mutated tumors in vivo. Anti-TGF-β antibody combined with anti-PD-1 antibody significantly inhibited the proliferation of recombinant EGFR-mutated tumors in C57BL/6N mice, which was superior to their monotherapy. Mechanistically, the combination of anti-TGF-β and anti-PD-1 antibodies significantly increased the infiltration of CD8+ T cells and enhanced the anti-tumor function of CD8+ T cells. Moreover, we found that the expression of TGF-β1 in EGFR-TKI resistant cell lines was significantly higher than that in parental cell lines. The combination of anti-TGF-β and nivolumab significantly inhibited the proliferation of EGFR-TKI resistant tumors in humanized M-NSG mice and prolonged their survival. Conclusions: Our results reveal that TGF-β expression is upregulated in NSCLC with EGFR mutations through the EGFR-ERK1/2-p90RSK signaling pathway. High TGF-β expression inhibits the infiltration and anti-tumor function of CD8+ T cells, contributing to the "cold" TME of EGFR-mutated tumors. Blocking TGF-β can reshape the TME and enhance the therapeutic efficacy of anti-PD-1 in EGFR-mutated tumors, which provides a potential combination immunotherapy strategy for advanced NSCLC patients with EGFR mutations. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

17. Clinical definition of secondary resistance to immunotherapy in non‐small cell lung cancer

Author

Huang, Dingzhi, primary, Lin, Gen, additional, Chu, Qian, additional, Hu, Yi, additional, Wang, Jun, additional, Wang, Zhijie, additional, Yang, Fan, additional, Zhong, Wenzhao, additional, Zhou, Chengzhi, additional, Zhu, Bo, additional, Ai, Xinghao, additional, Cao, Baoshan, additional, Cao, Yabing, additional, Chen, Mingqiu, additional, Chen, Xiaohui, additional, Chu, Tianqing, additional, Duan, Jianchun, additional, Fan, Yun, additional, Fang, Yong, additional, Feng, Shuitu, additional, Feng, Weineng, additional, Guo, Hui, additional, Han, Chengbo, additional, He, Yong, additional, Hong, Shaodong, additional, Hu, Jie, additional, Huang, Meijuan, additional, Huang, Yan, additional, Jiang, Da, additional, Jiang, Kan, additional, Jiang, Richeng, additional, Jin, Bo, additional, Jin, Shi, additional, Li, Jisheng, additional, Li, Min, additional, Li, Ziming, additional, Li, Chao, additional, Lin, Jie, additional, Liu, Anwen, additional, Liu, Si‐Yang Maggie, additional, Liu, Yutao, additional, Liu, Zhefeng, additional, Liu, Zhe, additional, Liu, Zhenhua, additional, Liu, Zhentian, additional, Liu, Zhigang, additional, Lu, Yuping, additional, Lv, Tangfeng, additional, Ma, Zhiyong, additional, Miao, Qian, additional, Peng, Min, additional, Pu, Xingxiang, additional, Ren, Xiu Bao, additional, Shan, Jianzhen, additional, Shan, Jinlu, additional, Shen, Peng, additional, Shen, Bo, additional, Shi, Meiqi, additional, Song, Yong, additional, Song, Zhengbo, additional, Su, ChunXia, additional, Sun, Jianguo, additional, Tian, Panwen, additional, Wang, Jinliang, additional, Wang, Feng, additional, Wang, Huijuan, additional, Wang, Jialei, additional, Wang, Qian, additional, Wang, Wenxian, additional, Wang, Yan, additional, Wu, Lin, additional, Wu, Fang, additional, Xia, Yang, additional, Xie, Congying, additional, Xie, Conghua, additional, Xin, Tao, additional, Xiong, Jianping, additional, Xu, Haipeng, additional, Xu, Song, additional, Xu, Yiquan, additional, Xu, Bin, additional, Xu, Chunwei, additional, Yan, Xiaolong, additional, Yang, Zhenzhou, additional, Yao, Wenxiu, additional, Yu, Yao, additional, Feng, Ye, additional, Yu, Zongyang, additional, Yu, Yongfeng, additional, Yue, Dongsheng, additional, Zhang, Haibo, additional, Zhang, HongMei, additional, Zhang, Li, additional, Zhang, Longfeng, additional, Zhang, Qiuyu, additional, Zhang, Tongmei, additional, Zhang, Bicheng, additional, Zhao, Jun, additional, Zhao, Mingfang, additional, Zheng, Xiaobin, additional, Zhong, Fengqiao, additional, Zhou, Jin, additional, Zhou, Penghui, additional, Zhu, Zhengfei, additional, Zou, Juntao, additional, and Zou, Zihua, additional

Published
2023
Full Text
View/download PDF

19. Chinese multidisciplinary expert consensus on the management of adverse drug reactions associated with savolitinib.

Author

Zhang, Li, Wang, Yongsheng, Lin, Lizhu, Yu, Yongfeng, and Lu, Shun

Abstract

Savolitinib is a highly selective mesenchymal-epithelial transition (MET) tyrosine kinase inhibitor (TKI). Based on its significant efficacy shown in clinical studies, savolitinib was conditionally approved for marketing in China on 22 June 2021, for the treatment of advanced non-small cell lung cancer (NSCLC) with MET 14 exon skipping mutation. Additionally, many studies showed that MET TKIs were equally effective in patients with advanced solid tumors with MET gene amplification or MET protein overexpression. Several relevant registered clinical studies are in progress. The most common adverse reactions (ARs) due to savolitinib administration are nausea, vomiting, peripheral edema, pyrexia, and hepatotoxicity. This consensus was developed through two rounds of extensive national surveys involving multidisciplinary experts in China, aiming to guide clinicians to prevent and treat various ARs scientifically, and improve the efficacy of the drug and the quality of life of patients. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

22. Supplementary File from Genome-wide DNA Methylation Analysis Reveals GABBR2 as a Novel Epigenetic Target for EGFR 19 Deletion Lung Adenocarcinoma with Induction Erlotinib Treatment

Author

Niu, Xiaomin, primary, Liu, Fatao, primary, Zhou, Yi, primary, Zhou, Zhen, primary, Zhou, Daizhan, primary, Wang, Ting, primary, Li, Ziming, primary, Ye, Xiangyun, primary, Yu, Yongfeng, primary, Weng, Xiaoling, primary, Zhang, Hong, primary, Ye, Junyi, primary, Liao, Meilin, primary, Liu, Yun, primary, Chen, Zhiwei, primary, and Lu, Shun, primary

Published
2023
Full Text
View/download PDF

24. Figure S1 from Genome-wide DNA Methylation Analysis Reveals GABBR2 as a Novel Epigenetic Target for EGFR 19 Deletion Lung Adenocarcinoma with Induction Erlotinib Treatment

Author

Niu, Xiaomin, primary, Liu, Fatao, primary, Zhou, Yi, primary, Zhou, Zhen, primary, Zhou, Daizhan, primary, Wang, Ting, primary, Li, Ziming, primary, Ye, Xiangyun, primary, Yu, Yongfeng, primary, Weng, Xiaoling, primary, Zhang, Hong, primary, Ye, Junyi, primary, Liao, Meilin, primary, Liu, Yun, primary, Chen, Zhiwei, primary, and Lu, Shun, primary

Published
2023
Full Text
View/download PDF

35. Abstract CT034: Phase II study of SCC244 in NSCLC patients harboring MET exon 14 skipping (METex14) mutations (GLORY study)

Author

Lu, Shun, primary, Yu, Yongfeng, additional, Zhou, Jianya, additional, Goto, Koichi, additional, Li, Xingya, additional, Sakakibara-Konishi, Jun, additional, Nishino, Kazumi, additional, Kentaro, Tanaka, additional, Wu, Lin, additional, Min, Xuhong, additional, Zhang, Wei, additional, Huang, Dingzhi, additional, Shu, Yongqian, additional, Zhou, Chengzhi, additional, Li, Min, additional, Dong, Xiaorong, additional, Bai, Chong, additional, Li, Lu, additional, Cui, Jiuwei, additional, Zhang, Li, additional, Cao, Lejie, additional, Li, Xiaoling, additional, Zang, AiMin, additional, Kobayashi, Haruki, additional, Zhang, Yiping, additional, Yu, Yan, additional, Wang, Xiuwen, additional, Kato, Terufumi, additional, Yamamoto, Shoichiro, additional, Shinno, Yuki, additional, Lin, Xiaoyan, additional, Zhao, Yanqiu, additional, Hu, Yanping, additional, Yu, Qitao, additional, Wang, Ziping, additional, Kodani, Masahiro, additional, Fang, Jian, additional, Wang, Jialei, additional, Shi, Meiqi, additional, Zhong, Diansheng, additional, Dong, Wen, additional, Tanaka, Hiroshi, additional, Yoneshima, Yasuto, additional, Sun, Minghui, additional, Zhou, Jun, additional, Wu, Qiuxia, additional, and Li, Meng, additional

Published
2022
Full Text
View/download PDF

36. Maintenance therapy improves survival outcomes in patients with advanced non-small cell lung cancer: a meta-analysis of 14 studies

Author

Lu, Shun, Yu, Yongfeng, Chen, Zhiwei, Ye, Xiangyun, Li, Ziming, and Niu, Xiaomin

Subjects
Patients -- Care and treatment, Lung cancer, Non-small cell -- Research -- Development and progression -- Care and treatment -- Patient outcomes -- Complications and side effects, Health
Abstract

Purpose The aim was to investigate whether maintenance therapy (MT) is sufficient or not to improve overall survival (OS) and progress-free survival (PFS) of advanced non-small cell lung cancer (NSCLC) patients. Methods Randomized controlled trials (RCTs) published between 1990 and 2013 were retrieved from PubMed, EMBASE, ISTP, clinicaltrials.org, and ASCO conference proceeding. Patients' characteristics, OS, progress-free survival, and hazard ratios were extracted. Data were analyzed using RevMan 5.2. Fourteen RCTs involving 6198 individuals were included. Results Compared with placebo, observation or best supportive care (BSC), patients receiving single agent (SA) MT had an improved OS (hazard ratio, HR 0.85, 95 % CI 0.79-0.91; p < 0.05) and PFS (HR 0.65, 95 % CI 0.57-0.73; p < 0.05). In a sub-group analysis of SA MT versus placebo, observation or BSC, we found that switch MT using SA provided an improved OS (HR 0.85, 95 % CI 0.79-0.91; p < 0.05). For multiple agent (MA) versus SA MT, a prolonged PFS (HR 0.68, 95 % CI 0.52-0.88; p < 0.05) but not OS (HR 0.96, 95 % CI 0.86-1.07; p > 0.05) was observed for MA. A significant prolonged PFS was observed in MA switch MT (HR 0.71, 95 % CI 0.58-0.86; p < 0.05) versus SA MT. However, no significant improvement in OS was observed for MA versus SA MT, indicating that switch MT (HR 0.90, 95 % CI 0.73-1.12; p > 0.05) and continuous MT (HR 0.98, 95 % CI 0.86-1.11; p > 0.05) showed similar effect on OS. Conclusion SA switch MT is associated with improved OS and PFS in patients with advanced NSCLC. MA switch MT is sufficient to improve PFS, but not OS. Keywords Maintenance therapy * Advanced non-small cell lung cancer * Meta-analysis, Introduction Non-small cell lung cancer (NSCLC) accounts for more than 70 % of lung cancers, and is often found at advanced stage with limited treatment options and poor outcomes [1]. [...]

Published
2015
Full Text
View/download PDF

39. Homologous recombination deficiency (HRD) can predict the therapeutic outcomes of immuno-neoadjuvant therapy in NSCLC patients

Author

Zhou, Zhen, primary, Ding, Zhengping, additional, Yuan, Jie, additional, Shen, Shengping, additional, Jian, Hong, additional, Tan, Qiang, additional, Yang, Yunhai, additional, Chen, Zhiwei, additional, Luo, Qingquan, additional, Cheng, Xinghua, additional, Yu, Yongfeng, additional, Niu, Xiaomin, additional, Qian, Liqiang, additional, Chen, Xiaoke, additional, Gu, Linping, additional, Liu, Ruijun, additional, Ma, Shenglin, additional, Huang, Jia, additional, Chen, Tianxiang, additional, Li, Ziming, additional, Ji, Wenxiang, additional, Song, Liwei, additional, Shen, Lan, additional, Jiang, Long, additional, Yu, Zicheng, additional, Zhang, Chao, additional, Tai, Zaixian, additional, Wang, Changxi, additional, Chen, Rongrong, additional, and Lu, Shun, additional

Published
2022
Full Text
View/download PDF

40. Additional file 2 of Homologous recombination deficiency (HRD) can predict the therapeutic outcomes of immuno-neoadjuvant therapy in NSCLC patients

Author

Zhou, Zhen, Ding, Zhengping, Yuan, Jie, Shen, Shengping, Jian, Hong, Tan, Qiang, Yang, Yunhai, Chen, Zhiwei, Luo, Qingquan, Cheng, Xinghua, Yu, Yongfeng, Niu, Xiaomin, Qian, Liqiang, Chen, Xiaoke, Gu, Linping, Liu, Ruijun, Ma, Shenglin, Huang, Jia, Chen, Tianxiang, Li, Ziming, Ji, Wenxiang, Song, Liwei, Shen, Lan, Jiang, Long, Yu, Zicheng, Zhang, Chao, Tai, Zaixian, Wang, Changxi, Chen, Rongrong, Carbone, David P., Xia, Xuefeng, and Lu, Shun

Abstract

Additional file 2: Supplementary Results.

Published
2022
Full Text
View/download PDF

41. sj-docx-2-tam-10.1177_17588359221112474 – Supplemental material for Comprehensive analysis of MET mutations in NSCLC patients in a real-world setting

Author

Ai, Xinghao, Yu, Yongfeng, Zhao, Jun, Sheng, Wang, Bai, Jing, Fan, Zaiwen, Liu, Xuemei, Ji, Wenxiang, Chen, Rongrong, and Lu, Shun

Subjects
110203 Respiratory Diseases, FOS: Clinical medicine, 111702 Aged Health Care, FOS: Health sciences, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental material, sj-docx-2-tam-10.1177_17588359221112474 for Comprehensive analysis of MET mutations in NSCLC patients in a real-world setting by Ai Xinghao, Yongfeng Yu, Jun Zhao, Wang Sheng, Jing Bai, Zaiwen Fan, Xuemei Liu, Wenxiang Ji, Rongrong Chen and Shun Lu in Therapeutic Advances in Medical Oncology

Published
2022
Full Text
View/download PDF

42. Additional file 3 of Homologous recombination deficiency (HRD) can predict the therapeutic outcomes of immuno-neoadjuvant therapy in NSCLC patients

Author

Zhou, Zhen, Ding, Zhengping, Yuan, Jie, Shen, Shengping, Jian, Hong, Tan, Qiang, Yang, Yunhai, Chen, Zhiwei, Luo, Qingquan, Cheng, Xinghua, Yu, Yongfeng, Niu, Xiaomin, Qian, Liqiang, Chen, Xiaoke, Gu, Linping, Liu, Ruijun, Ma, Shenglin, Huang, Jia, Chen, Tianxiang, Li, Ziming, Ji, Wenxiang, Song, Liwei, Shen, Lan, Jiang, Long, Yu, Zicheng, Zhang, Chao, Tai, Zaixian, Wang, Changxi, Chen, Rongrong, Carbone, David P., Xia, Xuefeng, and Lu, Shun

Abstract

Additional file 3: Supplementary Figures. Figure S1. Mutation statistics of all curated samples. (A) Regimen combinations of the 14 NSCLC patients. (B) Chemotherapeutic drugs and immunotherapy agents used by each patient. (C) Neoadjuvant immunotherapy duration time distribution between MPR and Non-MPR groups. (D) Correlation between PD-L1 expression and percentage of viable tumor cells in patients. (E) Mutation number retained after filtrations. (F) Mutation number distribution on MPR/Non-MPR and FFPE/Frozen specimen. (G) Six substitution type spectrum plot in two patient groups. P-values were shown above each substitution category. (H) Exposures of three types of mutation signature to known database. SBS: single base substitution; ID: InDel; DBS: double base substitution. Either sum or log2 fold change of the absolute exposures was addedly depicted. (I) Percentage of altered tumor suppressor genes enriched in multiple DNA repair-related pathways in MPR and Non-MPR group. Figure S2. SCNA statistical and signature analyses results in groups with distinct therapeutic response. (A) Chromosome level CNV burden between MPR/Non-MPR groups. (B) Arm level CNV burden in two groups. (C) Chromosome level CNV burden between squamous cell carcinoma (SCC) MPR/Non-MPR patients. (D) Arm level CNV burden in two SCC patient groups. (E) Correlation between chromosome level CNV burden and percentage of viable tumor cells. (F) Correlation between arm level CNV burden and percentage of viable tumor cells. (G) Percentage of genome amplified in two groups. (H) Percentage of genome deleted in two groups. (I) Percentage of genome amplified in SCC patients. (J) Percentage of genome deleted in SCC patients. (K) SCNA heatmap only plotting copy number alternations exceeding the threshold +/-0.2. Two aneuploid samples were marked by pink circles. (L) Contributions of three SCNA signatures in all samples. Adenocarcinoma samples were marked with pink triangles. (M) Proportion of SCNA Signature 1 in patients. (N) Proportion of SCNA Signature 7 in patients. (O) Proportion of SCNA Signature 5 in patients. (P) Proportion of SCNA Signature 1 in SCC patients. (Q) Proportion of SCNA Signature 7 in SCC patients. (R) Proportion of SCNA Signature 5 in SCC patients. Figure S3. HRDscore in non-aneuploid SCC samples. (A) HRDscore calculated on non-aneuploid SCC samples. (B) Correlation between HRDscore and percentage of viable tumor cells in all non-aneuploid SCC samples. Figure S4. Multidimensional ITH comparison results between MPR/Non-MPR groups. (A) TMB value distribution in SCC samples. (B) Correlation between TMB and percentage of viable tumor cells in SCC samples. (C) Clonal TMB value in two groups, only selecting SCC patients. (D) Correlation between clonal TMB and percentage of viable tumor cells on SCC samples. (E) Germline HR pathway gene mutation number in MPR/Non-MPR groups. (F) Subclonal SCNA fragment number in two groups, only selecting SCC samples. (G) Correlation between subclonal SCNA fragment number and percentage of viable tumor cells, only selecting SCC samples. Figure S5. Investigation on the neoantigen load difference between patients with distinct outcomes. (A) TNB in two groups. (B) Correlation between TNB values and percentage of viable tumor cells. (C) TNB in two groups, only selecting SCC samples. (D) Correlation between TNB values and percentage of viable tumor cells, only selecting SCC samples. (E) Clonal TNB value distribution in two groups, only selecting SCC samples. (F) Correlation between clonal TNB and percentage of viable tumor cells, only selecting SCC samples. (G) HLA LOH frequency in MPR/Non-MPR groups. (H) TNB value on kept HLA alleles. (I) Correlation between TNB on kept HLA alleles and percentage of viable tumor cells. (J) TNB value on kept HLA alleles, only selecting SCC samples. (K) Correlation between TNB on kept HLA alleles and percentage of viable tumor cells, only selecting SCC samples. (L) Clonal TNB value on kept HLA alleles. (M) Correlation between clonal TNB on kept HLA alleles and percentage of viable tumor cells. (N) Clonal TNB value on kept HLA alleles, only selecting SCC samples. (O) Correlation between clonal TNB on kept HLA alleles and percentage of viable tumor cells, only selecting SCC samples. Figure S6. Comprehensive analysis on clonal neoantigen and SCNA bring insights to the function of SCNA on neoantigen generation. (A) Percentage of kept clonal neoantigens on SCNA regions in two groups. (B) Correlation between CNV-related neoantigen proportion and percentage of viable tumor cells. (C) Percentage of kept clonal neoantigens on SCNA regions in two groups, only selecting SCC samples. (D) Correlation between CNV-related neoantigen proportion and percentage of viable tumor cells, only selecting SCC samples. (E) Number of clonal neoantigen on kept HLA alleles and their distribution on 3 types of SCNAs. Adenocarcinoma samples were marked with pink triangles. The sample with ATR mutation was marked with arrow. (F) Correlation between clonal neoantigen number on amplified segments and percentage of viable tumor cells. (G) Correlation between clonal neoantigen number on amplified segments and percentage of viable tumor cells, only selecting SCC samples. (H) Correlation values on all 11 non-aneuploid and high purity samples, calculated by Spearman Correlation Coefficient (SCC). (I) Similar with (H), the correlation values were on MPR samples. (J) Similar with (H), the correlation values were on Non-MPR samples. Figure S7. Investigation on HR pathway gene alternation frequency in patients with distinct response from public cohorts. (A) HR pathway gene mutation frequency in J Immunother Cancer. 2020 neoadjuvant dataset. Left: in pCR patients. Middle: in other patients. Right: percentage of distinct mutant reads for each HR gene mutation detected. Mutations from pCR patients were marked with red color. (B) HR pathway gene mutation frequency in N Engl J Med. 2018 neoadjuvant dataset. Left: in MPR patients. Middle: in Non-MPR patients. Right: percentage of distinct mutant reads for each HR gene mutation detected. Mutations from MPR patients were marked with red color. (C) HR pathway gene mutation frequency in DCB patients from Nat Genet. 2018 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (D) Similar with (C), but using non-DCB patients. (E) Similar with (C), but using J Clin Oncol. 2018 dataset. (F) Similar with (C), but using NDB patients. (G) HR pathway gene mutation frequency in patients achieved DCB in chemotherapy from Cancer Discov. 2017 dataset. (H) Similar with (G), but in NDB patients. (I) HR pathway gene SCNA frequency in patients achieved DCB in chemotherapy from Cancer Discov. 2017 dataset. Left: HR gene SCNAs. Right: HR core pathway gene SCNAs. (J) Similar with (I), but in NDB patients. (K) HR pathway gene mutation frequency in patients achieved CR and PR in chemotherapy from Nat Med. 2018 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (L) Similar with (K), but in other patients. (M) Similar with (K), but using patients received immunotherapy. (N) Similar with (M), but in other patients. Figure S8. Comparisons on TMB values stratified by therapeutic response and HR pathway gene status from public cohorts. (A) TMB and clonal TMB value distribution between DCB and other patients in non-squamous population from Nat Genet. 2018 dataset. Left: TMB. Right: clonal TMB. (B) Similar with (A) but using HR pathway gene clonal mutations as the stratification strategy. (C) TMB value distribution between DCB and NDB patients in J Clin Oncol. 2018. (D) Similar with (C) but focusing on different histological subtypes. Left: in adenocarcinoma subgroup. Right: in squamous subgroup. (E) Similar with (D) but using HR pathway gene mutation as the classification strategy. (F) Clonal TMB value distribution between patients stratified by HRD event in J Clin Oncol. 2018 dataset. Left: between patients with and without HR pathway gene mutation. Right: between patients with and without HR pathway gene clonal mutation. (G) TMB value distribution between CR+PR and other immunotherapy patients in Nat Med. 2018 dataset. (H) Similar with (G) but focusing on different histological subtypes. Left: in non-squamous subgroup. Right: in squamous subgroup. (I) TMB value distribution between CR+PR patients with HRD and other CR+PR patients in Nat Med. 2018. (J) Similar with (F), but in Nat Med. 2018 dataset. Figure S9. Survival analyses and TMB comparisons on HRD event-stratified patients in multiple public cohorts. (A) PFS of patients stratified by HR gene mutational condition in Nat Genet. 2018 dataset. Left: between patients with and without HR gene mutation. Right: between patients with and without HR gene clonal mutation. (B) Similar with (A), but on OS data. (C) Similar with (A) but in all patients from J Clin Oncol. 2018 cohort. (D) Similar with (C) but in adenocarcinoma patients. (E) Similar with (C), but in squamous patients. (F) PFS and TMB of all DCB patients with and without HR gene mutations in Nat Genet. 2018 cohort. (G) Similar with (F) but on OS data. (H) Similar with (F) but using HR pathway gene clonal mutations as the classification strategy. (I) Similar with (H) but on OS data. (J) Similar with (F) but in patients from J Clin Oncol. 2018 cohort. Figure S10. Survival analyses and TMB comparisons conducted on Nat Med. 2018 blood cohort. (A) Survival and TMB of all chemo-treated non-squamous patients with and without HR gene mutations. Left: OS. Middle: PFS. Right: TMB distribution. (B) Similar with (A) but on squamous patients. (C) Similar with (A) but on patients received immunotherapy. (D) Similar with (C) but on squamous patients. (E) Survival and TMB of all HR pathway-mutant patients treated with immunotherapy and chemotherapy. Left: OS. Middle: PFS. Right: TMB distribution. (F) Similar with (E) but among patients with HR gene clonal mutations. Figure S11. Investigation on HR pathway gene alternation frequency and HRD events in multiracial untreated patients. (A) HR pathway gene mutation frequency in Sci Rep. 2015 dataset. Left: all and clonal HR gene mutations. Right: all and clonal HR core pathway mutations. (B) Similar with (A), but in J Thorac Oncol. 2020 cohort. (C) Similar with (A), but in TCGA-LUAD dataset. (D) Similar with (A), but in TCGA-LUSC dataset. (E) HR pathway gene alternation frequency in TCGA-LUAD dataset. (F) Similar with (E), but in TCGA-LUSC dataset. (G) Correlation between CNV burden and HRDscore in TCGA-LUAD dataset. (H) Similar with (G), but in TCGA-LUSC dataset.

Published
2022
Full Text
View/download PDF

43. sj-docx-1-tam-10.1177_17588359221133546 – Supplemental material for Circulating tumour DNA biomarkers in savolitinib-treated patients with non-small cell lung cancer harbouring MET exon 14 skipping alterations: a post hoc analysis of a pivotal phase 2 study

Author

Yu, Yongfeng, Ren, Yongxin, Fang, Jian, Cao, Lejie, Liang, Zongan, Guo, Qisen, Han, Sen, Ji, Zimei, Wang, Ye, Sun, Yulan, Chen, Yuan, Li, Xingya, Xu, Hua, Zhou, Jianying, Jiang, Liyan, Cheng, Ying, Han, Zhigang, Shi, Jianhua, Chen, Gongyan, Ma, Rui, Fan, Yun, Sun, Sanyuan, Jiao, Longxian, Jia, Xiaoyun, Wang, Linfang, Lu, Puhan, Xu, Qian, Luo, Xian, Su, Weiguo, and Lu, Shun

Subjects
110203 Respiratory Diseases, FOS: Clinical medicine, 111702 Aged Health Care, FOS: Health sciences, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental material, sj-docx-1-tam-10.1177_17588359221133546 for Circulating tumour DNA biomarkers in savolitinib-treated patients with non-small cell lung cancer harbouring MET exon 14 skipping alterations: a post hoc analysis of a pivotal phase 2 study by Yongfeng Yu, Yongxin Ren, Jian Fang, Lejie Cao, Zongan Liang, Qisen Guo, Sen Han, Zimei Ji, Ye Wang, Yulan Sun, Yuan Chen, Xingya Li, Hua Xu, Jianying Zhou, Liyan Jiang, Ying Cheng, Zhigang Han, Jianhua Shi, Gongyan Chen, Rui Ma, Yun Fan, Sanyuan Sun, Longxian Jiao, Xiaoyun Jia, Linfang Wang, Puhan Lu, Qian Xu, Xian Luo, Weiguo Su and Shun Lu in Therapeutic Advances in Medical Oncology

Published
2022
Full Text
View/download PDF

45. Circulating tumour DNA biomarkers in savolitinib-treated patients with non-small cell lung cancer harbouring MET exon 14 skipping alterations: a post hoc analysis of a pivotal phase 2 study

Author

Yu, Yongfeng, primary, Ren, Yongxin, additional, Fang, Jian, additional, Cao, Lejie, additional, Liang, Zongan, additional, Guo, Qisen, additional, Han, Sen, additional, Ji, Zimei, additional, Wang, Ye, additional, Sun, Yulan, additional, Chen, Yuan, additional, Li, Xingya, additional, Xu, Hua, additional, Zhou, Jianying, additional, Jiang, Liyan, additional, Cheng, Ying, additional, Han, Zhigang, additional, Shi, Jianhua, additional, Chen, Gongyan, additional, Ma, Rui, additional, Fan, Yun, additional, Sun, Sanyuan, additional, Jiao, Longxian, additional, Jia, Xiaoyun, additional, Wang, Linfang, additional, Lu, Puhan, additional, Xu, Qian, additional, Luo, Xian, additional, Su, Weiguo, additional, and Lu, Shun, additional

Published
2022
Full Text
View/download PDF

46. Landscape and Predictive Significance of the Structural Classification of EGFR Mutations in Chinese NSCLCs: A Real-World Study.

Author

Gu, Linping, Huang, Huayan, Xu, Zhangwendi, Niu, Xiaomin, Li, Ziming, Xia, Liliang, Yu, Yongfeng, and Lu, Shun

Subjects
EPIDERMAL growth factor receptors, NON-small-cell lung carcinoma
Abstract

Background: Non-classical EGFR mutations demonstrate heterogeneous and attenuated responsiveness to EGFR TKIs. Non-small cell lung cancer (NSCLC) patients with atypical EGFR mutations have limited therapeutic options. A recent study established a novel structural-based classification of EGFR mutations and showed its value in predicting the response to TKI. We sought to interrogate the distribution of different structural types and to validate the predictive value in Chinese NSCLCs. Methods: A total of 837 tumor samples were retrospectively recruited from 522 patients with unresectable EGFR-mutant NSCLC. EGFR mutations were classified into four groups: classical-like, T790M-like, Ex20ins-L, and PACC. Treatment information and clinical outcomes were obtained from 436 patients. The time to treatment failure (TTF) was determined on a per-sample basis. Results: Of the 837 EGFR-mutant samples, 67.9%, 18.5%, 9.0%, and 3.1% harbored classical-like, T790M-like, PACC, and Ex20ins-L mutations, respectively. Thirteen (1.6%) samples carried mutations beyond the four types. Among the 204 samples with atypical mutations, 33.8%, 36.7%, 12.7%, and 10.3% were classical-like, PACC, Ex20ins-L, and T790M-like, respectively. In patients with PACC mutations, second-generation TKIs demonstrated a significantly longer TTF than first-generation TKIs (first-line: 15.3 vs. 6.2 months, p = 0.009; all-line: 14.7 vs. 7.1 months, p = 0.003), and a trend of longer TTF than third-generation TKIs (all-line: 14.7 vs. 5.1 months, p = 0.135). Conclusions: Our study depicted the landscape of structural types of EGFR mutations in Chinese NSCLC patients. Our results also suggest that the structural classification can serve as a predictive marker for the efficacy of various EGFR TKIs, which would guide therapeutic decision making. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

48. Prevalence of MET exon 14 skipping mutation in pulmonary sarcomatoid carcinoma patients without common targetable mutations: A single-institute study

Author

Yu, Yongfeng, Zhang, Qing, Zhang, Jie, and Lu, Shun

Subjects
Lung cancer -- Diagnosis -- Genetic aspects, DNA sequencing -- Usage, Gene mutation -- Research, Genes, Cancer treatment, Cancer research, Crizotinib, Carcinoma, Cancer, Cancer genetics, Cabozantinib, Health
Abstract

Byline: Yongfeng. Yu, Qing. Zhang, Jie. Zhang, Shun. Lu Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare histologic subtype of nonsmall cell lung cancer with limited targeted treatment options. In [...]

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results