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Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy

Tumor growth rate (TGR; percent size change per month [%/m]) is postulated as an early radio-graphic predictor of response to anti-cancer treatment to overcome limitations of RECIST. We aimed to evaluate the predictive value of pre-treatment TGR (TGR(0)) for outcomes of advanced non-small cell lung...

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Autores principales: He, Li-na, Zhang, Xuanye, Li, Haifeng, Chen, Tao, Chen, Chen, Zhou, Yixin, Lin, Zuan, Du, Wei, Fang, Wenfeng, Yang, Yunpeng, Huang, Yan, Zhao, Hongyun, Hong, Shaodong, Zhang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863973/
https://www.ncbi.nlm.nih.gov/pubmed/33552993
http://dx.doi.org/10.3389/fonc.2020.621329
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author He, Li-na
Zhang, Xuanye
Li, Haifeng
Chen, Tao
Chen, Chen
Zhou, Yixin
Lin, Zuan
Du, Wei
Fang, Wenfeng
Yang, Yunpeng
Huang, Yan
Zhao, Hongyun
Hong, Shaodong
Zhang, Li
author_facet He, Li-na
Zhang, Xuanye
Li, Haifeng
Chen, Tao
Chen, Chen
Zhou, Yixin
Lin, Zuan
Du, Wei
Fang, Wenfeng
Yang, Yunpeng
Huang, Yan
Zhao, Hongyun
Hong, Shaodong
Zhang, Li
author_sort He, Li-na
collection PubMed
description Tumor growth rate (TGR; percent size change per month [%/m]) is postulated as an early radio-graphic predictor of response to anti-cancer treatment to overcome limitations of RECIST. We aimed to evaluate the predictive value of pre-treatment TGR (TGR(0)) for outcomes of advanced non-small cell lung cancer (aNSCLC) patients treated with anti-PD-1/PD-L1 monotherapy. We retrospectively screened all aNSCLC patients who received PD-1 axis inhibitors in Sun Yat-Sen University Cancer Center between August 2016 and June 2018. TGR(0) was calculated as the percentage change in tumor size per month (%/m) derived from two computed tomography (CT) scans during a “wash-out” period before the initiation of PD-1 axis inhibition. Final follow-up date was August 28, 2019. The X-tile program was used to identify the cut-off value of TGR(0) based on maximum progression-free survival (PFS) stratification. Patients were divided into two groups per the selected TGR(0) cut-off. The primary outcome was the difference of PFS between the two groups. The Kaplan-Meier methods and Cox regression models were performed for survival analysis. A total of 80 eligible patients were included (54 [67.5%] male; median [range] age, 55 [30-74] years). Median (range) TGR(0) was 21.1 (-33.7-246.0)%/m. The optimal cut-off value of TGR(0) was 25.3%/m. Patients with high TGR(0) had shorter median PFS (1.8 months; 95% CI, 1.6 - 2.1 months) than those with low TGR(0) (2.7 months; 95% CI, 0.5 - 4.9 months) (P = 0.005). Multivariate Cox regression analysis revealed that higher TGR(0) independently predicted inferior PFS (hazard ratio [HR] 1.97; 95% CI, 1.08-3.60; P = 0.026). Higher TGR(0) was also significantly associated with less durable clinical benefit rate (34.8% vs. 8.8%, P = 0.007). High pre-treatment TGR was a reliable predictor of inferior PFS and clinical benefit in aNSCLC patients undergoing anti-PD-1/PD-L1 monotherapy. The findings highlight the role of TGR(0) as an early biomarker to predict benefit from immunotherapy and could allow tailoring patient’s follow-up.
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spelling pubmed-78639732021-02-06 Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy He, Li-na Zhang, Xuanye Li, Haifeng Chen, Tao Chen, Chen Zhou, Yixin Lin, Zuan Du, Wei Fang, Wenfeng Yang, Yunpeng Huang, Yan Zhao, Hongyun Hong, Shaodong Zhang, Li Front Oncol Oncology Tumor growth rate (TGR; percent size change per month [%/m]) is postulated as an early radio-graphic predictor of response to anti-cancer treatment to overcome limitations of RECIST. We aimed to evaluate the predictive value of pre-treatment TGR (TGR(0)) for outcomes of advanced non-small cell lung cancer (aNSCLC) patients treated with anti-PD-1/PD-L1 monotherapy. We retrospectively screened all aNSCLC patients who received PD-1 axis inhibitors in Sun Yat-Sen University Cancer Center between August 2016 and June 2018. TGR(0) was calculated as the percentage change in tumor size per month (%/m) derived from two computed tomography (CT) scans during a “wash-out” period before the initiation of PD-1 axis inhibition. Final follow-up date was August 28, 2019. The X-tile program was used to identify the cut-off value of TGR(0) based on maximum progression-free survival (PFS) stratification. Patients were divided into two groups per the selected TGR(0) cut-off. The primary outcome was the difference of PFS between the two groups. The Kaplan-Meier methods and Cox regression models were performed for survival analysis. A total of 80 eligible patients were included (54 [67.5%] male; median [range] age, 55 [30-74] years). Median (range) TGR(0) was 21.1 (-33.7-246.0)%/m. The optimal cut-off value of TGR(0) was 25.3%/m. Patients with high TGR(0) had shorter median PFS (1.8 months; 95% CI, 1.6 - 2.1 months) than those with low TGR(0) (2.7 months; 95% CI, 0.5 - 4.9 months) (P = 0.005). Multivariate Cox regression analysis revealed that higher TGR(0) independently predicted inferior PFS (hazard ratio [HR] 1.97; 95% CI, 1.08-3.60; P = 0.026). Higher TGR(0) was also significantly associated with less durable clinical benefit rate (34.8% vs. 8.8%, P = 0.007). High pre-treatment TGR was a reliable predictor of inferior PFS and clinical benefit in aNSCLC patients undergoing anti-PD-1/PD-L1 monotherapy. The findings highlight the role of TGR(0) as an early biomarker to predict benefit from immunotherapy and could allow tailoring patient’s follow-up. Frontiers Media S.A. 2021-01-19 /pmc/articles/PMC7863973/ /pubmed/33552993 http://dx.doi.org/10.3389/fonc.2020.621329 Text en Copyright © 2021 He, Zhang, Li, Chen, Chen, Zhou, Lin, Du, Fang, Yang, Huang, Zhao, Hong and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
He, Li-na
Zhang, Xuanye
Li, Haifeng
Chen, Tao
Chen, Chen
Zhou, Yixin
Lin, Zuan
Du, Wei
Fang, Wenfeng
Yang, Yunpeng
Huang, Yan
Zhao, Hongyun
Hong, Shaodong
Zhang, Li
Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy
title Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy
title_full Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy
title_fullStr Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy
title_full_unstemmed Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy
title_short Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy
title_sort pre-treatment tumor growth rate predicts clinical outcomes of patients with advanced non-small cell lung cancer undergoing anti-pd-1/pd-l1 therapy
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863973/
https://www.ncbi.nlm.nih.gov/pubmed/33552993
http://dx.doi.org/10.3389/fonc.2020.621329
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