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 (TGR0) for outcomes of advanced non-small cell lung ca...
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Frontiers Media S.A.
2021-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2020.621329/full |
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| author | Li-na He Li-na He Li-na He Xuanye Zhang Xuanye Zhang Xuanye Zhang Haifeng Li Haifeng Li Haifeng Li Tao Chen Tao Chen Tao Chen Chen Chen Chen Chen Chen Chen Yixin Zhou Yixin Zhou Yixin Zhou Zuan Lin Zuan Lin Zuan Lin Wei Du Wei Du Wei Du Wenfeng Fang Wenfeng Fang Wenfeng Fang Yunpeng Yang Yunpeng Yang Yunpeng Yang Yan Huang Yan Huang Yan Huang Hongyun Zhao Hongyun Zhao Hongyun Zhao Shaodong Hong Shaodong Hong Shaodong Hong Li Zhang Li Zhang Li Zhang |
| author_facet | Li-na He Li-na He Li-na He Xuanye Zhang Xuanye Zhang Xuanye Zhang Haifeng Li Haifeng Li Haifeng Li Tao Chen Tao Chen Tao Chen Chen Chen Chen Chen Chen Chen Yixin Zhou Yixin Zhou Yixin Zhou Zuan Lin Zuan Lin Zuan Lin Wei Du Wei Du Wei Du Wenfeng Fang Wenfeng Fang Wenfeng Fang Yunpeng Yang Yunpeng Yang Yunpeng Yang Yan Huang Yan Huang Yan Huang Hongyun Zhao Hongyun Zhao Hongyun Zhao Shaodong Hong Shaodong Hong Shaodong Hong Li Zhang Li Zhang Li Zhang |
| author_sort | Li-na He |
| collection | DOAJ |
| 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 (TGR0) 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. TGR0 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 TGR0 based on maximum progression-free survival (PFS) stratification. Patients were divided into two groups per the selected TGR0 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) TGR0 was 21.1 (-33.7-246.0)%/m. The optimal cut-off value of TGR0 was 25.3%/m. Patients with high TGR0 had shorter median PFS (1.8 months; 95% CI, 1.6 - 2.1 months) than those with low TGR0 (2.7 months; 95% CI, 0.5 - 4.9 months) (P = 0.005). Multivariate Cox regression analysis revealed that higher TGR0 independently predicted inferior PFS (hazard ratio [HR] 1.97; 95% CI, 1.08-3.60; P = 0.026). Higher TGR0 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 TGR0 as an early biomarker to predict benefit from immunotherapy and could allow tailoring patient’s follow-up. |
| first_indexed | 2024-12-14T11:30:41Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-14T11:30:41Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-30715b95c2c24c239ea3a35129fc4dd32022-12-21T23:03:18ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-01-011010.3389/fonc.2020.621329621329Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 TherapyLi-na He0Li-na He1Li-na He2Xuanye Zhang3Xuanye Zhang4Xuanye Zhang5Haifeng Li6Haifeng Li7Haifeng Li8Tao Chen9Tao Chen10Tao Chen11Chen Chen12Chen Chen13Chen Chen14Yixin Zhou15Yixin Zhou16Yixin Zhou17Zuan Lin18Zuan Lin19Zuan Lin20Wei Du21Wei Du22Wei Du23Wenfeng Fang24Wenfeng Fang25Wenfeng Fang26Yunpeng Yang27Yunpeng Yang28Yunpeng Yang29Yan Huang30Yan Huang31Yan Huang32Hongyun Zhao33Hongyun Zhao34Hongyun Zhao35Shaodong Hong36Shaodong Hong37Shaodong Hong38Li Zhang39Li Zhang40Li Zhang41State Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of VIP Region, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Guangzhou, ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, ChinaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaTumor 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 (TGR0) 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. TGR0 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 TGR0 based on maximum progression-free survival (PFS) stratification. Patients were divided into two groups per the selected TGR0 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) TGR0 was 21.1 (-33.7-246.0)%/m. The optimal cut-off value of TGR0 was 25.3%/m. Patients with high TGR0 had shorter median PFS (1.8 months; 95% CI, 1.6 - 2.1 months) than those with low TGR0 (2.7 months; 95% CI, 0.5 - 4.9 months) (P = 0.005). Multivariate Cox regression analysis revealed that higher TGR0 independently predicted inferior PFS (hazard ratio [HR] 1.97; 95% CI, 1.08-3.60; P = 0.026). Higher TGR0 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 TGR0 as an early biomarker to predict benefit from immunotherapy and could allow tailoring patient’s follow-up.https://www.frontiersin.org/articles/10.3389/fonc.2020.621329/fullprogression-free survivalnon-small cell lung cancerNSCLCanti-PD-1/PD-L1 therapyimmunotherapytumor growth rate |
| spellingShingle | Li-na He Li-na He Li-na He Xuanye Zhang Xuanye Zhang Xuanye Zhang Haifeng Li Haifeng Li Haifeng Li Tao Chen Tao Chen Tao Chen Chen Chen Chen Chen Chen Chen Yixin Zhou Yixin Zhou Yixin Zhou Zuan Lin Zuan Lin Zuan Lin Wei Du Wei Du Wei Du Wenfeng Fang Wenfeng Fang Wenfeng Fang Yunpeng Yang Yunpeng Yang Yunpeng Yang Yan Huang Yan Huang Yan Huang Hongyun Zhao Hongyun Zhao Hongyun Zhao Shaodong Hong Shaodong Hong Shaodong Hong Li Zhang Li Zhang Li Zhang Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy Frontiers in Oncology progression-free survival non-small cell lung cancer NSCLC anti-PD-1/PD-L1 therapy immunotherapy tumor growth rate |
| 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 | progression-free survival non-small cell lung cancer NSCLC anti-PD-1/PD-L1 therapy immunotherapy tumor growth rate |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2020.621329/full |
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