Utility of comprehensive genomic profiling in directing treatment and improving patient outcomes in advanced non-small cell lung cancer.
Utility of comprehensive genomic profiling in directing treatment and improving patient outcomes in advanced non-small cell lung cancer.
复制标题
综合基因组谱在指导晚期非小细胞肺癌治疗和改善患者预后中的应用。
DOI：
10.1186/s12916-021-02089-z
复制
发表时间：
2021-10-01
期刊：
影响因子：
9.3
通讯作者：
Zhang L
中科院分区：
文献类型：
--
作者：
Zhao S;Zhang Z;Zhan J;Zhao X;Chen X;Xiao L;Wu K;Ma Y;Li M;Yang Y;Fang W;Zhao H;Zhang L
随着新的靶向驱动因子的发现和新型靶向药物的出现，越来越多的人主张在晚期NSCLC的临床护理中使用全面的基因组分析来代替经典驱动因子的常规检测。然而，证明这种做法合理的关键假设，即全面的基因组分析可以导致有效的抗癌治疗并改善患者的预后，仍然没有得到证实。在1564例晚期NSCLC患者中前瞻性应用综合基因组分析，以确定潜在的可操作基因组改变。根据分析结果，将患者分配至基因型匹配的靶向治疗或不匹配的治疗。其在指导治疗方面的效用取决于接受基因型匹配靶向治疗的患者比例和入组基因型匹配临床试验的患者比例。通过比较接受基因型匹配和非匹配治疗的患者的无进展生存期（PFS）和总生存期（OS），评估其对患者结局的影响。从2016年10月至2019年10月，在1166例患者中建立了肿瘤基因组图谱，导致37.7%（n = 440）的患者接受了匹配的靶向治疗，20.9%（n = 244）的患者入组了基因型匹配的试验。在781例患者（67.0%）中检测到潜在的可采取行动的改变。对于这些患者，与非匹配治疗相比，基因组分析导向的匹配治疗显著改善了PFS（9.0个月vs 4.9个月，P < 0.001）和OS（3.9年vs 2.5年，P < 0.001）。排除接受标准靶向治疗的患者，基因组分析导致16.7%（n = 24）的患者接受了匹配的靶向治疗，11.2%（n = 16）的患者入组了匹配的试验。在该人群中，使用基因型匹配的靶向治疗未观察到PFS（4.7个月vs 4.6个月，P = 0.530）或OS（1.9年vs 2.4年，P = 0.238）获益。综合基因组分析在辅助治疗选择、促进临床试验入组和改善晚期NSCLC患者结局方面具有临床实用性。然而，对于携带变异但未使用标准治疗靶向药物的患者，应谨慎解读基因组分析结果，因为在当前治疗环境中，该人群从研究性或标签外使用靶向治疗中获益的可能性较低。ChiCTR 1900027582（于2019年11月19日回顾性注册）在线版本包含补充材料，可通过10.1186/s12916-021-02089-z获得。
With the identification of new targetable drivers and the recent emergence of novel targeted drugs, using comprehensive genomic profiling in lieu of the routine testing for classic drivers in the clinical care for advanced NSCLC has been increasingly advocated. However, the key assumption justifying this practice, that comprehensive genomic profiling could lead to effective anticancer therapies and improve patient outcomes, remains unproved. Comprehensive genomic profiling was prospectively applied in 1564 advanced NSCLC patients to identify potentially actionable genomic alterations. Patients were assigned to genotype-matched targeted therapies or nonmatched therapies based on the profiling results. Its utility in directing treatments was determined by the proportion of patients receiving genotype-matched targeted therapies and the proportion of patients being enrolled into genotype-matched clinical trials. Its impacts on patient outcomes were assessed by comparing progression-free survival (PFS) and overall survival (OS) between patients who received a genotype-matched and nonmatched therapy. From October 2016 to October 2019, tumor genomic profiles were established in 1166 patients, leading to a matched targeted therapy in 37.7% (n = 440) and a genotype-matched trial enrollment in 20.9% of patients (n = 244). Potentially actionable alterations were detected in 781 patients (67.0%). For these patients, a genomic profiling-directed matched therapy significantly improved PFS (9.0 months vs 4.9 months, P < 0.001) and OS (3.9 years vs 2.5 years, P < 0.001) compared with a nonmatched therapy. Excluding patients with standard targeted therapies, genomic profiling led to a matched targeted therapy in 16.7% (n = 24) and a matched trial enrollment in 11.2% (n = 16) of patients. No PFS (4.7 months vs 4.6 months, P = 0.530) or OS (1.9 years vs 2.4 years, P = 0.238) benefit was observed with the use of genotype-matched targeted therapies in this population. Comprehensive genomic profiling is of clinical utility in assisting treatment selection, facilitating clinical trial enrollment, and improving patient outcomes in advanced NSCLC. However, for patients carrying alterations without standard-of-care targeted drugs, the interpretation of genomic profiling results should be careful given the low likelihood of benefit from the investigational or off-label use of targeted therapies in this population in the current treatment landscape. ChiCTR1900027582 (retrospectively registered on 19 November 2019) The online version contains supplementary material available at 10.1186/s12916-021-02089-z.
登录
查看更多内容
DOI：
10.1056/nejmoa2005653
发表时间：
2020-08-27
期刊：
The New England journal of medicine
影响因子：
--
作者：
Drilon A;Oxnard GR;Tan DSW;Loong HHF;Johnson M;Gainor J;McCoach CE;Gautschi O;Besse B;Cho BC;Peled N;Weiss J;Kim YJ;Ohe Y;Nishio M;Park K;Patel J;Seto T;Sakamoto T;Rosen E;Shah MH;Barlesi F;Cassier PA;Bazhenova L;De Braud F;Garralda E;Velcheti V;Satouchi M;Ohashi K;Pennell NA;Reckamp KL;Dy GK;Wolf J;Solomon B;Falchook G;Ebata K;Nguyen M;Nair B;Zhu EY;Yang L;Huang X;Olek E;Rothenberg SM;Goto K;Subbiah V
通讯作者：
Subbiah V
影响因子：
51.1
作者：
Planchard, David;Smit, Egbert F.;Johnson, Bruce E.
通讯作者：
Johnson, Bruce E.
影响因子：
120.7
作者：
Kris, Mark G.;Johnson, Bruce E.;Berry, Lynne D.;Kwiatkowski, David J.;Iafrate, A. John;Wistuba, Ignacio I.;Varella-Garcia, Marileila;Franklin, Wilbur A.;Aronson, Samuel L.;Su, Pei-Fang;Shyr, Yu;Camidge, D. Ross;Sequist, Lecia V.;Glisson, Bonnie S.;Khuri, Fadlo R.;Garon, Edward B.;Pao, William;Rudin, Charles;Schiller, Joan;Haura, Eric B.;Socinski, Mark;Shirai, Keisuke;Chen, Heidi;Giaccone, Giuseppe;Ladanyi, Marc;Kugler, Kelly;Minna, John D.;Bunn, Paul A.
通讯作者：
Bunn, Paul A.
DOI：
10.6004/jnccn.2019.0059
发表时间：
2019-12-01
影响因子：
13.4
作者：
Ettinger, David S.;Wood, Douglas E.;Hughes, Miranda
通讯作者：
Hughes, Miranda
影响因子：
5.3
作者：
Tan, Aaron C.;Lai, Gillianne G. Y.;Tan, Daniel S. W.
通讯作者：
Tan, Daniel S. W.