Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis.
Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis.
复制标题
DOI：
10.1371/journal.pmed.1002201
复制
发表时间：
2016-12
期刊：
影响因子：
15.8
通讯作者：
André F
中科院分区：
文献类型：
--
作者：
Lefebvre C;Bachelot T;Filleron T;Pedrero M;Campone M;Soria JC;Massard C;Lévy C;Arnedos M;Lacroix-Triki M;Garrabey J;Boursin Y;Deloger M;Fu Y;Commo F;Scott V;Lacroix L;Dieci MV;Kamal M;Diéras V;Gonçalves A;Ferrerro JM;Romieu G;Vanlemmens L;Mouret Reynier MA;Théry JC;Le Du F;Guiu S;Dalenc F;Clapisson G;Bonnefoi H;Jimenez M;Le Tourneau C;André F
早期乳腺癌(EBC)基因组图谱的研究已取得重大进展。转移性乳腺癌(MBC)与不良预后相关，但关于这种疾病的基因组图谱的信息有限。这项研究旨在利用下一代测序技术破译MBC的突变图谱。在SAFIR01、SAFIR02、SHIVA或癌症治疗优化分子筛查(Moscato)前瞻性试验的背景下，对216例接受活组织检查的MBC患者的肿瘤-血液对进行了全外显子组测序。用来自癌症基因组图谱(TCGA)的772个原发乳腺肿瘤的突变图谱作为比较原发突变图谱和单核细胞突变图谱的参考。12个基因(TP53、PIK3CA、GATA3、ESR1、MAP3K1、CDH1、AKT1、MAP2K4、RB1、PTEN、Cbfb和CDKN2A)在MBC中显著突变(假发现率为0.1)。8个基因(ESR1、FSIP2、FRAS1、OSBPL3、EDC4、PALB2、IGFN1和AGRN)在MBC中的突变频率高于EBC(FDR<；0.01)。ESR1基因同时被鉴定为驱动基因和转移基因(n=22，优势比=29，95%CI[9-155]，p=1.2e-12)，并对31例ESR1突变或扩增的MBC进行了焦点扩增(n=9)，其中包括27例激素受体阳性(HR+)和HER2阴性(HER2-−)的MBC(19%)。与HR+/HER2−−相比，HR+/HER2 MBC有较高的突变率(分别为6%和0.7%，P=0.0004)。在HR+MBC中，ERBB4(n=8)、NOTCH3(n=7)和ALK(n=7)等其他可作用基因的突变频率较高。突变特征分析显示，与原发−样本(p<；2e-16)相比，在HR+/HER2TCGA转移瘤中APOBEC介导的突变显著增加。这项研究的主要局限性包括没有骨转移和队列的大小，这可能无法识别罕见的突变及其对生存的影响。这项工作报告了第一次大规模研究MBC突变图谱的分析结果。这项研究揭示了与治疗耐药性有关的基因组变化和突变特征，包括可操作的突变。Fabrice Andre和他的同事描述了在一大群转移性乳腺癌患者中发生的突变。乳腺癌在转移到远处器官后通常会导致不良的预后，但是，尽管人们在分子水平上广泛地描述了原发乳腺癌的特征，但对转移的病变却知之甚少。这项研究旨在通过对大量转移性乳腺癌和相应的血液样本进行全外显子测序和分析，来表征转移性乳腺癌的突变情况。了解转移性肿瘤的突变情况应该会为评估对治疗的耐药性和开发更好的治疗方法开辟新的途径。作者从乳腺癌转移瘤的DNA和每个患者相应的未突变DNA中生成了大量完整外显子组测序数据，以识别肿瘤特有的突变和基因拷贝数变化。生物信息学分析确定了转移性肿瘤中反复突变的基因，并通过比较它们与原发乳腺肿瘤的突变频率，揭示了与转移性疾病相关的基因。这项研究可以确定受影响的基因和突变特征，这些突变特征与原发肿瘤相比在转移性肿瘤中更常见，可能与耐药有关。与乳腺癌转移相关的突变和拷贝数改变的鉴定表明，肿瘤是在治疗的压力下演变的。除原发肿瘤外，转移灶的突变和拷贝数变化的特征应有助于为患者量身定做治疗，并有可能改善临床结果。
Major advances have been achieved in the characterization of early breast cancer (eBC) genomic profiles. Metastatic breast cancer (mBC) is associated with poor outcomes, yet limited information is available on the genomic profile of this disease. This study aims to decipher mutational profiles of mBC using next-generation sequencing. Whole-exome sequencing was performed on 216 tumor–blood pairs from mBC patients who underwent a biopsy in the context of the SAFIR01, SAFIR02, SHIVA, or Molecular Screening for Cancer Treatment Optimization (MOSCATO) prospective trials. Mutational profiles from 772 primary breast tumors from The Cancer Genome Atlas (TCGA) were used as a reference for comparing primary and mBC mutational profiles. Twelve genes (TP53, PIK3CA, GATA3, ESR1, MAP3K1, CDH1, AKT1, MAP2K4, RB1, PTEN, CBFB, and CDKN2A) were identified as significantly mutated in mBC (false discovery rate [FDR] < 0.1). Eight genes (ESR1, FSIP2, FRAS1, OSBPL3, EDC4, PALB2, IGFN1, and AGRN) were more frequently mutated in mBC as compared to eBC (FDR < 0.01). ESR1 was identified both as a driver and as a metastatic gene (n = 22, odds ratio = 29, 95% CI [9–155], p = 1.2e-12) and also presented with focal amplification (n = 9) for a total of 31 mBCs with either ESR1 mutation or amplification, including 27 hormone receptor positive (HR+) and HER2 negative (HER2−) mBCs (19%). HR+/HER2− mBC presented a high prevalence of mutations on genes located on the mechanistic target of rapamycin (mTOR) pathway (TSC1 and TSC2) as compared to HR+/HER2− eBC (respectively 6% and 0.7%, p = 0.0004). Other actionable genes were more frequently mutated in HR+ mBC, including ERBB4 (n = 8), NOTCH3 (n = 7), and ALK (n = 7). Analysis of mutational signatures revealed a significant increase in APOBEC-mediated mutagenesis in HR+/HER2− metastatic tumors as compared to primary TCGA samples (p < 2e-16). The main limitations of this study include the absence of bone metastases and the size of the cohort, which might not have allowed the identification of rare mutations and their effect on survival. This work reports the results of the analysis of the first large-scale study on mutation profiles of mBC. This study revealed genomic alterations and mutational signatures involved in the resistance to therapies, including actionable mutations. Fabrice Andre and colleagues describe the mutations occurring in a large group of patients with metastatic breast cancer. Breast cancer often results in poor outcomes after it has metastasized to distant organs, but, while primary breast tumors have been extensively characterized at the molecular level, metastatic lesions are poorly understood. This study aims to characterize the mutational landscape of metastatic breast cancer by performing and analyzing whole-exome sequencing of a large collection of metastatic breast tumors and corresponding blood samples. Understanding of the mutational landscape of metastatic tumors should open new avenues for assessing resistance to therapy and developing better treatments. The authors generated a large collection of whole-exome sequencing data from the DNA of breast cancer metastases and from each patient’s corresponding unmutated DNA in order to identify mutations and gene copy number alterations specific to the tumors. The bioinformatics analyses identified recurrently mutated genes in metastatic tumors and revealed the genes specifically involved in metastatic disease by comparing their mutational frequency to those of primary breast tumors. The study allowed identification of the affected genes and of mutational signatures that were more prevalent in metastatic as compared with primary tumors and that may be involved in the resistance to therapies. The identification of mutational and copy number alterations specifically involved in breast cancer metastasis demonstrated that tumors evolve under the pressure of therapy. Characterization of mutations and copy number alterations in metastatic lesions in addition to primary tumors should help to tailor treatment for patients, with the potential for improved clinical outcomes.
登录
查看更多内容
DOI：
10.1093/annonc/mdu479
发表时间：
2015-01
期刊：
Annals of oncology : official journal of the European Society for Medical Oncology
影响因子：
--
作者：
Favero F;Joshi T;Marquard AM;Birkbak NJ;Krzystanek M;Li Q;Szallasi Z;Eklund AC
通讯作者：
Eklund AC
影响因子：
64.8
作者：
Ellis, Matthew J.;Ding, Li;Shen, Dong;Luo, Jingqin;Suman, Vera J.;Wallis, John W.;Van Tine, Brian A.;Hoog, Jeremy;Goiffon, Reece J.;Goldstein, Theodore C.;Ng, Sam;Lin, Li;Crowder, Robert;Snider, Jacqueline;Ballman, Karla;Weber, Jason;Chen, Ken;Koboldt, Daniel C.;Kandoth, Cyriac;Schierding, William S.;McMichael, Joshua F.;Miller, Christopher A.;Lu, Charles;Harris, Christopher C.;McLellan, Michael D.;Wendl, Michael C.;DeSchryver, Katherine;Allred, D. Craig;Esserman, Laura;Unzeitig, Gary;Margenthaler, Julie;Babiera, G. V.;Marcom, P. Kelly;Guenther, J. M.;Leitch, Marilyn;Hunt, Kelly;Olson, John;Tao, Yu;Maher, Christopher A.;Fulton, Lucinda L.;Fulton, Robert S.;Harrison, Michelle;Oberkfell, Ben;Du, Feiyu;Demeter, Ryan;Vickery, Tammi L.;Elhammali, Adnan;Piwnica-Worms, Helen;McDonald, Sandra;Watson, Mark;Dooling, David J.;Ota, David;Chang, Li-Wei;Bose, Ron;Ley, Timothy J.;Piwnica-Worms, David;Stuart, Joshua M.;Wilson, Richard K.;Mardis, Elaine R.
通讯作者：
Mardis, Elaine R.
影响因子：
17.1
作者：
McGranahan N;Favero F;de Bruin EC;Birkbak NJ;Szallasi Z;Swanton C
通讯作者：
Swanton C
影响因子：
51.1
作者：
Cybulski, Cezary;Kluzniak, Wojciech;Lubinski, Jan
通讯作者：
Lubinski, Jan
影响因子：
50.5
作者：
Lobbezoo, D. J. A.;van Kampen, R. J. W.;Tjan-Heijnen, V. C. G.
通讯作者：
Tjan-Heijnen, V. C. G.