骨髓增殖性肿瘤的分类与个体化预后
Classification and Personalized Prognosis in Myeloproliferative Neoplasms.
机构信息
From the Wellcome-MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research (J.G., C.E.M., F.L.N., A.R.G., P.J.C.), the Department of Haematology, University of Cambridge (J.G., E.J.B., C.M., J.C., C.E.M., F.L.N., A.R.G.), and the Department of Haematology, Cambridge University Hospitals NHS Foundation Trust (J.G., E.J.B., A.L.G., C.M., J.C., A.R.G.), Cambridge, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus (J.N., D.C.W., N.A., E.P., G.G., L.O., S.O., J.W.T., A.P.B., N.W., P.J.C.), and the European Molecular Biology Laboratory, European Bioinformatics Institute (R.C., M.G.), Hinxton, Big Data Institute, University of Oxford, Oxford (D.C.W.), the Department of Haematology, Queen's University Belfast, Belfast (M.F.M.), and the Department of Haematology, Guy's and St. Thomas' NHS Foundation Trust, London (C.N.H.) - all in the United Kingdom; the Center for Molecular Oncology and the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York (E.P., G.G.); the Department of Hematology, Zealand University Hospital, Roskilde, and the University of Copenhagen, Copenhagen (C.L.A., H.C.H.); and the Department of Experimental and Clinical Medicine, Center of Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy (P.G., A.M.V.).
出版信息
N Engl J Med. 2018 Oct 11;379(15):1416-1430. doi: 10.1056/NEJMoa1716614.
BACKGROUND
Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.
METHODS
We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.
RESULTS
A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.
CONCLUSIONS
Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.).
背景
骨髓增生性肿瘤，如真性红细胞增多症、原发性血小板增多症和骨髓纤维化，是具有不同进展速度的慢性血液系统癌症。骨髓增生性肿瘤患者的基因组特征分析可为个性化诊断、风险分层和治疗提供可能。
方法
我们对骨髓增生性肿瘤患者的 69 个髓系肿瘤基因的编码外显子进行了测序，全面注释了驱动突变和拷贝数变化。我们为骨髓增生性肿瘤建立了一种基因组分类，并为预测个体患者的预后开发了多阶段预后模型。在外部队列中对分类和预后模型进行了验证。
结果
共有 2035 例患者纳入分析。共有 33 个基因的驱动突变至少出现在 5 例患者中，其中 45%的患者仅存在 JAK2、CALR 或 MPL 基因突变。驱动突变的数量随年龄和疾病进展而增加。驱动突变、种系多态性和人口统计学变量可独立预测患者被诊断为真性红细胞增多症还是原发性血小板增多症，或被诊断为慢性期疾病还是骨髓纤维化。我们定义了 8 个基因组亚组，这些亚组表现出不同的临床表型，包括血细胞计数、白血病转化风险和无事件生存。整合 63 个临床和基因组变量，我们创建了能够对慢性期骨髓增生性肿瘤和骨髓纤维化患者的临床结局进行个体化预测的预后模型。在训练队列的内部交叉验证和独立的外部队列中，预测结果与观察结果相关性良好。即使在现有的预后方案的个别类别中，我们的模型也大大提高了预测准确性。
结论
全面的基因组特征分析确定了不同的遗传亚组，并基于因果生物学机制对骨髓增生性肿瘤进行了分类。基因组数据与临床变量的整合实现了对患者结局的个体化预测，可能有助于对骨髓增生性肿瘤患者的治疗。（由威康信托基金会等资助）。
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