臺灣博碩士論文加值系統

急性骨髓性白血病是成人最常見的白血病之一，在台灣癌症造成的死亡率中歷年排名皆約第十名。。急性骨髓性白血病是一種臨床和生物學上相當異質的血液惡性腫瘤，其特點是不成熟的造血細胞不受控制地增殖，同時喪失分化能力。為了改善患者的治療效果與提高醫療照護品質，結合精準醫學的醫療照顧是非常關鍵的，包括診斷時的風險分層和基於治療反應的治療策略調整。近來，學界已經發現了許多影響預後的因素，著眼於這幾年生物技術方法學的發展，急性骨髓性白血病病患的存活率有望更進一步的提升。本論文將利用次世代定序技術來解決這些與急性骨髓性白血病預後相關的關鍵問題，分為三個部分：病患初診斷時的基因突變、非編碼核糖核酸的表現量和接受化學治療之後的可測/最小殘餘疾病之監測。
第一部分是初診斷時的cohesin基因突變。在其他骨髓性惡性腫瘤中，cohesin基因突變的臨床使用與預後意義已有文獻闡述，但其在急性骨髓性白血病的臨床預後意義和在治療過程中的動態變化仍有待發掘。本研究針對391位新診斷的急性骨髓性白血病患者，發現有9.5%的患者存在cohesin基因突變，最常見的是RAD21（3.8%）和STAG2（3.1%）基因突變。這些cohesin基因突變是總體存活期和無復發存活期的獨立的良好預後因子。在病患接受治療的追蹤中，有兩名患者在過程中失去了原有的cohesin基因突變，而在初診斷沒有任何cohesin基因突變的患者在復發時都沒有獲得新的突變。在其功能方面的分析顯示，cohesin基因突變涉及造血系統、骨髓與血液細胞相關的功能。
第二部分是初診斷時的長鏈非編碼核糖核酸（lncRNAs）表現量。近年來有些文獻認為，長鏈非編碼核糖核酸的表現量可能可以作為急性骨髓性白血病患者的預後評估依據。然而，將長鏈非編碼核糖核酸表現量納入目前使用最廣的2017年歐洲白血病網（ELN）風險分類中，是否能進一步改善預後評估系統仍有待研究。在本論文中，我們將275位急性骨髓性白血病患者隨機分為訓練組（n = 183）和驗證組（n = 92）。在訓練組中，我們建構了一個由五個長鏈非編碼核糖核酸表現量組成的評分系統，在這個評分系統中，高分患者的總體存活期與無復發存活期皆比低分患者要來的短，這個發現在驗證組中得到進一步證實。多變項統計分析顯示，此長鏈非編碼核糖核酸表現量組成的評分系統是獨立於2017年歐洲白血病網分類的預後因子。
第三部分是治療之後可測/最小殘餘疾病（MRD）之監測。次世代定序（NGS）目前已知可用於急性骨髓病白血病患者的殘餘疾病的監測，但該檢測的最佳應用時間點仍不明確。在本論文中，我們收集了303位急性骨髓性白血病患者在診斷時、首次完全緩解後（第一個時間點）、首次鞏固化療後（第二個時間點），共三個時間點的骨髓細胞，進行了次世代定序的分析。在第一個和第二個時間點分別有44.6%和27.7%的患者檢測到殘餘疾病。在第一或第二個時間點帶有殘餘疾病的患者，其累計復發率較高，總體存活期與無復發存活期較短。第一個時間點帶有殘餘疾病但在第二個時間點沒有殘餘疾病的患者，其預後和第一與第二個時間點皆無殘餘疾病的患者一樣好。
總結本論文發現，cohesin基因突變對AML患者的預後有良好的影響。這些突變在疾病進展中可能並不關鍵，因為在追蹤過程中，沒有任何原本不帶有cohesin突變的患者獲得突變。在2017年歐洲白血病網分類中納入長鏈非編碼核糖核酸評分系統可以改善AML患者的風險預後評估。最後，使用次世代定序來監測可測/最小殘餘疾病，發現第二個時間點偵測得到的可測/最小殘餘疾病，可以幫助預測患者的臨床預後。希望藉由在診斷和追蹤階段納入這些預後指標，臨床醫師可以更進一步擬定最有勝算的治療計畫與提供精準到位的治療，提升急性骨髓性白血病患者的存活率。

Acute myeloid leukemia (AML) is one of the most common forms of leukemia in adults and is associated with significant mortality in Taiwan. AML is a clinically and biologically heterogeneous hematologic malignancy characterized by uncontrolled proliferation of hematopoietic precursors along with loss of their ability to differentiate. To improve quality health care and patient outcomes, incorporation of precision medicine, including risk stratification at diagnosis and treatment strategy modifications based on the treatment response, is very critical. Recently, numerous risk factors have been identified using traditional techniques. With the advancement of biotechnology methodology, the prognosis of AML is expected to be more favorable. This thesis will address these crucial issues associated with AML prognosis using the technique of next-generation sequencing (NGS), which is divided into three parts: coding, non-coding, and measurable/minimal residual disease monitoring.
Mutations in the cohesin complex genes have been reported in myeloid malignancies, but their prognostic implications and dynamic changes during the clinical course of AML remain undefined. This thesis involved a study on 391 newly diagnosed de novo non-M3 AML patients. We found that 9.5% of patients had cohesin gene mutations, most prevalent in RAD21 (3.8%) and STAG2 (3.1%) genes. These cohesion gene mutations were independent favorable factors for both overall survival (OS) and relapse-free survival (RFS), irrespective of other prognostic factors. Serial analyses showed that two patients lost the original cohesin mutations during disease evolution, while none of the patients without any cohesin gene mutation acquired a novel mutation at relapse. Gene set enrichment analysis demonstrated that cohesin gene mutations were involved in functions related to hematopoiesis, myeloid, or blood cells.
The expression of long non-coding RNAs (lncRNAs) has recently been recognized as a potential prognostic marker for AML. However, it remains unclear whether incorporation of lncRNA expression in the 2017 European LeukemiaNet (ELN) risk classification can further improve prognostic prediction. In the study described in this thesis, 275 non-M3 AML patients were enrolled and randomly assigned to the training (n=183) and validation cohorts (n=92). In the training cohort, a lncRNA scoring system comprising five lncRNAs whose expression had a significant impact on treatment outcomes was constructed. Patients with higher scores were found to have shorter OS and RFS, which was further confirmed in both internal and external validation cohorts. Multivariate analyses revealed that the lncRNA score was an independent prognostic factor in AML, irrespective of the risk based on the 2017 ELN classification.
NGS has also been used for measurable/minimal residual disease (MRD) monitoring in AML patients, but the optimal time point for the test remains unclear. In our study described in this thesis, we performed targeted NGS for 54 genes of the bone marrow cells serially obtained at diagnosis, after first complete remission (1st time point), and after the first consolidation chemotherapy (2nd time point) from 303 de novo AML patients. MRD was detected in 44.6% and 27.7% of patients at the 1st (MRD1st) and 2nd (MRD2nd) time points, respectively. Patients with detectable NGS MRD at either time point had a significantly higher cumulative incidence of relapse and shorter OS and RFS. Patients with positive MRD1st but negative MRD2nd had a similar good prognosis to those with negative MRD at both time points.
This thesis concludes that cohesin gene mutations have a favorable prognostic impact in patients with AML. These mutations may not be critical in disease progression since none of the patients with wild-type cohesins acquired any mutation during follow-ups. Incorporation of the lncRNA scoring system in the 2017 ELN classification can improve the risk stratification of patients with AML. Finally, the use of NGS MRD, especially at the 2nd time point, can help predict the clinical outcomes of patients with AML. By incorporating these prognosticators at diagnosis and follow-up stages, physicians can additionally refine the current risk stratification system and further improve quality care and patient outcomes in AML cases.

誠摯感謝 i
中文摘要 ii
Abstract iv
Table of contents vii
List of Figures ix
List of Tables xiii
Chapter 1. Introduction 1
1.1 Principles of AML treatment 1
1.2 Literature view: Risk stratification at the time of diagnosis 2
1.2.1 The coding event: Cohesin as an example 4
1.2.2 The non-coding event: long non-coding RNA as an example 5
1.3 Literature view: The measurable/minimal residual disease monitoring after treatment 6
Chapter 2. Methods and Materials 8
2.1 Subjects and treatment 8
2.1.1 The cohesin complex gene mutations in AML 8
2.1.2 Long non-coding RNA expression profile in AML 8
2.1.3 Sequential MRD monitoring via NGS in AML 9
2.2 Cytogenetic analysis 9
2.3 Gene mutation analysis 9
2.3.1 Sequential MRD monitoring via NGS in AML 11
2.4 Gene expression analysis 11
2.4.1 The cohesin complex gene mutations in AML 11
2.4.2 Long Non-coding RNA Expression Profile in AML 12
2.5 Integrated gene expression analysis and pathway analysis 13
2.6 Statistical analysis 13
2.6.1 Long non-coding RNA expression profile in AML 14
2.6.2 Sequential MRD monitoring via NGS in AML 15
Chapter 3. Results 17
3.1 The cohesin complex gene mutations in AML 17
3.1.1 Mutations in cohesin complex genes 17
3.1.2 Comparison of clinical and laboratory features between the patients with and without cohesin gene mutations 17
3.1.3 Prognostic impact of cohesin gene mutations 19
3.1.4 Dynamic changes in cohesin gene mutations 20
3.1.5 The possible physiological pathways underlying cohesin gene mutations 22
3.2 Long non-coding RNA expression profile in AML 23
3.2.1 The lncRNA risk score 23
3.2.2 Patient characteristics 23
3.2.3 Comparison of cytogenetic abnormalities and gene mutations between patients with higher and lower lncRNA scores 24
3.2.4 Prognostic impact of lncRNA scores on OS and RFS 25
3.2.5 Correlation of the lncRNA signature with gene expression to investigate potential underlying mechanisms 27
3.3 The measurable/minimal residual disease (MRD) monitoring after treatment 28
3.3.1 AML patient characteristics 28
3.3.2 The prognostic impact of detectable MRD attributable to DNMT3A, TET2, and ASXL1 (DTA) mutations, the most common CHIP-related gene mutations 28
3.3.3 MRD at CR1 and after first consolidation chemotherapy 29
3.3.4 The association of NGS MRD with clinical features and outcomes 30
3.3.5 Prognostic impact of serial NGS MRD 31
3.3.6 Integration of NGS MRD and MFC MRD 32
3.3.7 The prognostic impact of HSCT at CR1 in patients with detectable NGS MRD 33
Chapter 4. Discussion 34
4.1 The cohesin complex gene mutations in AML 34
4.2 Long non-coding RNA expression profile in AML 37
4.3 The measurable/minimal residual disease monitoring after treatment 41
Chapter 5. Conclusion 42
Figures 44
Tables 112
References 153
Appendix 161

1.Access, G.B.D.H., et al., Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990-2015: a novel analysis from the Global Burden of Disease Study 2015. Lancet, 2017. 390(10091): p. 231-266.
2.衛生福利部統計處, 107年國人死因統計結果. 2018.
3.衛生福利部國民健康署, 105年癌症登記報告. 2018.
4.Arber, D.A., et al., The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood, 2016. 127(20): p. 2391-405.
5.Löwenberg, B., Acute myeloid leukemia: the challenge of capturing disease variety. Hematology Am Soc Hematol Educ Program, 2008: p. 1-11.
6.Bennett, J.M., et al., Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. Br J Haematol, 1976. 33(4): p. 451-8.
7.Hou, H.A., et al., TP53 mutations in de novo acute myeloid leukemia patients: longitudinal follow-ups show the mutation is stable during disease evolution. Blood Cancer J, 2015. 5: p. e331.
8.Chou, W.C., et al., The prognostic impact and stability of Isocitrate dehydrogenase 2 mutation in adult patients with acute myeloid leukemia. Leukemia, 2011. 25(2): p. 246-53.
9.Chou, W.C., et al., TET2 mutation is an unfavorable prognostic factor in acute myeloid leukemia patients with intermediate-risk cytogenetics. Blood, 2011. 118(14): p. 3803-10.
10.Papaemmanuil, E., et al., Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med, 2016. 374(23): p. 2209-2221.
11.Ley, T.J., et al., DNMT3A mutations in acute myeloid leukemia. N Engl J Med, 2010. 363(25): p. 2424-33.
12.Falini, B., et al., Cytoplasmic nucleophosmin in acute myelogenous leukemia with a normal karyotype. N Engl J Med, 2005. 352(3): p. 254-66.
13.Schnittger, S., et al., Analysis of FLT3 length mutations in 1003 patients with acute myeloid leukemia: correlation to cytogenetics, FAB subtype, and prognosis in the AMLCG study and usefulness as a marker for the detection of minimal residual disease. Blood, 2002. 100(1): p. 59-66.
14.Metzeler, K.H., et al., ASXL1 mutations identify a high-risk subgroup of older patients with primary cytogenetically normal AML within the ELN Favorable genetic category. Blood, 2011. 118(26): p. 6920-9.
15.Mendler, J.H., et al., RUNX1 mutations are associated with poor outcome in younger and older patients with cytogenetically normal acute myeloid leukemia and with distinct gene and MicroRNA expression signatures. J Clin Oncol, 2012. 30(25): p. 3109-18.
16.Paschka, P., et al., Wilms' tumor 1 gene mutations independently predict poor outcome in adults with cytogenetically normal acute myeloid leukemia: a cancer and leukemia group B study. J Clin Oncol, 2008. 26(28): p. 4595-602.
17.Whitman, S.P., et al., The MLL partial tandem duplication in adults aged 60 years and older with de novo cytogenetically normal acute myeloid leukemia. Leukemia, 2012. 26(7): p. 1713-7.
18.Hou, H.A., et al., Splicing factor mutations predict poor prognosis in patients with de novo acute myeloid leukemia. Oncotarget, 2016. 7(8): p. 9084-101.
19.Tsai, C.H., et al., Genetic alterations and their clinical implications in older patients with acute myeloid leukemia. Leukemia, 2016. 30(7): p. 1485-92.
20.Patel, J.P., et al., Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med, 2012. 366(12): p. 1079-89.
21.Hou, H.A., et al., Integration of cytogenetic and molecular alterations in risk stratification of 318 patients with de novo non-M3 acute myeloid leukemia. Leukemia, 2014. 28(1): p. 50-8.
22.Grimwade, D., et al., The importance of diagnostic cytogenetics on outcome in AML: analysis of 1,612 patients entered into the MRC AML 10 trial. The Medical Research Council Adult and Children's Leukaemia Working Parties. Blood, 1998. 92(7): p. 2322-33.
23.Grimwade, D., et al., Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood, 2010. 116(3): p. 354-65.
24.Slovak, M.L., et al., Karyotypic analysis predicts outcome of preremission and postremission therapy in adult acute myeloid leukemia: a Southwest Oncology Group/Eastern Cooperative Oncology Group Study. Blood, 2000. 96(13): p. 4075-83.
25.Dohner, K., et al., Prognostic significance of partial tandem duplications of the MLL gene in adult patients 16 to 60 years old with acute myeloid leukemia and normal cytogenetics: a study of the Acute Myeloid Leukemia Study Group Ulm. J Clin Oncol, 2002. 20(15): p. 3254-61.
26.Thiede, C., et al., Analysis of FLT3-activating mutations in 979 patients with acute myelogenous leukemia: association with FAB subtypes and identification of subgroups with poor prognosis. Blood, 2002. 99(12): p. 4326-35.
27.Linch, D.C., et al., Impact of FLT3(ITD) mutant allele level on relapse risk in intermediate-risk acute myeloid leukemia. Blood, 2014. 124(2): p. 273-6.
28.Pratcorona, M., et al., Favorable outcome of patients with acute myeloid leukemia harboring a low-allelic burden FLT3-ITD mutation and concomitant NPM1 mutation: relevance to post-remission therapy. Blood, 2013. 121(14): p. 2734-8.
29.Dohner, H., et al., Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood, 2017. 129(4): p. 424-447.
30.Thiede, C., et al., Prevalence and prognostic impact of NPM1 mutations in 1485 adult patients with acute myeloid leukemia (AML). Blood, 2006. 107(10): p. 4011-20.
31.Schlenk, R.F., et al., Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N Engl J Med, 2008. 358(18): p. 1909-18.
32.Wouters, B.J., et al., Double CEBPA mutations, but not single CEBPA mutations, define a subgroup of acute myeloid leukemia with a distinctive gene expression profile that is uniquely associated with a favorable outcome. Blood, 2009. 113(13): p. 3088-91.
33.Whitman, S.P., et al., The MLL partial tandem duplication: evidence for recessive gain-of-function in acute myeloid leukemia identifies a novel patient subgroup for molecular-targeted therapy. Blood, 2005. 106(1): p. 345-52.
34.Chou, W.C., et al., Distinct clinical and biological features of de novo acute myeloid leukemia with additional sex comb-like 1 (ASXL1) mutations. Blood, 2010. 116(20): p. 4086-94.
35.Thol, F., et al., Prognostic significance of ASXL1 mutations in patients with myelodysplastic syndromes. J Clin Oncol, 2011. 29(18): p. 2499-506.
36.Chen, T.C., et al., Dynamics of ASXL1 mutation and other associated genetic alterations during disease progression in patients with primary myelodysplastic syndrome. Blood Cancer J, 2014. 4: p. e177.
37.Chou, W.C., et al., Distinct clinical and biologic characteristics in adult acute myeloid leukemia bearing the isocitrate dehydrogenase 1 mutation. Blood, 2010. 115(14): p. 2749-54.
38.Wagner, K., et al., Impact of IDH1 R132 mutations and an IDH1 single nucleotide polymorphism in cytogenetically normal acute myeloid leukemia: SNP rs11554137 is an adverse prognostic factor. J Clin Oncol, 2010. 28(14): p. 2356-64.
39.Lin, C.C., et al., IDH mutations are closely associated with mutations of DNMT3A, ASXL1 and SRSF2 in patients with myelodysplastic syndromes and are stable during disease evolution. Am J Hematol, 2014. 89(2): p. 137-44.
40.Green, C.L., et al., The prognostic significance of IDH2 mutations in AML depends on the location of the mutation. Blood, 2011. 118(2): p. 409-12.
41.Metzeler, K.H., et al., TET2 mutations improve the new European LeukemiaNet risk classification of acute myeloid leukemia: a Cancer and Leukemia Group B study. J Clin Oncol, 2011. 29(10): p. 1373-81.
42.Hou, H.A., et al., DNMT3A mutations in acute myeloid leukemia: stability during disease evolution and clinical implications. Blood, 2012. 119(2): p. 559-68.
43.Hou, H.A., et al., WT1 mutation in 470 adult patients with acute myeloid leukemia: stability during disease evolution and implication of its incorporation into a survival scoring system. Blood, 2010. 115(25): p. 5222-31.
44.Becker, H., et al., Mutations of the Wilms tumor 1 gene (WT1) in older patients with primary cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study. Blood, 2010. 116(5): p. 788-92.
45.Wang, M., et al., Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling. Leukemia, 2017. 31(10): p. 2029-2036.
46.Nasmyth, K., Segregating sister genomes: the molecular biology of chromosome separation. Science, 2002. 297(5581): p. 559-65.
47.Gruber, S., C.H. Haering, and K. Nasmyth, Chromosomal cohesin forms a ring. Cell, 2003. 112(6): p. 765-77.
48.Nasmyth, K. and C.H. Haering, Cohesin: its roles and mechanisms. Annu Rev Genet, 2009. 43: p. 525-58.
49.Hirano, T., SMC proteins and chromosome mechanics: from bacteria to humans. Philos Trans R Soc Lond B Biol Sci, 2005. 360(1455): p. 507-14.
50.Kagey, M.H., et al., Mediator and cohesin connect gene expression and chromatin architecture. Nature, 2010. 467(7314): p. 430-5.
51.Panigrahi, A.K. and D. Pati, Higher-order orchestration of hematopoiesis: is cohesin a new player? Exp Hematol, 2012. 40(12): p. 967-73.
52.Duployez, N., et al., Comprehensive mutational profiling of core binding factor acute myeloid leukemia. Blood, 2016. 127(20): p. 2451-9.
53.Lindsley, R.C., et al., Acute myeloid leukemia ontogeny is defined by distinct somatic mutations. Blood, 2015. 125(9): p. 1367-76.
54.Kihara, R., et al., Comprehensive analysis of genetic alterations and their prognostic impacts in adult acute myeloid leukemia patients. Leukemia, 2014. 28(8): p. 1586-95.
55.Cancer Genome Atlas Research, N., et al., Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med, 2013. 368(22): p. 2059-74.
56.Thol, F., et al., Mutations in the cohesin complex in acute myeloid leukemia: clinical and prognostic implications. Blood, 2014. 123(6): p. 914-20.
57.Thota, S., et al., Genetic alterations of the cohesin complex genes in myeloid malignancies. Blood, 2014. 124(11): p. 1790-8.
58.Kon, A., et al., Recurrent mutations in multiple components of the cohesin complex in myeloid neoplasms. Nat Genet, 2013. 45(10): p. 1232-7.
59.Yoshida, K., et al., The landscape of somatic mutations in Down syndrome-related myeloid disorders. Nat Genet, 2013. 45(11): p. 1293-9.
60.Garzon, R., et al., MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood, 2008. 111(6): p. 3183-9.
61.Marcucci, G., et al., MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J Med, 2008. 358(18): p. 1919-28.
62.Schwind, S., et al., Prognostic significance of expression of a single microRNA, miR-181a, in cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study. J Clin Oncol, 2010. 28(36): p. 5257-64.
63.Marcucci, G., et al., Clinical role of microRNAs in cytogenetically normal acute myeloid leukemia: miR-155 upregulation independently identifies high-risk patients. J Clin Oncol, 2013. 31(17): p. 2086-93.
64.Chuang, M.K., et al., A 3-microRNA scoring system for prognostication in de novo acute myeloid leukemia patients. Leukemia, 2015. 29(5): p. 1051-9.
65.Kitagawa, M., et al., Cell cycle regulation by long non-coding RNAs. Cell Mol Life Sci, 2013. 70(24): p. 4785-94.
66.Hu, W., et al., Long noncoding RNA-mediated anti-apoptotic activity in murine erythroid terminal differentiation. Genes Dev, 2011. 25(24): p. 2573-8.
67.Geisler, S. and J. Coller, RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat Rev Mol Cell Biol, 2013. 14(11): p. 699-712.
68.Garzon, R., et al., Expression and prognostic impact of lncRNAs in acute myeloid leukemia. Proc Natl Acad Sci U S A, 2014. 111(52): p. 18679-84.
69.De Clara, E., et al., Long non-coding RNA expression profile in cytogenetically normal acute myeloid leukemia identifies a distinct signature and a new biomarker in NPM1-mutated patients. Haematologica, 2017. 102(10): p. 1718-1726.
70.Papaioannou, D., et al., Prognostic and biologic significance of long non-coding RNA profiling in younger adults with cytogenetically normal acute myeloid leukemia. Haematologica, 2017. 102(8): p. 1391-1400.
71.Tang, J.L., et al., AML1/RUNX1 mutations in 470 adult patients with de novo acute myeloid leukemia: prognostic implication and interaction with other gene alterations. Blood, 2009. 114(26): p. 5352-61.
72.Schuurhuis, G.J., et al., Minimal/measurable residual disease in AML: a consensus document from the European LeukemiaNet MRD Working Party. Blood, 2018. 131(12): p. 1275-1291.
73.Walter, R.B., et al., Significance of minimal residual disease before myeloablative allogeneic hematopoietic cell transplantation for AML in first and second complete remission. Blood, 2013. 122(10): p. 1813-21.
74.Ivey, A., et al., Assessment of Minimal Residual Disease in Standard-Risk AML. N Engl J Med, 2016. 374(5): p. 422-33.
75.Hourigan, C.S., et al., Measurable residual disease testing in acute myeloid leukaemia. Leukemia, 2017. 31(7): p. 1482-1490.
76.Grimwade, D. and S.D. Freeman, Defining minimal residual disease in acute myeloid leukemia: which platforms are ready for "prime time"? Blood, 2014. 124(23): p. 3345-55.
77.Ravandi, F., et al., Effective treatment of acute promyelocytic leukemia with all-trans-retinoic acid, arsenic trioxide, and gemtuzumab ozogamicin. J Clin Oncol, 2009. 27(4): p. 504-10.
78.Grimwade, D., et al., Prospective minimal residual disease monitoring to predict relapse of acute promyelocytic leukemia and to direct pre-emptive arsenic trioxide therapy. J Clin Oncol, 2009. 27(22): p. 3650-8.
79.Zhu, H.H., et al., MRD-directed risk stratification treatment may improve outcomes of t(8;21) AML in the first complete remission: results from the AML05 multicenter trial. Blood, 2013. 121(20): p. 4056-62.
80.Buccisano, F. and R.B. Walter, Should patients with acute myeloid leukemia and measurable residual disease be transplanted in first complete remission? Curr Opin Hematol, 2017. 24(2): p. 132-138.
81.Jongen-Lavrencic, M., et al., Molecular Minimal Residual Disease in Acute Myeloid Leukemia. N Engl J Med, 2018. 378(13): p. 1189-1199.
82.Kim, T., et al., Next-generation sequencing-based posttransplant monitoring of acute myeloid leukemia identifies patients at high risk of relapse. Blood, 2018. 132(15): p. 1604-1613.
83.Thol, F., et al., Measurable residual disease monitoring by NGS before allogeneic hematopoietic cell transplantation in AML. Blood, 2018. 132(16): p. 1703-1713.
84.Morita, K., et al., Clearance of Somatic Mutations at Remission and the Risk of Relapse in Acute Myeloid Leukemia. J Clin Oncol, 2018. 36(18): p. 1788-1797.
85.Kronke, J., et al., Monitoring of minimal residual disease in NPM1-mutated acute myeloid leukemia: a study from the German-Austrian acute myeloid leukemia study group. J Clin Oncol, 2011. 29(19): p. 2709-16.
86.Tien, H.F., et al., Correlation of cytogenetic results with immunophenotype, genotype, clinical features, and ras mutation in acute myeloid leukemia. A study of 235 Chinese patients in Taiwan. Cancer Genet Cytogenet, 1995. 84(1): p. 60-8.
87.Seabright, M., A rapid banding technique for human chromosomes. Lancet, 1971. 2(7731): p. 971-2.
88.Nomenclature, I.S.C.o.H.C., ISCN : an international system for human cytogenomic nomenclature (2016), ed. A.S. Jean McGowan-Jordan, Michael Schmid. 2016, Switzerland: Karger.
89.Smith, T.F. and M.S. Waterman, Identification of common molecular subsequences. J Mol Biol, 1981. 147(1): p. 195-7.
90.Li, H., et al., The Sequence Alignment/Map format and SAMtools. Bioinformatics, 2009. 25(16): p. 2078-9.
91.Basara, N., et al., Early related or unrelated haematopoietic cell transplantation results in higher overall survival and leukaemia-free survival compared with conventional chemotherapy in high-risk acute myeloid leukaemia patients in first complete remission. Leukemia, 2009. 23(4): p. 635-40.
92.O'Leary, N.A., et al., Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res, 2016. 44(D1): p. D733-45.
93.Robinson, J.T., et al., Integrative genomics viewer. Nat Biotechnol, 2011. 29(1): p. 24-6.
94.Genomes Project, C., et al., A global reference for human genetic variation. Nature, 2015. 526(7571): p. 68-74.
95.Lek, M., et al., Analysis of protein-coding genetic variation in 60,706 humans. Nature, 2016. 536(7616): p. 285-91.
96.Forbes, S.A., et al., COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res, 2015. 43(Database issue): p. D805-11.
97.Sherry, S.T., et al., dbSNP: the NCBI database of genetic variation. Nucleic Acids Res, 2001. 29(1): p. 308-11.
98.Landrum, M.J., et al., ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res, 2014. 42(Database issue): p. D980-5.
99.Adzhubei, I., D.M. Jordan, and S.R. Sunyaev, Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet, 2013. Chapter 7: p. Unit7 20.
100.Ng, P.C. and S. Henikoff, SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res, 2003. 31(13): p. 3812-4.
101.Papaemmanuil, E., et al., Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood, 2013. 122(22): p. 3616-27; quiz 3699.
102.Volders, P.J., et al., An update on LNCipedia: a database for annotated human lncRNA sequences. Nucleic Acids Res, 2015. 43(Database issue): p. D174-80.
103.Harrow, J., et al., GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res, 2012. 22(9): p. 1760-74.
104.Subramanian, A., et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 2005. 102(43): p. 15545-50.
105.Simon, R., et al., Analysis of gene expression data using BRB-ArrayTools. Cancer Inform, 2007. 3: p. 11-7.
106.Kuhn, M., Building Predictive Models inRUsing thecaretPackage. Journal of Statistical Software, 2008. 28(5): p. 26.
107.Blanche, P., J.F. Dartigues, and H. Jacqmin-Gadda, Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med, 2013. 32(30): p. 5381-97.
108.Leiserson, M.D., et al., Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat Genet, 2015. 47(2): p. 106-14.
109.Nik-Zainal, S., et al., The life history of 21 breast cancers. Cell, 2012. 149(5): p. 994-1007.
110.Tien, F.M., et al., Hyperleukocytosis is associated with distinct genetic alterations and is an independent poor-risk factor in de novo acute myeloid leukemia patients. Eur J Haematol, 2018. 101(1): p. 86-94.
111.Ng, S.W., et al., A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature, 2016. 540(7633): p. 433-437.
112.Eppert, K., et al., Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med, 2011. 17(9): p. 1086-93.
113.Gal, H., et al., Gene expression profiles of AML derived stem cells; similarity to hematopoietic stem cells. Leukemia, 2006. 20(12): p. 2147-54.
114.Gnad, F., et al., Assessment of computational methods for predicting the effects of missense mutations in human cancers. BMC Genomics, 2013. 14 Suppl 3: p. S7.
115.Miosge, L.A., et al., Comparison of predicted and actual consequences of missense mutations. Proc Natl Acad Sci U S A, 2015. 112(37): p. E5189-98.
116.Solomon, D.A., et al., Mutational inactivation of STAG2 causes aneuploidy in human cancer. Science, 2011. 333(6045): p. 1039-43.
117.Mullenders, J., et al., Cohesin loss alters adult hematopoietic stem cell homeostasis, leading to myeloproliferative neoplasms. J Exp Med, 2015. 212(11): p. 1833-50.
118.Faber, Z.J., et al., The genomic landscape of core-binding factor acute myeloid leukemias. Nat Genet, 2016. 48(12): p. 1551-1556.
119.Zhang, N., et al., Characterization of the interaction between the cohesin subunits Rad21 and SA1/2. PLoS One, 2013. 8(7): p. e69458.
120.Hauf, S., et al., Dissociation of cohesin from chromosome arms and loss of arm cohesion during early mitosis depends on phosphorylation of SA2. PLoS Biol, 2005. 3(3): p. e69.
121.Viny, A.D., et al., Dose-dependent role of the cohesin complex in normal and malignant hematopoiesis. J Exp Med, 2015. 212(11): p. 1819-32.
122.Mazumdar, C., et al., Leukemia-Associated Cohesin Mutants Dominantly Enforce Stem Cell Programs and Impair Human Hematopoietic Progenitor Differentiation. Cell Stem Cell, 2015. 17(6): p. 675-688.
123.Huang, Y., et al., MAPK/ERK2 phosphorylates ERG at serine 283 in leukemic cells and promotes stem cell signatures and cell proliferation. Leukemia, 2016. 30(7): p. 1552-61.
124.Malcovati, L., et al., SF3B1 mutation identifies a distinct subset of myelodysplastic syndrome with ring sideroblasts. Blood, 2015. 126(2): p. 233-41.
125.Beck, D., et al., A four-gene LincRNA expression signature predicts risk in multiple cohorts of acute myeloid leukemia patients. Leukemia, 2018. 32(2): p. 263-272.
126.Harada, Y., et al., Prognostic analysis according to the 2017 ELN risk stratification by genetics in adult acute myeloid leukemia patients treated in the Japan Adult Leukemia Study Group (JALSG) AML201 study. Leuk Res, 2018. 66: p. 20-27.
127.Mer, A.S., et al., Expression levels of long non-coding RNAs are prognostic for AML outcome. J Hematol Oncol, 2018. 11(1): p. 52.
128.Li, Z., et al., Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study. J Clin Oncol, 2013. 31(9): p. 1172-81.
129.Metzeler, K.H., et al., An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood, 2008. 112(10): p. 4193-201.
130.Bullinger, L., et al., Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med, 2004. 350(16): p. 1605-16.
131.Nagalakshmi, U., et al., The transcriptional landscape of the yeast genome defined by RNA sequencing. Science, 2008. 320(5881): p. 1344-9.
132.Wang, Z., M. Gerstein, and M. Snyder, RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 2009. 10(1): p. 57-63.
133.Camarena, L., et al., Molecular mechanisms of ethanol-induced pathogenesis revealed by RNA-sequencing. PLoS Pathog, 2010. 6(4): p. e1000834.
134.Fang, Z. and X. Cui, Design and validation issues in RNA-seq experiments. Brief Bioinform, 2011. 12(3): p. 280-7.
135.Feng, L., et al., Power of deep sequencing and agilent microarray for gene expression profiling study. Mol Biotechnol, 2010. 45(2): p. 101-10.
136.Dean, M., T. Fojo, and S. Bates, Tumour stem cells and drug resistance. Nat Rev Cancer, 2005. 5(4): p. 275-84.
137.Ho, T.C., et al., Evolution of acute myelogenous leukemia stem cell properties after treatment and progression. Blood, 2016. 128(13): p. 1671-8.
138.Heery, R., et al., Long Non-Coding RNAs: Key Regulators of Epithelial-Mesenchymal Transition, Tumour Drug Resistance and Cancer Stem Cells. Cancers (Basel), 2017. 9(4).
139.Asadi Fakhr, Z., et al., Evaluation of the utility of peripheral blood vs bone marrow in karyotype and fluorescence in situ hybridization for myelodysplastic syndrome diagnosis. J Clin Lab Anal, 2018. 32(9): p. e22586.
140.Mohamedali, A.M., et al., Comparison of Peripheral Blood and Bone Marrow Molecular Profiling in Primary Myelodysplastic Syndromes (MDS). Blood, 2014. 124(21): p. 4655-4655.
141.Loken, M.R., et al., Normalization of bone marrow aspirates for hemodilution in flow cytometric analyses. Cytometry B Clin Cytom, 2009. 76(1): p. 27-36.
142.Boddu, P., et al., Treated secondary acute myeloid leukemia: a distinct high-risk subset of AML with adverse prognosis. Blood Adv, 2017. 1(17): p. 1312-1323.
143.Klco, J.M., et al., Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia. JAMA, 2015. 314(8): p. 811-22.
144.Krzywinski, M., et al., Circos: an information aesthetic for comparative genomics. Genome Res, 2009. 19(9): p. 1639-45.
145.Miller, C.A., et al., Visualizing tumor evolution with the fishplot package for R. BMC Genomics, 2016. 17(1): p. 880.
146.Herold, T., et al., Validation and refinement of the revised 2017 European LeukemiaNet genetic risk stratification of acute myeloid leukemia. Leukemia, 2020.
147.Tsai, C.H., et al., Incorporation of long non-coding RNA expression profile in the 2017 ELN risk classification can improve prognostic prediction of acute myeloid leukemia patients. EBioMedicine, 2019. 40: p. 240-250.