AbstractLong non-coding ribonucleic acids (RNAs) are a novel class of RNA molecules, which are increasingly recognized as important molecular players in solid and hematologic malignancies. Herein we investigated whether long non-coding RNA expression is associated with clinical and molecular features, as well as outcome of younger adults (aged <60 years) with de novo cytogenetically normal acute myeloid leukemia. Whole transcriptome profiling was performed in a training (n=263) and a validation set (n=114). Using the training set, we identified 24 long non-coding RNAs associated with event-free survival. Linear combination of the weighted expression values of these transcripts yielded a prognostic score. In the validation set, patients with high scores had shorter disease-free (P<0.001), overall (P=0.002) and event-free survival (P<0.001) than patients with low scores. In multivariable analyses, long non-coding RNA score status was an independent prognostic marker for disease-free (P=0.01) and event-free survival (P=0.002), and showed a trend for overall survival (P=0.06). Among multiple molecular alterations tested, which are prognostic in cytogenetically normal acute myeloid leukemia, only double CEBPA mutations, NPM1 mutations and FLT3-ITD associated with distinct long non-coding RNA signatures. Correlation of the long non-coding RNA scores with messenger RNA and microRNA expression identified enrichment of genes involved in lymphocyte/leukocyte activation, inflammation and apoptosis in patients with high scores. We conclude that long non-coding RNA profiling provides meaningful prognostic information in younger adults with cytogenetically normal acute myeloid leukemia. In addition, expression of prognostic long non-coding RNAs associates with oncogenic molecular pathways in this disease. clinicaltrials.gov Identifier: 00048958 (CALGB-8461), 00899223 (CALGB-9665), and 00900224 (CALGB-20202).
Acute myeloid leukemia (AML) is a highly heterogeneous disease with regard to genetic abnormalities and clinical course.1 The prognosis of adult AML is generally poor. Only 40% of younger adult (aged <60 years) and 10% of older (aged ≥60 years) AML patients achieve long-term survival.1 Currently, chromosomal aberrations42 and recurrent gene mutations85 are considered the most reliable and reproducible prognostic markers in AML, and are used in the clinic to identify patients at high risk of death and to guide treatment. Aberrant levels of messenger RNA (mRNA)119 and microRNA (miR) transcripts1312 also have prognostic significance, and efforts have been made to incorporate gene-expression profiling into prognostic algorithms.1614
Long non-coding RNAs (lncRNAs) are a novel class of RNA molecules that are longer than 200 nucleotides, have no protein coding potential and are either located within the intergenic stretches of the genome or overlap (in sense or antisense direction) protein coding genes.1817 These transcripts regulate key cellular functions, such as chromosome dosage compensation,19 imprinting,20 cell cycle progression,21 and differentiation.22 In cancer, individual lncRNAs have been shown to play an important role in malignant transformation.2523 Despite the growing understanding of the biologic significance of deregulated lncRNA expression in malignant diseases, the value of these molecules as potential biomarkers in the clinical setting has not been extensively studied.2726 With regard to cytogenetically normal AML (CN-AML), the prognostic and biologic significance of lncRNAs in younger adult patients remains unknown. Therefore, we analyzed, using whole transcriptome sequencing (RNA-seq), the lncRNA profiles of younger adults with de novo CN-AML, who were comprehensively characterized with regard to molecular abnormalities and outcome. Herein, we show that lncRNA profiling provides independent prognostic information in these patients. We also show that expression levels of prognostic lncRNAs correlate with distinct mRNA and miR signatures, and provide insights into the leukemogenic pathways that these lncRNAs potentially regulate.
Patients and treatment
Pretreatment bone marrow (BM) or blood samples were obtained from a training (n=263) and a validation set (n=114) of younger adult patients (aged 17–59 years) with de novo CN-AML, who received intensive cytarabine/anthracycline-based first-line therapy on Cancer and Leukemia Group B (CALGB)/Alliance for Clinical Trials in Oncology (Alliance) trials and were alive 30 days after initiation of treatment. Per protocol, no patient received allogeneic stem cell transplantation in first complete remission (CR). Details regarding treatment protocols are provided in the Online Supplementary Appendix. All patients provided written informed consent, and all study protocols were in accordance with the Declaration of Helsinki and approved by institutional review boards at each center.
Cytogenetic and molecular analyses
Cytogenetic analyses were performed in CALGB/Alliance-approved institutional laboratories and results confirmed by central karyotype review.28 The diagnosis of normal karyotype was based on at least 20 metaphase cells analyzed in BM specimens subjected to short-term (24- or 48-hour) unstimulated cultures.
Targeted amplicon sequencing using the MiSeq platform (Illumina) was used to analyze DNA samples for presence of gene mutations that have been reported to associate with clinical outcome of CN-AML patients (i.e., mutations in the ASXL1, DNMT3A [R882 and non-R882], IDH1, IDH2 [R140 and R172], NPM1, RUNX1, TET2 or WT1 genes, and FLT3-tyrosine kinase domain [FLT3-TKD] mutations), as described previously.2926 A variant allele frequency of ≥10% was used as the cutoff to distinguish between mutated versus wild-type alleles of these genes. The presence of mutations in the CEBPA gene and FLT3-internal tandem duplications (FLT3-ITD) were evaluated using Sanger sequencing30 and fragment analysis,31 as described previously. Since only double CEBPA mutations are favorable prognostic markers in CN-AML,32 we considered only this genotype as mutated.
RNA samples of all studied patients (n=377) were analyzed with total RNA sequencing (after depletion of ribosomal and mitochondrial RNA) using the Illumina HiSeq 2500 platform. Due to RNA quality restrictions, a subset of 300 patients could be additionally analyzed with small RNA sequencing, for profiling of miR expression. Further details are provided in the Online Supplementary Appendix. To determine the expression status of patients (i.e., high versus low expressers) with regard to prognostic expression markers (e.g., BAALC), the median values of normalized RNA sequencing reads were used as the cutoff.
Clinical endpoint definitions are given in the Online Supplementary Appendix. Baseline demographic, clinical, and molecular features were compared between patients with low and those with high lncRNA scores (later on referred to as favorable and unfavorable, see below), and between the training and validation sets using the Wilcoxon rank-sum and Fisher’s exact tests for continuous and categorical variables, respectively.33 The estimated probabilities of disease-free (DFS), overall (OS) and event-free survival (EFS) were calculated using the Kaplan–Meier method, and the log-rank test evaluated differences between survival distributions.34 Cox proportional hazard models were used to calculate hazard ratios (HR) for DFS, OS and EFS.33 Multivariable proportional hazards models were constructed using a backward selection procedure. All statistical analyses were performed by The Alliance Statistics and Data Center.
Global expression of lncRNAs
To investigate the role of lncRNAs in AML, we first identified all known lncRNAs which were present in the transcriptomes of the younger CN-AML patients who were studied (n=377). After exclusion of contaminating ribosomal RNA molecules, we identified 22,166 non-coding RNA transcripts. According to the GENCODE v22 database,35 23% of these transcripts were categorized as processed pseudogenes, 21% as intergenic/intervening lncRNAs, 21% as antisense lncRNAs, 4% as sense intronic/overlapping lncRNAs and 31% were classified as other transcripts (e.g., as unitary pseudogenes, unprocessed pseudogenes etc.; Figure 1).
Generation of a prognostic lncRNA score in the training set
To assess the prognostic significance of lncRNA expression in younger adults with CN-AML, we performed exploratory analysis in a training set (n=263) of younger CN-AML patients and used a separate patient cohort to validate our findings (validation set, n=114). Comparison of clinical and molecular characteristics at diagnosis between the training and validation sets showed that they were relatively similar, with the exceptions that patients in the training set had higher percentages of blood blasts (P=0.03), were more frequently FLT3-TKD-positive (P=0.02), and had higher ERG (P=0.01) and BAALC (P=0.002) expression levels (Online Supplementary Table S1).
We first identified all lncRNAs that were highly associated with EFS (P<10) in the training set by univariable Cox analysis (Figure 2). EFS was used because it comprehensively evaluates the lncRNAs that are associated with response to chemotherapy, probability of relapse and probability of survival. We detected 24 lncRNAs associated with EFS (P<10; Online Supplementary Table S2). Next, we derived a prognostic lncRNA score by linear combination of the weighted expression values of these 24 lncRNAs. The median value of the lncRNA score was used to dichotomize the training set of patients. Patients with low lncRNA scores (n=132) had longer DFS (P<0.001), OS (P<0.001) and EFS (P<0.001) than patients with high lncRNA scores (n=131). We therefore classified low lncRNA scores as “favorable” and high as “unfavorable” (Online Supplementary Table S3 and Online Supplementary Figure S1).
Association of lncRNA score with patient characteristics and clinical outcome in the training set
With regard to clinical and molecular characteristics, patients with favorable lncRNA scores in the training set were more likely to present with higher hemoglobin levels (P=0.02), lower white blood cell (WBC) counts (P<0.001), and lower percentages of BM blasts (P=0.02). They were also less likely to harbor FLT3-ITD (P<0.001), DNMT3A (P=0.01) and RUNX1 (P=0.009) mutations and more likely to harbor double CEBPA mutations (P<0.001). Patients with favorable lncRNA scores in the training set differed with regard to their distribution in the Risk Categories of the European LeukemiaNet (ELN) classification of AML,1 when compared with patients with unfavorable lncRNA scores (P<0.001); patients with favorable lncRNA scores were more frequently classified as favorable and less frequently as intermediate or adverse risk than those with unfavorable lncRNA scores (Online Supplementary Table S4). Favorable lncRNA score status also associated with high expression of miR-181a (P<0.001) and low expression of miR-155 (P=0.03, Online Supplementary Table S4). Association of a favorable lncRNA score with longer DFS, OS and EFS remained significant in multivariable analyses (P<0.001 for all 3 end points, Online Supplementary Table S5), after adjusting for other co-variates.
Association of lncRNA score with patient characteristics and clinical outcome in the validation set
We used the median value of the lncRNA score, as calculated in the training set to divide the validation set into favorable and unfavorable lncRNA score groups (Figure 2). Patients with favorable lncRNA scores (n=57) were less likely to present with higher WBC counts at the time of diagnosis (P=0.009) or to harbor FLT3-ITD (P=0.007). lncRNA score status also associated with significantly different distribution of the patients in the Risk Categories of the ELN guidelines (P=0.02).1 Patients with favorable lncRNA scores were more likely to belong to the favorable and less likely to belong to the intermediate or adverse risk category. Patients with favorable lncRNA scores in the validation set were less likely to be miR-155 high-expressers (P<0.001) than patients with unfavorable lncRNA scores (n=57; Table 1).
Patients with favorable lncRNA scores had longer DFS than those with unfavorable lncRNA scores (P<0.001; Figure 3A). Five years after diagnosis, 51% of patients with favorable lncRNA scores remained alive and leukemia-free, in contrast to only 17% of those with unfavorable lncRNA scores. Favorable lncRNA score status also associated with longer OS (P=0.002, 5-year rates, 52% versus 26%; Figure 3B) and longer EFS (P<0.001, 5-year rates, 46% versus 16%; Figure 3C, Online Supplementary Table S6). The prognostic value of the lncRNA score in the validation set remained significant when it was analyzed as a continuous variable. Increasingly favorable lncRNA scores associated with longer DFS (P<0.001), OS (P=0.007) and EFS (P=0.002).
In multivariable analyses, favorable lncRNA score status was an independent marker for longer DFS (P=0.01), after adjusting for miR-155 expression status, and EFS (P=0.002), after adjusting for the presence of FLT3-ITD (Table 2). With regard to OS, patients with a favorable lncRNA score had a trend for longer survival (P=0.06), after adjustment for FLT3-ITD and MN1 expression status.
Associations of recurrent gene mutations with lncRNA expression
We evaluated if recurrent prognostic gene mutations in CN-AML associated with distinct expression patterns of lncRNAs in younger adults with CN-AML. For this purpose, mutation-related lncRNA signatures were derived in the training set using stringent criteria (for details see Methods and the Online Supplementary Appendix).
Double-mutated CEBPA showed the strongest association with lncRNA expression; 82 lncRNAs were upregulated and 186 lncRNAs were downregulated in patients who harbored double-mutated CEBPA (Figure 4A, Online Supplementary Table S7). Among the CEBPA mutation-related lncRNAs, NEAT1 was significantly underexpressed in the group of patients with CEBPA mutations. This lncRNA has been involved in myeloid differentiation of acute promyelocytic leukemia cells after all-trans retinoic acid treatment.36
Mutations in the NPM1 gene also strongly associated with a lncRNA signature, which comprised 35 transcripts upregulated and 37 transcripts downregulated in patients harboring NPM1 mutations (Figure 4B, Online Supplementary Table S8). Thirty-three of the 35 lncRNAs overexpressed in patients with NPM1 mutations, were downregulated in patients with CEBPA mutations. This finding is consistent with the observation that double CEBPA and NPM1 mutations rarely co-occur in CN-AML. NPM1 mutations were positively associated with lncRNAs embedded within the HOX gene loci (HOXA-AS3, HOXB-AS3) and other lncRNAs implicated in myelopoiesis (EGOT137) or carcinogenesis (e.g., PCAT1838 and LUCAT139).
The FLT3-ITD-related lncRNA signature consisted of 26 transcripts, 19 of which were upregulated and 7 downregulated in patients with this mutation (Figure 4C, Online Supplementary Table S9). The host gene of miR-155 (MIR155HG) was among the lncRNAs overexpressed in FLT3-ITD-positive patients. High MIR155HG expression independently associates with poor outcome in CN-AML.40 The WT1-AS lncRNA was also highly expressed among FLT3-ITD-positive patients; it has been reported to post-translationally regulate the protein levels of WT1.41
To assess the capacity of gene mutation-related lncRNA signatures to detect their corresponding molecular alterations in CN-AML patients, we applied these signatures to the validation set. The mutated CEBPA-related signature showed the highest level of accuracy (specificity and sensitivity of mutated CEBPA detection: ≥93% and ≥98%, respectively), followed by the mutated NPM1-related (sensitivity: ≥80%, specificity ≥73%) and the FLT3-ITD-related signatures (sensitivity ≥70%, specificity: ≥76%). The remaining prognostic gene mutations that were tested either did not associate with differential expression of lncRNAs (i.e., TET2 mutations) or generated signatures that failed to reliably detect the mutational status of patients in the validation set (e.g., DNMT3A, WT1 mutations).
Biologic implications of the lncRNA score
To gain biologic insights into the molecular pathways that may be affected by differences in the lncRNA score, we examined the correlation between the lncRNA score and the mRNA/miRNA expression in 300 younger CN-AML patients who had available mRNA and miRNA profiling data.
We identified 410 mRNA transcripts whose expression levels correlated with the lncRNA score, 172 of which correlated positively and 238 negatively with unfavorable lncRNA scores (Figure 5A, Online Supplementary Table S10). Among highly expressed genes in patients with unfavorable lncRNA scores, putative oncogenes and key mediators of the oncogenic AP-1 pathway such as ATF3, FOS, FOSB, JUN, and MAFF were identified. With regard to hematopoiesis, the AP-1 pathway has been shown to regulate proliferation of erythroleukemia cells,42 to mediate monocyte/macrophage differentiation of myeloid cells43 and to co-regulate miR-155 expression in stimulated macrophages.44 Genes that regulate immune responses (e.g., IL1B, IRF7, CD80) and genes that mediate immune evasion (e.g., IER3, LILRB4) were also highly expressed in patients with unfavorable lncRNA profiles. Finally, oncogenes promoting proliferation of malignant cells (e.g., RET, ETS2, PLK2, NEK6, PLK3 and SRC) were found to be overexpressed in patients with unfavorable lncRNA scores. Gene ontology analysis revealed that genes involved in lymphocyte/leukocyte activation, inflammation, response to wounding and regulation of apoptosis were enriched in the subset of patients with unfavorable lncRNA scores (Figure 5B, Online Supplementary Table S11).
Among mRNA molecules downregulated in patients with unfavorable lncRNA scores, we detected transcripts with reported tumor-suppressive function (APC, JADE1, BRMS1L, and ING3). Gene ontology analysis showed that genes that participate in the regulation of transcription, the regulation of RNA metabolic processes and DNA binding were underexpressed in the group of patients with unfavorable lncRNA scores (Figure 5C, Online Supplementary Table S11).
With regard to miR expression, 10 miRs were found to correlate positively (miR-660, miR-502, miR-532-5p, miR-155, miR-500a-3p, miR-500a-5p, miR-532-3p, miR-362, miR-339 and miR-23a) and 4 miRs to correlate negatively (miR-192, miR-625, miR-100 and miR-194) with unfavorable lncRNA scores (Online Supplementary Table S12). Among the 10 miRs that positively correlated with unfavorable lncRNA scores, 7 were located in close proximity on chromosome X; miR-660, miR-502, miR-532-5p, miR-500a-3p, miR-500a-5p, miR-532-3p and miR-362 are all imbedded in intron 3 of the CLCN5 gene. This miR cluster mediates an anti-apoptotic effect in chronic lymphocytic leukemia cells.45 miR-155, which also positively correlated with unfavorable lncRNA scores, is an established adverse prognosticator in CN-AML40 and has been implicated in leukemogenesis of FLT3-ITD-positive AML.46
Over the past 5 years, lncRNAs have emerged as new players in cancer biology and biomarker discovery.47 Our group has previously reported that distinctive lncRNA signatures are associated with prognostic gene mutations in older CN-AML patients, and that expression levels of a small group of lncRNAs have prognostic significance in these patients.26 Since CN-AML in younger adults differs with regard to clinical features, associated molecular abnormalities and outcome from that in older patients, we investigated the prognostic value and biologic implications of lncRNA expression in a total of 377 CN-AML adult patients younger than 60 years.
First, we identified 24 lncRNAs highly correlated with EFS. Similarly to the previously reported older CN-AML patients,26 only a small number of these prognostic lncRNAs associated with prognostic gene mutations: MIR155HG was upregulated in patients who harbor FLT3-ITD, AC006129.2 was upregulated in patients with double CEBPA mutations, whereas AL122127.25, RP11-946L16.2, SDHAP3 and SENC3 were downregulated in patients with double CEBPA mutations. Of the 24 prognostic lncRNA genes, only MIR155HG has previously been associated with the clinical outcome of CN-AML patients.4840
Linear combination of the weighted expression values of lncRNA transcripts yielded a prognostic score, which strongly associated with DFS, EFS and OS duration in younger adult CN-AML patients. Favorable lncRNA score status was an independent marker for longer DFS and EFS (and also showed a strong trend towards significance for longer OS). We were intrigued to find no overlap between the 48 prognostic lncRNAs that we previously identified in older CN-AML patients26 and the 24 transcripts reported herein in younger patients. This finding could be interpreted as an additional biologic difference between CN-AML of younger and that of older patients, similar to the age-dependent difference in frequency of some recurrent prognostic gene mutations (e.g., mutations in the ASXL1 and RUNX1 genes).1
We also examined the associations between recurrent prognostic gene mutations and lncRNA expression, and found double CEBPA and NPM1 mutations and FLT3-ITD to associate with distinct lncRNA signatures. We identified several lncRNAs that were commonly associated with these gene mutations in both younger and older CN-AML patients26 (e.g., the HOX-loci embedded lncRNAs in the NPM1 mutation-related lncRNA signature, WT1-AS in the FLT3-ITD-related signature, etc.). On the other hand, such gene mutations as RUNX1 and ASXL1 that are more frequent in older CN-AML patients and were found to associate with differential expression of lncRNAs26 could not be tested in younger CN-AML patients, because too few younger patients harbored these mutations. Of note, mutations in the TET2 gene showed no correlation with differential expression of lncRNA molecules in either older26 or younger CN-AML patients, despite their impact on the epigenome49 and adequate numbers of patients in both studied cohorts.
To gain insights into biologic pathways affected by differences in the lncRNA score, we investigated correlations between mRNA and miR expression signatures and lncRNA scores. In concordance with the adverse outcome that unfavorable lncRNA scores bestow, a number of previously described oncogenes and oncomiRs were found overexpressed in patients with unfavorable lncRNA score status. Similarly, genes with reported tumor-suppressive activity were found downregulated in this patient group. Only a small fraction of these transcripts have been reported in gene mutation-related mRNA signatures or other prognostic gene-expression signatures.1614 These findings indicate that, in addition to being independent of prognostic mutations, the differential expression of prognostic lncRNAs may regulate distinct molecular pathways in CN-AML. Notably, 5 important mediators of the AP-1 pathway (ATF3, FOS, FOSB, JUN, and MAFF) were found upregulated in patients with unfavorable lncRNA scores. The high number of cell cycle regulators and proliferation-inducing kinases that were also upregulated in this patient group is consistent with aberrant activation of the AP-1 pathway.
In this work, we used whole transcriptome sequencing techniques to identify and measure the expression of prognostic lncRNA molecules in younger adults with CN-AML. While this technology is becoming rapidly cheaper and widely available, it will most likely continue to serve as a research tool rather than a diagnostic test to guide patient treatment. Despite this, alternative techniques for measuring RNA transcripts in a clinically applicable manner are available and are used to risk stratify patients with certain solid malignancies.50 Similar assays could be developed in order to obtain targeted measurements of prognostic lncRNAs in a fast and clinically meaningful manner. The potential of such assays to improve risk stratification of AML patients should be evaluated in future prospective clinical studies.
The authors would like to thank: Donna Bucci and Wacharaphon Vongchucherd of The Alliance NCTN Biorepository and Biospecimen Resource for sample processing and storage services, Karl Kroll for technical support, and Lisa J. Sterling and Christine Finks of The Ohio State University, Comprehensive Cancer Center, Columbus, OH for data management.
- ↵* RG and CDB contributed equally to this work.
- Check the online version for the most updated information on this article, online supplements, and information on authorship & disclosures: www.haematologica.org/content/102/8/1391
- FundingThis work was supported by the National Cancer Institute of the National Institutes of Health under Award Numbers CA180821 and CA180882 (to the Alliance for Clinical Trials in Oncology), CA077658, CA180850, CA180861, CA140158, CA16058, and CA197734. This work was also supported in part by the Leukemia Clinical Research Foundation, D Warren Brown Foundation, and the Pelotonia Fellowship Program. The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health.
- Received February 7, 2017.
- Accepted May 2, 2017.
- Döhner H, Estey E, Grimwade D. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017; 129(4):424-447. PubMedhttps://doi.org/10.1182/blood-2016-08-733196Google Scholar
- Byrd JC, Mrózek K, Dodge RK. Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood. 2002; 100(13):4325-4336. PubMedhttps://doi.org/10.1182/blood-2002-03-0772Google Scholar
- Grimwade D, Hills RK, Moorman AV. 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):354-365. PubMedhttps://doi.org/10.1182/blood-2009-11-254441Google Scholar
- Mrózek K, Heerema NA, Bloomfield CD. Cytogenetics in acute leukemia. Blood Rev. 2004; 18(2):115-136. PubMedhttps://doi.org/10.1016/S0268-960X(03)00040-7Google Scholar
- Papaemmanuil E, Gerstung M, Bullinger L. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med. 2016; 374(23):2209-2221. PubMedhttps://doi.org/10.1056/NEJMoa1516192Google Scholar
- Metzeler KH, Herold T, Rothenberg-Thurley M. Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia. Blood. 2016; 128(5):686-698. PubMedhttps://doi.org/10.1182/blood-2016-01-693879Google Scholar
- Patel JP, Gönen M, Figueroa ME. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med. 2012; 366(12):1079-1089. PubMedhttps://doi.org/10.1056/NEJMoa1112304Google Scholar
- Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. N Engl J Med. 2015; 373(12):1136-1152. PubMedhttps://doi.org/10.1056/NEJMra1406184Google Scholar
- Baldus CD, Tanner SM, Ruppert AS. Blood. 2003; 102(5):1613-1618. PubMedhttps://doi.org/10.1182/blood-2003-02-0359Google Scholar
- Marcucci G, Baldus CD, Ruppert AS. J Clin Oncol. 2005; 23(36):9234-9242. PubMedhttps://doi.org/10.1200/JCO.2005.03.6137Google Scholar
- Schwind S, Marcucci G, Kohlschmidt J. Blood. 2011; 118(15):4188-4198. PubMedhttps://doi.org/10.1182/blood-2011-06-357764Google Scholar
- Garzon R, Volinia S, Liu C-G. MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood. 2008; 111(6):3183-3189. PubMedhttps://doi.org/10.1182/blood-2007-07-098749Google Scholar
- Marcucci G, Radmacher MD, Maharry K. MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J Med. 2008; 358(18):1919-1928. PubMedhttps://doi.org/10.1056/NEJMoa074256Google Scholar
- Bullinger L, Döhner K, Bair E. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med. 2004; 350(16):1605-1616. PubMedhttps://doi.org/10.1056/NEJMoa031046Google Scholar
- Metzeler KH, Hummel M, Bloomfield CD. An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood. 2008; 112(10):4193-4201. PubMedhttps://doi.org/10.1182/blood-2008-02-134411Google Scholar
- Li Z, Herold T, He C. 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):1172-1181. PubMedhttps://doi.org/10.1200/JCO.2012.44.3184Google Scholar
- Gibb EA, Brown CJ, Lam WL. The functional role of long non-coding RNA in human carcinomas. Mol Cancer. 2011; 10:38. PubMedhttps://doi.org/10.1186/1476-4598-10-38Google Scholar
- Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs. Cell. 2009; 136(4):629-641. PubMedhttps://doi.org/10.1016/j.cell.2009.02.006Google Scholar
- Zhao J, Sun BK, Erwin JA, Song J-J, Lee JT. Polycomb proteins targeted by a short repeat RNA to the mouse X chromosome. Science. 2008; 322(5902):750-756. PubMedhttps://doi.org/10.1126/science.1163045Google Scholar
- Bartolomei MS, Zemel S, Tilghman SM. Parental imprinting of the mouse H19 gene. Nature. 1991; 351(6322):153-155. PubMedhttps://doi.org/10.1038/351153a0Google Scholar
- Hung T, Wang Y, Lin MF. Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters. Nat Genet. 2011; 43(7):621-629. PubMedhttps://doi.org/10.1038/ng.848Google Scholar
- Ulitsky I, Shkumatava A, Jan CH, Sive H, Bartel DP. Conserved function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution. Cell. 2011; 147(7):1537-1550. PubMedhttps://doi.org/10.1016/j.cell.2011.11.055Google Scholar
- Yildirim E, Kirby JE, Brown DE. Xist RNA is a potent suppressor of hematologic cancer in mice. Cell. 2013; 152(4):727-742. PubMedhttps://doi.org/10.1016/j.cell.2013.01.034Google Scholar
- Gupta RA, Shah N, Wang KC. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 2010; 464(7291):1071-1076. PubMedhttps://doi.org/10.1038/nature08975Google Scholar
- Trimarchi T, Bilal E, Ntziachristos P. Genome-wide mapping and characterization of Notch-regulated long noncoding RNAs in acute leukemia. Cell. 2014; 158(3):593-606. PubMedhttps://doi.org/10.1016/j.cell.2014.05.049Google Scholar
- Garzon R, Volinia S, Papaioannou D. Expression and prognostic impact of lncRNAs in acute myeloid leukemia. Proc Natl Acad Sci USA. 2014; 111(52):18679-18684. PubMedhttps://doi.org/10.1073/pnas.1422050112Google Scholar
- Zhou M, Guo M, He DA. A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer. J Transl Med. 2015; 17(13):231. Google Scholar
- Mrózek K, Carroll AJ, Maharry K. Central review of cytogenetics is necessary for cooperative group correlative and clinical studies of adult acute leukemia: the Cancer and Leukemia Group B experience. Int J Oncol. 2008; 33(2):239-244. PubMedGoogle Scholar
- Kroll KW, Eisfeld AK, Lozanski G, Bloomfield CD, Byrd JC, Blachly JS. MuCor: mutation aggregation and correlation. Bioinformatics. 2016; 32(10):1557-1558. PubMedhttps://doi.org/10.1093/bioinformatics/btw028Google Scholar
- Marcucci G, Maharry K, Radmacher MD. J Clin Oncol. 2008; 26(31):5078-5087. PubMedhttps://doi.org/10.1200/JCO.2008.17.5554Google Scholar
- Whitman SP, Archer KJ, Feng L. Cancer Res. 2001; 61(19):7233-7239. PubMedGoogle Scholar
- Wouters BJ, Löwenberg B, Erpelinck-Verschueren CAJ, van Putten WLJ, Valk PJM, Delwel R. Blood. 2009; 113(13):3088-3091. PubMedhttps://doi.org/10.1182/blood-2008-09-179895Google Scholar
- Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival and repeated measures models. Springer: New York, NY; 2005. Google Scholar
- Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958; 53(282):457-481. https://doi.org/10.2307/2281868Google Scholar
- Harrow J, Frankish A, Gonzalez JM. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012; 22(9):1760-1774. PubMedhttps://doi.org/10.1101/gr.135350.111Google Scholar
- Zeng C, Xu Y, Xu L. Inhibition of long non-coding RNA NEAT1 impairs myeloid differentiation in acute promyelocytic leukemia cells. BMC Cancer. 2014; 14:693. Google Scholar
- Wagner LA, Christensen CJ, Dunn DM. EGO, a novel, noncoding RNA gene, regulates eosinophil granule protein transcript expression. Blood. 2007; 109(12):5191-5198. PubMedhttps://doi.org/10.1182/blood-2006-06-027987Google Scholar
- Crea F, Watahiki A, Quagliata L. Identification of a long non-coding RNA as a novel biomarker and potential therapeutic target for metastatic prostate cancer. Oncotarget. 2014; 5(3):764-774. PubMedhttps://doi.org/10.18632/oncotarget.1769Google Scholar
- Thai P, Statt S, Chen CH, Liang E, Campbell C, Wu R. Characterization of a novel long noncoding RNA, SCAL1, induced by cigarette smoke and elevated in lung cancer cell lines. Am J Respir Cell Mol Biol. 2013; 49(2):204-211. PubMedhttps://doi.org/10.1165/rcmb.2013-0159RCGoogle Scholar
- Marcucci G, Maharry KS, Metzeler KH. J Clin Oncol. 2013; 31(17):2086-2093. PubMedhttps://doi.org/10.1200/JCO.2012.45.6228Google Scholar
- Moorwood K, Charles AK, Salpekar A, Wallace JI, Brown KW, Malik K. Antisense WT1 transcription parallels sense mRNA and protein expression in fetal kidney and can elevate protein levels in vitro. J Pathol. 1998; 185(4):352-359. PubMedhttps://doi.org/10.1002/(SICI)1096-9896(199808)185:4<352::AID-PATH119>3.0.CO;2-#Google Scholar
- Smith MJ, Prochownik EV. Blood. 1992; 79(8):2107-2115. PubMedGoogle Scholar
- Gaynor R, Simon K, Koeffler P. Blood. 1991; 77(12):2618-2623. PubMedGoogle Scholar
- O’Connell RM, Taganov KD, Boldin MP, Cheng G, Baltimore D. MicroRNA-155 is induced during the macrophage inflammatory response. Proc Natl Acad Sci USA. 2007; 104(5):1604-1609. PubMedhttps://doi.org/10.1073/pnas.0610731104Google Scholar
- Ruiz-Lafuente N, Alcaraz-García MJ, Sebastián-Ruiz S. IL-4 up-regulates miR-21 and the miRNAs hosted in the CLCN5 gene in chronic lymphocytic leukemia. PLoS One. 2015; 10(4):e0124936. PubMedhttps://doi.org/10.1371/journal.pone.0124936Google Scholar
- Gerloff D, Grundler R, Wurm AA. NF-κB/STAT5/miR-155 network targets PU.1 in FLT3-ITD-driven acute myeloid leukemia. Leukemia. 2015; 29(3):535-547. PubMedhttps://doi.org/10.1038/leu.2014.231Google Scholar
- Huarte M. The emerging role of lncRNAs in cancer. Nat Med. 2015; 21(11):1253-1261. PubMedhttps://doi.org/10.1038/nm.3981Google Scholar
- Marcucci G, Yan P, Maharry K. Epigenetics meets genetics in acute myeloid leukemia: clinical impact of a novel seven-gene score. J Clin Oncol. 2014; 32(6):548-556. PubMedhttps://doi.org/10.1200/JCO.2013.50.6337Google Scholar
- Ko M, Huang Y, Jankowska AM. Nature. 2010; 468(7325):839-843. PubMedhttps://doi.org/10.1038/nature09586Google Scholar
- Liu MC, Pitcher BN, Mardis ER. PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline- and taxane-based chemotherapy: correlative analysis of C9741 (Alliance). NPJ Breast Cancer. 2016; 2:15023. Google Scholar