Prognostic and Biologic Relevance of Clinically Applicable Long Non-Coding RNA Profiling in Older Patients with Cytogenetically Normal Acute Myeloid Leukemia

Abstract

We have previously shown that expression levels of 48 long non-coding RNAs (lncRNAs) can generate a prognostic lncRNA score that independently associates with outcome of older patients with cytogenetically normal acute myeloid leukemia (CN-AML). However, the techniques used to identify and measure prognostic lncRNAs (i.e., RNA sequencing and microarrays) are not tailored for clinical testing. Herein we report on an assay (based on the nCounter platform) that is designed to produce targeted measurements of prognostic lncRNAs in a clinically applicable manner. We analyzed a new cohort of 76 older CN-AML patients and found that the nCounter assay yielded reproducible measurements and that the lncRNA score retained its prognostic value; patients with high lncRNA scores had lower complete remission (CR) rates (P=0.009; 58% vs. 87%), shorter disease-free (P=0.05; 3-year rates: 0% vs. 21%), overall (OS, P=0.02; 3-year rates: 10% vs. 29%) and event-free survival (EFS; P=0.002, 3-year rates: 0% vs. 18%) than patients with low lncRNA scores. In multivariable analyses, the lncRNA score independently associated with CR rates (P=0.02), OS (P=0.02) and EFS (P=0.02). To gain biological insights, we examined our initial cohort of 71 older CN-AML patients, previously analyzed with RNA sequencing. Genes involved in immune response and B-cell receptor signaling were enriched in patients with high lncRNA scores. We conclude that clinically applicable lncRNA profiling is feasible and potentially useful for risk stratification of older CN-AML patients. Furthermore, we identify potentially targetable molecular pathways that are active in the high-risk patients with high lncRNA scores.

Introduction

Acute myeloid leukemia (AML) is a heterogeneous disease with regard to the underlying molecular abnormalities and its clinical course (1, 2). The outcome of patients with AML is generally poor (2), especially in the case of patients who are 60 years of age or older. Only 10% of older AML patients who are fit to receive induction chemotherapy will remain alive and leukemia-free five years after their diagnosis (2). Thus, it is important to identify molecular markers that could distinguish between the patients who will respond to standard treatment from those who will not and who could benefit from experimental therapeutic approaches. Currently, chromosomal alterations, which are detected in approximately 55–60% of all AML cases, are used in the clinic to guide treatment decisions (2–5). In patients who lack microscopically detectable chromosomal abnormalities and thus have cytogenetically normal AML (CN-AML), recurrent gene mutations that associate with clinical outcome have been identified and are currently used to risk-stratify the treatment of CN-AML patients (6–8).

Long non-coding RNAs (lncRNAs) comprise a novel class of non-coding RNA molecules, which are equal to or longer than 200 nucleotides (9). LncRNAs have been shown to regulate many key cellular functions (10–12) and have been implicated in cancer pathogenesis (13–15). Our group has previously shown that expression levels of lncRNAs have prognostic significance in older patients with CN-AML (16). Specifically, we have demonstrated that a weighted summary expression score of 48 lncRNAs (called lncRNA score) provides independent prognostic information in this patient population.

Although two independent techniques of transcriptome interrogation (i.e., a microarray platform and total RNA sequencing) were used to generate and validate the prognostic lncRNA score in the aforementioned study (16), these techniques are not suitable for patient testing in the clinic. Consequently, there is a need for development of a fast, reproducible and clinically applicable assay that would enable the translation of lncRNA profiling from the bench to the bedside. To address this need, we designed an assay allowing targeted measurements of prognostic lncRNAs using the nCounter analysis system (NanoString Technologies, Inc.). The nCounter platform has been developed to provide RNA measurements in a single reaction without amplification and is compatible with real-life clinical testing. This technology is currently used as the basis of an FDA-approved assay that measures the expression of RNA molecules for risk stratification of breast cancer patients (17, 18). Herein, we analyzed a cohort of 76 older patients with CN-AML, and report on the prognostic value of the lncRNA score, as measured by the nCounter lncRNA assay. In addition, to identify potentially targetable molecular pathways in the subset of patients with high lncRNA scores, we performed transcriptome analyses in our initial cohort of older CN-AML patients who had been previously analyzed with total RNA sequencing (RNA seq) (16).

Materials and Methods

Patients and treatment

In this study, we analyzed pretreatment bone marrow (BM) or blood of older patients (aged ≥60 years) with de novo CN-AML, who received intensive cytarabine/anthracycline-based therapy on Cancer and Leukemia Group B (CALGB)/Alliance for Clinical Trials in Oncology (Alliance) frontline clinical trials. We studied only patients who were alive 30 days after initiation of induction chemotherapy. Per protocol, no patient received allogeneic stem cell transplantation in first complete remission (CR). Details regarding treatment protocols are provided in the Supplementary Data. There were no patients who were selected for the study but not included in the final analyses because of poor RNA quality or failure of the profiling experiments. All patients provided written informed consent for the analyses of their samples and data. 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 the results were confirmed by central karyotype review (19). 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 the 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 and WT1 genes, and FLT3-tyrosine kinase domain (FLT3-TKD) mutations], as described previously (16, 20). A variant allele frequency of ≥10% was used as the cut-off 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 sequencing (21) and fragment analysis (22), respectively as described previously. As recent publications (2, 23, 24) indicate that only biallelic CEBPA mutations confer prognostic significance, we considered patients with this genotype as mutated.

Transcriptome analyses

The nCounter analysis system from NanoString allows direct profiling of individual RNA molecules in a single reaction without amplification. The custom assay we designed includes 46 of the 48 lncRNAs that are used to generate the prognostic lncRNA score, as well as selected mRNAs and microRNAs (miRs) whose expression were previously reported as associated with clinical outcome of CN-AML patients. These include BAALC (25), ERG (26), MN1 (27), miR-155 (28), miR-3151 (29) and miR-181a (30), and the seven genes that comprise the integrated genetic-epigenetic score (31) [CD34, MIR155HG, RHOC, SCRN1, F2RL1, FAM92A1 and VWA8]. Internal controls for normalization (e.g., GAPDH, ABL) were also included per NanoString guidelines. These probes were designed and synthesized by NanoString Technologies and the experiments were performed at The Ohio State University using the nCounter Diagnostic analysis system. Total RNA extracted with Trizol reagent was used as input material for the assay. For the details concerning calculation of the prognostic lncRNA score with the nCounter assay measurements see the Supplementary Data. The nCounter profiling experiments have been submitted to the GEO repository under the accession number GSE130923.

The expression status (i.e., high or low expresser) of each of the aforementioned prognostic mRNA and miR transcripts (e.g., BAALC, miR-155) was determined for each patient using the median expression value measured by the nCounter assay as the cut-off.

Statistical analyses

Clinical endpoint definitions are given in the Supplementary Data. Baseline demographic, clinical, and molecular features were compared between patients with high and those with low lncRNA scores using the Wilcoxon rank sum and Fisher’s exact tests for continuous and categorical variables, respectively (32). 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 (33). Cox proportional hazards models were used to calculate hazard ratios for DFS, OS and EFS (34). Multivariable Cox proportional hazards models were constructed using a forward selection procedure. All statistical analyses were performed by the Alliance Statistics and Data Center. The researchers who conducted the laboratory profiling experiments were blinded to patient outcome results.

Results

Design of nCounter assay and reproducibility of the measurements

nCounter probes capable of interrogating the lncRNA expression levels could be generated for 46 of the 48 prognostic lncRNAs described previously (16) (Supplementary Table S1). The sequences of the designed probes are provided in the Supplementary Data section (Supplementary Table S2). The assay also included mRNA and miRNA transcripts that have been previously reported to be prognostic in AML and reference genes for quality control of the analyzed material (Supplementary Table S3). Samples of 76 older patients with de novo CN-AML were measured with the nCounter platform in five independent experiments. To evaluate the reproducibility of lncRNA measurements across time and measured batches, we measured 16 of the 76 samples repeatedly. Eleven samples were measured twice in separate batches, three samples were measured 3 times in the same batch, and 2 samples measured four times in the same batch, yielding a total of 39 replicate runs. Analyses of samples that were measured repeatedly showed satisfactory reproducibility within and across measured batches in all but one case. The median of the Pearson’s r squared values for the correlation of the nCounter measurements for each pair of replicate runs was 0.94 (range: 0.44–0.98, Supplementary Figures S1 and S2). The samples of three patients that were measured twice in the same or separate batches (Fig. 1A–C) and of one patient, which were measured three times in the same batch (Fig. 1D–F) are depicted in Figure 1 as examples.

Figure 1.

Scatterplots depicting the correlation of nCounter results in repeated measurement of samples. A-C, Measurements of three samples that were analyzed twice in separate batches, D-F, Measurements of one sample that was analyzed three times in one batch. The Pearson’s r squared value is annotated on the top of each plot. G, Multidimensional scaling plots showing relationships between the total measurements of the 76 samples analyzed with the nCounter assay. Physical distance between samples indicates similarity: the shorter the distance, the higher the similarity of the measurements. Each dot represents the combined measurements of an individual sample. Measurements are colored by batch: black indicates batch 1; red, batch 2; green, batch 3; deep blue, batch 4 and light blue, batch 5.

To study whether the separation of the measurements in different batches impacted on the nCounter results, we generated multidimensional scaling plots. These plots display the pairwise Euclidean distance between the samples in two dimensions, and the physical distance between samples represents sample similarity (i.e., the shorter the distance between two annotated measurements, the higher the similarity between them). We observed that when all analyzed samples were depicted simultaneously, there was no segregation of the nCounter measurements by batches (Fig. 1G).

Association of lncRNA score with pretreatment characteristics of patients

To examine whether the lncRNA score retained its prognostic value when determined by nCounter assay measurements, we performed outcome analysis in our new cohort of 76 older patients with CN-AML. Specifically, we used the median value of the lncRNA score, as measured with the nCounter assay, to divide our dataset into patients with high and patients with low lncRNA scores. With regard to pretreatment characteristics, patients with high lncRNA scores were older (P=0.04), and harbored mutated NPM1 (P=0.003) less frequently and mutated RUNX1 (P=0.03) and FLT3-ITD (P=0.005) more frequently than patients with low lncRNA scores. Patients with high lncRNA scores were also more frequently classified in the Intermediate or Adverse Risk Groups of the ELN Classification (2) (P<0.001), and were high expressers of ERG (P<0.001), BAALC (P=0.003) and miR-155 (P=0.04), and low expressers of MN1 (P=0.04) more often than patients with low lncRNA scores (Table 1).

Table 1.

Comparison of clinical and molecular characteristics by low and high long non-coding RNA (lncRNA) score in the cohort of 76 older patients (aged ≥60 years) with cytogenetically normal acute myeloid leukemia, who were analyzed with the nCounter assay.

Characteristic	Low lncRNA Score (n=38)	High lncRNA Score (n=38)	P
Age, years			0.04
Median	66	71
Range	60–81	60–82
Sex, n (%) of females	21 (55)	19 (50)	0.82
Race, n (%)			1.00
White	35 (95)	34 (92)
Non-white	2 (5)	3 (8)
Hemoglobin, g/dL			0.15
Median	9	9.4
Range	6.5–11.9	6.8–11.3
Platelet count, x109/L			0.67
Median	70	70
Range	18–507	5–592
WBC count, x109/L			0.54
Median	29.7	28.5
Range	1.4–343.6	1.0–173.1
Blood blasts, %			0.18
Median	36	58
Range	0–90	0–95
Bone marrow blasts, %			0.56
Median	68	64
Range	6–99	0–95
Extramedullary involvement, n (%)	7 (19)	4 (13)	0.53
NPM1, n (%)			0.003
Mutated	28 (80)	15 (44)
Wild type	7 (20)	19 (56)
FLT3-ITD, n (%)			0.005
Mutated	8 (26)	20 (63)
FLT3-ITD allelic raio ≥0.50	4	7
FLT3-ITD allelic ratio <0.50	4	13
Wild type	23 (74)	12 (38)
CEBPA, n (%)			1.00
Biallelic mutations	1 (4)	0 (0)
Wild type or monoallelic mutations	27 (96)	25 (100)
FLT3-TKD, n (%)			1.00
Present	2 (6)	2 (6)
Absent	32 (94)	31 (94)
WT1, n (%)			0.71
Mutated	3 (9)	4 (12)
Wild type	31 (91)	29 (88)
TET2, n (%)			0.59
Mutated	11 (32)	8 (24)
Wild type	23 (68)	25 (76)
IDH1, n (%)			0.75
Mutated	5 (15)	6 (18)
Wild type	29 (85)	27 (82)
IDH2, n (%)			0.51
Mutated	4 (12)	6 (18)
Wild type	30 (88)	27 (82)
ASXL1, n (%)			0.43
Mutated	2 (6)	4 (12)
Wild type	32 (94)	29 (88)
DNMT3A, n (%)			0.12
Mutated	15 (44)	8 (24)
Wild type	19 (56)	25 (76)
RUNX1, n (%)			0.03
Mutated	1 (3)	7 (21)
Wild type	33 (97)	26 (79)
ELN Risk Category,a n (%)			<0.001
Favorable	22 (79)	6 (23)
Intermediate	5 (18)	9 (35)
Adverse	1 (4)	11 (42)
ERG expression group,b n (%)			<0.001
High	11 (29)	27 (71)
Low	27 (71)	11 (29)
BAALC expression group,b n (%)			0.003
High	12 (32)	26 (68)
Low	26 (68)	12 (32)
MN1 expression group,b n (%)			0.04
High	24 (63)	14 (37)
Low	14 (37)	24 (63)
miR-181a expression group,b n (%)			0.82
High	20 (53)	18 (47)
Low	18 (47)	20 (53)
miR-3151,b n (%)			0.82
High	20 (53)	18 (47)
Low	18 (47)	20 (53)
miR-155 expression group,b n (%)			0.04
High	14 (37)	24 (63)
Low	24 (63)	14 (37)

Abbreviations: n, number; WBC, white blood cell; ELN, European LeukemiaNet; FLT3-ITD, internal tandem duplication of the FLT3 gene; FLT3-TKD, tyrosine kinase domain mutation in the FLT3 gene.

^aAmong patients with cytogenetically normal acute myeloid leukemia (CN-AML), the ELN Favorable Risk Category comprises patients with biallelic mutations in CEBPA and patients with mutated NPM1 without FLT3-ITD or with FLT3-ITD^low. The ELN Intermediate Risk Category includes patients with wild-type CEBPA and either wild-type NPM1 without FLT3-ITD, wild-type NPM1 and FLT3-ITD^low or mutated NPM1 and FLT3-ITD^high. The ELN Adverse Risk Category comprises patients with wild-type CEBPA and wild-type NPM1 with FLT3-ITD^high, patients with mutated TP53, and patients with mutated RUNX1 and/or mutated ASXL1 (if these mutations do not co-occur with Favorable-risk AML subtype). FLT3-ITD^low is defined by a FLT3-ITD/FLT3 wild-type allelic ratio of less than 0.5 and FLT3-ITD^high is defined as by a FLT3-ITD/FLT3 wild-type allelic ratio of equal to or more than 0.5.

^bThe median expression value was used as the cut point.

Association of lncRNA score with clinical outcome

Survival analyses in the new cohort of older CN-AML patients showed that patients with high lncRNA scores were less likely to achieve a CR than those with low lncRNA scores (58% vs. 87%, P=0.009). High lncRNA score status associated with shorter DFS (P=0.05; Fig. 2A). None of the patients with high lncRNA scores were alive and leukemia free three years after diagnosis in contrast to 21% of the patients with low lncRNA scores who were. Patients with high lncRNA scores also had shorter OS (P=0.02, 3-year rates: 10% vs. 29%; Fig. 2B). In addition, high lncRNA scores associated with shorter EFS (P=0.02; Fig. 2C, Table 2). Three years after diagnosis 18% of the patients with low lncRNA scores were alive and had not experienced an event in comparison to none of the patients with high lncRNA scores.

Figure 2.

Outcomes of older patients (aged ≥60 years) with cytogenetically normal acute myeloid leukemia with low and high long non-coding RNA (lncRNA) scores. A, Disease-free survival, B, overall survival and C, event-free survival. The lncRNA score of each individual patient was computed as a weighted score, based on the nCounter assay measurements of 46 prognostic lncRNAs.

Table 2.

Outcome of older patients (aged ≥ 60 years) with cytogenetically normal acute myeloid leukemia by low and high long non-coding RNA (lncRNA) score status

End point	Low lncRNA score (n=38)	High lncRNA score (n=38)	P
Complete remission			0.009
n, (%)	33 (87)	22 (58)
Disease-free survival			0.05
Median, years	0.5	0.5
Disease-free at 3 years, % (95% CI)	21 (9–36)	0
Disease-free at 5 years,% (95% CI)	21 (9–36)	0
Overall survival			0.02
Median, years	1.2	0.9
Alive at 3 years, % (95% CI)	29 (16–44)	10 (3–22)
Alive at 5 years, % (95% CI)	26 (13–40)	5 (0–18)
Event-free survival			0.002
Median, years	0.6	0.3
Event-free at 3 years, % (95% CI)	18 (8–32)	0
Event-free at 5 years,% (95% CI)	18 (8–32)	0

Abbreviations: n, number; CI, confidence interval.

Multivariable analyses

To assess whether the prognostic lncRNA score provides independent prognostic information in the context of other established prognostic markers, we constructed multivariable proportional hazards models. High lncRNA scores independently associated with a lower CR rate (P=0.02), after adjusting for BAALC expression status (P=0.02). High lncRNA score status also independently associated with shorter OS (P=0.02), after adjusting for white blood cell (WBC) counts (P=0.005) and the sex of patients (P=0.02). Finally, high lncRNA score was an independent marker of shorter EFS (P=0.02); patients with high lncRNA scores had approximately a two-fold increase in their risk of experiencing an event than those with low lncRNA score, after adjusting for WBC counts (P=0.002) and BAALC expression status (P=0.02, Table 3). The lncRNA score status did not remain significantly associated with DFS duration in multivariable analysis.

Table 3.

Multivariable analyses for outcome in the dataset of older (aged ≥ 60 years) cytogenetically normal acute myeloid leukemia patients

Variables in final modelsa	Complete Remission		Overall survival		Event-free survival
	OR (95% CI)	P	HR (95% CI)	P	HR (95% CI)	P
lncRNA score, high versus low	0.18 (0.04–0.75)	0.02	1.84 (1.09, 3.10)	0.02	1.88 (1.08, 3.27)	0.02
BAALC, high versus low	0.17 (0.04, 0.74)	0.02	-	-	1.88 (1.10, 3.21)	0.02
White blood cell counts, continuous	-	-	1.34 (1.09, 1.64)	0.005	1.38 (1.13, 1.68)	0.002
Sex, male versus female	-	-	1.90 (1.14, 3.17)	0.01	-	-

Abbreviations: OR, odds ratio; HR, hazard ratio; CI, confidence interval; lncRNA, long non-coding RNA.

^aThe lncRNA score status did not remain significantly associated with disease-free survival in multivariable analysis.

NOTE: Odds ratios less than one indicate lower chances for achieving complete remission. Hazard ratios greater than one indicate higher risk for failure to achieve complete remission or death (overall survival) or for failure to achieve complete remission, relapse or death (event-free survival) for the first category listed. Variables considered for model inclusion were: lncRNA score status (high vs. low), age (as a continuous variable, in 10-year increments), sex (male vs. female), race (white vs. non-white), white blood cell count [as a continuous variable, in 50-unit increments], hemoglobin (as a continuous variable, in 1-unit increments), platelet count (as a continuous variable, in 50-unit increments), extramedullary involvement (present vs. absent), ASXL1 mutations (mutated vs. wild-type), CEBPA mutations (biallelic mutations vs. monoallelic mutations or wild-type), DNMT3A mutations (mutated vs. wild-type), FLT3-ITD (present vs. absent), FLT3-TKD (present vs. absent), IDH1 mutations (mutated vs. wild-type), IDH2 mutations (mutated vs. wild-type), NPM1 mutations (mutated vs. wild-type), RUNX1 mutations (mutated vs. wild-type), TET2 mutations (mutated vs. wild-type), WT1 mutations (mutated vs. wild-type), BAALC expression levels (high vs. low), ERG expression levels (high vs. low), MN1 expression levels (high vs. low), miR-181a expression levels (high vs. low), miR-3151 (expressed vs. not expressed), and miR-155 expression levels (high vs. low). For BAALC, ERG, MN1, miR-181a and miR-155 the median expression value, as calculated by the nCounter assay, was used as the cut point to divide patients into high and low expressers.

Biological insights regarding the molecular pathways that associate with the lncRNA score

Using the nCounter assay, we could demonstrate that lncRNA score profiling could distinguish between the patients who were more likely to respond to standard chemotherapy from those who were not. However, older patients often have comorbidities that preclude intensification of chemotherapy and allogeneic stem cell transplantation as therapeutic options. It is therefore important to identify targetable molecular pathways, in particular in those patients that are predicted not to benefit from standard therapy. To this end, we performed correlation analysis in our initial cohort of 71 older CN-AML patients, who were analyzed with total RNA sequencing (16). This sequencing technique provides comprehensive information of both coding and non-coding fractions of the transcriptome. We applied stringent criteria [P value of <0.001 and false discovery rate (FDR) of <0.05] and identified 115 transcripts that were upregulated and three transcripts which were downregulated in patients with high lncRNA scores (Fig. 3A and Supplementary Table S4)

Figure 3.

Messenger RNA (mRNA) transcripts which associate with the prognostic long non-coding RNA (lncRNA) score in older patients (aged ≥60 years) with cytogenetically normal acute myeloid leukemia (CN-AML). A, Heat map of the gene-expression signature associated with the lncRNA score. Rows represent protein-coding genes and columns represent patients. Patients are grouped by lncRNA score: low on the left and high on the right. The lncRNA score of each individual patient was computed as a weighted score of 46 prognostic lncRNAs. Expression values of the mRNA transcripts are represented by color: green, expression less than median value; red, expression greater than median value. B, Gene Ontology functional groups that positively correlate with high lncRNA scores in older patients with CN-AML. Gene Ontology functional groups are ranked according to fold enrichment.

Among transcripts overexpressed in patients with high lncRNA scores, we identified genes that are key regulators of the immune system, such as CD74 and CIITA that are implicated in peptide processing and presentation in antigen presenting cells; BTK and SYK implicated in B-cell receptor signaling; and IL1RAP, IL6R and TLR6, which are important cytokine receptors. We also found aberrant overexpression of CD34 in patients with high lncRNA scores, a surface marker of leukemic stem cells whose expression has been related to chemotherapy resistance and poor outcome in AML (35). In addition, genes implicated in leukemogenesis such as DAPK1 (36) and IDH1 (37) were also upregulated in patients with high lncRNA scores. Finally, in keeping with the adverse prognostic impact of high lncRNA score, we found mRNAs, which are established markers of poor outcome in CN-AML, such as BAALC (25) to be enriched in the subset of patients with high lncRNA scores.

To further characterize and classify the genes and molecular pathways that are active in patients with high lncRNA scores we performed Gene Ontology analysis (38). Gene Ontology analysis revealed enrichment for genes involved in leukocyte migration, inflammatory response, mitochondrial function, apoptosis and immune response in patients with high lncRNA scores (Fig. 3B).

Discussion

LncRNAs are gaining increasing recognition as key regulators of important cellular functions including imprinting, cell cycle regulation and apoptosis (39–43). Over the past years, it has become evident that lncRNAs are functionally associated with malignant diseases (43–45) and that they affect clinical outcome of cancer patients. In CN-AML, we reported a prognostic score, which is based on the expression levels of 48 lncRNAs and provides independent prognostic information in older CN-AML patients (16). Importantly, we found that the prognostic lncRNA score showed no association with recurrent prognostic gene mutations that are currently used for risk stratification of CN-AML patients, such as biallelic CEBPA mutations, NPM1 mutations or FLT3-ITD. For this reason, we hypothesized that the lncRNA score could further refine risk stratification of older CN-AML patients. However, the techniques that were used to identify and measure the prognostic lncRNA molecules are not clinically applicable. To acquire fast and reproducible transcriptome measurements, which would facilitate translation of lncRNA profiling to the clinic, we designed a prognostic lncRNA-measuring assay using the nCounter technology. The nCounter platform has been specifically developed to serve as the basis of clinically applicable tests, and is currently used in FDA-approved transcriptome profiling assays (17, 18).

To test the efficacy and reproducibility of our nCounter lncRNA assay, we analyzed a new cohort of 76 older CN-AML patients, who were treated on frontline CALGB/Alliance studies. We performed a total of five experiments using standard RNA extraction techniques and methods. We sought to evaluate the performance of the assay in real-life conditions and, therefore, did not discard any samples on the basis of RNA yield or quality. We performed multiple measurements of individual samples, so as to evaluate the robustness and reproducibility of our assay. We found a satisfactory correlation of the repeated measurements when these were conducted within the same run of the assay or in independent experiments in all but one case.

To examine whether the nCounter-based lncRNA score retained its prognostic value, we performed outcome analyses in our new cohort of older CN-AML patients. We found that the lncRNA score was significantly associated with outcome and that patients with high lncRNA scores were less likely to achieve a CR and had shorter DFS, OS and EFS than patients with low lncRNA scores. We also detected associations of the lncRNA score status with prognostic mutations, such as those in the RUNX1 gene and FLT3-ITD. Despite these associations, in multivariable analyses, the nCounter assay lncRNA score was shown to be an independent prognosticator for achievement of CR, as well as OS and EFS duration after adjusting for other co-variates.

Our current study was conducted in a cohort of older CN-AML patients of relatively small size and was designed to evaluate the feasibility and utility of lncRNA profiling by the use of a nCounter assay in the clinical setting. LncRNA score-based risk assessment in the current study was dependent on profiling of a group of patients to establish the median lncRNA score value for this group that was then used to distinguish low- from high-risk patients. Nevertheless, because the nCounter platform allows individualized transcriptomic measurements it could be potentially used for risk-assignment of individual patients in the future. To achieve this goal, a larger number of older CN-AML patients should be analyzed to establish the optimal lncRNA score value that should be used as a widely accepted cut-off between patients with a low and those with a high lncRNA score in the clinic.

While it is important to identify those older CN-AML patients that will respond to conventional therapeutic modalities, those who will not represent a therapeutic challenge. Confounding comorbidities often preclude the use of such options as intensification of chemotherapy or allogeneic stem cell transplantation that have been proven to be efficacious in younger adult AML patients. To gain biological insights and identify potentially targetable pathways active in patients with high lncRNA scores, we examined which mRNA transcripts correlate with unfavorable lncRNA profiles and performed gene ontology analyses in our initial cohort of 71 older CN-AML patients, previously analyzed with RNA seq (16). We found genes involved in the regulation of the immune response and B-cell receptor signaling, such as BTK and SYK, to be overexpressed in patients with high lncRNA scores. High expression levels of immune response-related genes are reminiscent of the mRNA expression signature associated with RUNX1 mutations in CN-AML patients (46). The relatively small number of patients with RUNX1 mutations in our initial cohort (n=8) renders it unlikely that these mutations are the sole drivers of the detected lncRNA score-related gene expression signature. It could be hypothesized instead that high expression of the prognostically unfavorable lncRNAs has a similar impact on the transcriptome to RUNX1 mutations.

In recent years, targeting BTK with inhibitory molecules has proven to be a successful therapeutic approach for certain lymphoid malignancies (47, 48) and BTK inhibitors are currently included among the standard-of-care therapeutic agents for these diseases. Use of BTK inhibitors has also yielded encouraging preclinical results in AML (49). The high expression of BTK in patients with high lncRNA score that we detected could provide the rationale for exploring the efficacy of BTK-targeting agents in these patients. Thus, lncRNA profiling could be potentially used not only to risk-stratify treatment of older CN-AML patients but also to guide novel therapeutic approaches in patients who are at high risk of treatment failure.

In summary, we demonstrate the technical feasibility of using the nCounter assay for prognostic lncRNA profiling in a clinically applicable manner. We have also validated the prognostic value of lncRNA expression in older CN-AML patients, in our new cohort of patients analyzed using a different profiling method than the ones used previously. We believe that the value of the nCounter assay for improving risk stratification of AML patients warrants evaluation in future prospective clinical trials.