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A MicroRNA Cluster at 14q32 Drives Aggressive Lung Adenocarcinoma

Ernest Nadal1*, Jinjie Zhong1,2, Jules Lin1, Rishindra M. Reddy1, Nithya Ramnath3, Mark B. Orringer1, Andrew C. Chang1, David G. Beer1*, Guoan Chen1*

1Section of Thoracic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan, 48109, United States

2Xinjiang Medical University, Xinjiang, 830011, China

3Division of Medical Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, 48109, United States

Address correspondence to:

Ernest Nadal, M.D., David G. Beer, Ph.D, or Guoan Chen, M.D., Ph.D., Thoracic Surgery, Department of Surgery, University of Michigan Medical School, 1500 E. Medical Center Drive, Ann Arbor, Michigan 48109. E-mail: [email protected], [email protected]

or [email protected]

Running title: microRNAs located at 14q32 are prognostic in resected lung adenocarcinomas

Keywords: lung adenocarcinoma, microRNA, expression profile, prognosis

Funds: The Bonnie J. Addario Lung Cancer Foundation and the University of Michigan Comprehensive Cancer Center. E.N was supported by a Spanish Society of Medical Oncology Fellowship.

Conflict of Interest: The authors have no conflict of interest to disclose.

Word count: 4,935 words.

Number of figures and tables: 5 figures and 1 table.

Number of supplementary files: 8 figures (S1-S8) and 9 tables (S1-S9).

Research. on October 3, 2020. © 2014 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on May 15, 2014; DOI: 10.1158/1078-0432.CCR-13-3348

http://clincancerres.aacrjournals.org/

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Statement of Translational Relevance

MicroRNAs (miRs) are small noncoding RNAs involved in post-transcriptional regulation of gene

expression. Lung cancer miR expression profiles have identified not only miRs differentially

expressed among the distinct histological subtypes of lung cancer, but also miRs that predict

prognosis in early stage non-small cell lung cancer. In this study, we foundmiRs differentially

expressed among distinct morphological subtypes of lung adenocarcinoma which may have

potential diagnostic utility in the future. In addition, we identified 3 miRs encoded at 14q32

region (miR-411, miR-370 and miR-376a) whose expression was associated with poor survival

and these miRs were validated as independent prognostic markers in early stage lung

adenocarcinoma patients. These miRs associated with aggressive subtypes of lung

adenocarcinoma may be actionable targets in the future.




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Abstract

Purpose: To determine whether different subtypes of lung adenocarcinoma (AC) have distinct

microRNA (miR) expression profiles and to identify miRs associated with aggressive subgroups

of resected lung AC.

Experimental Design: miR expression profile analysis was performed in 91 resected lung AC

and 10 matched nonmalignant lung tissues using a PCR-based array. An independent cohort of

60 lung ACs was used for validating by quantitative PCR the top 3 prognostic miRs. Gene-

expression data from 51 miR profiled tumors was used for determining transcript-specific miR

correlations and gene-enrichment pathway analysis.

Results: Unsupervised hierarchical clustering of 356 miRs identified 3 major clusters of lung AC

correlated with stage (P = 0.023), tumor differentiation (P < 0.003) and IASLC histological

subtype of lung AC (P < 0.005). Patients classified in cluster 3 had worse survival as compared

to the other clusters. Eleven out of 22 miRs associated with poor survival were encoded in a

large miR cluster at 14q32. The top 3 prognostic 14q32 miRs (miR-411, miR-370 and miR-

376a) were validated in an independent cohort of 60 lung AC. A significant association with cell

migration and cell adhesion was found by integrating gene-expression data with miR-411, miR-

370 and miR-376a expression. MiR-411 knockdown significantly reduced cell migration in lung

AC cell lines and this miR was overexpressed in tumors from patients who relapsed

systemically.

Conclusions: Different morphological subtypes of lung AC have distinct miR expression

profiles and 3 miRs encoded at 14q32 (miR-411, miR-370 and miR-376a) were associated with

poor survival after lung AC resection.




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Introduction

Lung cancer is the leading cause of cancer-related deaths for both sexes in

industrialized countries (1, 2). Adenocarcinoma (AC) is the most common histologic subtype,

accounting for about 40% of lung cancer diagnoses and 65,000 deaths each year in the United

States. Lung AC is a heterogeneous disease and includes tumors with remarkably diverse

clinical, pathological and molecular features. A new multidisciplinary lung AC classification has

been recommended based on histopathology as well as clinical, radiological and molecular

features (3). According to the predominant histological pattern, lung AC can be further classified

in differentiated (comprising lepidic, invasive mucinous AC, acinar, papillary and micropapillary)

and undifferentiated/solid subtypes.

During the last decade, significant advances in understanding the critical molecular

mechanisms and the tumor heterogeneity in lung AC have provided clinically-relevant

biomarkers that stratify patients according to their outcome. Additionally these biomarkers have

contributed to the development of novel therapeutic strategies by identifying new targets as well

as predictive markers for specific drugs (4). Analyses using mRNA genomic profiling from large

cohorts of lung ACs have also provided significant information complementing histological

evaluation (5, 6).

MicroRNAs (miRs) are short non-coding RNAs involved in many developmental

processes that can negatively regulate gene expression by base pairing to a complementary

sequence in the 3’ untranslated region of a target mRNA, leading to translational repression. In

human cancer, miRs play a pathogenic role in the disease process by acting as oncogenes or

tumor suppressor genes (7, 8). Since each miR can regulate hundreds of targeted genes, miR

profiling has been considered superior for classifying cancer subtype, tumor differentiation or

predicting overall survival compared with expression profiles of protein-coding genes (9-13).




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Several studies have identified different miRs associated with lung cancer survival by profiling

large sets of non-small cell lung cancer (NSCLC) samples, including lung AC (14-19).

In this study, we carried out global miR profiling on 10 nonmalignant lung samples and

a cohort of 91 lung AC tumors classified according to the IASLC/ATS/ERS International

Multidisciplinary Classification of Lung Adenocarcinoma (3) with the aim of identifying relevant

miRs associated with survival and with specific morphological subtypes of lung AC.

Material and Methods

Clinical samples

We used 151 frozen primary tumors and 10 nonmalignant lung samples matched to the

associated tumor from lung AC patients who underwent resection at the University of Michigan

Health System from 1991-2007. Informed consent was obtained for each subject and clinical

investigations were conducted after approval by the Institutional Review Board. Tumor

specimens were immediately frozen following resection and stored at -80°C. Regions containing

a minimum of 70% tumor cellularity were used for RNA isolation. Tumor grade assessment as

well as histopathological analysis of sections adjacent to regions used for RNA isolation was

performed according the IASLC/ATS/ERS International Multidisciplinary Classification of Lung

Adenocarcinoma (3) by 2 independent investigators. None of the patients included in this study

received preoperative radiation or chemotherapy. Clinical data was retrospectively collected

from the medical records and all cases were staged according to the revised 7th TNM

classification criteria (20). The median follow-up time was 8.12 years among the patients that

remained alive. Primary tumors were randomly assigned to 2 independent sets: training and

validation set, consisting of 91 and 60 samples respectively. Patient characteristics are provided

in Supplementary Table S1.




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RNA isolation and miR profiling

We profiled 91 lung AC and 10 nonmalignant lung samples using TaqMan OpenArray Human

microRNA panel (Applied Biosystems) which includes 754 miRs plus 3 controls (U6, RNU44

and RNU48). Details on the RNA extraction, quality control procedures, array preparation and

data normalization are provided in the Supplementary Material.

Validation of miR expression by quantitative RT-PCR

Quantitative RT-PCR (qPCR) was performed using TaqMan microRNA assays (Applied

Biosystems) to determine the expression values of 3 miRs (miR-411, miR-370 and miR-376a) in

an independent cohort of 60 resected lung AC. Details about the qPCR preparation and data

normalization are provided in the Supplementary Material. A patient’s Risk Score was

calculated as the sum of the expression levels (v) of the 3 prognostic miRs in the test set,

weighted by the corresponding regression coefficients (β) derived from the Cox regression

analysis in the training set, as previously reported (21). The risk score was used to classify

patients into high- or low-risk groups, with a high-risk score indicating poorer survival. The

distribution of risk scores was similar in both sets (Supplementary Figure S1). In the test set,

the median of the risk score was used as the cutoff value.

Lung AC cell lines, transfection and Trans-well migration assay

Two lung AC cells endogenously expressing high miR-411 (SK-LU-1 and NCI-H2228) were

purchased from American Type Culture Collection (Manassas) and were authenticated by

genotyping using the Identifier Plus kit (Applied Biosystems). These cells were transfected with

miRCURY LNA microRNA power inhibitors (Exiqon) either with non-target control A or

antisense against miR-411 using Lipofectamine RNAimax (Invitrogen) and their migration ability

was tested by using Boyden chambers (8 μm pore size, BD Biosciences).




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Integration of miR profile with other genomic data

Single Nucleotide Polymorphism (SNP) array data from 216 lung ACs was used for calculating

the copy number of regions encoding selected miRs (22). Available Affymetrix U133A gene

expression microarray data from 51 miR profiled lung AC tumors were utilized from a previous

study (5). The original gene sets of embryonic stem cell, Myc targets and Notch pathway were

obtained from previous publications (23) and average expression of each gene set was

calculated for each tumor. Using a 5% FDR, the correlation between specific miRs and mRNA

expression was determined using 2 approaches: independently of whether they can be targeted

by these specific miRs to capture genes that may be indirectly regulated and restricting the

analysis to the predicted conserved targets downloaded from TargetScan v.6.2 and the Miranda

and miRWalk websites (24-26). To assess biological processes associated with selected miRs,

a Gene Ontology enrichment analysis was performed based on significantly correlated genes

using DAVID bioinformatics website (27).

Statistical analysis

Significance Analysis of Microarrays (SAM) algorithms for paired and unpaired samples were

used for identifying differentially expressed miRs among tumor and nonmalignant samples using

5,000 permutations as previously described (28). DIANA-miRPath software version 2.0 was

used for pathway enrichment for miRs discriminating lung AC and nonmalignant samples (29).

To identify miR expression patterns, an unsupervised hierarchical centroid linkage cluster

analysis was performed using Cluster v3.0 (30) after mean-centering miRs and arrays and heat

maps were visualized using TreeView software (31). Pearson’s Chi square and ANOVA tests

were used to determine the correlation between the clusters and the clinicopathological

variables. ANOVA tests were used for comparing the mean expression of ESC, Myc and Notch

gene sets among the miR clusters. A multivariate regression analysis adjusted by tumor grade

was performed for miRs significantly up- or down-regulated in each specific histologic subtype




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for identifying miRs differentially expressed in solid, lepidic and invasive mucinous AC subtypes.

Spearman’s correlation coefficients and a linear regression analysis, adjusted by gender, were

performed to test the association between tobacco consumption (measured in pack year) and

miR expression. Survival curves were plotted using the Kaplan-Meier method and survival

differences were assessed by the log-rank test using the median of each individual miR as a

cutoff. Univariate or multivariate Cox proportional hazards were calculated considering

individual miR as a continuous variable. Multivariate analysis was adjusted by age, gender and

stage. To identify miRs associated with metastatic recurrence, the expression of miRs from

patients with lung AC who developed metastasis within 5 years of follow-up was compared with

recurrence-free patients at 5 years.

Results

Identification of differentially-expressed miRs in lung AC versus nonmalignant lung

A total of 78 miRs were differentially expressed in lung AC as compared to nonmalignant

lung tissue by paired class-comparison analysis at a false discovery rate (FDR) of 0.65% (SAM

plot is shown in Supplementary Fig. S2). Thirty-seven were found to be significantly up-

regulated and 41 down-regulated in the tumor tissues (Fig. 1). Using TCGA miRNAseq data

from 126 lung AC versus 78 nonmalignant lung samples, 31 out 78 miRs were validated as

differentially expressed by class comparison analysis in this independent cohort at a FDR <1%

(Supplementary Table S2).

The top 10 deregulated miRs discriminating lung AC from nonmalignant lung included:

miR-21, miR-210, miR-183-3p, miR-135b, miR-182 (up-regulated) and miR-144, miR-126,

miR30a-3p, miR-195 and miR-145-3p (down-regulated). Interestingly, pathway enrichment

analysis based on the predicted gene targets for the top 10 discriminating miRs yielded




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pathways relevant in lung cancer biology such as p53, PI3K-Akt, MAPK, TGF-β, WNT, MAPK

and ErbB signaling pathways, as well as regulation of transcription and post-translational

processes such as ubiquitin mediated proteolysis (Supplementary Fig. S3).

Hierarchical clustering yielded three clusters of lung AC significantly associated with

clinicopathological features and outcome

Unsupervised hierarchical clustering analysis based upon 356 expressed miRs yielded

three major clusters of lung AC, with all nonmalignant lung samples clustering tightly together

within Cluster 1 (Fig. 2A). When nonmalignant samples were not included in the analysis, we

observed that tumors classified in Cluster 3 still clustered together and most tumors

overexpressed miRs encoded at 14q32 (Supplementary Fig. S4). Using unsupervised

clustering analysis and the most variable miRs expressed in a large cohort of lung ACs, a

subset of tumors overexpressing 14q32 miRs was clearly identified (Supplementary Fig. S5).

The correlation of these 3 clusters with the clinicopathological features was determined

and detailed information is provided in Table 1. A significant association between cluster and

stage (P = 0.023) as well as differentiation (P = 0.003) was found. Cluster 1 included more well

differentiated tumors (70%) and fewer stage II-III patients (17% and 6% respectively), whereas

Cluster 2 and 3 contained more poorly differentiated tumors (48% and 45% respectively), and

more stage II-III patients. As an embryonic stem cell (ESC) profile correlates with poorly

differentiated lung AC (23), we analyzed using microarray gene expression data on 51 lung AC

the expression of the ESC, Myc and Notch gene sets according to the 3 miR clusters.

Remarkably, Cluster 3 had significantly higher expression of Notch (P = 0.035) and Myc target

genes (P = 0.032) as compared to Custers 1 and 2 (Supplementary Fig. S6). On the other

hand, Cluster 2 and 3 overexpressed ESC signature genes (P < 0.001) as compared to Cluster

1.




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Strikingly, these miR clusters were highly correlated with the predominant histological

pattern according to the IASCL/ATS/ERS classification (P = 0.005). Cluster 1 included fewer

acinar and solid tumors, whereas nearly all tumors categorized as lepidic or mucinous invasive

adenocarcinomas were included in this cluster. Conversely, Clusters 2 and 3 were enriched for

acinar and solid tumors and included fewer lepidic and invasive mucinous adenocarcinomas.

These results suggest that miR profiles might recapitulate to some extent the morphological

classification of lung AC. To identify individual miRs associated with predominant histologic

subtypes, a logistic regression adjusted by tumor grade was performed and 19 miRs were

independently associated with a predominant solid pattern within the tumor sample and 30 were

associated with lepidic pattern or mucinous invasive adenocarcinoma (Supplementary Table

S3).

A borderline association was also found between cluster and patient age (P = 0.066)

and between cluster and smoking history (P = 0.097). Clusters 2 and 3 contained more heavy

smokers than Cluster 1. We determined the association between miR expression and the

tobacco pack-year consumed and 13 miRs were significantly associated with patient’s smoking

intensity: miR-210 was positively correlated with pack-year, while miR-342, miR-151, miR-501-

3p, miR-29b, miR-30d, miR-497, miR-222, miR-505, miR-34b, miR-500 and miR-99a-3p were

inversely correlated with pack-year consumed.

Furthermore we found that the clusters were associated with clinical outcome, and

patients classified in Cluster 3 had a significantly worse disease-free survival (DFS) and overall

survival (OS) (Log-rank test P = 0.002 and P = 0.001 respectively, Fig. 2B and C) as compared

to other patients.

Prognostic miRs and validation of the top 3 prognostic miRs in an independent cohort of

lung AC




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The statistically significant clinicopathological covariates in the multivariate Cox model

for OS were used in the multivariate analysis examining the prognostic value of individual miRs

(Supplementary Table S4). The univariate and multivariate analysis (adjusted by age, gender

and stage) for OS generated 46 and 40 significant miRs respectively, including 22 overlapped in

both analyses (Supplementary Table S5). Eleven out of these 22 miRs were structurally

associated by their genomic location on the long arm of chromosome 14 (14q32). Interestingly,

this region encodes one of the larger miR clusters in humans and is organized into 2 miR

segments (14q32.2 and 14q32.31).

We selected 3 miRs which were significantly associated with survival in the univariate

and multivariate analysis (Supplementary Fig. S7) that are encoded at 14q32.2 (miR-370) and

14q32.31 (miR-411 and miR-376a) for validating their prognostic value in an independent cohort

of 60 lung AC by qPCR. Patients were classified as high-risk or low-risk according to their

median risk score value. In this test set, patients with a high-risk score had a shorter median

DFS (25.3 months, 95% confidence interval (CI) 19.0 – 31.6) as compared to low-risk patients

(median not reached, Log-rank P = 0.002, Fig. 3A). The estimated DFS rate at 24 months for

high and low-risk patients were 53.3% ± 9.1% and 80.0% ± 7.3 respectively. Similarly, high-risk

patients had a significantly shorter median OS (46.3, 95% CI 29.7 – 62.9) as compared to low-

risk patients (median not reached, Log-rank P = 0.005, Fig. 3B). In the multivariate Cox model

(Supplementary Table S6), a high-risk score remained as an independently prognostic marker

for DFS (HR = 3.32, 95% CI 1.61 - 6.88, P = 0.001) as well as for OS (HR = 3.37, 95% CI 1.42

– 8.00, P = 0.006). The prognostic value of these 3 miRs encoded at the 14q32 (miR-411, miR-

370 and miR-376a) was therefore validated in an independent set of resected lung AC.

Potential mechanisms of regulation of the miRs encoded in the 14q32 region




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The 14q32 region is an imprinted locus depicted in Fig. 4A which contains multiple

paternally imprinted genes (DLK1, DIO3 and RTL1) and maternally-imprinted non-coding RNAs

(MEG3, MEG8 and asRTL1) (32). The 14q32 miRs are only expressed from the maternal allele

and are organized into 2 clusters: the 14q32.2 cluster which contains 10 miRs that overlap with

the gene antisense RTL1 (asRTL1) and the 14q32.31 which encodes more than 40 miRs.

These 2 miR segments are separated by a C/D small nucleolar RNA (snoRNA) segment which

overlaps with the noncoding RNA MEG8. As multiple miRs encoded at 14q32 were associated

with outcome, it is likely that these miRs share a common mechanism of regulation. Several

reasons might explain why only specific 14q32 miRs were associated with prognosis such as

the diversity of target genes regulated by individual miRs, differences in the transcript

processing or maturation and the fact that 14q32 miRs are not all consistently expressed in lung

AC. Correspondingly, we found that not all the expressed 14q32 miRs were significantly

correlated among them (Supplementary Fig. S8).

To examine whether structural chromosomal changes are associated with the

expression of the miRs encoded at the 14q32 region, we used dense Single Nucleotide

Polymorphism (SNP) 250K data from 216 lung AC (22). The median copy number value was

calculated for the 14q32 region including both miR clusters and only 1 patient (0.5%) harbored a

deletion whereas 2 patients (0.9%) had amplification at this region (Fig. 4B). These results

suggest that copy number is not the main mechanism influencing expression of the miRs

encoded at this chromosomal region. Next, we determined the correlation of 14q32 miRs with

other genes encoded at 14q32 region using mRNA expression data available from the same 51

lung ACs included in this study. The mean of 24 expressed miRs located at 14q32 was

calculated and correlated to the expression values of 25 probes corresponding to 18 transcripts

(coding and non-coding) flanking the 14q32 miRs. The mean expression of 14q32 miRs was

significantly correlated to DLK1 and MEG3 expression (P = 0.041 and 0.003, respectively; Fig.

4C). We hypothesize that 14q32 miRs are epigenetically regulated, since genes and miRs




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encoded within this imprinted locus might be regulated by DNA methylation of two differentially-

methylated regions located upstream of the transcription initiation site of MEG3 (32-35).

Genes correlated with miR-370, miR-411 and miR-376a expression and gene ontology

analysis

We determined the correlation between the expression of the top 3 prognostic miRs

(miR-370, miR-411 and miR-376a) and mRNA expression of 22,823 probes in a subset of 51

lung AC which had both miRNA and mRNA expression data. Using a 5% FDR, we detected a

significant correlation (P < 0.05) among 3621 unique miR-mRNA pairs. A gene set enrichment

analysis was performed based upon genes significantly correlated with each of the 3 miR gene

sets, focusing on Gene Ontology (GO) gene set collections. The top 12 GO terms for these 3

miRs are provided in Supplemental Table S7. Interestingly, all 3 miRs were significantly

associated with regulation of cell migration or cell motion (P < 0.001), whereas miR-370 and

miR-411 target genes were also associated with cell adhesion (P ≤ 0.001). In addition, the

predicted genes targeted by miR-370, miR-411 and miR-376a were downloaded from

TargetScan v6.2, Miranda and mirWalk to determine whether the miR-correlated genes are

potential direct miR targets. When the gene enrichment analysis was restricted to genes which

are predicted targets and significantly correlated with these miRs (959 miR-mRNA pairs), the

top terms were involved in the regulation of cell migration and motility as well.

miR-411 expression is associated with metastatic relapse and EMT genes

As regulation of genes involved in cell motility and cell adhesion can contribute to the

acquisition of a more metastatic phenotype, we examined which miRs were differentially

expressed in tumors from patients who developed distant relapse as compared to patients

metastasis-free during the follow-up. Strikingly, miR-411 was identified among the 7 miRs

differentially expressed in tumors from patients with metastatic disease progression (Fig. 5A).




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Accordingly, patients with higher miR-411 expression had a significantly shorter median

metastasis-free survival as compared to patients expressing low levels of miR-411 in both the

training and in the validation set (Fig. 5B and C). Next, we determined the functional impact of

miR-411 knockdown on migration by transfecting 2 lung AC cell lines (SK-LU-1 and NCI-H2228)

which endogenously overexpress miR-411. A significant reduction in cell migration was

observed in SK-LU-1 and NCI-H2228 cells transfected with an anti-miR-411 as compared to

non-target (Fig. 5D). Interestingly, a significant number of genes overexpressed during

epithelial-mesenchymal transition (EMT) (36) were found positively correlated with miR-411

expression, such as ZEB1, SNAI1, SNAI2, MMP2, COL5A2 or SPARC (Fig. 5E,

Supplementary Table S8).

Discussion

MiR expression is deregulated in human cancers, including lung cancer, and contributes

to cancer initiation and progression by targeting genes involved in biological processes that are

hallmarks of cancer such as sustained proliferative signaling, resisting cell death, activating cell

migration and invasion, inducing angiogenesis and avoiding immune destruction or deregulating

cell energetics (37). The investigation of miRs associated with a more aggressive behavior in

lung cancer may provide prognostic and disease monitoring markers and potentially define

novel therapeutic targets.

In this study, we identified 78 miRs differentially expressed in lung AC comparing to

nonmalignant lung tissue. Interestingly, 7 of these deregulated miRs were found differentially

expressed in lung squamous cell carcinoma (SCC) versus nonmalignant lung in our previous

miR study (11): miR-210, miR-200c, miR-200a, miR-106b, miR-182 and miR-183 were up-

regulated whereas let-7e was down-regulated. Previous studies demonstrated that miR profiles




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can discriminate the main histological subtypes of lung cancer, small cell lung cancer and

NSCLC as well as between lung AC and SCC (14, 16, 38). However it is unknown whether

different morphologic and molecular subtypes of lung AC have specific miR expression profiles.

We identified 3 major clusters of lung AC by unsupervised hierarchical clustering analysis based

on the expression of 356 miRs. These clusters were significantly associated not only with tumor

differentiation grade and disease stage, but also with the predominant morphological subtype of

lung AC according to the IASLC/ATS/ERS Classification. Cluster 1 was significantly enriched for

lepidic and mucinous invasive tumors, whereas Cluster 3 contained more solid lung AC. These

results suggest that miR expression profiles may recapitulate to some extent the significant

heterogeneity of lung AC and specific miRs are dysregulated in distinct morphological types of

lung AC. The morphological subtypes are indicative of biological behavior and therefore, miR

expression profiles, which reflect the morphological subtypes of lung AC may be potential

surrogate markers of the underlying lung cancer and have potential clinical utility as diagnostic

tools in the future. Further prospective studies will be needed for validating these findings. Of

note, differential miR expression profiles have been reported to be reflective of distinct

histological subtypes of breast or ovarian tumors as well (39, 40).

When we examined the effect of tobacco smoking on miR expression, we found several

miRs were significantly correlated with the amount of tobacco consumed. MiR-210, which was

highly expressed in heavy smokers, has been associated with hypoxia in lung cancer and

positively regulates HIF-1α (41). Certain miRs negatively correlated with smoking status (such

as miR-342-5p and miR-34b) may act as tumor suppressor miRs, and their expression may be

regulated by epigenetic mechanisms (42).

In the survival analysis, we identified a set of overexpressed miRs associated with poor

outcome after lung AC resection which were encoded in the long arm of chromosome 14

(14q32). Overexpression of 14q32 miRs has been also associated with poor outcome in other

solid tumors (43, 44), but it has not been previously reported in lung cancer. We compared the




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prognostic miRs identified in previous studies and a modest overlap was observed across all

these studies (Supplementary Table S9). This minimal overlap might be influenced by tumor

heterogeneity, differences in assay platforms and normalization methods, different tumor

histology distribution and patient selection. Large collaborative studies such as The Cancer

Genome Atlas (TCGA) or the Strategic Partnering to Evaluate Cancer Signatures (SPECS) will

be extremely valuable for testing the accuracy and reliability of miRs as prognostic markers.

The top 3 prognostic miRs encoded at 14q32 (miR-411, miR-376a and miR-370) were

selected for validation by qPCR in an independent cohort of lung AC. This supervised miR

signature appeared to be superior to the clusters identified by unsupervised clustering since it

not only predicted poor outcome independently of other clinical variables, but it is also more

practicable when analyzing clinical samples.

We were interested in understanding the mechanisms of regulation of 14q32 region

which contains several prognostic miRs. Our analysis of 14q32 copy number using SNP 250K

data from a large cohort of lung AC did not reveal a significant frequency of genomic events.

Since miR expression may be transcriptionally linked to the expression of host genes (protein-

coding and noncoding RNAs), we determined the correlation of miRs encoded at 14q32 with

other genes located in this region. We found that expression of 14q32 miRs correlated with two

genes (DLK1 and MEG3) located in an imprinted locus which is regulated by DNA methylation

and histone modifications. We hypothesize that 14q32 miRs may also be epigenetically

regulated but further studies are needed to elucidate the mechanism that controls the

expression of this miR cluster. MiRs encoded at 14q32 region play an important role in stem cell

pluripotency as well as lung development and differentiation. Indeed, they were overexpressed

in the lungs of mice at the late embryonic stage or newborn mice and then decrease

dramatically during the first weeks of life (45, 46). A recent study that performed RNAseq in

KRASG12D transgenic tumors found that miRs encoded at the Dlk1-Dio3 locus on mouse

chromosome 12qF1 were consistently increased in tumors as compared with nonmalignant lung




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(47). Interestingly, these miRs were repressed when KRASG12D cells were grown in vitro and

were reactivated when transplanted in vivo.

To investigate the biological processes related to specific 14q32 miRs, we determined

which genes were correlated with specific miRs using gene-expression data from 51 lung ACs.

We recognize a limitation of using this approach since miRs may reduce protein expression

without affecting mRNA stability. Nevertheless this approach might be hypothesis-generating for

identifying cellular processes that are deregulated in primary tumors overexpressing certain

miRs. We found that several gene families were consistently correlated with expression of these

miRs, such as regulation of cell motility which may confer a more aggressive phenotype. We

also observed that migration of lung AC cells was reduced after knocking-down miR-411.

Furthermore miR-411 was significantly overexpressed in tumors from patients who developed a

distant recurrence and miR-411 expression correlated with genes involved in EMT. Indeed, it

has been recently reported that the expression of various miRs encoded at 14q32 significantly

increased after treating lung AC cells with TGF-β and knockdown of these miRs inhibited the

EMT process (48).

In conclusion, miR expression profiles not only identified miRs differentially expressed in

lung AC compared to nonmalignant lung, but also were correlated with tumor differentiation and

morphological subtype of lung AC, suggesting that miR profiles may subclassify lung AC. We

identified specific miRs located at 14q32 independently associated with poor outcome after lung

AC resection. We focused on miR-411 which was overexpressed in patients who develop

metastasis after surgery correlated with genes involved in cell migration and EMT. These

findings may have implications for the diagnosis of lung AC and specific miRs might be relevant

targets in the future using novel therapeutic approaches such as antisense oligonucleotides.

Acknowledgments: We gratefully acknowledge Dr. Gabriel Capellà for scientific input and

critical review of this manuscript.




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48. Kitamura K, Seike M, Okano T, Matsuda K, Miyanaga A, Mizutani H, et al. MiR-134/487b/655 Cluster Regulates TGF-beta-induced Epithelial-Mesenchymal Transition and Drug Resistance to Gefitinib by Targeting MAGI2 in Lung Adenocarcinoma Cells. Mol Cancer Ther. 2013.




21

Table 1. Correlation between clinicopathological characteristics and clusters defined by

unsupervised hierarchical clustering analysis. Histological type based upon the IASLC/ATS/ERS

classification of lung adenocarcinoma.

Characteristics Cluster 1 (n=24)

Cluster 2 (n=33)

Cluster 3 (n=34)

P-value

Age- years Mean (SD)

65.9 (± 9.9)

62.5 (± 10.4)

68.1 (± 8.9)

0.066

Sex –n (%) Male Female

10 (25%) 14 (28%)

11 (27.5%) 22 (43%)

19 (47.5%) 15 (29%)

0.172 Smoking –n (%) Never smokers Smokers

5 (50%)

19 (24%)

4 (40%)

28 (35%)

1 (10%)

33 (41%)

0.097 Stage –n (%) I II III

19 (39%) 4 (17%) 1 (6%)

17 (35%) 10 (42%) 6 (33%)

13 (26%) 10 (42%) 11 (61%)

0.023 Histological subtype –n (%) Acinar predominant Lepidic predominant Papillary predominant Invasive mucinous AC Solid predominant

10 (19%) 6 (86%)

3 (37.5%) 3 (60%) 2 (11%)

23 (43%) 1 (14%)

1 (12.5%) 1 (20%) 7 (39%)

20 (38%)

0 (0%) 4 (50%) 1 (20%) 9 (50%)

0.005 Differentiation – n (%) Well Moderate Poor

7 (70%)

15 (29%) 2 (7%)

1 (10%)

18 (35%) 14 (48%)

2 (20%)

19 (37%) 12 (45%)

0.003

Life status –n (%) Dead Alive

9 (19%)

15 (35%)

14 (29%) 19 (44%)

25 (52%) 9 (21%)

0.008 Recurrence –n (%) Yes No

9 (21%)

15 (34%)

11 (26%)

20 (45.5%)

23 (53%) 9 (20.5%)

0.006 Metastasis –n (%) Yes No

5 (15%)

19 (35%)

9 (27%)

22 (41%)

19 (58%) 13 (24%)

0.006




22

MAIN FIGURES LEGENDS:

Figure 1. Supervised clustering of 78 differentially expressed miRs among 10 lung AC vs. 10

matched nonmalignant lung samples. Substantially elevated (yellow) or decreased (blue)

expression of the miRs is observed for individual tumors.

Figure 2. Hierarchical clustering of miR expression in lung ACs. (A) Three major clusters of

tumors were identified by unsupervised clustering analysis based on 356 miRs expressed in 91

tumors and 10 nonmalignant samples. Samples are depicted in columns and miRs in rows.

Lung AC predominant histological subtypes of lung AC are displayed by different colors at the

top of the heat map. We highlighted a set of miRs noticeably overexpressed (yellow) in cluster 3

tumors that included several miRs encoded at the 14q32 region. (B) Kaplan-Meier plot of

disease-free survival according to the cluster subgroups. The estimated DFS rate for patients

classified in cluster 3 was significantly lower (n = 34, 32.4% ± 0.1) as compared to cluster 1 and

2 patients (n = 24 and 33, 70.8% ± 0.9 and 60.6% ± 0.8 respectively). (C) Kaplan-Meier plot of

overall survival according to the cluster subgroups. The estimated OS rate for patients classified

in cluster 3 was significantly lower (n = 34, 35.3% ± 0.8) as compared to cluster 1 and 2 patients

(n = 24 and 33, 75.0% ± 0.8 and 63.6% ± 0.8 respectively).

Figure 3. Validation of the prognostic value of 3 miRs (miR-370, miR-411 and miR-376a)

located at 14q32 in an independent cohort of 60 surgically-resected lung AC patients. Patients

with risk score ≥ or < median were classified as high-risk (n = 30) low-risk (n = 30) respectively.

Kaplan-Meier plot of DFS (A) and OS (B) according to the risk score.

Figure 4. Regulation of 14q32 region. (A) Genomic map of 14q32 miR clusters. The paternally

imprinted genes (DLK1, RTL1 and DIO3) are depicted in black and the maternally imprinted

non-coding RNAs (MEG3, anti-RTL1 and MEG8) are depicted in red. The miRs at 14q32 region




23

are organized in 2 segments separated by a C/D snoRNA cluster. The intergenic differentially

methylated region (IG-DMR) is methylated in the paternal chromosome. Bold miRs were

associated with poor outcome in the univariate and multivariate analysis. (B) Median copy

number value was calculated using SNP 250K data available from 216 lung AC patients for the

region 100,445,178 - 100,582,998. Dashed lines indicate the criteria for considering the

samples amplified (≥ 3) or deleted (≤ 1). (C) Scatterplot showing the significant correlation

among the mean of 14q32 miRs expression and MEG3 expression (r = 0.42, n = 51, P = 0.003).

Figure 5. Association of miR-411 with metastatic recurrence. (A) Volcano plot showing the log-

transformed P-values plotted against the log-transformed fold-change in expression among lung

ACs from patients who develop distant relapse vs. lung ACs from metastasis-free patients.

Seven miRs were differentially expressed within these two groups of tumors. Kaplan-Meier plot

of metastasis-free survival (MFS) according to miR-411 expression in the training (B) and

validation set (C) of lung AC. Patients with tumors expressing higher levels of miR-411 had

significantly median shorter MFS (Log-rank P-value = 0.004 and 0.033, respectively) compared

to the patients with lower levels of miR-411. (D) Two lung AC cell lines expressing high miR-411

levels (SK-LU-1 and H2228) were transfected with non-target or anti-miR-411 inhibitors for

assessing the functional impact of this miR on migration. A significant decrease in cell migration

(p < 0.05) was observed in both cell lines when miR-411 was inhibited. Relevant knock-down of

miR-411 was obtained. (E) Box plots showing the expression of represented EMT genes which

were positively correlated with miR-411 expression in 51 lung AC.




Fig. 1




Cl t 1 Cl t 2 Cl t 3

A

nonmalignant

Fig. 2

Cluster 1 Cluster 2 Cluster 3mucinous

nonmalignantlepidic

papillaryacinarsolid

miR-337miR-127miR-411miR-323-3pmiR-376amiR-410miR-539miR-379c

iR 370miR-370miR-487bmiR-409-3pmiR-889miR-485-3pmiR-432miR-758miR-493miR-494miR-655miR 654 3pmiR-654-3pmiR-409miR-708

obab

ility

B 1

0.8

0.6

obab

ility

1

0.8

0.6

Log-rank P = 0.002 Log-rank P = 0.001C

Months after surgery

DFS

pro

0 10 20 30 40 50 60

0.4

0.2

0

Months after surgery0 10 20 30 40 50 60

OS

pro

0.4

0.2

0 Cluster 3

Cluster 1Cluster 2

Cluster 3

Cluster 1Cluster 2




Fig. 3y

A 1

DFS

pro

babi

lity 0.8

0.6

0.4

0.2Low indexHigh index


B

0 10 20 30 40 50 60

0 Log-rank P = 0.002

S pr

obab

ility

1

0.8

0.6

0.4

OS

Months after surgery0 10 20 30 40 50 60

0.2

0

Low indexHigh index

Log-rank P = 0.005




DLK1 DIO3

MEG3 MEG8

miR-431

miR-433

miR-127

miR-432

miR-136

miR-370

IG-DMR MEG3-DMR 14q32.2 miR

cluster

snoRNA cluster 14q32.31 miR

cluster B

miR-379

miR-411

miR-299

miR-380

miR-1197

miR-323a

miR-758

miR-329

miR-494

miR-1193

miR-543

miR-495

miR-376c

miR-376a

miR-654

miR-376b

miR-300

miR-1185

miR-381

miR-487b

miR-539

miR-889

miR-544

miR-655

miR-487a

miR-382

miR-134

miR-668

miR-485

miR-323b

miR-154

miR-496

miR-377

miR-541

miR-409

miR-412

miR-369

miR-410

miR-656

miR-770

miR-493

miR-337

miR-665

PAT

MAT

A

C

Fig. 4

B




Fig. 5 A

Log 2 (metastatic rec. vs no rec.)

P-v

alu

e (

-lo

g 1

0)

Non-target Anti-miR-411 D

MF

S p

rob

ab

ilit

y

0 10 20 30 40 50 60

1

0.8

0.6

0.4

0.2

0


MF

S p

rob

ab

ilit

y

0 10 20 30 40 50 60

1

0.8

0.6

0.4

0.2

0


C

B

Low High Low High Low High

P = 0.02 P = 0.044

P = 0.002 P = 0.009

Low High

E

Low miR-411

High miR-411

Log-rank P = 0.033

Log-rank P = 0.002

Low miR-411

High miR-411




Published OnlineFirst May 15, 2014.Clin Cancer Res Ernest Nadal, Jinjie Zhong, Jules Lin, et al. AdenocarcinomaA MicroRNA Cluster at 14q32 Drives Aggressive Lung

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