Abstract
Background
Patients with non-small cell lung cancer (NSCLC) receiving curative surgery have a risk of relapse, and adjuvant treatments only translate into a 5% increase in 5-year survival. We assessed the clinical significance of epithelial–mesenchymal transition (EMT) and explored its association with the [SNAIL/miR-34]:[ZEB/miR-200] regulation hub to refine prognostic information.
Methods
We validated a 7-gene EMT score using a consecutive series of 176 resected NSCLC. We quantified EMT transcription factors, microRNAs (miRs) of the miR-200, miR-34 families and miR-200 promoter hypermethylation to identify outcome predictors.
Results
Most tumours presented with an EMT-hybrid state and the EMT score was not predictive of outcome. Individually, all miR-200 were inversely associated with the EMT score, but only chromosome-1 miRs, miR-200a, b, 429, were associated with disease-free survival (p = 0.08, 0.05 and 0.025) and overall survival (p = 0.013, 0.003 and 0.006). We validated these associations on The Cancer Genome Atlas data. Tumour unsupervised clustering based on miR expression identified two good prognostic groups, unrelated to the EMT score, suggesting that miR profiling may have an important clinical value.
Conclusion
miR-200 family members do not have similar predictive value. Core EMT-miR, regulators and not EMT itself, identify NSCLC patients with a low risk of relapse after surgery.
Subject terms: Prognostic markers, Non-small-cell lung cancer
Introduction
Despite major advances, non-small cell lung cancer (NSCLC) remains the major cause of cancer-related death in developed countries [1]. Metastasis and drug resistance are the main factors contributing to relapse and death [2]. Patients with stage I to IIIA are at risk of relapse and death after surgical resection, with a 40–70% risk of recurrence depending on the stage. Adjuvant treatments have been shown to reduce cancer recurrence risk; however, patients with low relapse risk have a moderate benefit from adjuvant therapy. With various upcoming practice changes in adjuvant options as the use of immunotherapies or targeted therapies [3], there is a need for identifying markers to guide treatment selection. The identification and validation of molecular factors linked to relapse risk that predicts which patients are most likely to benefit from adjuvant treatment remain a challenge. Recurrence after complete resection of NSCLC has been attributed to micrometastatic cancer cells and disseminated tumour cells that may have acquired a new phenotype with increased expression of mesenchymal markers and decreased expression of epithelial markers, referred to as epithelial-to-mesenchymal transition (EMT). Although the exact role of EMT in tumour metastasis and cancer prognosis remains a matter of debate, there is a consensus on considering tumour plasticity as the major key point as it allows cells to switch back and forth from E to M states. EMT is a complex molecular and cellular process of tissue remodelling that plays essential roles in cell invasion, migration and drug resistance in many cancer types including NSCLC [4]. During EMT, cells acquire increased mobility, invasiveness, stem-cell properties and resistance to apoptosis [5]. Many signalling pathways control EMT according to different cellular contexts [6, 7]. Pathways involved in EMT are transforming growth factor-β [8], epidermal growth factor (EGF) [9], which activate transcription factors (TFs) [10], including SNAIL superfamily members, ZEB family members, E47, the Krüpple-like factor 8 [11, 12], TWIST family members, Goosecoid, E2.2/TCF4 and FOXC2. Up-regulation of these TFs and loss of E-cadherin are hallmarks of EMT and are related to carcinogenesis and metastasis [13].
MicroRNAs (miRNAs or miRs) are important regulators of EMT-driver genes and are involved in cancer progression and metastasis [5, 14, 15]. MiR-34 and miR-200 are the two main miR families involved in the regulation of cell fate and plasticity through EMT. The regulatory unit of miRs and EMT-TFs [SNAIL/miR-34]:[ZEB/miR-200] is the core regulatory system for the EMT process where miRs regulate EMT-TF, and, in turn, EMT-TF regulates miRs through complex regulation loops. Besides their involvement in driving EMT, miR-200 was described to regulate angiogenesis [16], to restrict metastasis and to modulate intratumoral immunosuppression [17]. In some studies, a prometastatic and protumoral effect of miR-200 was also reported, emphasising the complexity of EMT regulation [16, 18]. MiR-200 family splits into two clusters located on chromosome 1 (chr-1) (miR-200a, b and 429) and chr-12 (miR-200c and 141). Those two clusters are differently expressed and their effects on EMT may depend among tumour types. In an insulinoma mouse model, it was shown that the miR-200c family—and not the closely related miR-141—was responsible for EMT regulation through ZEB1 activation [19]. In breast cancer, miR-200c silencing and ZEB1 regulation were associated with aggressiveness [20]. In colorectal cancer, low miR-200a expression was related to poor prognosis [21]. In the lung, nearly all miR-200s were repressed in metastases as compared to primary tumours in a mouse metastatic lung adenocarcinoma, suggesting a global miR-200 involvement in lung cancer progression [22]. However, only miR-141 and miR-200c were markers of overall survival (OS) in early-stage NSCLC [23], suggesting that the prognostic value of miR-200 family members remains a matter of debate.
To assess their proper relevance to survival, we quantified all members of the miR-200/ZEB/miR-34/SNAIL circuit and downstream EMT targets in a series of 176 resected NSCLC. We validated and used a 7-genes score to classify samples. We correlated miRs and TFs with the EMT score and assessed their value in predicting OS and relapse-free survival (RFS) individually or using hierarchical clustering. Finally, this study underlines highly significant differences between chr-1 and chr-12 miR-200 in predicting relapse and death in NSCLC.
Materials and methods
Study design and patients
This study, conducted at the European Georges Pompidou hospital, was approved by CPP Ile de France 2 ethics committee (nos. 2012-08-09 and 2012-08-09 A1) and registered in clinical trial.gov (NCT03509779). Patients with NSCLC treated by surgery for curative intent signed informed consent for research and tumour tissues banking. A series of 176 primary lung cancer were prospectively collected from October 2011 to December 2014. Samples were stored frozen (−80 °C) at the Biological Resources centre and Tumour Bank Platform (PRB-HEGP BB-0033-00063) before nucleic acid extraction. Baseline demographics and clinical variables were collected using the Epithor national database, and survival data were updated using patients’ medical records.
Molecular characterisation
Tumours were cut prepared on a cryostat and reviewed by the pathologist before DNA and RNA extractions. Mean tumour cell content was 52% ± 25; all samples with <20% were excluded. DNA and RNA were extracted using QIAamp DNA Mini Kit (Qiagen) and miRNeasy Mini Kit (Qiagen) extraction kits; DNAs and RNAs were quantified by Qubit Fluorometric Quantitation (Thermo Fisher Scientific) and stored frozen.
Samples were characterised for molecular alterations by targeted next-generation sequencing (NGS) (Ion AmpliSeq™ Colon-Lung Cancer Research Panel v2, Life Technologies™, Carlsbad, CA). Briefly, the multiplex barcoded libraries are generated from 10 ng of DNA following the manufacturer’s recommendations (Ion Ampliseq Library Kit V2) and are normalised using the Ion Library Equalizer™ Kit. The pooled libraries (max 96) are processed on Ion Chef™ System for template preparation and chip loading (Ion PI HI-Q Chef Kit, Ion PI Chip V3), and sequenced on the Ion Proton™ System (Life Technologies™). The FASTQs sequencing data are aligned to the human genome (hg19) and processed using IonTorrent Suite V5.0.4.0 This package included the Torrent Variant Caller V5.0.4.0 using the built-in ‘Somatic-low stringency’ with optimised parameters to automatically call variants with allelic ratio >2%.
Real-time quantitative PCRs (qPCRs) were performed to characterise EMT and stem-cell markers in tumours using a set of molecular markers; five mesenchymal markers (TWIST1, N-CADHERIN, ZEB1, SNAI1 and VIMENTIN), two epithelial markers (E-CADHERIN and JUP) and two stem-cells markers (TCF3 and CD44), as recommended by guidelines [24]. RNAs (1 μg) were reverse transcribed using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystem, Foster City, CA). E-CADHERIN, JUP, TWIST1, N-CADHERIN, ZEB1, SNAI1, VIMENTIN, TCF3 and CD44 messenger RNA (mRNA) levels were quantified using FAM Taqman probes on an ABI Prism 7900 Sequence Detector System (TaqMan™ Gene Expression Assay, Applied Biosystems). Measures were performed in triplicate with 10 ng/µL of complementary DNA (cDNA) using Taqman Gene Expression Master Mix (Applied Biosystems). In each run, normal human lung RNAs from a pool of normal lung tissues (n = 10) was used as calibrator. We performed duplex analysis using 18S RNAs (VIC probe, Applied Biosystems) as an endogenous gene control. Low-quality RNA samples (18S RNA Ct > 16) were discarded from the analysis. Expression levels were calculated as described by Livak and Schmittgen using the ΔΔCt method [25]. Data were analysed using the RT2 profiler PCR array Data Analysis Web portal (Qiagen). Fold change between controls and samples defined down (between 0 and 0.5), stable (0.5–2) or up-regulated (>2) markers. As we quantified two epithelial markers and five mesenchymal markers, we weighted results to create an EMT score to classify tumours: for epithelial markers (E-CADHERIN and JUP), down, stable and up-regulation were weighted as 10, 5 and 0 points; for mesenchymal markers (TWIST1, N-CADHERIN, ZEB1, SNAI1 and VIMENTIN), down, stable and up-regulation were weighted as 0, 2 and 6 points. EMT score was the sum of epithelial and mesenchymal markers. Low to high EMT scores classify sample phenotypes from epithelial to mesenchymal. This classification was established a priori, and tumours were classified blindly of miR levels and clinical data. This score was validated by comparison to two previously published EMT signatures using linear regression (Spearman’s correlation) 76GS: 76 Gene signature method [26] (Supplemental Fig. 1A) and KS: a two-sample Kolmogorov–Smirnov test (Supplemental Fig. 1B) [27]. We found a good correlation between our EMT score and published signatures (r = −0.58, p < 10−5 and r = 0.65, p < 10−5, respectively), consistent with the one existing between the two validated signatures (r = −0.77, p < 10−5, Supplemental Fig. 1C) [28]. Our 7-genes score is an approximation of minima of the EMT status and can be used as a surrogate marker to score EMT.
MiRs were quantified by quantitative reverse transcription-PCR. RNAs (10 ng/µL) were reversed transcribed using the TaqMan Advanced miRNA cDNA Synthesis Kit (Applied Biosystem). MiR levels were quantified by qPCR using Taqman probes (Taqman Advanced miRNA Assay, clone ID 478490_mir and clone ID 477849_mir, Thermo Fisher Scientific) on an ABI Prism 7900 System (Applied Biosystems). Real-time PCR was performed in triplicate with 3.3 ng of cDNA and the Taqman Fast Advanced Master Mix (Applied Biosystems). In each run, normal human lung RNA as described above was used as a calibrator. MiR-423 and miR-425 (clone ID 478327_mir and clone ID 478094_mir, Thermo Fisher Scientific) were used for normalisation and expression levels were calculated as described for EMT markers.
DNA methylation
DNA methylation level at miR-200 promoters was measured by fragment analysis. DNA underwent bisulfite treatment. Bisulfite and methylation-sensitive primers were designed with Methprimer. For a specific CpG site, two couples were designed with R primer localised on a CpG site and F outside of any CpG site. R primers were HEX tagged for methylated fragments and FAM tagged for non-methylated fragments. Migration was performed on ABI3730xl sequencer data and analysed with Genemapper software (Thermo Fisher diagnostics). Methylation level was defined as the ratio of methylated peak height/(methylated + non-methylated peak height). Methylation states were grouped in quartiles for analysis.
Statistical analyses
The association between continuous variables was assessed by Pearson’s correlation and the association between categorical variables was assessed by Wilcoxon’s test. Survival curves were estimated by the Kaplan–Meier method. Statistical comparisons between survival distributions were made using the log-rank test. Multivariate analyses were performed using the Cox proportional hazards model for overall and disease-free survival (DFS) analyses. All data analyses were conducted with two-sided tests; a p value < 0.05 was statistically significant (R V3.5.2). miR clustering was done using the Ward method with JMP (SAS Institute JMP, France).
Results
Patients and tumour characteristics
Clinical characteristics are shown in Table 1: the average age of patients was 65 years. The disease stage was distributed as follows: 78 patients stage I (44%), 45 (25%) stage II, 47 stage III (26%) and 6 (3%) stage IV. Patients had lobectomy (n = 131), bilobectomy (n = 7), pneumonectomy (n = 17) or atypical resection (n = 11). The median follow-up duration was 42 months. Less than a half of the patients had adjuvant chemotherapy (n = 64) or radiotherapy (n = 26) and 53 patients had relapsed or died at 3 years. The most frequent genetic alteration was, as expected, TP53 mutations followed by KRAS and EGFR (Table 2A). Molecular alterations in TP53, KRAS or EGFR were not linked to age, stage, relapse or death at 3 years. EGFR mutations were more frequent in women (p = 0.03) and in non-smokers (p = 0.006) and TP53 mutations were more frequent in smokers (p = 0.03), no relation with tobacco exposure was identified in the KRAS-mutated group.
Table 1.
Clinical, surgical and oncological characteristics of samples.
| Clinics | |
| Sex ratio (F/M) | 55/121 |
| Age (yr) ± SD | 65 ± 11 |
| IMC (kg/m2) ± SD | 25 ± 4 |
| VEMS (L/min) ± SD | 81 ± 21 |
| Comorbidities | |
| Cardiovascular | 81 (46%) |
| Diabetes | 23 (13%) |
| Cancer (other) | 45 (26%) |
| Surgery | |
| Surgical approach | |
| Thoracotomy, n (%) | 157 (89%) |
| Video-assisted, n (%) | 17 (10%) |
| Robotic, n (%) | 2 (1%) |
| Surgical resection | |
| Sub-lobar, n (%) | 12 (7%) |
| Lobectomy, n (%) | 136 (77%) |
| Bilobectomy, n (%) | 9 (6%) |
| Pneumonectomy, n (%) | 19 (10%) |
| Complete resection (R0), n (%) | 176 (100%) |
| Side (D/G) | 103/73 |
| Lymph nodes resection | |
| Radical, n (%) | 166 (94%) |
| Sampling, n (%) | 10 (6%) |
| Duration (min) ± SD | 182 ± 64 |
| Post-operative course | |
| Infection, n (%) | 25 (14%) |
| Cardiovascular, n (%) | 16 (9%) |
| Prolonged air leaks, n (%) | 21 (12%) |
| Death, n (%) | 4 (2%) |
| Length of stay (days) ± SD | 13 ± 8 |
| Oncology | |
| Histological type | |
| Adenocarcinomas, n (%) | 117 (66%) |
| Squamous cell, n (%) | 46 (26%) |
| Large cell, n (%) | 9 (5%) |
| Carcinosarcomas n (%) | 4 (2%) |
| TNM (IASLC 2009) | |
| Tx, n (%) | 3 (2%) |
| T1, n (%) | 55 (31%) |
| T2, n (%) | 77 (44%) |
| T3, n (%) | 35 (20%) |
| T4, n (%) | 6 (3%) |
| Nx | 6 (3%) |
| N0, n (%) | 99 (56%) |
| N1, n (%) | 32 (18%) |
| N2, n (%) | 39 (22%) |
| M0, n (%) | 170 (97%) |
| M1, n (%) | 6 (3%) |
| Stage | |
| IA, n (%) | 43 (24%) |
| IB, n (%) | 35 (20%) |
| IIA, n (%) | 9 (5%) |
| IIB, n (%) | 36 (20%) |
| IIIA, n (%) | 43 (24%) |
| IIIB, n (%) | 4 (2%) |
| IV, n (%) | 6 (3%) |
| Perioperative treatment | |
| Neoadjuvant chemotherapy | 20 (11%) |
| Adjuvant chemotherapy | 66 (38%) |
| Adjuvant radiation therapy | 26 (15%) |
Table 2.
Description of molecular alterations.
| (A) Mutations | |||
|---|---|---|---|
| Mutated genes | Alteration in n cases (%) | ||
| KRAS | 51 (31%) | ||
| BRAF | 5 (3%) | ||
| NRAS | 4 (2%) | ||
| TP53 | 75 (45%) | ||
| PIK3CA | 7 (4%) | ||
| CTNNB1 | 3 (2%) | ||
| SMAD4 | 2 (1%) | ||
| FBXW7 | 1 (1%) | ||
| STK11 | 10 (6%) | ||
| EGFR | 17 (10%) | ||
| ERBB2 | 2 (1%) | ||
| AKT1 | 2 (1%) | ||
| ERBB4 | 3 (2%) | ||
| FGFR3 | 1 (1%) | ||
| PTEN | 2 (1%) | ||
| DDR2 | 1 (1%) | ||
| MAP2K1 | 1 (1%) | ||
| At least 1 alteration | 138 (84%) | ||
| 1 alteration | 75 (45%) | ||
| 2 alterations | 49 (30%) | ||
| 3 alterations or more | 14 (8%) | ||
| (B) MiRs and EMT marker | |||
| Up | Stable | Down | |
| miR | |||
| miR-200a-3p | 52 (32%) | 91 (55%) | 21 (13%) |
| miR-200b-3p | 91 (57%) | 54 (34%) | 14 (9%) |
| miR-429-3p | 83 (51%) | 64 (39%) | 17 (10%) |
| miR-200c-3p | 72 (45%) | 70 (44%) | 18 (11%) |
| miR-141-3p | 104 (65%) | 42 (26%) | 14 (9%) |
| Epithelial markers | |||
| E-cadherin | 51 (35%) | 57 (39%) | 37 (26%) |
| JUP | 58 (40%) | 59 (41%) | 28 (19%) |
| Mesenchymal markers | |||
| Twist1 | 72 (50%) | 32 (22%) | 41 (28%) |
| N-cadherin | 62 (43%) | 43 (30%) | 40 (28%) |
| ZEB1 | 24 (17%) | 46 (32%) | 75 (52%) |
| SNAI1 | 20 (14%) | 43 (30%) | 82 (57%) |
| VIM | 24 (17%) | 44 (30%) | 77 (53%) |
| Stem-cell markers | |||
| TCF3 | 44 (30%) | 62 (43%) | 39 (27%) |
| CD44 | 35 (24%) | 53 (37%) | 57 (39%) |
| EMT score (mean ± SD) | 19.7 ± 7.6 | ||
Mutations, using NGS Colon-Lung v2 Cancer Panel (A); miR-200a, miR-429, miR-200b, miR-200c, miR-141, EMT and stem-cell markers expression levels (B) defined as up, stable and low as described in ‘Materials and methods’.
(A) Mutations and (B) MiRs and EMT marker.
EMT tumour profiles
EMT gene expression and miR quantification are described in Table 2B. Because EMT progresses through dynamic intermediate states, we explored EMT’s value using a 7-genes score as described in the ‘Materials and methods’ section. EMT score ranged from 0 to 40 (theoretical maximum: 0–50); mean was 19.7 [18.5–21]IC95% (quartiles 15–24). The existence of tumours expressing epithelial and mesenchymal markers and the existence of a correlation between epithelial and mesenchymal markers illustrates the predominance of hybrid states (Fig. 1).
Fig. 1. Correlation matrix of miR-34, miR-200, promoter methylation, epithelial and mesenchymal marker expression.
Circles represent the correlation coefficient when significant; the cross represents the absence of correlation.
EMT score inversely correlated with miR-429 (p = 0.004), miR-200b (p = 0.004), miR-200a (p < 0.0001), miR-200c (p = 0.003) and miR-141 (p = 0.001) was associated with methylation at chr-1 (p = 0.0003) and chr-12 (p = 0.0002) (Supplemental Fig. 2) and with high expression of stem-cell markers (p < 0.0001). EMT score was not linked to smoking, tumour mutational status, sex, age or stage (data not shown).
EMT’s prognostic value was explored using the EMT score either tested as a continuous variable or grouped in quartiles with the lower quartile defining the epithelial state, the two intermediate quartiles the transition state and the higher quartile the mesenchymal state. No association was found between the EMT score and RFS or OS (Supplemental Fig. 3).
MiR tumour profiles
Expression of miR-200 family members strongly correlated together within chromosome clusters: miR-200a, b and miR-429 on chr-1, and miR-200c and miR-141 on chr-12 (Fig. 1). However, correlations were weak or absent between chr-1 or chr-12. MiR-200a inversely correlated with SNAI1, TCF3 and CD44, whereas miR-34a positively correlates with CDH1, ZEB1, VIM, TCF7 and CD44. MiR promoter methylation at chromosome 1 was related to low miR-200a, b and 429, methylation at chr-12 was not related to low chr-12 miR-200s. High miR-429 (p = 0.02) and miR-200b (p = 0.002) associate with the female gender; no links were found between miRs and other clinical data including stage and node metastasis. Up-regulation of chr-12 miRs, miR-200c (p = 0.04) and miR-141 (p = 0.03), were associated with KRAS mutations at the opposite; up-regulation of chr-1 miRs, miR-200b (p = 0.02) and miR-429 (p = 0.004), were associated with EGFR mutations. TP53 mutations were inversely correlated with miR-200a (p = 0.004), miR-200c (p = 0.005) and miR-141 (p = 0.03). As miRs analysed individually moderately associate with EMT markers, we tested whether miR profiles might be more informative.
miR profile analyses
We used hierarchical clustering to classify tumours based on miR expression and explored whether identified clusters exhibited different prognostic profiles (Fig. 2). MiR expression data and methylation status at chr-1 and chr-12 were used to create clusters. Parallel plots were generated to identify gene profiles within each cluster. Cluster 1 is defined by high expression of miR-429, miR-200a and intermediate miR-200b, but low or absence of expression of miR-141 and miR-200c; cluster 2 has lower expression of miR-429, miR-200a and higher expression of miR-200b, miR-141 and miR-200c; cluster 3 is characterised by high to intermediate methylation at chr-1 and miR-34a expression; cluster 4 is characterised by high methylation at chr-1 and chr-12 and very low expression of all miRs markers, and cluster 5 is defined by high expression of miR-34b and miR-34c (Fig. 2). MiR clusters were associated with the EMT score; lower scores defining epithelial samples are seen in clusters 1 and 2 (Supplemental Fig. 4). Expression of ZEB1, SNAIL and TWIST was statistically different between miR clusters (p = 0.02, 0.03 and 0.002, respectively) and TP53 mutations were more frequent in cluster 4 (p = 0.03) (Supplemental Fig. 5). No association was found with sex, age, stage or node metastasis.
Fig. 2. Unsupervised hierarchical clustering based on miR-34, miR-200 expression and promoter methylation status.
Five different clusters with differential methylation or miRs expression profiles are identified.
Core EMT miRs and prognosis in NSCLC
We assessed links between miRs, clinical data and tumour phenotype. Univariate analysis showed that age, TP53, EGFR or KRAS mutations, individual EMT markers or EMT score had no impact on OS or RFS (data not shown). RFS was associated to stage (p < 0.0001) and to chr-1-miR-200 expression miR-200a-3p (p = 0.08), miR-200b (p = 0.05) and miR-429 (p = 0.025) (Supplemental Fig. 6A–C). No association was identified with chr-12-miR-200s or miR-34s. OS was associated to stage (p < 0.0001), neoadjuvant chemotherapy (p < 0.001), lymph nodes invasion (p < 0.001), to chr-1-miR-200 expression miR-200a (p = 0.016), miR-429 (p = 0.034) and miR-200b (p = 0.08) (Supplemental Fig. 6D–F). Multivariate analysis including stage showed that chr-1 miR-200s were independent predictors of OS and a trend was seen for DFS, especially for miR-429 (p = 0.059) (Supp Table 1). No prognostic value was found for chr-12 miR-200s or miR-34s. Kaplan–Meir survival curves confirmed that low expression of miR-200a, mir-200b and miR-429 was linked to shorter OS (p = 0,013, p = 0.003, p = 0.006, respectively) and shorter DFS (p = 0.08, 0.05 and 0.025, respectively) (Fig. 3). Our results were validated by The Cancer Genome Atlas (TCGA) data showing that indeed chr-1 miR-200s and not chr-12 miR-200s had a predictive value for OS in NSCLC (Supplemental Fig. 7).
Fig. 3. Kaplan–Meier OS and RFS plots.
Disease-free survival according to miR-200a (a), miR-200b (b) and miR-429 (c) expressions. Overall survival according to miR-200a (d), miR-200b (e) and miR-429 (f) expressions.
Methylation status was associated with low miR-200 expression (Fig. 1). Using 80% methylation ratio as a cut-off, lower OS was identified in the high methylated group (p = 0.0079), and a trend was identified for lower DFS (p = 0.057) (Fig. 4a, b).
Fig. 4. Kaplan–Meier OS and RFS plots.
Disease-free survival (a) and overall survival (b) according to different miR clusters showing two sub-groups, clusters 1 and 5, with low relapse risk (a) and longer OS (b). Disease-free survival (c) and overall survival (d) according to miR-200-chromosome-1 methylation status.
Considering miR clusters, we showed that clusters 2, 3 and 4 were linked to higher death (p = 0.002) and relapse (p = 0.02) at 3 years, and nearly 50% of patients had died or relapsed as compared to clusters 1 and 5, for which only 2 out of 18 and 1 out of 17 had died at 3 years in clusters 1 and 5, respectively. Kaplan–Meir survival curves confirmed that tumours with miR profiles corresponding to clusters 1 and 5 are of low risk of relapse (p = 0.064) (Fig. 4c) and death (p = 0.021) (Fig. 4d).
Discussion
Patients treated by surgery for localised lung cancer have distinct prognosis and relapse risk. Perioperative treatments have an unsatisfactory benefit, with a 5-year OS rate increase ranging from 5 to 8% [29, 30]. The management of patients with localised diseases is rapidly evolving with new perioperative treatments available as immunotherapies and targeted therapies [3]. With the diversification of treatment options, tumour molecular characterisation will be necessary for patients with localised diseases to identify patients with a high risk of relapse. Epithelial-to-mesenchymal transition is a cellular process that drives metastasis and resistance to treatment, suggesting that the tumour EMT phenotype could be a valuable marker of aggressiveness. The absence of prognosis value of the EMT status itself may seem contradictory; however, due to EMT plasticity, the strength of EMT evaluation to predict survival might be low. Moreover, low miR may reflect the propensity of cells to adapt through an EMT programme, and the hybrid E/M phenotype may have multiple advantages as compared to a complete mesenchymal state to favour metastasis due to collective migrating cell clusters [31]. Gene signatures were used to score EMT. A 76-gene signature [26] predicted resistance to EGFR inhibitors in vitro, but had no prognostic value in vivo; a 16-gene signature [32] was linked to immune tumour infiltration, but not to prognosis, and finally, a 12-gene signature predicted responses to immune checkpoint inhibitors and was linked to OS and RFS in patients receiving immune checkpoint inhibitors [33]. Our EMT score was not associated with OS or RFS in line with the previous series. EMT signatures may not capture well enough cell plasticity that may be of importance to evaluate tumour aggressiveness. Indeed, most tumours showed co-expression of epithelial and mesenchymal markers corresponding to different hybrid states. Analysing upstream EMT regulatory loops might drive important information on cell capacities to undergo E to M switches. The miR-200, miR-34, ZEB1, SNAIL regulation circuit is a hub that governs cell fate [34]. We hypothesis that quantifying core circuit regulators may help identify tumours that are prone to acquire EM plasticity. In this series, miR-200 down-regulation and high EMT scores were observed in broadly 10% of samples. No association between miR-200s down-regulation and up-regulation of their direct target ZEB1 was found. Bracken et al. showed that the promoter shared by miR-200a, miR-200b and miR-429 was repressed in mesenchymal cells by ZEB1 and SIP1 [35, 36], and Lindner et al. showed that ZEB1 alters the epigenetic landscape in colorectal cancer cells [37]. Depending on extracellular stimulations, the miR-200/ZEB1/2 equilibrium may turn on epithelial or mesenchymal markers, and feedback loops may explain the absence of association between miR-200s and ZEB1/2 in this series. Our study showed that miRs belonging to the miR-200 family but not miR belonging to the miR-34 family were linked to the EMT score. The miR-34/SNAIL loop may act as a side regulator that does not give rise to phenotypic transition by itself. However, hierarchical clustering showed that the miR-34b- and miR-34c-positive sub-group was of good prognosis and low relapse risk, even though these samples had low miR-200s and high EMT scores. In line with this, differences between miR-34a, c and b were shown and miR-34b and c were found more effective tumour suppressors as compared to miR-34a [38]. Here we found that miR-34b/c expression could rescue miR-200 loss. One of the main results brought by this study is the identification of major differences between both miR-200 clusters. We showed that the molecular background, as for KRAS, EGFR and TP53 associates with miR-200s expression. TP53 mutations were more frequent in cluster 4, characterised by high methylation and low miR-200s. Links between miR-200 and TP53 exist: in TP53-mutated breast cancer, loss of miR-200c promotes carcinogenesis. Moreover, wild-type TP53 transactivates miR-200s, and, in turn, regulates ZEB1/2 [39]. Our results suggested that in NSCLC, mutant TP53 could lose its ability to up-regulate miR-200s and therefore be an important regulator of EMT. The seed miR sequences that differ among miR-200 may direct at different 3′-untranslated region sequences of target mRNAs. In lung cancer models, miR-200 exhibits a dual function in lung cancer progression: often described as tumour suppressors, they have also been associated with tumour progression as for miR-429 in cellular models [18, 40] and for miR-141 [41]. This suggests that first the biological functions of miR-200s members may be different and second may depend on environment and tissue types. Up-regulation of miR-200c and miR-141 has been associated with poor survival rates in early-stage NSCLC [23, 42]. Here we showed, in accordance with TCGA data, that miR-141 and miR-200c do not predict survival in NSCLC. In line with that, a meta-analysis testing the prognostic value of miR-141 in cancer showed large differences between cancer types and studies [43]. In our study, chr-12 miRs seemed much less regulated by promoter methylation as compared to chr-1 miRs. Considering the miR-ZEB1-SNAIL regulation hub, methylation might unbalance the regulation loops towards the mesenchymal side. The hypothesis that a methylator phenotype or DNMT dysregulation could be at the origin of miR-200a, b and miR429 down-regulation in lung cancer needs to be validated. In colorectal cancer, miR-200 promoter methylation was associated with the CMS4 subtype that is known to have a mesenchymal phenotype and a worse prognosis [44]. The importance of methylation in driving EMT and prognosis through down-regulation of chr-1 miR-200s brings new opportunities. Indeed, miR-200 promoter hypermethylation may be used as a surrogate marker of miR quantification and a target to reverse the EMT process. As EMT was largely associated with resistance to treatments, combinations might increase response rates. MiR-200 was implicated in immunomodulation by targeting PDL-1 [45], in angiogenesis [16] and in targeted therapies resistance [46], suggesting a wide range of situations in which EMT-miR evaluation might be of interest to personalise treatment options.
This work is the first identification of a different impact on prognosis between chr-1 and chr-12 miR-200 clusters confirmed on the TCGA data set. We show an unrevealed link between down-regulation of chr-1 miR-200s (miR-200a, miR-200b and miR-429) and survival or relapse in localised NSCLC. Moreover, using miR clustering, we identified a group of samples with low relapse risk and good overall survival characterised by high EMT score, low miR-200 but high miR-34b and c. This study provides new insights on the clinical impact of miR-200s family members in localised NSCLC. We believe that using EMT markers with the TNM classification could trigger adjuvant treatment decisions and that EMT itself could be targeted in a subset of lung cancer patients. Here we show that miR-200 should no longer be considered as a homogeneous group, and that core EMT regulators quantification is promising to identify NSCLC patients with a low risk of relapse after surgery.
Supplementary information
Acknowledgements
We thank Claudia DeToma, Marine Largeau, Elodie Michel and Lauriane Chambolle for their work and implication at the Biological Resources centre and Tumour Bank Platform PRB-HEGP (BB-0033-00063).
Author contributions
Conception or design: HB, SG and AL. Data collection: J-BL, FLP-B, AL, LG, CB, TD and AP. Data analysis: SG and AL. Redaction: AL, SG and HB. Final approval: SG, AD, TD, AP, GB, J-BL, J-BO, LG, CB, FLP-B, PL-P, AL and HB.
Funding information
This study was supported by a grant from the integrated site of cancer research ‘cancer research for personalised medicine’ (SIRIC CARPEM). AL received personal research grants from Fondation de la Recherche Médicale. We thank the Tumour Bank Platform (PRB-HEGP BB-0033-00063) for its support.
Ethics approval and consent to participate
This study, conducted at the European Georges Pompidou hospital, was approved by CPP Ile de France 2 ethics committee (nos. 2012-08-09 and 2012-08-09 A1) and registered in clinical trial.gov (NCT03509779). Patients with NSCLC treated by surgery for curative intent signed informed consent for research and tumour tissues banking. Samples were stored frozen (−80 °C) at the Biological Resources centre and Tumour Bank Platform (PRB-HEGP BB-0033-00063).
Competing interests
AL reports grants from Fondation de la Recherche Médicale, during the conduct of the study. HB reports a grant from Site de la Recherche Intégrée sur le Cancer (SIRIC) CARPEM for this study.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Antoine Legras, Helene Blons.
Supplementary information
The online version contains supplementary material available at 10.1038/s41416-021-01568-7.
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