Biyun Wang, Yannan Zhao, Yi Li and Yingying Xu contributed equally to this work.
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Abkürzungen
ASO
Antisense oligonucleotides
AUC
Area under the curve
Bcl-2
B-cell lymphoma-2
CI
Confidence interval
CLDN6
Claudin 6
ER
Estrogen receptor
GADD45α
Growth arrest and DNA damage inducible alpha
HER2
ErbB2 receptor
HR
Hazard ratio
MBD
Methyl-CpG-binding domain
Mcl-l
Myeloid cell leukemia-1
MeCP2
Methyl-CpG-binding protein 2
mTNBC
Metastatic triple negative breast cancer
MYOD1
Myogenic differentiation 1
ncRNA
Non-coding RNA
OS
Overall survival
PFS
Progress-free survival
PR
Progesterone receptor
RIP
RNA immunoprecipitation
ROC
Receiver operating characteristic
snoRNAs
Small nucleolar RNAs
TNBC
Triple negative breast cancer
Main text
Breast cancer is the most common cancer in women [1]. Triple-negative breast cancer (TNBC), defined by the absence of estrogen receptor (ER), progesterone receptor (PR) and ErbB2 receptor (HER2), accounts for 10–20% of all invasive breast cancers [2]. TNBC has limited treatment options, is prone to recurrence and metastasis, and has a poor prognosis [3]. Platinum has been proved efficacious and is widely used in the neoadjuvant and metastatic treatment of TNBC [4]. However, many patients are refractory to platinum-based regimens and quickly relapse. Here, we aimed to identify a reliable, robust and convenient liquid biopsy approach for rapid prediction of platinum response in TNBC patients.
First, we generated a cisplatin (DDP)-resistant TNBC cell line, MDA-MB-231/DDP, which is 5-fold more resistant to cisplatin than its parental line MDA-MB-231 (estimated IC50 values: 39.6 μmol/L versus 7.93 μmol/L) (Fig. 1a). A total of 277 RNA were identified to be aberrantly expressed in 231/DDP cells using RNA-seq analyses (Fig. 1c and S1a). Interestingly, small nucleolar RNAs (snoRNAs) showed the highest variation rate (12.3%), compared with other groups, such as mRNA, lncRNAs and miRNAs (Fig. S1a, right panel). snoRNAs are non-coding RNA (ncRNA) molecules of ~ 60–300 nucleotides in length and can be detected in plasma or serum [5]. The amount of snoRNAs in blood indicates different disease status and associates with clinicopathological features and prognosis [6‐9]. Compared with miRNA, ctDNA and exosome, snoRNAs are more stable, technically easy to enrich and detect, therefore, more suitable to serve as prognostic biomarkers [7, 10‐13]. We then validated the expression of snoRNAs using qRT-PCR. Among all changed snoRNAs, SNORD33 showed the largest fold (32.2) of change (Fig. 1c), and with relative high abundance (Fig. 1b-d). The antisense oligonucleotide (ASO2) successfully silenced SNORD33 expression in MDA-MB-231, MDA-MB-468 and SUM149PT cells (Fig. S1b). SNORD33 knockdown reduced cisplatin-induced cell death, as measured by CCK-8 assay (Fig. 1e) and colony formation assay (Fig. S2a). Furthermore, a significantly decreased apoptotic cells (Fig. S2b), along with significantly increased level of anti-apoptotic proteins (Mcl-1 and Bcl-2) and reduced expression of pro-apoptotic proteins (cleaved caspase-3 and caspase-9), were observed in SNORD33 knockdown cells upon cisplatin treatment (Fig. S2c, d), indicating that reduced SNORD33 promotes cisplatin resistance in TNBC cells.
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Considering the potential of SNORD33 as a liquid biopsy biomarker, we investigated the association between the baseline plasma expression of SNORD33 and corresponding prognosis in mTNBC (metastatic triple-negative breast cancer) patients who received first-line platinum-based chemotherapy (Fig. 1f). In the training cohort, 81 patients were divided into high-SNORD33 and low-SNORD33 expression groups according to the median 2-ΔΔCt value of SNORD33 expression in baseline plasma. Baseline characteristics were well-balanced between these two groups (Table S1). As shown in Fig. 1g, low-SNORD33 group had significantly shorter progression-free survival (PFS) than high-SNORD33 group (6.6 versus 10.1 months; P = 0.005). Consistently, in both validation cohort of 128 samples (Fig. 1h) and the combined cohort (Fig. S3a), low-SNORD33 group showed significant worse PFS compared with high-SNORD33 group (validation cohort: 6.5 versus 10.1 months; P < 0.001; combined cohorts: 6.6 versus 10.1 months; P < 0.001). Likewise, low-SNORD33 group showed significantly reduced overall survival (OS) compared with high-SNORD33 group (22.2 versus 40.6 months; P = 0.016, Fig. S3b) in the combined cohort. Univariate Cox regression analysis showed that low SNORD33 expression, number of metastatic sites, visceral metastasis and liver metastasis were significantly associated with reduced PFS and OS in the combined cohort (Table S2). In the multivariate analysis, the signature of SNORD33 was an independent predictive factor of the PFS with platinum-based chemotherapy in the combined cohort (Table S2). Plasma SNORD33 was significantly higher in patients reaching clinical benefit (CB) (defined as complete response (CR), partial response (PR) and stable disease (SD)>6 months) compared with those who failed to reach CB in the training cohort (11.88 versus 10.98, P = 0.012), validation group (11.72 versus 11.23, P = 0.006) and combined cohorts (11.88 versus 10.98, P = 0.038) (Fig. 1j). Furthermore, Kaplan-Meier survival curves from patients treated with non-platinum chemotherapy (gemcitabine plus paclitaxel) (Table S3) revealed that variation in baseline plasma SNORD33 profile did not associate with PFS (5.5 versus 6.5 months; P = 0.535, Fig. 1i), indicating that the predictive value of SNORD33 expression is platinum-specific. BRCA mutation status, the most studied biomarker for platinum sensitivity in breast cancer, did not correlate with plasma SNORD33 level (Fig. 1k), nor related to PFS in patients with platinum-based chemotherapy (Fig. 1l).
ROC test was used to evaluate the value of SNORD33 in predicting PFS in mTNBC patients treated with platinum-based regimens in the combined cohort. SNORD33 signature showed the strongest predictive value (AUC = 0.652) for PFS, compared with other single clinicopathological risk factors (liver metastasis, AUC = 0.575; number of metastatic sites, AUC = 0.365), suggesting better performance of SNORD33 signature as a surrogate predictor for platinum-based chemotherapy outcome (Fig. 1m). Based on the estimated coefficients in the multivariate Cox regression model included SNORD33 expression, liver metastasis and number of metastatic sites, we then built a prognostic nomogram to assign PFS probability at 2, 4, 6, 10, 12 and 18 months after first-line platinum treatment. In this nomogram, the point system work by ranking the regression coefficients, with SNORD33 expression converted into 100 points, followed by number of metastatic sites and liver metastasis (yes/no). The final PFS probability was calculated by adding up the score of each item using the nomogram depicted in Fig. 1n. The prediction performance of the nomogram was assessed by the discrimination and calibration. The concordance index (C index), estimating the discriminative ability of the nomogram, was 0.664. A calibration curve also showed high consistency between the predicted and observed PFS probability (Fig. 1o). This nomogram can predict PFS possibility upon platinum-based treatment in different months, information that could be factored in during regimen selection.
Furthermore, a decrease of cisplatin-induced cell toxicity was observed in SNORD33 knocked-down lung adenocarcinoma and urinary bladder carcinoma cell lines (Fig. S4). In 50 metastatic NSCLC patients treated with first-line cisplatin/carboplatin-based regimens, low baseline plasma SNORD33 group had significantly shorter PFS (7.7 versus 15.5 months; P = 0.023) than high-SNORD33 group (Table S4, Fig. S5a). And plasma SNORD33 was significantly higher in patients reaching CB compared with those who failed to reach CB (1.36 versus 0.34, P = 048, Fig. S5b). The signature of SNORD33 was an independent predictive factor of the PFS with first-line cisplatin/carboplatin-based regimens in lung cancer (Table S5). These data suggest SNORD33 might serve as a feasible predicting biomarker for platinum.
We next investigated the mechanisms underlying SNORD33 affected platinum sensitivity. SNORD33 locates in intron 4 of host gene ribosomal protein L13a (RPL13A) [14]. It participates in the 2-O′- ribose methylation of 18S rRNA [14]. Does SNORD33 regulate cisplatin resistance by i) its host gene; ii) sliced into functional miRNA; iii) rRNA modification and subsequent protein translation? We knocked down RPL13A (host gene), DICER enzyme (an endoribonuclease), or SNORD32a (homologue to SNORD33 modifying 18S rRNA during cell proliferation) in MDA-MB-231 cells (Fig. 2a), and found that the cell viability in response to platinum was not changed (Fig. 2b). We then employed RNA-pull down assay combined with mass spectrometry to identify SNORD33 binding proteins. Methyl-CpG-binding protein 2 (MeCP2), an important member of the methyl-CpG-binding domain (MBD) family with epigenetic control functions [15], was identified as a potential target to convey SNORD33 drug resistance (Fig. 2c, d and Fig. S6a). The interaction between SNORD33 and MeCP2 was further confirmed by RNA immunoprecipitation (RIP) assays (Fig. 2d). The knockdown of SNORD33 in MDA-MB-231 cells did not alter mRNA and protein levels of MeCP2 (Fig. 2e), but reduced mRNA levels of most MeCP2 target genes, including GADD45α, MYOD1, and CLDN6, which promotes cell apoptosis (Fig. 2f). MeCP2 can repress target-gene expression by binding to CpG-methyl groups on DNA via its MBD domain [16]. In another model, MeCP2, via recruiting mSIN3A and HDAC1, induces histone deacetylation, chromatin compaction, resulting in gene repression [16] (Fig. S6b). We observed that SNORD33 knockdown promoted MeCP2 binding to target gene promoter in MDA-MB-231 cells by chromatin immunoprecipitation (ChIP) assay (Fig. 2g), while the formation of MeCP2/mSIN3A/HDAC1 complex was not affected (Fig. 2h-i). Therefore, SNORD33 may regulate MeCP2 targeted gene expression through disrupting its binding with methylated CpG DNA (Fig. S6b). We then knocked down MeCP2 in SNORD33-knockdown MDA-MB-231, MDA-MB-468 and SUM149PT cell lines (Fig. S6c). Down-regulation of MeCP2 expression can partially reverse the platinum resistance of TNBC cell lines caused by loss of SNORD33, as measured by CCK-8 assay (Fig. 2j) and colony formation assay (Fig. S7). Consistently, MeCP2 down-regulation attenuated SNORD33-knockdown induced decreased apoptotic cells and pro-apoptotic proteins, and increased anti-apoptotic proteins (Fig. 2k, l). Together, these data imply that MeCP2 contributes to SNORD33 mediated platinum resistance in TNBC cells.
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Conclusions
Together, we identified plasma SNORD33 signature as a predictor for the sensitivity of platinum-based chemotherapy in mTNBC patients. Furthermore, we showed that MeCP2 could convey SNORD33 associated platinum resistance. Our findings may have immediate translational relevance for precision platinum-based chemotherapy.
Acknowledgements
We thank Lina Ma for plasma sample collection and processing.
Declarations
Ethics approval and consent to participate
Peripheral blood specimens were obtained from three clinical trials CBCSG006, NCT02546934 and NCT02341911. The collection of blood samples and access to clinical data for research were approved by the Institutional Review Board of the Fudan University Shanghai Cancer Center (FUSCC).
Consent for publication
Not applicable.
Competing interests
The authors declare no conflict of interest.
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