Integrated mRNA-lncRNA Signature Research Articles

Intensive chemotherapy versus standard chemotherapy among patients with high risk, operable, triple negative breast cancer based on integrated mRNA-lncRNA signature (BCTOP-T-A01): randomised, multicentre, phase 3 trial

ObjectiveTo evaluate the feasibility of using a multigene signature to tailor individualised adjuvant therapy for patients with operable triple negative breast cancer.DesignRandomised, multicentre, open label, phase 3 trial.Setting7 cancer centres...

Adjuvant chemotherapy guided by an integrated mRNA–lncRNA signature in triple-negative breast cancer (BCTOP-T-A01): A randomized, open-label, multicenter phase 3 trial.

525 Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with variable clinical outcomes. We have previously classified patients with TNBC into high- and low-risk groups based on a multigene signature. In this study, we aimed to evaluate the feasibility of utilizing a multigene signature to tailor individualized adjuvant therapy for patients with operable TNBC. Methods: This multicenter, open-label, randomized, phase 3 trial was conducted at 7 cancer centers in China. We included female patients aged 18-70 years with early TNBC after definitive surgery (histologically confirmed axillary lymph node-positive or lymph node-negative with tumor diameter >10 mm). After categorization using the integrated signature, high-risk patients were randomized (1:1) to receive an intensive adjuvant treatment comprising four cycles of docetaxel, epirubicin, and cyclophosphamide followed by four cycles of gemcitabine and cisplatin (TEC-GP; arm A), or the standard four cycles of epirubicin and cyclophosphamide followed by four cycles of docetaxel (EC-T; arm B). Low-risk patients received the same adjuvant chemotherapy as arm B (arm C). The primary endpoint was disease-free survival (DFS) in the intention-to-treat analysis of arm A versus arm B. Results: Between January 2016 and July 2023, 504 patients (336 high-risk and 168 low-risk; median age, 52 years; 488 with T1-2 tumors; 263 with node-positive disease) were recruited. At a median follow-up of 45.1 months, the 3-year DFS rates were 90.9% for patients in arm A and 80.6% for patients in arm B (hazard ratio [HR], 0.51; 95% confidence interval [CI], 0.28 to 0.95; P = 0.030). Intensive chemotherapy improved DFS relative to standard therapy irrespective of tumor size and nodal status. The 3-year RFS was 92.6% in arm A and 83.2% in arm B (HR, 0.50; 95% CI, 0.25 to 0.98; P = 0.039). The groups did not differ in OS (HR, 0.58; 95% CI, 0.22 to 1.54; P = 0.267; 3-year OS, 98.2% vs 91.3%). Upon receiving the same chemotherapy regimen, patients in arm C had significantly higher DFS (HR, 0.57; 95% CI, 0.33-0.98; P = 0.038; 3-year DFS, 90.1% vs 80.6%), RFS (HR, 0.42; 95% CI, 0.22-0.81; P = 0.007; 3-year RFS, 94.5% vs 83.2%) and OS (HR, 0.14; 95% CI, 0.03-0.61; P = 0.002; 3-year OS, 100% vs 91.3%) compared to patients in arm B. The incidence of grade 3 to 4 treatment-related adverse. events for arms A, B and C, respectively, were thrombocytopenia (14.1%, 2.5%, 1.8%), febrile neutropenia (9.9%, 4.3%, 4.9%), anemia (5.8%, 1.9%, 1.8%), nausea (7.0%, 3.7%, 4.9%), vomiting (8.3%, 4.3%, 4.9%) and myalgia (2.6%, 5.6%, 5.5%). There were no treatment-related deaths. Conclusions: The multigene signature showed potential for tailoring adjuvant chemotherapy for patients with operable TNBC. Intensive regimens incorporating GP into anthracycline/taxane-based therapy improves survival with manageable toxicity. Clinical trial information: NCT02641847 .

An integrated mRNA-lncRNA signature for overall survival prediction in cholangiocarcinoma.

The combination of mRNA and lncRNA profiles for establishing an integrated mRNA-lncRNA prognostic signature has remained unexplored in cholangiocarcinoma (CCA) patients. We utilized a training dataset of 36 samples from The Cancer Genome Atlas dataset and a validation cohort (GSE107943) of 30 samples from Gene Expression Omnibus. Two mRNAs (CFHR3 and PIWIL4) and 2 lncRNAs (AC007285.1 and AC134682.1) were identified to construct the integrated signature through a univariate Cox regression (P-value = 1.35E-02) and a multivariable Cox analysis (P-value = 3.07E-02). Kaplan-Meier curve showed that patients with low risk scores had notably prolonged overall survival than those with high risk scores (P-value = 4.61E-03). Subsequently, the signature was validated in GSE107943 cohort with an area under the curve of 0.750 at 1-year and 0.729 at 3-year. The signature was not only independent from diverse clinical features (P-value = 3.07E-02), but also surpassed other clinical characteristics as prognostic biomarkers with area under the curve of 0.781 at 3-year. Moreover, the weighted gene co-expression network analysis and gene enrichment analyses found that the integrated signature were associated with metabolic-related biological process and lipid metabolism pathway, which has been implicated in the pathogenesis of CCA. Taken together, we developed an integrated mRNA-lncRNA signature that had an independent prognostic value in the risk stratification of patients with CCA.

Integrated DNA and RNA sequencing reveals early drivers involved in metastasis of gastric cancer

Gastric cancer (GC) is the second cause of cancer-related death and metastasis is an important cause of death. Considering difficulties in searching for metastatic driver mutations, we tried a novel strategy here. We conducted an integrative genomic analysis on GC and identified early drivers lead to metastasis. Whole-exome sequencing (WES), transcriptomes sequencing and targeted-exome sequencing (TES) were performed on tumors and matched normal tissues from 432 Chinese GC patients, especially the comparative analysis between higher metastatic-potential (HMP) group with T1 stage and lymph-node metastasis, and lower metastatic-potential (LMP) group without lymph-nodes or distant metastasis. HMP group presented higher mutation load and heterogeneity, enrichment in immunosuppressive signaling, more immune cell infiltration than LMP group. An integrated mRNA-lncRNA signature based on differentially expressed genes was constructed and its prognostic value was better than traditional TNM stage. We identified 176 candidate prometastatic mutations by WES and selected 8 genes for following TES. Mutated TP53 and MADCAM1 were significantly associated with poor metastasis-free survival. We further demonstrated that mutated MADCAM1 could not only directly promote cancer cells migration, but also could trigger tumor metastasis by establishing immunosuppressive microenvironment, including promoting PD-L1-mediated immune escape and reprogramming tumor-associated macrophages by regulating CCL2 through Akt/mTOR axis. In conclusion, GCs with different metastatic-potential are distinguishable at the genetic level and we revealed a number of potential metastatic driver mutations. Driver mutations in early-onset metastatic GC could promote metastasis by establishing an immunosuppressive microenvironment. This study provided possibility for future target therapy of GC.

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value for long-term survival in diffuse large B-cell lymphoma.

For patients with diffuse large B-cell lymphoma (DLBCL), survival at 24 months is a milestone for long-term survival. The purpose of this study was to develop a multigene risk score (MGRS) to refine the International Prognostic Index (IPI) model to identify patients with DLBCL at high risk of death within 24 months. Using a robust statistical strategy, we built a MGRS incorporating nine mRNAs and two lncRNAs. Stratification and multivariable Cox regression analysis confirmed the MGRS as an independent risk factor. A nomogram based on IPI+MGRS model was constructed and its calibration plot showed close agreement between predicted 2-year survival rate and observed rate. The 2-year AUC was bigger with the IPI+MGRS model (ΔAUC=0.162; 95%CI 0.1295–0.1903) than with the IPI model, and the IPI+MGRS model more accurately predicted the prognostic risk of DLBCL. The 2-year survival decision curve revealed the IPI+MGRS model was more useful clinically than the IPI model. Functional enrichment analysis showed that the MGRS correlated with cell cycle, DNA replication and repair. The results were validated using an independent external dataset. In conclusion, we successfully developed an integrated mRNA–lncRNA signature to refine the IPI model for predicting long-term survival of patients with DLBCL.

Bioinformatics analysis of esophageal cancer unveils an integrated mRNA-lncRNA signature for predicting prognosis.

Esophageal cancer (ESCA) carries a poor prognosis among gastrointestinal malignancies. The present study developed a signature based on mRNAs and long non-coding RNAs (lncRNAs) to predict prognosis in ESCA by using The Cancer Genome Atlas database. By using least absolute shrinkage and selection operator penalized regression, a set of RNAs (three mRNAs and two lncRNAs) was identified and used to build a risk score system of ESCA prognosis, which was used to stratify patients having considerable diverse survival in the training set [hazard ratio (HR), 3.932; 95% CI, 1.555–9.944; P<0.002] into high- and low-risk groups. The authentication of the results was achieved through the test set (HR, 3.150; 95% CI, 1.113–8.918; P<0.02) and the entire set (HR, 3.181; 95% CI, 1.686–6.006; P<0.0002). The results from multivariate Cox proportional hazard regression analysis in the entire set suggested that the prognostic significance of this signature may be independent of patients' clinicopathological characteristics. Furthermore, this signature was associated with several molecular signaling pathways of cancer according to Gene Set Enrichment Analysis. In addition, a nomogram was built and the risk score and TNM stage were integrated to estimate the 1- and 3-year overall survival rates. The results from the present study demonstrated that the integrated mRNA-lncRNA signature may be considered as a novel biomarker for the prognosis of ESCA.

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value of early relapse in colon cancer.

The purpose of our study was to develop a multigene signature based on transcriptome profiles of both mRNAs and lncRNAs to identify a group of patients who are at high risk of early relapse in stages II-III colon cancer. Firstly, propensity score matching was conducted between patients in early relapse group and long-term survival group from GSE39582 training series (N = 359) and patients were matched 1:1. Global transcriptome analysis was then performed between the paired groups to identify tumor specific mRNAs and lncRNAs. Finally, using LASSO Cox regression model, we built a multigene early relapse classifier incorporating 15 mRNAs and three lncRNAs. The prognostic and predictive accuracy of the signature was internally validated in 102 colon cancer patients and externally validated in other 241 patients. In the training set, patients with high risk score were more likely to suffer from relapse than those with low risk score (HR: 2.67, 95% CI: 2.07-3.46, P < 0.001). The results were validated in the internal validation set (HR: 2.23, 95% CI: 1.23-3.78, P = 0.003) and external validation (HR 1.88, 95% CI 1.42-2.48; P < 0.001) set. Time-dependent receiver operating curve at 1 year showed that the integrated mRNA-lncRNA signature [area under curve (AUC) = 0.742] had better prognostic accuracy than AJCC TNM stage (AUC = 0.615) in the entire 702 patients. In addition, survival decision curve analyses at 12 months revealed a good clinical usefulness of the integrated mRNA-lncRNA signature. In conclusion, we successfully developed an integrated mRNA-lncRNA signature that can accurately predict early relapse.

Transcriptome Analysis of Triple-Negative Breast Cancer Reveals an Integrated mRNA-lncRNA Signature with Predictive and Prognostic Value.

While recognized as a generally aggressive disease, triple-negative breast cancer (TNBC) is highly diverse in different patients with variable outcomes. In this prospective observational study, we aimed to develop an RNA signature of TNBC patients to improve risk stratification and optimize the choice of adjuvant therapy. Transcriptome microarrays for 33 paired TNBC and adjacent normal breast tissue revealed tumor-specific mRNAs and long noncoding RNAs (lncRNA) that were associated with recurrence-free survival. Using the Cox regression model, we developed an integrated mRNA-lncRNA signature based on the mRNA species for FCGR1A, RSAD2, CHRDL1, and the lncRNA species for HIF1A-AS2 and AK124454 The prognostic and predictive accuracy of this signature was evaluated in a training set of 137 TNBC patients and then validated in a second independent set of 138 TNBC patients. In addition, we enrolled 82 TNBC patients who underwent taxane-based neoadjuvant chemotherapy (NCT) to further verify the predictive value of the signature. In both the training and validation sets, the integrated signature had better prognostic value than clinicopathologic parameters. We also confirmed the interaction between the administration of taxane-based NCT and different risk groups. In the NCT cohort, patients in the low-risk group were more likely to achieve pathologic complete remission after taxane-based NCT (P = 0.014). Functionally, we showed that HIF1A-AS2 and AK124454 promoted cell proliferation and invasion in TNBC cells and contributed there to paclitaxel resistance. Overall, our results established an integrated mRNA-lncRNA signature as a reliable tool to predict tumor recurrence and the benefit of taxane chemotherapy in TNBC, warranting further investigation in larger populations to help frame individualized treatments for TNBC patients. Cancer Res; 76(8); 2105-14. ©2016 AACR.

Comprehensive Transcriptome Profiling Reveals Multigene Signatures in Triple-Negative Breast Cancer.

By integrating expression profiles of mRNAs and long noncoding RNAs (lncRNA), we tried to develop and validate novel multigene signatures to facilitate individualized treatment of triple-negative breast cancer (TNBC) patients. We analyzed 165 TNBC samples and 33 paired normal breast tissues using transcriptome microarrays. Tumor-specific mRNAs and lncRNAs were identified and correlated with patients' recurrence-free survival (RFS). Using Cox regression model, we built two multigene signatures incorporating mRNAs and lncRNAs. The prognostic and predictive accuracy of the signatures were tested in a training set of 165 TNBC patients and validated in other 101 TNBC patients. We successfully developed an mRNA and an integrated mRNA-lncRNA signature based on eight mRNAs and two lncRNAs. In the training set, patients in the high-risk group were more likely to suffer from recurrent disease than patients in the low-risk group in both signatures [HR, 10.00; 95% confidence interval (CI), 2.53-39.47, P= 0.001; HR = 4.46, 95% CI, 1.34-14.91, P= 0.015 for integrated signature and mRNA signature, respectively). Results were validated in the validation set (P= 0.019 and 0.030, respectively). In addition, time-dependent receiver operating curve showed that the integrated mRNA-lncRNA signature had a better prognostic value than both the eight-mRNA-only signature and the clinicopathologic risk factors in both sets. We also found through interaction analysis that patients classified into the low-risk group by the integrated mRNA-lncRNA signature had a more favorable response to adjuvant taxane chemotherapy. The multigene signature we developed can accurately predict clinical outcome and benefit of taxane chemotherapy in TNBC patients.

Abstract P6-08-28: Prognostic and predictive value of an integrated mRNA-lncRNA signature in triple-negative breast cancer: A comprehensive transcriptome analysis

Abstract Purpose Triple-negative breast cancer (TNBC) is a highly diverse group of disease, and clinical outcome of patients with TNBC is highly variable. Due to the heterogeneity of TNBCs, there is limited molecular signature routinely used for predicting the risk of disease recurrence and benefit of adjuvant chemotherapy. Our study aims to develop and validate a RNA signature, integrating messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) together, for TNBC patients to improve risk stratification and avoid unnecessary adjuvant therapy. Methods Using transcriptome microarrays, we analyzed 33 paired TNBC and adjacent normal breast tissues, and identified 1,644 mRNAs and 1,047 lncRNAs which were differentially expressed between tumors and normal tissues. We further determined the expression of these mRNAs and lncRNAs in an additional 134 TNBC samples using transcriptome microarrays, and confirmed their associations with patients' recurrence-free survival (RFS). Using the LASSO Cox regression model, we built an integrated mRNA-lncRNA signature incorporating seven mRNAs and three lncRNAs. Prognostic and predictive accuracy of the signature was tested in the training set of 167 TNBC patients and further validated in an independent validation set of 143 TNBC patients. Results In the training set, we identified 36 mRNAs and 32 lncRNAs which were tumor-specific and significantly associated with patients' RFS. Using the LASSO Cox regression model, an integrated mRNA-lncRNA signature based on seven mRNAs (CCR4, CTSB, ERO1L, HIF1A, IGFR1R, SRD5A1 and TFF1) and three lncRNAs (n381928, n333541 and TCONS_I2_00013109-XLOC_I2_007048) was developed in the training set and subsequently validated in the validation set. Patients were classified to the high-risk (high risk of recurrence) and low-risk (low risk of recurrence) groups according to their scores in the signature. In the training set, multivariate analysis showed that the predicted high-risk group had higher risk of developing recurrent disease within five years of surgery than the low-risk patients (hazard ratio [HR] = 6.12, 95% confidence interval [CI] 2.76-13.56, P&amp;lt;0.001). In the validation set, the predicted high-risk group also had poorer RFS in multivariate analysis (HR = 5.09, 95% CI 1.82-10.96, P&amp;lt;0.001). Furthermore, analyzing the areas under the time-dependent receiver operating curve for five-year RFS, we proved the integrated mRNA-lncRNA signature had better prognostic value than the seven-mRNA-only signature and clinicopathological risk factors in both the training and validation sets. Finally, Cox proportional hazards models were utilized to test the interaction between adjuvant chemotherapy and different risk groups. Patients in the low-risk group had a more favorable response to adjuvant chemotherapy both in the training and validation sets. Conclusion The integrated mRNA-lncRNA signature is a reliable tool for predicting disease recurrence and benefit of adjuvant chemotherapy in TNBC patients. If further validated in larger population, it could facilitate patient counseling and individualize treatment of TNBC. Citation Format: Yizhou Jiang, Yi-Rong Liu, Ke-Da Yu, Xin Hu, Xiao-En Xu, Ling Yao, Zhi-Ming Shao. Prognostic and predictive value of an integrated mRNA-lncRNA signature in triple-negative breast cancer: A comprehensive transcriptome analysis [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-08-28.