Dataset Information

A combined ANXA2-NDRG1-STAT1 gene signature predicts response to chemoradiotherapy in cervical cancer.

ABSTRACT:

Background

A better understanding of locally advanced cervical cancer (LACC) is mandatory for further improving the rates of disease control, since a significant proportion of patients still fail to respond or undergo relapse after concurrent chemoradiation treatment (CRT), and survival for these patients has generally remained poor.

Methods

To identify specific markers of CRT response, we compared pretreatment biopsies from LACC patients with pathological complete response (sensitive) with those from patients showing macroscopic residual tumor (resistant) after neoadjuvant CRT, using a proteomic approach integrated with gene expression profiling. The study of the underpinning mechanisms of chemoradiation response was carried out through in vitro models of cervical cancer.

Results

We identified annexin A2 (ANXA2), N-myc downstream regulated gene 1 (NDRG1) and signal transducer and activator of transcription 1 (STAT1) as biomarkers of LACC patients' responsiveness to CRT. The dataset collected through qPCR on these genes was used as training dataset to implement a Random Forest algorithm able to predict the response of new patients to this treatment. Mechanistic investigations demonstrated the key role of the identified genes in the balance between death and survival of tumor cells.

Conclusions

Our results define a predictive gene signature that can help in cervical cancer patient stratification, thus providing a useful tool towards more personalized treatment modalities.

SUBMITTER: Buttarelli M

PROVIDER: S-EPMC6595690 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

A combined ANXA2-NDRG1-STAT1 gene signature predicts response to chemoradiotherapy in cervical cancer.

Buttarelli Marianna M Babini Gabriele G Raspaglio Giuseppina G Filippetti Flavia F Battaglia Alessandra A Ciucci Alessandra A Ferrandina Gabriella G Petrillo Marco M Marino Carmela C Mancuso Mariateresa M Saran Anna A Villani Maria Elena ME Desiderio Angiola A D'Ambrosio Chiara C Scaloni Andrea A Scambia Giovanni G Gallo Daniela D

Journal of experimental & clinical cancer research : CR 20190626 1

<h4>Background</h4>A better understanding of locally advanced cervical cancer (LACC) is mandatory for further improving the rates of disease control, since a significant proportion of patients still fail to respond or undergo relapse after concurrent chemoradiation treatment (CRT), and survival for these patients has generally remained poor.<h4>Methods</h4>To identify specific markers of CRT response, we compared pretreatment biopsies from LACC patients with pathological complete response (sensi ...[more]

PMID: 31242951

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Project description:Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy has been proposed as an alternative treatment approach for cervical cancer (CC) in stage Ib2-IIb, who had a strong desire to be treated with surgery. Our study aims to develop a model based on multimodal MRI by using radiomics and deep learning to predict the treatment response in CC patients treated with neoadjuvant chemoradiotherapy (NACRT). From August 2009 to June 2013, CC patients in stage Ib2-IIb (FIGO 2008) who received NACRT at Fujian Cancer Hospital were enrolled in our study. Clinical information, contrast-enhanced T1-weighted imaging (CE-T1WI), and T2-weighted imaging (T2WI) data were respectively collected. Radiomic features and deep abstract features were extracted from the images using radiomics and deep learning models, respectively. Then, ElasticNet and SVM-RFE were employed for feature selection to construct four single-sequence feature sets. Early fusion of two multi-sequence feature sets and one hybrid feature set were performed, followed by classification prediction using four machine learning classifiers. Subsequently, the performance of the models in predicting the response to NACRT was evaluated by separating patients into training and validation sets. Additionally, overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan-Meier survival curves. Among the four machine learning models, SVM exhibited the best predictive performance (AUC=0.86). Among the seven feature sets, the hybrid feature set achieved the highest values for AUC (0.86), ACC (0.75), Recall (0.75), Precision (0.81), and F1-score (0.75) in the validation set, outperforming other feature sets. Furthermore, the predicted outcomes of the model were closely associated with patient OS and DFS (p = 0.0044; p = 0.0039). A model based on MRI images with features from multiple sequences and different methods could precisely predict the response to NACRT in CC patients. This model could assist clinicians in devising personalized treatment plans and predicting patient survival outcomes.

Project description:Purpose: We aimed to investigate the distinct transcriptional landscape in the poor responders to concurrent chemoradiotherapy (CCRT) and to gain mechanistic insights on treatment resistance in cervical cancer. Methods: RNA sequencing was performed using archived pre-treatment tissue biopsies of patients with locally advanced cervical cancer treated with concurrent platinum-based CCRT. Transcriptome data of patients with no durable benefit (NDB; progression-free period < 3 years) and durable clinical benefit (DCB; progression-free period > 5 years) after were compared. NDB score was estimated for each patient using the differentially expressed genes between NDB and DCB patients. Three independent cohorts, including The Cancer Genome Atlas cervical cancer (TCGA-CESC) cohort, were utilized for validation of NDB score. Potential response to PD-1 blockade was estimated with Tumor Immune Dysfunction and Exclusion (TIDE) score and T-cell-inflamed gene expression profile (GEP). Results: Patients with NDB exhibited a distinct transcriptional profile compared to those with DCB such as higher signatures of extracellular matrix organization and epithelial-to-mesenchymal transition. The fraction of cancer-associated fibroblasts (CAFs) within the tumor was significantly higher in NDB than DCB patients. Higher NDB score was significantly associated with poor survival in the TCGA-CESC cohort (n = 274; P = 0.015), but only in patients that received curative aim radiotherapy (P = 0.002) and not in patients who received other treatments (P = 0.57). The transcriptomic characteristics of patients with NDB were recapitulated in patients with high NDB score in TCGA-CESC. Patients with high NDB score displayed significantly higher TIDE prediction score and lower T-cell-inflamed GEP score compared to patients with low NDB scores. Conclusion: Cervical cancer patients with poor treatment outcome following CCRT exhibited a distinct gene signature that was able to predict treatment outcomes. For poor responders, immune checkpoint inhibitors may be less effective and CAF-targeting treatments may be a promising approach.

Dataset Information

A combined ANXA2-NDRG1-STAT1 gene signature predicts response to chemoradiotherapy in cervical cancer.

Background

Methods

Results

Conclusions

Publications

A combined ANXA2-NDRG1-STAT1 gene signature predicts response to chemoradiotherapy in cervical cancer.

Similar Datasets

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