Identification of DNA methylation signatures associated with poor outcome in lower-risk Stage, Size, Grade and Necrosis (SSIGN) score clear cell renal cell cancer
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ABSTRACT:
PROVIDER: PRJNA631980 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11778224_1.fastq.gz | Fastqsanger.gz | |||
| SRR11778224_2.fastq.gz | Fastqsanger.gz | |||
| SRR11778225_1.fastq.gz | Fastqsanger.gz | |||
| SRR11778225_2.fastq.gz | Fastqsanger.gz | |||
| SRR13362400_1.fastq.gz | Fastqsanger.gz |
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Project description:BackgroundDespite using prognostic algorithms and standard surveillance guidelines, 17% of patients initially diagnosed with low risk clear cell renal cell carcinoma (ccRCC) ultimately relapse and die of recurrent disease, indicating additional molecular parameters are needed for improved prognosis.ResultsTo address the gap in ccRCC prognostication in the lower risk population, we performed a genome-wide analysis for methylation signatures capable of distinguishing recurrent and non-recurrent ccRCCs within the subgroup classified as 'low risk' by the Mayo Clinic Stage, Size, Grade, and Necrosis score (SSIGN 0-3). This approach revealed that recurrent patients have globally hypermethylated tumors and differ in methylation significantly at 5929 CpGs. Differentially methylated CpGs (DMCpGs) were enriched in regulatory regions and genes modulating cell growth and invasion. A subset of DMCpGs stratified low SSIGN groups into high and low risk of recurrence in independent data sets, indicating that DNA methylation enhances the prognostic power of the SSIGN score.ConclusionsThis study reports a global DNA hypermethylation in tumors of recurrent ccRCC patients. Furthermore, DMCpGs were capable of discriminating between aggressive and less aggressive tumors, in addition to SSIGN score. Therefore, DNA methylation presents itself as a potentially strong biomarker to further improve prognostic power in patients with low risk SSIGN score (0-3).
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2016-04-05 | E-GEOD-79910 | biostudies-arrayexpress
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Project description:BackgroundThe tumor stage, size, grade, and necrosis (SSIGN) score was originally defined using patients treated with radical nephrectomy (RN) between 1970 and 1998 for clear cell renal cell carcinoma (ccRCC), excluding patients treated with partial nephrectomy (PN).ObjectiveTo characterize the original SSIGN score cohort with longer follow-up and evaluate a contemporary series of patients treated with RN and PN.Design, setting, and participantsRetrospective single-institution review of 3600 consecutive surgically treated ccRCC patients grouped into three cohorts: original RN, contemporary (1999-2010) RN, and contemporary PN.InterventionRN or PN.Outcome measurements and statistical analysisThe association of the SSIGN score with risk of death from RCC was assessed using a Cox proportional hazards regression model, and predictive ability was summarized with a C-index.Results and limitationsThe SSIGN scores differed significantly between the original RN, contemporary RN, and contemporary PN cohorts (p<0.001), with SSIGN ≥4 in 53.5%, 62.7%, and 4.7%, respectively (p<0.001). The median durations of follow-up for these groups were 20.1, 9.2, and 7.6 yr, respectively. Each increase in the SSIGN score was predictive of death from RCC (hazard ratios [HRs]: 1.41 for original RN, 1.37 for contemporary RN, and 1.70 for contemporary PN; all p<0.001). The C-indexes for these models were 0.82, 0.84, and 0.82 for original RN, contemporary RN, and contemporary PN, respectively. After accounting for an era-specific improvement in survival among RN patients (HR: 0.53 for contemporary vs original RN; p<0.001), the SSIGN score remained predictive of death from RCC (HR: 1.40; p<0.001).ConclusionsThe SSIGN score remains a useful prediction tool for patients undergoing RN with 20-yr follow-up. When applied to contemporary RN and PN patients, the score retained strong predictive ability. These results should assist in patient counseling and help guide surveillance for ccRCC patients treated with RN or PN.Patient summaryWe evaluated the validity of a previously described tool to predict survival following surgery in contemporary patients with kidney cancer. We found that this tool remains valid even when extended to patients significantly different than were initially used to create the tool.
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Project description:PurposeClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics.MethodsA multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT).ResultsThe manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set.ConclusionPreoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.
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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.
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