Prognostic classifier to identify intermediate and high-risk clear-cell renal cell carcinoma patients at higher risk for tumor progression
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ABSTRACT:
PROVIDER: PRJNA733142 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14667200_1.fastq.gz | Fastqsanger.gz | |||
| SRR14667200_2.fastq.gz | Fastqsanger.gz | |||
| SRR14667201_1.fastq.gz | Fastqsanger.gz | |||
| SRR14667201_2.fastq.gz | Fastqsanger.gz | |||
| SRR14667202_1.fastq.gz | Fastqsanger.gz |
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Project description:Background. Current clinicopathological factors are not accurate enough to predict tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC). Objective: To develop a prognostic classifier for intermediate/high-risk ccRCC based on gene expression and histopathological prognostic factors. Design, setting and participants: Retrospective, multicenter study including 84 intermediate/high-risk ccRCC patients who underwent surgery. Global gene expression patterns were analyzed in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 2000 kit. Expression levels of 22 selected genes were assessed by nCounter Analysis in an independent series of 71 ccRCC. A combined genetic-clinicopathological classifier for predicting tumor progression was developed. Outcomes measurements and statistical analysis: Logistic regression analysis was used to identify independent prognostic factors. Results and Limitations: A total of 1202 genes were found differentially expressed between progressive and non-progressive intermediate/high-risk ccRCC. In the independent cohort, 7 genes remained significant differentially expressed between the groups. Expression of HS6ST2, pT stage, tumor size and ISUP grade were found independent prognostic factors for tumor progression (p<0.05). A risk score generated using these variables was able to distinguish a subset of patients at higher-risk of progression (HR 7,27; p<0,001), improving the individual discriminative performance of each of these variables on their own. Conclusions: A novel prognostic algorithm based on genetic and clinicopathological factors was successfully developed. This model may aid physicians to select high-risk patients for further adjuvant target therapy or immune therapy.
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Project description:The molecular profile of endometrial cancer has become an important tool in determining patient prognosis and their optimal adjuvant treatment. In addition to The Cancer Genome Atlas (TCGA), simpler tools have been developed, such as the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE). We attempted to determine a genetic signature to build a recurrence risk score in patients diagnosed with low- and intermediate-risk endometrial cancer. A case-control study was conducted. The eligible patients were women diagnosed with recurrence low- and intermediate-risk endometrial cancer between January 2009 and December 2014 at a single institution; the recurrence patients were matched to two nonrecurrence patients with the same diagnosis by age and surgical staging. Following RNA isolation of 51 cases, 17 recurrence and 34 nonrecurrence patients, the expression profile was determined using the nCounter® PanCancer Pathways Panel, which contains 770 genes. The expression profile was successfully characterized in 49/51 (96.1%) cases. We identified 12 genes differentially expressed between the recurrence and nonrecurrence groups. The ROC curve for each gene was generated, and all had AUCs higher than 0.7. After backward stepwise logistic regression, four genes were highlighted: FN1, DUSP4, LEF1, and SMAD9. The recurrence risk score was calculated, leading to a ROC curve of the 4-gene model with an AUC of 0.93, sensitivity of 100%, and specificity of 72.7%. We identified a four-gene signature that is associated the risk of recurrence in patients with low- and intermediate-risk endometrial cancer. This finding suggests a new prognostic factor in this poorly explored group of patients with endometrial cancer.
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Project description:The probability of tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC) is highly variable, underlining the lack of predictive accuracy of the current clinicopathological factors. To develop an accurate prognostic classifier for these patients, we analyzed global gene expression patterns in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 4000. Expression levels of 22 selected differentially expressed genes (DEG) were assessed by nCounter analysis in an independent series of 71 ccRCCs. A clinicopathological-molecular model for predicting tumor progression was developed and in silico validated in a total of 202 ccRCC patients using the TCGA cohort. A total of 1202 DEGs were found between progressive and non-progressive intermediate/high-risk ccRCC in RNAseq analysis, and seven of the 22 DEGs selected were validated by nCounter. Expression of HS6ST2, pT stage, tumor size, and ISUP grade were found to be independent prognostic factors for tumor progression. A risk score generated using these variables was able to distinguish patients at higher risk of tumor progression (HR 7.27; p < 0.001), consistent with the results obtained from the TCGA cohort (HR 2.74; p < 0.002). In summary, a combined prognostic algorithm was successfully developed and validated. This model may aid physicians to select high-risk patients for adjuvant therapy.
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Project description:Purpose: Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population. Method:A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 235 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. Patients treated with RP between 2000 and 2006 were identified from the Mayo Clinic tumor registry for a case-cohort study design. This involved identification of all patients with metastasis and a representative of the full cohort .Thus, men at high risk of recurrence post-RP (open/robotic) with no prior neoadjuvant/prostate cancer treatment were selected based on any of preoperative PSA >20 ng/mL, pathological Gleason score (GS) â¥8, SVI, or GPSM (GS; preoperative PSA; SVI; margins) score â¥10.The cohort of 1,010 men included 73 cases with metastasis as evidenced by CT or bone scan. A 20% random sample (n=202) was drawn from the cohort. This included 19 of 73 metastatic cases. To increase sampling of metastasis, the remaining 54 metastatic cases were added, resulting in a final study set of 256 patients. 21 samples did not pass QC and were eliminated from this study. Patients not experiencing metastasis regardless of BCR (defined as follow-up PSA â¥0.4 ng/mL >30 days post-RP) were censored at last follow-up. The study was approved by Mayo Clinic Institutional Review Board.
2014-11-13 | E-GEOD-62116 | biostudies-arrayexpress
Project description:BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of “low-risk” warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease. 680 CEL files from 639 BEC specimens, 152 benign and 487 malignant samples
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Project description:Radical prostatectomy (RP) is a primary treatment option for men with intermediate- and high-risk prostate cancer. Although many are effectively cured with local therapy alone, these men are by definition at higher risk of adverse pathologic features. It has been shown previously that genomic data can be used to predict tumor aggressiveness. Our objective was to evaluate genomic data and it's relationship to pathological stage and grade in a cohort of men that received no treatment other than radical prostatectomy surgery.
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