DNA Methylation Markers Predict Recurrence-free Interval in Triple-Negative Breast Cancer.
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ABSTRACT:
PROVIDER: PRJNA593505 | ENA |
REPOSITORIES: ENA
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Project description:We lack tools to risk-stratify triple-negative breast cancer (TNBC). Our goal was to develop molecular tools to predict disease recurrence. Methylation array analysis was performed on 110 samples treated by locoregional therapy obtained from Institutional cohorts. Discovered marker sets were then tested by Kaplan Meier analyses in a prospectively collected TNBC cohort of 49 samples from the no chemotherapy arms of IBCSG trials VIII and IX, and by logistic regression in a chemotherapy-treated cohort of 121 TNBCs from combined IBCSG trials and Institutional repositories. High methylation was associated with shorter recurrence-free interval in the no chemotherapy arm of the IBCSG studies, as well as in the chemotherapy-treated patients within the combined Institutional and IBCSG chemotherapy cohorts (100 marker panel, P=0.002; 30 marker panel, P=0.05). Chromosome 19 sites were enriched among these loci. In conclusion, our hypermethylation signatures identify increased recurrence risk independent of whether patients receive chemotherapy.
2019-12-05 | GSE141441 | GEO
Project description: Not available
| S-EPMC6994477 | biostudies-literature
Project description:Background:; One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results:; The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions:; Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Experiment Overall Design: Methods: Experiment Overall Design: At first, we selected the statistically significant genes from the analysis of time-to-recurrence and censoring information from 138 whole-genome wide microarray data. Then, we further reduced the number of genes which could be reliably reproducible by RTQ-PCR. With these assayable genes and clinical parameters, construction of recurrence prediction model by Cox proportional hazard regression was done. After validation into two independent cohorts (n=59 and n=56), the model was transformed into recurrence prediction for the each patient.
2008-06-17 | E-GEOD-8894 | biostudies-arrayexpress
Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared. Adequate DASL assay profiles were obtained in 300 patients and we selected 12 genes for the gene set model. When the proportions of patients were categorized as having a low or high risk by the prognostic scores using these genes from LOOCV procedure, the Kaplan-Meier curve showed significant different recurrence rate between two groups. Clinical model was developed using FIGO stage as well as post-surgical pathological findings.
2013-05-01 | E-GEOD-44001 | biostudies-arrayexpress
Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared.
2013-05-01 | GSE44001 | GEO
Project description:To find metastatic recurrence biomarkers of triple negative breast cancer (TNBC) treated by neoadjuvant chemotherapy and anti-EGFR antibodies (NAT), we evaluated tumor genomic, transcriptomic and immune features, using MSK-IMPACT assay, gene arrays, Nanostring technology and TIL assessment on H&E. Six patients experienced a rapid fatal recurrence (RR) and other 6 had later non-fatal recurrences (LR). Before NAT, RR had low expression of 6 MHC class I and 13 MHC class II genes but were enriched in upregulated genes involved in the cell cycle-related pathways. Their TIL number before NAT in RR was very low (<5%) and did not increase after treatment. In post-NAT residual tumors, RR cases showed high expression of SOX2 and CXCR4. Our results indicate that high expression of cell cycle genes, combined with cold immunological phenotype, may predict strong TNBC resistance to NAT and rapid progression after it. This biomarker combination is worth validation in larger studies.
2021-07-28 | GSE180775 | GEO
Project description:To find metastatic recurrence biomarkers of triple negative breast cancer (TNBC) treated by neoadjuvant chemotherapy and anti-EGFR antibodies (NAT), we evaluated tumor genomic, transcriptomic and immune features, using MSK-IMPACT assay, gene arrays, Nanostring technology and TIL assessment on H&E. Six patients experienced a rapid fatal recurrence (RR) and other 6 had later non-fatal recurrences (LR). Before NAT, RR had low expression of 6 MHC class I and 13 MHC class II genes but were enriched in upregulated genes involved in the cell cycle-related pathways. Their TIL number before NAT in RR was very low (<5%) and did not increase after treatment. In post-NAT residual tumors, RR cases showed high expression of SOX2 and CXCR4. Our results indicate that high expression of cell cycle genes, combined with cold immunological phenotype, may predict strong TNBC resistance to NAT and rapid progression after it. This biomarker combination is worth validation in larger studies.
2021-07-26 | GSE180717 | GEO
Project description:The DNA methylation value in early-stage hepatocellular carcinoma was undetermined. The Illumina Infinium 450k Human DNA methylation Beadchip was used to identify recurrence-related abbrent CpG methylation. This study was performed in a total of 66 early-stage HCC samples, including 29 recurrence samples and 37 recurrence-free samples
2017-04-30 | E-GEOD-75041 | biostudies-arrayexpress
Project description:The objective of this study is to identify a gene set to predict recurrence of colorectal cancer (CRC) patients. We generated RNA-seq data of 110 primary CRC samples and identified significant genes associated with recurrence of CRC. Through diverse statistical methods including generalized linear model likelihood ratio test, significant 10 genes were identified. In the validation cohorts, a risk classifier consisting of the 10 genes was an independent risk factor in colorectal cancers.
2019-10-29 | GSE107422 | GEO
Project description:Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. Though, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case-control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 54 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone
2022-11-02 | GSE216872 | GEO