Identification and validation of gene expression models that predict clinical outcome in patients with early stage laryngeal cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE25727 | GEO | 2011/11/26
SECONDARY ACCESSION(S): PRJNA133859
REPOSITORIES: GEO
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Project description:Background Despite improvement in diagnostic and therapeutic techniques, a significant percentage of patients with early stage laryngeal cancer still recur after treatment. Gene expression models prognostic of recurrence risk could suggest which patients with early stage laryngeal cancer would be more appropriate for testing adjuvant strategies. Patients and Methods Expression profiling using whole genome DASL arrays was performed on 56 formalin-fixed paraffin-embedded tumor samples of patients with early stage laryngeal cancer, treated with surgery or radiation therapy. We split the samples into a training set and a validation set. Using the supervised principal components survival analysis in the first cohort, we identified multiple gene expression profiles that predict the risk of recurrence. These profiles were then validated in the second independent cohort. Results Gene models comprising different number of genes (40-100) identified a subgroup of patients who were at high risk of recurrence. Of these, the best prognostic model distinguished between a high- and a low-risk group (median DFS: 92 and 123 months, log rank p<0.005, permutation p<0.05), Hazard Ratio (HR): 8.51 (95% CI, 1.01 to 71.77; p<0.05). These models performed similarly in the independent cohort of our study (median DFS: 38 vs 161 months, log rank p=0.018), HR=5.19 (95% CI, 1.14 to 23.57; p<0.05). Conclusions We have identified gene expression prognostic models which can refine the estimation of a patient’s risk of recurrence. These findings, if further validated, should aid in patient stratification for testing adjuvant treatment strategies. 56 patients with early stage laryngeal cancer were included in this study.
2011-11-26 | E-GEOD-25727 | biostudies-arrayexpress
Project description:Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities. Training set comprises 59 samples and validation set 50 samples
2013-10-30 | E-GEOD-27020 | biostudies-arrayexpress
Project description:Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities.
2013-10-30 | GSE27020 | GEO
Project description:We report a kidney cancer tissue-based prognostic biomarker encompassing 15 genes (15G score) to classify patients into low versus high risk for recurrence after curative nephrectomy. The 15G score was independently associated with disease free survival adjusting for clinicopathologic variables as well as existing clinical risk calculators or nomograms. By improving risk stratification of patients with ccRCC, the 15G score has the capacity to facilitate selection of biopsy confirmed small renal cancers (T1a) for treatment versus surveillance; inform intensity and duration of surveillance after curative nephrectomy; and to potentially facilitate patient selection for adjuvant systemic therapy. We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched based on grade/stage to patients without recurrence. Capture whole transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNAseq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease specific survival (DSS) and overall survival (OS) in two validation datasets (combined n=761).
2024-04-01 | GSE254566 | GEO
Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment. 38 patients with papillary thyroid carcinoma from a consecutive cohort of patients, no replicates
2015-01-20 | E-GEOD-65074 | biostudies-arrayexpress
Project description:About 50% of resected pancreatic ductal adenocarcinoma (PDAC) recur within just one year following surgery. Prognostic molecular markers predicting rapid recurrence are currently unavailable. We hypothesized that epigenetic differences at the level of chromatin accessibility, potentially linked to distinct differentiation states, might distinguish rapidly recurrent from non-recurrent tumors. Therefore, we interrogated genome-wide chromatin accessibility using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) on EpCAM+ PDAC malignant cells sorted from a cohort of 54 treatment-naïve resected tumors, in hopes of defining a tumor-intrinsic chromatin signature associated with recurrence. We discovered a signature of ~1000 loci that were differentially accessible between recurrent (disease free survival (DFS) < 1 year) and non-recurrent patients (DFS > 1 year). Through transcription factor (TF) binding motif analysis using supervised learning, we identified candidate TFs whose accessible motifs were differentially associated with recurrence. Nuclear localization of two such TFs as selected by top hits, ZKSCAN1 and HNF1b, were assessed by both immunohistochemistry and immunofluorescence on the tissue microarrays (TMA) of 40 out of 54 patients. Nuclear staining of HNF1b was strong in the non-recurrent and weak or absent in the recurrent patients but ZKSCAN1 was not significantly associated with recurrence. In an independent TMA of PDAC cohort (n=97) preselected for 52 long (OS 6 years)- and 45 short (OS 6 months)- term survivors, the number of nuclear positive cells for HNF1b was 52-fold higher in the long-term compared to the short-term survivors and that for ZKSCAN1 was 5.3-fold higher in the short-term compared to the long-term survivors. Altogether, these results provide novel prognostic molecular markers of early recurrence in PDAC and also suggest that the global epigenetic landscape is a prognostic feature in this disease.
2021-03-31 | GSE124231 | GEO
Project description:About 50% of resected pancreatic ductal adenocarcinoma (PDAC) recur within just one year following surgery. Prognostic molecular markers predicting rapid recurrence are currently unavailable. We hypothesized that epigenetic differences at the level of chromatin accessibility, potentially linked to distinct differentiation states, might distinguish rapidly recurrent from non-recurrent tumors. Therefore, we interrogated genome-wide chromatin accessibility using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) on EpCAM+ PDAC malignant cells sorted from a cohort of 54 treatment-naïve resected tumors, in hopes of defining a tumor-intrinsic chromatin signature associated with recurrence. We discovered a signature of ~1000 loci that were differentially accessible between recurrent (disease free survival (DFS) < 1 year) and non-recurrent patients (DFS > 1 year). Through transcription factor (TF) binding motif analysis using supervised learning, we identified candidate TFs whose accessible motifs were differentially associated with recurrence. Nuclear localization of two such TFs as selected by top hits, ZKSCAN1 and HNF1b, were assessed by both immunohistochemistry and immunofluorescence on the tissue microarrays (TMA) of 40 out of 54 patients. Nuclear staining of HNF1b was strong in the non-recurrent and weak or absent in the recurrent patients but ZKSCAN1 was not significantly associated with recurrence. In an independent TMA of PDAC cohort (n=97) preselected for 52 long (OS 6 years)- and 45 short (OS 6 months)- term survivors, the number of nuclear positive cells for HNF1b was 52-fold higher in the long-term compared to the short-term survivors and that for ZKSCAN1 was 5.3-fold higher in the short-term compared to the long-term survivors. Altogether, these results provide novel prognostic molecular markers of early recurrence in PDAC and also suggest that the global epigenetic landscape is a prognostic feature in this disease.
2021-03-30 | GSE124230 | GEO
Project description:About 50% of resected pancreatic ductal adenocarcinoma (PDAC) recur within just one year following surgery. Prognostic molecular markers predicting rapid recurrence are currently unavailable. We hypothesized that epigenetic differences at the level of chromatin accessibility, potentially linked to distinct differentiation states, might distinguish rapidly recurrent from non-recurrent tumors. Therefore, we interrogated genome-wide chromatin accessibility using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) on EpCAM+ PDAC malignant cells sorted from a cohort of 54 treatment-naïve resected tumors, in hopes of defining a tumor-intrinsic chromatin signature associated with recurrence. We discovered a signature of ~1000 loci that were differentially accessible between recurrent (disease free survival (DFS) < 1 year) and non-recurrent patients (DFS > 1 year). Through transcription factor (TF) binding motif analysis using supervised learning, we identified candidate TFs whose accessible motifs were differentially associated with recurrence. Nuclear localization of two such TFs as selected by top hits, ZKSCAN1 and HNF1b, were assessed by both immunohistochemistry and immunofluorescence on the tissue microarrays (TMA) of 40 out of 54 patients. Nuclear staining of HNF1b was strong in the non-recurrent and weak or absent in the recurrent patients but ZKSCAN1 was not significantly associated with recurrence. In an independent TMA of PDAC cohort (n=97) preselected for 52 long (OS 6 years)- and 45 short (OS 6 months)- term survivors, the number of nuclear positive cells for HNF1b was 52-fold higher in the long-term compared to the short-term survivors and that for ZKSCAN1 was 5.3-fold higher in the short-term compared to the long-term survivors. Altogether, these results provide novel prognostic molecular markers of early recurrence in PDAC and also suggest that the global epigenetic landscape is a prognostic feature in this disease.
2021-03-30 | GSE124229 | GEO
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:Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of 'high-risk' at diagnosis could help improving patient stratification and predicting outcome. In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.
2023-07-14 | MTBLS4294 | MetaboLights