Molecular prognosis in multiple myeloma
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE7039 | GEO | 2008/08/05
SECONDARY ACCESSION(S): PRJNA98499
REPOSITORIES: GEO
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Project description:Importance: Severe alcoholic hepatitis (AH) is a highly lethal disease with a 3-month mortality up to 50%. Corticosteroids are the current standard of care although nearly 40% of the patients do not respond and accurate pre-treatment predictors of survival are lacking. Objective: To develop a prognostic score based on molecular and clinical variables before initiation of corticosteroids. Design, Setting, and Participants: Gene expression profiling of fixed liver biopsy obtained for diagnosis of severe AH assessed in 3 independent cohorts (1 prospective cohort for prognostic gene signature and integrative score derivation, and 2 retrospective cohorts for validation). Samples were obtained from 4 European and 1 US medical centers between July 2006 and February 2015. Exposures: Biopsy proven severe AH patients treated with corticosteroids. Main Outcome Measures: Identification of a gene signature combined with a validated clinical index to develop an integrative prognostic score of survival without death or liver transplantation in a derivation cohort (n=71). Prognostic performance of the score was validated in an independent multi-center validation cohort 1 (n=48). Finally, the score was implemented in an FDA-approved clinical diagnostic platform (NanoString) and tested in another independent validation cohort 2 (n=20). Results: A prognostic score, integrating a 123-gene signature and the model for end-stage liver disease (gs-MELD score), was defined in the derivation cohort, in which poor- and good-prognosis patients were discriminated with a cut-off value of 2.66 at 3 months (event-free survival rates of 32% vs. 76%, respectively, p<.001) and 6 months (26% vs. 65%, respectively, p<.001). In the validation cohort 1, the score similarly discriminated poor- and good-prognosis patients at 3 months (43%, [95% CI, 26%-70%] vs. 96% [95% CI, 89%-100%], respectively, p<.001, c-index of 0.83 [95% CI, 0.66-0.99]) and 6 months (34% [95% CI, 18%-61%] vs 84% [95% CI, 72%-100%], respectively, p<.001, c-index of 0.80 [95% CI, 0.66-0.94]), and outperformed other existing clinical indices. The NanoString-based gs-MELD score remained predictive of 3- (p=.03) and 6-month (p=.009) even-free survival in the validation cohort 2. Conclusion and Relevance: The gs-MELD score, incorporating clinically applicable gene signature test and the MELD score, enables pre-treatment survival prediction in severe AH patients.
2018-02-18 | GSE95246 | GEO
Project description:Background: Accurate survival stratification in early-stage NSCLC could inform the use of adjuvant therapy. We developed a clinically-implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables. Methods: Gene expression profiles from 1106 non-squamous NSCLCs were used for generation and internal validation of a 9-gene molecular prognostic index (MPI). Expression of the MPI genes was determined within sorted tumor cell subpopulations. A quantitative PCR (qPCR) assay was developed and validated on an independent cohort of FFPE tissues. A prognostic score using clinical variables was generated using Surveillance Epidemiology and End Results (SEER) data and combined with the MPI. Results: The MPI stratified stage I patients into prognostic categories in four independent validation datasets, including three microarray and one FFPE qPCR cohorts (HR=2.4, 95% CI, 1.8-3.3, P=7x10-9 in the largest microarray cohort; and HR=2.5, 95% CI 1.1-6.0, P=.03 in stage I patients of the qPCR validation cohort). Prognostic genes were expressed in distinct tumor cell subpopulations and expression of genes implicated in cellular proliferation and stem cells portended poor outcomes, while expression of genes involved in normal lung differentiation and immune infiltration was associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR=3.3, 95% CI 2.4-4.6; P=2x10-15 in the largest microarray cohort; and HR=3.6, 95% CI 1.5-8.8, P=.003 in stage I patients of the qPCR validation cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK. Conclusion: The MPI incorporates genes expressed in the tumor and its microenvironment, and designates risk of death for patients with early-stage non-squamous NSCLC. The MPI can be implemented clinically using qPCR assays on FFPE tissues and a composite model integrating the MPI with clinical variables provides the most accurate risk stratification.
2015-08-24 | GSE67639 | GEO
Project description:A seven-lncRNA signature was identified in the training set, which is significantly associated with overall survival (OS) (Hazard Ratio [HR]: 3.535, 95% confidence interval [CI]: 2.113-5.915, P < 0.001) and disease-free survival (DFS) (Hazard Ratio [HR]: 2.413, 95% confidence interval [CI]: 1.573-3.703, P < 0.001). Patients in high-risk group have shorter overall survival (HR: 3.555, 95% CI: 2.195-5.757, p < 0.001) and disease-free survival (HR: 2.537, 95% CI: 1.646-3.909, p < 0.001) in the training set, and we found the same conclusion in the independent set for OS (HR 2.665, p < 0.001) and DFS (HR 2.360, p = 0.001). Combination of the signature and TNM staging system was more powerful than TNM staging system alone in predicting outcomes in both the training set (AUC: 0.772 vs 0.681, p = 0.002) and in the independent set (AUC: 0.772 vs 0.660, p = 0.003).
2020-02-10 | GSE92986 | GEO
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:Glioblastomas are among the most lethal cancers; however, recent advances in survival have increased the need for better prognostic markers. microRNAs (miRNAs) hold great prognostic potential being deregulated in glioblastomas and highly stable in stored tissue specimens. Moreover, miRNAs control multiple genes representing an additional level of gene regulation possibly more prognostically powerful than a single gene. The aim of the study was to identify a novel miRNA signature with the ability to separate patients into prognostic subgroups. Samples from 40 glioblastoma patients were included retrospectively; patients were comparable on all clinical aspects except overall survival enabling patients to be categorized as short-term or long-term survivors based on median survival. A miRNome screening was employed, and a prognostic profile was developed using leave-one-out cross-validation. We found that expression patterns of miRNAs; particularly the four miRNAs: hsa-miR-107_st, hsa-miR-548x_st, hsa-miR-3125_st and hsa-miR-331-3p_st could determine short- and long-term survival with a predicted accuracy of 78%. Heatmap dendrograms dichotomized glioblastomas into prognostic subgroups with a significant association to survival in univariate (HR 8.50; 95% CI 3.06-23.62; p<0.001) and multivariate analysis (HR 9.84; 95% CI 2.93-33.06; p<0.001). Similar tendency was seen in The Cancer Genome Atlas (TCGA) using a 2-miRNA signature of miR-107 and miR-331 (miR sum score), which were the only miRNAs available in TCGA. In TCGA, patients with O6-methylguanine-DNA-methyltransferase (MGMT) unmethylated tumors and low miR sum score had the shortest survival. Adjusting for age and MGMT status, low miR sum score was associated with a poorer prognosis (HR 0.66; 95% CI 0.45-0.97; p=0.033). A Kyoto Encyclopedia of Genes and Genomes analysis predicted the identified miRNAs to regulate genes involved in cell cycle regulation and survival. In conclusion, the biology of miRNAs is complex, but the identified 4-miRNA expression pattern could comprise promising biomarkers in glioblastoma stratifying patients into short- and long-term survivors.
2017-10-04 | GSE104554 | GEO
Project description:Background: The JBR.10 trial demonstrated significant survival benefit from adjuvant cisplatin/vinorelbine (ACT) in stage IB-II NSCLC (HR 0.69, p=0.04), but stage IB patients did not derive significant benefit (HR: 0.94, p= 0.79). We hypothesized that expression profiling could identify stage-independent subgroups of patients who might benefit from adjuvant chemotherapy. Methods: Gene expression profiling was conducted on mRNA isolated from frozen JBR.10 tumor samples (either from patients under observation [OBS], or treated with ACT). The minimum gene set that selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified and this gene signature was used to classify patients into high and low risk for death after surgery, and predict ACT effect. The prognostic gene signature was additionally tested on ACT patients and publicly available microarray datasets. Results: A 15-gene signature separated OBS patients into equal high and low risk subgroups with significantly different prognoses (HR 15.02, 95% CI 5.12-44.04, p=0.0001). The signature was prognostic in both stage IB and II. It was also predictive of improved survival following ACT treatment in high-risk patients (HR 0.33, 95% CI 0.17-0.63, p=0.0005), but not in low risk patients (HR 3.67, 95% CI 1.22-11.06, p=0.0133; interaction p=0.0001). The prognostic effect of the signature was validated in two independent gene expression datasets of 169 stage I-II adenocarcinoma and 106 squamous cell carcinoma patients. Conclusions: This microarray-based 15-gene prognostic expression signature is stage and histology independent and may select early stage NSCLC patients who are most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine. Keywords: Expression profiling by microarray; prognosis prediction 90 samples
2010-09-09 | E-GEOD-14814 | biostudies-arrayexpress
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.
2009-12-02 | E-GEOD-17537 | biostudies-arrayexpress
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.
2009-12-02 | E-GEOD-17536 | biostudies-arrayexpress
Project description:Background & Aims: The recurrence determines the postoperative prognosis of patients with hepatocellular carcinoma (HCC). It is unknown whether de novo HCCs derive from the liver with disability of an organic anion transport. This study was designed to elucidate the link between such transporters and the multicentric occurrence (MO) after radical hepatectomy. Results: SLC22A7 expression was the best predictor of metastasis-free survival (MFS) as judged by the GA (Fold, 0.726; P=0.001). High SLC22A7 gene expression in noncancerous tissue prevent HCC occurrence after hepatectomy (Odds Ratio (OR), 0.2; 95%CI, 0.1-0.6; P=0.004). Multivariate analyses of MFS revealed the independent risk factor to be SLC22A7 expression (OR, 0.3; 95%CI, 0.1-1.0, P=0.043). Low SLC22A7 expression caused MO of HCC significantly (log-rank, P=0.001). In the validation study, multivariate analyses of MFS revealed the independent risk factor to be SLC22A7 expression (OR, 0.5; 95%CI, 0.3-0.8; P=0.012). As judged by Gene set-enrichment analysis, SLC22A7 down-regulation associated with mitochondrion (P=0.008; false discovery rate (FDR)=0.199; normalized enrichment score (NES) =1.804), oxidoreductase activity (P=0.006; FDR=0.157; NES=1.854) and fatty acid metabolic process (P=0.021; FDR=0.177; NES=1.723). Sirtuin3 also determined MFS (P= 0.018). Conclusions: These pathways involving SLC22A7 dysfunction may promote the occurrence of HCC. The 49 noncancerous liver tissues of HCC patients within Milan criteria, treated at our institution between January 2004 and August 2008, were examined as a training set by genome-wide gene expression analysis (GA). Cox proportional hazards regression analyses for MO-free survival (MFS) were performed to estimate the risk factors. Using the independent two institutional cohorts of 134 patients between September 2008 and December 2009, a validation study was employed using tissue microarrays.
2013-04-01 | E-GEOD-40873 | biostudies-arrayexpress
Project description:Molecular prognostic assays, such as Oncotype DX, are increasingly incorporated into the management of patients with invasive breast carcinoma. BreastPRS is a new molecular assay developed and validated from a meta-analysis of publically available genomic datasets. We applied the assay to matched fresh-frozen (FF) and formalin-fixed paraffin embedded (FFPE) tumor samples to translate the assay to FFPE. A linear relationship of the BreastPRS prognostic score was observed between tissue preservation formats. BreastPRS recurrence scores were compared with Oncotype DX recurrence scores from 246 patients with invasive breast carcinoma and known Oncotype DX results. Using this series, a 120-gene linear discriminant algorithm (LDA) was trained to predict Oncotype DX risk groups and then applied to series of untreated, node-negative, estrogen receptor (ER) – positive patients from previously published studies with known clinical outcomes. Correlation of recurrence score and risk group between Oncotype DX and BreastPRS was statistically significant (P<0.0001). 59 of 260 (23%) patients from four previously published studies were classified as intermediate-risk when the 120-gene LDA was applied. BreastPRS reclassified the 59 patients into binary risk groups (high vs. low-risk). 23 (39%) patients were classified as low-risk 36 (61%) as high-risk [P=0.029, HR: 3.64, 95% CI: 1.40 to 9.50]. At 10 years from diagnosis, the low-risk group had a 90% recurrence-free survival (RFS) rate, compared to 60% for the high-risk group. BreastPRS recurrence score is comparable to Oncotype DX and can reclassify Oncotype DX intermediate-risk patients into two groups with significant differences in RFS. Further studies are needed to validate these findings. Expression profiles of 246 invasive breast carcinomas.
2013-06-23 | E-GEOD-47109 | biostudies-arrayexpress