AML subtype is a major determinant of the association between prognostic gene expression signatures and their clinical significance.
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE117994 | GEO | 2019/09/10
REPOSITORIES: GEO
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Project description:Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.
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Project description:Non-muscle-invasive bladder cancer (NMIBC) is clinically heterogeneous, and many NMIBC patients fail to respond to treatment and frequently experience disease relapse. We aim to identify a prognostic and predictive signature of the NMIBC heterogeneity. We constructed a transcriptomic data set by RNA-seq experiments from an independent NMIBC patient cohort (n=32). This cohort contains the patients stratified into three prognostic subgroups: Group 1 (patients with non-relapse; n=15), Group 2 (patients experiencing recurrence, n=9), and Group 3 (patients experiencing progression; n=8).
2021-02-16 | GSE163899 | GEO
Project description:Context: In many cancers, specific subpopulations of cells appear to be uniquely capable of initiating and maintaining tumors. The strongest support for this cancer stem cell model comes from transplantation assays in immune-deficient mice indicating that human acute myeloid leukemia (AML) is organized as a cellular hierarchy driven by self-renewing leukemia stem cells (LSC). This model has significant implications for the development of novel therapies, but its clinical significance remains unclear. Objective: To measure associations between a leukemic stem cell expression signature and clinical outcomes in AML. Design, Setting, and Patients: We defined a gene expression signature of LSC-enriched subpopulations from primary AML patient samples and xenografts, based on a functional definition in transplantation assays. Using previously published gene expression data of bulk AML from four independent cohorts totaling 1047 patients, we performed a retrospective cohort study, defining an LSC score and evaluating it for associations with known predictors of risk including cytogenetic subtype and molecular mutations, and as an independent prognostic factor. Main Outcome Measures: Reproducible associations between a leukemic stem cell signature and overall, event-free, and relapse-free survival. Results: The LSC score was similar across most AML subtypes, but was lower in promyelocytic leukemia, and prognostically favorable cases harboring NPM1 or CEBPA mutations. Strikingly, high scores associated with inferior overall (OS), event-free (EFS), and relapse-free survival (RFS) in these independent cohorts, whether considering patients with a normal karyotype [hazard ratio (HR) range for OS 1.13-1.18, p<0.012 in all cases], or those with cytogenetic anomalies (HR range for OS 1.07-1.15, p<0.01 in all cases). In multivariate analysis, the LSC score was associated with poor outcomes independently of age, FLT3 or NPM1 mutations, and cytogenetic risk group, and added to their prognostic value. Conclusions: High expression of a leukemic stem cell gene expression signature is independently associated with adverse outcomes in AML
2010-12-22 | GSE24006 | 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:Although approximately 80% of adult patients with cytogenetically normal acute myeloid leukemia (CN-AML) achieve a complete remission (CR), over half of them relapse. Better identification of patients who are likely to relapse can help inform clinical decisions. We performed RNA sequencing on pretreatment samples from 268 adults with de novo CN-AML younger than 60 years who achieved a CR after induction treatment with standard “7+3” chemotherapy. After filtering for genes whose expressions were associated with gene mutations known to impact on outcome (i.e., CEBPA, NPM1, and FLT3-internal tandem duplication [FLT3-ITD]), we identified a 10-gene signature strongly predictive of patient relapse (area under the curve [AUC]=0.81). The signature consisted of seven coding genes: GAS6, PSD3, PLCB4, DEXI, JMY, NRP1, C10orf55, and three long non-coding RNAs. In multivariable analysis, the 10-gene signature was strongly associated with relapse (P<.001), after adjustment for the FLT3-ITD, CEBPA and NPM1 mutational status. Validation of the expression signature in an independent patient set from The Cancer Genome Atlas showed the signature’s strong predictive value with AUC=0.78. Implementation of the 10-gene signature into clinical prognostic stratification could be useful for identifying patients likely to relapse.
2021-03-16 | GSE165430 | GEO
Project description:Acute myeloid leukemia (AML) is a heterogeneous disease and AML with normal karyotype (AML-NK) is categorized as an intermediate-risk group. Over the past years molecular analyses successfully identified biomarkers that will further allow to dissecting clinically meaningful subgroups in this disease. Thus far, somatic mutations were identified which elucidate the disturbance of cellular growth, proliferation, and differentiation processes in hematopoietic progenitor cells. In AML-NK, acquired gene mutations with prognostic relevance were identified for FLT3, CEBPA, and NPM1. FLT3-ITD mutations were associated with short relapse-free and overall survival, while mutations in CEBPA or NPM1 (without concomitant FLT3-ITD) had a more favorable outcome. Here, we present a multicenter study investigating gene expression profiles of 251 cases of de novo AML-NK. In three centers whole-genome microarray analyses were performed to delineate robust and common expression signatures for molecular markers in AML-NK. All samples were obtained from untreated patients at the time of diagnosis. Cells used for microarray analysis were collected from the purified fraction of mononuclear cells after Ficoll density centrifugation (Dresden, n=78; Munich, n=97; Ulm, n=77). Each laboratory performed its routine diagnostic algorithms, including the characterization of molecular markers.
2009-12-31 | E-GEOD-15434 | biostudies-arrayexpress
Project description:We performed genome wide miRNA expression analysis to investigate the ability of miRNAs in predicting leukemia relapse from 33 t(8;21) pediatric AMLs at diagnosis divided into a training and a validation cohort. COX regression analysis and ROC curves were used to identify a powerful miRNA signature able to predict relapse in t(8;21) AML patients. miRNA profiling showed that patients with high- or low- risk of relapse had different expression profiles. We support the role of microRNAs in modulating the prognosis of t(8;21)-AML and the use of a miRNA expression signature at diagnosis to better define standard risk cases in pediatric AML.
2017-09-14 | GSE97135 | GEO
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis.
2011-07-19 | GSE13861 | GEO
Project description:In AML, most patients are initiated on standard chemotherapy and afterwards assigned to a post-remission strategy based on genetically-defined risk categories. However, outcomes remain heterogeneous, indicating the need for novel biomarker tests that can rapidly and accurately identify high-risk patients, allowing better stratification of both induction and post-remission therapy. As patient outcomes are linked to leukemia stem cell (LSC) properties that confer therapy resistance and drive relapse, LSC-based biomarkers may be highly informative. We tested 227 CD34/CD38 cell fractions from 78 AML patients for LSC activity in xenotransplantation assays. Comparison of microarray-based gene expression (GE) profiles between 138 LSC+ and 89 LSC? fractions identified 104 differentially-expressed LSC-specific genes. To obtain prognostic signatures, we performed statistical regression analysis of LSC GE against patient outcome using a training cohort of 495 AML patients treated with curative intent. A score calculated as the weighted sum of expression of 17 LSC signature genes (LSC17) was strongly associated with survival in 4 independent datasets (716 AML cases) spanning all risk categories in multi-variate analysis; an optimized 3-gene sub-score (LSC3) was prognostic in favorable risk subsets. These scores were robust across GE technology platforms, including the clinically serviceable NanoString system (LSC17: HR=2.73, P<0.0001; LSC3: HR=6.3, P<0.02). The LSC17 and LSC3 scores provide rapid and accurate identification of high-risk patients for whom conventional chemotherapy is non-curative. These scores will enable evaluation in clinical trials of whether such patients may benefit from novel and/or more intensified therapies during induction or in the post-remission setting.
2016-12-02 | GSE76008 | GEO
Project description:In AML, most patients are initiated on standard chemotherapy and afterwards assigned to a post-remission strategy based on genetically-defined risk categories. However, outcomes remain heterogeneous, indicating the need for novel biomarker tests that can rapidly and accurately identify high-risk patients, allowing better stratification of both induction and post-remission therapy. As patient outcomes are linked to leukemia stem cell (LSC) properties that confer therapy resistance and drive relapse, LSC-based biomarkers may be highly informative. We tested 227 CD34/CD38 cell fractions from 78 AML patients for LSC activity in xenotransplantation assays. Comparison of microarray-based gene expression (GE) profiles between 138 LSC+ and 89 LSC? fractions identified 104 differentially-expressed LSC-specific genes. To obtain prognostic signatures, we performed statistical regression analysis of LSC GE against patient outcome using a training cohort of 495 AML patients treated with curative intent. A score calculated as the weighted sum of expression of 17 LSC signature genes (LSC17) was strongly associated with survival in 4 independent datasets (716 AML cases) spanning all risk categories in multi-variate analysis; an optimized 3-gene sub-score (LSC3) was prognostic in favorable risk subsets. These scores were robust across GE technology platforms, including the clinically serviceable NanoString system (LSC17: HR=2.73, P<0.0001; LSC3: HR=6.3, P<0.02). The LSC17 and LSC3 scores provide rapid and accurate identification of high-risk patients for whom conventional chemotherapy is non-curative. These scores will enable evaluation in clinical trials of whether such patients may benefit from novel and/or more intensified therapies during induction or in the post-remission setting.
2016-12-02 | GSE76004 | GEO