Transcription profiling of human acute myeloid leukemia samples - a two-gene classifier for predicting response to the farnesyltransferase inhibitor tipifarnib
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Jack Yu
PROVIDER: E-GEOD-8970 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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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:Histologic findings on 1-year biopsies such as inflammation with fibrosis and transplant glomerulopathy predict renal allograft loss by 5 years. However, almost half of the patients with graft loss have a 1-year biopsy that is either normal or has only interstitial fibrosis. The goal of this study was to determine if there was a gene expression profile in these relatively normal 1-year biopsies that predicted subsequent decline in renal function. Using transcriptome microarrays we measured intragraft mRNA levels in a retrospective Discovery cohort (170 patients with a normal/minimal fibrosis 1-year biopsy, 54 with progressive decline in function/graft loss and 116 with stable function) and developed a nested 10-fold cross-validated gene classifier that predicted progressive decline in renal function (positive predictive value=38±34%%; negative predictive value=73±30%, c-statistic=0.59). In a prospective, multicenter Validation cohort (270 patients with Normal/Interstitial Fibrosis [IF]), the classifier had a 20% positive predictive value, 85% negative predictive value and 0.58 c-statistic. Importantly, the majority of patients with graft loss in the prospective study had 1-year biopsies scored as Normal or IF. We conclude predicting graft loss in many renal allograft recipients (i.e. those with a relatively normal 1-year biopsy and eGFR >40) remains difficult.
2021-08-31 | GSE181757 | GEO
Project description:We hypothesized that DNA methylation distributes into specific patterns in cancer cells, which reflect critical biological differences. We therefore examined the methylation profiles of 344 patients with acute myeloid leukemia (AML). Clustering of these patients by methylation data segregated patients into 16 groups. Five of these groups defined new AML subtypes that shared no other known feature. In addition, DNA methylation profiles segregated patients with CEBPA aberrations from other subtypes of leukemia, defined four epigenetically distinct forms of AML with NPM1 mutations, and showed that established AML1-ETO, CBFb-MYH11 and PML-RARA leukemia entities are associated with specific methylation profiles. We report a 15-gene methylation classifier predictive of overall survival in an independent patient cohort (p<0.001, adjusted for known covariates). Keywords: DNA methylation profiling
2010-01-12 | GSE18700 | GEO
Project description:We hypothesized that DNA methylation distributes into specific patterns in cancer cells, which reflect critical biological differences. We therefore examined the methylation profiles of 344 patients with acute myeloid leukemia (AML). Clustering of these patients by methylation data segregated patients into 16 groups. Five of these groups defined new AML subtypes that shared no other known feature. In addition, DNA methylation profiles segregated patients with CEBPA aberrations from other subtypes of leukemia, defined four epigenetically distinct forms of AML with NPM1 mutations, and showed that established AML1-ETO, CBFb-MYH11 and PML-RARA leukemia entities are associated with specific methylation profiles. We report a 15-gene methylation classifier predictive of overall survival in an independent patient cohort (p<0.001, adjusted for known covariates). Keywords: DNA methylation profiling DNA methylation profiling of a cohort of 344 AML patients from Erasmus Medical Center and enrolled in clinical trials from the Dutch-German cooperative group HOVON. Additionally, a control group consisting of 8 CD34+ bone marrow samples from healthy donors was also studied.
2010-01-12 | E-GEOD-18700 | biostudies-arrayexpress
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial. Experiment Overall Design: The was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission. Experiment Overall Design: Note: Failure (relapse) samples F8,12 and F19 were removed for reasons of failure other than relapse.
2009-02-09 | E-GEOD-14613 | biostudies-arrayexpress
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial. Experiment Overall Design: This was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission. Experiment Overall Design: NOTE: non-sequential numbers were removed for reasons other than relapse, or poor hybridization during chip preparation.
2009-02-09 | E-GEOD-14615 | biostudies-arrayexpress
Project description:Background: Validated markers to predict outcome in rectal cancer patients treated with multimodal therapy remain elusive. Identifying molecular profiles for disease prognosis would be required for the design of clinical trials aimed at optimizing risk-adapted therapies. We have therefore used whole genome expression profiling of tumors of a large cohort of patients enrolled in the multicenter trials of the German Rectal Cancer Study Group (GRCSG) to identify molecular profiles for individualized therapy. Methods: We prospectively collected pretherapeutic biopsies from patients (n=300) treated according to the GRCSG trial guidelines from seven different German surgical departments using rigid quality controls. These samples were profiled by global gene expression analysis and a classifier developed to predict postoperative lymph node status and Disease Free Survival (DFS). The performance of the classifier was validated with an independent, prospectively collected set of samples. Findings: The final training and test set included 198 patients. Analyzes for postoperative nodal status and DFS revealed 69 and 674 differentially regulated genes, respectively. Depending on the classifier the accuracy to predict lymph node status ranged from 64% to 69% with negative predictive values between 72% and 74%. Stratification according to DFS resulted in a good (n=99), bad (n=96) and a small very bad prognosis group (n=3). Based on linear discriminant analysis the classifier for positive lymph node status was validated in 47 independent patients and revealed an accuracy of 72% and a positive predictive value of 100%. Thereby, prediction based on molecular profiling is superior to prediction based on conventional clinical markers. Interpretation: Whole genome expression analysis of pretherapeutical biopsies resulted in a molecular classifier of disease prognosis. This classifier was successfully validated. The high positive predictive value for post therapeutic lymph node status allows identification of patients requiring alternative or intensified treatment protocols. These data are currently validated for their clinical applicability and should be taken as basis for future molecular driven clinical trials to develop risk-adapted treatments.
2023-03-31 | GSE40492 | GEO
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial. This SuperSeries is composed of the following subset Series:; GSE14613: Microarray analyses of induction failure in T-ALL (COG study 8704); GSE14615: Microarray analyses of induction failure in T-ALL (COG study 9404) Experiment Overall Design: Refer to individual Series Experiment Overall Design: This was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission.
2009-02-07 | E-GEOD-14618 | biostudies-arrayexpress
Project description:A previously predictive CEBPA double mutant (CEBPAdm) signature was hampered by the recently reported CEBPA silenced AML cases that carry a similar gene expression profile (GEP). Two independent AML cohorts were used to train and evaluate the predictive value of the CEBPAdm signature in terms of sensitivity and specificity. A predictive signature was created, containing 25-probe sets by using a logistic regression model with Lasso regularization, which selects discriminative probe sets between the classes, CEBPAdm and all other AML cases, CEBPA wild type (CEBPAwt) and CEBPA single mutant (CEBPAsm). Subsequently, a classifier was trained on the entire HOVON-SAKK cohort based on a two-class approach; CEBPAdm versus all other cases (CEBPAwt and CEBPAsm). This trained classifier subsequently classified 16 candidate CEBPAdm cases in the AMLSG-cohort out of 154 AML cases. This approach showed perfect sensitivity and specificity (both 100%). In addition, we have performed a classification between CEBPAdm ,CEBPAsm, and CEBPAwt to infer if we were able to accurately classify CEBPAsm cases. We observed that all CEBPAsm cases were classified as CEBPAwt, thus CEBPAsm cases do not have a consistent gene expression pattern and are different from the CEBPAdm group. 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. Routine diagnostic algorithms, including the characterization of molecular markers are performed.
2011-08-03 | E-GEOD-22845 | biostudies-arrayexpress