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accession-icon GSE2034
Breast cancer relapse free survival
  • organism-icon Homo sapiens
  • sample-icon 285 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

This series represents 180 lymph-node negative relapse free patients and 106 lymph-node negate patients that developed a distant metastasis.

Publication Title

Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE12093
The 76-gene Signature Defines High-Risk Patients that Benefit from Adjuvant Tamoxifen Therapy
  • organism-icon Homo sapiens
  • sample-icon 136 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Classification of tamixifen-treated breast cancer patients into high and low risk groups using the 76-gene signature

Publication Title

The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE4573
Gene expression signatures for predicting prognosis of squamous cell lung carcinomas
  • organism-icon Homo sapiens
  • sample-icon 130 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Gene signatures were derived to separate high risk patients from low risk ones..

Publication Title

Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE3726
Prognostic gene signatures can be measured with samples stored in RNAlater
  • organism-icon Homo sapiens
  • sample-icon 104 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

A number of breast or colon specific genes predictive of the relapse status were used in comparing the outcome from matched fresh frozen and stored in RNAlater preservative.

Publication Title

Prognostic gene expression signatures can be measured in tissues collected in RNAlater preservative.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE5122
Identification of molecular predictors of response in a study of tipifarnib treatment in relapsed and refractory AML
  • organism-icon Homo sapiens
  • sample-icon 58 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Gene signatures were derived to separate responders from nonresponders by tipifarnib treatment.

Publication Title

Identification of molecular predictors of response in a study of tipifarnib treatment in relapsed and refractory acute myelogenous leukemia.

Sample Metadata Fields

Sex, Age

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accession-icon GSE8970
A two-gene classifier for predicting response to the farnesyltransferase inhibitor tipifarnib in acute myeloid leukemia
  • organism-icon Homo sapiens
  • sample-icon 33 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Currently there is no method available to predict response to farnesyltransferase inhibitors (FTI). We analyzed gene expression profiles from the bone marrow of patients from a phase 2 study of the FTI tipifarnib, in older adults with previously untreated acute myeloid leukemia (AML). The RASGRP1:APTX gene expression ratio was found to predict response to tipifarnib with the greatest accuracy. This two-gene ratio was validated by quantitative PCR (QPCR) in the newly diagnosed AML cohort. We further demonstrated that this classifier could predict response to tipifarnib in an independent set of 54 samples from relapsed or refractory AML, with a negative predictive value (NPV) and positive predictive value (PPV) of 92% and 28%, respectively (odds ratio of 4.4). The classifier also predicted for improved overall survival (154 vs 56 days, p = 0.0001), which was shown to be independent of other prognostic factors including a previously described gene expression classifier predictive of overall survival. Therefore, these data indicate that a two-gene expression assay may have utility in categorizing a population of AML patients who are more likely to respond to tipifarnib.

Publication Title

A 2-gene classifier for predicting response to the farnesyltransferase inhibitor tipifarnib in acute myeloid leukemia.

Sample Metadata Fields

Sex, Age, Disease

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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