Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients

Purpose This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. Patients and Methods A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. Results Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. Conclusion Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression–based predictive index can be used to select patients for PMRT.

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Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen

Breast Cancer Research and Treatment ◽

10.1007/s10549-008-9939-y ◽

2008 ◽

Vol 113 (2) ◽

pp. 275-283 ◽

Cited By ~ 45

Author(s):

Marleen Kok ◽

Sabine C. Linn ◽

Ryan K. Van Laar ◽

Maurice P. H. M. Jansen ◽

Teun M. van den Berg ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Patients ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Breast Cancer Patients

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Prognostic impact of the glypican family of heparan sulfate proteoglycans on the survival of breast cancer patients

Journal of Cancer Research and Clinical Oncology ◽

10.1007/s00432-021-03597-4 ◽

2021 ◽

Author(s):

Paulina Karin Grillo ◽

Balázs Győrffy ◽

Martin Götte

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Heparan Sulfate ◽

Cancer Patients ◽

Heparan Sulfate Proteoglycans ◽

Prognostic Impact ◽

Breast Cancer Patients ◽

Relapse Free Survival ◽

Free Survival ◽

The Impact

Abstract Purpose Dysregulated expression of proteoglycans influences the outcome and progression of numerous cancers. Several studies have investigated the role of individual glypicans in cancer, however, the impact of the whole glypican family of heparan sulfate proteoglycans on prognosis of a large patient cohort of breast cancer patients has not yet been investigated. In the present study, our aim was to investigate the prognostic power of the glypicans in breast cancer patients. Methods We used a public database including both gene expression data and survival information for 3951 breast cancer patients to determine the prognostic value of glypicans on relapse-free survival using Cox regression analysis. Moreover, we performed quantitative Real-Time PCR to determine glypican gene expression levels in seven representative breast cancer cell lines. Results We found that high GPC3 levels were associated with a better prognosis in overall breast cancer patients. When stratified by hormone receptor status, we found that in worse prognosis subtypes low GPC1 levels correlate with a longer relapse-free survival, and in more favorable subtypes low GPC6 was associated with longer survival. Conclusion Our study concludes that glypicans could act as subtype-specific biomarkers for the prognosis of breast cancer patients and sparks hope for future research on glypicans possibly eventually providing targets for the treatment of the disease.

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Gene expression profiles in circulating tumor cells to predict prognosis in metastatic breast cancer patients

Annals of Oncology ◽

10.1093/annonc/mdu557 ◽

2015 ◽

Vol 26 (3) ◽

pp. 510-516 ◽

Cited By ~ 33

Author(s):

B. Mostert ◽

A.M. Sieuwerts ◽

J. Kraan ◽

J. Bolt-de Vries ◽

P. van der Spoel ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Metastatic Breast Cancer ◽

Cancer Patients ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Metastatic Breast ◽

Breast Cancer Patients

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Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

The Scientific World JOURNAL ◽

10.1100/2012/380495 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Mark Burton ◽

Mads Thomassen ◽

Qihua Tan ◽

Torben A. Kruse

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Patients ◽

Expression Profiles ◽

External Validation ◽

Gene Expression Profiles ◽

Microarray Gene Expression Data ◽

Classification Methods ◽

Breast Cancer Patients ◽

Microarray Gene Expression

Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

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Abstract 2896: Impact of intratumoral heterogeneity on gene expression profiles and known biomarkers in FFPE tumor biopsies from early stage breast cancer patients

10.1158/1538-7445.am2019-2896 ◽

2019 ◽

Author(s):

Lucas Delmonico ◽

Joan Chen ◽

Trushna Desai ◽

Lisa Barnett ◽

Aladdin Shadyab ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Patients ◽

Early Stage ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Early Stage Breast Cancer ◽

Breast Cancer Patients ◽

Ffpe Tumor ◽

Tumor Biopsies

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Gene expression profiles of circulating tumor cells in metastatic breast cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.10504 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 10504-10504

Author(s):

Bianca Mostert ◽

Anieta M Sieuwerts ◽

Jaco Kraan ◽

Joan Bolt-de Vries ◽

Dieter Peeters ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Metastatic Breast Cancer ◽

Cancer Patients ◽

Prognostic Value ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Metastatic Breast ◽

Breast Cancer Patients ◽

Gene Profile

10504 Background: A circulating tumor cell (CTC) count is an established prognostic factor in metastatic breast cancer. Besides enumeration, CTC characterization promises to further improve outcome prediction and treatment guidance. We have previously shown the feasibility of measuring the expression of a panel of 96 clinically relevant genes in CTCs in a leukocyte background, and in the current study, we determined the prognostic value of CTC gene expression profiling in metastatic breast cancer. Methods: CTCs were isolated and enumerated from blood of 130 metastatic breast cancer patients prior to start of first-line systemic, endocrine or chemotherapeutic, therapy. Of these, 103 were evaluable for mRNA gene expression levels measured by quantitative RT-PCR in relation to time to treatment switch (TTS). Separate prognostic CTC gene profiles were generated by leave-one-out cross validation for all patients and for patients with ≥5 CTCs per 7.5 mL blood, and cut-offs were chosen to ensure optimal prediction of patients who might benefit from an early therapy switch. Results: In the total cohort, of whom 56% received chemotherapeutic and 44% endocrine therapy, baseline CTC count (≥5 versus <5 CTCs/7.5 mL blood) predicted for TTS (Hazard Ratio (HR) 2.92 [95% Confidence Interval (CI) 1.71 – 4.95] P <0.0001). A 16-gene CTC profile for all patients and a separate 9-gene CTC profile applicable for patients with ≥5 CTCs were identified, which distinguished those patients with TTS or death within 9 months from those with a more favorable outcome. Test performance for both profiles was favorable; the 16-gene profile had 90% sensitivity, 38% specificity, 50% positive predictive value (PPV) and 85% negative predictive value (NPV), and the 9-gene profile performed slightly better at 92% sensitivity, 52% specificity, 66% PPV and 87% NPV. In multivariate Cox regression analysis, the 16-gene profile was the only factor independently associated with TTS (HR 3.15 [95%CI 1.35 – 7.33] P 0.008). Conclusions: Two CTC gene expression profiles were discovered, which provide prognostic value in metastatic breast cancer patients. This study further underscores the potential of molecular characterization of CTCs.

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Differences in gene-expression profiles in breast cancer between African and European-ancestry women

Carcinogenesis ◽

10.1093/carcin/bgaa035 ◽

2020 ◽

Vol 41 (7) ◽

pp. 887-893 ◽

Cited By ~ 1

Author(s):

Jie Ping ◽

Xingyi Guo ◽

Fei Ye ◽

Jirong Long ◽

Loren Lipworth ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Patients ◽

Expression Profiles ◽

Breast Cancer Mortality ◽

Tumor Biology ◽

Gene Expression Profiles ◽

Gene Signature ◽

European Ancestry ◽

Breast Cancer Patients

Abstract African American (AA) women have an excess breast cancer mortality than European American (EA) women. To investigate the contribution of tumor biology to this survival health disparity, we compared gene expression profiles in breast tumors using RNA sequencing data derived from 260 AA and 155 EA women who were prospectively enrolled in the Southern Community Cohort Study (SCCS) and developed breast cancer during follow-up. We identified 59 genes (54 protein-coding genes and 5 long intergenic non-coding RNAs) that were expressed differently between EA and AA at a stringent false discovery rate (FDR) < 0.01. A gene signature was derived with these 59 genes and externally validated using the publicly available Cancer Genome Atlas (TCGA) data from180 AA and 838 EA breast cancer patients. Applying C-statistics, we found that this 59-gene signature has a high discriminative ability in distinguishing AA and EA breast cancer patients in the TCGA dataset (C-index = 0.81). These findings may provide new insight into tumor biological differences and the causes of the survival disparity between AA and EA breast cancer patients.

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