Topological analysis of gene expression arrays identifies high risk molecular subtypes in breast cancer

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

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A Prospective Randomized Trial Evaluating Gene Expression Arrays to Select Neoadjuvant Chemotherapy Regimen for Operable Breast Cancer: First Report of the Remagus04 Trial

Annals of Oncology ◽

10.1016/s0923-7534(20)34355-6 ◽

2012 ◽

Vol 23 ◽

pp. ixe3

Author(s):

J. Pierga ◽

B. Asselain ◽

S. Alsafadi ◽

E. Brain ◽

F. Lerebours ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Neoadjuvant Chemotherapy ◽

Randomized Trial ◽

Chemotherapy Regimen ◽

Operable Breast Cancer ◽

Prospective Randomized Trial ◽

First Report ◽

Expression Arrays ◽

Gene Expression Arrays

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Molecular biology in breast cancer: Should molecular classifiers be assessed by conventional tools or by gene expression arrays?

Critical Reviews in Oncology/Hematology ◽

10.1016/j.critrevonc.2012.08.003 ◽

2012 ◽

Vol 84 ◽

pp. e58-e69 ◽

Cited By ~ 9

Author(s):

Debora Fumagalli ◽

Fabrice Andre ◽

Martine J. Piccart-Gebhart ◽

Christos Sotiriou ◽

Christine Desmedt

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Molecular Biology ◽

Expression Arrays ◽

Gene Expression Arrays

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Immune Effects of Transtuzumab in HER2 Positive Breast Cancer

Revista de Chimie ◽

10.37358/rc.19.8.7428 ◽

2019 ◽

Vol 70 (8) ◽

pp. 2791-2794

Author(s):

Anca Zgura ◽

Laurentia Gales ◽

Bogdan Haineala ◽

Elvira Bratila ◽

Claudia Mehedintu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

B Lymphocytes ◽

Immunogenic Cell Death ◽

Immune Markers ◽

Her2 Positive ◽

Expression Arrays ◽

Her2 Positive Breast Cancer ◽

Gene Expression Arrays ◽

Positive Breast Cancer

The immune system could mediate the antitumor activity of several anticancer treatments. Several chemotherapy compounds, including anthracyclines and oxaliplatin, induce immunogenic cell death that in turn activates the antitumor immune response. Trastuzumab induces antibody-dependant cell-mediated cytotoxicity. On the basis of this background, immune markers have recently been the focus of intense translational research to predict and monitor the efficacy of treatments. Gene expression arrays and immunohistochemistry have assessed immune activation and infiltration by macrophages, natural killer, and T and B lymphocytes. In this paper we present the results of a study that included 22 patients diagnosed with Her2 positive breast cancer undergoing treatment with Transtuzumab.

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Combining multigene profiling of molecular subtypes with the 70-gene profile for classification of breast cancer

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.15_suppl.11083 ◽

2009 ◽

Vol 27 (15_suppl) ◽

pp. 11083-11083

Author(s):

L. Stork-Sloots ◽

O. Krijgsman ◽

P. Roepman ◽

F. A. de Snoo ◽

R. A. Bender ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Survival Rate ◽

High Risk ◽

Molecular Subtypes ◽

Low Risk ◽

Gene Profile ◽

Risk Of Recurrence ◽

Risk Patients

11083 Background: Classification of breast cancers into molecular subtypes may be important for accurate selection of therapy for patients. Herein we report a multigene profile for classification of breast cancer into molecular subtypes. The profile separates tumors into hormone receptor (HR)+/luminal-like, HER2+/ERBB2-like, and triple negative/basal-like subclasses. Methods: A multi-gene profile was developed based on a series of 200 tumor samples of known ER, PR and HER2 receptor status (concordant IHC and gene expression result) hybridized on 44k microarrays. The profile classifies 96% concordant to the molecular subtypes named luminal-, ERBB2- or basal-type as published by Perou et al (Perou et al, Nature, 2000; Fan et al, NEJM, 2005). The profile was validated using 469 independent samples as well as on two publically available gene expression datasets (n=251 and n=159). Results: The profile classified 66% (712) as luminal-like, 18% (194) ERBB2-like, and 16% (173) as basal-like. As compared to single-marker readout for the presence of ER, PR and HER2, 13% of the samples that were scored positive for presence of ER/PR did not express a luminal-like gene profile. Samples with a ERBB2-like or basal-like gene profile showed equally poor 5-year survival rates of ∼65%. However, the ERBB2-like subset of MammaPrint low risk patients (15%) showed an 89% (95%CI, 71–100) survival rate without trastuzumab treatment. When the luminal-like subtype was separated into “high-” and “low-risk” by MammaPrint the survival rate was 56% (95%CI, 46–68) for high-risk luminal-like samples and 94% (95%CI, 90–99) for low-risk samples. Conclusions: The developed multigene profile can classify breast tumors into luminal-, ERBB2- and basal-like subgroups. By combining this molecular subtyping with MammaPrint risk-classification specific groups of patients can be recognized that that are at high risk of recurrence. The low risk patients within the luminal- and ERBB2-like subclasses have a very low risk of recurrence. Implementation of this knowledge can improve the clinical management of breast cancer patients. [Table: see text]

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Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv166 ◽

2015 ◽

Vol 23 (3) ◽

pp. 617-626 ◽

Cited By ~ 1

Author(s):

Nophar Geifman ◽

Sanchita Bhattacharya ◽

Atul J Butte

Keyword(s):

Gene Expression ◽

Large Scale ◽

Biomedical Literature ◽

Cytokine Gene Expression ◽

Future Research ◽

Cytokine Gene ◽

Medical Subject Headings ◽

Expression Arrays ◽

Gene Expression Arrays ◽

Subject Headings

Abstract Objective Cytokines play a central role in both health and disease, modulating immune responses and acting as diagnostic markers and therapeutic targets. This work takes a systems-level approach for integration and examination of immune patterns, such as cytokine gene expression with information from biomedical literature, and applies it in the context of disease, with the objective of identifying potentially useful relationships and areas for future research. Results We present herein the integration and analysis of immune-related knowledge, namely, information derived from biomedical literature and gene expression arrays. Cytokine-disease associations were captured from over 2.4 million PubMed records, in the form of Medical Subject Headings descriptor co-occurrences, as well as from gene expression arrays. Clustering of cytokine-disease co-occurrences from biomedical literature is shown to reflect current medical knowledge as well as potentially novel relationships between diseases. A correlation analysis of cytokine gene expression in a variety of diseases revealed compelling relationships. Finally, a novel analysis comparing cytokine gene expression in different diseases to parallel associations captured from the biomedical literature was used to examine which associations are interesting for further investigation. Discussion We demonstrate the usefulness of capturing Medical Subject Headings descriptor co-occurrences from biomedical publications in the generation of valid and potentially useful hypotheses. Furthermore, integrating and comparing descriptor co-occurrences with gene expression data was shown to be useful in detecting new, potentially fruitful, and unaddressed areas of research. Conclusion Using integrated large-scale data captured from the scientific literature and experimental data, a better understanding of the immune mechanisms underlying disease can be achieved and applied to research.

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