A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Nwosu, Ifeanyichukwu O.; Piccolo, Stephen R.

doi:10.1080/15384047.2021.1953902

Cited by 2 publications

(3 citation statements)

References 113 publications

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“…This standardized resource has the potential to reduce time that breast-cancer researchers spend on finding and preparing publicly available gene-expression data and allow them to focus more on answering biomedical questions. We have previously 9 surveyed the literature on types of analyses that have been commonly performed using gene-expression data from breast-cancer patients. Examples include predicting patient outcomes, defining breast-cancer subtypes, and identifying differentially expressed genes.…”

Section: Discussionmentioning

confidence: 99%

“…Microarrays and RNA sequencing have been used in hundreds of thousands of gene-expression studies (spanning many biomedical contexts) 8 , helping to identify differentially expressed genes, define disease subtypes, inform treatment and patient-management strategies, predict patient outcomes, etc. 9 Much of the data from these studies has been deposited in public databases such as Gene Expression Omnibus (GEO) 8,10 and ArrayExpress 11 . These databases offer opportunities for reuse, including studies that combine insights from multiple datasets.…”

Section: Introductionmentioning

confidence: 99%

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Annotated Compendium of 102 Breast Cancer Gene-Expression Datasets

Nwosu,

Tabler,

Chipman

et al. 2023

Preprint

Self Cite

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Transcriptomic data from breast-cancer patients are widely available in public repositories. However, before a researcher can perform statistical inferences or make biological interpretations from such data, they must find relevant datasets, download the data, and perform quality checks. In many cases, it is also useful to normalize and standardize the data for consistency and to use updated genome annotations. Additionally, researchers need to parse and interpret metadata: clinical and demographic characteristics of patients. Each of these steps requires computational and/or biomedical expertise, thus imposing a barrier to reuse for many researchers. We have identified and curated 102 publicly available, breast-cancer datasets representing 17,151 patients. We created a reproducible, computational pipeline to download the data, perform quality checks, renormalize the raw gene-expression measurements (when available), assign gene identifiers from multiple databases, and annotate the metadata against the National Cancer Institute Thesaurus, thus making it easier to infer semantic meaning and compare insights across datasets. We have made the curated data and pipeline freely available for other researchers to use. Having these resources in one place promises to accelerate breast-cancer research, enabling researchers to address diverse types of questions, using data from a variety of patient populations and study contexts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Annotated Compendium of 102 Breast Cancer Gene-Expression Datasets

Nwosu,

Tabler,

Chipman

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, having prior knowledge of how each subtype is distinguished is vital for research into new treatments. Although the existing IHC approach is useful, it is too broad [ 39 ]. Individuals with the same IHC subtype, for example, may not benefit from the same treatment regimens.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer

Khan¹,

Seema²

2022

JPM

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Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment. However, there is still room for improvement in the performance of the existing models based on the aforementioned combination. In this work, we propose two models that complement the treatment of breast cancer patients. First, we discuss our deep learning-based model for breast cancer subtype classification. Second, we propose DCNN-DR, a deep convolute.ion neural network-drug response method for predicting the effectiveness of drugs on in vitro and in vivo breast cancer datasets. Finally, we applied DCNN-DR for predicting effective drugs for the basal-like breast cancer subtype and validated the results with the information available in the literature. The models proposed use late integration methods and have fairly better predictive performance compared to the existing methods. We use the Pearson correlation coefficient and accuracy as the performance measures for the regression and classification models, respectively.

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A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Cited by 2 publications

References 113 publications

Annotated Compendium of 102 Breast Cancer Gene-Expression Datasets

Annotated Compendium of 102 Breast Cancer Gene-Expression Datasets

Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer

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