Dataset Information

Detection and Differentiation of Breast Cancer Sub-Types using a cPLA2? Activatable Fluorophore.

ABSTRACT: Cytosolic phospholipase A2? (cPLA2?) has been shown to be elevated in breast cancer and is a potential biomarker in the differentiation of molecular sub-types. Using a cPLA2? activatable fluorophore, DDAO arachidonate, we explore its ability to function as a contrast agent in fluorescence-guided surgery. In cell lines ranging in cPLA2? expression and representing varying breast cancer sub-types, we show DDAO arachidonate activates with a high correlation to cPLA2? expression level. Using a control probe, DDAO palmitate, in addition to cPLA2? inhibition and genetic knockdown, we show that this activation is a result of cPLA2? activity. In mouse models, using an ex vivo tumor painting technique, we show that DDAO arachidonate activates to a high degree in basal-like versus luminal-like breast tumors and healthy mammary tissue. Finally, we show that using an in vivo model, orthotopic basal-like tumors give significantly high probe activation compared to healthy mammary fat pads and surrounding tissue. Together we conclude that cPLA2? activatable fluorophores such as DDAO arachidonate may serve as a useful contrast agent for the visualization of tumor margins in the fluorescence-guided surgery of basal-like breast cancer.

SUBMITTER: Chiorazzo MG

PROVIDER: S-EPMC6467920 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Detection and Differentiation of Breast Cancer Sub-Types using a cPLA2α Activatable Fluorophore.

Chiorazzo Michael G MG Tunset Hanna Maja HM Popov Anatoliy V AV Johansen Berit B Moestue Siver S Delikatny E James EJ

Scientific reports 20190416 1

Cytosolic phospholipase A2α (cPLA2α) has been shown to be elevated in breast cancer and is a potential biomarker in the differentiation of molecular sub-types. Using a cPLA2α activatable fluorophore, DDAO arachidonate, we explore its ability to function as a contrast agent in fluorescence-guided surgery. In cell lines ranging in cPLA2α expression and representing varying breast cancer sub-types, we show DDAO arachidonate activates with a high correlation to cPLA2α expression level. Using a contr ...[more]

PMID: 30992473

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Project description:BACKGROUND:Breast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an important part of clinical decision-making. Although this problem has been addressed using machine learning methods in the past, there remains unexplained heterogeneity within the established sub-types that cannot be resolved by the commonly used classification algorithms. METHODS:In this paper, we propose a novel deep learning architecture, called DeepTRIAGE (Deep learning for the TRactable Individualised Analysis of Gene Expression), which uses an attention mechanism to obtain personalised biomarker scores that describe how important each gene is in predicting the cancer sub-type for each sample. We then perform a principal component analysis of these biomarker scores to visualise the sample heterogeneity, and use a linear model to test whether the major principal axes associate with known clinical phenotypes. RESULTS:Our model not only classifies cancer sub-types with good accuracy, but simultaneously assigns each patient their own set of interpretable and individualised biomarker scores. These personalised scores describe how important each feature is in the classification of any patient, and can be analysed post-hoc to generate new hypotheses about latent heterogeneity. CONCLUSIONS:We apply the DeepTRIAGE framework to classify the gene expression signatures of luminal A and luminal B breast cancer sub-types, and illustrate its use for genes as well as the GO and KEGG gene sets. Using DeepTRIAGE, we calculate personalised biomarker scores that describe the most important features for classifying an individual patient as luminal A or luminal B. In doing so, DeepTRIAGE simultaneously reveals heterogeneity within the luminal A biomarker scores that significantly associate with tumour stage, placing all luminal samples along a continuum of severity.

Dataset Information

Detection and Differentiation of Breast Cancer Sub-Types using a cPLA2? Activatable Fluorophore.

Publications

Detection and Differentiation of Breast Cancer Sub-Types using a cPLA2α Activatable Fluorophore.

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