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Live-cell imaging of tumor proteolysis: impact of cellular and non-cellular microenvironment.

ABSTRACT: Our laboratory has had a longstanding interest in how the interactions between tumors and their microenvironment affect malignant progression. Recently, we have focused on defining the proteolytic pathways that function in the transition of breast cancer from the pre-invasive lesions of ductal carcinoma in situ (DCIS) to invasive ductal carcinomas (IDCs). We use live-cell imaging to visualize, localize and quantify proteolysis as it occurs in real-time and thereby have established roles for lysosomal cysteine proteases both pericellularly and intracellularly in tumor proteolysis. To facilitate these studies, we have developed and optimized 3D organotypic co-culture models that recapitulate the in vivo interactions of mammary epithelial cells or tumor cells with stromal and inflammatory cells. Here we will discuss the background that led to our present studies as well as the techniques and models that we employ. This article is part of a Special Issue entitled: Proteolysis 50 years after the discovery of lysosome.

SUBMITTER: Rothberg JM

PROVIDER: S-EPMC3232330 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Live-cell imaging of tumor proteolysis: impact of cellular and non-cellular microenvironment.

Rothberg Jennifer M JM Sameni Mansoureh M Moin Kamiar K Sloane Bonnie F BF

Biochimica et biophysica acta 20110805 1

Our laboratory has had a longstanding interest in how the interactions between tumors and their microenvironment affect malignant progression. Recently, we have focused on defining the proteolytic pathways that function in the transition of breast cancer from the pre-invasive lesions of ductal carcinoma in situ (DCIS) to invasive ductal carcinomas (IDCs). We use live-cell imaging to visualize, localize and quantify proteolysis as it occurs in real-time and thereby have established roles for lyso ...[more]

PMID: 21854877

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Project description:BackgroundThe abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict the abundance of various cell types in the TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell types in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging.MethodsWe performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell type abundance, we performed linear regression on each radiomic feature/cell type abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell type infiltration status (i.e. "high" vs "low") prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models' performance was measured via area under the receiver operating characteristic curve (AUC).ResultsUnivariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell type invasion predictions.ConclusionsOn two independent breast cancer cohorts, breast MRI-derived radiomics are associated with the tumor's microenvironment in terms of the abundance of several cell types. Further evaluation with larger cohorts is needed.

Dataset Information

Live-cell imaging of tumor proteolysis: impact of cellular and non-cellular microenvironment.

Publications

Live-cell imaging of tumor proteolysis: impact of cellular and non-cellular microenvironment.

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