Spatial topology of organelle is a new breast cancer cell classifier

Wang, Ling; Goldwag, Joshua; Bouyea, Megan; Barra-Carrasco, Jonathan; Matteson, Kailie; Maharjan, Niva; Eladdadi, Amina; Embrechts, Mark J.; Intes, Xavier; Krüger, Uwe; Barroso, Margarida

doi:10.1016/j.isci.2023.107229

Cited by 3 publications

References 82 publications

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Peripheral positioning of lysosomes supports melanoma aggressiveness

Jerabkova-Roda,

Mousson,

Peralta

et al. 2023

Preprint

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Emerging evidences suggest that both function and position of organelles are pivotal for tumor cell dissemination. Among them, lysosomes stand out as they integrate metabolic sensing with gene regulation and secretion of proteases. Yet, how the function of lysosomes is linked to their position and how this controls metastatic progression remains elusive. Here, we analyzed lysosome subcellular distribution in micropatterned patient-derived melanoma cells and found that lysosome spreading scales with their aggressiveness. Peripheral lysosomes promote invadopodia-based matrix degradation and invasion of melanoma cells, which is directly linked to their lysosomal and cell transcriptional programs. When controlling lysosomal positioning using chemo-genetical heterodimerization in melanoma cells, we demonstrated that perinuclear clustering impairs lysosomal secretion, matrix degradation and invasion. Impairing lysosomal spreading in a zebrafish metastasis model significantly reduces invasive outgrowth. Our study provides a mechanistic demonstration that lysosomal positioning controls cell invasion, illustrating the importance of organelle adaptation in carcinogenesis.

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Peripheral positioning of lysosomes supports melanoma aggressiveness

Jerabkova-Roda,

Mousson,

Peralta

et al. 2023

Preprint

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Organelle-based breast cancer cell classification with deep learning and visual explanations

Yee,

Matteson,

Goldwag

et al. 2024

Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII

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Breast cancer cell analysis has traditionally focused on cell and intracellular organelle morphology. Recent research has demonstrated that organelle topology-based cancer cell classification is considerably more accurate when using handcrafted feature extraction and machine learning-based classifiers on fluorescent confocal microscopy images. However, feature extraction and classification through this methodology requires manual segmentation and computational organelle rendering. Herein, we employ convolutional neural networks (CNN) and Gradient-weighted Class Activation Mapping (GradCAM) for fast end-to-end classification and visual interpretation of confocal fluorescent microscopy images based on spatial organelle features. First, raw 3D images are filtered and preprocessed into 2D image patches for the CNN. To replicate feature analysis of the surface-surface contact area, marginal intermediate fusion CNN is implemented to classify each patch. GradCAM is then used post hoc to generate a representative heatmap of important areas used for classification. All relevant heatmap patches are then reconstructed based on the extraction of their respective patches to obtain an overall heatmap of the entire microscopy image. Furthermore, finer-grained heatmaps were obtained through the use of patch overlap and weighting during initial patch preprocessing. On a dataset consisting of 6 different breast cancer cell lines, this methodology resulted in a classification accuracy of 95.7% while also providing visualization of areas indicative of certain cancer cell lines. These findings demonstrate the efficacy of using deep learning and GradCAM for fast and interpretable organelle-based cancer cell classification.

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