Abstract:
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Cancer is an accumulation of rare, random, and spontaneous mutations, which makes detecting and studying pre-malignancy difficult. Modern microscopy enables collecting images and extracting molecular features from individual cells grown in the lab. We analyze such single-cell-level data derived from patients with and without a genetic predisposition to cancer. Using image processing techniques, we track cells over time and extract shape characteristics like "solidity," a geometric measure of cell boundary smoothness. We use feature selection and classification techniques to identify what characteristics of cell shape consistently appear in cells with the pre-cancerous mutation. In particular, we benchmark our performance against a convolutional neural network. A consistent challenge is accounting for experimental bias in the seeding, staining, and imaging of cells. To test the biological validity of our discoveries, we demonstrate the predictive power of these cell shape features in determining genetic variation. Future work will correlate cellular features with genetic features to discover biological pathways relevant to cancer development.
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