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HAVOC: Mapping of Cancer Biodiversity Using Deep Neural Networks
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HAVOC: Mapping of Cancer Biodiversity Using Deep Neural Networks
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HAVOC: Mapping of Cancer Biodiversity Using Deep Neural Networks
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Dissertation
HAVOC: Mapping of Cancer Biodiversity Using Deep Neural Networks
2022
Overview
Spatially distinct areas of tumor biodiversity wreak havoc on current precision medicine strategies. To address this challenge, I developed a pipeline that leverages unsupervised clustering of regional neural network-defined histomorphologic signatures to generate Histomic Atlases of Variation Of Cancers (HAVOC). Using spatially resolved mass-spectrometry-based proteomic and immunohistochemical readouts of characteristically heterogeneous glioma specimens, I demonstrated how these personalized atlases of histomic variation capture regional tumoral populations with distinct molecular signatures and biologic programs. I further validated HAVOC on existing spatial DNA copy number and transcriptomic datasets and assigned distinct histologic differences downstream of these genetically distinct subclones. Finally, I extended HAVOC to large tumor resection specimens to demonstrate its utility in automating the topographic organization of cancer biodiversity across multiple centimeters. Together, these findings establish HAVOC as a versatile tool capable of generating small-scale heterogeneity maps and guiding regional deployment of limited molecular resources to relevant and biodiverse tumor niches.
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9798837516429
