The tumor microenvironment (TME) is a complex milieu around the tumor, whereby cancer cells interact with stromal, immune, vascular, and extracellular components. The TME is being increasingly recognized as a key determinant of tumor growth, disease progression, and response to therapies. We build a generalizable and robust tensor-based framework capable of integrating dissociated single-cell and spatially resolved RNA-seq data for a comprehensive analysis of the TME. Tensors are a generalization of matrices to higher dimensions. Tensor methods are known to be able to successfully incorporate data from multiple sources and perform a joint analysis of heterogeneous high-dimensional data sets. The methodologies developed as part of this effort will advance our understanding of the TME in multiple directions. These include cellular heterogeneity within the TME, crosstalks between cells, and tumor-intrinsic pathways stimulating tumor growth and immune evasion.
Further Information
(taken from https://nerimantokcan.com/)
Neriman Tokcan's research focuses on formulating novel, mathematically sound theoretical frameworks to perform analysis of multi-modal, multi-dimensional data while preserving the integrity of their structure. Her work on the generalization of matrix-based compression, noise elimination, and dimension reduction methods to higher dimensions. Her background is at the intersection of algebraic geometry, multi-linear algebra, combinatorics, and representation theory. I explore applications in bioinformatics and cancer genomics.
Currently, Neriman is working on the formulation of the novel, mathematically sound tensor-based frameworks, and the development of computational tools to model tumor microenvironments.
Neriman will join the University of Massachusetts Boston as a Tenure-Track Assistant Professor of Applied Mathematics in January 2023.