Using label-free Raman microscopy to predict therapeutic resistance of TNBC cells

University Of Texas At Austin, Austin TX

Investigators

Linked publications & trials

Abstract

This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-23-045. Cellular heterogeneity has become critically important, limiting the effectiveness of therapeutics in cancer treatment. The manifestations of cellular heterogeneity can be observed at the biochemical (molecular composition and structure), morphological, and mechanical level, ultimately affecting cell function. When considering non-invasive single-cell methods, the primary methods to assess population biochemical heterogeneity are based on fluorescence activated cell sorting (FACS) of cell surface markers. While extremely rapid, this method quantifies only specific molecules chosen by the user; but what if the marker is unreliable? For example, studies have shown that enriching cells based on FACS of CD44 surface markers only provides a population of cells in which 5% show stem-like behavior. What about all the other potential molecular changes in lipids or nucleic acid chemistry that were invisible in the FACS experiment, which might be informative as differentiating features in cells? In this supplement request, we propose to develop a label-free, molecular imaging approach to characterize cellular Raman fingerprints and train a convolutional neural network (CNN) to predict therapeutic resistance in heterogenous populations. We will use high-speed, label-free nonlinear Raman scattering, which captures a holistic molecular fingerprint of a cell by quantifying abundance of metabolites, lipids, proteins, and nucleic acids and couple this data with CNN training and functional drug screening. Building off our preliminary findings, a CNN will be developed and trained by culturing different breast cancer subpopulations with increasing concentrations of doxorubicin and measuring both live and dead cell Raman fingerprints (Aim 1). The CNN developed in Aim 1 will be tested for its ability to predict subpopulation therapeutic susceptibility in co-cultures of the two subpopulations (Aim 2). This project offers not only a new take on phenotyping heterogeneous cancer cells, but it will also be fully compatible with downstream functional or molecular testing such as tumor initiation or RNAseq measurements.