A tumor is a heterogeneous mixture of cancerous cells and multiple types of normal cells. Decoding this heterogeneity and identifying the genomic events that drive cancer development are key to studying tumor evolution and progression. Spatially resolved transcriptomics (SRT) technologies sequence expressed RNAs across thousands of locations in a tumor slice; the resulting gene expression signatures reveal the localization of cancer and adjacent normal cell types forming the tumor microenvironment. However, gene expression signatures alone are insufficient to identify cancer clones – subpopulations of cancerous cells that share the same genetic lineage – or to reconstruct the evolution of these clones. In this talk, I will introduce our recently published method, CalicoST, and our on-going improvements. CalicoST simultaneously infers allele-specific copy number aberrations (CNAs) and the spatial distribution of cancer clones using SRT data from one or more tumor slices. CalicoST identifies important types of CNAs – including copy-neutral loss of heterozygosity (LOH) and mirrored subclonal copy number aberrations – that are invisible to existing approaches. From these CNAs, CalicoST infers a phylogeny describing the ancestral relationships between the clones and revealing the spatial evolution of the tumor. Although CalicoST achieves state-of-the-art accuracy in inferring allele-specific CNAs and cancer clones, the optimization problem it tackles is challenging and prone to falling into local optima. To address this, we evaluated the challenges in clone inference using idealized simulations and developed a method to infer potential missing cancer clones for each CalicoST inference result.
Research Links: https://www.nature.com/articles/s41592-024-02438-9
Dr. Cong Ma is an assistant professor at the University of Michigan, having started this position last September. She earned her Ph.D. in computational biology from Carnegie Mellon University under the supervision of Dr. Carl Kingsford, and subsequently completed a postdoctoral fellowship at Princeton University in Dr. Ben Raphael’s group before joining the University of Michigan. Dr. Ma was awarded the prestigious Damon Runyon Quantitative Biology Fellowship, underscoring the significance of her work in developing computational methods to study spatial tumor evolution.
Speaker: Cong Ma, PhD
Affiliation: University of Michigan Medical School
Position: Assistant Professor of Computational Medicine and Bioinformatics
Host: Youn Kim, PhD, Gibson Lab
Date: Monday May 19th, 2025
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866
Transcription in individual cells is an inherently stochastic process, with non-deterministic numbers of RNA molecules produced per gene in transcriptional events across cells of the same cell type and condition. These distributions can be mathematical modeled according to biophysical hypotheses that specify system parameters of interest, such as rates of RNA splicing and degradation. While such stochastic models have been applied to infer RNA processing rates in fluorescence based assays, the scale of high-throughput sequencing data makes inference in this setting computationally challenging, especially as more molecular species, such as unspliced and spliced RNA molecules, can be quantified. We adapt an inference tool from machine learning, the variational autoencoder, that allows summarization and analysis of high-dimensional datasets to work with biophysical models of transcriptional dynamics. While preserving the variational autoencoder’s ability to reduce dimensionality of and denoise data, this work presents two major advances: one, principled integration and treatment of multimodal data (i.e., unspliced and spliced RNA molecules) through the employment of specific biophysical models; and two, inference of biophysical rates that parameterize those models, allowing the exploration of RNA dynamics across thousands of individual genes and cells.
Medical imaging plays a critical role in modern healthcare, yet its complexity necessitates advanced computational tools to enhance analysis, interpretability, and diagnostic accuracy. In this presentation, we will discuss our research efforts in developing computational imaging biomarkers and frameworks for precision medicine in real clinical scenarios involving imperfect data. We will cover a spectrum of computational techniques grounded in both biological and domain-specific insights that facilitate the early detection and evaluation of treatment responses across various diseases.
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