Biology subjects

Burkard, M. E.

Publications and source records attributed to Burkard, M. E..

3 recordsLinked to original sources

bioRxiv2021

Integrating Subclonal Response Heterogeneity to Define Cancer Organoid Therapeutic Sensitivity

Tumor heterogeneity is predicted to confer inferior clinical outcomes, however modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to the multitude of available treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain numerous individual organoids with the potential to recapitulate heterogeneity, though PCOs are most commonly studied in bulk ignoring any diversity in the molecular profile or treatment response. Here we demonstrate the advantage of evaluating individual PCOs in conjunction with cellular level optical metabolic imaging to characterize the largely ignored heterogeneity within these cultures to predict clinical therapeutic response, identify subclonal populations, and determine patient specific mechanisms of resistance.

cancer biology↗

bioRxiv2021

Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference

Chromosomal instability (CIN) -- persistent chromosome gain or loss through abnormal karyokinesis -- is a hallmark of cancer that drives aneuploidy. Intrinsic chromosome mis-segregation rates, a measure of CIN, can inform prognosis and are a likely biomarker for response to anti-microtubule agents. However, existing methodologies to measure this rate are labor intensive, indirect, and confounded by karyotype selection reducing observable diversity. We developed a framework to simulate and measure CIN, accounting for karyotype selection, and recapitulated karyotype-level clonality in simulated populations. We leveraged approximate Bayesian computation using phylogenetic topology and diversity to infer mis-segregation rates and karyotype selection from single-cell DNA sequencing data. Experimental validation of this approach revealed extensive chromosome mis-segregation rates caused by the chemotherapy paclitaxel (17.5{+/-}0.14/division). Extending this approach to clinical samples revealed the inferred rates fell within direct observations of cancer cell lines. This work provides the necessary framework to quantify CIN in human tumors and develop it as a predictive biomarker.

cancer biology↗

bioRxiv2020

CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

BackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data. ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets. ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

bioinformatics↗