Abstract (Expand)

The initiation and progression of cancers reflect the underlying process of somatic evolution, in which the diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination-a key diversification mechanism throughout all strata of life. On the basis of observations of spontaneous fusions involving cancer cells, the reported genetic instability of polypoid cells and the precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions between genetically distinct cancer cells could produce parasexual recombination. Using differentially labelled tumour cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modelling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumour cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape (relative to strictly asexual populations), which may substantially accelerate a tumour's ability to adapt to new selective pressures.

Authors: D. Miroshnychenko, E. Baratchart, M. C. Ferrall-Fairbanks, R. V. Velde, M. A. Laurie, M. M. Bui, A. C. Tan, P. M. Altrock, D. Basanta, A. Marusyk

Date Published: 20th Jan 2021

Publication Type: Journal

Abstract (Expand)

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected low-glycolytic breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA sequencing analysis of WE clones identified the transcription factor KLF4 as potential inducer of the WE phenotype. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a WE phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

Authors: M. Damaghi, J. West, M. Robertson-Tessi, L. Xu, M. C. Ferrall-Fairbanks, P. A. Stewart, E. Persi, B. L. Fridley, P. M. Altrock, R. A. Gatenby, P. A. Sims, A. R. A. Anderson, R. J. Gillies

Date Published: 19th Jan 2021

Publication Type: Journal

Abstract (Expand)

Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for researchers to easily assay tumor heterogeneity at the transcriptomic level with high resolution. However, ongoing research focuses on different ways to analyze this big data and how to compare across multiple different samples. In this chapter, we provide a practical guide to calculate inter- and intrasample diversity metrics from single-cell RNA sequencing datasets. These measures of diversity are adapted from commonly used metrics in statistics and ecology to quantify and compare sample heterogeneity at single-cell resolution.

Authors: M. C. Ferrall-Fairbanks, P. M. Altrock

Date Published: 14th Sep 2020

Publication Type: InBook

Abstract (Expand)

Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

Authors: M. C. Ferrall-Fairbanks, C. A. Kieslich, M. O. Platt

Date Published: 11th Feb 2020

Publication Type: Journal

Abstract

Not specified

Authors: M. C. Ferrall-Fairbanks, D. J. Glazar, R. J. Brady, G. J. Kimmel, M. U. Zahid, P. M. Altrock, H. Enderling

Date Published: 31st May 2019

Publication Type: Journal

Abstract (Expand)

PURPOSE: Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor heterogeneity serves as a reservoir for resistance, which often occurs as a result of the selection of minor cellular subclones. On the level of gene expression, clonal heterogeneity can only be revealed using high-dimensional single-cell methods. We propose using a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution. MATERIALS AND METHODS: We focused on individual patient samples that were probed with single-cell RNA (scRNA) sequencing to describe heterogeneity. We developed a pipeline to analyze single-cell data via sample normalization, clustering, and mathematical interpretation using a generalized diversity measure, as well as to exemplify the utility of this platform using single-cell data. RESULTS: We focused on three sources of patient scRNA sequencing data: two healthy bone marrow (BM) donors, two patients with acute myeloid leukemia-each sampled before and after BM transplantation, four samples of presorted lineages-and six patients with lung carcinoma with multiregion sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified the ways in which these samples differed. Whereas a widely used Shannon diversity index sometimes reveals fewer differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparison of pre- and post-BM transplantation acute myeloid leukemia samples did not reveal differences in heterogeneity, although biological differences can exist. CONCLUSION: GDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach can be widely applied to quantify heterogeneity across samples and conditions.

Authors: M. C. Ferrall-Fairbanks, M. Ball, E. Padron, P. M. Altrock

Date Published: 18th Apr 2019

Publication Type: Journal

Abstract (Expand)

IMPACT STATEMENT: The ability to freeze, revive, and prolong the lifetime of tissue-engineered skeletal muscle without incurring any loss of function represents a significant advancement in the field of tissue engineering. Cryopreservation enables the efficient fabrication, storage, and shipment of these tissues. This in turn facilitates multidisciplinary collaboration between research groups, enabling advances in skeletal muscle regenerative medicine, organ-on-a-chip models of disease, drug testing, and soft robotics. Furthermore, the observation that freezing undifferentiated skeletal muscle enhances functional performance may motivate future studies developing stronger and more clinically relevant engineered muscle.

Authors: L. Grant, R. Raman, C. Cvetkovic, M. C. Ferrall-Fairbanks, G. J. Pagan-Diaz, P. Hadley, E. Ko, M. O. Platt, R. Bashir

Date Published: 10th Nov 2018

Publication Type: Journal

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