Biology subjects

Brock, A.

Publications and source records attributed to Brock, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Galectin-1 identifies a unique subpopulation of highly invasive glioblastoma cells and enables their migration

Glioblastoma (GBM), the most common primary brain tumor, is characterized by extensive infiltration into surrounding brain tissue. GBM tumors exhibit substantial intratumoral heterogeneity making it difficult to identify and target invasive cell subpopulations. Here, we use an in vitro model of the mechanical transitions at the tumor-brain interface to isolate highly invasive GBM cells from populations derived from unique patient tumors for downstream transcriptomic analysis or further culture. Using single-cell RNA sequencing combined with cell barcodes we were able to trace distinct cell lineages during migration and identify an intrinsically invasive subpopulation. This invasive subpopulation exhibits a distinct pre-invasive transcriptomic profile characterized by overexpression of galectin-1, a {beta}-galactoside binding protein. Our findings reveal galectin-1 overexpression is an innate characteristic of invasive GBM subpopulations, where expression level positively correlates with invasion rate and inhibition of galectin-1 binding to cell surface glycoproteins effectively prevented migration. While some studies have reported that galetcin-1 aids in cell migration, this study identifies and confirms that galectin-1 expression is a pre-existing characteristic of invading GBM cells and a target to prevent tumor recurrence.

Cancer Biology↗

bioRxiv2025

Cell Barcoding Reveals Lineage-dependent Outcomes in hiPSC Cardiac Differentiation

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have potential applications in treating cardiovascular disease but are currently limited in their clinical translation. A primary limitation is the poor clinical scalability of hiPSC-CMs, with the heterogeneity of hiPSC cardiac differentiation significantly contributing to this limitation. We hypothesize that clinical scalability can be improved by tracking and controlling hiPSC clonal heterogeneity, a variable often overlooked in current differentiation approaches. "Fate priming", wherein clonal lineage identity determines differentiation fate, has been demonstrated in other stem cell differentiation pathways. We investigated fate priming in hiPSC cardiac differentiation using the ClonMapper cell barcoding platform to label, track, and isolate distinct hiPSC lineages from the same cell line. We show that certain hiPSC lineages preferentially differentiate into hiPSC-CMs or non-CMs. After isolating lineages with apparent fate priming, we found significant differences in cardiac differentiation outcomes between these single-clone populations and heterogeneous, multi-clone hiPSC populations. These findings indicate that lineage identity influences hiPSC cardiac differentiation outcomes. SIGNIFICANCE STATEMENTCardiovascular disease is a significant global health concern that can be addressed by engineering artificial tissues to develop new treatments for heart disease or to directly replace damaged heart tissue. Stem cells are a useful tool for engineering these tissues because of their ability to become cardiomyocytes. However, their clinical translation is limited by variability in the process of differentiating stem cells into cardiomyocytes. This article reports findings that show different lineages of genetically identical human induced pluripotent stem cells have different capacities for differentiating into cardiomyocytes, which may contribute to the variability observed.

bioengineering↗

bioRxiv2025

Remodeling of KRAS interactome induced by clinically relevant RAS inhibitors reveals convergent responses and a KRAS-mediated regulation of directional cell migration

KRAS is commonly mutated in lung, colorectal, and pancreatic cancers. Small molecule inhibitors targeting KRAS with distinct mechanisms-of-action and variable specificities have entered the clinic, but a comprehensive view of their effect on the RAS signaling network has not been reported. Here, we describe the impact of RAS inhibition on the KRAS protein interactome using Proximity-dependent Biotinylation Identification. Two inhibitors were used: panRAS-ON, which forms a ternary complex between cyclophilin and RAS in the GTP bound state; and panKRAS-off, which binds specifically to the KRAS switch II pocket. RAS inhibitors were found to significantly alter 16.5% of proteins in proximity to KRAS. Despite their distinct mechanisms-of-action, the KRAS inhibitors induced highly correlated changes in the KRAS interactomes. Among proteins in close proximity to KRAS, Afadin was found to be highly regulated by RAS inhibition. AFDN has been characterized as a key regulator of cell motility, invasion, and metastasis. Analysis of AFDN phosphorylation revealed that AKT only partially modulates p-Ser1718, while inhibition of KRAS is sufficient to abolish EGF-mediated AFDN phosphorylation. Knockdown of AFDN in a KRAS-driven non-small cell lung cancer model abolished chemotaxis in a transwell migration assay and disrupted directional movement in an EGF-driven wound healing model. These results suggest that KRAS is a central node in regulating growth factor induced cell migration, and that KRAS inhibition plays a broader role than MAPK-mediated cell proliferation and survival.

cancer biology↗

bioRxiv2025

Lineage Tracing Reveals Clone-Specific Responses to Doxorubicin in Triple-Negative Breast Cancer

Triple-negative breast cancer, characterized by aggressive growth and high intratumor heterogeneity, presents a significant clinical challenge. Here, we use a lineage-tracing system, ClonMapper, which couples heritable clonal identifying tags with single-cell RNA-sequencing (scRNA-seq), to better elucidate the response to doxorubicin in a model of TNBC. We demonstrate that, while there is a dose-dependent reduction in overall clonal diversity, there is no pre-existing resistance signature among surviving clones. Separately, we found the existence of two transcriptomically distinct clonal subpopulations that remain through the course of treatment. Among clones persisting across multiple samples we identified divergent phenotypes, suggesting a response to treament independent of clonal identity. Finally, a subset of clones harbor novel changes in expression following treatment. The clone and sample specific responses to treatment identified herein highlight the need for better personalized treatment strategies to overcome tumor heterogeneity.

cancer biology↗

bioRxiv2024

Mapping cell-cell fusion at single-cell resolution

Cell-cell fusion is a tightly controlled process in the human body known to be involved in fertilization, placental development, muscle growth, bone remodeling, and viral response. Fusion between cancer cells results first in a whole-genome doubled state, which may be followed by the generation of aneuploidies; these genomic alterations are known drivers of tumor evolution. The role of cell-cell fusion in cancer progression and treatment response has been understudied due to limited experimental systems for tracking and analyzing individual fusion events. To meet this need, we developed a molecular toolkit to map the origins and outcomes of individual cell fusion events within a tumor cell population. This platform, ClonMapper Duo ( CMDuo), identifies cells that have undergone cell-cell fusion through a combination of reporter expression and engineered fluorescence-associated index sequences paired to randomly generated nucleotide barcodes. scRNA-seq of the indexed barcodes enables the mapping of each set of parental cells and fusion progeny throughout the cell population. In triple-negative breast cancer cells CMDuo uncovered subclonal transcriptomic hybridization and unveiled distinct cell-states which arise in direct consequence of homotypic cell-cell fusion. CMDuo is a platform that enables mapping of cell-cell fusion events in high-throughput single cell data and enables the study of cell fusion in disease progression and therapeutic response.

systems biology↗

bioRxiv2024

Differentiation Protocol-Dependent Variability in hiPSC-Derived Endothelial Progenitor Functionality

PurposeWith multiple groups generating methods for differentiating induced pluripotent stem cells (hiPSCs) into endothelial cells/endothelial progenitors (ECs/EPs), there is a need to better understand the specific endothelial subtypes that different differentiation protocols produce. This is especially important for accurate tissue modeling as researchers continue to incorporate the endothelium into engineered tissues. MethodsWe illustrated the heterogeneity of cells produced from different differentiation protocols by focusing on two selected protocols, one driving differentiation exclusively using small molecules (SM Protocol) and one driving differentiation primarily using growth factors (GF Protocol). We characterized the cells through a combination of vasculogenic computational analysis following encapsulation in 3D hydrogels, RNA sequencing, and measurement of soluble factor release. ResultsVasculogenic computational analysis indicates that cells from the GF protocol formed more dense and interconnected vasculature compared to cells from the SM protocol. Likewise, RNA-seq analysis showed GF-derived cell enrichment in pathways involved in cell migration and angiogenesis. In addition, GF-derived cells favor differentiation to arterial endothelial cells as well as predisposition to undergoing a partial endothelial-to-mesenchymal transition, whereas SM-derived cells resemble immature progenitors based on higher proliferation rates as well as ECM remodeling. These trends also persisted following extended 3D culture. ConclusionsThe results demonstrate that despite using the same starting hiPSC population and isolating EPs using the same surface marker, the two differentiation protocols yield highly distinct cell populations. This work highlights the importance of understanding the specific endothelial subtypes produced by different differentiation protocols for the creation of more accurate tissue models. Lay SummaryAlthough multiple groups have created protocols for differentiating human induced pluripotent stem cells (hiPSCs) to endothelial progenitors (EPs), it is not clear if different differentiation protocols generate different endothelial subtypes, matching the variance seen in vivo To that end, we performed an in-depth characterization of two selected hiPSC-EP differentiation protocols, primarily focusing on cells cultured in 3D hydrogels, to better understand the specific endothelial subtypes produced. We found significant differences in both cell maturity and cell phenotype that persisted even after extended culture. Future WorkFuture work will involve extending the existing study to include additional hiPSC-EP differentiation protocols as well as conducting single-cell RNAseq to better understand the makeup of different cell populations both before and after isolating CD34+ cells. Because smooth muscle cells play an important role in vessel stabilization, we will also incorporate hiPSC-derived smooth muscle cells into the system.

bioengineering↗

bioRxiv2024

Ketomimetic Medium Promotes Metastatic Disposition and Chemoresistance in Breast Cancer Cells through Hypersialylation and Lipid Synthesis

While the triggers for the metastatic transformation of breast cancer (BC) cells remain unknown, recent evidence suggests that intrinsic cellular metabolism could be a crucial driver of migratory disposition and chemoresistance. Aiming to decode the molecular mechanisms involved in BC cell metabolic maneuvering, we study how a ketomimetic (ketone body-rich, low glucose) medium affects Doxorubicin (DOX) susceptibility and invasive disposition of BC cells. We quantified glycocalyx sialylation and found an inverse correlation with DOX-induced cytotoxicity and DOX internalization. These measurements were coupled with single-cell metabolic imaging, bulk migration studies, along with transcriptomic and metabolomic analyses. Our findings revealed that a ketomimetic medium enhances chemoresistance and invasive disposition of BC cells via two main oncogenic pathways: hypersialylation and lipid synthesis. We propose that the crosstalk between these pathways, juxtaposed at the synthesis of the glycan precursor UDP-GlcNAc, furthers advancement of a metastatic phenotype in BC cells under ketomimetic conditions.

cancer biology↗

bioRxiv2024

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

cancer biology↗

bioRxiv2024

Dynamic Electrical Stimulation Promotes hiPSC-CM Differentiation and Functionality

Human induced pluripotent stem cell differentiated cardiomyocytes (hiPSC-CMs) hold great potential to resolve cardiovascular disease but are stymied by their functional immaturity. The complex electric potentials measured during cardiogenesis point to the potential of exogenous electrical stimulation in improving cardiac differentiation and functionality. Herein, we create, validate, and implement a low-cost electrical stimulation device to stimulate hiPSCs during cardiac differentiation. Notably, our open-source device enables the generation of complex electrical stimulation regimes that may vary in frequency and pulse duration over time. Our results demonstrate that dynamic stimulation during differentiation improves cardiac differentiation efficiency, calcium handling, and flow velocity, and promotes significant transcriptomic pathway enrichment compared to static stimulation or no stimulation controls. Moreover, dynamic stimulation enhances electrochemical coupling and promotes the expression of cardiogenic pathways, potentially via sarcomere development. We anticipate that more complex dynamic electrical stimulation regimens may be generated to further optimize hiPSC-CM functionality and maturity.

bioengineering↗

bioRxiv2022

Drug interaction mapping with proximity dependent enzyme recruiting chimeras

Proximity dependent labeling using engineered enzymes has been used extensively to identify protein-protein interactions, and map protein complexes in-vitro and in-vivo. Here, we extend the use of engineered promiscuous biotin ligases to the identification of small molecule protein targets. Chimeric bi-functional chemical probes ("recruiters") are used to effectively recruit tagged biotin ligases for proximity dependent labeling of target and target interactors. The broad applicability of this approach is demonstrated with probes developed from a multi-kinase inhibitor, a bromodomain targeting moiety, and an FKBP targeting molecule. While complementary to traditional chemo-proteomic strategies such as photo-affinity labeling (PAL), and activity-based protein profiling (ABPP), this approach is a useful addition to the target ID toolbox with opportunities for tunability based on the inherent labeling efficiencies of different engineered enzymes and control over the enzyme cellular localization.

molecular biology↗

bioRxiv2022

A heterogeneous drug tolerant persister state in BRAF-mutant melanoma is characterized by ion channel dysregulation and susceptibility to ferroptosis

Drug-tolerant persisters (DTPs) represent a major obstacle to durable responses in targeted cancer therapy. DTPs are commonly described as distinct single-cell states that survive drug treatment via reversible, non-genetic mechanisms and drive tumor recurrence. Recent work demonstrates that multiple DTPs can coexist, reflecting diversity in lineage, signaling programs, or stress responses. However, each DTP is still generally viewed as a uniform cellular phenotype. Building on our prior work describing a population-level DTP termed "idling" [Paudel et al., Biophys. J. (2018) 114, 1499-1511], here we present evidence supporting a fundamentally different view: that DTPs are not single-cell states, but rather heterogeneous populations composed of multiple sub-states with distinct division and death rates that balance to produce near-zero net population growth. Using single-cell transcriptomics and lineage barcoding, we identify multiple phenotypic states within idling DTP populations, with reduced heterogeneity compared to untreated populations, and find that idling DTP cells emerge from nearly all lineages. Transcriptomic and functional analyses further reveal altered ion-channel activity in idling DTPs, which we confirm experimentally. Moreover, drug-response assays reveal increased susceptibility of idling DTPs to ferroptosis, a non-apoptotic form of regulated cell death, indicating the emergence of vulnerabilities associated with drug tolerance. Altogether, our results support a population-level view of tumor drug tolerance in which DTPs comprise stable collections of phenotypic states, shaped by treatment-defined phenotypic landscapes, which are potentially vulnerable to subsequent interventions. This perspective implies that eradicating DTPs will require a fundamental shift away from cell-type-centric strategies toward sequential treatments that progressively reduce phenotypic heterogeneity by modulating the molecular and cellular processes that establish the DTP landscape, an approach previously termed "targeted landscaping."

systems biology↗

bioRxiv2021

Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin

The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that the tumor is composed of two subpopulations: drug-resistant cells, which continue proliferating after treatment, and drug-sensitive cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor growth dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.

cancer biology↗

bioRxiv2021

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present work characterizing dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600-800 hours, and this data is used to calibrate a family of cancer growth models derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-106%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

cancer biology↗

bioRxiv2020

An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines

We present the development and validation of a mathematical model that predicts how glucose dynamics influence metabolism and therefore tumor cell growth. Glucose, the starting material for glycolysis, has a fundamental influence on tumor cell growth. We employed time-resolved microscopy to track the temporal change of the number of live and dead tumor cells under different initial glucose concentrations and seeding densities. We then constructed a family of mathematical models (where cell death was accounted for differently in each member of the family) to describe overall tumor cell growth in response to the initial glucose and confluence conditions. The Akaikie Information Criteria was then employed to identify the most parsimonious model. The selected model was then trained on 75% of the data to calibrate the system and identify trends in model parameters as a function of initial glucose concentration and confluence. The calibrated parameters were applied to the remaining 25% of the data to predict the temporal dynamics given the known initial glucose concentration and confluence, and tested against the corresponding experimental measurements. With the selected model, we achieved an accuracy (defined as the fraction of measured data that fell within the 95% confidence intervals of the predicted growth curves) of 77.2 {+/-} 6.3% and 87.2 {+/-} 5.1% for live BT-474 and MDA-MB-231 cells, respectively.

cancer biology↗

bioRxiv2020

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

cancer biology↗

bioRxiv2019

Amperometric measurements of cocaine cue and novel context-evoked glutamate and nitric oxide release in the nucleus accumbens core

Cue-induced reinstatement of cocaine seeking after self-administration (SA) and extinction relies on glutamate release in the nucleus accumbens core (NAcore), which in turn activates neuronal nitric oxide synthase (nNOS) interneurons. Nitric oxide (NO) is required for structural plasticity in NAcore medium spiny neurons (MSNs), as well as cued cocaine seeking. However, NO release in the NAcore during reinstatement has yet to be directly measured. Further, the temporal relationship between glutamate release, and the induction of a NO response also remains unknown. Using wireless amperometric recordings in awake behaving rat, we quantified the magnitude and temporal dynamics of novel context- and cue-induced reinstatement-evoked glutamate and NO release in the NAcore. We found that re-exposure to cocaine-conditioned stimuli following SA and extinction increased extracellular glutamate, leading to release of NO in the NAcore. In contrast, exposing drug-naive rats to a novel context led to a lower magnitude rise in glutamate in the NAcore relative to cue-induced reinstatement. Interestingly, novel context exposure evoked a higher magnitude NO response relative to cue-induced reinstatement. Despite differences in magnitude, novel context evoked-NO release in the NAcore was also temporally delayed when compared to glutamate. These results demonstrate a dissociation between the magnitude of cocaine cue- and novel context-evoked glutamate and NO release in the NAcore, yet similarity in the temporal dynamics of their release. Together, these data contribute to a greater understanding of the relationship between glutamate and NO, two neurotransmitters implicated in encoding the valence of distinct contextual stimuli.

neuroscience↗

bioRxiv2019

Single cell transcriptome profiling of the human alcohol-dependent brain

BackgroundAlcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain.\n\nResultsWe utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16,000 nuclei isolated from prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes, and microglia.\n\nConclusionsTo our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species, and the first such analysis in humans for any addictive substance. These findings greatly advance understanding of transcriptomic changes in the brain of alcohol-dependent individuals.

neuroscience↗

bioRxiv2019

Expressed barcodes enable clonal characterization of chemotherapeutic responses in chronic lymphocytic leukemia

The remarkable evolutionary capacity of cancer is a major challenge to current therapeutic efforts. Fueling this evolution is its vast clonal heterogeneity and ability to adapt to diverse selective pressures. Although the genetic and transcriptional mechanisms underlying these responses have been independently evaluated, the ability to couple genetic alterations present within individual clones to their respective transcriptional or functional outputs has been lacking in the field. To this end, we developed a high-complexity expressed barcode library that integrates DNA barcoding with single-cell RNA sequencing through use of the CROP-seq sgRNA expression/capture system, and which is compatible with the COLBERT clonal isolation workflow for subsequent genomic and epigenomic characterization of specific clones of interest. We applied this approach to study chronic lymphocytic leukemia (CLL), a mature B cell malignancy notable for its genetic and transcriptomic heterogeneity and variable disease course. Here, we demonstrate the clonal composition and gene expression states of HG3, a CLL cell line harboring the common alteration del(13q), in response to front-line cytotoxic therapy of fludarabine and mafosfamide (an analog of the clinically used cyclophosphamide). Analysis of clonal abundance and clonally-resolved single-cell RNA sequencing revealed that only a small fraction of clones consistently survived therapy. These rare highly drug tolerant clones comprise 94% of the post-treatment population and share a stable, pre-existing gene expression state characterized by upregulation of CXCR4 and WNT signaling and a number of DNA damage and cell survival genes. Taken together, these data demonstrate at unprecedented resolution the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population. Further, this approach provides a template for the high-resolution study of thousands of clones and the respective gene expression states underlying their response to therapy.

cancer biology↗