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Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

Solid tumors such as triple-negative breast cancer (TNBC) have characteristically high rates of aneuploidy and polyploidy. Chromosome-level aneuploidies are known to arise through errors in cytokinesis, but many of the mechanisms which drive large-scale ploidy alterations such as whole genome doubling (WGD) are still under investigation. WGD and subsequent ploidy reduction has been implicated as a key event in cancer evolution across patient samples. Further, in vitro studies have suggested that cells may undergo polyploidization to survive certain chemotherapies. Here, we apply lineage tracing technologies to test the hypotheses that some cells are primed to undergo WGD through endocycling or cell-cell fusion and that active polyploidization is a mechanism to resist chemotherapy. To deconvolve WGD by endocycling from WGD by cell-cell fusion, we transduced the TNBC cell line HCC1806 to create parallel populations with transcribed DNA barcodes on H2B-GFP or H2B-mCherry. All experiments were performed with homotypic cocultures containing equal proportion of cells from each fluorescent barcode population. With this method, ploidy alterations due to endoreplication were identifiable as monofluorescent single-barcoded cells with increased ploidy, while ploidy changes due to fusion were identifiable as double positive (GFP+mCherry+) double-barcoded cells with increased ploidy. The cell population was treated with an LD75 dose of doxorubicin and parallel plates were monitored by live-cell imaging. At each timepoint (pre-treatment, then 3, 5, and 14 days post-treatment), triplicate plates were stained with Hoechst and sorted into two fractions for lysis and DNA extraction: Near diploid (~2N) and ii) Hyperploid (> 4N). Barcode composition of cells from each fraction and each timepoint was determined through Illumina sequencing of barcode amplicons. Here, we will present preliminary results revealing the subclonal dynamics of gross ploidy alterations through normal culture conditions and chemotherapeutic perturbation. This study will elucidate whether pre-existing or de novo polyploidy promotes cell survival during chemotherapy, and determine if gross ploidy alterations occur randomly or through deterministic selection. Citation Format: Andrea L. Gardner, Lan Zheng, Patrik Parker, Kennedy Howland, Daylin Morgan, Amy Brock. The subclonal dynamics of gross ploidy alterations in TNBC cells under chemotherapeutic pressure [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 122.

There is increasing interest in cancer cell subpopulations that can withstanding treatment via non-genetic mechanisms, such as tumor cell plasticity and adaptation. These cell populations may be comprised of cells with diverse phenotypes, e.g., quiescent or slow cycling. Such populations have been broadly termed “drug-tolerant persisters” (DTPs) and may be responsible for minimal residual disease following anticancer treatment and acquired resistance. Understanding molecular mechanisms that drive emergence of DTPs could lead to new strategies to improve therapeutic outcomes. Recently, we reported that BRAF-mutant melanoma cells under prolonged BRAF inhibition enter a DTP state with balanced cell death and division, which we termed “idling.” Here, we apply single cell barcoding to show that idling DTP populations emerge via cell state transitions, rather than selection of a few pre-existing drug-tolerant clones. Within the time frame of our experiments, DTPs exhibit varying proportions of fast- and slow-cycling cells within each lineage, suggesting that entry into the DTP state is a stochastic process. Furthermore, single-cell transcriptomics and bulk epigenomics reveal common gene expression and ontology signatures in DTP lineages that are consistent with rebalancing of ion channels. Calcium flux experiments uncover a reduction of divalent cation reserves in intracellular organelles, likely leading to endoplasmic reticulum stress. Accordingly, idling DTPs are more prone to ferroptotic cell death, as indicated by increased sensitivity to inhibition of glutathione peroxidase 4 (GPX4), which prevents removal of toxic lipid peroxides. In summary, we propose that ion channel homeostasis is a central process underlying idling DTP emergence in BRAF-mutated melanoma. Future studies will investigate translational aspects of this insight.

The ability to track and isolate unique cell lineages from large heterogeneous populations increases the resolution at which cellular processes can be understood under normal and pathogenic states beyond snapshots obtained from single-cell RNA sequencing (scRNA-seq). Here, we describe the Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT) method in which unique single guide RNA (sgRNA) barcodes are used as functional tags to identify and recall specific lineages of interest. An sgRNA barcode is stably integrated and actively transcribed, such that all cellular progeny will contain the parental barcode and produce a functional sgRNA. The sgRNA barcode has all the benefits of a DNA barcode and added functionalities. Once a barcode pertaining to a lineage of interest is identified, the lineage of interest can be isolated using an activator variant of Cas9 (such as dCas9-VPR) and a barcode-matched sequence upstream of a fluorescent reporter gene. CRISPR activation of the fluorescent reporter will only occur in cells producing the matched sgRNA barcode, allowing precise identification and isolation of lineages of interest from heterogeneous populations.

Lineage-tracing methods have enabled characterization of clonal dynamics in complex populations, but generally lack the ability to integrate genomic, epigenomic and transcriptomic measurements with live-cell manipulation of specific clones of interest. We developed a functionalized lineage-tracing system, ClonMapper, which integrates DNA barcoding with single-cell RNA sequencing and clonal isolation to comprehensively characterize thousands of clones within heterogeneous populations. Using ClonMapper, we identified subpopulations of a chronic lymphocytic leukemia cell line with distinct clonal compositions, transcriptional signatures and chemotherapy survivorship trajectories; patterns that were also observed in primary human chronic lymphocytic leukemia. The ability to retrieve specific clones before, during and after treatment enabled direct measurements of clonal diversification and durable subpopulation transcriptional signatures. ClonMapper is a powerful multifunctional approach to dissect the complex clonal dynamics of tumor progression and therapeutic response. Wu and colleagues develop a barcoding tool, ClonMapper, which permits single-cell lineage tracing and clonal isolation and demonstrate its utility to study clonal dynamics in human CLL cells in the context of chemotherapy treatment and resistance.

Tumors are comprised of dynamic, heterogenous cell populations characterized by numerous genetic and nongenetic alterations that accumulate and change with disease progression and treatment. Retrospective analyses of tumor evolution have relied on the measurement of genetic markers (such as copy number variants) to infer clonal dynamics. However, these approaches neglect the critical contributions of nongenetic drivers of disease. Techniques that harness the power of prospective clone tracking via heritable barcode tags provide an alternative strategy. In this review, we discuss methods for high-resolution, quantitative clone tracking, including recent advancements to pair barcode-specific functionality with scRNA-seq, clonal cell isolation, and in situ hybridization and imaging. We discuss these approaches in the context of cancer cell heterogeneity and treatment resistance.

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 a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize 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, each consisting of a system of ordinary differential equations, 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%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

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.

Heterogeneity across individual cancer cells and clonal populations impacts growth rate, tumor composition, and response to therapy. To improve treatment, new tools are required to measure and control the contributions of diverse cell subpopulations. Our team has developed a high-complexity expressed barcode system, ClonMapper, that integrates expressed cell barcoding with single-cell RNA-sequencing and clonal isolation to characterize and track subpopulation trajectories. Using this approach, we uncovered subsets of cells from cell models with distinct transcriptional signatures and chemotherapy survivorship trajectories. To gain a deeper understanding of the process of clonal diversification, we profiled clones and retrieved sub-clones over the course of expansion and treatment. Supervised learning indicated that clonal subpopulations have characteristic transcriptomic signatures that are well-conserved under a variety of therapeutic perturbations. By providing the capability for systematic dissection of complex clonal dynamics, ClonMapper enables the delineation of an underlying engine of clonal diversification in cancer cell populations and refines our understanding of clonal identity. Citation Format: Amy Brock. Tracking population heterogeneity and chemoresistance with functionalized cell barcodes [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr IA004.

Cell-cell fusion between cancer cells and genetically mismatched populations has been shown to generate genotypic diversity and more pathological phenotypes which may alter tumor evolution and treatment response, but little is known on how the process of cell-cell fusion itself affects progeny from genetically matched cells. To investigate the hypothesis that the process of cell-cell fusion alone leaves durable effects in daughter cell populations derived from homotypic cell-cell fusion, GFP and mCherry labeled cells from the triple negative breast cancer (TNBC) cell line HCC1806 were co-cultured and subclones were generated through single-cell FACS isolation of spontaneously generated fusion progeny (double positive cells; n=9) and control cells (single positive cells; n=9) from the same population. Subclonal populations were expanded for >19 generations (1 to ~106 cells) before experimental measurements and perturbations. The morphological features, growth rate, chemotactic ability, motility, karyotype, gene expression profile, and chemotherapeutic response of each fusion and control subclone were characterized. We find no durable differences between fusion and control subclones in terms of growth rate, chemotactic ability, and response to 5-fluorouracil. However, subclonal populations generated from homotypic cell-cell fusion show remarkably consistent and statistically significant increased sensitivity to paclitaxel and doxorubicin compared to control subclones. Further, differential expression analysis of RNA sequencing data revealed loss of expression of cancer-testis antigen (CTA) MAGEC2 in fusion cells compared to controls. The MAGE family of CTAs have shown promise as an immunotherapeutic target, but this study suggests that loss or downregulation of MAGEC2 after homotypic cell-cell fusion could allow immune escape. While fusion progeny may be more protected from immune threats, fusion progeny are simultaneously sensitized to paclitaxel and doxorubicin which highlights the potential need for standard chemotherapeutics after CTA immunotherapy. Where current literature posits cell-cell fusion as a pathologically disruptive event which may increase therapeutic resistance, this is the first study to show that fusion progeny are counterintuitively more sensitive to standard chemotherapeutics and to indicate homotypic cell-cell fusion as a potential mechanism of immunotherapeutic escape. Citation Format: Andrea Gardner, Lan Zheng, Kennedy Howland, Chisom Iloegbunam, Patrik Barrera, Carolina De Santiago, Amy Brock. Homotypic cell-cell fusion in TNBC cells downregulates cancer testis antigen MAGEC2 and increases therapeutic sensitivity to paclitaxel and doxorubicin [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR007.

This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR007) of the Conference Proceedings. Citation Format: Andrea Gardner, Lan Zheng, Kennedy Howland, Chisom Iloegbunam, Patrik Barrera, Carolina De Santiago, Amy Brock. Homotypic cell-cell fusion in TNBC cells downregulates cancer testis antigen MAGEC2 and increases therapeutic sensitivity to paclitaxel and doxorubicin [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B017.

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 types of longitudinal data. 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 model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

SummaryA 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.

Nongenetic heterogeneity in cancer plays a critical role in disease progression and response to therapy. While variability in cellular phenotypes results from both gene expression noise and different stable phenotypic states, in this chapter we will focus on theory and evidence for nongenetic heterogeneity due to phenotypic plasticity of cancer cells. To elucidate the theory that allows for heterogeneous populations of cells regardless of the genomic state, we incorporate the concept of the phenotypic landscape—in which cells reside in stable “attractor” states. Cells have the ability to transition to different states, and the probability of these transitions may be in part dependent on environmental conditions and independent of the genome. Mathematical models allow us to build a simplified understanding of heterogeneous states and the transition rates between states within a cancer cell population. Here we discuss ways that cell states are identified and measured to investigate nongenetic heterogeneity in various empirical settings. For example, we describe one of the most well-characterized cell state transitions, the epithelial-to-mesenchymal transition (EMT) observed in cancer cells in response to environmental cues. Because EMT is a physiological process in development and tissue repair, the accompanying cell state transitions are well defined and can be confirmed by multiple readouts. Additionally, we describe instances of nongenetic heterogeneity and phenotypic state switching defined by other cell states with functional relevance to cancer progression and drug response. To make progress in preventing the seemingly inexorable onset of chemoresistance, it is necessary to elucidate how drug exposure directly induces cell state transitions between sensitive and resistant cell states. We discuss here the evidence that exposure to cytotoxic or targeted therapeutic treatments may cause cells to activate transcriptional or cell signaling programs that render them insensitive to treatment via a variety of different resistance mechanisms.

INTRODUCTION: A defining hallmark of cancer is aberrant cell proliferation. Efforts to understand the generative properties of cancer cells span all biological scales: from genetic deviations and alterations of metabolic pathways, to physical stresses due to overcrowding, as well as the effects of therapeutics and the immune system. While these factors have long been studied in the laboratory, mathematical and computational techniques are being increasingly applied to help understand and forecast tumor growth and treatment response. Advantages of mathematical modeling of proliferation include the ability to simulate and predict the spatiotemporal development of tumors across multiple experimental scales. Central to proliferation modeling is the incorporation of available biological data and validation with experimental data. AREAS COVERED: We present an overview of past and current mathematical strategies directed at understanding tumor cell proliferation. We identify areas for mathematical development as motivated by available experimental and clinical evidence, with a particular emphasis on emerging, non-invasive imaging technologies. EXPERT COMMENTARY: The data required to legitimize mathematical models are often difficult or (currently) impossible to obtain. We suggest areas for further investigation to establish mathematical models that more effectively utilize available data to make informed predictions on tumor cell proliferation.

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.

Precision Oncology seeks to identify and target the mutation that drives a tumor. Despite its straightforward rationale, concerns about its effectiveness are mounting. What is the biological explanation for the "imprecision?" First, Precision Oncology relies on indiscriminate sequencing of genomes in biopsies that barely represent the heterogeneous mix of tumor cells. Second, findings that defy the orthodoxy of oncogenic "driver mutations" are now accumulating: the ubiquitous presence of oncogenic mutations in silent premalignancies or the dynamic switching without mutations between various cell phenotypes that promote progression. Most troublesome is the observation that cancer cells that survive treatment still will have suffered cytotoxic stress and thereby enter a stem cell–like state, the seeds for recurrence. The benefit of “precision targeting” of mutations is inherently limited by this counterproductive effect. These findings confirm that there is no precise linear causal relationship between tumor genotype and phenotype, a reminder of logician Carveth Read's caution that being vaguely right may be preferable to being precisely wrong. An open-minded embrace of the latest inconvenient findings indicating nongenetic and "imprecise" phenotype dynamics of tumors as summarized in this review will be paramount if Precision Oncology is ultimately to lead to clinical benefits. Cancer Res; 77(23); 6473–9. ©2017 AACR.

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.ResultsWe 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.ConclusionsTo 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.

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.

The widely accepted model of tumor growth assumes tumors grow exponentially at a constant rate during early tumorigenesis when populations are small. The possibility that tumors might exhibit altered slower growth dynamics or even net cell death below a critical tumor size has yet to be fully explored. Deterministic growth models are capable of describing larger populations because population variation becomes small compared with the average, but when the population being modeled is small, the inherent stochasticity of the birth and death process produces significant variation. Recent advances in high throughput data collection allow for precise and sufficiently large data sets needed to capture this variation. Therefore, we present a stochastic modeling framework to describe and test the potential for altered growth dynamics at small tumor populations.

Immunotherapy has emerged as a promising approach to treat cancer. However, partial responses across multiple clinical trials support the significance of characterizing intertumor and intratumor heterogeneity to achieve better clinical results and as potential tools in selecting patients for different types of cancer immunotherapies. Yet, the type of heterogeneity that informs clinical outcome and patient selection has not been fully explored. In particular, the lack of characterization of immune response-related genes in cancer cells hinders the further development of metrics to select and optimize immunotherapy. Therefore, we analyzed single-cell RNA-Seq data from lung adenocarcinoma patients and cell lines to characterize the intratumor heterogeneity of immune response-related genes and demonstrated their potential impact on the efficacy of immunotherapy. We discovered that IFN-γ signaling pathway genes are heterogeneously expressed and coregulated with other genes in single cancer cells, including MHC class II (MHCII) genes. The downregulation of genes in IFN-γ signaling pathways in cell lines corresponds to an acquired resistance phenotype. Moreover, analysis of 2 groups of tumor-restricted antigens, namely neoantigens and cancer testis antigens, revealed heterogeneity in their expression in single cells. These analyses provide a rationale for applying multiantigen combinatorial therapies to prevent tumor escape and establish a basis for future development of prognostic metrics based on intratumor heterogeneity.

Summary We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response., Graphical Abstract, Bioengineering; Systems Biology; Cancer; In Silico Biology

The immense evolutionary capacity of cancer poses major challenges to current therapeutic efforts, and results from the vast clonal heterogeneity and ability of individual cancer cells to adapt to diverse selective pressures. More complete mechanistic understanding of the basis of therapeutic resistance has been limited by difficulties in coupling genetic identity of cells to their respective transcriptomic and functional outputs. To this end, we developed a novel high-complexity expressed barcode system, ClonMapper, that integrates DNA barcoding with single-cell RNA-sequencing and clonal isolation to characterize thousands of clones within a mixed cancer cellpopulation. In applying this system to breast cancer and B cell cancer cell lines in the setting of resistance to doxorubicin and fludarabine-based chemotherapy, we discover pretreatment sub-populations with distinct expression profiles that not only confer distinct treatment survivorship trajectories, but also provide the basis for long-term clonal equilibrium between co-existing clones in the absence of treatment. These data reveal the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population and highlight the unprecedented resolution that can be achieved using ClonMapper. Citation Format: Eric A. Brenner, Daylin Morgan, Aziz Al'Khafaji, Catherine Gutierrez, Catherine J. Wu, Amy Brock. High resolution analysis of clonal dynamics using lineage tracing and single cell transcriptomics [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-097.

Lineage tracking delivers essential quantitative insight into dynamic, probabilistic cellular processes, such as somatic tumor evolution and differentiation. Methods for high diversity lineage quantitation rely on sequencing a population of DNA barcodes. However, manipulation of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool, Control of Lineages by Barcode Enabled Recombinant Transcription (COLBERT), that enables high diversity lineage tracing and lineage-specific manipulation of gene expression. This modular platform utilizes expressed barcode gRNAs to both track cell lineages and direct lineage-specific gene expression.

The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. Although several molecular mechanisms of chemotherapeutic resistance are well studied, a quantitative understanding of the dynamics of resistant subpopulations within a heterogeneous tumor cell population remains elusive. While mathematical models describing the dynamics of heterogeneous cancer cell populations have been proposed, few have been experimentally validated due to the complex nature of resistance that limits the ability of a single phenotypic marker to sufficiently isolate drug resistant subpopulations. In this work, we address this problem with a combined experimental and modeling system that uses drug sensitivity data to reveal the composition of multiple subpopulations differing in their level of drug resistance. We calibrate time-resolved dose-response data to three mathematical models to interrogate the models’ ability to capture the dynamics of drug. All three models demonstrated an increase in population level resistance following drug exposure. The candidate models were compared by Akaike information criterion and the model selection criteria identified a multi-state model incorporating the role of population heterogeneity and cellular plasticity. To validate the ability of this model to identify the composition of subpopulations, we mixed wild-type MCF-7 and MCF-7/ADR resistant cells at various proportions and evaluated the corresponding model output. Our blinded two-state model was able to estimate the proportions of cell subtypes, with the measured proportions falling within the 95 percent confidence intervals on the parameter estimations and at an R-squared value of 0.986. To the best of our knowledge, this contribution represents the first work to combine experimental time-resolved drug sensitivity data with a mathematical model of resistance development.