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Combination drug therapies have become an integral part of precision oncology, and while evidence of clinical effectiveness continues to grow, the underlying mechanisms supporting synergy are poorly understood. Immortalized human lymphoblastoid cell lines (LCLs) have been proven as a particularly useful, scalable and low-cost model in pharmacogenetics research, and are suitable for elucidating the molecular mechanisms of synergistic combination therapies. In this review, we cover the advantages of LCLs in synergy pharmacogenomics and consider recent studies providing initial evidence of the utility of LCLs in synergy research. We also discuss several opportunities for LCL-based systems to address gaps in the research through the expansion of testing regimens, assessment of new drug classes and higher-order combinations, and utilization of integrated omics technologies.

A major topic of debate in developmental biology centers on whether development is continuous, discontinuous, or a mixture of both. Pseudo-time trajectory models, optimal for visualizing cellular progression, model cell transitions as continuous state manifolds and do not explicitly model real-time, complex, heterogeneous systems and are challenging for benchmarking with temporal models. We present a data-driven framework that addresses these limitations with temporal single-cell data collected at discrete time points as inputs and a mixture of dependent minimum spanning trees (MSTs) as outputs, denoted as dynamic spanning forest mixtures (DSFMix). DSFMix uses decision-tree models to select genes that account for variations in multimodality, skewness and time. The genes are subsequently used to build the forest using tree agglomerative hierarchical clustering and dynamic branch cutting. We first motivate the use of forest-based algorithms compared to single-tree approaches for visualizing and characterizing developmental processes. We next benchmark DSFMix to pseudo-time and temporal approaches in terms of feature selection, time correlation, and network similarity. Finally, we demonstrate how DSFMix can be used to visualize, compare and characterize complex relationships during biological processes such as epithelial–mesenchymal transition, spermatogenesis, stem cell pluripotency, early transcriptional response from hormones and immune response to coronavirus disease. Our results indicate that the expression of genes during normal development exhibits a high proportion of non-uniformly distributed profiles that are mostly right-skewed and multimodal; the latter being a characteristic of major steady states during development. Our study also identifies and validates gene signatures driving complex dynamic processes during somatic or germline differentiation.

During an adaptive immune response from pathogen invasion, multiple cytokines are produced by various immune cells interacting jointly at the cellular level to mediate several processes. For example, studies have shown that regulation of interleukin-4 (IL-4) correlates with interleukin-2 (IL-2) induced lymphocyte proliferation. This motivates the need to better understand and model the mechanisms driving the dynamic interplay of proliferation of lymphocytes with the complex interaction effects of cytokines during an immune response. To address this challenge, we adopt a hybrid computational approach comprising of continuous, discrete and stochastic non-linear model formulations to predict a system-level immune response as a function of multiple dependent signals and interacting agents including cytokines and targeted immune cells. We propose a hybrid ordinary differential equation-based (ODE) multicellular model system with a stochastic component of antigen microscopic states denoted as Multiscale Multicellular Quantitative Evaluator (MMQE) implemented using MATLAB. MMQE combines well-defined immune response network-based rules and ODE models to capture the complex dynamic interactions between the proliferation levels of different types of communicating lymphocyte agents mediated by joint regulation of IL-2 and IL-4 to predict the emergent global behavior of the system during an immune response. We model the activation of the immune system in terms of different activation protocols of helper T cells by the interplay of independent biological agents of classic antigen-presenting cells (APCs) and their joint activation which is confounded by the exposure time to external pathogens. MMQE quantifies the dynamics of lymphocyte proliferation during pathogen invasion as bivariate distributions of IL-2 and IL-4 concentration levels. Specifically, by varying activation agents such as dendritic cells (DC), B cells and their joint mechanism of activation, we quantify how lymphocyte activation and differentiation protocols boost the immune response against pathogen invasion mediated by a joint downregulation of IL-4 and upregulation of IL-2. We further compare our in-silico results toin-vivoandin-vitroexperimental studies for validation. In general, MMQE combines intracellular and extracellular effects from multiple interacting systems into simpler dynamic behaviors for better interpretability. It can be used to aid engineering of anti-infection drugs or optimizing drug combination therapies against several diseases.

Cancer is a complex landscape of aberrant cell signaling programs, often initiated by oncogenes. The causal oncogene drives the preponderance of accumulated mutations that enable tumorigenesis. Cancers arising this way can be reliant on the initiating oncogene, a term known as oncogene addiction. The proto-oncogene c-MYC is a transcription factor that regulates much of the genome and is deregulated in many cancers. The transgenic Eµ-tTA/Tet-O-MYC mouse model of T-cell acute lymphoblastic leukemia (T-ALL), allows for modulation of c-MYC expression (ON vs OFF). When c-MYC is expressed, T-ALL progression is observed; and when c-MYC expression is revoked, tumor regression occurs. Using cells and microarray data from this model we employed a nested effects model (NEM) that infers hierarchical relationships anchored to master transcription factors that govern critical aspects of cell biology. We identified a critical node governed by a class of histone demethylases influencing transcriptional availability across the genome. KDM5B/JARID1b is known as a transcriptional repressor, and is downregulated in the T-ALL model when c-MYC is overexpressed. Using CRISPR/Cas9 mediated mutagenesis to disrupt KDM5B expression, we observed a potent reduction in cell death when c-MYC expression is abrogated; suggesting KDM5B mediates cell death responses in T-ALL. KDM5B is downregulated in the primary murine model when c-MYC is overexpressed, and upregulated concordantly with the loss of MYC expression. Human lymphoma cell lines that are sensitive to the bromodomain inhibitor JQ1 show downregulation of c-MYC and KDM5B upregulation. Xenografts of KDM5B knockout cells in NOD-SCIDIL-2Rg-/- (NSG) mice show increased tumor progression and burden, and more aggressive time to relapse after c-MYC expression is revoked. Future studies examining the KDM5B/c-MYC axis and its influence on chromatin architecture are ongoing. These studies aim to demonstrate a novel paradigm in understanding how c-MYC promotes tumor development through repression of a tumor suppressive epigenetic landscape regulated by KDM5B and identify therapeutic options for treating c-MYC-induced T-ALL. Citation Format: Gabrielle Dewson, Benedict Anchang, Rosalie Sears, Daniel F. Liefwalker. Evasion of apoptosis in MYC dependent T-ALL through epigenetic control [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 825.

The distal lung contains terminal bronchioles and alveoli that facilitate gas exchange and is affected by disorders including interstitial lung disease, cancer, and SARS-CoV-2-associated COVID-19 pneumonia. Investigations of these localized pathologies have been hindered by a lack of 3D in vitro human distal lung culture systems. Further, human distal lung stem cell identification has been impaired by quiescence, anatomic divergence from mouse and lack of lineage tracing and clonogenic culture. Here, we developed robust feeder-free, chemically-defined culture of distal human lung progenitors as organoids derived clonally from single adult human alveolar epithelial type II (AT2) or KRT5+ basal cells. AT2 organoids exhibited AT1 transdifferentiation potential, while basal cell organoids progressively developed lumens lined by differentiated club and ciliated cells. Organoids consisting solely of club cells were not observed. Upon single cell RNA-sequencing (scRNA-seq), alveolar organoids were composed of proliferative AT2 cells; however, basal organoid KRT5+ cells contained a distinct ITGA6+ITGB4+ mitotic population whose proliferation segregated to a TNFRSF12Ahi subfraction. Clonogenic organoid growth was markedly enriched within the TNFRSF12Ahi subset of FACS-purified ITGA6+ITGB4+ basal cells from human lung or derivative organoids. In vivo, TNFRSF12A+ cells comprised ~10% of KRT5+ basal cells and resided in clusters within terminal bronchioles. To model COVID-19 distal lung disease, we everted the polarity of basal and alveolar organoids to rapidly relocate differentiated club and ciliated cells from the organoid lumen to the exterior surface, thus displaying the SARS-CoV-2 receptor ACE2 on the outwardly-facing apical aspect. Accordingly, basal and AT2 “apical-out” organoids were infected by SARS-CoV-2, identifying club cells as a novel target population. This long-term, feeder-free organoid culture of human distal lung alveolar and basal stem cells, coupled with single cell analysis, identifies unsuspected basal cell functional heterogeneity and exemplifies progenitor identification within a slowly proliferating human tissue. Further, our studies establish a facile in vitro organoid model for human distal lung infectious diseases including COVID-19-associated pneumonia.

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies., Intermediate transitions between epithelial and mesenchymal states are associated with tumor progression. Here using mass cytometry, Plevritis and colleagues develop a computational framework to resolve and map these trajectories in lung cancer cells and clinical specimens.

Significance Single-cell high-throughput technologies enable the ability to identify combination cancer therapies that account for intratumoral heterogeneity, a phenomenon that has been shown to influence the effectiveness of cancer treatment. We developed and applied an approach that identifies top-ranking drug combinations based on the single-cell perturbation response when an individual tumor sample is screened against a panel of single drugs. This approach optimizes drug combinations by choosing the minimum number of drugs that produce the maximal intracellular desired effects for an individual sample., An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple (>40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.

Purpose: To identify an optimal drug combination for addressing intratumor heterogeneity by measuring single drug responses at the level of single cells. Background: Drug combination strategies have been derived from drug-drug and drug-target interaction inferences made based on genomic network analysis, such as synthetic lethality network analysis and protein network analysis. A key limitation with these approaches is that they do not account for intratumor heterogeneity. A next generation high throughput single cell technology such as Cytometry Time-of-Flight (CyTOF) has the potential to circumvent this limitation by providing the key information for optimally targeting intratumor heterogeneity within the context of personalized medicine. Methods: We propose a systems level modeling framework for analyzing heterogeneous drug response data across a population of single cells. Our approach, called Mixture Nested Effects Models (MNEM), identifies multi-target drug combinations from single drug effects measured at the level of single cells. MNEM analyzes drug responses among subpopulations (aka clusters) of similar cells, where the subpopulations are defined by cellular surface markers. MNEM infers the nested drug target hierarchy (directed network) from a heterogeneous population of drug effects by combining subpopulation identification, subpopulation matching to infer the drug response pre- and post-treatment, and nested effects modeling for each subpopulation individually or through a mixture of subpopulations. An MNEM infers that Drug A targets are a superset of Drug B targets, if the heterogeneous downstream target effects resulting from Drug B are a noisy subset of those resulting from Drug A. This relationship is represented as a directed edge from Drug A to Drug B. From the graphical model of nested drug effects, a scoring function is superimposed to identify the optimal drug combinations. Results: We apply MNEM to CyTOF drug screening data derived from treating three well-established cancer cell lines: HeLa (ovarian), NCIH460 (lung) and HCT116 (colon) with Mek, mTOR, pP38MAPK, JnK I and PI3K inhibitors at different optimal dose levels in addition to the base line treatment TRAIL, an apoptotic stimulator. MNEM indicates that pP38MAPK and Mek inhibitors are important candidates for targeting the ovarian and lung cancer cell lines. The MNEM analysis from the colon cancer cell line data indicates that several optimal drug regimens may have equivalent efficacy. Survival data from drug combination invitro experiments using clonogenic assays support the results from MNEM. Overall, these analyses suggest that MNEM could be used to optimize drug combinations based on responses from single drugs measured at the single cell level. Conclusion: Intratumor heterogeneity in tumor response, measured at the level of single cells, upon exposure to single drugs may be combined through Mixture Nested Effects Modeling (MNEM) to infer optimal drug combinations. Citation Format: Benedict Anchang, Harris Fienberg, Sean Bendall, Robert Tibshirani, Sylvia K. Plevritis. Multi-target drug combinations from single drug responses measured at the level of single cells using Mixture Nested Effects Models (MNEMs) applied to cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-39.

MYC-induced T-ALL exhibit oncogene addiction. Addiction to MYC is a consequence of both cell-autonomous mechanisms, such as proliferative arrest, cellular senescence, and apoptosis, as well as non-cell autonomous mechanisms, such as shutdown of angiogenesis, and recruitment of immune effectors. Here, we show, using transgenic mouse models of MYC-induced T-ALL, that the loss of either p19ARF or p53 abrogates the ability of MYC inactivation to induce sustained tumor regression. Loss of p53 or p19ARF, influenced the ability of MYC inactivation to elicit the shutdown of angiogenesis; however the loss of p19ARF, but not p53, impeded cellular senescence, as measured by SA-beta-galactosidase staining, increased expression of p16INK4A, and specific histone modifications. Moreover, comparative gene expression analysis suggested that a multitude of genes involved in the innate immune response were expressed in p19ARF wild-type, but not null, tumors upon MYC inactivation. Indeed, the loss of p19ARF, but not p53, impeded the in situ recruitment of macrophages to the tumor microenvironment. Finally, p19ARF null-associated gene signature prognosticated relapse-free survival in human patients with ALL. Therefore, p19ARF appears to be important to regulating cellular senescence and innate immune response that may contribute to the therapeutic response of ALL.

The distal lung contains terminal bronchioles and alveoli that facilitate gas exchange and is affected by disorders including interstitial lung disease, cancer, and SARS-CoV-2-associated COVID-19 pneumonia. Investigations of these localized pathologies have been hindered by a lack of 3D in vitro human distal lung culture systems. Further, human distal lung stem cell identification has been impaired by quiescence, anatomic divergence from mouse and lack of lineage tracing and clonogenic culture. Here, we developed robust feeder-free, chemically-defined culture of distal human lung progenitors as organoids derived clonally from single adult human alveolar epithelial type II (AT2) or KRT5 (+) basal cells. AT2 organoids exhibited AT1 transdifferentiation potential, while basal cell organoids progressively developed lumens lined by differentiated club and ciliated cells. Organoids consisting solely of club cells were not observed. Upon single cell RNA-sequencing (scRNA-seq), alveolar organoids were composed of proliferative AT2 cells; however, basal organoid KRT5 (+) cells contained a distinct ITGA6 (+) ITGB4 (+) mitotic population whose proliferation segregated to a TNFRSF12A (hi) subfraction. Clonogenic organoid growth was markedly enriched within the TNFRSF12A (hi) subset of FACS-purified ITGA6 (+) ITGB4 (+) basal cells from human lung or derivative organoids. In vivo, TNFRSF12A (+) cells comprised ~10% of KRT5 (+) basal cells and resided in clusters within terminal bronchioles. To model COVID-19 distal lung disease, we everted the polarity of basal and alveolar organoids to rapidly relocate differentiated club and ciliated cells from the organoid lumen to the exterior surface, thus displaying the SARS-CoV-2 receptor ACE2 on the outwardly-facing apical aspect. Accordingly, basal and AT2 apical-out organoids were infected by SARS-CoV-2, identifying club cells as a novel target population. This long-term, feeder-free organoid culture of human distal lung alveolar and basal stem cells, coupled with single cell analysis, identifies unsuspected basal cell functional heterogeneity and exemplifies progenitor identification within a slowly proliferating human tissue. Further, our studies establish a facile in vitro organoid model for human distal lung infectious diseases including COVID-19-associated pneumonia.

We have found evidence suggesting that ARF and p53 are essential for tumor regression upon MYC inactivation through distinct mechanisms ARF through p53-independent affect, is required to for MYC to regulate the expression of genes that are required for both the induction of cellular senescence as well as recruitment of innate immune activation. Our observations have possible implications for mechanisms of therapeutic resistance to targeted oncogene inactivation.

Bioinformatics, 30 (3), ISSN:1367-4803, ISSN:1460-2059

Cell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.

The role of EMT in cancer has been well reported and has been shown to prime cells for invasion and metastasis. EMT can be adopted or reversed (i.e. mesenchymal to epithelial transition, MET) by cells, revealing plasticity that can also lead to stemness and drug resistance. Although it is appreciated that EMT is not a binary process of two extremes but instead a continuum of intermediate states of partial EMT phenotypes, these are poorly defined. Given that intermediate EMT cancer states are viewed as critical for understanding and clinically targeting EMT processes, our aim was to dynamically capture and characterize intermediate EMT states in TGF beta treated lung cancer cells and clinical specimens. With single cell analysis we identified 4 distinct transition states. These states patterned an EMT axis featuring: (1) an epithelial, (2) a partial EMT and (3) a mesenchymal state that branches off to (4) a subset of phenotypically stem-like cells. Transition was reflected by gradual changes in E Cadherin, Vimentin, CD44 and CD24 levels. “True” mesenchymal state (E Cad- Vim+) was isolated to the stem-like cells (CD44hiCD24lo), which were negative for Twist protein expression. To interrogate the dynamism of EMT and MET processes, we performed TGF beta withdrawal experiments, which showed that most cells were able to reverse their behavior. Simultaneous analysis of 30 parameters with CyTOF confirmed the 4-state transition, offering deep dynamic views of a variety of cell surface, signaling, cell cycle, and transcriptional markers. We then proceeded to computationally identify and define additional states of the EMT/MET spectrum that are visited by transitioning cells in a step-wise manner, in order to construct a lung cancer EMT/MET proteomic landscape. To tackle this, we applied CCAST, an algorithm that employs decision trees to identify and discretize homogeneous cell subpopulations among heterogeneous single cell data. When we assembled the high dimensional data in a timely order, we were able to visualize the emerging states that cells visited during their transitions. This revealed the existence of more than one possible EMT/MET trajectories as well as the existence of a transient subpopulation of cells with a distinct phenotype (Twist+, CD34+, E Cad-, Cytokeratin 7+, pEGFR-). Finally, CyTOF analysis and projection of 2 lung adenocarcinoma specimens on the constructed EMT/MET landscape confirmed the existence of states observed in our cell line studies, including the aforementioned Twist+ transient subpopulation. In summary, we provide a lung cancer cell proteomic map that dissects EMT phenotypic plasticity. Clinically, this type of EMT/MET proteomic mapping could help identify, predict and target EMT mechanisms known to have a role in cancer progression, drug resistance and disease recurrence. Citation Format: Loukia G. Karacosta, Benedict Anchang, Samuel Kimmey, Matt van de Rijn, Joseph B. Shrager, Sean C. Bendall, Sylvia K. Plevritis. Identifying dynamic EMT states and constructing a proteomic EMT landscape of lung cancer using single cell multidimensional analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4997.

Currently, cancer drug combinations primarily focus on mutational heterogeneity of the primary tumor and do not account for single-cell variations that can give rise to drug resistance. Moreover, even with the increasing number of potential FDA-approved targeted drugs including immunotherapies, methods are needed to identify better combination therapy that leverages intratumor heterogeneity, thereby potentially mitigating the need for trials with large numbers of patients. Despite advances in single-cell technologies that capture intratumor heterogeneity, there are no drug combination strategies that utilize single-cell platforms. One idea that has been proposed is to use Mass Cytometry Time-of-Flight (CyTOF) for drug screening by producing drug perturbation effects at the level of the single cell, but the required analytics for the resulting complex data were not addressed. To address this unmet need, we have developed a novel algorithm to optimize combination therapy for an individual patient by analyzing distinct single-cell drug perturbation responses on a tumor sample. This model framework, called “DRUGNEM,” can be applied to CyTOF data, single-cell RNA-seq, or any single-cell imaging data currently available. DRUGNEM is composed of three steps: (1) identify the subpopulations that make up the tumor and may respond differently to treatment; (2) reconstruct a drug-nested-effects model that integrates the drug effects across all subpopulations to capture sub-setting relationships among individual drug effects; and (3) systematically score potential drug combinations to identify or prioritize strategies that will be clinically (and economically) sustainable. Currently, DRUGNEM is optimized to select the minimum number of drugs that produces the maximal desired intracellular effect, predicated on the premise that fewer drugs lower treatment-related toxicities and costs, but the final selection criterion can be easily modified. As proof of concept, we applied the DRUGNEM framework to individualize drug combinations based on CyTOF data generated on de-identified malignant research samples from 30 ALL pediatric patients before and after exposure to 3 targeted FDA -pproved single drugs (dasatinib, tofacitinib and BEZ235). We found that the most common combination treatment strategy (dasatinib and BEZ235) might not be optimal for all 30 ALL patients, with 2 of the 30 likely responding best to tofacitinib alone. Using in vitro survival assays, we validated the DRUGNEM prediction of BEZ235 and dasatinib as a potential synergistic combination on ALL cell lines. In summary, DRUGNEM is a novel framework using single-cell technologies to guide drug-combination strategies and can be adapted to incorporate complementary molecular data and computational methods to ultimately achieve more effective therapy for the individual cancer patient. Citation Format: Benedict Anchang, Kara Davis, Harris Fienberg, Sean Bendall, Loukia Karacosta, Garry Nolan, Sylvia K. Plevritis. Individualized drug combination based on single-cell drug perturbations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2275.

Several cell populations have been reported to possess intestinal stem cell (ISC) activity during homeostasis and injury-induced regeneration. Here, we explored inter-relationships between putative mouse ISC populations by comparative RNA-sequencing (RNA-seq). The transcriptomes of multiple cycling ISC populations closely resembled Lgr5+ ISCs, the most well-defined ISC pool, but Bmi1-GFP+ cells were distinct and enriched for enteroendocrine (EE) markers, including Prox1. Prox1-GFP+ cells exhibited sustained clonogenic growth invitro, and lineage-tracing of Prox1+ cells revealed long-lived clones during homeostasis and after radiation-induced injury invivo. Single-cell mRNA-seq revealed two subsets of Prox1-GFP+ cells, one of which resembled mature EE cells while the other displayed low-level EE gene expression but co-expressed tuft cell markers, Lgr5 and Ascl2, reminiscent of label-retaining secretory progenitors. Our data suggest that the EE lineage, including mature EE cells, comprises a reservoir of homeostatic and injury-inducible ISCs, extending our understanding of cellular plasticity and stemness.

A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations., Author Summary Sorting out homogenous subpopulations in a heterogeneous population of single cells enables downstream characterization of specific cell types, such as cell-type specific genomic profiling. This study proposes a data-driven gating strategy, CCAST, for sorting out homogeneous subpopulations from a heterogeneous population of single cells without relying on expert knowledge thereby removing human bias and variability. In a fully automated manner, CCAST identifies the relevant gating markers, gating hierarchy and partitions that isolate homogeneous cell subpopulations. CCAST is optimized for cell sorting but can be applied to the identification and analysis of homogeneous subpopulations. CCAST is shown to identify more homogeneous breast cancer subpopulations in SUM159 compared to prior sorting strategies. When applied to normal bone marrow single cell data, CCAST proposes an efficient strategy for gating out T-cells without relying on expert knowledge; on B-cells, it reveals a new characterization of mature B-cell subtypes not revealed by manual gating.

Cellular decision making in differentiation, proliferation, or cell death is mediated by molecular signaling processes, which control the regulation and expression of genes. Vice versa, the expression of genes can trigger the activity of signaling pathways. We introduce and describe a statistical method called Dynamic Nested Effects Model (D-NEM) for analyzing the temporal interplay of cell signaling and gene expression. D-NEMs are Bayesian models of signal propagation in a network. They decompose observed time delays of multiple step signaling processes into single steps. Time delays are assumed to be exponentially distributed. Rate constants of signal propagation are model parameters, whose joint posterior distribution is assessed via Gibbs sampling. They hold information on the interplay of different forms of biological signal propagation. Molecular signaling in the cytoplasm acts at high rates, direct signal propagation via transcription and translation act at intermediate rates, while secondary effects operate at low rates. D-NEMs allow the dissection of biological processes into signaling and expression events, and analysis of cellular signal flow. An application of D-NEMs to embryonic stem cell development in mice reveals a feed-forward loop dominated network, which stabilizes the differentiated state of cells and points to Nanog as the key sensitizer of stem cells for differentiation stimuli.

c-MYC is a bHLH transcription factor that heterodimerizes with MAX to form an active transcription factor complex that binds to promoter regions across the genome. MYC is a potent oncogene that promotes tumorigenesis in many cell types. Several studies have shown that tumor cells can be dependent on proto-oncogenes such as MYC, a state called “oncogene addiction.” Recent findings have revealed that c-myc amplifies active gene programs, and our group has previously identified a unique MYC transcriptional signature. In an effort to understand how MYC regulates discrete transcriptional programs across the genome we searched for potential cofactors that coordinate with MYC. Using computational approaches we examined several publically available human data sets as well as our own conditional MYC murine model for potential co-factor interactions. We have determined that several Jumonji family members coordinate with MYC expression. Currently, we are using CRISPR/Cas9 mediated disruption of target genes in murine cells to investigate whether these genes have a casual role in MYC oncogene addiction. Citation Format: Daniel Koch, Stacey Adams, Andrew Gentles, Benedict Anchang, Delaney Sullivan, Sylvia Plevritis, Dean Felsher. Gene expression signatures associated with MYC oncogene addiction in lymphoma. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr A48.