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STING is an intracellular sensor of cyclic di-nucleotides involved in response to pathogen- or self-derived DNA that induces protective immunity, or if dysregulated, autoimmunity. STING trafficking is tightly linked to its activity. We aimed to systematically characterize genes regulating STING trafficking and to define their impact on STING responses. Based on proximity-ligation proteomics and genetic screens, an ESCRT complex containing HGS, VPS37A and UBAP1 was found to be required for STING degradation and signaling shutdown. Analogous to phosphorylated STING creating a platform for IRF3 recruitment, oligomerization-driven STING ubiquitination by UBE2N formed a platform for ESCRT recruitment at the endosome, responsible for STING signaling shutdown. A UBAP1 mutant that underlies human spastic paraplegia and disrupts ESCRT function led to STING-dependent type I IFN responses at the steady-state, defining ESCRT as a homeostatic regulator of STING signaling.

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.

Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements - an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses.

Pancreatic cancer is a lethal disease in part because tumor cells exist in distinct transcriptional phenotypes (e.g. basal and classical states), each with a selective ability to evade current chemotherapy regimens. Two major mechanisms have been suggested for treatment evasion: 1) intrinsic resistance of certain phenotypes to particular chemotherapy regimens and 2) plasticity of treatment sensitive phenotypes to adopt more resistant phenotypes. However, the relative contribution of these mechanisms to treatment resistance is still poorly understood. Whereas previous work has described the redistribution of tumor cell states under selective treatment pressure, there is no direct evidence that tumor cells exhibit phenotypic plasticity at steady state or with treatment. By leveraging technological advancements in single-cell methods, lineage tracing and functional genomics, we have now shown direct evidence of phenotypic state switching in human pancreatic cancer cell lines. By performing single-cell RNA-seq on 5 barcoded PDAC cell lines over a steady state timecourse and under chemotherapy selective pressure (>600k cells total), we identify unique plasticity phenotypes within these cell lines and infer regulators of these plastic states. We validate the role of several of these regulators using bulk phenotypic CRISPRi screens in these cell lines. We next perform CRISPRi perturbations along with lineage tracing and single-cell multiomics (>300k cells) to dissect the regulatory relationships that underlie these cell states. We identify several novel epithelial and mesenchymal biasing factors, including those with unique roles in the most plastic clones. Collectively, we nominate several regulators that bias PDAC cell states thus posing a paradigm whereby perturbations may be used to homogenize tumor populations towards treatment-sensitive phenotypes. We believe this approach combined with current chemotherapy regimens could benefit pancreatic cancer patients by targeting residual, resistant tumor cells in the localized and metastatic disease settings to improve patient survival. Citation Format: Arnav Mehta, Lynn Bi, Aziz Al'Khafaji, Martin Jankowiak, Milan Parikh, Mehrtash Babadi, Alex Bloemendal, Marc Schwartz, Glen Munson, Joeseph Chan, Cassandra Burdziak, Elisa Donnard, Ryan Park, Chen Lu, Philippe Rigollet, Andrew Aguirre, Vidya Subramanian, Ray Jones, Eric S. Lander, David T. Ting, Dana Pe'er, Nir Hacohen. Quantifying and dissecting pancreatic cancer cell phenotypic plasticity using lineage tracing, single-cell multiomics and CRISPR perturbations reveals novel regulators of plastic states [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B016.

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.

Treatment-persistent residual tumors impede curative cancer therapy. To understand this cancer cell state we generate models of treatment persistence that simulate the residual tumors. We observe that treatment-persistent tumor cells in organoids, xenografts and in cancer patients adopt a distinct and reversible transcriptional program resembling that of embryonic diapause, a dormant stage of suspended development triggered by stress and associated with suppressed Myc activity and overall biosynthesis. In cancer cells, depleting Myc or inhibiting Brd4, a Myc transcriptional co-activator, abrogates drug cytotoxicity through a dormant diapause-like adaptation with reduced apoptotic priming. Conversely, inducible Myc upregulation enhances acute chemotherapeutic activity. Maintaining residual cells in dormancy after chemotherapy by inhibiting Myc activity, or interfering with the diapause-like adaptation by inhibiting cyclin-dependent kinase 9 (CDK9), represent potential therapeutic strategies against chemotherapy-persistent tumor cells. Our study demonstrates that cancer co-opts a mechanism similar to diapause with adaptive inactivation of Myc to persist during treatment.

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.

Chemo-persistent residual tumors are a major barrier for curative cancer therapy and provide a reservoir of cancer cells for eventual relapse. This clinically critical tumor cell population is poorly understood and lacks faithful in vitro models. We compared the transcriptional profiles of paired samples from patient with solid tumors or hematological neoplasias at the stage of post-treatment residual tumors or minimal residual disease vs. at their respective baselines. For a large proportion of patients, post-treatment tumor cells had a molecular signature that resembled that of embryonic diapause, a dormant stage of suspended development in early mammalian embryos triggered by stress and induced by suppression of Myc activity and overall biosynthesis. This Myc-inactivated molecular program was acquired during treatment and not pre-existing at baseline. Remarkably, baseline tumors with multiple MYC copy numbers maintained their MYC amplification in the post-treatment persistent tumor cells but suppressed Myc activity. We simulated this cancer cell state using preclinical models of breast and prostate cancer and multiple myeloma, including patient-derived cancer organoids and PDX models. Parallel approaches of genome sequencing and DNA barcode-mediated clonal tracking in vitro and in vivo independently indicated that the residual cancer cell subpopulations that persisted after treatment with common chemotherapeutic classes were not driven by rare pre-existing clones or de novo genetic events. Transcriptional analysis of the treatment-persistent cancer cells in 3D organoid cultures and in xenograft/PDX models indicated a diapause-like transcriptional adaptation with inactivated Myc, similar to that in patients. We functionally examined the role of Myc suppression in in vitro models of breast cancer and multiple myeloma using genetic (CRISPR/Cas9 editing and interference) and pharmacological (inhibition of Myc transcriptional co-activator Brd4) approaches. Suppression of Myc in cancer cells induced an embryonic diapause-like molecular profile and attenuated the acute chemotherapeutic cytotoxicity. Myc-inactivated cancer cells had reduced apoptotic priming and maintained low redox stress even in the presence of cytotoxic agents, which may contribute to cell survival during treatment. High-throughput screening of chemo-persistent cells revealed that inhibitors of CDK9 could revert the biosynthetic pause, reactivate Myc, attenuate the diapause-like state, and re-sensitize the residual cancer cells to chemotherapy. Overall, our study shows that cancer cells can co-opt the stress survival mechanism of embryonic diapause by dynamically suppressing Myc to enter transient drug-refractory dormancy. Citation Format: Eugen Dhimolea, Ricardo De Matos Simoes, Dhvanir Kansara, Caroline Vilas, Aziz Al'Khafaji, Juliette Bouyssou, Aedin Culhane, Constantine S. Mitsiades. Treatment-induced embryonic diapause-like adaptation through suppression of Myc activity as mediator of drug persistence in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 42.

Amongst the novel putative drivers identified by large-scale sequencing studies of chronic lymphocytic leukemia (CLL) is the ribosomal protein RPS15. Mutated in 5.3% of CLL, it co-occurs with heterozygous TP53 alterations in 36% of RPS15-mutated samples. Mutation of this mediator of ribosome maturation and translation is associated with poor disease prognosis and enriched in cohorts with del(17p) and relapsed CLL, suggesting a role in disease progression and therapeutic resistance. However, the impact of RPS15 mutation on B cell function and CLL development, in the presence or absence of TP53 mutation, has yet to be characterized. To this end, we developed overexpression HG3 CLL cell lines modeling four common RPS15 mutations (G134R, H137Y, S138F, and S139F) and a conditional knock-in mouse model of the S138F mutation with and without heterozygous Trp53 deletion (generated by crossing Rps15 and Trp53 mutant mice with Cd19-Cre mice). To characterize the impact of RPS15 mutation on transcription, we performed RNA-sequencing on splenic B cells from 3-month-old Rps15WT, Rps15Het and Rps15Hom mice (3 per cohort). We identified 255 and 670 upregulated and 596 and 777 downregulated genes in the Rps15MT vs Rps15WT mice (Rps15Het and Rps15Hom, respectively; log2FC>0.5, p1, nominal p-value To evaluate whether RPS15 mutant proteins incorporate into ribosomes, we performed polysome profiling of the HG3 lines. All overexpressed RPS15-WT and MT proteins were observed to integrate into the small ribosomal subunit and mature ribosomes, potentially impacting translation. Next, ribosome profiling of HG3 RPS15-WT and S138F cells revealed 2,334 genes with differential translation efficiency (TE) between RPS15-S138F vs WT cells and 2,425 genes between RPS15-S138F vs WT in TP53 knock-out cells (log2FC>0.5, p To determine whether Rps15 mutation can drive CLL-like disease, we engineered 6 novel mouse lines with B cell restricted expression of alterations through crossing with CD19-Cre mice: Rps15WT, Rps15Het, and Rps15Hom mutant mice alone or co-expressing Trp53 deletion. We detected circulating CLL-like (B220+CD5+) cells in 5 of 30 (17%) Rps15Het mice by 20 months of age, but not in 30 age-matched Rps15WT mice. We also detected CLL-like cells in 6 of 30 (20%) Trp53+/- mice by 17 months, indicating that Trp53 deletion alone can induce CLL-like disease. Interestingly, we found CLL-like cells in 2 of 30 Rps15Het/Trp53+/- mice as early as 15 months of age. The cohorts of Rps15Hom and Rps15Hom/Trp53+/- mice, however, have been monitored for 18 months of age with no disease occurrences, indicating that a double dosage of Rps15 mutation may be detrimental to disease formation. Altogether, Rps15 heterozygous mutation can drive CLL development in mice, and our early data hint that co-mutation with Trp53 may shorten the latency of CLL-like disease. Overall, RPS15 mutant protein can incorporate into the ribosome and induce changes in mRNA translation, resulting in MYC activation predominantly in the context of TP53 loss. Our mouse studies indicate that mut-Rps15 drives CLL development, with a more aggressive disease course when combined with Trp53 deletion. Our results collectively suggest that RPS15 and TP53 co-mutation drives CLL development through translational dysregulation and MYC-mediated signaling. Disclosures Neuberg: Pharmacyclics: Research Funding; Celgene: Research Funding; Madrigak Pharmaceuticals: Current equity holder in publicly-traded company. Getz:Broad Institute: Patents & Royalties: MuTect, ABSOLUTE, MutSig, MSMuTect, MSMutSig, POLYSOLVER and TensorQTL; Pharmacyclics: Research Funding; IBM: Research Funding; Scorpion Therapeutics: Consultancy, Current equity holder in publicly-traded company, Other: Founder. Wu:BionTech: Current equity holder in publicly-traded company; Pharmacyclics: Research Funding.

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 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.

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 the population of DNA barcodes. However, molecular analysis of specific individual lineages is not possible with this approach. To address this challenge, we developed a functionalized lineage tracing tool 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.

Cancer's ability to evolve and adapt is a major challenge to therapeutic success. Fueling this evolution is vast tumor heterogeneity, with constituent clones varying in their genetics, epigenetics and response to therapy. As a field, however, we have yet to couple the genetic alterations present within individual clones to their transcriptional or functional outputs. Here, we applied a novel adaptation of clone tracing that integrates DNA barcoding with single-cell RNA sequencing (scRNA-seq) to HG3, a CLL cell line harboring del(13q) and no other known cancer drivers, to model in vitro responses to front-line chemotherapy with fludarabine and cyclophosphamide at clone-level resolution. To generate a high-diversity barcode library compatible with scRNA-seq, a random pool of 20 base pair DNA barcodes was introduced into the 3'UTR of a reporter gene in a lentiviral expression vector. This viral barcode library was transduced into HG3 cells (at MOI 0.1 to minimize multiple barcode-tagging of cells) and 1.2x106 barcoded cells were sorted and expanded to establish the parental barcoded HG3 population. We subsequently treated this barcoded population with an LD95 combined dose of fludarabine and mafosfamide (the in vitro analog of cyclophosphamide) in 8 parallel experiments. Cell barcodes were sequenced prior to treatment (TP1) and following outgrowth from treatment (TP2) for analysis of clonal composition. 10,000 cells each from TP1 and from 2 of 8 parallel replicates at TP2 were processed for scRNA-seq. We observed a massive decrease in viability across all 8 replicates, with regrowth occurring at 20 days post-treatment. Barcode analysis revealed a marked decrease in clonal diversity from TP1 to TP2 (11,827 to 2,622 ± 380, n=8; or ~78%), and clones that survived treatment did so consistently such that 94% of surviving cells in each replicate had a clonal identity that was present in all 8 replicates. Analysis of clonally-resolved transcriptional profiles revealed that clones consistently fell into one of two stable gene expression states (clusters) prior to treatment, with nominal intermixing between populations. Treatment predominantly selected for clones comprising the smaller of these two clusters (TP1-'high tolerance'), with only a minimal number of resistant clones originating from the larger cluster at TP1 (TP1-'low tolerance'). Pathway and gene set enrichment analysis of these two TP1 clusters demonstrated that TP1-high tolerance had a stark upregulation of common CLL signaling pathways (i.e. WNT and CXCR4, an inflammatory/migratory phenotype) and a reliance on chromatin modification pathways. TP1-low tolerance, on the other hand, exhibited upregulated type 2 antigen presentation and prostaglandin biosynthesis/metabolism which has a known role in driving inflammation and migration in adjacent cells (Wang et al, BMJ 2006). These gene expression states remained stable after treatment, but with the added upregulation of well-described mechanisms of resistance to cyclophosphamide, with TP1-high tolerance exhibiting upregulation of GSTP1, a glutathione S-transferase that is a main mediator of cyclophosphamide metabolism, and TP1-low tolerance exhibiting upregulation of members of the ALDH family thought to ameliorate toxicity from chemotherapy-induced ROS (Andersson et al, Acta Oncologica 1995). Through this work, we resolved the underlying clonal composition of a CLL cell line and observed that the constituent clones exhibit stable and discrete gene expression states that differentially respond to chemotherapy. We noted two different axes of resistance - a more successful avenue that relies on WNT and CXCR4 signaling as well as cyclophosphamide clearance for resistance, and a less-successful avenue that involves clearance of reactive oxygen species for survival. The intersection of these two critical CLL pathways in in vitro resistance to first-line CLL therapy is of particular interest given the FAT1 (WNT regulator) mutations and CXCR4 upregulation frequently seen in chemo-refractory CLL (Messina et al, Blood 2014; Burger et al, Blood 2006), and future efforts will assess the stability and interplay of these two pathways in patient samples collected upon relapse to fludarabine and cyclophosphamide. Further, our approach provides a template for the high-resolution study of tens of thousands of clones and their respective phenotypes in a mixed leukemic population. Disclosures Neuberg: Pharmacyclics: Research Funding; Madrigal Pharmaceuticals: Equity Ownership; Celgene: Research Funding. Wu:Pharmacyclics: Research Funding; Neon Therapeutics: Other: Member, Advisory Board.