Multiomic Single Cell Sequencing Identifies Stemlike Nature of Mixed Phenotype Acute Leukemia and Provides Novel Risk Stratification.

Mixed phenotype acute leukemia (MPAL) is a leukemia whose biologic drivers are poorly understood, therapeutic strategy remains unclear, and prognosis is poor. We performed multiomic single cell (SC) profiling of 14 newly diagnosed adult MPAL patients to characterize the immunophenotypic, genetic, and transcriptional landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. However, progressive acquisition of mutations is associated with increased expression of immunophenotypic markers of immaturity. Using SC transcriptional profiling, we find that MPAL blasts express a stem cell-like transcriptional profile distinct from other acute leukemias and indicative of high differentiation potential. Further, patients with the highest differentiation potential demonstrated inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in this cohort, is applicable to bulk RNA sequencing data and was predictive of survival in an independent patient cohort, suggesting utility for clinical risk stratification.

Microfluidics-free single-cell genomics with templated emulsification.

Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.

An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.

Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment.

An Integrative Gene Expression and Mathematical Flux Balance Analysis Identifies Targetable Redox Vulnerabilities in Melanoma Cells.

Melanomas harboring BRAF mutations can be treated with BRAF inhibitors (BRAFi), but responses are varied and tumor recurrence is inevitable. Here we used an integrative approach of experimentation and mathematical flux balance analyses in BRAF-mutated melanoma cells to discover that elevated antioxidant capacity is linked to BRAFi sensitivity in melanoma cells. High levels of antioxidant metabolites in cells with reduced BRAFi sensitivity confirmed this conclusion. By extending our analyses to other melanoma subtypes in The Cancer Genome Atlas, we predict that elevated redox capacity is a general feature of melanomas, not previously observed. We propose that redox vulnerabilities could be exploited for therapeutic benefits and identify unsuspected combination targets to enhance the effects of BRAFi in any melanoma, regardless of mutational status. SIGNIFICANCE: An integrative bioinformatics, flux balance analysis, and experimental approach identify targetable redox vulnerabilities and show the potential for modulation of cancer antioxidant defense to augment the benefits of existing therapies in melanoma.

Drug-Tolerant Idling Melanoma Cells Exhibit Theory-Predicted Metabolic Low-Low Phenotype.

Cancer cells adjust their metabolic profiles to evade treatment. Metabolic adaptation is complex and hence better understood by an integrated theoretical-experimental approach. Using a minimal kinetic model, we predicted a previously undescribed Low/Low (L/L) phenotype, characterized by low oxidative phosphorylation (OXPHOS) and low glycolysis. Here, we report that L/L metabolism is observed in BRAF-mutated melanoma cells that enter a drug-tolerant "idling state" upon long-term MAPK inhibition (MAPKi). Consistently, using publicly available RNA-sequencing data of both cell lines and patient samples, we show that melanoma cells decrease their glycolysis and/or OXPHOS activity upon MAPKi and converge toward the L/L phenotype. L/L metabolism is unfavorable for tumor growth, yet supports successful cell division at ~50% rate. Thus, L/L drug-tolerant idling cells are a reservoir for accumulating mutations responsible for relapse, and it should be considered as a target subpopulation for improving MAPKi outcomes in melanoma treatment.

A Nonquiescent "Idling" Population State in Drug-Treated, BRAF-Mutated Melanoma.

Targeted therapy is an effective standard of care in BRAF-mutated malignant melanoma. However, the duration of tumor remission varies unpredictably among patients, and relapse is almost inevitable. Here, we examine the responses of several BRAF-mutated melanoma cell lines (including isogenic subclones) to BRAF inhibitors. We observe complex response dynamics across cell lines, with short-term responses (<100 h) varying from cell line to cell line. In the long term, however, we observe equilibration of all drug-treated populations into a nonquiescent state characterized by a balanced rate of death and division, which we term the "idling" state, and to our knowledge, this state has not been previously reported. Using mathematical modeling, we propose that the observed population-level dynamics are the result of cells transitioning between basins of attraction within a drug-modified phenotypic landscape. Each basin is associated with a drug-induced proliferation rate, a recently introduced metric of an antiproliferative drug effect. The idling population state represents a new dynamic equilibrium in which cells are distributed across the landscape such that the population achieves zero net growth. By fitting our model to experimental drug-response data, we infer the phenotypic landscapes of all considered melanoma cell lines and provide a unifying view of how BRAF-mutated melanomas respond to BRAF inhibition. We hypothesize that the residual disease observed in patients after targeted therapy is composed of a significant number of idling cells. Thus, defining molecular determinants of the phenotypic landscape that idling populations occupy may lead to "targeted landscaping" therapies based on rational modification of the landscape to favor basins with greater drug susceptibility.