Follicular lymphoma (FL) is a low-grade B-cell malignancy that transforms into a highly aggressive and lethal disease at a rate of 2% per year. Perfect isolation of the malignant B-cell population from a surgical biopsy is a significant challenge, masking important FL biology, such as immune checkpoint coexpression patterns. To resolve the underlying transcriptional networks of follicular B-cell lymphomas, we analyzed the transcriptomes of 34 188 cells derived from 6 primary FL tumors. For each tumor, we identified normal immune subpopulations and malignant B cells, based on gene expression. We used multicolor flow cytometry analysis of the same tumors to confirm our assignments of cellular lineages and validate our predictions of expressed proteins. Comparison of gene expression between matched malignant and normal B cells from the same patient revealed tumor-specific features. Malignant B cells exhibited restricted immunoglobulin (Ig) light chain expression (either Igκ or Igλ), as well the expected upregulation of the BCL2 gene, but also downregulation of the FCER2, CD52, and major histocompatibility complex class II genes. By analyzing thousands of individual cells per patient tumor, we identified the mosaic of malignant B-cell subclones that coexist within a FL and examined the characteristics of tumor-infiltrating T cells. We identified genes coexpressed with immune checkpoint molecules, such as CEBPA and B2M in regulatory T (Treg) cells, providing a better understanding of the gene networks involved in immune regulation. In summary, parallel measurement of single-cell expression in thousands of tumor cells and tumor-infiltrating lymphocytes can be used to obtain a systems-level view of the tumor microenvironment and identify new avenues for therapeutic development.

Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

Oligodendrocyte progenitor cells (OPC) undergo asymmetric cell division (ACD) to generate one OPC and one differentiating oligodendrocyte (OL) progeny. Loss of pro-mitotic proteoglycan and OPC marker NG2 in the OL progeny is the earliest immunophenotypic change of unknown mechanism that indicates differentiation commitment. Here, we report that expression of the mouse homolog of Drosophila tumor suppressor Lethal giant larvae 1 (Lgl1) is induced during OL differentiation. Lgl1 conditional knockout OPC progeny retain NG2 and show reduced OL differentiation, while undergoing more symmetric self-renewing divisions at the expense of asymmetric divisions. Moreover, Lgl1 and hemizygous Ink4a/Arf knockouts in OPC synergistically induce gliomagenesis. Time lapse and total internal reflection microscopy reveals a critical role for Lgl1 in NG2 endocytic routing and links aberrant NG2 recycling to failed differentiation. These data establish Lgl1 as a suppressor of gliomagenesis and positive regulator of asymmetric division and differentiation in the healthy and demyelinated murine brain.

Breast cancer progresses in a multistep process from primary tumor growth and stroma invasion to metastasis. Nutrient-limiting environments promote chemotaxis with aggressive morphologies characteristic of invasion. It is unknown how coexisting cells differ in their response to nutrient limitations and how this impacts invasion of the metapopulation as a whole. In this study, we integrate mathematical modeling with microenvironmental perturbation data to investigate invasion in nutrient-limiting environments inhabited by one or two cancer cell subpopulations. Subpopulations were defined by their energy efficiency and chemotactic ability. Invasion distance traveled by a homogeneous population was estimated. For heterogeneous populations, results suggest that an imbalance between nutrient efficacy and chemotactic superiority accelerates invasion. Such imbalance will spatially segregate the two populations and only one type will dominate at the invasion front. Only if these two phenotypes are balanced, the two subpopulations compete for the same space, which decelerates invasion. We investigate ploidy as a candidate biomarker of this phenotypic heterogeneity and discuss its potential to inform the dose of mTOR inhibitors (mTOR-I) that can inhibit chemotaxis just enough to facilitate such competition. Significance: This study identifies the double-edged sword of high ploidy as a prerequisite to personalize combination therapies with cytotoxic drugs and inhibitors of signal transduction pathways such as mTOR-Is.

The phenotypic efficacy of somatic copy number alterations (SCNAs) stems from their incidence per base pair of the genome, which is orders of magnitudes greater than that of point mutations. One mitotic event stands out in its potential to significantly change a cell’s SCNA burden–a chromosome missegregation. A stochastic model of chromosome mis-segregations has been previously developed to describe the evolution of SCNAs of a single chromosome type. Building upon this work, we derive a general deterministic framework for modeling missegregations of multiple chromosome types. The framework offers flexibility to model intra-tumor heterogeneity in the SCNAs of all chromosomes, as well as in missegregation- and turnover rates. The model can be used to test how selection acts upon coexisting karyotypes over hundreds of generations. We use the model to calculate missegregation-induced population extinction (MIE) curves, that separate viable from non-viable populations as a function of their turnover- and missegregation rates. Turnover- and missegregation rates estimated from scRNA-seq data are then compared to theoretical predictions. We find convergence of theoretical and empirical results in both the location of MIE curves and the necessary conditions for MIE. When a dependency of missegregation rate on karyotype is introduced, karyotypes associated with low missegregation rates act as a stabilizing refuge, rendering MIE impossible unless turnover rates are exceedingly high. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering MIE unlikely.

Tetraploidy is an aneuploidy-permissive condition that can fuel tumorgenesis. The tip-over hypothesis of cytotoxic therapy sensitivity proposes that therapy is effective if it pushes a cell's aneuploidy above a viable tipping point. But elevated aneuploidy alone may not account for this tipping point. Tissue microenvironments that lack sufficient resources to support tetraploid cells can explain the fitness cost of aneuploidy. Raw materials needed to generate deoxynucleotides, the building blocks of DNA, are candidate rate-limiting factors for the evolution of high-ploidy cancer cells. Understanding the resource cost of high ploidy is key to uncover its therapeutic vulnerabilities across tissue sites with versatile energy supplies.