Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Single-cell landscape of primary central nervous system diffuse large B-cell lymphoma

Understanding tumor heterogeneity and immune infiltrates within the tumor-immune microenvironment (TIME) is essential for the innovation of immunotherapies. Here, combining single-cell transcriptomics and chromatin accessibility sequencing, we profile the intratumor heterogeneity of malignant cells and immune properties of the TIME in primary central nervous system diffuse large B-cell lymphoma (PCNS DLBCL) patients. We demonstrate diverse malignant programs related to tumor-promoting pathways, cell cycle and B-cell immune response. By integrating data from independent systemic DLBCL and follicular lymphoma cohorts, we reveal a prosurvival program with aberrantly elevated RNA splicing activity that is uniquely associated with PCNS DLBCL. Moreover, a plasmablast-like program that recurs across PCNS/activated B-cell DLBCL predicts a worse prognosis. In addition, clonally expanded CD8 T cells in PCNS DLBCL undergo a transition from a pre-exhaustion-like state to exhaustion, and exhibit higher exhaustion signature scores than systemic DLBCL. Thus, our study sheds light on potential reasons for the poor prognosis of PCNS DLBCL patients, which will facilitate the development of targeted therapy.

Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space

Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two cell state geometries for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; (i) by using partial differential equations on a graph representing intermediate cell states between known cell types, and (ii) by using the equations on a multi-dimensional continuous cell state-space. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell states during the pathogenesis of acute myeloid leukemia. We particularly focus on comparing the strength and weakness of the graph model and multi-dimensional model.

Single Cell Gene Expression Analysis in a 3D Microtissue Liver Model Reveals Cell Type-Specific Responses to Pro-Fibrotic TGF-β1 Stimulation

3D cell culture systems are widely used to study disease mechanisms and therapeutic interventions. Multicellular liver microtissues (MTs) comprising HepaRG, hTERT-HSC and THP-1 maintain multicellular interactions and physiological properties required to mimic liver fibrosis. However, the inherent complexity of multicellular 3D-systems often hinders the discrimination of cell type specific responses. Here, we aimed at applying single cell sequencing (scRNA-seq) to discern the molecular responses of cells involved in the development of fibrosis elicited by TGF-β1. To obtain single cell suspensions from the MTs, an enzymatic dissociation method was optimized. Isolated cells showed good viability, could be re-plated and cultured in 2D, and expressed specific markers determined by scRNA-seq, qRT-PCR, ELISA and immunostaining. The three cell populations were successfully clustered using supervised and unsupervised methods based on scRNA-seq data. TGF-β1 led to a fibrotic phenotype in the MTs, detected as decreased albumin and increased αSMA expression. Cell-type specific responses to the treatment were identified for each of the three cell types. They included HepaRG damage characterized by a decrease in cellular metabolism, prototypical inflammatory responses in THP-1s and extracellular matrix remodeling in hTERT-HSCs. Furthermore, we identified novel cell-specific putative fibrosis markers in hTERT-HSC (COL15A1), and THP-1 (ALOX5AP and LAPTM5).

Investigating Inter- and Intrasample Diversity of Single-Cell RNA Sequencing Datasets

Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for researchers to easily assay tumor heterogeneity at the transcriptomic level with high resolution. However, ongoing research focuses on different ways to analyze this big data and how to compare across multiple different samples. In this chapter, we provide a practical guide to calculate inter- and intrasample diversity metrics from single-cell RNA sequencing datasets. These measures of diversity are adapted from commonly used metrics in statistics and ecology to quantify and compare sample heterogeneity at single-cell resolution.

Spontaneous cell fusions as a mechanism of parasexual recombination in tumour cell populations

The initiation and progression of cancers reflect the underlying process of somatic evolution, in which the diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination-a key diversification mechanism throughout all strata of life. On the basis of observations of spontaneous fusions involving cancer cells, the reported genetic instability of polypoid cells and the precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions between genetically distinct cancer cells could produce parasexual recombination. Using differentially labelled tumour cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modelling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumour cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape (relative to strictly asexual populations), which may substantially accelerate a tumour's ability to adapt to new selective pressures.

Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments

High-throughput sequencing (HTS) has revolutionized researchers' ability to study the human transcriptome, particularly as it relates to cancer. Recently, HTS technology has advanced to the point where now one is able to sequence individual cells (i.e., "single-cell sequencing"). Prior to single-cell sequencing technology, HTS would be completed on RNA extracted from a tissue sample consisting of multiple cell types (i.e., "bulk sequencing"). In this chapter, we review the various bioinformatics and statistical methods used in the processing, quality control, and analysis of bulk and single-cell RNA sequencing methods. Additionally, we discuss how these methods are also being used to study tumor heterogeneity.

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer

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.

Single-Cell Analysis of Different Stages of Oral Cancer Carcinogenesis in a Mouse Model

Oral carcinogenesis involves the progression of the normal mucosa into potentially malignant disorders and finally into cancer. Tumors are heterogeneous, with different clusters of cells expressing different genes and exhibiting different behaviors. 4-nitroquinoline 1-oxide (4-NQO) and arecoline were used to induce oral cancer in mice, and the main factors for gene expression influencing carcinogenesis were identified through single-cell RNA sequencing analysis. Male C57BL/6J mice were divided into two groups: a control group (receiving normal drinking water) and treatment group (receiving drinking water containing 4-NQO (200 mg/L) and arecoline (500 mg/L)) to induce the malignant development of oral cancer. Mice were sacrificed at 8, 16, 20, and 29 weeks. Except for mice sacrificed at 8 weeks, all mice were treated for 16 weeks and then either sacrificed or given normal drinking water for the remaining weeks. Tongue lesions were excised, and all cells obtained from mice in the 29- and 16-week treatment groups were clustered into 17 groups by using the Louvain algorithm. Cells in subtypes 7 (stem cells) and 9 (keratinocytes) were analyzed through gene set enrichment analysis. Results indicated that their genes were associated with the MYC_targets_v1 pathway, and this finding was confirmed by the presence of cisplatin-resistant nasopharyngeal carcinoma cell lines. These cell subtype biomarkers can be applied for the detection of patients with precancerous lesions, the identification of high-risk populations, and as a treatment target.

Assessing Cell Activities rather than Identities to Interpret Intra-Tumor Phenotypic Diversity and Its Dynamics

Despite advances in single-cell and molecular techniques, it is still unclear how to best quantify phenotypic heterogeneity in cancer cells that evolved beyond normal, known classifications. We present an approach to phenotypically characterize cells based on their activities rather than static classifications. We validated the detectability of specific activities (epithelial-mesenchymal transition, glycolysis) in single cells, using targeted RT-qPCR analyses and in vitro inductions. We analyzed 50 established activity signatures as a basis for phenotypic description in public data and computed cell-cell distances in 28,513 cells from 85 patients and 8 public datasets. Despite not relying on any classification, our measure correlated with standard diversity indices in populations of known structure. We identified bottlenecks as phenotypic diversity reduced upon colorectal cancer initiation. This suggests that focusing on what cancer cells do rather than what they are can quantify phenotypic diversity in universal fashion, to better understand and predict intra-tumor heterogeneity dynamics.

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Cells categorized as having the same identity can perform different activities

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Single-cell expression data can be used to infer the activities cells take part in

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Activity profiles provide a basis to measure phenotypic cell-cell divergence

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Cell activity can quantify intra-tumor heterogeneity more fully than identity