The Role of the Tumor Microenvironment in CSC Enrichment and Chemoresistance: 3D Co-culture Methods

Cancer stem-like cells (CSC) are responsible for tumor progression, chemoresistance, recurrence, and poor outcomes in many cancers, making them critical research and therapeutic targets. One of the critical components potentiating CSC chemoresistance is the interactions between CSC and the surrounding cells in the tumor microenvironment. Our lab has developed several 3D co-culture models to study ovarian CSC interactions with stromal or immune cells found in ovarian tumor microenvironments. In this chapter, we use ovarian cancer as a model to describe the methodologies developed in our lab; however, these techniques are applicable to a wide range of cancers. First, we discuss our method for isolating CSC from heterogeneous tumors and for creating 3D self-assembled tumoroids in hanging drop plates, in either monoculture or co-culture with mesenchymal stem cells or monocytes/macrophages. We then discuss methods for analyzing these models with a focus on isolating cell-type-specific changes and mechanism investigation. Specifically, we describe lentiviral transduction and flow cytometry as established and robust methods to identify and separate each cell type for downstream analysis. We then describe methods to examine CSC functionality with transwell migration assays and colorimetric MTS-based proliferation assays. Finally, we demonstrate enzyme-linked immunosorbent assays (ELISA ) and quantitative polymerase chain reaction (qPCR) methods as mechanistic investigation tools to decouple paracrine and juxtacrine interactions. These methods have wide-reaching applications in cancer research from basic biological investigations, to drug discovery, and personalized drug screening for precision medicine.

Applications of single-cell sequencing in cancer research: progress and perspectives

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Breast cancers, mammary stem cells, and cancer stem cells, characteristics, and hypotheses

The cellular heterogeneity of breast cancers still represents a major therapeutic challenge. The latest genomic studies have classified breast cancers in distinct clusters to inform the therapeutic approaches and predict clinical outcomes. The mammary epithelium is composed of luminal and basal cells, and this seemingly hierarchical organization is dependent on various stem cells and progenitors populating the mammary gland. Some cancer cells are conceptually similar to the stem cells as they can self-renew and generate bulk populations of nontumorigenic cells. Two models have been proposed to explain the cell of origin of breast cancer and involve either the reprogramming of differentiated mammary cells or the dysregulation of mammary stem cells or progenitors. Both hypotheses are not exclusive and imply the accumulation of independent mutational events. Cancer stem cells have been isolated from breast tumors and implicated in the development, metastasis, and recurrence of breast cancers. Recent advances in single-cell sequencing help deciphering the clonal evolution within each breast tumor. Still, few clinical trials have been focused on these specific cancer cell populations.

An engineered pancreatic cancer model with intra-tumoral heterogeneity of driver mutations

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease with significant intra-tumoral heterogeneity (ITH). Currently, no reliable PDAC tumor model is available that can present ITH profiles in a controlled manner. We develop an in vitro microfluidic tumor model mimicking the heterogeneous accumulation of key driver mutations of human PDAC using cancer cells derived from genetically engineered mouse models. These murine pancreatic cancer cell lines have KPC (Kras and Trp53 mutations) and KIC genotypes (Kras mutation and Cdkn2a deletion). Also, the KIC genotypes have two distinct phenotypes - mesenchymal or epithelial. The tumor model mimics the ITH of human PDAC to study the effects of ITH on the gemcitabine response. The results show gemcitabine resistance induced by ITH. Remarkably, it shows that cancer cell-cell interactions induce the gemcitabine resistance potentially through epithelial-mesenchymal-transition. The tumor model can provide a useful testbed to study interaction mechanisms between heterogeneous cancer cell subpopulations.

Early Prediction of Single-Cell Derived Sphere Formation Rate Using Convolutional Neural Network Image Analysis

Functional identification of cancer stem-like cells (CSCs) is an established method to identify and study this cancer subpopulation critical for cancer progression and metastasis. The method is based on the unique capability of single CSCs to survive and grow to tumorspheres in harsh suspension culture environment. Recent advances in microfluidic technology have enabled isolating and culturing thousands of single cells on a chip. However, tumorsphere assay takes a relatively long period of time, limiting the throughput of this assay. In this work, we incorporated machine learning with single-cell analysis to expedite tumorsphere assay. We collected 1,710 single-cell events as the database and trained a convolutional neural network model that predicts whether a single cell could grow to a tumorsphere on Day 14 based on its Day 4 image. With this future-telling model, we precisely estimated the sphere formation rate of SUM159 breast cancer cells to be 17.8% based on Day 4 images. The estimation was close to the ground truth of 17.6% on Day 14. The preliminary work demonstrates not only the feasibility to significantly accelerate tumorsphere assay but also a synergistic combination between single-cell analysis with machine learning, which can be applied to many other biomedical applications.