Issue 22 : August, 2019 PDF Icon

CTD² Guest Editorial
Dissecting Cellular Heterogeneity using Single-cell RNA Sequencing

Single-cell RNA sequencing enables analysis of the transcriptome of individual cell, provides information on cell-states, and allows a high-resolution characterization of heterogeneous tumor microenvironment. It can be used to discover therapeutic targets and can enable mechanistic understanding of target inhibition.

HCMI Program Highlights
Scientific Applications of Next-generation Cancer Models

HCMI is providing the scientific community with next-gen cancer models that more closely resemble primary tumors, and that are annotated with genomic and clinical data. The article provides examples of how next-gen models have been applied in research.

CTD² Guest Editorial
GenePattern Notebook: Integration of Electronic Notebooks with Bioinformatics Tools for Genomic Data Analysis

The GenePattern Notebook is an electronic notebook that enables integrative genomic analyses. These analyses are displayed in a user-friendly form and allows scientists even without programming experience to share, collaborate, and publish the results.

HCMI Program Highlights
Collecting Uniform Clinical Data for a Community Resource

HCMI’s clinical Case Report Forms (CRFs) standardize the clinical data that are collected from participating Tissue Source Sites (TSSs) collaborating with HCMI. The article discusses the process of developing cancer-type specific CRFs to ensure uniformity and compatibility for use at TSSs across the globe.

OCG Perspective
Leveraging a Genomics Background to Facilitate Molecular Characterization of HCMI Models

This perspective article introduces Dr. Lauren Hurd, a new Scientific Program Manager for the Human Cancer Models Initiative. Dr. Hurd discusses her background in genomics and its applications in her current role.

CTD² Guest Editorial
Dissecting Cellular Heterogeneity using Single-cell RNA Sequencing

Anuja Sathe, M.B.B.S., Ph.D. and Hanlee P. Ji, M.D.
Stanford University
Anuja Sathe, M.B.B.S., Ph.D. and Hanlee P. Ji, M.D.

Gene expression analysis using RNA sequencing has contributed immensely to our knowledge of cancer. However, bulk sequencing methods average signals by pooling information from a mass of cells. These methods are thus unable to fully resolve the complexity of intra-tumor heterogeneity in cancer1. Single-cell RNA sequencing (scRNA-seq) enables the analysis of the transcriptome of each individual cell and allows a high-resolution characterization of tumors. Moreover, tumors are not isolated masses of cancer cells but are surrounded by a unique microenvironment composed of different cell types. scRNA-seq enables the analysis of this heterogenous tumor microenvironment (TME) that is increasingly important in improving treatment strategies such as immunotherapy. Unlike other single-cell analysis methods such as mass cytometry or CyTOF, scRNA-seq allows an unbiased assessment of cellular phenotypes. This enables the identification of not just heterogenous cell types but provides information on individual cell states.

In recent years, scRNA-seq has been used to construct single cell atlases of several tumor types2. These studies have revealed novel targets in sub-populations of cancer cells as well as in the TME. We have successfully used scRNA-seq in the characterization of lymphoma TME using patient biopsies and in an organoid mouse model of gastric cancer3,4. We are applying it to understand the TME of gastrointestinal cancers from fresh surgical specimens as well as in cell line models to delineate clonal heterogeneity5.

Several technologies have been developed for scRNA-seq6. They differ in their method of cell isolation (e.g. plate or microfluidics-based), capture and length of transcript (full-length, 3’ or 5’ end) as well as the chemistry used for reverse transcription and amplification. Choosing a particular platform for an experiment depends on the question being investigated. For example, microfluidics-based techniques are more high-throughput than plate-based ones. Methods that capture the 3’ or 5’ transcript do not allow the detection of splicing events, isoforms, or quantifying allelic expression.

Following sequencing, a typical analytical workflow begins with data matrices containing entries of molecular counts corresponding to each gene and cell, which are represented in respective rows or columns. This high-dimensional data is generally analyzed using dimensionality reduction (e.g. with principal component analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE)) followed by clustering of cells with similar transcriptional profiles (Figure). Understanding differences across clusters is aided by differential expression testing.

Schematic representation of microfluidics based scRNA-seq workflow

Figure: Schematic representation of microfluidics based scRNA-seq workflow: Tissue specimens are dissociated into a single-cell suspension followed by a microfluidics based scRNA-seq protocol. cDNA from each individual cell is tagged with a unique barcode and made into a sequencing library. The resulting data is a matrix of molecular counts per transcript per cell. High dimensional data is processed using dimensionality reduction and clustering approaches. (Image credit: Created with BioRender)

A major challenge in scRNA-seq analysis results from the noise and variability of the assay owing to a high number of zero transcript counts. These dropouts can be biological, related to the stochasticity of mRNA expression, or technical, owing to the low amount of transcript material and limited efficiency of its capture. This can result in distorted false negative or false positive profiles. scRNA-seq requires careful attention to quality control and use of appropriate computational methods that are suited for such data distribution7. Another limitation of scRNA-seq is that it does not retain any spatial information. Moreover, disaggregation methods are required to produce a single-cell suspension that could introduce artifacts in the gene expression program. Commercial assays and instrumentation can also be expensive.

A number of technical and computational approaches are enabling improvements in scRNA-seq that overcome many of these challenges. For example, conducting parallel experiments such as RNA in situ hybridization, imaging mass cytometry, or spatial transcriptomics can enable integration of spatial information. As sequencing costs reduce, the current cost for an scRNA-seq experiment with a microfluidics-based platform works out to be less than 1 USD/cell (https://satijalab.org/costpercell). This is additionally aided by modifications to the assay using antibodies to tag each cell that enable sample multiplexing8. Several quality control and imputation methodologies are also being developed to improve data analytics9,10. Novel assay developments such as single-cell DNA sequencing, single-cell Assay for Transposase Accessible Chromatin (ATAC) sequencing, single-cell T Cell Receptor (TCR) sequencing, and single-cell epitope sequencing can also be integrated with scRNA-seq to reveal a wealth of information at high granularity.

scRNA-seq is thus equipped to answer several important questions in cancer biology including target discovery and can enable a mechanistic understanding of target inhibition. It also has tremendous potential in translational applications such as longitudinal patient monitoring of treatment response11.

References
  1. Andor N, Graham TA, Jansen M, et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med. 2016 Jan;22(1):105-13. (PMID: 26618723)
  2. Valdes-Mora F, Handler K, Law AMK, et al. Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology. Front Immunol. 2018 Nov 12;9:2582. (PMID: 30483257)
  3. Chen J, Lau BT, Andor N, et al. Single-cell transcriptome analysis identifies distinct cell types and niche signaling in a primary gastric organoid model. Sci Rep. 2019 Mar 14;9(1):4536. (PMID: 30872643)
  4. Andor N, Simonds EF, Czerwinski DK, et al. Single-cell RNA-Seq of follicular lymphoma reveals malignant B-cell types and coexpression of T-cell immune checkpoints. Blood. 2019 Mar 7;133(10):1119-1129. (PMID: 30591526)
  5. Andor N, Lau BT, Catalanotti C, et al. Joint single cell DNA-Seq and RNA-Seq of gastric cancer reveals subclonal signatures of genomic instability and gene expression. bioRxiv. 2018 Oct 17. doi: 10.1101/445932
  6. Haque A, Engel J, Teichmann SA, et al. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017 Aug 18;9(1):75. (PMID: 28821273)
  7. Chen G, Ning B, Shi T. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front Genet. 2019 Apr 5;10:317. (PMID: 31024627)
  8. Stoeckius M, Zheng S, Houck-Loomis B, et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 2018 Dec 19;19(1):224. (PMID: 30567574)
  9. Huang M, Wang J, Torre E, et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods. 2018 Jul;15(7):539-542. (PMID: 29941873)
  10. Ilicic T, Kim JK, Kolodziejczyk AA, et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016 Feb 17;17:29. (PMID: 26887813)
  11. Shalek AK, Benson M. Single-cell analyses to tailor treatments. Sci Transl Med. 2017 Sep 20;9(408). (PMID: 28931656)

HCMI Program Highlights
Scientific Applications of Next-generation Cancer Models

Cindy Kyi, Ph.D.
Office of Cancer Genomics, NCI
HCMI banner with an organoid

As a contribution to efforts in precision oncology, the National Cancer Institute’s (NCI) Human Cancer Models Initiative (HCMI) is developing next-generation (next-gen) cancer models, which include 3D organoids, neurospheres, 2D adherent, and conditionally reprogrammed cells. These next-gen models are derived from a variety of cancer types including poor outcome cancers, rare cancers, and cancers from ethnic and racial minorities, as well as pediatric populations. HCMI was founded by the NCI, Cancer Research UK, Hubrecht Organoid Technology, and Wellcome Sanger Institute to develop about 1,000 next-gen cancer models. The next-gen cancer models are annotated with molecular characterization data, as well as clinical data to address challenges of traditional cancer cell lines. The goal of HCMI is to provide the research community with a rich resource of diverse, fully annotated next-gen models (NGCMs) to better study disease biology.

Historically, traditional cancer cell lines have provided a platform to conduct large scale studies, such as investigating molecular regulations of cancer cell growth and progression, identifying genetic and biological markers, and predicting drug sensitivities. The Cancer Cell Line Encyclopedia, a repository of cancer cell lines with associated molecular data and analyses, is a valuable resource of over 1,100 cell lines generated from numerous cancer types. Another resource is the Cancer Therapeutics Response Portal (CTRP) which houses a large dataset of quantitative small-molecule sensitivity data of cancer cell lines. This resource could be used to mine for lineages or mutations, enriched among cell lines, that are sensitive to small-molecules and identify new therapeutic vulnerabilities. The next-gen cancer models aim to address limitations of most available cell lines such as poor or unknown representation of the cellular architecture of the original tumor, heterogeneity of cell types and  genetic drivers of cancer subtypes1.

Next-generation models

The successful culture and expansion of organoids from murine small intestinal tissue paved the way for early organoid models generated from mouse colon and human small intestine and colonic epithelium2. Human intestinal organoids were observed to mimic in vivo cellular differentiation; however, adaptations of the culture media were needed to successfully grow organoids from different tissue types. Sato and colleagues reported optimized cell culture methods utilizing growth factors, Notch protein inhibitors, nicotinamide, and kinase enzyme inhibitors for culturing primary human epithelial cells from small intestine, colon, adenoma, adenocarcinomas, and Barrett’s esophagus. The models were hypothesized to be more representative of the tumor biology than colon cancer cell lines2. The group recently published their protocols for generating next-gen cancer models (NGCMs) from breast normal and tumor tissues3.

Identifying an optimized culture medium for each cancer and tissue type is critical for successfully growing next-gen models which retain their originating tumor characteristics. According to Ince and colleagues, ovarian tumor cell lines grown in standard culture media: “(1) had very low success rate (less than one percent) [sic of being established in culture], (2) had long lag times for the first passage, (3) could only be propagated for up to 15 passages and (4) lacked the phenotype of original tumor”4. The authors developed 25 diverse ovarian cell lines using optimized culture media compositions for each human ovarian cancer subtype. The resulting ovarian cancer cell lines retained the genomic landscape, histopathology, and molecular characteristics of the original tumors from which they were derived. The expression profiles and drug responses of these cell lines were also found to correlate with patient outcomes4.

Applications of next-generation models

Next-gen models have been shown to be excellent research tools to carry out ex vivo experiments as they recapitulate the biology and tissue architecture of primary tumors. A few examples of applications of next-gen models in research include studying disease progression, identifying genomic and molecular drivers of diseases, and screening compounds or small molecules for treatment sensitivity and/or resistance.

  • Studying disease progression in pancreatic cancers can be challenging due to lack of patient-derived models that cover the full spectrum of disease progression, lack of clinical correlations, and accumulation of genetic aberrations. Boj and colleagues were able to generate human derived organoids from normal and neoplastic ductal cells using modified culture conditions5. Through targeted sequencing of cancer-associated genes on organoids derived from human normal and tumor tissues, the authors identified oncogenic KRAS mutations in majority of the tumor-derived models indicating the organoids represented the cancer driver mutations observed in the originating human tumors.
  • Patient-derived organoid models of pancreatic ductal adenocarcinoma (PDAC) were used to test chemosensitivity and chemoresistance of individual tumors. One of the limitations of traditional cell lines is that due to genetic drift after multiple passages, there are differences in genetic profiles between the original tumor and the derived cell lines; such as copy number alterations, DNA methylation, molecular subtypes, and resulting phenotypes. Tiriac and colleagues found that the patient-derived PDAC organoids harbored genetic alterations that are consistent with known pathogenic mutations in PDAC6. The authors concluded that the organoids recapitulated the mutational spectrum and molecular subtypes of primary pancreatic cancer and, therefore, are excellent models to accurately examine and predict responses to chemotherapeutic agents6.
  • The lack of model systems that reflect the pathology of the primary disease and responses to therapy presents a challenge in studying esophageal adenocarcinoma (EAC)1. Using patient tumor-derived organoid models, Li and colleagues could identify tumor drivers of EAC through histological and genomic characterization1. The molecular annotation of the EAC organoids showed that they retained patient-specific gene expression, disrupted cellular polarity, intra-tumor heterogeneity, and drug sensitivity1. Based on these findings, the use of patient-derived organoids provides model systems to accurately study disease.
  • The variability in drug response due to cellular heterogeneity presents another challenge in cancer research using traditional cell lines. Next-gen cancer models were shown to produce reliable responses that resemble those of the originating tumor when screening targeted therapy compounds7.Similar to the responses found in mouse organoids, treatment of patient-derived organoids with Itraconazole, a cell cycle inhibitor compound8, led to inhibited organoid growth and cell death7. Buczacki and colleagues suggested that they found a therapeutic potential of Itraconazole in inducing cancer cell death and preventing late recurrence in colorectal cancer7. Hence, patient-derived organoids could be used to identify novel drug targets