Single-Cell Omics: Deciphering Tumor Clonal Architecture

CHAPTER 5

Single-Cell Omics: Deciphering Tumor Clonal Architecture Kevin Dzobo*,†, Nicholas Ekow Thomford‡, Arielle Rowe*, Dimakatso Alice Senthebane*,†, Collet Dandara‡ *International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Cape Town, South Africa † Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa ‡ Pharmacogenetics Research Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

5.1

INTRODUCTION

Despite scientists’ concerted efforts to elucidate its underlying molecular mechanisms, cancer remains one of the deadliest diseases, with the number of new cases increasing worldwide. The process of DNA replication is a tightly controlled process with mistakes occurring rarely (Groth et al., 2007; MacAlpine and Almouzni, 2013). While, somatic alterations of DNA can occur in normal cells, mostly these are caused by environmental factors, inherited mutations, or unresolved mistakes during transcription or replication (Ortega et al., 2017; Tomasetti et al., 2013). If these genetic alterations occur in the noncoding regions of the DNA, they usually show no apparent effect on cellular behavior and characteristics (Blokzijl et al., 2016; Jager et al., 2018). The accumulation of mutations over time causes significant variation in the genetic makeup of parent and daughter cells (Blokzijl et al., 2016; Jager et al., 2018). Genetic changes that confer uncontrolled growth and the subsequent clonal expansion of a cell lead to cancer. Genetic alterations in key genes, sometimes referred to as driver genes, give cells harboring such mutations characteristics associated with increased growth rates, the ability to evade apoptosis, and resistance to therapy (Li and Zhu, 2014; Palle et al., 2015; Tomasetti et al., 2013). Thus, mutations to driver genes confer selective advantages on the Single-Cell Omics. https://doi.org/10.1016/B978-0-12-814919-5.00005-1 © 2019 Elsevier Inc. All rights reserved.

61

62

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

founding cell and subsequent generations. These cellular properties smooth the path to cancer development and progression (Robinson et al., 2015, 2017). Determining whether a mutation is a “driver” or a “passenger” requires functional analysis of such a mutation in several samples to determine whether the mutation affects the phenotype of a cancer cell (Sottoriva et al., 2017; Stratton et al., 2009). The distribution of driver mutations should be determined through bioinformatic algorithms to show the distribution in several tumor samples (Leiserson et al., 2013; Vandin et al., 2012; Zhang et al., 2014). In addition, passenger mutations are not necessarily irrelevant, as they point to how cancer cells have evolved over time (Gillespie, 2000; Smith and Haigh, 2007). In cancer evolution studies, passenger mutations can be used as a signal. To understand the human body and diseases such as cancer, genetic variation between cells need to be studied and elucidated. This requires the analysis of single cells using the latest omics technologies. Concurrently, several highthroughput methods and computational algorithms have been developed that analyze single-cell data (Sengupta et al., 2011; Tang et al., 2010). Currently, many novel methods and algorithms allow the analysis of single cells with increased sensitivity and coverage of the genome (Baran-Gale et al., 2018; Picelli et al., 2014). Several questions, unanswered for generations, can now be addressed through the use of omics technologies. The integration of single-cell analysis methods allows the concurrent analysis of multiple properties of single cells, such as genomics, transcriptomics, epigenomics, and proteomics (Lappalainen et al., 2013; Wu et al., 2016). The amount of starting genetic material required for sequencing analysis has been dramatically reduced so that it is now possible to sequence single cells (Shapiro et al., 2013; Zong et al., 2012). This has allowed the sequencing of organisms that cannot be cultured in vitro and the analysis of scarce circulating tumor cells (Kalisky et al., 2011; Ramskold et al., 2012). Single-cell sequencing now allows the elucidation of tumor heterogeneity that would otherwise be masked in bulk analysis; such sequencing also enables the study of early embryonic differentiation (Cristofanilli et al., 2005; Dalerba et al., 2011). Imaging and mass spectrometric analysis can also be used for tracking single cells (Managh et al., 2013; Shapiro et al., 2013). The introduction of microfluidics has allowed automation of cell isolation and subsequent analysis (Dong et al., 2016; Stumpf et al., 2015). Recently, microfluidics and emulsion-based methods have been combined to enable single-cell isolation and analysis (Zhang et al., 2012, 2013). The elucidation of the clonal architecture of a cancer is important in the identification of subpopulation driver mutations that are responsible for expansion, invasion, and metastasis. Although the genomic diversity within tumors has

5.2

Tumor Evolution and Heterogeneity

been recognized for decades, it is only with the advent of technologies such as deep- and single-cell sequencing that the full extent of this diversity has been revealed (Burrell and Swanton, 2014b; Marusyk et al., 2012). The sequencing of multiple regions of a single tumor has also shown the full extent of clonal diversity within tumors (Hu and Curtis, 2016; Lu et al., 2016; Schmidt and Efferth, 2016). In comparison, some tumors have homogenous coding mutations. For example, lung cancer has an elevated clonal mutation burden due to exposure to exogenous mutagens over a long period of time (Kim et al., 2015a; McGranahan and Swanton, 2017). It is important to note that tumor heterogeneity is not just a result of coding mutations; it can also result from epigenetic mechanisms. DNA methylation, remodeling of chromatin, and histone modifications contribute to the diversity observed in tumors (Mazor et al., 2015; McGranahan and Swanton, 2015; Meacham and Morrison, 2013). Tumor heterogeneity due to genomic copy number variations has been observed extensively in tumors such as clear cell renal cell carcinoma (Martinez et al., 2013). Thus it is important to consider both mutation data and copy number when inferring the evolutionary history of tumors (de Bruin et al., 2014; McPherson et al., 2016). This chapter discusses how the advent of omics techniques is driving new research involved in characterizing intratumor heterogeneity. The integration of single-cell omics analyses will result in information that has never been seen before regarding human cell lineage, with huge implications for medicine. Due to the transient nature of research in this area, many deserving works may not be cited and discussed.

5.2

TUMOR EVOLUTION AND HETEROGENEITY

The understanding that cancer is a disorder of aberrant genetic programming has driven the search for acquired somatic mutations and hallmark characteristics (Ellsworth et al., 2017; Kuipers et al., 2017). Such acquired mutations and hallmarks allow tumors to progress by providing a selective advantage to certain cells, resulting in clonal expansion of cells with these particular mutations and hallmarks (Nowell, 1976; Vogelstein et al., 1988). Tumors resulting from competition—and sometimes cooperation—between clones are therefore heterogeneous and complex in nature and present treatment challenges (Dexter et al., 1978; Heppner, 1984). A tumor can be viewed as an ecosystem in which all subclones interact with one another positively or negatively (Gallaher and Anderson, 2013; Greaves, 2015). The availability of large data sets—for example, through The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC)—revealed that tumors are heterogeneous entities resulting from competition between clones and expansion of the surviving

63

64

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

clones (Hudson et al., 2010; Merlo et al., 2006). In-depth analysis of recent genomic data shows a complicated clonal structure in different cancers, with no tumor being similar to another (Burrell and Swanton, 2016). The mutational inventory of a tumor illustrates its historical record, showing all the mutations accumulated over time (McGranahan and Swanton, 2017; Meacham and Morrison, 2013). To understand the order of events—in this case, mutations—differences can be inferred between tumor cells within a single tumor. Common mutations in tumor cells represent the trunk of the tumor’s evolutionary tree (Lee et al., 2015). Mutations found in a subset of cancer cells, referred to as subclonal, make up the branches of the evolutionary tree ( Jiao et al., 2014; Lee et al., 2015; McGranahan and Swanton, 2015). Several bioinformatic algorithms are available to decode the manner in which mutations were acquired and to evaluate clonal and subclonal mutations (Alexandrov et al., 2013; de Bruin et al., 2014; Hu and Curtis, 2016). Most of the available bioinformatic algorithms are used for somatic point mutations and use different assumptions, including the assumption that only one single mutation is present at the same copy number state in each single-cell (Yu et al., 2016, 2017). Distinguishing the type of mutations present depends on how many regions of the tumor are sequenced, how pure the sequenced samples are, and whether single-cell sequencing was performed (de Bruin et al., 2014; Gerlinger et al., 2014a, 2015). Tumor heterogeneity can result in chemoresistance as one or two clones might not respond to therapy (Dzobo et al., 2018). Single-cell analysis offers hope for understanding the heterogeneity of a tumor. At best, clonal evolution is explained by the Darwinian tree model: Several biopsies taken from different regions of the same tumor are used in high-throughput sequencing processes. A detailed understanding of tumor heterogeneity is obtained from a phylogenetic reconstruction of the clonal evolution. A driver mutation occurs in the tree trunk, and subclones arise later due to subsequent mutations. While the Darwinian nature of tumors has presented challenges for treatment, it may also hold great promise for the effective control of tumor progression and metastasis. In studies of esophageal adenocarcinoma in which clonal diversity was used as a biomarker, diverse clones were observed to spur tumor progression (Kuipers et al., 2017; Merlo et al., 2010). Clonal diversity and the accompanying tumor diversity are prerequisites for tumor drug resistance (Ding et al., 2012; Greaves and Maley, 2012; Shiba et al., 2016). Targeting the dominant clones in a tumor may result in the emergence of smaller subclones and the development of treatment resistance (Burrell and Swanton, 2014b; Gillies et al., 2012). After treatment, the smaller subclone can become the dominant clone. The sequence in which mutations are acquired is also important in tumor progression and treatment resistance (Kent et al., 2015;

5.2

Tumor Evolution and Heterogeneity

Ortmann et al., 2015). By revealing the complex nature of tumors, the generation of large cancer genomic data sets has also allowed for the development of precise or targeted therapies (McGranahan and Swanton, 2015; Stratton et al., 2009).

5.2.1

Methods for Resolving Intratumor Heterogeneity

Although next-generation sequencing methods can detect many mutations in tumors, these methods require bulk tissue and they provide only part of the information on the number and occurrence of clones within a tumor (Davis et al., 2017; Mardis, 2011). Several methods have been developed to address this. Multiregion sequencing, deep sequencing and single-cell sequencing are the latest techniques for elucidating the clonal structure of tumors (Nik-Zainal et al., 2012; Shah et al., 2012). Deep sequencing measures mutant allele frequencies, and when it is coupled with computational algorithms it can identify subpopulations of clones assumed to have the same mutant allele frequencies (Davis et al., 2017; Shah et al., 2012). The accuracy of this approach to resolve clonal subpopulation structure in a tumor is not guaranteed. Multiregion sequencing involves obtaining several samples from different regions of the tumor (Gerlinger et al., 2014a, 2015; McPherson et al., 2016). Exome sequencing is then performed to try to resolve the clonal structure of tumors. Mixed subclones in different locations of the tumor cannot be resolved. Single-cell sequencing, on the other hand, can resolve mixed subclones, because it involves single-cell isolation, then genomic amplification, followed by sequencing. Data from multiple cells is then used to reconstruct the clonal lineages (Navin et al., 2011; Wang and Navin, 2015; Wang et al., 2014).

5.2.2

What Is a Clone?

Although evidence suggests that tumors originate from an individual cell and therefore are clonal, tumor evolution continues throughout the life of a tumor or an organism (Burrell et al., 2013; Burrell and Swanton, 2014a,b). The definition of clone as applied to cancer is the different subpopulations of tumor cells present in a single tumor (Almendro et al., 2013; Marusyk and Polyak, 2010; Marusyk et al., 2014; Sottoriva et al., 2017). It is important to note that this definition is based on measured genotypes, not on phenotypes. However, defining clone in this way is problematic as the relationship between genotypes and phenotypes is complex. Other definitions that have been suggested for clone include the following: cells that share the same driver mutation, cells with the same genome, cancer cells from the same ancestor, and cells with the same measurable phenotype (Almendro et al., 2013; Gillespie, 2000; Marusyk et al., 2012; Sottoriva et al., 2017). Each of the above definitions has weaknesses. In cancer biology, the standard and common definition defines clone as “a group

65

66

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

of cancer cells from the same ancestor” (Sottoriva et al., 2017). The plastic nature of cancer cells makes defining clone using phenotype difficult (Gentric et al., 2016; Gerlinger et al., 2014b, 2015).

5.2.3 Tumor Evolution Models to Explain Clonal Lineages Observed in Tumors The complex process of tumor evolution involves a single cancer cell accumulating genetic aberrations over time and being able to transform. Distinct cancer cells form clonal lineages, resulting in different cancer cell subpopulations within a single tumor. Indeed, early studies showed morphological differences in the cells present in a single tumor. The term tumor evolution refers to “the field of biology employing the knowledge of population genetics and evolution to reveal how tumor cells respond to different selective pressures” (Nowell, 1976). To date, several models have been suggested to explain the observed tumor evolution: the linear evolution model, the branched evolution model, the neutral evolution model, and the punctuated evolution model (Davis et al., 2017; Davis and Navin, 2016).

5.2.3.1

Linear Tumor Evolution

The linear evolution model predicts that mutations are acquired linearly over time, with the result being more malignant or aggressive cancer stages (Davis et al., 2017; Fearon and Vogelstein, 1990). Acquiring new driver mutations provides the cancer cells with a strong selective advantage, allowing them to outcompete previous clones (Fig. 5.1) (Davis et al., 2017; Kroigard et al., 2015; Sidow and Spies, 2015). At each stage of tumor growth, a dominant clone is present, with the possibility of a minor clone as well (Fig. 5.1A and B). The phylogenetic tree expected from the linear tumor evolution model is shown in Fig. 5.2. Colon cancer was shown to progress through stepwise acquisition of mutations, resulting in more malignant tumor growth (Davis et al., 2017; Fearon and Vogelstein, 1990). Data from advanced tumors do not support the linear evolution model.

5.2.3.2

Branched Tumor Evolution

In the branched evolution model, clones diverge from the ancestral cell and gradually develop in the tumor without outcompeting each other. The clones all have increased fitness and grow concurrently, resulting in a “rainbow tumor” scenario in terms of the clones revealed when analyzed (Fig. 5.3A and B). The phylogenetic trees observed in branching evolution show clones that have expanded as a result of driver mutations in subclonal lineages. Several next-generation sequencing studies have shown the branching evolution model in many cancer types. For example, single-cell sequencing has revealed point mutations in breast, ovarian, and blood cancer (Gao et al., 2016; Gawad

5.2

Tumor Evolution and Heterogeneity

FIG. 5.1 Linear tumor evolution model. (A) An illustration of the linear evolution model showing the acquisition of mutations in a linear fashion over time. (B) The changing intratumor heterogeneity landscape in linear evolution model during tumor growth. Different colors show different clones with different genotypes.

et al., 2014; McPherson et al., 2016; Navin et al., 2011; Shah et al., 2012). As expected, studies supporting the branching evolution model differ in the trajectory of the inferred evolutionary tree (Davis et al., 2017). Evolution is continuous in branching evolution, with new driver mutations being acquired all the time, resulting in the appearance of new clones. An exome-sequencing study on single triple-negative breast cancer cells revealed subclonal driver mutations leading to clonal expansion of three subpopulations (Wang et al., 2014). Single-cell exome sequencing of colon cancer samples revealed subclonal mutations that resulted in the growth of a tumor subpopulation (Davis et al., 2017;

FIG. 5.2 Inferred evolutionary tree from the linear evolution model. Clones with different genotypes are shown in different colors.

67

68

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

FIG. 5.3 Branched tumor evolution model. (A) An illustration of the branched evolution model showing diverging clones that have similar fitness and therefore do not outcompete each other over time. (B) The changing intratumor heterogeneity landscape in linear evolution model during tumor growth. Different colors show different clones with different genotypes.

Yu et al., 2014a). The presence of multiple clones as predicted in the branching evolution model suggests clonal cooperation, with several studies showing clones contributing to expansion of other clones (Cleary et al., 2014; Marusyk et al., 2014). Other studies have shown that some clones do not compete at all, allowing them to concurrently expand without interference (Inda et al., 2010). The phylogenetic tree expected from the branched tumor evolution model is shown in Fig. 5.4.

FIG. 5.4 Inferred evolutionary tree from the branched evolution model. Clones have similar fitness and are able to coexist in the same tumor. Clones with different genotypes are shown in different colors.

5.2

5.2.3.3

Tumor Evolution and Heterogeneity

Neutral Evolution

In the neutral evolution model, random mutations are acquired over time, resulting in extensive intratumor heterogeneity, making this an extraordinary form of branching evolution (Fig. 5.5A and B) (Davis et al., 2017; Kimura, 1983). There is no selection pressure in the lifetime of the tumor, with multiple clones coexisting, resulting in extensive intratumor heterogeneity. The phylogenetic tree inferred by neutral evolution is extensively branched. Several studies have shown the presence of multiple subclones in tumors through multiregion sequencing and have revealed that, indeed, neutral evolution occurs in many tumors (Ling et al., 2015; Sottoriva et al., 2017; Williams et al., 2016). The phylogenetic tree expected from neutral tumor evolution model is shown in Fig. 5.6.

5.2.3.4

Punctuated Tumor Evolution

Not all tumors acquire mutations in a stepwise fashion over time. The consequence of the sequential acquisition of mutations over time is that intratumor heterogeneity is at its highest point at later stages of tumor growth. In the punctuated evolution model, however, rapid short bursts of genetic change may occur within a short period of time, followed by the growth of one or two dominant clones (Fig. 5.7A and B) (Davis et al., 2017; Sottoriva et al., 2017). In this model, intratumor heterogeneity is at its peak in the early stages of tumor

FIG. 5.5 Neutral tumor evolution model. (A) An illustration of the neutral evolution model showing the extensive acquisition of mutations resulting in extensive intratumor heterogeneity. (B) The changing intratumor heterogeneity landscape in neutral evolution model during tumor growth. Neutral evolution is an extraordinary form of branching evolution displaying extensive intratumor heterogeneity. Different colors show different clones with different genotypes.

69

70

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

FIG. 5.6 Inferred evolutionary tree from the neutral evolution model. Clones with different genotypes are shown in different colors.

growth, with the resulting tumor composed of one or two clones. The inferred evolutionary tree is one of a long root node followed by one or two clones. There are no intermediate clones, and this model is also called the “big bang”

FIG. 5.7 Punctuated tumor evolution model. (A) An illustration of the punctuated evolution model, showing rapid short bursts of acquisition of mutations in a short period of time. (B) The changing intratumor heterogeneity landscape in the punctuated evolution model during tumor growth, showing intratumor heterogeneity at its peak during the early stages of tumor growth. Different colors show different clones with different genotypes.

5.2

Tumor Evolution and Heterogeneity

FIG. 5.8 Inferred evolutionary tree from the punctuated evolution model. The evolutionary tree is characterized by having a long root node followed by one or two clones. Clones with different genotypes are shown in different colors.

tumor evolution model (Sievers et al., 2016; Sottoriva et al., 2015). The phylogenetic tree expected from the punctuated tumor evolution model is shown in Fig. 5.8. Several studies utilizing single-cell DNA sequencing support the punctuated evolution model. Genome-wide copy number variations were revealed in invasive breast cancer patients through single-cell DNA sequencing (Navin et al., 2011). A study of colorectal cancer patients revealed several genetic alterations that suited the “big bang” model (Sottoriva et al., 2015).

5.2.4

Tumor Heterogeneity and Evolutionary Histories

In tumor biology, the ultimate goal of sequencing and interrogating single cells is to understand the evolutionary history of a tumor and associated tumor heterogeneity. The initiation and progression, and even the metastasis, of a tumor is driven by the accumulative acquisition of genetic variants in individual cells (Schmitt et al., 2012; Zhang and Zhang, 2015). To understand tumor biology, it is important to elucidate the variants present in tumors (Akbani et al., 2014; Weinstein et al., 2013). When a bulk sample is used, sensitivity of detection of variants is limited, which means that some variants might not be detected (Campbell et al., 2015; Freed and Pevsner, 2016; Samuels and Friedman, 2015). Due to technological challenges, most strategies for understanding tumor heterogeneity and clonal expansion and diversity have used bulk samples, where DNA from thousands or millions of cells is mixed before sequencing (Greaves and Maley, 2012; Heppner, 1984; Kuipers et al., 2017; Meyerson et al., 2010). The result is an estimation of the different variants within the sample, with no precise knowledge of the evolutionary history of the tumor. Bulk sequencing

71

72

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

shows variant allele frequency data, which cannot differentiate different evolutionary histories (Allison and Sledge, 2014; Kuipers et al., 2017; Zare et al., 2014). Allele frequency data alone cannot infer the true evolutionary history that matches the clonal expansion observed in tumors. One way to improve this situation is by taking multiple samples from the tumor or by taking the samples at different times (Donmez et al., 2017; Jiang et al., 2016; Zare et al., 2014). Even with such improvements, low-frequency mutations are not easily detected and therefore are grouped with other mutations (Navin, 2014; Van Loo and Voet, 2014). Achieving the highest resolution of a tumor’s evolutionary history requires single-cell analysis (Donmez et al., 2017; Ellsworth et al., 2017; Kuipers et al., 2017; Navin, 2014). Constructing the genealogical tree from single cells requires the assumption that once a mutation occurs it is present in subsequent generations and occurs only once. Perfect calling of cellular mutations results in the efficient reconstruction of the phylogeny. On the downside, mutation calling errors can be large with single-cell sequencing data (Donmez et al., 2017; Ellsworth et al., 2017; Navin, 2014).

5.3

BULK SEQUENCING

Most evolutionary histories have been constructed using bulk sequencing data (Kuipers et al., 2017; Satas and Raphael, 2017). However, a bulk sample is made up of millions of cells, and the resultant sequencing shows allele frequencies of individual mutations (Fig. 5.9) (Ellsworth et al., 2017; Navin, 2014). Important information—such as the number of subclones, the extent of their occurrence, and their genealogy—is undetermined (Ellsworth et al., 2017; Navin, 2014).

5.4 SINGLE-CELL SEQUENCING: DECIPHERING THE CLONAL ARCHITECTURE OF TUMORS The arrival of next-generation sequencing methods (deep sequencing, multiregion sequencing, single-cell genomic sequencing) and the lowering of genomic analysis costs signaled the “era of big data” and the increased push to understand tumor diversity and heterogeneity. Thousands of tumor specimens and the data obtained from their analysis were deposited in their respective public data banks, such as the TCGA and the ICGC (Hudson et al., 2010). In deep sequencing, mutant allele frequencies can be measured when next-generation sequencing is performed at high coverage depth (Davis et al., 2017; Shah et al., 2012). Multiple samplings of the same tumor show remarkable intratumor heterogeneity in many cancer types (Burrell and Swanton, 2014b; Gerlinger et al., 2015; Mattos-Arruda et al., 2018; Mazio et al., 2018). Both deep

5.4

Single-Cell Sequencing: Deciphering the Clonal Architecture of Tumors

FIG. 5.9 The stepwise process of bulk sample sequencing and the phylogenetic reconstruction process.

sequencing and multiregion sequencing are limited. Despite lingering challenges, single-cell sequencing has the capacity to revolutionize many fields of biology, including cancer, especially in resolving the tumor heterogeneity conundrum. One major challenge related to single-cell sequencing is the limited starting genetic material available in a single-cell. To overcome this limitation, genetic material has to be amplified before sequencing. Four main steps are involved in single-cell genomic sequencing: (1) the isolation of single cells from a sample, (2) cell lysis, (3) isolation of genetic material, and (4) amplification. Each of these steps carries the potential for introduction of errors and experimental artifacts (Navin et al., 2011). A detailed description of these steps is beyond the scope of this chapter.

5.4.1

Single-Cell Isolation From Tumor Samples

To be able to isolate an individual cell from a sample, the sample must first be disintegrated, cells be suspended in a solution, and the cells must be viable. Disintegration of a solid sample requires either mechanical or enzymatic disintegration; further, it must not be harsh and must not be biased toward a specific cell

73

74

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

type or subpopulation. Tissues from different parts of the body and those with a pathological condition can have different disintegration kinetics compared to normal tissues. Further research is needed to optimize protocols used for the disintegration of these different tissues (Dressler and Visscher, 2001; Leelatian et al., 2017). One method commonly used for the isolation of genetic material from single cells is laser-capture microdissection (Bonner et al., 1997; Datta et al., 2015; Frost et al., 2015). It is a slow process, however, with the sequencing data obtained from single cells of relatively poor quality (Gawad et al., 2016). In this method cells are isolated directly from their native tissue with no chemical or physical disintegration of sample or tissue. A thermoplastic film is used to cover the target tissue, and a laser is fired to melt the film. The film adheres to a single-cell, allowing its mechanical isolation from the rest of the tissue. Possible contamination may come from microenvironmental material. New methods, including microfluidics, have been used for specific purposes, such as enriching for specific tumor cells (Navin, 2014). Methods such as serial dilution, microwell dilution, and the use of optical tweezers can be employed to isolate individual cells from a suspension (Lan et al., 2017; Zilionis et al., 2017). fluorescence-activated cell sorting (FACS) analysis can also be employed to isolate individual cells from a suspension (Navin et al., 2011). FACS relies on fluorescence signals and light-scattering parameters of a cell. Target cells within a cell suspension are immunoreacted with fluorescence-tagged antibodies. The antibodies are specific for surface markers or other markers on the target cell. Labeled cells are passed through the FACS machine, with the flow controlled so that each droplet contains one cell. The light-scattering properties of the cell help identify the cell. Magnetic-activated cell sorting takes advantage of the immunoreactivity of antigens on cell surfaces and antibody-coated magnetic beads. The mixture is then sorted by a magnetic activated cell sorting (MACS) column using a MACS separator. This technique has been useful in the isolation of circulating tumor cells.

5.4.2

Single-Cell Sequencing of Tumor Samples

One major challenge with sequencing and interrogating single cells is the small amount of starting genetic material. On average, single cells contain 10–30 pg of total RNA and approximately 6 pg of DNA (Gawad et al., 2016; Kuipers et al., 2017). This genetic material is all that is available for amplification before sequencing. As for protein, only about 250 pg are available in each single-cell. Several computational algorithms have been developed to interrogate singlecell data and to address amplification errors such as false positives and false negatives (Fu et al., 2015; Huang et al., 2015; Yu et al., 2014b). Without these computational algorithms and bioinformatic tools, it is difficult if not

5.4

Single-Cell Sequencing: Deciphering the Clonal Architecture of Tumors

FIG. 5.10 The stepwise process of single-cell sequencing and phylogenetic reconstruction.

impossible to interrogate next-generation sequencing data and to accurately identify genetic heterogeneity. Researchers can use single-cell sequencing data obtained from whole-genome and whole-exome sequencing to reconstruct the tumor evolutionary tree. Using DNA and RNA material for single-cell sequencing would entail the amplification of the initial single copy to have enough material for sequencing (Fig. 5.10, illustration for DNA). Several amplification methods are in use today. A method commonly used for amplification is multiple displacement amplification (MDA). MDA results in higher coverage compared to earlier version of PCRbased amplifications (Hou et al., 2012; Kuipers et al., 2017; Xu et al., 2012). One advantage of several of the latest methods used in single-cell sequencing is the low rate of false positives (Hou et al., 2012; Kuipers et al., 2017). Currently, false positives are in the 10%–20% range, and they remain an important part of modeling single-cell sequencing data (Hou et al., 2012; Kuipers et al., 2017). To reduce the cost of sequencing and to attain low error rates, deep bulk sequencing can be done first, followed by selection of sites that are assumed to have mutations. After

75

76

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

coming up with a panel for the patients involved, final sequencing and mutation calling can be done (Gawad et al., 2014). When used for six leukemia patients, this approach reduced the number of false positives (Kuipers et al., 2017).

5.5 SINGLE-CELL RNA SEQUENCING OF TUMOR SAMPLES RNA sequencing (RNA-Seq) has been used in bulk sample analysis for quite some time now, and it provides valuable information on cancer types and progression (Cancer Genome Atlas Research Network, 2017a,b; Robinson et al., 2017; Varn et al., 2015). For researchers to gain a better understanding of the heterogeneous nature of tumor samples, RNA sequencing of single cells is necessary (Tang et al., 2009; Yang et al., 2017). Some tumor constituents can be concealed in bulk RNA-Seq (Tirosh et al., 2016; Yang et al., 2017). The amount of published RNA-Seq data is on the rise since RNA-Seq was first used in 2009, with single-cell RNA-Seq demonstrating clear advantages over bulk RNA-Seq. First, single-cell RNA-Seq can elucidate and accurately show the cell-to-cell variations between individual cells. Transcriptomic data is very difficult to interpret, however, due to technical noise as well as the problem of lowly expressed genes. Second, and more important, single-cell RNA-Seq data can be used to reconstruct clonal evolution more accurately than bulk RNA-Seq (Hoeijmakers et al., 2013; Maekawa et al., 2014; Tang et al., 2010). In addition, single-cell RNA sequencing is useful for monitoring changes in subclonal populations during therapeutic treatment; in this way it helps doctors make informed decisions about any changes in treatment regimens (Huang et al., 2018; Maeda et al., 2018). The major concern in the amplification of low concentrations of RNA from single cells is the introduction of bias. Several molecular approaches are available for creating single-cell RNA-Seq libraries, including Smart-Seq, BAT-Seq, and CEL-Seq (Picelli et al., 2013, 2014; Ziegenhain et al., 2017). These approaches differ in their sensitivities, meaning they differ in the amount of initial RNA needed for amplification in order for the results to be trustworthy (Ortega et al., 2017; Ziegenhain et al., 2017). A comparison of different RNA-Seq methods shows that chip-based methods are more sensitive, while dropletbased microfluidic methods allow a higher number of cells to be counted (Svensson et al., 2017; Ziegenhain et al., 2017). Several computational algorithms have been developed to deal with noise and to elucidate tumor cell heterogeneity through reconstructing evolution timelines from RNA-Seq data (Buettner et al., 2015; Trapnell et al., 2014). Some of these algorithms are based on dimensional reduction; others are based on nearest-neighbor graphs; a third type of lineage reconstruction algorithms is based on cluster networks.

5.6

Phylogeny Reconstruction

RNA-Seq has been used in the study of several cancer types, and it has enabled researchers to gather invaluable gene expression data for following cancer development and metastasis (Hoeijmakers et al., 2013; Hou et al., 2012; Robinson et al., 2017; Sho et al., 2017; Tang et al., 2010; Varn et al., 2015; Yang et al., 2017). Patel and colleagues used the Smart-Seq protocol to perform single-cell RNA-Seq analysis on 430 glioblastomas, revealing tumor heterogeneity in diverse regulatory programs important in glioblastoma biology and treatment (Patel et al., 2014). An improved protocol of Smart-Seq, now referred to as Smart-Seq2, was used to analyze thousands of cells from oligodendrogliomas; this protocol showed the presence of cancer stem cells, potentially revealing the source of oligodendrogliomas (Picelli et al., 2013; Tirosh et al., 2016). By revealing clones that otherwise would be concealed, single-cell RNA-Seq allows discovery of biomarkers that might have been undetectable if bulk RNA-Seq were used. Studies are looking into combining single-cell RNA-Seq data with available bulk RNA-Seq data for a deeper and broader data-mining potential (Ortega et al., 2017; Venteicher et al., 2017). Single-cell RNA-Seq has also been used to profile circulating tumor cells (Beije et al., 2016; Jordan et al., 2016; Ramskold et al., 2012).

5.6 5.6.1

PHYLOGENY RECONSTRUCTION Phylogenetic Reconstruction Using Bulk Data

Given the various technological challenges and the ease with which bulk sampling of tumors is done, most evolutionary histories have been constructed using bulk sequencing data (Kuipers et al., 2017; Satas and Raphael, 2017; Zaccaria et al., 2018). However, a bulk sample is made up of millions of cells, and the resultant sequencing will show the allele frequencies of individual mutations (Ellsworth et al., 2017; Navin, 2014). Important information such as the number of subclones, the extent of their occurrence, and their genealogy remain undetermined (Ellsworth et al., 2017; Navin, 2014). Table 5.1 Clonal Reconstruction Algorithms Based on Bulk Data. Algorithm

Bulk Data

Inference

Reference

PhyloSub PyClone SciClone CloneHD SubcloneSeeker CANOPY

SNVs SNVs SNVs SNVs and CNAs SNVs and CNAs SNVs and CNAs

Markov chain Monte Carlo Markov chain Monte Carlo Beta mixture model Hidden Markov model Exhaustive enumeration Markov chain Monte Carlo

Jiao et al. (2014) Roth et al. (2014) Miller et al. (2014) Fischer et al. (2014) Qiao et al. (2014) Jiang et al. (2016)

77

78

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

Many computational tools have been developed to reconstruct tumor evolution using single nucleotide variant data (Table 5.1) (El-Kebir et al., 2015; Griffith et al., 2015; Jiao et al., 2014; Roth et al., 2014; Strino et al., 2013; Zare et al., 2014). These include PhyloSub, PyClone, and SciClone. To make precise estimations about the extent of mutations in a cell when using single nucleotide variation data, it is important to correct allele frequencies for ploidy anomalies (Marotti et al., 2017; Roth et al., 2014; Shah et al., 2012). All strategies utilizing single-nucleotide variant (SNV) data for phylogeny reconstruction apply an assumption that restricts the space of mutations histories. The infinite sites assumption holds that no site on the genome has more than one mutation throughout the tumor evolutionary history, and the mutation will be maintained in subsequent generations. SNV-based strategies also have to fix the clustering of mutations with approximate allele frequencies (Huang et al., 2016; Larson and Fridley, 2013). It is assumed that variants with similar frequencies are either present or absent in every subpopulation (Kuipers et al., 2017; Larson and Fridley, 2013; Shah et al., 2012). The problem of erroneously clustering mutations together is known to be increased when the number of subclones is higher (Strino et al., 2013). The process of arranging mutations in a tree format is based on the prevalence of mutations in a cell, and it can be done once clustering has been fixed. Having several samples from one patient provides an advantage. Usually, subclones with a higher frequency are easily placed in the tree during reconstruction from SNV bulk data, as there is enough discriminative power to show their evolutionary relationships (Kuipers et al., 2017; Navin, 2014; Van Loo and Voet, 2014). For low-frequency subclones, the signal from mixed variant allele frequencies is too weak for a definitive reconstruction (Kuipers et al., 2017; Navin, 2014; Van Loo and Voet, 2014). Only a few strategies are available that use copy number alterations data alone to infer subclones (Oesper et al., 2013, 2014; Yu et al., 2016). Combining SNV and copy number alteration (CNA) data makes it possible to increase the discriminative power during the reconstruction process. Examples of such methods include SubcloneSeeker, MixClone, CloneHD, CHAT, and SCHISM (Table 5.1) (Fischer et al., 2014; Li and Li, 2014; Li and Xie, 2015; Qiao et al., 2014). These methods have their strengths and weaknesses. Only SubcloneSeeker infers phylogenetic trees based on cellular prevalences of both SNV and CNA data, but it also relies on other tools for the estimation of the prevalences (Kuipers et al., 2017; Qiao et al., 2014). Like SubcloneSeeker, SCHISM requires other tools to estimate the cellular prevalences (Niknafs et al., 2015). Tree inference strategies make use of the infinite sites assumption. Copy number alterations affect larger segments of the genetic material, so the chance of the segments overlapping is increased, meaning that in this case the infinite sites assumption might not hold. Recent

5.6

Phylogeny Reconstruction

strategies such as CANOPY and SPRUCE attempt to solve issues associated with earlier approaches such as recognizing that CNA are interdependent and should be viewed as sequences of events rather than independent alterations to chromosomes (El-Kebir et al., 2016; Jiang et al., 2016; Kuipers et al., 2017). At this time, most tree inference models do not consider structural rearrangements, although work in this area is ongoing (Greenman et al., 2012; Purdom et al., 2013). Especially important for phylogeny reconstruction are two studies that estimated the order of genomic rearrangement events and applied the HapMap data, which can be useful when combined with other models (Greenman et al., 2012).

5.6.2

Phylogenetic Reconstruction Using Single-Cell Data

Cells in a tumor are interconnected, having originated from the same ancestor (Caiado et al., 2016; Clifford, 2012; Ding et al., 2012; Engle et al., 2015; Greaves and Maley, 2012; Kuipers et al., 2017). Phylogenetic algorithms such as hierarchical clustering struggle with noisy data are straightforward and easy to use for reconstruction. The needs and specifics of single-cell data mean that the present approaches have to be modified in terms of phylogenetic inference (Table 5.2). Probabilistic approaches are now being used to select the best possible phylogenetic tree (Caiado et al., 2016; Kuipers et al., 2017). These approaches are applied based on how they explain the available single-cell data, and they must take into considerations all the inconsistencies and errors introduced during the experiment (Kuipers et al., 2017; La Porta and Zapperi, 2017; Lan et al., 2017; Lapa et al., 2017). A measure of the phylogenetic tree to the data can be taken by the Bayes theorem, which finds the probability of a tree from the data ( Jiao et al., 2014; Kim and Simon, 2014; Navin et al., 2011; Yuan et al., 2015). In the case of single-cell data, mutations have to be present in none or one of the allele and not at arbitrary frequencies. An analysis of mutations present in single cells will show a vast number of possible phylogenetic trees. It is important to find optimal trees resulting from the data analysis.

Table 5.2 Clonal Reconstruction Algorithms Based on Single-Cell Data. Algorithm

Inference

Reference

SCITE OncoNEM BitPhylogeny

Markov chain Monte Carlo Greedy structure search Tree-structure Markov chain Monte Carlo

Jahn et al. (2016) Ross and Markowetz (2016) Yuan et al. (2015)

79

80

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

5.7

SINGLE-CELL EPIGENETICS

Epigenetics refers to the heritable phenotypic differences through genetic expression, but it does not involve changes to the DNA. Examples include nucleic acid modification and such histone modifications as methylation and acetylation (Chen et al., 2016; Fu and He, 2012). Cellular diversity introduced through epigenetics adds to cellular heterogeneity and thus to tumor heterogeneity (Dawson and Kouzarides, 2012; Kanwal et al., 2015). DNA methylation is associated with transcriptional repression of genes, while hypomethylation results in transcriptional activation (Dawson and Kouzarides, 2012; Kanwal et al., 2015). By studying epigenetics at the individual cell level, scientists gain more knowledge about how methylation patterns are maintained throughout clonal evolution (Beltran et al., 2016; Kim et al., 2015b). Single-cell bisulfite sequencing is one method used to infer cytosine modification (Guo et al., 2015; Smallwood et al., 2014). A major drawback of this method is that DNA fragmentation occurs, reducing the quality of the input. Two other techniques— single-cell reduced representation bisulfite sequencing (scRRBS) and single-cell whole-genome bisulfite sequencing (scWGBS-Seq)—are also available, each with its own strengths and weaknesses.

5.8

SINGLE-CELL PROTEOMICS

The final frontier in studying single-cell heterogeneity is single-cell proteomics. Currently, single-cell proteomic studies are hampered by the lack of sensitivity, as protein cannot be amplified like DNA and RNA (Gavasso et al., 2016; Hoppe et al., 2014). In addition, functional proteins are transient in nature, making quantification complicated (Heath et al., 2016). Several methods have been developed to address the problem of low protein quantity in an individual cell. One of these is single-cell time-of-flight mass cytometry (CyTOF) (Cheung and Utz, 2011; Kay et al., 2016). CyTOF is limited in the number of parameters it can measure, yielding a lower throughput than scRNA-Seq. Single-cell proteomic by mass spectrometry (SCoPE-MS) is a new method for the isolation of proteins from individual cells in preparation for mass spectrometry; individual cells are manually isolated before lysis. Other nonmass spectrometry approaches make use of chip-based isolation. In the single-cell barcoded chip method, individual cells are isolate into separate wells; then antibodies are used to probe the proteins before analysis with a microarray scanner (Shi et al., 2012). A number of computational algorithms are now available for individual cell protein analysis and for inference of subpopulations and specific markers; these algorithms include SPADE, Phenograph, and Wishbone (Su et al., 2017).

5.9

5.9

Single-Cell Multiomics

SINGLE-CELL MULTIOMICS

Multiomics strategies allow researchers to get the full picture of all factors contributing to cellular phenotype. A general trend is revealed when bulk analysis is done, but this masks low-occurring cell subtypes (Li et al., 2017). The challenge in using multiomics lies in preserving the different molecules isolated from an individual cell. The observed phenotype of a cell is the outcome of the interplay between the genetic material (DNA, RNA), proteins, and the epigenome. Thus the analysis of individual molecules present in the cell gives an incomplete picture of the cell phenotype (Macaulay et al., 2017). To obtain a deeper knowledge of the cellular state requires determining different molecules—such as RNA, DNA, and protein—simultaneously. Understanding the interactions of different molecules in the cell during development and diseases allows for the development of mathematical and mechanistic models that explain processes such as protein expression dynamics. “Feature expression” matrices can be created through read alignment, quality control, and processing steps and displayed in a graph with individual cells as vectors and phenotypic features as columns. Phenotypic features can be gene expression and methylation, for example. Independent analysis of different omics matrices results in detection of cell subpopulations. Used together, multiomics data can infer the underlying features and mechanisms responsible for cellular identity and function. As for tumors, neoplastic predisposition markers can be identified and can provide a basis for treatment strategies. The genotype-phenotype axis of single cells provided by multiomics allows the elucidation of biological and pathological processes, especially in cancer where genomic mutations and the downstream products drive tumor evolution (Gerlinger et al., 2015; Kuipers et al., 2017; Seoane and De Mattos-Arruda, 2014). Besides providing a picture of the tumor evolution, multiomics approaches allow the understanding of tumor therapy resistance. Only through the concurrent study of more cellular molecules such as DNA, RNA, and proteins (genomic, transcriptional state, and proteomic) from individual cells are scientists able to identify the molecular targets that are causing the diversity in therapy responsiveness. One important area in which single-cell multiomics is being used is the reconstruction of phylogenetic lineage of tumor evolution. By evaluating the degree of shared somatic variants inherited from a common ancestral cell, researchers can reconstruct the cell lineage (Shapiro et al., 2013). Once the clonal architecture of a tumor has been reconstructed using single-cell sequencing, lineages within the tumor can be annotated using transcriptomic cell states. Combining genomic-inferred lineage trees and transcriptomic cell states allows the understanding of the biology of genomic-transcriptomic cellular heterogeneity in cancer, especially during therapy. The acquisition of a driver mutation

81

82

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

FIG. 5.11 Single-cell multiomics. The acquired mutations, revealed by DNA sequencing analysis, can be overlaid with information from the transcriptomic analysis of the individual cell, as done by RNA sequencing. The integration of DNA- and RNA-sequencing data reveals the gene expression profiles of tumor subclones.

in a single-cell results in its transformation into a malignant cell. The clonal expansion of the cell, accompanied by acquisition of more driver and passenger mutations, results in the creation of tumor subclones with similar and specific DNA mutations. DNA and RNA sequencing analyses reveal the acquired DNA mutations in a single-cell and the transcriptomic state of the cell, respectively. Information from DNA mutation is used to reconstruct the cell lineage tree, while data from RNA sequencing reveals the transcriptomic states of the cells (Fig. 5.11). Any multicellular organism has cells with different genotypes due to mutations acquired during the organism’s development (Shapiro et al., 2013). These mutations can be used to illuminate the lineage of cells in an organism. By combining information from the analysis of several molecules, the cellular phylogeny of an organism or diseased tissue—for example, a tumor—can be inferred using multiomics strategies (Macaulay et al., 2017; Shapiro et al., 2013). Several previous multiomics studies have focused primarily on linking epigenetics and transcriptional variations. Macaulay and colleagues established a sequencing technique to measure genetic variation and gene expression concurrently (Macaulay et al., 2015). The method used in the study, single-cell G&T– Seq, is an improvement on Smart-Seq2, allowing Smart-Seq2 to perform transcriptome analysis; it can be used together with several methods for DNA amplification. This improved method can analyze gene expression as well as transcript coverage lengths (Ortega et al., 2017). Several single-cell studies of tumors revealed that the connection between methylomes and transcriptomes differs between cells in a single tumor (Angermueller et al., 2016; Macaulay

5.10

et al., 2017; Ortega et al., 2017). Another method, called CITE-Seq, is used to integrate cellular protein markers and transcriptome in individual cells through the use of oligonucleotide-labeled antibodies (Stoeckius et al., 2017). As technological advances continue, three omics single-cell analyses are becoming possible. Recent work shows that it is possible to perform single-cell genomic copy number variation, DNA methylation, and transcription gene expression analyses concurrently (Hou et al., 2016). One method that can be used in this way is single-cell Trio-Seq (scTrio-Seq).

5.10

DISCUSSION

To elucidate the evolutionary history and heterogeneity of tumors, computational algorithms are needed—both to analyze the sequencing data and to resolve the phylogeny (Gawad et al., 2016). Bulk data analysis evaluates the prevalence of mutations in samples. Better accuracy using deeper sequencing can distinguish the occurrence of mutations and can help to resolve a tumor’s evolutionary history (Griffith et al., 2015). In bulk sequencing, the DNA or RNA obtained from thousands if not millions of cells makes it difficult to untangle the clonal structure; additional difficulty arises in resolving lowfrequency mutations (Griffith et al., 2015; Kuipers et al., 2017). To perform single-cell sequencing, researchers must amplify the initial DNA or RNA; this introduces noise in the sequencing data. Computational algorithms have been applied to solve problems associated with single-cell sequencing, such as mutation-calling and correcting errors in the calling by using clustering (Roth et al., 2016; Zafar et al., 2016). Future models for single-cell sequencing will have to account for such technical errors as sampling doublets (Roth et al., 2016). Results from several instances of single-cell sequencing show that the infinite sites assumption is not followed all the time; the assumption needs to be replaced or used with other rules that capture the full complexity of tumor evolution (Gawad et al., 2016; Kim and Simon, 2014; Yuan et al., 2015). Limitations of single-cell sequencing include the difficulty of using individual cells to obtain a better picture of the occurrence of clones and their mutations; this is especially true for heterogeneous tumors. It is also possible to miss lowfrequency clones. One solution to some of these limitations is to sequence more cells, but that is a costly exercise. The most effective solution would be to combine single-cell sequencing and bulk sequencing, taking advantage of their individual characteristics. For example, to obtain a detailed analysis of breast cancer clonal architecture and the separation into subclones, single-cell sequencing was performed on 47 single cells ( Jahn et al., 2016; Wang et al., 2014), and about 40 mutations were uncovered. Taking into account the uncertainties in the mutations observed, the researchers used probabilistic

Discussion

83

84

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

phylogenetic models to infer the tree structure ( Jahn et al., 2016; Kuipers et al., 2017). Based on available single-cell data, at least 50 single cells had to be sequenced for the data to give a high-resolution picture of the tumor. What is needed most at the moment are methods that can combine both single-cell sequencing data and bulk sequencing. Single-cell sequencing offers the possibility of explaining how metastasis would fit into the evolutionary history of a tumor (Kuipers et al., 2017; Reiter et al., 2017).

5.11

FUTURE DIRECTIONS IN CANCER RESEARCH

The advent of new technologies has allowed the field of single-cell omics— including genomics, transcriptomics, proteomics, and epigenetics—to begin to be used to decipher very important biological questions that have been unanswered for generations. This is even more true for cancer biology and evolution. With the rapid developments in single-cell sequencing, it will soon be possible to analyze almost all single-cell genomic variations, including structural variants in noncoding regions of the genome. It is important that the right analysis be done in accordance with the question being asked. This analysis may be whole-genome sequencing, whole-exome sequencing, or targeted sequencing. The cost, speed, and desired quality of the data must be factored into the decision-making process. It is encouraging that several improvements are being made in computational algorithms, as this increases the accuracy of variant calling and the clonal structures identified. It is also important that the nomenclature used in cancer sequencing studies be standardized, so that comparisons can made more easily across studies and to avoid confusion that could hinder the advancement of the field. Important in the same vein is a definition of clone. As the sensitivity of technology continues to increase, variants unique to individual cells are being identified. A consensus must be reached on whether rare mutations can be used to call such cells an independent clonal population (Gawad et al., 2016). Single-cell genomic sequencing can now be done at the same time as transcriptomic, epigenomic, and proteomic analyses, allowing answers to be found for some of the most difficult biological questions.

References Akbani, R., Ng, P.K., Werner, H.M., Shahmoradgoli, M., Zhang, F., Ju, Z., Liu, W., Yang, J.Y., Yoshihara, K., Li, J., Ling, S., Seviour, E.G., Ram, P.T., Minna, J.D., Diao, L., Tong, P., Heymach, J.V., Hill, S.M., Dondelinger, F., Stadler, N., Byers, L.A., Meric-Bernstam, F., Weinstein, J.N., Broom, B.M., Verhaak, R.G., Liang, H., Mukherjee, S., Lu, Y., Mills, G.B., 2014. A pan-cancer proteomic perspective on the cancer genome atlas. Nat. Commun. 5, 3887.

References

Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Aparicio, S.A.J.R., Behjati, S., Biankin, A.V., Bignell, G.R., Bolli, N., Borg, A., Børresen-Dale, A.-L., Boyault, S., Burkhardt, B., Butler, A.P., Caldas, C., Davies, H.R., Desmedt, C., Eils, R., Eyfj€ ord, J.E., Foekens, J.A., Greaves, M., Hosoda, F., Hutter, B., Ilicic, T., Imbeaud, S., Imielinski, M., J€ager, N., Jones, D.T.W., Jones, D., Knappskog, S., Kool, M., Lakhani, S.R., Lo´pez-Otı´n, C., Martin, S., Munshi, N.C., Nakamura, H., Northcott, P.A., Pajic, M., Papaemmanuil, E., Paradiso, A., Pearson, J.V., Puente, X.S., Raine, K., Ramakrishna, M., Richardson, A.L., Richter, J., Rosenstiel, P., Schlesner, M., Schumacher, T.N., Span, P.N., Teague, J.W., Totoki, Y., Tutt, A.N.J., ValdesMas, R., van Buuren, M.M., van ’t Veer, L., Vincent-Salomon, A., Waddell, N., Yates, L.R., Australian Pancreatic Cancer Genome Initiative, ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, ICGC PedBrain, Zucman-Rossi, J., Andrew Futreal, P., McDermott, U., Lichter, P., Meyerson, M., Grimmond, S.M., Siebert, R., Campo, E., Shibata, T., Pfister, S.M., Campbell, P.J., Stratton, M.R., 2013. Signatures of mutational processes in human cancer. Nature 500, 415. Allison, K.H., Sledge, G.W., 2014. Heterogeneity and cancer. Oncology (Williston Park) 28 (9), 772–778. Almendro, V., Marusyk, A., Polyak, K., 2013. Cellular heterogeneity and molecular evolution in cancer. Annu. Rev. Pathol. 8, 277–302. Angermueller, C., Clark, S.J., Lee, H.J., Macaulay, I.C., Teng, M.J., Hu, T.X., Krueger, F., Smallwood, S., Ponting, C.P., Voet, T., Kelsey, G., Stegle, O., Reik, W., 2016. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13 (3), 229–232. Baran-Gale, J., Chandra, T., Kirschner, K., 2018. Experimental design for single-cell RNA sequencing. Brief. Funct. Genomics 17, 233–239. Beije, N., Onstenk, W., Kraan, J., Sieuwerts, A.M., Hamberg, P., Dirix, L.Y., Brouwer, A., de Jongh, F.E., Jager, A., Seynaeve, C.M., Van, N.M., Foekens, J.A., Martens, J.W., Sleijfer, S., 2016. Prognostic impact of HER2 and ER status of circulating tumor cells in metastatic breast cancer patients with a HER2-negative primary tumor. Neoplasia 18 (11), 647–653. Beltran, H., Prandi, D., Mosquera, J.M., Benelli, M., Puca, L., Cyrta, J., Marotz, C., Giannopoulou, E., Chakravarthi, B.V., Varambally, S., Tomlins, S.A., Nanus, D.M., Tagawa, S.T., Van Allen, E.M., Elemento, O., Sboner, A., Garraway, L.A., Rubin, M.A., Demichelis, F., 2016. Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat. Med. 22 (3), 298–305. Blokzijl, F., de Ligt, J., Jager, M., Sasselli, V., Roerink, S., Sasaki, N., Huch, M., Boymans, S., Kuijk, E., Prins, P., Nijman, I.J., Martincorena, I., Mokry, M., Wiegerinck, C.L., Middendorp, S., Sato, T., Schwank, G., Nieuwenhuis, E.E., Verstegen, M.M., van der Laan, L.J., de Jonge, J., IJzermans, J.N., Vries, R.G., van de Wetering, M., Stratton, M.R., Clevers, H., Cuppen, E., van Boxtel, R., 2016. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538 (7624), 260–264. Bonner, R.F., Emmert-Buck, M., Cole, K., Pohida, T., Chuaqui, R., Goldstein, S., Liotta, L.A., 1997. Laser capture microdissection: molecular analysis of tissue. Science 278 (5342), 1481–1483. Buettner, F., Natarajan, K.N., Casale, F.P., Proserpio, V., Scialdone, A., Theis, F.J., Teichmann, S.A., Marioni, J.C., Stegle, O., 2015. Computational analysis of cell-to-cell heterogeneity in singlecell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33 (2), 155–160. Burrell, R.A., McGranahan, N., Bartek, J., Swanton, C., 2013. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501 (7467), 338–345. Burrell, R.A., Swanton, C., 2014a. The evolution of the unstable cancer genome. Curr. Opin. Genet. Dev. 24, 61–67. Burrell, R.A., Swanton, C., 2014b. Tumour heterogeneity and the evolution of polyclonal drug resistance. Mol. Oncol. 8 (6), 1095–1111.

85

86

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

Burrell, R.A., Swanton, C., 2016. Re-evaluating clonal dominance in cancer evolution. Trends Cancer 2 (5), 263–276. Caiado, F., Silva-Santos, B., Norell, H., 2016. Intra-tumour heterogeneity—going beyond genetics. FEBS J. 283 (12), 2245–2258. Campbell, I.M., Shaw, C.A., Stankiewicz, P., Lupski, J.R., 2015. Somatic mosaicism: implications for disease and transmission genetics. Trends Genet. 31 (7), 382–392. Cancer Genome Atlas Research Network, et al., 2017a. Integrated genomic and molecular characterization of cervical cancer. Nature 543 (7645), 378–384. Cancer Genome Atlas Research Network, et al., 2017b. Integrated genomic characterization of oesophageal carcinoma. Nature 541 (7636), 169–175. Chen, K., Zhao, B.S., He, C., 2016. Nucleic acid modifications in regulation of gene expression. Cell Chem. Biol. 23 (1), 74–85. Cheung, R.K., Utz, P.J., 2011. Screening: CyTOF-the next generation of cell detection. Nat. Rev. Rheumatol. 7 (9), 502–503. Cleary, A.S., Leonard, T.L., Gestl, S.A., Gunther, E.J., 2014. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 508 (7494), 113–117. Clifford, S.C., 2012. Cancer genetics: evolution after tumour spread. Nature 482 (7386), 481–482. Cristofanilli, M., Hayes, D.F., Budd, G.T., Ellis, M.J., Stopeck, A., Reuben, J.M., Doyle, G.V., Matera, J., Allard, W.J., Miller, M.C., Fritsche, H.A., Hortobagyi, G.N., Terstappen, L.W., 2005. Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer. J. Clin. Oncol. 23 (7), 1420–1430. Dalerba, P., Kalisky, T., Sahoo, D., Rajendran, P.S., Rothenberg, M.E., Leyrat, A.A., Sim, S., Okamoto, J., Johnston, D.M., Qian, D., Zabala, M., Bueno, J., Neff, N.F., Wang, J., Shelton, A.A., Visser, B., Hisamori, S., Shimono, Y., van de Wetering, M., Clevers, H., Clarke, M.F., Quake, S.R., 2011. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29 (12), 1120–1127. Datta, S., Malhotra, L., Dickerson, R., Chaffee, S., Sen, C.K., Roy, S., 2015. Laser capture microdissection: big data from small samples. Histol. Histopathol. 30 (11), 1255–1269. Davis, A., Gao, R., Navin, N., 2017. Tumor evolution: linear, branching, neutral or punctuated? Biochim. Biophys. Acta 1867 (2), 151–161. Davis, A., Navin, N.E., 2016. Computing tumor trees from single cells. Genome Biol. 17 (1), 113. Dawson, M.A., Kouzarides, T., 2012. Cancer epigenetics: from mechanism to therapy. Cell 150 (1), 12–27. de Bruin, E.C., McGranahan, N., Mitter, R., Salm, M., Wedge, D.C., Yates, L., Jamal-Hanjani, M., Shafi, S., Murugaesu, N., Rowan, A.J., Gronroos, E., Muhammad, M.A., Horswell, S., Gerlinger, M., Varela, I., Jones, D., Marshall, J., Voet, T., Van Loo, P., Rassl, D.M., Rintoul, R.C., Janes, S.M., Lee, S.M., Forster, M., Ahmad, T., Lawrence, D., Falzon, M., Capitanio, A., Harkins, T.T., Lee, C.C., Tom, W., Teefe, E., Chen, S.C., Begum, S., Rabinowitz, A., Phillimore, B., Spencer-Dene, B., Stamp, G., Szallasi, Z., Matthews, N., Stewart, A., Campbell, P., Swanton, C., 2014. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346 (6206), 251–256. Dexter, D.L., Kowalski, H.M., Blazar, B.A., Fligiel, Z., Vogel, R., Heppner, G.H., 1978. Heterogeneity of tumor cells from a single mouse mammary tumor. Cancer Res. 38 (10), 3174–3181. Ding, L., Ley, T.J., Larson, D.E., Miller, C.A., Koboldt, D.C., Welch, J.S., Ritchey, J.K., Young, M.A., Lamprecht, T., McLellan, M.D., McMichael, J.F., Wallis, J.W., Lu, C., Shen, D., Harris, C.C., Dooling, D.J., Fulton, R.S., Fulton, L.L., Chen, K., Schmidt, H., Kalicki-Veizer, J., Magrini, V.J., Cook, L., McGrath, S.D., Vickery, T.L., Wendl, M.C., Heath, S., Watson, M.A., Link, D.C., Tomasson, M.H., Shannon, W.D., Payton, J.E., Kulkarni, S., Westervelt, P.,

References

Walter, M.J., Graubert, T.A., Mardis, E.R., Wilson, R.K., DiPersio, J.F., 2012. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481 (7382), 506–510. Dong, L., Chen, D.W., Liu, S.J., Du, W., 2016. Automated chemotactic sorting and single-cell cultivation of microbes using droplet microfluidics. Sci. Rep. 6, 24192. Donmez, N., Malikic, S., Wyatt, A.W., Gleave, M.E., Collins, C.C., Sahinalp, S.C., 2017. Clonality inference from single tumor samples using low-coverage sequence data. J. Comput. Biol. 24 (6), 515–523. Dressler, L.G., Visscher, D., 2001. Handling, storage, and preparation of human tissues. Curr. Protoc. Cytom. (Chapter 5, Unit 5.2). Dzobo, K., Senthebane, D.A., Thomford, N.E., Rowe, A., Dandara, C., Parker, M.I., 2018. Not everyone fits the mold: intratumor and intertumor heterogeneity and innovative cancer drug design and development. OMICS 22 (1), 17–34. El-Kebir, M., Oesper, L., Acheson-Field, H., Raphael, B.J., 2015. Reconstruction of clonal trees and tumor composition from multi-sample sequencing data. Bioinformatics 31 (12), i62–i70. El-Kebir, M., Satas, G., Oesper, L., Raphael, B.J., 2016. Inferring the mutational history of a tumor using multi-state perfect phylogeny mixtures. Cell Syst. 3 (1), 43–53. Ellsworth, D.L., Blackburn, H.L., Shriver, C.D., Rabizadeh, S., Soon-Shiong, P., Ellsworth, R.E., 2017. Single-cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis. Clin. Transl. Med. 6 (1), 15. Engle, E.K., Fisher, D.A., Miller, C.A., McLellan, M.D., Fulton, R.S., Moore, D.M., Wilson, R.K., Ley, T.J., Oh, S.T., 2015. Clonal evolution revealed by whole genome sequencing in a case of primary myelofibrosis transformed to secondary acute myeloid leukemia. Leukemia 29 (4), 869–876. Fearon, E.R., Vogelstein, B., 1990. A genetic model for colorectal tumorigenesis. Cell 61 (5), 759–767. Fischer, A., Vazquez-Garcia, I., Illingworth, C.J.R., Mustonen, V., 2014. High-definition reconstruction of clonal composition in cancer. Cell Rep. 7 (5), 1740–1752. Freed, D., Pevsner, J., 2016. The contribution of mosaic variants to autism spectrum disorder. PLoS Genet. 12 (9), e1006245. Frost, A.R., Eltoum, I.E., Siegal, G.P., Emmert-Buck, M.R., Tangrea, M.A., 2015. Laser microdissection. Curr. Protoc. Mol. Biol. 112, 25a.1.1–25a.1.30. Fu, Y., He, C., 2012. Nucleic acid modifications with epigenetic significance. Curr. Opin. Chem. Biol. 16 (5–6), 516–524. Fu, Y., Li, C., Lu, S., Zhou, W., Tang, F., Xie, X.S., Huang, Y., 2015. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proc. Natl. Acad. Sci. U. S. A. 112 (38), 11923–11928. Gallaher, J., Anderson, A.R., 2013. Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance. Interface Focus 3 (4), 20130016. Gao, R., Davis, A., McDonald, T.O., Sei, E., Shi, X., Wang, Y., Tsai, P.C., Casasent, A., Waters, J., Zhang, H., Meric-Bernstam, F., Michor, F., Navin, N.E., 2016. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48 (10), 1119–1130. Gavasso, S., Gullaksen, S.E., Skavland, J., Gjertsen, B.T., 2016. Single-cell proteomics: potential implications for cancer diagnostics. Expert. Rev. Mol. Diagn. 16 (5), 579–589. Gawad, C., Koh, W., Quake, S.R., 2014. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc. Natl. Acad. Sci. U. S. A. 111 (50), 17947–17952.

87

88

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

Gawad, C., Koh, W., Quake, S.R., 2016. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17 (3), 175–188. Gentric, G., Mieulet, V., Mechta-Grigoriou, F., 2016. Heterogeneity in cancer metabolism: new concepts in an old field. Antioxid. Redox Signal. Gerlinger, M., Catto, J.W., Orntoft, T.F., Real, F.X., Zwarthoff, E.C., Swanton, C., 2015. Intratumour heterogeneity in urologic cancers: from molecular evidence to clinical implications. Eur. Urol. 67 (4), 729–737. Gerlinger, M., Horswell, S., Larkin, J., Rowan, A.J., Salm, M.P., Varela, I., Fisher, R., McGranahan, N., Matthews, N., Santos, C.R., Martinez, P., Phillimore, B., Begum, S., Rabinowitz, A., SpencerDene, B., Gulati, S., Bates, P.A., Stamp, G., Pickering, L., Gore, M., Nicol, D.L., Hazell, S., Futreal, P.A., Stewart, A., Swanton, C., 2014a. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46 (3), 225–233. Gerlinger, M., McGranahan, N., Dewhurst, S.M., Burrell, R.A., Tomlinson, I., Swanton, C., 2014b. Cancer: evolution within a lifetime. Annu. Rev. Genet. 48, 215–236. Gillespie, J.H., 2000. Genetic drift in an infinite population. The pseudohitchhiking model. Genetics 155 (2), 909–919. Gillies, R.J., Verduzco, D., Gatenby, R.A., 2012. Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat. Rev. Cancer 12 (7), 487–493. Greaves, M., 2015. Evolutionary determinants of cancer. Cancer Discov. 5 (8), 806–820. Greaves, M., Maley, C.C., 2012. Clonal evolution in cancer. Nature 481 (7381), 306–313. Greenman, C.D., Pleasance, E.D., Newman, S., Yang, F., Fu, B., Nik-Zainal, S., Jones, D., Lau, K.W., Carter, N., Edwards, P.A., Futreal, P.A., Stratton, M.R., Campbell, P.J., 2012. Estimation of rearrangement phylogeny for cancer genomes. Genome Res. 22 (2), 346–361. Griffith, M., Miller, C.A., Griffith, O.L., Krysiak, K., Skidmore, Z.L., Ramu, A., Walker, J.R., Dang, H.X., Trani, L., Larson, D.E., Demeter, R.T., Wendl, M.C., McMichael, J.F., Austin, R.E., Magrini, V., McGrath, S.D., Ly, A., Kulkarni, S., Cordes, M.G., Fronick, C.C., Fulton, R.S., Maher, C.A., Ding, L., Klco, J.M., Mardis, E.R., Ley, T.J., Wilson, R.K., 2015. Optimizing cancer genome sequencing and analysis. Cell Syst. 1 (3), 210–223. Groth, A., Rocha, W., Verreault, A., Almouzni, G., 2007. Chromatin challenges during DNA replication and repair. Cell 128 (4), 721–733. Guo, H., Zhu, P., Guo, F., Li, X., Wu, X., Fan, X., Wen, L., Tang, F., 2015. Profiling DNA methylome landscapes of mammalian cells with single-cell reduced-representation bisulfite sequencing. Nat. Protoc. 10 (5), 645–659. Heath, J.R., Ribas, A., Mischel, P.S., 2016. Single-cell analysis tools for drug discovery and development. Nat. Rev. Drug Discov. 15 (3), 204–216. Heppner, G.H., 1984. Tumor heterogeneity. Cancer Res. 44 (6), 2259–2265. Hoeijmakers, W.A., Bartfai, R., Stunnenberg, H.G., 2013. Transcriptome analysis using RNA-Seq. Methods Mol. Biol. 923, 221–239. Hoppe, P.S., Coutu, D.L., Schroeder, T., 2014. Single-cell technologies sharpen up mammalian stem cell research. Nat. Cell Biol. 16 (10), 919–927. Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., Peng, J., 2016. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26 (3), 304–319. Hou, Y., Song, L., Zhu, P., Zhang, B., Tao, Y., Xu, X., Li, F., Wu, K., Liang, J., Shao, D., Wu, H., Ye, X., Ye, C., Wu, R., Jian, M., Chen, Y., Xie, W., Zhang, R., Chen, L., Liu, X., Yao, X., Zheng, H., Yu, C., Li, Q., Gong, Z., Mao, M., Yang, X., Yang, L., Li, J., Wang, W., Lu, Z., Gu, N., Laurie, G., Bolund, L., Kristiansen, K., Wang, J., Yang, H., Li, Y., Zhang, X., Wang, J., 2012. Single-cell

References

exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148 (5), 873–885. Hu, Z., Curtis, C., 2016. Inferring tumor phylogenies from multi-region sequencing. Cell Syst. 3 (1), 12–14. Huang, G., Wang, S., Wang, X., You, N., 2016. An empirical Bayes method for genotyping and SNP detection using multi-sample next-generation sequencing data. Bioinformatics 32 (21), 3240–3245. Huang, H., Wang, C., Liu, F., Li, H.Z., Peng, G., Gao, X., Dong, K.Q., Wang, H.R., Kong, D.P., Qu, M., Dai, L.H., Wang, K.J., Zhou, Z., Yang, J., Yang, Z.Y., Cheng, Y.Q., Tian, Q.Q., Liu, D., Xu, C.L., Xu, D.F., Cui, X.G., Sun, Y.H., 2018. Reciprocal network between cancer stem-like cells and macrophages facilitates the progression and androgen deprivation therapy resistance of prostate cancer. Clin. Cancer Res. 24, 4612–4626. Huang, L., Ma, F., Chapman, A., Lu, S., Xie, X.S., 2015. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu. Rev. Genomics Hum. Genet. 16, 79–102. Hudson, T.J., Anderson, W., Artez, A., Barker, A.D., Bell, C., Bernabe, R.R., Bhan, M.K., Calvo, F., Eerola, I., Gerhard, D.S., Guttmacher, A., Guyer, M., Hemsley, F.M., Jennings, J.L., Kerr, D., Klatt, P., Kolar, P., Kusada, J., Lane, D.P., Laplace, F., Youyong, L., Nettekoven, G., Ozenberger, B., Peterson, J., Rao, T.S., Remacle, J., Schafer, A.J., Shibata, T., Stratton, M.R., Vockley, J.G., Watanabe, K., Yang, H., Yuen, M.M., Knoppers, B.M., Bobrow, M., CambonThomsen, A., Dressler, L.G., Dyke, S.O., Joly, Y., Kato, K., Kennedy, K.L., Nicolas, P., Parker, M.J., Rial-Sebbag, E., Romeo-Casabona, C.M., Shaw, K.M., Wallace, S., Wiesner, G.L., Zeps, N., Lichter, P., Biankin, A.V., Chabannon, C., Chin, L., Clement, B., de Alava, E., Degos, F., Ferguson, M.L., Geary, P., Hayes, D.N., Hudson, T.J., Johns, A.L., Kasprzyk, A., Nakagawa, H., Penny, R., Piris, M.A., Sarin, R., Scarpa, A., Shibata, T., van de Vijver, M., Futreal, P.A., Aburatani, H., Bayes, M., Botwell, D.D., Campbell, P.J., Estivill, X., Gerhard, D.S., Grimmond, S.M., Gut, I., Hirst, M., Lopez-Otin, C., Majumder, P., Marra, M., McPherson, J.D., Nakagawa, H., Ning, Z., Puente, X.S., Ruan, Y., Shibata, T., Stratton, M.R., Stunnenberg, H.G., Swerdlow, H., Velculescu, V.E., Wilson, R.K., Xue, H.H., Yang, L., Spellman, P.T., Bader, G.D., Boutros, P.C., Campbell, P.J., Flicek, P., et al., 2010. International network of cancer genome projects. Nature 464 (7291), 993–998. Inda, M.M., Bonavia, R., Mukasa, A., Narita, Y., Sah, D.W., Vandenberg, S., Brennan, C., Johns, T.G., Bachoo, R., Hadwiger, P., Tan, P., Depinho, R.A., Cavenee, W., Furnari, F., 2010. Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma. Genes Dev. 24 (16), 1731–1745. Jager, M., Blokzijl, F., Sasselli, V., Boymans, S., Janssen, R., Besselink, N., Clevers, H., van Boxtel, R., Cuppen, E., 2018. Measuring mutation accumulation in single human adult stem cells by whole-genome sequencing of organoid cultures. Nat. Protoc. 13 (1), 59–78. Jahn, K., Kuipers, J., Beerenwinkel, N., 2016. Tree inference for single-cell data. Genome Biol. 17, 86. Jiang, Y., Qiu, Y., Minn, A.J., Zhang, N.R., 2016. Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing. Proc. Natl. Acad. Sci. U. S. A. 113 (37), E5528–E5537. Jiao, W., Vembu, S., Deshwar, A.G., Stein, L., Morris, Q., 2014. Inferring clonal evolution of tumors from single nucleotide somatic mutations. BMC Bioinform. 15, 35. Jordan, N.V., Bardia, A., Wittner, B.S., Benes, C., Ligorio, M., Zheng, Y., Yu, M., Sundaresan, T.K., Licausi, J.A., Desai, R., O’Keefe, R.M., Ebright, R.Y., Boukhali, M., Sil, S., Onozato, M.L., Iafrate, A.J., Kapur, R., Sgroi, D., Ting, D.T., Toner, M., Ramaswamy, S., Haas, W., Maheswaran, S., Haber, D.A., 2016. HER2 expression identifies dynamic functional states within circulating breast cancer cells. Nature 537 (7618), 102–106.

89

90

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

Kalisky, T., Blainey, P., Quake, S.R., 2011. Genomic analysis at the single-cell level. Annu. Rev. Genet. 45, 431–445. Kanwal, R., Gupta, K., Gupta, S., 2015. Cancer epigenetics: an introduction. Methods Mol. Biol. 1238, 3–25. Kay, A.W., Strauss-Albee, D.M., Blish, C.A., 2016. Application of mass cytometry (CyTOF) for functional and phenotypic analysis of natural killer cells. Methods Mol. Biol. 1441, 13–26. Kent, D.G., Ortmann, C.A., Green, A.R., 2015. Effect of mutation order on myeloproliferative neoplasms. N. Engl. J. Med. 372 (19), 1865–1866. Kim, K.I., Simon, R., 2014. Using single cell sequencing data to model the evolutionary history of a tumor. BMC Bioinform. 15, 27. Kim, K.T., Lee, H.W., Lee, H.O., Kim, S.C., Seo, Y.J., Chung, W., Eum, H.H., Nam, D.H., Kim, J., Joo, K.M., Park, W.Y., 2015a. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 16, 127. Kim, T.M., An, C.H., Rhee, J.K., Jung, S.H., Lee, S.H., Baek, I.P., Kim, M.S., Lee, S.H., Chung, Y.J., 2015b. Clonal origins and parallel evolution of regionally synchronous colorectal adenoma and carcinoma. Oncotarget 6 (29), 27725–27735. Kimura, M., 1983. Rare variant alleles in the light of the neutral theory. Mol. Biol. Evol. 1 (1), 84–93. Kroigard, A.B., Larsen, M.J., Laenkholm, A.V., Knoop, A.S., Jensen, J.D., Bak, M., Mollenhauer, J., Kruse, T.A., Thomassen, M., 2015. Clonal expansion and linear genome evolution through breast cancer progression from pre-invasive stages to asynchronous metastasis. Oncotarget 6 (8), 5634–5649. Kuipers, J., Jahn, K., Beerenwinkel, N., 2017. Advances in understanding tumour evolution through single-cell sequencing. Biochim. Biophys. Acta 1867 (2), 127–138. La Porta, C.A., Zapperi, S., 2017. Complexity in cancer stem cells and tumor evolution: toward precision medicine. Semin. Cancer Biol. 44, 3–9. Lan, F., Demaree, B., Ahmed, N., Abate, A.R., 2017. Single-cell genome sequencing at ultra-highthroughput with microfluidic droplet barcoding. Nat. Biotechnol. 35 (7), 640–646. Lapa, C., Schirbel, A., Samnick, S., Luckerath, K., Kortum, K.M., Knop, S., Wester, H.J., Buck, A.K., Schreder, M., 2017. The gross picture: intraindividual tumour heterogeneity in a patient with nonsecretory multiple myeloma. Eur. J. Nucl. Med. Mol. Imaging. Lappalainen, T., Sammeth, M., Friedlander, M.R., t Hoen, P.A., Monlong, J., Rivas, M.A., GonzalezPorta, M., Kurbatova, N., Griebel, T., Ferreira, P.G., Barann, M., Wieland, T., Greger, L., van Iterson, M., Almlof, J., Ribeca, P., Pulyakhina, I., Esser, D., Giger, T., Tikhonov, A., Sultan, M., Bertier, G., MacArthur, D.G., Lek, M., Lizano, E., Buermans, H.P., Padioleau, I., Schwarzmayr, T., Karlberg, O., Ongen, H., Kilpinen, H., Beltran, S., Gut, M., Kahlem, K., Amstislavskiy, V., Stegle, O., Pirinen, M., Montgomery, S.B., Donnelly, P., McCarthy, M.I., Flicek, P., Strom, T.M., Lehrach, H., Schreiber, S., Sudbrak, R., Carracedo, A., Antonarakis, S.E., Hasler, R., Syvanen, A.C., van Ommen, G.J., Brazma, A., Meitinger, T., Rosenstiel, P., Guigo, R., Gut, I.G., Estivill, X., Dermitzakis, E.T., 2013. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501 (7468), 506–511. Larson, N.B., Fridley, B.L., 2013. PurBayes: estimating tumor cellularity and subclonality in nextgeneration sequencing data. Bioinformatics 29 (15), 1888–1889. Lee, J.Y., Yoon, J.K., Kim, B., Kim, S., Kim, M.A., Lim, H., Bang, D., Song, Y.S., 2015. Tumor evolution and intratumor heterogeneity of an epithelial ovarian cancer investigated using nextgeneration sequencing. BMC Cancer 15, 85. Leelatian, N., Doxie, D.B., Greenplate, A.R., Sinnaeve, J., Ihrie, R.A., Irish, J.M., 2017. Preparing viable single cells from human tissue and tumors for cytomic analysis. Curr. Protoc. Mol. Biol. 118, 25c.1.1–25c.1.23.

References

Leiserson, M.D., Blokh, D., Sharan, R., Raphael, B.J., 2013. Simultaneous identification of multiple driver pathways in cancer. PLoS Comput. Biol. 9 (5), e1003054. Li, B., Li, J.Z., 2014. A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data. Genome Biol. 15 (9), 473. Li, H., Courtois, E.T., Sengupta, D., Tan, Y., Chen, K.H., Goh, J.J.L., Kong, S.L., Chua, C., Hon, L.K., Tan, W.S., Wong, M., Choi, P.J., Wee, L.J.K., Hillmer, A.M., Tan, I.B., Robson, P., Prabhakar, S., 2017. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49 (5), 708–718. Li, Y., Xie, X., 2015. MixClone: a mixture model for inferring tumor subclonal populations. BMC Genomics 16 (Suppl. 2), S1. Li, Y., Zhu, B., 2014. Acute myeloid leukemia with DNMT3A mutations. Leuk. Lymphoma 55 (9), 2002–2012. Ling, S., Hu, Z., Yang, Z., Yang, F., Li, Y., Lin, P., Chen, K., Dong, L., Cao, L., Tao, Y., Hao, L., Chen, Q., Gong, Q., Wu, D., Li, W., Zhao, W., Tian, X., Hao, C., Hungate, E.A., Catenacci, D.V., Hudson, R.R., Li, W.H., Lu, X., Wu, C.I., 2015. Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution. Proc. Natl. Acad. Sci. U. S. A. 112 (47), E6496–E6505. Lu, Y.W., Zhang, H.F., Liang, R., Xie, Z.R., Luo, H.Y., Zeng, Y.J., Xu, Y., Wang, L.M., Kong, X.Y., Wang, K.H., 2016. Colorectal cancer genetic heterogeneity delineated by multi-region sequencing. PLoS One 11 (3), e0152673. MacAlpine, D.M., Almouzni, G., 2013. Chromatin and DNA replication. Cold Spring Harb. Perspect. Biol. 5 (8), a010207. Macaulay, I.C., Haerty, W., Kumar, P., Li, Y.I., Hu, T.X., Teng, M.J., Goolam, M., Saurat, N., Coupland, P., Shirley, L.M., Smith, M., Van der Aa, N., Banerjee, R., Ellis, P.D., Quail, M.A., Swerdlow, H.P., Zernicka-Goetz, M., Livesey, F.J., Ponting, C.P., Voet, T., 2015. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12 (6), 519–522. Macaulay, I.C., Ponting, C.P., Voet, T., 2017. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33 (2), 155–168. Maeda, S., Tomiyasu, H., Tsuboi, M., Inoue, A., Ishihara, G., Uchikai, T., Chambers, J.K., Uchida, K., Yonezawa, T., Matsuki, N., 2018. Comprehensive gene expression analysis of canine invasive urothelial bladder carcinoma by RNA-Seq. BMC Cancer 18 (1), 472. Maekawa, S., Suzuki, A., Sugano, S., Suzuki, Y., 2014. RNA sequencing: from sample preparation to analysis. Methods Mol. Biol. 1164, 51–65. Managh, A.J., Edwards, S.L., Bushell, A., Wood, K.J., Geissler, E.K., Hutchinson, J.A., Hutchinson, R.W., Reid, H.J., Sharp, B.L., 2013. Single cell tracking of gadolinium labeled CD4+ T cells by laser ablation inductively coupled plasma mass spectrometry. Anal. Chem. 85 (22), 10627–10634. Mardis, E.R., 2011. A decade’s perspective on DNA sequencing technology. Nature 470 (7333), 198–203. Marotti, J.D., de Abreu, F.B., Wells, W.A., Tsongalis, G.J., 2017. Triple-negative breast cancer: nextgeneration sequencing for target identification. Am. J. Pathol. 187 (10), 2133–2138. Martinez, P., Birkbak, N.J., Gerlinger, M., McGranahan, N., Burrell, R.A., Rowan, A.J., Joshi, T., Fisher, R., Larkin, J., Szallasi, Z., Swanton, C., 2013. Parallel evolution of tumour subclones mimics diversity between tumours. J. Pathol. 230 (4), 356–364. Marusyk, A., Almendro, V., Polyak, K., 2012. Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12 (5), 323–334. Marusyk, A., Polyak, K., 2010. Tumor heterogeneity: causes and consequences. Biochim. Biophys. Acta 1805 (1), 105–117.

91

92

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

Marusyk, A., Tabassum, D.P., Altrock, P.M., Almendro, V., Michor, F., Polyak, K., 2014. Non-cellautonomous driving of tumour growth supports sub-clonal heterogeneity. Nature 514 (7520), 54–58. Mattos-Arruda, L., Weigelt, B., Cortes, J., Won, H.H., Ng, C.K.Y., Nuciforo, P., Bidard, F.C., Aura, C., Saura, C., Peg, V., Piscuoglio, S., Oliveira, M., Smolders, Y., Patel, P., Norton, L., Tabernero, J., Berger, M.F., Seoane, J., Reis-Filho, J.S., 2018. Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle. Ann. Oncol. Mazio, C., Casale, C., Imparato, G., Urciuolo, F., Netti, P.A., 2018. Recapitulating spatiotemporal tumor heterogeneity in vitro through engineered breast cancer microtissues. Acta Biomater. Mazor, T., Pankov, A., Johnson, B.E., Hong, C., Hamilton, E.G., Bell, R.J.A., Smirnov, I.V., Reis, G.F., Phillips, J.J., Barnes, M.J., Idbaih, A., Alentorn, A., Kloezeman, J.J., Lamfers, M.L.M., Bollen, A.W., Taylor, B.S., Molinaro, A.M., Olshen, A.B., Chang, S.M., Song, J.S., Costello, J.F., 2015. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 28 (3), 307–317. McGranahan, N., Swanton, C., 2015. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27 (1), 15–26. McGranahan, N., Swanton, C., 2017. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168 (4), 613–628. McPherson, A., Roth, A., Laks, E., Masud, T., Bashashati, A., Zhang, A.W., Ha, G., Biele, J., Yap, D., Wan, A., Prentice, L.M., Khattra, J., Smith, M.A., Nielsen, C.B., Mullaly, S.C., Kalloger, S., Karnezis, A., Shumansky, K., Siu, C., Rosner, J., Chan, H.L., Ho, J., Melnyk, N., Senz, J., Yang, W., Moore, R., Mungall, A.J., Marra, M.A., Bouchard-Cote, A., Gilks, C.B., Huntsman, D.G., McAlpine, J.N., Aparicio, S., Shah, S.P., 2016. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48 (7), 758–767. Meacham, C.E., Morrison, S.J., 2013. Tumour heterogeneity and cancer cell plasticity. Nature 501 (7467), 328–337. Merlo, L.M., Pepper, J.W., Reid, B.J., Maley, C.C., 2006. Cancer as an evolutionary and ecological process. Nat. Rev. Cancer 6 (12), 924–935. Merlo, L.M., Shah, N.A., Li, X., Blount, P.L., Vaughan, T.L., Reid, B.J., Maley, C.C., 2010. A comprehensive survey of clonal diversity measures in Barrett’s esophagus as biomarkers of progression to esophageal adenocarcinoma. Cancer Prev. Res. (Phila.) 3 (11), 1388–1397. Meyerson, M., Gabriel, S., Getz, G., 2010. Advances in understanding cancer genomes through second-generation sequencing. Nat. Rev. Genet. 11 (10), 685–696. Miller, C.A., White, B.S., Dees, N.D., Griffith, M., Welch, J.S., Griffith, O.L., Vij, R., Tomasson, M.H., Graubert, T.A., Walter, M.J., Ellis, M.J., Schierding, W., DiPersio, J.F., Ley, T.J., Mardis, E.R., Wilson, R.K., Ding, L., 2014. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput. Biol. 10 (8), e1003665. Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., Muthuswamy, L., Krasnitz, A., McCombie, W.R., Hicks, J., Wigler, M., 2011. Tumour evolution inferred by single-cell sequencing. Nature 472 (7341), 90–94. Navin, N.E., 2014. Cancer genomics: one cell at a time. Genome Biol. 15 (8), 452. Nik-Zainal, S., Van Loo, P., Wedge, D.C., Alexandrov, L.B., Greenman, C.D., Lau, K.W., Raine, K., Jones, D., Marshall, J., Ramakrishna, M., Shlien, A., Cooke, S.L., Hinton, J., Menzies, A., Stebbings, L.A., Leroy, C., Jia, M., Rance, R., Mudie, L.J., Gamble, S.J., Stephens, P.J., McLaren, S., Tarpey, P.S., Papaemmanuil, E., Davies, H.R., Varela, I., McBride, D.J., Bignell, G.R., Leung, K., Butler, A.P., Teague, J.W., Martin, S., Jonsson, G., Mariani, O., Boyault, S., Miron, P., Fatima, A., Langerod, A., Aparicio, S.A., Tutt, A., Sieuwerts, A.M.,

References

Borg, A., Thomas, G., Salomon, A.V., Richardson, A.L., Borresen-Dale, A.L., Futreal, P.A., Stratton, M.R., Campbell, P.J., 2012. The life history of 21 breast cancers. Cell 149 (5), 994–1007. Niknafs, N., Beleva-Guthrie, V., Naiman, D.Q., Karchin, R., 2015. SubClonal hierarchy inference from somatic mutations: automatic reconstruction of cancer evolutionary trees from multiregion next generation sequencing. PLoS Comput. Biol. 11 (10), e1004416. Nowell, P.C., 1976. The clonal evolution of tumor cell populations. Science 194 (4260), 23–28. Oesper, L., Mahmoody, A., Raphael, B.J., 2013. THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data. Genome Biol. 14 (7), R80. Oesper, L., Satas, G., Raphael, B.J., 2014. Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data. Bioinformatics 30 (24), 3532–3540. Ortega, M.A., Poirion, O., Zhu, X., Huang, S., Wolfgruber, T.K., Sebra, R., Garmire, L.X., 2017. Using single-cell multiple omics approaches to resolve tumor heterogeneity. Clin. Transl. Med. 6 (1), 46. Ortmann, C.A., Kent, D.G., Nangalia, J., Silber, Y., Wedge, D.C., Grinfeld, J., Baxter, E.J., Massie, C.E., Papaemmanuil, E., Menon, S., Godfrey, A.L., Dimitropoulou, D., Guglielmelli, P., Bellosillo, B., Besses, C., Dohner, K., Harrison, C.N., Vassiliou, G.S., Vannucchi, A., Campbell, P.J., Green, A.R., 2015. Effect of mutation order on myeloproliferative neoplasms. N. Engl. J. Med. 372 (7), 601–612. Palle, K., Mani, C., Tripathi, K., Athar, M., 2015. Aberrant GLI1 activation in DNA damage response, carcinogenesis and chemoresistance. Cancers (Basel) 7 (4), 2330–2351. Patel, A.P., Tirosh, I., Trombetta, J.J., Shalek, A.K., Gillespie, S.M., Wakimoto, H., Cahill, D.P., Nahed, B.V., Curry, W.T., Martuza, R.L., Louis, D.N., Rozenblatt-Rosen, O., Suva, M.L., Regev, A., Bernstein, B.E., 2014. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344 (6190), 1396–1401. Picelli, S., Bjorklund, A.K., Faridani, O.R., Sagasser, S., Winberg, G., Sandberg, R., 2013. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10 (11), 1096–1098. Picelli, S., Faridani, O.R., Bjorklund, A.K., Winberg, G., Sagasser, S., Sandberg, R., 2014. Full-length RNA-seq from single cells using smart-seq2. Nat. Protoc. 9 (1), 171–181. Purdom, E., Ho, C., Grasso, C.S., Quist, M.J., Cho, R.J., Spellman, P., 2013. Methods and challenges in timing chromosomal abnormalities within cancer samples. Bioinformatics 29 (24), 3113–3120. Qiao, Y., Quinlan, A.R., Jazaeri, A.A., Verhaak, R.G., Wheeler, D.A., Marth, G.T., 2014. SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization. Genome Biol. 15 (8), 443. Ramskold, D., Luo, S., Wang, Y.C., Li, R., Deng, Q., Faridani, O.R., Daniels, G.A., Khrebtukova, I., Loring, J.F., Laurent, L.C., Schroth, G.P., Sandberg, R., 2012. Full-length mRNA-Seq from singlecell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30 (8), 777–782. Reiter, J.G., Makohon-Moore, A.P., Gerold, J.M., Bozic, I., Chatterjee, K., Iacobuzio-Donahue, C.A., Vogelstein, B., Nowak, M.A., 2017. Reconstructing metastatic seeding patterns of human cancers. Nat. Commun. 814114. Robinson, D., Van Allen, E.M., Wu, Y.M., Schultz, N., Lonigro, R.J., Mosquera, J.M., Montgomery, B., Taplin, M.E., Pritchard, C.C., Attard, G., Beltran, H., Abida, W., Bradley, R.K., Vinson, J., Cao, X., Vats, P., Kunju, L.P., Hussain, M., Feng, F.Y., Tomlins, S.A., Cooney, K.A., Smith, D.C., Brennan, C., Siddiqui, J., Mehra, R., Chen, Y., Rathkopf, D.E., Morris, M.J., Solomon, S.B., Durack, J.C., Reuter, V.E., Gopalan, A., Gao, J., Loda, M., Lis, R.T., Bowden, M., Balk, S.P., Gaviola, G., Sougnez, C., Gupta, M., Yu, E.Y., Mostaghel, E.A., Cheng, H.H., Mulcahy, H., True, L.D., Plymate, S.R., Dvinge, H.,

93

94

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

Ferraldeschi, R., Flohr, P., Miranda, S., Zafeiriou, Z., Tunariu, N., Mateo, J., Perez-Lopez, R., Demichelis, F., Robinson, B.D., Schiffman, M., Nanus, D.M., Tagawa, S.T., Sigaras, A., Eng, K.W., Elemento, O., Sboner, A., Heath, E.I., Scher, H.I., Pienta, K.J., Kantoff, P., de Bono, J.S., Rubin, M.A., Nelson, P.S., Garraway, L.A., Sawyers, C.L., Chinnaiyan, A.M., 2015. Integrative clinical genomics of advanced prostate cancer. Cell 161 (5), 1215–1228. Robinson, D.R., Wu, Y.M., Lonigro, R.J., Vats, P., Cobain, E., Everett, J., Cao, X., Rabban, E., KumarSinha, C., Raymond, V., Schuetze, S., Alva, A., Siddiqui, J., Chugh, R., Worden, F., Zalupski, M.M., Innis, J., Mody, R.J., Tomlins, S.A., Lucas, D., Baker, L.H., Ramnath, N., Schott, A.F., Hayes, D.F., Vijai, J., Offit, K., Stoffel, E.M., Roberts, J.S., Smith, D.C., Kunju, L.P., Talpaz, M., Cieslik, M., Chinnaiyan, A.M., 2017. Integrative clinical genomics of metastatic cancer. Nature 548 (7667), 297–303. Ross, E.M., Markowetz, F., 2016. OncoNEM: inferring tumor evolution from single-cell sequencing data. Genome Biol. 17, 69. Roth, A., Khattra, J., Yap, D., Wan, A., Laks, E., Biele, J., Ha, G., Aparicio, S., Bouchard-Cote, A., Shah, S.P., 2014. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11 (4), 396–398. Roth, A., McPherson, A., Laks, E., Biele, J., Yap, D., Wan, A., Smith, M.A., Nielsen, C.B., McAlpine, J.N., Aparicio, S., Bouchard-Cote, A., Shah, S.P., 2016. Clonal genotype and population structure inference from single-cell tumor sequencing. Nat. Methods 13 (7), 573–576. Samuels, M.E., Friedman, J.M., 2015. Genetic mosaics and the germ line lineage. Genes (Basel) 6 (2), 216–237. Satas, G., Raphael, B.J., 2017. Tumor phylogeny inference using tree-constrained importance sampling. Bioinformatics 33 (14), i152–i160. Schmidt, F., Efferth, T., 2016. Tumor heterogeneity, single-cell sequencing, and drug resistance. Pharmaceuticals (Basel). 9(2). Schmitt, M.W., Prindle, M.J., Loeb, L.A., 2012. Implications of genetic heterogeneity in cancer. Ann. N. Y. Acad. Sci. 1267, 110–116. Sengupta, S., Bolin, J.M., Ruotti, V., Nguyen, B.K., Thomson, J.A., Elwell, A.L., Stewart, R., 2011. Single read and paired end mRNA-Seq Illumina libraries from 10 nanograms total RNA. J. Vis. Exp. (56), e3340. Seoane, J., De Mattos-Arruda, L., 2014. The challenge of intratumour heterogeneity in precision medicine. J. Intern. Med. 276 (1), 41–51. Shah, S.P., Roth, A., Goya, R., Oloumi, A., Ha, G., Zhao, Y., Turashvili, G., Ding, J., Tse, K., Haffari, G., Bashashati, A., Prentice, L.M., Khattra, J., Burleigh, A., Yap, D., Bernard, V., McPherson, A., Shumansky, K., Crisan, A., Giuliany, R., Heravi-Moussavi, A., Rosner, J., Lai, D., Birol, I., Varhol, R., Tam, A., Dhalla, N., Zeng, T., Ma, K., Chan, S.K., Griffith, M., Moradian, A., Cheng, S.W., Morin, G.B., Watson, P., Gelmon, K., Chia, S., Chin, S.F., Curtis, C., Rueda, O.M., Pharoah, P.D., Damaraju, S., Mackey, J., Hoon, K., Harkins, T., Tadigotla, V., Sigaroudinia, M., Gascard, P., Tlsty, T., Costello, J.F., Meyer, I.M., Eaves, C.J., Wasserman, W.W., Jones, S., Huntsman, D., Hirst, M., Caldas, C., Marra, M.A., Aparicio, S., 2012. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486 (7403), 395–399. Shapiro, E., Biezuner, T., Linnarsson, S., 2013. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14 (9), 618–630. Shi, Q., Qin, L., Wei, W., Geng, F., Fan, R., Shin, Y.S., Guo, D., Hood, L., Mischel, P.S., Heath, J.R., 2012. Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl. Acad. Sci. U. S. A. 109 (2), 419–424. Shiba, N., Yoshida, K., Shiraishi, Y., Okuno, Y., Yamato, G., Hara, Y., Nagata, Y., Chiba, K., Tanaka, H., Terui, K., Kato, M., Park, M.J., Ohki, K., Shimada, A., Takita, J., Tomizawa, D.,

References

Kudo, K., Arakawa, H., Adachi, S., Taga, T., Tawa, A., Ito, E., Horibe, K., Sanada, M., Miyano, S., Ogawa, S., Hayashi, Y., 2016. Whole-exome sequencing reveals the spectrum of gene mutations and the clonal evolution patterns in paediatric acute myeloid leukaemia. Br. J. Haematol. 175 (3), 476–489. Sho, S., Court, C.M., Winograd, P., Lee, S., Hou, S., Graeber, T.G., Tseng, H.R., Tomlinson, J.S., 2017. Precision oncology using a limited number of cells: optimization of whole genome amplification products for sequencing applications. BMC Cancer 17 (1), 457. Sidow, A., Spies, N., 2015. Concepts in solid tumor evolution. Trends Genet. 31 (4), 208–214. Sievers, C.K., Leystra, A.A., Clipson, L., Dove, W.F., Halberg, R.B., 2016. Understanding intratumoral heterogeneity: lessons from the analysis of at-risk tissue and premalignant lesions in the colon. Cancer Prev. Res. (Phila.) 9 (8), 638–641. Smallwood, S.A., Lee, H.J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S.R., Stegle, O., Reik, W., Kelsey, G., 2014. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11 (8), 817–820. Smith, J.M., Haigh, J., 2007. The hitch-hiking effect of a favourable gene. Genet. Res. 89 (5–6), 391–403. Sottoriva, A., Barnes, C.P., Graham, T.A., 2017. Catch my drift? Making sense of genomic intratumour heterogeneity. Biochim. Biophys. Acta 1867 (2), 95–100. Sottoriva, A., Kang, H., Ma, Z., Graham, T.A., Salomon, M.P., Zhao, J., Marjoram, P., Siegmund, K., Press, M.F., Shibata, D., Curtis, C., 2015. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47 (3), 209–216. Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P.K., Swerdlow, H., Satija, R., Smibert, P., 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14 (9), 865–868. Stratton, M.R., Campbell, P.J., Futreal, P.A., 2009. The cancer genome. Nature 458 (7239), 719–724. Strino, F., Parisi, F., Micsinai, M., Kluger, Y., 2013. TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Res. 41 (17), e165. Stumpf, F., Schoendube, J., Gross, A., Rath, C., Niekrawietz, S., Koltay, P., Roth, G., 2015. Single-cell PCR of genomic DNA enabled by automated single-cell printing for cell isolation. Biosens. Bioelectron. 69, 301–306. Su, Y., Shi, Q., Wei, W., 2017. Single cell proteomics in biomedicine: high-dimensional data acquisition, visualization, and analysis. Proteomics. 17(3–4). Svensson, V., Natarajan, K.N., Ly, L.H., Miragaia, R.J., Labalette, C., Macaulay, I.C., Cvejic, A., Teichmann, S.A., 2017. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14 (4), 381–387. Tang, F., Barbacioru, C., Nordman, E., Li, B., Xu, N., Bashkirov, V.I., Lao, K., Surani, M.A., 2010. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5 (3), 516–535. Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B.B., Siddiqui, A., Lao, K., Surani, M.A., 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6 (5), 377–382. Tirosh, I., Venteicher, A.S., Hebert, C., Escalante, L.E., Patel, A.P., Yizhak, K., Fisher, J.M., Rodman, C., Mount, C., Filbin, M.G., Neftel, C., Desai, N., Nyman, J., Izar, B., Luo, C.C., Francis, J.M., Patel, A.A., Onozato, M.L., Riggi, N., Livak, K.J., Gennert, D., Satija, R., Nahed, B.V., Curry, W.T., Martuza, R.L., Mylvaganam, R., Iafrate, A.J., Frosch, M.P., Golub, T.R., Rivera, M.N., Getz, G., Rozenblatt-Rosen, O., Cahill, D.P., Monje, M., Bernstein, B.E., Louis, D.N., Regev, A., Suva, M.L., 2016. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539 (7628), 309–313.

95

96

C HA PT E R 5 :

Single-Cell Omics: Deciphering Tumor Clonal Architecture

Tomasetti, C., Vogelstein, B., Parmigiani, G., 2013. Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor initiation. Proc. Natl. Acad. Sci. U. S. A. 110 (6), 1999–2004. Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N.J., Livak, K.J., Mikkelsen, T.S., Rinn, J.L., 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32 (4), 381–386. Van Loo, P., Voet, T., 2014. Single cell analysis of cancer genomes. Curr. Opin. Genet. Dev. 24, 82–91. Vandin, F., Upfal, E., Raphael, B.J., 2012. De novo discovery of mutated driver pathways in cancer. Genome Res. 22 (2), 375–385. Varn, F.S., Ung, M.H., Lou, S.K., Cheng, C., 2015. Integrative analysis of survival-associated gene sets in breast cancer. BMC Med. Genet. 8, 11. Venteicher, A.S., Tirosh, I., Hebert, C., Yizhak, K., Neftel, C., Filbin, M.G., Hovestadt, V., Escalante, L.E., Shaw, M.L., Rodman, C., Gillespie, S.M., Dionne, D., Luo, C.C., Ravichandran, H., Mylvaganam, R., Mount, C., Onozato, M.L., Nahed, B.V., Wakimoto, H., Curry, W.T., Iafrate, A.J., Rivera, M.N., Frosch, M.P., Golub, T.R., Brastianos, P.K., Getz, G., Patel, A.P., Monje, M., Cahill, D.P., Rozenblatt-Rosen, O., Louis, D.N., Bernstein, B.E., Regev, A., Suva, M.L., 2017. Decoupling genetics, lineages, and microenvironment in IDHmutant gliomas by single-cell RNA-seq. Science 355 (6332), eaai8478. Vogelstein, B., Fearon, E.R., Hamilton, S.R., Kern, S.E., Preisinger, A.C., Leppert, M., Nakamura, Y., White, R., Smits, A.M., Bos, J.L., 1988. Genetic alterations during colorectal-tumor development. N. Engl. J. Med. 319 (9), 525–532. Wang, Y., Navin, N.E., 2015. Advances and applications of single-cell sequencing technologies. Mol. Cell 58 (4), 598–609. Wang, Y., Waters, J., Leung, M.L., Unruh, A., Roh, W., Shi, X., Chen, K., Scheet, P., Vattathil, S., Liang, H., Multani, A., Zhang, H., Zhao, R., Michor, F., Meric-Bernstam, F., Navin, N.E., 2014. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512 (7513), 155–160. Weinstein, J.N., Collisson, E.A., Mills, G.B., Shaw, K.R., Ozenberger, B.A., Ellrott, K., Shmulevich, I., Sander, C., Stuart, J.M., 2013. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45 (10), 1113–1120. Williams, M.J., Werner, B., Barnes, C.P., Graham, T.A., Sottoriva, A., 2016. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48 (3), 238–244. Wu, H., Wu, X., Zhang, Y., 2016. Base-resolution profiling of active DNA demethylation using MABseq and caMAB-seq. Nat. Protoc. 11 (6), 1081–1100. Xu, X., Hou, Y., Yin, X., Bao, L., Tang, A., Song, L., Li, F., Tsang, S., Wu, K., Wu, H., He, W., Zeng, L., Xing, M., Wu, R., Jiang, H., Liu, X., Cao, D., Guo, G., Hu, X., Gui, Y., Li, Z., Xie, W., Sun, X., Shi, M., Cai, Z., Wang, B., Zhong, M., Li, J., Lu, Z., Gu, N., Zhang, X., Goodman, L., Bolund, L., Wang, J., Yang, H., Kristiansen, K., Dean, M., Li, Y., Wang, J., 2012. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148 (5), 886–895. Yang, J., Tanaka, Y., Seay, M., Li, Z., Jin, J., Garmire, L.X., Zhu, X., Taylor, A., Li, W., Euskirchen, G., Halene, S., Kluger, Y., Snyder, M.P., Park, I.H., Pan, X., Weissman, S.M., 2017. Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors. Nucleic Acids Res. 45 (3), 1281–1296. Yu, C., Yu, J., Yao, X., Wu, W.K., Lu, Y., Tang, S., Li, X., Bao, L., Li, X., Hou, Y., Wu, R., Jian, M., Chen, R., Zhang, F., Xu, L., Fan, F., He, J., Liang, Q., Wang, H., Hu, X., He, M., Zhang, X., Zheng, H., Li, Q., Wu, H., Chen, Y., Yang, X., Zhu, S., Xu, X., Yang, H., Wang, J., Zhang, X., Sung, J.J., Li, Y., Wang, J., 2014a. Discovery of biclonal origin and a novel oncogene SLC12A5 in colon cancer by single-cell sequencing. Cell Res. 24 (6), 701–712.

References

Yu, Z., Li, A., Wang, M., 2016. CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data. BMC Bioinform. 17, 310. Yu, Z., Li, A., Wang, M., 2017. CLImAT-HET: detecting subclonal copy number alterations and loss of heterozygosity in heterogeneous tumor samples from whole-genome sequencing data. BMC Med. Genet. 10 (1), 15. Yu, Z., Lu, S., Huang, Y., 2014b. Microfluidic whole genome amplification device for single cell sequencing. Anal. Chem. 86 (19), 9386–9390. Yuan, K., Sakoparnig, T., Markowetz, F., Beerenwinkel, N., 2015. BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome Biol. 16, 36. Zaccaria, S., El-Kebir, M., Klau, G.W., Raphael, B.J., 2018. Phylogenetic copy-number factorization of multiple tumor samples. J. Comput. Biol. Zafar, H., Wang, Y., Nakhleh, L., Navin, N., Chen, K., 2016. Monovar: single-nucleotide variant detection in single cells. Nat. Methods 13 (6), 505–507. Zare, H., Wang, J., Hu, A., Weber, K., Smith, J., Nickerson, D., Song, C., Witten, D., Blau, C.A., Noble, W.S., 2014. Inferring clonal composition from multiple sections of a breast cancer. PLoS Comput. Biol. 10 (7), e1003703. Zhang, H., Jenkins, G., Zou, Y., Zhu, Z., Yang, C.J., 2012. Massively parallel single-molecule and single-cell emulsion reverse transcription polymerase chain reaction using agarose droplet microfluidics. Anal. Chem. 84 (8), 3599–3606. Zhang, J., Liu, J., Sun, J., Chen, C., Foltz, G., Lin, B., 2014. Identifying driver mutations from sequencing data of heterogeneous tumors in the era of personalized genome sequencing. Brief. Bioinform. 15 (2), 244–255. Zhang, X., Zhang, Y., 2015. Bladder cancer and genetic mutations. Cell Biochem. Biophys. 73 (1), 65–69. Zhang, Y., Ho, Y.P., Chiu, Y.L., Chan, H.F., Chlebina, B., Schuhmann, T., You, L., Leong, K.W., 2013. A programmable microenvironment for cellular studies via microfluidics-generated double emulsions. Biomaterials 34 (19), 4564–4572. Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., Enard, W., 2017. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65 (4), 631–643.e4. Zilionis, R., Nainys, J., Veres, A., Savova, V., Zemmour, D., Klein, A.M., Mazutis, L., 2017. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12 (1), 44–73. Zong, C., Lu, S., Chapman, A.R., Xie, X.S., 2012. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338 (6114), 1622–1626.

97