Sequencing a Single Circulating Tumor Cell for Genomic Assessment

C H A P T E R

15 Sequencing a Single Circulating Tumor Cell for Genomic Assessment Lei Xu1,2, Nuria Coll Bastus1 and Yong-Jie Lu1 1

Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom 2Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China

Advances in genomic technology and molecular diagnosis will allow the implementation of precision medicine in cancer patients in the near future. Such advances in knowledge and technologies have already enabled clinical scientists to come closer than ever to dissecting the genetic and molecular features linked to the causes, initiation, and progression of human malignancies, as well as the implementation of patient stratification for treatment and the prediction of the therapeutic responses (Garraway, 2013). Precision medicine is not just individualized treatment but also the timely monitoring of the molecular disease profile to adjust the treatment. However, current genomic characterization and molecular diagnosis are mainly performed in tumor tissues, which are obtained through timeconsuming and invasive procedures. Frequent re-biopsy of tumor tissues for real-time disease monitoring can be very challenging and often unfeasible. Therefore, treatments are frequently based on the information generated from tissues obtained years ago at diagnosis or before relapse, which does not represent the disease mutational

Oncogenomics DOI: https://doi.org/10.1016/B978-0-12-811785-9.00015-6

profile and biologic characteristics at later treatment times. Liquid biopsy is gaining research focus for cancer diagnostic and monitoring applications because it can be sampled frequently in a non- or minimally invasive manner. Circulating tumor cells (CTCs) are cancer cells that have entered the blood circulation. The advantage of analyzing CTCs over circulating tumor DNA is that CTCs can provide information about the genetic and molecular characteristics of the cancer, as well as tumor heterogeneity at the cellular level. CTCs allow single-cell analysis, which offers a unique opportunity to uncover stochastic changes of individual cells as well as unknown regulatory pathways of cancer relapse or treatment resistance. Therefore, CTCs can serve as a rich resource for the identification of both cancer biological and genetic features, which can be used for real-time monitoring of cancer development and progression as well as understanding mechanisms of cancer metastasis (Lohr et al., 2014; Xu, Shamash, & Lu, 2015). Enumeration of CTCs has already been used for cancer prognosis (Xu et al., 2015). The technological improvement

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to harvest CTCs with increased purity enables researchers to perform genomic analysis for CTCs (Lohr et al., 2014). Single-cell CTC genomic analysis is attractive for two reasons. First, none of the current CTC isolation technologies can efficiently harvest CTCs without a significant amount of lymphocyte contamination. Second, and more important, single-cell analysis reveals intratumor cancer genetic heterogeneity and helps identify otherwise undetectable subpopulations of cancer cells, which may be critical for cancer metastasis and therapeutic resistance (Buettner et al., 2015). Single-cell analysis of primary tumors and metastatic lesions has been used to trace cancer evolutionary history, to establish a mono- or multiclonal origin of certain cancers, and to identify low-prevalence subpopulation mutations (Hou et al., 2012; Lima et al., 2012; Navin et al., 2011; Yu et al., 2014). In this chapter, we review the current state of technologies for single CTC analysis, including CTC capture, whole-genome amplification for next-generation sequencing (NGS), and bioinformatics data analysis, with examples and prospects for research and clinical applications.

SINGLE-CELL WHOLE-GENOME SEQUENCING TECHNOLOGIES The two major challenges to sequence a single CTC are efficiently capturing single CTCs and unbiased amplification of the tiny amount of DNA from one cell.

Isolation of a Single CTC Efficiently capturing a single CTC of interest is critical and one of the main challenges for single-cell CTC sequencing. Currently, fluorescence-activated cell sorting (FACS) (Dent et al., 2016; Neves et al., 2014; Swennenhuis, Reumers, Thys, Aerssens, & Terstappen, 2013), micromanipulation systems (Dey, Kester,

Spanjaard, Bienko, & van Oudenaarden, 2015; Kroneis et al., 2011; Neumann et al., 2016; Peeters et al., 2013; Polzer et al., 2014), and microfluidics platforms (Buettner et al., 2015; Chen et al., 2016; Xin et al., 2016; Yang et al., 2015; Yeo et al., 2016) are the three main techniques developed for single-cell capture from enriched CTC populations from whole blood using various isolation systems (Fig. 15.1 and Table 15.1). The pre-enrichment can be performed by either label-dependent systems, such as positive selection (CellSearch and IsoFlux, μp CTC-Chip, etc.) and negative selection (e.g., EasySep and RosetteSep for CD451 cells depletion) platforms or label-independent systems, such as size-based (e.g., Parsortix and ISET) platforms (Alix-Panabieres & Pantel, 2014; Xu, Mao, et al., 2015). Because label-independent approaches usually utilize magnetic beads to capture cells of interest, an extra step to get rid of these beads may be required before or after single-cell preparation (Swennenhuis et al., 2013). Another difference among CTC preenrichment platforms is the fixation of cells. Cells are required to be fixed in the CellSave tubes, in which blood is collected for preenrichment using CellSearch. This step is optional for most of the other platforms but is not preferred for certain systems, aiming to isolate viable cells, such as Parsortix (Xu, Mao, et al., 2015). Cells may become sticky after fixation, which would affect the efficiency of CTC isolation, especially for sizeand/or deformability based systems. The cell fixation also has a substantial influence on the final DNA content, resulting in decreased matching variants to pooled sample and higher false discovery rate compared to unfixed single cells (Swennenhuis et al., 2013). Sorting cells with a flow cytometer is very fast and enables the isolation of cells of interest based on both labeled fluorescence and physical features (e.g., cell size). In the past, it was challenging to capture single CTCs with this technique due to the low sensitivity in

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FIGURE 15.1 Overview of single CTC isolation methods. Schematic of single CTC isolation showing steps from CTC pre-enrichment to singe CTC pick-up.

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TABLE 15.1 Comparison of Three Major Single Cell Capture Platforms Single CTC Capture Method

FACS Sorting

Micromanipulation

Microfluidics

Mode

Automated

Semiautomated

Automated

Speed

Fast

Time-consuming

Fast

Cell selection principle

Enable selection based on cell fluorescent markers, cell size, and DNA content

Enable selection based on cell morphology and fluorescent markers

Enable selection based on cell size

Supervision

No visualization to supervise cell selection

With supervision for each selected cell

With supervision for each selected cell

Missed cells

May lose tumor cells that do not express protein marker

May lose tumor cells that do not express protein marker

May lose tumor cells that are not in the selected size range

capturing rare events among millions of background cells (Allan et al., 2005). With the advances in affinity reagent technology, hardware technology (e.g., lasers and detectors), instrument calibration methodology, and data analysis capabilities (O’Donnell, Ernst, & Hingorani, 2013), as well as in CTC pre-enrichment using various approaches, FACS reattracted attention for CTC isolation. Recovery rates for single-cell CTCs sorted from enriched samples ranged between 50% and 90%, and the variability is associated with the use of different FACS instruments, gate setting, and protein expression across cancer types (Dent et al., 2016; Neves et al., 2014; Swennenhuis et al., 2013). However, the incapability of FACS to downscale reactions for harvesting targeted cells to the nanoliter-scale volume has not yet been overcome, facing the potential higher reagent cost per cell in the following step, DNA amplification. Micromanipulation systems, such as DEPArray (Peeters et al., 2013; Polzer et al., 2014) and CellCelector (Neumann et al., 2016), and micropipette aspiration (Dey et al., 2015) enable semiautomated image-based isolation of single cells. After images are scanned, all the previously mentioned systems can identify and

capture cells with the desired fluorescence labeling and morphological characteristics. Because such isolation is supervised under microscopic vision for each cell attempt, this approach is time-consuming and low throughput. Cell loss happens in these semiautomated approaches as well. Taking DEPArray as an example, the mean recovery rate is reported to be around 60% and cell loss mainly results from the dead volume injected into the microchamber of the system (Peeters et al., 2013). Similar to FACS sorting, another drawback of these capture systems is the microliter instead of nanoliter scaled volume of harvested cells, necessitating a higher reagent cost per cell (Kolodziejczyk, Kim, Svensson, Marioni, & Teichmann, 2015). Recent technical advances in microfluidic systems have resulted in their application in single-cell isolation (Chen et al., 2016; Yang et al., 2015; Yeo et al., 2016). The Fluidigm C1 system (Buettner et al., 2015; Xin et al., 2016) is one of the microfluidic platforms suitable for single-cell sequencing. Cells are captured into individual reaction chambers in the exclusive integrated fluidic circuit, which currently enables analysis of up to 96 cells per chip. Captured cells can then be examined by

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SINGLE-CELL WHOLE-GENOME SEQUENCING TECHNOLOGIES

microscopy for integrity, surface markers, or reporter genes. Locations of cells of interest can also be recorded for subsequent automated cell lyse and preamplification-on-chip in nanoliter volumes. This method is not yet perfected due to several limitations. First, it is only applicable to cells with a similar range of size as each chip has a pre-specified size range (currently three types of 5
10, 10
17, and 17
25 μm in diameter). Second, it requires at least 1,000 cells to be loaded into chips in order to allow a substantial number of cells for sample analysis, making those samples with low number of total mixed cells unsuitable for this method. Third, an optimal cell concentration is required for sample loading; otherwise, capture efficiency declines and less than half the chambers may capture cells. Therefore, a significant proportion of the sample is required to be used for concentration calibration, in some cases resulting in insufficient sample volume for single CTC capture. Finally, the C1 chip is expensive, increasing significantly the consumable cost for single-cell preparation compared to other platforms. On the other hand, the use of reagents at the nanoliter scale versus the microliter scales in other systems allows for a reduction in reagent costs (Kolodziejczyk et al., 2015).

Single-Cell Whole-Genome Amplification Methods A single human cell contains around 6 pg of DNA. Following single CTC isolation, the characterization of the genomes in a single cell remains technically challenging due to the limited amount of genetic material accessible for downstream analysis. Although it is possible to sequence individual DNA molecules of thousands of bases and DNA from a population of cells, current sequencing technology cannot directly sequence the genome of a single cell due to the low amount of DNA enclosed in it. During the past three decades, numerous efforts

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have been made to develop and optimize singlecell whole-genome amplification (scWGA) methods in order to enable the full characterization of the genome of individual cells (Fig. 15.2). The most commonly used scWGA methods are (1) polymerase chain reaction (PCR)-based scWGA, including linker adapter PCR, primer extension preamplification PCR (PEP
PCR), and degenerate oligonucleotide-primed PCR (DOP-PCR); (2) an isothermal amplification method called multiple displacement amplification (MDA); and (3) a hybrid method also known as multiple annealing and looping
based amplification cycles (MALBAC). Linker Adapter PCR Linker adapter PCR was first described in 1989 by Ludecke, Senger, Claussen, and Horsthemke for the amplification of chromosome regions and was later used for scWGA (Klein et al., 1999). In this method, the DNA is digested by restriction enzymes, and the ends are ligated to an aptamer. These aptamers are used as template to amplify each of the DNA fragments through PCR-based thermocycles (Fig. 15.3A). Linker adapter PCR can present bias associated with inefficient ligation, DNA polymerase errors, and nonuniformity of random primer annealing and extension. Small differences in the amplification among different sequences cause overamplified and underamplified regions of the genome due to the exponential amplification nature of the PCR (Geigl & Speicher, 2007; Klein et al., 1999). A kit based on this method that is often used for scWGA for copy number variations/structural variations (CNV/SV) analysis is the Ampli 1 WGA Kit from Silicon Biosystems (http:// www.siliconbiosystems.com/ampli1-wga-kit). Primer Extension Preamplification PCR Primer extension preamplification (PEP) PCR was also one of the first methods described for scWGA (Snabes et al., 1994; Zhang et al., 1992). This method uses DNA

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FIGURE 15.2

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The timeline of technology development applicable to single-cell whole-genome amplification.

polymerases and a set of totally degenerate 15-base random oligonucleotides (50 -30 ) to prime template DNA. PEP-PCR involves standard PCR thermocycles with very low annealing temperatures and requires 50 or more cycles to produce amplicons representative of the original genomic DNA. This method results in amplification bias due to nonuniformity of random primer annealing and extension. Throughout the years, efforts have been made to improve PEP-PCR. New protocols use DNA polymerase cocktails that include proofreading DNA polymerase to provide 30 -to-50 -exonuclease activity, in addition to Taq DNA polymerase to carry on the DNA extension (Arneson, Hughes, Houlston, & Done, 2008). Degenerate Oligonucleotide-Primed Polymerase Chain Reaction Soon after PEP-PCR was first described, a version of the PCR named degenerate oligonucleotide-primed PCR (DOP-PCR) was developed for genome mapping studies in which

less than 1 ng of DNA was used for amplification (Cheung & Nelson, 1996; Telenius et al., 1992). This method includes degenerate primers containing random hexamer primers at the 30 end and a partially fixed sequence at the 50 end. DOP-PCR involves two amplification stages. In the first stage, the 30 end primers bind to the DNA template at a low annealing temperature (30 C) and are extended at a raised temperature by DNA polymerases. In the second stage, the previous products are amplified with the partially fixed primer targeting the 50 end sequence at a higher annealing temperature (Fig. 15.3B). This method has been commonly used to efficiently amplify the whole genome, including DNA from formalinfixed paraffin-embedded tissue (Lu et al., 1998), as well as the transcriptome (Lu et al., 2001). It has also been used in the first DNA single-cell single-nucleus sequencing for genome-wide copy number analysis in combination with flow sorting for cell isolation and NGS (Baslan et al., 2012; Navin et al., 2011).

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(A)

Linker adapter PCR

(B)

Degenerate oligonucleotide–primed polymerase chain reaction (DOP-PCR)

DNA digestion

Preferential amplification Adapter ligation

Synthesis complementary strand

PCR amplification

(C) Multiple displacement amplification (MDA)

Further amplification

(D) Multiple annealing and looping–based amplification cycles (MALBAC)

Random primer and isothermal extension

Isothermal amplification

Strand displacement

Isothermal amplification and loop formation

Exponential amplification

PCR amplification

FIGURE 15.3 Overview of the main single-cell whole-genome amplification methods. (A) Linker adapter PCR uses restriction enzymes to digest the DNA, and ends are ligated to adaptors before PCR amplification. (B) Degenerate oligonucleotide primer PCR (DOP-PCR) uses degenerate random oligonucleotide primers followed by PCR amplification. (C) Multiple displacement amplification (MDA) uses random priming followed by isothermal exponential amplification with a DNA polymerase with high processivity and strand displacement activity. (D) Multiple annealing and looping-based amplification cycles (MALBAC) use random primers in an initial isothermal amplification in which only the original DNA and semiamplicons are linearly amplified before further PCR amplification.

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This method achieved approximately 10% coverage, enabling CNV analysis but not small-scale genomic analysis. One of the major limitations associated with this method is that the concentrations of the primers and polymerase directly affect the result of DOP-PCR. Similar to other PCR-based WGA approaches, this method involves exponential amplification of DNA, and small differences in the amplification among different sequences cause overamplified and underamplified regions in the genome, resulting in low coverage. Single-cell resequencing analysis showed that DOP-PCR results in high duplication ratio and poor genome coverage compared to MDA and MALBAC methods (Hou et al., 2015). The low amplification efficiency of this method is attributed to the random primers and the enzyme (Cheung & Nelson, 1996). Nonetheless, DOPPCR provides high reproducibility and accuracy for detection of CNVs (Hou et al., 2015). A kit available for scWGA using this method is the GenomePlex Single Cell WGA Kit from SigmaAldrich/Rubicon Genomics (https://www. sigmaaldrich.com/catalog/product/sigma/wga4? lang 5 en®ion 5 GB). Multiple Displacement Amplification Amplification bias such as under- and overamplified regions and duplicated amplicons observed in PCR-based scWGA were largely mitigated with the development of the multiple displacement amplification (MDA) method. MDA was first described in 2001 by Lasken’s group (Dean, Nelson, Giesler, & Lasken, 2001). This method carries out an isothermal genome amplification using a uniquely processive ϕ29 DNA polymerase and random hexamer primers (Dean et al., 2001). ϕ29 DNA polymerase is a highly stable single subunit DNA polymerase with high strand displacement activity able to incorporate more than 70,000 nucleotides without dissociating from the template

(Blanco et al., 1989). ϕ29 has a high replication fidelity because of its 3 - 5 exonuclease activity and proofreading activity. The random hexamer primers incorporate 30 thiophosphatecovered ends to protect them from the 30 exonuclease activity of the ϕ29 DNA polymerase. During MDA, ϕ29 DNA polymerase extends the random primers under isothermal conditions, producing 10- to 100-kb-long branched DNA fragments. Simultaneously, these branched fragments are extended by other primers, resulting in multibranched structures (Fig. 15.3C). Genome coverage of this method using single-cell resequencing analysis has been reported to range between 59% and 82%, depending on the kit used (de Bourcy et al., 2014; Hou et al., 2015). Although MDA provides a higher genome coverage than DOP-PCR and MALBAC (Hou et al., 2015), it involves exponential amplification, thus resulting in overamplification of certain genomic regions and underamplification in other regions. MDA is the most widely used WGA method due to its high fidelity and simplicity. Several MDA kits have been adapted for MDA scWGA, including REPLI-g Single Cell Kit from Qiagen, GenomiPhi DNA Amplification Kit from GenomiPhi DNA, and AmpliQ Genomic Amplifier Kit from AmpliqonIII (Sorensen et al., 2007). In 2014, Navin and coworkers reported a slightly modified MDA method called Nuc-seq that amplifies genomic DNA following MDA chemistry but from cells in the G2
M stage of the cell cycle. This method takes advantage of the fact that these cells have four copies of the genome, resulting in increased genome coverage (Yu et al., 2014). In 2013, an MDA system called the microwell displacement amplification system (MIDAS) was presented by Zhang and colleagues (Gole et al., 2013). This system enables the simultaneous amplification of thousands of single cells by using hundreds to thousands of nanoliter wells, thus increasing robustness and uniformity (Gole et al., 2013).

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SINGLE CTC NGS FOR GENETIC STUDY OF CTCS AND CLINICAL APPLICATIONS

Multiple Annealing and Looping-Based Amplification Cycles In order to overcome the low coverage of PCR-based methods and the low uniformity of the isothermal MDA chemistry, an scWGA hybrid approach that involves isothermal amplification followed by PCR amplification was developed. Multiple annealing and looping-based amplification cycles (MALBAC) is a single-cell WGA method that was first reported in 2012 by Xie and coworkers (Zong, Lu, Chapman, & Xie, 2012). This method involves a quasi-linear preamplification, in which DNA amplicons are always generated from the original genomic DNA, thus reducing the sequencedependent bias associated with exponential amplification (Fig. 15.3D). MALBAC uses thermally stable DNA polymerases with strand displacement activity and a pool of random primers, which can evenly hybridize to the DNA template at a very low temperature. The pool of primers includes a common 27nucleotide sequence that binds at the 50 end and eight random nucleotide primers that bind at the 30 end. During the preamplification, primers are annealed at 0 C to genomic DNA fragments (B10 to 100 kb) from a single human cell. DNA polymerase extends the random primers at a temperature of 65 C creating semiamplicons of variable lengths (0.5
1.5 kb). The newly synthesized strands are separated from the DNA template at 94 C. A second thermal cycle amplifies the semiamplicons and the initial genomic DNA, resulting in full amplicons with complementary ends and new semiamplicons, respectively. The amplified product is brought to 58 C to allow the looping and cross-hybridization of the full amplicons to prevent further amplification. The new semiamplicons and the starting DNA fragments remain linear in order to be used in the subsequent thermal cycle. This cycle is repeated 8
12 times. Genome recovery for this method has been reported to be slightly lower than MDA but higher than DOP-PCR (de

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Bourcy et al., 2014; Hou et al., 2015). Although MALBAC can also generate sequencedependent bias, these are highly reproducible from cell to cell. Suitable normalization of this bias allows this method to be highly accurate for CNV detection. On the other hand, the use of a DNA polymerase with lower fidelity than the ϕ29 polymerase results in a high false-positive rate for single nucleotid variant (SNV) analysis. Furthermore, underamplified genomic regions using MALBAC are sometimes completely lost due to the highly reproducible amplification of the same regions from the original genomic DNA (Lasken, 2013). MALBAC Single-Cell WGA Kit is available from Yikon Genomics. Another kit that also uses the amplicon looping and cross-hybridization approach is PicoPlex WGA Kit from Rubicon Genomics. PicoPlex WGA Kit is demonstrated to achieve 95% amplification success rate and have a high reproducibility in AT and GC-rich regions (http://www.rubicongenomics.com).

SINGLE CTC NGS FOR GENETIC STUDY OF CTCs AND CLINICAL APPLICATIONS One of the important applications of single CTC sequencing is to trace the origin of those tumor cells in blood circulation and investigate the evolution pattern of the tumor. Studies using single CTC sequencing initially focused on analyzing copy number variations in single tumor cells using low depth of genomic coverage (Carter et al., 2016; Navin et al., 2011; Ni et al., 2013). Using a 30X coverage, lineage relationships between the cells were attempted based on the point mutations (Hou et al., 2012; Xu et al., 2012). Further studies confirmed the feasibility to acquire the mutational landscapes of single CTCs at the whole-genome level (Chen et al., 2016; Heitzer et al., 2013), and such profiles were able to be traced back to either primary

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tumor or metastatic tumor cells, suggesting clinical utility of CTCs for noninvasive monitoring of disease progression. Moreover, some mutations were only observed in single CTCs, suggesting that these were either derived from a small subclone of the primary tumor or represented new variants in the metastatic cells. Some mutations were found to be present in the primary tumors at the subclonal level after additional deep sequencing (Heitzer et al., 2013). Certain mutations were truly exclusive of CTCs and conferred high risk for early death (Stoecklein et al., 2008). Mutated genes shared homogeneously among analyzed CTCs but highly distinct from the primary tumor of individual patients were also reported (Polzer et al., 2014). Minor subclones within the primary tumor may have turned into a predominant clone or specific mutations generated in CTCs during their blood vessel invasion. Perhaps the most important application of this technology is the identification of the genomic differences among individual tumor cells in each patient and the subsequent impact on clinical treatment and management decision. The genomic rearrangements in a circulating cell were found to be associated with specific driver mutations in primary tumor or metastasis (Heitzer et al., 2013). Using single CTC sequencing, researchers observed a higher number of genomic alterations in ERBB2-amplified CTCs compared to ERBB2-negative CTCs, whereas no such effect was seen for PIK3CA mutations (Polzer et al., 2014). Other analyses showed that exome point mutations were broadly different from cell to cell, and a large number of mutations were CTC specific (Lohr et al., 2014; Ni et al., 2013). One interesting finding is that the copy number profiles of single CTCs were highly similar and shared with the primary tumors (Ni et al., 2013). A recent study used this technology to classify chemosensitive and chemoresistant small cell lung cancer patients based on copy number aberrations (CNAs) of 88 CTCs from 13 patients (Carter et al., 2016).

This classifier achieved a concordance of 83.3% in 18 additional patients, suggesting the impact of different genetic profiles on clinical response and prognosis. However, in some of previously mentioned studies, CTC-specific mutations were not validated. Single-cell NGS has been documented as prone to artifacts, introduced during either amplification or sequencing (Hou et al., 2012; Lu et al., 2012; Wang, Fan, Behr, & Quake, 2012; Xu et al., 2012; Zong et al., 2012); thus, errors from technical limitations cannot be excluded. In our exome NGS analysis of single CTCs from two prostate cancer patients captured using the Fluidigm C1 system following Parsortix pre-enrichment, we found in both cases several clone mutations shared in more than two CTCs and the deep sequencing (around 1,000X) of mixed population of harvested CTCs. Although the mutations occurred in cancer-associated genes including androgen receptor pathway genes, the high proportion of homozygous mutations and the much higher mutation rate than the CTC percentage in the mixed population of the CTC isolation system harvested cells suggested that those mutations were artifacts induced during the presequencing genomic amplification (Lu et al., unpublished data).

CHALLENGES AND FUTURE PROSPECTS Single CTC sequencing remains out of reach for the majority of research and clinical laboratories due to the technical barrier, high cost, and lack of interpretation expertise. Dozens of platforms for CTC isolation emerged during the past decade, reducing the cost and increasing accessibility to most researchers. However, the subsequent singlecell capture still hinders some researchers due to its high cost, labor-intensive demand, and immature techniques causing uncertainty. The high cost of sequencing also limits the

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REFERENCES

implementation of this technique in many laboratories. However, sequencing cost is expected to drop with upcoming technological innovations inspired by industrial competition. Another barrier is the necessity for infrastructure and bioinformatics expertise required to analyze the data in a reliable and reproducible manner. More work has to be done to develop user-friendly computational models and statistical tools so that clinical scientists can acquire the independent ability to detect point mutations, indels, and structure variants in a single-cell database to accelerate its clinical application. The first and critical step to achieve success in scWGS is the implementation of scalable methods to isolate single cells. This is currently an active research area, focusing on producing novel tools to help improve capture performance and aiming to capture all tumor cells within a blood sample. The study of the genomic heterogeneity among individual cells provides the opportunity to implement novel therapeutic approaches and perform in-depth genomic instability evaluations. However, this makes clinical interpretation more complicated and difficult. Simple, user-friendly software to perform these comprehensive bioinformatics analysis is urgently required. A comprehensive overview of existing software tools for SNP calling from NGS data can be found in Nielsen, Paul, Albrechtsen, and Song (2011), and with the development of software and technologies, more state-of-the-art methods are emerging to help cover all analysis stages. The previously mentioned study using genetic features of CTCs to distinguish chemosensitive from chemorefractory tumors also showed that concordance of CTC CNAs with clinical status is 91% and 68% in patients with homogeneous and heterogeneous CTCs, respectively (Carter et al., 2016). Therefore, such analysis will require increased CTC sampling per patient for more accurate analysis.

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Although several scWGA kits are currently widely used for CNV and SNV analysis, the amplification process that these methods enclose is likely to introduce artificial aberrations on the amplified genome and even fail to represent some genomic regions. Although these shortfalls may not be a major drawback for CNV and SNV analysis, they are critical to evaluate genomic alterations at a smaller scale. Another difficulty with these methods is the frequent contamination with external DNA, because any trace of contamination could overwrite the data of interest. Therefore, an efficient and unbiased scWGA method is required to take full advantage of single-cell CTC NGS. Initial studies have provided hope for the feasibility of such investigations, but the degree of robustness of data quality due to sample bias and technical limitations warrants longer term and larger scale sample validation. Genomic variability among individual tumor cells exists and contributes to cancer progression and treatment resistance. Profiling for tumor cells in blood circulation at the cellular level to investigate tumor evolution and treatment response has a major advantage due to the easy accessibility to longitudinal samples. Such molecular analysis for these cells linked to targeted genomic instability for therapy improvement may represent the true meaning of how “liquid biopsy” can benefit the clinic.

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