Developing Bottom-Up Induced Pluripotent Stem Cell Derived Solid Tumor Models Using Precision Genome Editing Technologies

Kelsie L Becklin; Garrett M Draper; Rebecca A Madden; Mitchell G Kluesner; Tomoyuki Koga; Miller Huang; William A Weiss; Logan G Spector; David A Largaespada; Branden S Moriarity; Beau R Webber

doi:10.1089/crispr.2022.0032

. 2022 Aug 12;5(4):517–535. doi: 10.1089/crispr.2022.0032

Developing Bottom-Up Induced Pluripotent Stem Cell Derived Solid Tumor Models Using Precision Genome Editing Technologies

Kelsie L Becklin ^1,^2,^3,^†, Garrett M Draper ^1,^2,^3,^†, Rebecca A Madden ^1,^2,³, Mitchell G Kluesner ^1,^2,³, Tomoyuki Koga ^4,⁵, Miller Huang ^6,⁷, William A Weiss ^8,⁹, Logan G Spector ^1,², David A Largaespada ^1,^2,³, Branden S Moriarity ^1,^2,^3,^*, Beau R Webber ^1,^2,^3,^*

^¹Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA; Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^²Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, USA; Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^³Center for Genome Engineering, University of Minnesota, Minneapolis, Minnesota, USA; Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^⁴Ludwig Cancer Research San Diego Branch, La Jolla, California, USA; Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^⁵Department of Neurosurgery, University of Minnesota, Minneapolis, Minnesota, USA; Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^⁶Department of Pediatrics, University of Southern California, Los Angeles, California, USA; Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^⁷Cancer and Blood Disease Institute, Children's Hospital Los Angeles and The Saban Research Institute, Los Angeles, California, USA; Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^⁸Departments of Neurology, Pediatrics, Neurosurgery, Brain Tumor Research Center, and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA; and Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^⁹Departments of Pediatrics, Neurosurgery and Brain Tumor Research Center, University of California, San Francisco, San Francisco, California, USA.

^†These authors contributed equally to this work.

^{^*}

Address correspondence to: Beau R. Webber, Department of Pediatrics, University of Minnesota, 2231 6th Street SE, Minneapolis, MN 55455, USA, webb0178@umn.edu

^{^*}

Address correspondence to: Branden S. Moriarity, Department of Pediatrics, University of Minnesota, 2231 6th Street SE, Minneapolis, MN 55455, USA, mori0164@umn.edu

ⁱORCID ID (https://orcid.org/0000-0003-2904-7374).

ⁱⁱORCID ID (https://orcid.org/0000-0002-8330-117X).

PMCID: PMC9529369 PMID: 35972367

Abstract

Advances in genome and tissue engineering have spurred significant progress and opportunity for innovation in cancer modeling. Human induced pluripotent stem cells (iPSCs) are an established and powerful tool to study cellular processes in the context of disease-specific genetic backgrounds; however, their application to cancer has been limited by the resistance of many transformed cells to undergo successful reprogramming. Here, we review the status of human iPSC modeling of solid tumors in the context of genetic engineering, including how base and prime editing can be incorporated into “bottom-up” cancer modeling, a term we coined for iPSC-based cancer models using genetic engineering to induce transformation. This approach circumvents the need to reprogram cancer cells while allowing for dissection of the genetic mechanisms underlying transformation, progression, and metastasis with a high degree of precision and control. We also discuss the strengths and limitations of respective engineering approaches and outline experimental considerations for establishing future models.

Introduction

The breakthrough discovery that somatic cells could be reprogrammed to pluripotency through the addition of four transcription factors (SOX2, KLF4, cMYC, and OCT4) was first reported nearly 15 years ago.^1,2 In most non-transformed somatic cell types, the expression of these four factors induces global transcriptional and epigenetic changes that result in reversion of the somatic cell to a well-defined pluripotent state that is stably maintained throughout extended propagation in vitro.^3,4 In the years after this discovery, methods to differentiate iPSC into a diverse array of somatic cell lineages were rapidly produced.^5–7

With new differentiation methods continually being published, the fidelity of in vitro iPSC differentiation has only increased in efficiency and efficacy.^8–12 Aside from a few cell lineages that lack effective differentiation protocols, iPSC-based modeling represents a promising approach to model many human cancers.¹³ Cellular reprogramming has already proven useful for modeling inherited genetic diseases, as patient-derived iPSC (pd-iPSC) stably capture genetic alterations that underlie disease phenotype and can be differentiated into somatic lineages that recapitulate disease pathology.^14–18

In theory, reprogramming malignant cells could capture the genome of diverse human cancers, offering the opportunity to study the impact of genomic perturbations on cell development and function in the context of transformation.^19,20 This renewable source of tumor cells could be especially advantageous to study rare, difficult-to-procure tumors. However, extensive efforts to reprogram malignant cells have been stymied by the apparent intransigence of many transformed cells to reprogramming.^21,22 Aside from the few notable exceptions discussed later, efforts to reprogram cancer cells have been met with low efficiency or produced cells with an ill-defined, quasi-pluripotent phenotype.^23,24

The iPSC technology is best suited for isolation of non-transformed cells, allowing for the investigation of cancer initiation and development with the ability to address how single and complex mutations influence the transformation process. This has provided the impetus to pursue alternative approaches to recapitulate the genetic basis of many cancers in human iPSC models.

Over the past decade, advancements in our ability to efficiently and precisely modify the human genome and differentiate stem cells into somatic lineages have provided an opportunity to recapitulate many of the complex genetic alterations linked to human cancers in cells of the proper developmental state.^25,26 In an approach we coined “bottom-up” cancer modeling (Fig. 1), the combined use of iPSC and genetic engineering offers a new avenue to investigate discrete stages of cancer development in ways that can inform novel hypotheses and drive improved therapeutic development.

FIG. 1. — Bottom-up cancer modeling invokes the use of iPSC derived from somatic cells, which can then be differentiated into the cell-of-origin for a particular cancer alongside precise and timely genetic engineering to re-create cancer associated mutation/s. These models can be studied in many ways, including drug or genetic screens, used in organoid or mouse systems, or co-cultured with autologous or analogous immune cells to test new immunotherapies with the desired outcome of new therapeutics or new hypotheses. iPSC, induced pluripotent stem cell.

Here, we review bottom-up cancer models with a focus on solid tumors. We highlight the current challenges in iPSC cancer modeling and discuss the opportunities to further improve the development of these models. We also describe how new precision genome editing technologies, such as base and prime editing, can be combined with iPSC technology to study genomic alterations found in human cancer. For additional information on iPSC modeling of hematological cancers, we refer the reader to these excellent reviews.^27–31

Current iPSC-Based Cancer Models

The value of iPSCs in cancer modeling has begun to emerge in recent years. As we move forward into a new era of cancer modeling, we turn to landmark studies to help shape and guide the field. Here, we provide a brief overview of cancer models developed using iPSC technology. We separate these models into three categories: (1) reprogramming of primary tumor cells, (2) reprogramming of patient somatic cells containing cancer-predisposition mutations, and (3) bottom-up cancer models, with a particular emphasis on the engineering technologies used to generate these models and their unique utility.

Reprogramming tumor cells

Most cancers, particularly solid tumors, seem refractory to the reprogramming process, limiting their potential for research.^32–35 It is hypothesized that this is due to the underlying genomic and epigenetic alterations that prevent the cells from complete dedifferentiation, thus retaining the genetic and epigenetic state of the original cancer; pointing to why the initial attempts at reprogramming tumor cells into iPSC were largely unsuccessful.^21,22 However, a handful of tumor types have now been reprogrammed successfully (Table 1). From these tumor cell derived iPSC, researchers have analyzed how various genetic mutations predispose to, cause, or maintain cancer.

Table 1.

Successful reprogramming of primary tumor cells

Cancer diagnosis	Reprogramming method
Acute myeloid leukemia	Sendai virus,¹⁹ excisable lentivirus²⁰
Juvenile myelomonocytic leukemia	Lentivirus,²⁴¹ sendai virus,⁴⁰ lentivirus²⁴²
Chronic myeloid leukemia	Episomal²⁴³
Chronic myelomonocytic leukemia	Episomal,⁴³ sendai virus²⁴⁴
Solid plexiform neurofibroma	Retrovirus and/or sendai virus²⁴⁵
Pancreatic ductal adenocarcinoma	Lentivirus³⁸

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Reprogramming cancer-predisposed patient somatic cells

One major benefit of iPSC technology is the ability to “capture” genetic backgrounds by generating stem cells from patient-derived somatic cells.^36,37 Despite the limited success in generating iPSC from malignant cells, there have been many descriptions of iPSC derived from the somatic cells of patients with known germline cancer predisposition syndromes (Table 2). These pd-iPSC provide a novel method for in-depth studies of various genetic diseases that confer increased susceptibility to developing cancer.

Table 2.

Reprogramming of somatic cells from patients with cancer-predisposition syndromes

Cancer predisposing disease	Mutant gene	Reprogramming method
Li-Fraumeni Syndrome	TP53	Sendai virus³⁹
Rothmund-Thomson Syndrome	RECQL4	Sendai virus²⁴⁶
Werner Syndrome	WRN	Retrovirus,^247,248 excisable lentivirus^247,248
Diamond-Blackfan Anemia	RPS19 and RPL5	Excisable lentivirus,^249,250 episomal and sendai virus^249,250
Myelodysplastic Syndrome	del(7q)	Excisable lentivirus¹⁸⁴
Chronic Myeloproliferative Disorders	KRAS, NRAS, GATA2, del(7q)	Excisable lentivirus²⁰
Congenital Neutropenia	ELANE	Excisable lentivirus⁴⁵
Noonan Syndrome	PTPN11	Retrovirus²⁵¹
Gorlin Syndrome	PTCH1	Sendai virus^72–253
Neurofibromatosis Type I	NF1	Retrovirus and sendai virus²⁴⁵
Multiple Endocrine Neoplasia Type 2 Syndrome	RET	Sendai virus²⁵⁴
Hereditary Papillary Renal Cell Carcinoma	MET	Sendai virus⁴⁶
BRCA-Mutated Breast Cancer	BRCA1	Non-integrating mRNA,^47,255 sendai virus^47,255 Non-integrating episomal plasmid²⁵⁶
Familial Adenomatous Polyposis	APC	Lentivirus²¹⁶
Down Syndrome	Trisomy 21	Retrovirus²⁵⁷

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NF1, neurofibromatosis type 1.

With these pd-iPSC lines, researchers have been able to study differentiation capacity, conduct drug screens, investigate disease mechanisms, and generate detailed gene expression profiles on cells derived from the iPSCs.^38–47 However, iPSC derived from the somatic cells of individuals with cancer predisposing conditions are often devoid of obvious phenotypes, most likely due to the lack of secondary mutations required for transformation.^39,45 Thus, even with cancer-predisposed pd-iPSCs, genetic engineering can be valuable in modeling the transformation process through the introduction of known cooperating driver mutations.

Genetically Engineered “Bottom-Up” iPSC Models

Despite the novelty in combining these two fields and the technical challenges faced, some iPSC-based cancer modeling studies have benefited from genetic engineering strategies.^41,48–52 The feasibility of this approach has improved significantly through technological advances in gene editing methods, including reagent composition and delivery, allowing for more complex engineering approaches with higher efficiency.^53,54 These improvements allow the controlled introduction of cancer-associated genetic alterations in wild-type iPSC and their derivatives, which has unique utility in elucidating the fundamental mechanisms underlying transformation.⁵⁵

See Supplementary Table S1 for common cancer driving mutations and current gene editing tools that are suitable to introduce these mutations in bottom-up cancer models.^56–66 Later, we review various efforts to create bottom-up models of solid tumors with an emphasis on highlighting the unique ways in which these iPSC-based models can be used.

To identify novel biomarkers of synovial sarcoma (SS), Hayakawa et al utilized a piggyBac (PB) transposon vector in iPSC to randomly insert and overexpress the SYT-SSX2 fusion cDNA found in a subset of SS.⁶⁷ In contrast to previous findings, most genes were upregulated in iPSC by the fusion protein. To explain this confounding data, the group then induced SYT-SSX2 in iPSC and iPSC-derived neural crest cells (iNCC), a challenging cell type to obtain other than through iPSC differentiation. Expression of the SYT-SSX2 fusion protein in iNCC induced changes in gene expression similar to those observed in SS, unlike the iPSC findings, indicating that NCCs are the likely cell-of-origin for SS.⁶⁸

Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly solid tumor that lacks effective therapies or surveillance protocol.⁶⁹ In an attempt to describe novel drug targets or biomarkers, Huang et al used lentiviral expression of dominant negative TP53 and/or constitutively active KRAS to study PDAC genesis in iPSC.⁷⁰ Genetically engineered iPSC were differentiated into 3D pancreatic progenitor organoids that exhibited differential localization (cytoplasm or nuclear) of Sox9 based on the mutational profile (mutant TP53 or KRAS). The localization of Sox9 was assessed in patient samples and was found to influence Sox9 localization in the same manner as iPSC-derived organoids. Patient outcome was also associated with Sox9 localization, thus providing a novel biomarker that is useful for predicting patient outcome.

As more driver genes are described in specific cancer types, it is now possible to divide cancers into genetic subtypes that guide the course of treatment. This includes medulloblastoma (MB) where an iPSC-derived organoid model was developed using PB transposon-mediated integration to express OTX2/cMYC (OM) or GFI1/cMYC (GM), which are commonly overexpressed in G3 MB.⁷¹ On implantation into nude mice, these organoids developed tumors at 100% penetrance for both genotypes.

Further analysis showed that methylation and gene expression patterns of the iPSC-derived tumors strongly correlated to G3 MB, but of two different G3 subtypes. Based on the second-generation molecular subgrouping of MB, the OM cells modeled subgroup IV, standard-risk G3 MB whereas the GM cells depicted the subgroup II, high-risk MB facilitating future studies to investigate further differences in these subgroups.

In another model of MB, human iPSC-derived neuroepithelial stem (NES) cells were transduced with a MYCN expression cassette and implanted orthotopically into immunocompromised mice.⁷² MYCN overexpression was sufficient to generate tumors that resembled human MB histologically, transcriptomically and epigenetically. In fact, the iPSC-derived MYCN NES cell tumors resembled sonic hedgehog (SHH) MB, whereas MYCN-driven mouse models of MB aligned with G3 MB, a subgroup that typically shows amplification of MYC rather than MYCN in human patients.^73,74

To further investigate the phenotypic response to MYCN expression, iPSC and primary NES cells were transduced with a mutated MYCN^T58A or MYCN^WT lentivirus.⁷⁵ Both cell types formed tumors in mice regardless of which MYCN cassette received, but the MYCN^T58A iPSC-derived tumors formed faster with a more invasive phenotype. RNA-sequencing showed that MYCN expression in iPSC-derived or primary neuroepithelial cells developed into infant SHH-like MB with clinically relevant features validating the use of iPSC derived SHH-like MB models and reducing the need to obtain challenging primary cell types.

Glioblastoma (GBM) is the most common primary malignant tumor of the central nervous system,⁷⁶ and historical mouse models of GBM have failed to capture intratumor heterogeneity seen in human GBM.⁷⁷ To develop a novel model of GBM, Koga et al used CRISPR-Cas9 to knock out tumor suppressor genes (TSG) PTEN and NF1, or loss of TP53 and PDGFRA exons 8 and 9 resulting in a truncated constitutively active receptor.⁴⁹ These alterations mimic those found in mesenchymal or proneural GBM subtypes, respectively. Engineered iPSC clones were then differentiated to neural progenitor cells (NPCs) and injected orthotopically into immunocompromised mice, with resulting tumors resembling their respective subtypes.

Human cells were isolated from the tumors and cultured as neurospheres similar to previous GBM patient-derived xenograft experiments.⁷⁸ Tumor-derived cells showed the ability to self-renew in extreme limiting dilution assays and were able to form tumors in a secondary engraftment with significantly shortened latency. Interestingly, the researchers monitored cancer initiation and tumor evolution by performing single cell RNA-seq on primary and secondary spheres and tumors. They found modest levels of intratumor heterogeneity and differences between genotypes, which only became apparent over time. This study could aid in the creation of more accurate cell-based phenotypic drug discovery approaches in GBM reviewed elsewhere.⁷⁹

Historically, modeling the genetic cancer predisposing disease neurofibromatosis type 1 (NF1) employed immortalized human Schwann cells, patient derived xenografts, and several mouse models.⁸⁰ Recently, iPSC-based models have been shown to recapitulate several symptoms associated with NF1 syndrome. Anastasaki and Wegscheid et al used CRISPR-Cas9 to engineer NF1 patient-derived germline mutations into iPSC. Differentiation of these NF1 mutant iPSC clones to NPCs led to increased RAS-controlled proliferation.⁸¹

Using a cerebral organoid culture system, they found that a subset of NF1 patient germline NF1 mutations had normal levels of NPC proliferation, but reduced apoptotic activity, resulting in an 80% reduction in immature neurons. Dissecting the intricacies of “first hit” NF1 mutations in an isogenic system will allow us to better understand the genotype-to-phenotype correlations in NF1 patients.

Using a similar approach, Mo et al created an isogenic iPSC line harboring patient-specific NF1 mutations.⁸² Although NF1⁻^/− cells readily formed tumors in mice when differentiated into Schwann cells before injection, NF1⁻^/+ and wild-type cells did not. Tumors that developed in this system were found to exhibit high SOX10 expression, leading the investigators to conclude that SOX10 expressing cells are the cell of origin for neurofibromas. To confirm this, the group inactivated Nf1 in Sox10⁺ expressing cells in mice and observed tumors that closely recapitulated the human disease.

The success of these studies has spurred a surge of interest in the use of iPSC to model cancer development and progression. As of July 2022, our analysis of the NIH RePORTER database showed 328 active projects using the keywords “iPSC and cancer,” and 101 when searching “iPSC and cancer and CRISPR”; the numbers we anticipate to increase.⁸³ As additional iPSC-based modeling systems are developed, consideration must be given to the types of mutations that are installed and the developmental stage at which they are introduced. Depending on the malignancy or disease, there are several established and emerging genome editing tools available to precisely create the desired genetic alterations.

Overview of Genetic Engineering Strategies

Broadly, genetic engineering is employed to change or manipulate the genome through installation of a desired DNA sequence, but the methods utilized to accomplish this have evolved as new technologies are developed.^84,85 Early methods of genetic engineering relied on plasmids, viruses, and later transposons, to insert genetic cargo randomly into the genome with limited control of where the cargo was inserted.^86–88 Although the advent of zinc-finger nucleases, transcription activator-like effector nucleases, and engineered meganucleases initiated the era of precision genome engineering, these methods were challenging to design and implement without specialized expertise, limiting their widespread use.^89,90

In 2013, the CRISPR-Cas9 system was shown to produce targeted double-strand breaks in mammalian cells in a significantly more user-friendly fashion than prior technologies.^91–94 Further developments expanded the utility of CRISPR-Cas to include the introduction of single base changes and the insertion of genetic cargo without introducing double-strand breaks.^95–98 Here, we highlight select CRISPR-Cas-based systems and their utility in developing bottom-up cancer models in iPSC. For a more detailed description of these and other genetic engineering tools, please refer to these other reviews.^99–103

CRISPR-Cas9

CRISPR-Cas9 is an RNA-guided nuclease derived from a prokaryotic adaptive immune system that can be utilized to induce targeted Double strand breaks (DSBs) in human cells.^91,92 The CRISPR-Cas9 system commonly used today consists of a ∼100 bp chimeric single guide RNA (sgRNA) that is bound by the Cas9 protein and localizes to the complementary DNA sequence triggering DNA cleavage by the Cas9 enzyme (Fig. 2a). The sgRNA is a combination of the unique ∼20 bp CRISPR RNA (crRNA) complementary to the target genomic locus and the tracr RNA, which provides the required scaffolding for interaction with the Cas9 enzyme.^91,92

FIG. 2. — Precision genetic engineering tools. **(a)** CRISPR-Cas9 causes double-strand breaks when the sgRNA and DNA complement, activating the Cas9 enzyme. **(b)** CBE produces C → T transitions through the tethered cytidine deaminase to the nCas9 protein. **(c)** ABE produces A → G transitions via the tethered lab evolved DNA active adenosine deaminase to nCas9. **(d)** PE has a tethered RT to Cas9 and elongated sgRNA (pegRNA) such that the pegRNA is the template for the RT to incorporate into the DNA during repair. **(e)** CRISPRa/i has either an activator or a repressor tethered to a dCas9 to allow for site-specific gene activation or repression, respectively. **(f)** In many cases, an HR template will be desired for proper genetic engineering. These HR templates can be DNA plasmids, ssODNs, and AAV, among many other options. AAV, adeno-associated virus; ABE, Adenine base editor; CBE, Cytosine base editor; HR, homologous recombination; pegRNA, prime editor guide RNA; RT, reverse transcriptase; CRISPRa, CRISPR activation; CRISPRi, CRISPR interference; PE, prime editor; sgRNA, single guide RNA.

One restriction of the original CRISPR-Cas9 system is the requirement of a protospacer-adjacent motif (PAM) tri-nucleotide sequence where N stands for any nucleotide (ACGT) and GG stands for guanine adjacent to the crRNA complementary DNA sequence.⁹¹ However, evolved spCas9 and other Cas orthologs have been discovered with broadened or reduced PAM requirements, thereby increasing the scope of targetable sequences.^104–108 For the scope of this review, we will be discussing Streptococcus pyogenes (SpCas9), which remains the most common Cas system employed for genome engineering.

For information on additional Cas variants and/or uses, refer to these other reviews.^103,109,110 Site-specific Cas9 induced DSBs are repaired in human cells primarily via non-homologous end joining (NHEJ), leaving small insertions and/or deletions (indels) at the target site.^91,92 Conversely, homologous recombination (HR) can be engaged at the DSB site when a donor template or the sister chromatid is used as a repair substrate.⁹¹ Variants of the Cas9 enzyme have been developed to create only single-strand breaks (Cas9 nickase, nCas9) or no DNA strand breaks (dead Cas9, dCas9), allowing for expanded functionality.^91,111 Broadly speaking, NHEJ is typically leveraged for gene knockout, whereas HR is used to specifically change endogenous DNA sequences or introduce heterologous sequences.

CRISPR-derived DNA editors

Recently, new technologies have emerged to edit DNA without the necessity of a DSB or DNA donor template, namely Cas9 base editors (BEs) (Fig. 2b, c) and prime editors (PEs) (Fig. 2d).^95–98 By replacing Cas9 nuclease with the BE/PE technology, the introduction of specific gene edits can be performed safely and in most cases, more efficiently.^95,96,112 The major advantages of using BE or PE editing technologies over traditional nuclease mediated HR is the high efficiency of site-specific editing.^95,96,112 This is particularly true for BE, where depending on experimental optimization and desired outcome, high genomic editing efficiencies—in some cases exceeding 95%—can be achieved with regularity.^113–117 Further benefits are the lack of donor DNA, and critically, reduced or eliminated DSB, both of which are known to be toxic to primary stem cells.^118–120

Cytosine BEs (CBEs) consist of a cytidine deaminase fused to the amino terminus of nCas9 and a uracil glycosylase inhibitor tethered to its carboxyl terminus.⁹⁶ A separate sgRNA then complexes with nCas9 and targets the CBE to the specified genomic locus, where the cytidine deaminase acts on cytosines within a specific region of the sgRNA target site (positions 4–8), converting them to uracil. To increase efficiency, the nCas9 targets the opposite strand as the desired C → T transition causing mismatch repair pathways to select the nicked strand as divergent and read the uracil as a thymine, resulting in a stable C → T transition.

A similar configuration was used to develop an adenine base editor (ABE).⁹⁵ Using directed evolution of a TadA deoxyadenosine deaminase, the Liu lab created a DNA adenosine deaminase that was tethered to nCas9 (ABE).⁹⁵ The resulting ABE can efficiently deaminate adenosine to inosine within a defined editing window analogous to CBE. The inosine is read as guanosine by DNA polymerase, and again, nicking of the non-edited strand promotes the introduction of adenosine by mismatch repair, and subsequent replication resulted in the transition of an A → G in the absence of DSBs. Ongoing efforts are underway to narrow or manipulate the editing window to minimize non-target editing.^97,121

Recently, the use of an imperfect-guide RNA approach was reported to promote single base editing using CBE and ABE, thereby improving the probability of installing a desired single nucleotide polymorphism.¹²²

The more recently described PE platform, on the other hand, utilizes nCas9 fused to an optimized Moloney murine leukemia virus reverse transcriptase for so-called “search and replace” editing.⁹⁸ Here, the nCas9 cleaves one strand of the “R loop” such that a 3′ extended sgRNA, termed prime editor guide RNA (pegRNA), can anneal with a freed strand based on sequence complementarity and prime reverse transcription, thereby incorporating a designer sequence included in the 3′ pegRNA extension (Fig. 2d).

The nascent DNA strand created during reverse transcription is subsequently incorporated into the target site during DNA repair. As with BE, preferential incorporation of the desired edit during DNA repair can be stimulated through the introduction of a proximal DNA nick on the opposite strand using a second gRNA.⁹⁸ The primary benefit of PE over BE is the significantly broader diversity of desired edits that are possible using PE, including the ability to introduce transversion and transition mutations as well as defined deletions and insertions up to 80 and 44 bp, respectively.⁹⁸ At present, the primary limitation to PE is the lower efficiency of editing compared with BE; however, new versions continue to emerge and efficiency is likely to improve in the coming years.^98,123,124 Additional considerations surrounding the use of BE- and PE-derived technologies such as potential off-target effects, immune response, and delivery options are thoroughly reviewed elsewhere.^103,125

CRISPR-derived transcriptional modulators

Beyond its ability to alter DNA sequences, the CRISPR platform has also been adapted to regulate endogenous gene expression at the transcriptional level. Early research identified that dCas9 targeted to gene regulatory elements was capable of inhibiting transcription by interfering with transcriptional regulatory protein association with DNA, causing downregulation of target gene expression by up to ∼40%, a process coined CRISPR interference (CRISPRi).¹¹¹ Subsequent iterations of CRISPRi utilized dCas9 fused to transcriptional effector molecules, most commonly the KRAB repressor.^126–128

Alternatively, the fusion of dCas9 to transcriptional activators such as VP16 can induce endogenous gene expression at a defined locus, a process termed CRISPR activation (CRISPRa) (Fig. 2e).^129–132 These systems work by targeting dCas9 near the transcriptional start site, thus allowing the transcriptional effector molecule to have local impacts on gene regulation in up to ∼99% of cells tested.¹³⁰ These technologies may find utility in bottom-up cancer models where permanent gene modification is not desired, or where the functional effects of epigenetic changes to specific genomic loci are being investigated.

For example, epigenetic modifiers may prove useful to untangling mechanisms underlying metastasis, which has been increasingly associated with epigenetic alterations and their influence on the transient nature of the epithelial-to-mesenchymal transition.^133,134 When considering CRISPRi/a systems it should be considered that effect can take up to 7 h for full activation/repression of your target gene, and in settings of transient delivery, the desired effect will remain stable for 48 h dropping to baseline after 5–6 days post-electroporation.¹³⁰ Some applications may benefit from stable integration of CRISPRi/a machinery, with further control offered by including drug-regulated activity that can be used to modulate function of CRISPRa/i systems.

HR templates

To model many cancer-associated alterations, it is necessary to introduce larger cargo, such as conditionally regulated gene expression cassettes, which may be several kilobases (kb) in length. Site-specific integration of larger cargo is typically accomplished using HR from an exogenous DNA donor. This process is highly inefficient in human cells, but it can be enhanced substantially by the introduction of a DSB at the target site, typically using CRISPR-Cas9 or other targeted nucleases (Fig. 2f).^135,136

Common HR donor vector designs entail a cargo of interest flanked by regions of homology (homology arms, HA) flanking the target locus. The HA lengths of ∼1 kb are commonly employed; however, recent methodologies such as microhomology-mediated end joining and homology mediated end joining (HMEJ) can utilize substantially smaller HA (<50 bp).^137–142 Recently, several groups have utilized recombinant adeno-associated virus (rAAV) as a donor template for HR-mediated site specific integration in human iPSC.^143,144

One potential benefit of rAAV is its broad tropism in human tissues, including several serotypes with demonstrated tissue specificity, which may facilitate the transduction of developmental intermediates, or allow the selective engineering of subsets within heterogeneous cell populations.¹⁴⁵ One disadvantage to using rAAV is the small carrying capacity (4.7 kb), which limits the size of cargo that can be delivered, particularly considering the capacity that must be dedicated to HA sequences.

Single-stranded DNA oligonucleotides (ssODNs) are another HR template with particular utility for introducing small sequence changes, including single base mutations.^146–148 The ssODNs have been shown to have lower unintended integration events compared with double-stranded DNA templates, and ssODNs have been shown to induce point mutations at rates of 70% and insertion of LoxP sites in 40% of cells, making this a reliable HR template for some instances.^149,150 However, limitations in synthesis technologies continue to hinder the utility of ssODN for the introduction of larger and more complex DNA sequences.

Cancer Driving Mutations Possible with Genetic Engineering

The types of genetic modifications that can result in oncogenesis, as described in the hallmarks of cancer, are very diverse and include both small and large alterations.¹⁵¹ Here, we discuss many common types of mutations with the potential to drive cancer along with the ways these mutations can be genetically engineered into wild type iPSC to develop bottom-up cancer modeling systems (Fig. 3).

FIG. 3. — Common mutations found in cancer and suggested genetic engineering tools for induction.

Loss of TSG

Loss of TSG such as TP53, RB1, or BRCA1/2 is often required for cancer initiation and development.¹⁵² Thus, to effectively model transformation, it is pertinent that these mutations be accurately introduced in bottom-up cancer models, especially when germline mutations are involved in the development of cancer. The basic CRISPR-Cas9 approach for the introduction of DSBs will result in high efficiency gene knockout as a result of indel formation due to error-prone NHEJ DNA repair.^153–156

An additional method to induce the loss of TSG in iPSC is the use of ABE/CBE through the targeting of splice sites and the introduction of premature stop codons.^{113,114,157–159} Targeting splice sites can disrupt the reading frame of the protein of interest, resulting in truncation of the normal protein sequence. This is highly effective at creating protein knock-outs, but much lower efficiencies were found when a truncated protein was desired. Another method to cause loss of gene expression is to disrupt transcription through the use of CRISPRi.^129,160

Generation of single nucleotide variants

For some TSG, recurrent point mutations drive oncogenesis; for example, TP53 missense mutations with dominant negative function, or the truncating nonsense or frameshift mutations common of the APC gene.^161–163 For these cases, the use of CBE, ABE, and PE allows for the introduction of specific desired mutations without induction of a DSB. If the point mutation is a C → T or A → G mutation, CBE and ABE can be used, respectively, although it must be considered that bystander bases within the editing window are susceptible to deamination.⁹⁵

The use of editors with altered PAM requirements to shift the editing window and/or clonal isolation and screening may be required to isolate correctly altered clones. Unlike CBE/ABE, the use of prime editing can eliminate potential non-target editing within the editing window.⁹⁸ By using a user-defined RNA template to insert the desired mutation, PE allows more flexibility in the types of modification that can be introduced compared with standard ABE/CBE.

Activation of oncogenes

Many cancers are driven or maintained by the overexpression of oncogenes. Oncogene overexpression/activation can be induced by a variety of gene alterations, including but not limited to: changes in gene copy number, sequence or methylation changes in the promoters of the oncogene, missense single nucleotide variants resulting in a constitutively active protein, and chromosomal translocations, described in the next section. Some modern genetic engineering tools can aid in the induction of efficient and specific oncogene overexpression, such as the use of CRISPRa that was designed specifically for gene activation.¹³⁰

In addition, the use of an HR template alongside CRISPR-Cas9 induced DSBs allows for efficient insertion of an entire gene of interest or strong promoter upstream of the oncogene. These approaches can increase both copy number and expression level of oncogenes.¹⁶⁴ If the overexpression of an oncogene of interest is caused by a specific point mutation(s) in the promoter, such as is the case for TERT, ABE/CBE/PE could be used to replicate specific promoter mutations as well.^165,166

Translocations

Several types of cancer are driven by specific translocations that produce oncogenic fusion proteins or recruit enhancers and/or promoters into the vicinity of an oncogene, causing dysregulated expression.^167–170 The simplest method to reproduce this type of alteration is the use of dual CRISPR sgRNAs targeting each desired breakpoint simultaneously. A subset of cells will harbor chromosomal rearrangements and the desired translocation, but the frequency of this event is typically low.^171–174 The inclusion of a donor template or single stranded oligodeoxynucleotides (ssODN) with homology arms spanning sequences on two target chromosomes can increase the frequency of translocation.^174,175

In addition, a selectable marker flanked by loxP sites can be added within the homology arms to allow drug-based selection of translocation positive populations.^176,177 An effective way to introduce the donor template in iPSC may be through the use of rAAV6 or HMEJ, both of which can carry the homology arm cassette in addition to other genetic cargo such as a lox-stop-lox or fluorescent markers to allow tracking or conditional control over translocation activity.^178,179

It must be taken into account, when using the two-guide method, with or without a homology arm template, that there is a potential for repair of the individual DSBs at their respective loci, resulting in indel formation, and potentially the induction of inversions or deletions of chromosomal segments. Thorough sequencing should be utilized to rule out potential chromosomal rearrangements in the engineered cells before experimental use. Although PE-mediated translocations have not been explicitly demonstrated, the simultaneous insertion of attP and attB sites by PE and treatment with Bxb1 integrase has been shown to mediate large inversions (40 kb) and one can speculate that the development of similar technology could be used to create translocations.¹⁸⁰

Chromosomal deletions

Using the CRISPR-Cas9 system employing a single sgRNA targeting chromosome-specific repetitive sequences or multiple guides targeting adjacent regions on the chromosome can result in deletion of the intervening sequence and even entire chromosome loss if enough sites are targeted by CRISPR-Cas9.^181,182 It should be considered that with any multiple guide strategy there is a potential for inversions to occur; thus, this outcome should be evaluated by comprehensive sequencing of the target region.

In addition, the use of an HR template containing recombination sequences, such as inverted loxP sites, allows for Cre-mediated excision of the internal sequence, including large chromosomal deletions, with the ability to control timing of the genetic change.¹⁸³ This method was used to recreate the MDS associated del(7q) in wild type cells by Kotini et al.¹⁸⁴ Further, a recent study demonstrated precise deletion of up to 10 kb in cells using two PE simultaneously, where the 3′ pegRNA template of one PE encoded complementary DNA sequences to the nCas9 target sequence of the second PE and vice versa (PRIME-Del).^180,185,186 Advances in this area demonstrate feasible deletion of large chromosomal segments, although the deletion of entire chromosomes or chromosomal arms has not been reported to date and represents an opportunity to further broaden the scope of current cancer models.

Chromosomal duplications

Chromosomal duplications are commonly identified in tumors, particularly tumors that utilize alternative lengthening of telomeres for telomere maintenance or undergo chromothripsis.^187,188 Many chromosomal amplifications found in cancer are identified as double minutes (dmin), or extrachromosomal segments of DNA packaged as circular chromosomes that have been shown to independently replicate, resulting in duplications.^189,190 One hypothesis for the generation of dmin is through the breakage-fusion-bridge pathway or DNA excision and repair via NHEJ. Thus, to recreate this process in a cell, CRISPR-C was employed to determine whether dmin are created using the CRISPR system.¹⁹¹

CRISPR-C uses dual CRISPR guides to cause simultaneous DSB and in a portion of the cells the two ends of the DNA fragment liberated by paired DSB join, forming a circle through NHEJ activity. Using this system, a chromosome 18-derived ring structure was created in ∼2% of cells tested.¹⁹¹ Currently, this method is limited by dilution of the circular chromosome, suggesting that additional mutations may be required to stably retain the dmin.

Inversions

Gene inversions are commonly found in cancer and will be important to recreate in new bottom-up modeling systems. The creation of specific inversions can be accomplished through the introduction of two simultaneous DSBs, resulting in the inversion of the intervening sequence.¹⁹² The iPSC populations harboring such inversions can be screened to isolate desired clones through polymerase chain reaction analysis and sequencing. In a recent study by the Liu Lab, the use of TwinPE was able to induce inversions up to 40 kb and we anticipate this technology could be further developed to induce larger inversions.¹⁸⁰

Another method to reproduce inversions is through the addition of loxP sites on the same strand oriented in opposite directions, such that the addition of Cre-recombinase will result in an inversion.^193,194 The CRISPR-Cas9 system can be used to induce a double-strand break in combination with an HR template carrying the loxP site and homology arms to the target region. The main advantage to using loxP sites is the ability to control timing of the inversion through the addition of Cre-recombinase at various stages of cell differentiation, allowing investigation into the cell stage-specific responses to the genetic insult.

Temporal control of Cre expression can be accomplished with the tamoxifen inducible Cre-ERT2 fusion or doxycycline-inducible tTA/rtTA systems, where the addition of tamoxifen or doxycycline to the cell culture allows for the activation or suppression of Cre.^195–197 Developmental regulation of Cre expression can also be achieved by placing the Cre transgene under the control of a tissue-specific promoter within a transgene or by knocking at an endogenous locus.^198–200 These kinds of loxP systems require clonal selection and extensive characterization to confirm insertion of the loxP sites in trans, as well as faithful inversion after the addition of Cre-recombinase.

Initiation of chromothripsis

Chromothripsis results from a single punctuated event in which dozens to thousands of chromosomal rearrangements occur.²⁰¹ This event is common in genetically complex cancers, such as osteosarcoma, and is believed to drive cellular transformation through the loss of TSG and overexpression of oncogenes.¹⁸⁸ Although the precise mechanisms driving chromothripsis remain unclear, one current hypothesis is that during the first cell cycle after the induction of a DSB, chromosome misalignment results in missegregation of the chromosome into a micronuclei.²⁰²

During the following cell cycle, the micronucleus malfunctions, resulting in envelope disruption and chromosomal shattering. The resulting chromosomal pieces are randomly ligated back together by NHEJ in a canonical LIG4-dependent manner, resulting in the genomic rearrangements that are a hallmark of chromothripsis. Another proposed mechanism involves telomere fusion followed by chromosome bridge formation during cell division, resulting in chromosomal breaks on segregation followed by DNA fusion at the next cell cycle.²⁰¹

Currently, inducing chromothripsis on a genome wide scale using genetic engineering has not been accomplished, although there are several methods that have shown promise. One possibility is to disrupt genes involved in replication stress responses followed by DSB induction to promote aberrant replication fork collapse after treatment with hydroxyurea.²⁰³ An alternative method could involve disrupting kinetochore function, which could promote missegregation and an increase in micronuclei formation, thereby creating an environment that is conducive to chromothriptic events.²⁰²

The expression of Cas9 and 100 s of sgRNAs simultaneously targeting discrete regions throughout the genome or, alternatively, a single sgRNA targeting repetitive sequences found throughout the genome could conceivably induce a chromothripsis-like event, including mass genomic rearrangements and translocations, allowing for the study of cell behavior during and after global DNA rearrangement.¹¹⁴ It was recently shown that even on-target Cas9 editing with a single sgRNA could potentially initiate local chromothripsis, or genomic rearrangements on the target chromosome in proximity to the cut site, illustrating the feasibility of inducing local and genome wide chromothripsis events using the CRISPR system.⁶³

Conditional systems to model developmental cancers

One of the major benefits of using iPSC in investigating cancer is that iPSC allows access to developmental intermediate cell types that are not accessible in adult tissues. Mouse models of pediatric cancers have affirmed that developmental tumors can originate in stem or progenitor cells during specific developmental windows of time.^204–210 It is hypothesized that the cell of origin for developmental cancers can inform the precise cellular state permissive to the initial oncogenic insults that can promote self-renewal at the expense of orchestrated differentiation pathways.²¹¹

In this context, iPSC models provide a novel tool to understand the developmental, temporal, and sequential order of events en route to a mutational landscape permissive to oncogenesis. As has been done in mice, oncogene expression can be restricted to specific developmental time points using tamoxifen-inducible Cre or doxycycline-inducible tTA/rtTA systems, as well as through the use of tissue-specific promoters.^195–197 In this way, oncogene expression can be tailored to occur within specific lineages and/or specific cell-states, expanding the capacity to investigate links between development and transformation.

Remaining Challenges and Future Directions

Bottom-up cancer models show promise in recapitulating human disease. These models provide several advantages over current model systems, providing: (1) a renewable source of healthy non-tumor human cells to evaluate candidate genetic drivers of tumorigenesis, (2) isogenic control cell lines for iPSC-derived tumors undergoing chemical or genetic screening to filter out non-specific activity, (3) providing systems to investigate the role of chromosome copy number changes in tumorigenesis, and (4) to identify the cell types and target cells that can give rise to a specific tumor, including embryonic tissues.

Despite these benefits, significant and complex challenges continue to limit the realization of accurate stem cell models of human cancer. Many of these challenges are outside the scope of this perspective and will be discussed briefly but have been reviewed extensively elsewhere.^212,213

Any effort to apply bottom-up cancer modeling in iPSC must consider the amenability of the specific malignancy to this approach. Tumors with known genetic drivers that arise from a well described cell-of-origin for which defined iPSC differentiation protocols are available are the most straightforward to model using this method. Some examples include several brain tumors, melanoma, translocation-driven sarcomas, and colorectal cancers.^71,214–217

Cancers that are driven by punctuated mass genomic instability and complex mutational profiles such as osteosarcoma and liposarcoma are more difficult as replicating complex, less understood events such as chromothripsis and kataegis presents a significant challenge.²¹⁸ Similarly, cancers driven by environmental influences or cancers where the cell-of-origin is unknown or does not have an established method for generation from iPSC may present additional challenges.

It is important to consider that the biological variation between individual iPSC lines may influence results. It was previously found that out of 711 iPSC cell lines derived from 301 individuals, differences between individual donors were identified as the largest source of iPSC heterogeneity.²¹⁹ To circumvent this issue, iPSC donors from the different ends of polygenic risk of a particular cancer may be used to discern such influences. Additional considerations should include the potential for sex-specific differences, necessitating the study of tumor models created from iPSC lines derived from both sexes.

To avoid confounding results due to the reprogramming factors themselves, the use of non-integrating approaches to reprogramming should be prioritized to remove the potential for the re-expression of reprogramming transgenes and unintended gene alterations arising from rom random and stable transgene insertion.^220,221

Directed differentiation of human iPSC has been informed by decades of developmental biology knowledge and has been shown to faithfully recapitulate cellular development across numerous cell lineages; however, some limitations remain and should be considered.^5,222–225 The process of in vitro differentiation typically involves the provision of specific growth factors, small molecules, and extracellular matrices in the appropriate temporal sequence that is necessary to first specify the germ layer (e.g., endoderm, mesoderm, ectoderm) followed by a lineage of interest. In vitro, this occurs on a much shorter timeframe than that of human gestation, and in some cases results in a more immature or fetal phenotype.^226–229

Despite this, the resultant cells can still serve as a valuable model as most of the key functions and phenotypic attributes of the target lineage are present. Protocol efficiency and heterogeneity within the final cell population should also be considered; however, the use of immunomagnetic separation or fluorescence activated cell sorting can minimize the impact of such issues. The high complexity of some differentiation protocols can sometimes pose a challenge to reproducibility. This was aptly illustrated in a study where the same two iPSC lines were differentiated to neurons at five different laboratories with divergent results and concluded that the laboratory in which the cells were differentiated represented the largest source of variability.²³⁰

Thus, multi-center collaborations, or at minimum replicate differentiations using multiple iPSC lines should be considered to limit the influence of differentiation protocol and/or iPSC source. Finally, appropriate molecular, phenotypic, and functional assays should be performed to validate cells derived during differentiation.

Tumors are highly heterogeneous, and this heterogeneity is a determinant of therapeutic response and disease pathology.^231,232 How well tumors derived from iPSC models mimic this intratumoral heterogeneity is not well understood. However, heterogeneity may be forced by creating multiple iPSC-derivative tumors with differing genotypes that can be mixed to robustly evaluate the efficacy of treatment strategies. Additional methods to recapitulate tumor heterogeneity in genetically engineered human stem cell models of cancer will be critical to future model development.

In addition, although iPSC-derived cancer cells can be transplanted into immunocompromised animals, such models fail to recapitulate immune infiltration and the heterogeneous microenvironment of primary tumors.²³³ In particular, the lack of a functional immune system precludes the ability to evaluate immunotherapeutic approaches in vivo, although specialized murine models with a humanized immune system may overcome this limitation in the future.²³⁴

Recently, human stem cell models were generated in immunocompetent mice, but the human cells were implanted into gastrulating mouse embryos, before development of the immune system.²³⁵ Humanized mice, however, are costly and complex making it difficult for most research labs to routinely use these methods, and because the human immune cells and tumor cells did not arise from the same patient, there is still the possibility of rejection in a humanized mouse model.

Alternatively, using iPSC from the same person to generate both the tumor lines and immune cells will facilitate an in vitro co-culture environment that potentially mimics the microenvironment better than the humanized mouse model. The potential utility of immune cells, including T lymphocytes, natural killer cells, and macrophages, derived from human Embryonic Stem Cells (ESC) or iPSC for research of tumor biology and cancer immunotherapy has been extensively reviewed elsewhere.^236,237

Another level of genetic regulation in cancer is that of the epigenome. Investigation of epigenetic regulation during cancer initiation, development, and progression will be greatly aided by the use of bottom-up cancer models and the newly developed CRISPR-based epigenetic technologies. One such use is the use of dCas9 targeted to specific promoters to study the impacts of demethylation or histone modifications.^238,239 For additional information on epigenetic editing using CRISPR-based technology, here is an excellent review.²⁴⁰ The ability to edit the epigenome will be essential as we continue to develop more accurate and predictive cancer models.

Concluding Remarks

Historically, modeling cancer development beginning with normal cells has been studied using genetically engineered mouse models (GEMMs). The GEMMs have provided a breadth of knowledge and methods that we can now leverage to usher in a new era of cancer modeling in human iPSC. The capacity of iPSC to differentiate into nearly any somatic cell-type coupled with their amenability to manipulation at the genetic level using new genetic engineering strategies represents an unprecedented opportunity to expand our mechanistic understanding of human cancer development.

Further, the ability to derive iPSC from easily accessible somatic cell-types has facilitated the establishment and continued expansion of large iPSC banks from diverse genetic backgrounds, providing an unprecedented opportunity to fuse epidemiology and genomics with basic mechanistic studies at scales not previously possible. Realizing this potential will necessitate interdisciplinary collaboration spanning stem cell biology, bioengineering, cancer biology, genomics, and genome engineering. Moving forward, we envision bottom-up cancer modeling in iPSC as a highly flexible and powerful approach to glean novel mechanistic insights into the fundamental biology of human cancers.

Supplementary Material

Supplemental data

Supp_TableS1.docx^{(13.1KB, docx)}

Authors' Contributions

K.L.B., G.M.D., B.R.W., and B.S.M. conceived the scope of the review, wrote, and edited the article. R.A.M., T.K., M.H., and W.A.W. contributed to sections on model applications to specific cancers. M.G.K. assimilated data on mutational profiles of relevant cancers and identified optimal genetic engineering approaches to introduce mutations as outlined in Figure 3 and Supplementary Table S1. L.G.S. contributed epidemiological insights to sections pertaining to genetic diversity of iPSC lines. All authors reviewed and approved of the final article.

Author Disclosure Statement

D.A.L. is the co-founder and co-owner of several biotechnology companies, including NeoClone Biotechnologies, Inc., Discovery Genomics, Inc. (recently acquired by Immusoft, Inc.), B-MoGen Biotechnologies, Inc. (recently acquired by Biotechne Corporation), and Luminary Therapeutics, Inc. D.A.L. holds equity in, serves as a Senior Scientific Advisor for and a Board of Director member for Recombinetics, a genome editing company. D.A.L. consults for Genentech, Inc., which is funding some of his research. W.A.W. is a co-founder of StemSynergy Therapeutics and holds equity in Nalo Therapeutics and Auron Therapeutics. The business of all these companies is unrelated to the contents of this article.

Funding Information

B.R.W. acknowledges funding from NIH grants R21CA237789, R21AI163731, P01CA254849 Alex's Lemonade Stand Foundation, Children's Cancer Research Fund, and Rein in Sarcoma. Research in the Moriarity Lab is supported by NIH grants P50CA136393, P01CA254849, R01AI161017, and R01AI146009. D.A.L. acknowledges funding from the American Cancer Society Research Professor Award and the National Institutes of Health (R01NS115438). M.H. acknowledges funding from the Wright Trust Foundation, The Saban Research Institute, and National Institutes of Health (R00CA197484). Research in the Weiss lab is supported by NIH grants R01CA255369, R01NS106155, R01CA221969, P01CA217959, P30CA082103, P50CA097257, U01CA217864, U54CA243125, Alex's Lemonade Stand, The Brain Tumour Charity, Cancer Research UK grant A28592, St. Baldrick, and Samuel G. Waxman Foundations; and the Evelyn and Mattie Anderson Chair. Figures were created using BioRender (BioRender.com).

Supplementary Material

Supplementary Table S1

References

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PERMALINK

Developing Bottom-Up Induced Pluripotent Stem Cell Derived Solid Tumor Models Using Precision Genome Editing Technologies

Kelsie L Becklin

Garrett M Draper

Rebecca A Madden

Mitchell G Kluesner

Tomoyuki Koga

Miller Huang

William A Weiss

Logan G Spector

David A Largaespada

Branden S Moriarity

Beau R Webber

Abstract

Introduction

FIG. 1.

Current iPSC-Based Cancer Models

Reprogramming tumor cells

Table 1.

Reprogramming cancer-predisposed patient somatic cells

Table 2.

Genetically Engineered “Bottom-Up” iPSC Models

Overview of Genetic Engineering Strategies

CRISPR-Cas9

FIG. 2.

CRISPR-derived DNA editors

CRISPR-derived transcriptional modulators

HR templates

Cancer Driving Mutations Possible with Genetic Engineering

FIG. 3.

Loss of TSG

Generation of single nucleotide variants

Activation of oncogenes

Translocations

Chromosomal deletions

Chromosomal duplications

Inversions

Initiation of chromothripsis

Conditional systems to model developmental cancers

Remaining Challenges and Future Directions

Concluding Remarks

Supplementary Material

Authors' Contributions

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