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Review Biology and preclinical models of colorectal cancer metastasis
Yoojeong Seo1,*orcid, Jinho Jang1,*orcid, Jae-Il Park1,2,3orcid

DOI: https://doi.org/10.5217/ir.2025.00315
Published online: March 26, 2026

1Division of Radiation Oncology, Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

2Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

3Program in Genetics and Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Correspondence to Jae-Il Park, Division of Radiation Oncology, Department of Experimental Radiation Oncology, Genetics and Epigenetics Program, MD Anderson Cancer Center 6565 MD Anderson Blvd. Z6.3034, Unit 1052, Houston TX 77030, USA. E-mail: jaeil@mdanderson.org
*These authors contributed equally to this study as first authors.
• Received: December 9, 2025   • Revised: January 2, 2026   • Accepted: January 5, 2026

© 2026 Korean Association for the Study of Intestinal Diseases.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Metastatic colorectal cancer (mCRC) is the principal cause of CRC-related mortality, yet the biology of mCRC remains only partly understood and challenging to interrogate experimentally. Despite recent progress in mapping recurrent genetic and epigenetic alterations and treatment responses of mCRC, these efforts provide limited insight into how heterogeneous primary tumors breach tissue barriers, survive in circulation, and colonize distant organs. In this review, we summarize current experimental systems for studying mCRC, including genetically engineered mouse models, carcinogen-induced and transplant models, and patient-derived organoid and xenograft platforms, and discuss how each captures or fails to capture key steps of the metastatic cascade and organ-specific microenvironments. We highlight practical obstacles to longitudinal sampling and quantitative readouts of metastatic burden, as well as conceptual gaps in modeling immune and stromal influences. Finally, we outline how emerging approaches, including single-cell and spatial transcriptomics, and advances in longitudinal tracking of metastatic burden could be combined into an integrated framework that more faithfully links mechanistic insight to clinical behavior and ultimately to metastasis-specific therapies.
  • Keywords: Colorectal neoplasms; Neoplasm metastasis; Mice; Organoids; Multiomics
Colorectal cancer (CRC) is among the most common and lethal malignancies worldwide, with an estimated 1.9 million new cases and 900,000 deaths annually [1]. Although early detection and adjuvant therapy have significantly improved outcomes, metastatic CRC (mCRC) remains largely incurable and accounts for nearly 90% of CRC-related mortality [2]. The liver represents the predominant site of metastasis, followed by the lungs and peritoneum, reflecting the portal venous drainage of the colon and rectum [3]. Despite the integration of combination chemotherapy, targeted therapy, and, more recently, immunotherapy, 5-year survival for metastatic disease remains below 20% [4].
Contemporary management of mCRC combines cytotoxic chemotherapy, anti-vascular endothelial growth factor (VEGF) and anti-epidermal growth factor receptor (EGFR) or anti-BRAF–based targeted regimens, and—where applicable—immune checkpoint inhibitors. However, durable benefit is primarily restricted to molecularly selected subsets of a high level of microsatellite instability/deficiency in mismatch repair.
By contrast, most patients with microsatellite stable disease derive limited benefit and commonly develop primary or acquired resistance driven by clonal diversity, tumor heterogeneity, cell plasticity, stromal and immune remodeling, and organ-specific microenvironments [5-9]. These realities underscore an urgent clinical need: improving clinical outcomes requires a deeper mechanistic understanding of the metastatic cascade, including initiation, distant organ colonization, and subsequent therapeutic resistance.
Achieving that understanding requires moving beyond descriptive genomics to mechanism—dissecting the sequential steps of dissemination, intravascular survival, extravasation, organotropism, niche conditioning, and immune escape in CRC. However, CRC has been historically difficult to model compared with other solid tumors; many widely used systems capture primary tumorigenesis but incompletely recapitulate spontaneous and reproducible metastatic progression and therapeutic response [10,11]. Robust, disease-relevant preclinical platforms are therefore essential to translate biological insight into effective interventions for patients [12]. Here, we review the biology of CRC metastasis and the experimental models that support mechanistic and translational studies, highlighting how to align key biological questions with the capabilities and limitations of each system. An overview of the experimental models and integrative technologies discussed in this review is provided in Fig. 1.
CRC dissemination follows the canonical cascade: local invasion, intravasation, survival in circulation under shear/oxidative stress, arrest/extravasation (liver-first via the portal system), niche adaptation and outgrowth, therapy-conditioned relapse [13]. This trajectory is gated by tumor-intrinsic programs, including aberrant activation of key signaling pathways (WNT/β-catenin, MAPK, PI3K, TGF-β, Notch, Hippo/YAP, and hypoxia/HIFs) together with sequential clonal selection of recurrent driver mutations in canonical CRC oncogenes (KRAS, BRAF) and tumor suppressor genes (APC, TP53, SMAD4) [13,14]. In the context of genetic alterations, loss-of-function mutations in APC, TP53, and SMAD4 and gain-of-function mutations in KRAS and BRAF collectively sustain WNT and MAPK activation, promote genomic instability and apoptotic resistance, and subsequently rewire transforming growth factor β (TGF-β) from a tumor-suppressive to a pro-metastatic pathway [14]. In parallel, pervasive epigenetic remodeling, including CpG island hypermethylation [15,16], enhancer/super-enhancer rewiring, alterations in SWI/SNF and histone modifiers (KMT2C/D, SETD2) [17], noncoding RNA regulators (microRNAs/long noncoding RNAs) [15,18], and alternative splicing [16] enables cell plasticity (epithelial-mesenchymal plasticity, secretory/mucinous differentiation) [19], immune evasion [20], metabolic flexibility,21 and organ-specific colonization [22].
These tumor-intrinsic (genetic and epigenetic) and -extrinsic (immune cells, stromal cells, extracellular matrix [ECM]) layers converge on consensus molecular subtypes (CMS) [23], providing a framework for subtype-adapted therapeutic strategies [24]. CMS classification is based on genetic, epigenetic, and transcriptomic data, which reflect distinct biological behaviors and clinical outcomes. CMS is an important tool for personalized medicine in CRC, helping identify which patients are most likely to respond to specific therapies. Briefly, CMS1 (MSI-immune) displays genomic instability with strong immune infiltration; CMS2 (canonical) shows epithelial differentiation with WNT/MYC activation; CMS3 (metabolic) features KRAS mutations and metabolic reprogramming; and CMS4 (mesenchymal) exhibits prominent TGF-β/epithelial mesenchymal transition (EMT) signaling with fibroblast- and angiogenesis- rich stroma [23]. Among CMS, the metastatic landscape is predominantly shaped by CMS2 and CMS4. In metastatic disease, CMS assignments skew toward CMS2 and 4; notably, CMS4 is enriched in liver metastases and is associated with poorer prognosis and relative resistance to EGFR-targeted therapy, whereas high level of microsatellite instability/CMS1 is less frequent but may benefit from programmed death-ligand 1 blockade [25-27]. Subtype shifts between primary and metastatic sites further underscore plasticity and microenvironmental influence.
However, while CMS offers valuable insights, it is crucial to consider additional molecular subtyping methods. For instance, single-cell RNA sequencing provides higher resolution, capturing more tumor complexity than CMS based on bulk RNA-seq and genomics [27]. Bulk RNA-seq has limitations in capturing the full diversity of the tumor microenvironment. It may not represent the cellular heterogeneity present in tumors, which is essential for understanding metastasis and therapeutic resistance. These limitations in CMS, particularly when based on bulk RNA-seq, must be addressed in future studies, with approaches such as multiomic profiling to provide a more comprehensive view of CRC biology.
Over the past decade, several reviews have extensively discussed therapeutic strategies, clinical algorithms, and molecular subtypes of CRC [27]. These reviews collectively highlight the genomic complexity and clinical heterogeneity of the disease, with CMS providing a framework for precision therapy [23,27-29]. However, despite the genomic and transcriptomic granularity achieved in the clinic, our mechanistic understanding of how CRC spreads and colonizes distant organs remains limited.
Unlike breast or melanoma models [30,31], which produce spontaneous and reproducible metastases [32], CRC models often fail to capture the sequential steps of dissemination, intravascular survival, and colonization [33,34]. Such a gap between descriptive molecular knowledge and functional metastasis biology largely stems from experimental constraints.
For instance, classical Apc-mutant mouse models—while most widely used to study intestinal tumor initiation [35,36]—rarely develop distant metastases and often result in early lethality due to local tumor burden [37]. Similarly, inflammation-associated azoxymethane/dextran sulfate sodium models [38] and multiallelic combinations such as Apc; Kras; Trp53 mutations [34,39] can recapitulate advanced adenocarcinomas and tumor progression under chronic colitis. However, they seldom produce overt distant metastases in vivo [40]. This reflects a persistent paradox in CRC research: despite being one of the most genetically well-characterized malignancies, faithfully modeling metastatic dissemination in CRC remains experimentally challenging [41,42].
Furthermore, biological features unique to the colon exacerbate these challenges. The complex architecture of the intestinal epithelium, its microbiome-rich environment, and its dual vascular drainage create a distinct selective landscape for metastatic evolution [43]. The heterogeneity of the tumor microenvironment—ranging from immune-rich right-sided mucinous tumors to fibrotic, TGF-β–driven CMS4 subtypes—likely further limits the reproducibility of preclinical systems [23,44]. Consequently, most mechanistic insights into CRC metastasis remain inferential, derived from static genomic correlations rather than dynamic in vivo modeling.
Although CRC has been extensively modeled at the level of tumor initiation, translating these systems into tractable tools for metastasis research remains challenging. A major limitation stems less from the mere availability of models and more from their restricted temporal and spatial resolution, which makes it difficult to capture how metastatic competence emerges, evolves, and interacts with the host environment in real time.
1. Intrinsic Temporal Bottlenecks of in vivo Experiments
In vivo metastasis studies face fundamental temporal constraints that limit the ability to capture early dissemination dynamics. Rapid primary tumor expansion frequently triggers premature humane endpoints, reducing the time window in which premetastatic niches, circulating tumor cells, or sub-millimeter micrometastatic foci can be evaluated in a time-resolved manner [41,43]. These temporal challenges are further compounded in inducible CRC genetically engineered mouse models (GEMMs), where the timing and anatomical distribution of tumor initiation depend heavily on the properties of the CreERT2 drivers used. In CRC GEMMs, conditional knock-out (KO) of tumor suppressor genes (Apc or Trp53) or expression of oncogenes (e.g., KrasG12D), using cell lineage-specific promoters such as Cdx2-CreERT2 or Villin-CreERT2, partly mitigates this issue by enabling tamoxifen-inducible Cre-loxP genetic recombination in the gut epithelium. Cdx2-based CreERT2 drivers preferentially target the distal colon and rectum but show regionally restricted and often incomplete recombination [45], resulting in heterogeneous tumor initiation [46]. In contrast, Villin-CreERT2 is active along the intestinal epithelium [47] and shows the strongest expression in small intestinal villus enterocytes with lower levels in the colon. In practice, a Villin-CreERT2 driver exhibits a leaky (tamoxifen-independent) recombination [47,48], and recombination efficiencies vary along the crypt-villus axis, leading to mosaic and asynchronous lesions [49-51] Other gut-specific Cre drivers, such as Lgr5-EGFPIRES-Cre [52] and Fabp1-Cre [51,53], provide stem cell- or distal intestine-restricted targeting, respectively, but also introduce regional biases and variability in recombination efficiency. Consequently, while these inducible Cre systems are indispensable for modeling CRC and metastasis in a spatiotemporal manner, they can compromise experimental synchrony and spatial precision, similar to observations made for other tissue-specific CreERT2 lines [54].
2. Spatial Restrictions and Visualization Difficulties
Spatially, the colon’s anatomy itself restricts visualization and manipulation. The folded mucosa, crypt architecture, and dual blood supply impede intravital imaging compared with more accessible organs such as the skin or mammary gland [55]. Consequently, even when metastatic dissemination occurs, its earliest stages—local invasion and intravasation—often go unrecorded. Recent advances in two-photon [56-58] and light-sheet microscopy [59] have improved visualization of intestinal tumors, but sustained imaging over weeks remains technically and ethically challenging in live animals [55,60].
3. Immune and Stromal Context
Another barrier of mCRC preclinical models lies in biological reproducibility. Unlike breast or melanoma models that metastasize in a predictable manner [61-63], CRC models often display considerable inter-animal variability in tumor burden and metastatic frequency, including in matched-littermate settings where driver genotypes are identical. Differences in inbred background (C57BL/6 vs. FVB/N) [64,65], sex [66], and breeding cohort [67] can modulate the penetrance of spontaneous or GEMM-based colorectal tumors and liver metastases, so that Apc-driven strains show distinct polyp multiplicity, anatomical distribution, and metastatic propensity [68,69]. Factors such as microbiome composition, diet, cage environment, and inflammation further influence tumor behavior [70]. These variables are rarely standardized across laboratories, resulting in inconsistent metastatic frequency and anatomical tropism.
Immune and stromal context of CRC resists reductionist modeling. Subtypes such as CMS1 and CMS4 represent immunologically opposite extremes—one enriched for cytotoxic lymphocytes, the other dominated by fibroinflammatory stroma—yet both can metastasize [23,25,27]. Recapitulating these divergent ecosystems requires integrating epithelial, immune, and mesenchymal components within the same experimental system, a feature that remains largely unsolved. Even organoid or patient-derived xenograft (PDX) platforms, while powerful for molecular analysis, fail to fully recapitulate dynamic immune surveillance or the remodeling of premetastatic niches in distant organs [71-74].
4. Stem Cell Hierarchy and Plasticity in mCRC
Beyond stromal and immune heterogeneity, the hierarchical organization of CRC adds another layer of complexity to metastasis modeling. Cell lineage-tracing studies have demonstrated that Lgr5⁺ tumor cells possess the distinct capacity to initiate and sustain distant metastases, whereas Lgr5⁻ progenitors show limited seeding potential and fail to maintain long-term growth in secondary sites [75,76]. However, Lgr5⁺ cells display plasticity, as Lgr5⁻ populations can reacquire stem-like properties under selective pressure, challenging the concept of a fixed metastatic hierarchy [76]. Current GEMMs and organoid systems capture aspects of this cell plasticity but still fall short of reproducing its dynamic regulation by the microenvironment [71,72]. These challenges partly explain why progress in CRC metastasis research has lagged molecular characterization. They also highlight a conceptual gap: current models allow us to describe which genetic and epigenetic events occur, but not when, where, or under what ecological pressures metastatic potential arises. Bridging this gap will require longitudinal, multiscale approaches that integrate imaging, lineage tracing, and omics under physiologically relevant conditions.
Over the past three decades, multiple experimental platforms have been developed to model CRC and metastasis. Despite substantial progress in capturing genetic diversity and therapeutic responses, these systems rarely reproduce the sequential, spontaneous nature of human metastatic disease. In vivo, CRC cells derived from these platforms are typically introduced into mice through a few standard routes—subcutaneous flank injection, orthotopic implantation into the cecal or rectal wall, and intrasplenic, portal-vein, or tail vein injection—which in turn determine whether primary tumor growth, liver metastasis, or lung colonization is modeled. Each preclinical model—ranging from cell lines to organoids, PDXs, and GEMMs—offers complementary insights yet is constrained by distinct structural, temporal, and translational limitations that collectively hinder mechanistic discovery.
1. Cell Lines
Cell line-based models remain the most accessible and widely used tools in CRC research [77]. They are inexpensive, easy to propagate and cryopreserve, and highly amenable to genetic manipulation and high-throughput drug screening, and many lines are characterized at the genomic and pharmacologic levels. Human cell lines [78] such as SW480, SW620, and HCT116, together with murine cell lines MC38 and CT26 [79], have provided invaluable insights into oncogenic signaling, drug sensitivity, and EMT [80]. SW480 and SW620, derived from primary colon tumors and a lymph-node metastatic carcinoma from the same patient [78], respectively, offer a convenient paired system to compare molecular features associated with metastatic progression [81].
In vivo, these cell lines are most frequently used as cell line-derived xenografts. Subcutaneous implantation is the workhorse for tumor growth and drug-response studies, whereas the same lines can be used in orthotopic or intrasplenic/portalvein models described above to interrogate specific steps of metastatic dissemination.
However, long-term culture often leads to clonal drift, copy-number alterations, and transcriptomic divergence from the parental tumor [82]. Most cell lines represent late-stage or poorly differentiated tumors that have lost the hierarchical organization and cellular heterogeneity characteristic of in vivo lesions [83,84]. Furthermore, monolayer culture lacks stromal and immune components, eliminating the paracrine and mechanical cues essential for invasion and metastasis. Even the frequently cited SW480-SW620 pair captures only a snapshot of metastatic disease and does not recapitulate the dynamic, stepwise evolution of dissemination observed in patients. Thus, while CRC cell lines remain indispensable for reductionist mechanistic studies and scalable pharmacologic screens, their limited architecture and inability to represent full tumor heterogeneity must be carefully considered when extrapolating findings to human disease.
2. Patient-Derived Organoids
CRC organoids recapitulate histopathological features and allow genetic manipulation via clustered regularly interspaced short palindromic repeats (CRISPR) or short hairpin RNA [85], enabling systematic interrogation of key genetic alterations associated with CRC metastasis. Drug screening studies have shown notable concordance between organoid responses and clinical outcomes [86]. Beyond in vitro profiling, organoid platforms are also used directly to model mCRC in vivo. Orthotopic transplantation of genetically engineered human or murine CRC organoids into the cecal or rectal mucosa generates primary tumors that can spontaneously seed liver and lung metastases, enabling stepwise analysis of invasion, dissemination, and distant colonization in a controlled genetic and microenvironmental context [87-89]. Portal- or mesenteric-vein injection of organoid-derived cells produces stroma-rich liver lesions that recapitulate the fibroinflammatory niche of human CRC liver metastases and can be used to test stromal or niche-targeted interventions [90,91]. Syngeneic transplantation of genetically engineered murine organoids into immunocompetent hosts similarly preserves an intact immune system and has been leveraged for in vivo CRISPR-based screens to uncover metastasis drivers and therapeutic vulnerabilities [92] (see “Genetically Engineered Murine Organoids for Syngeneic Transplantation” for details). However, organoids remain inherently reductionistic, lacking the vasculature, fibroblasts, immune cells, and organized ECM organization necessary for invasion and metastasis [93-96]. Assembloids, co-culture systems combining organoids with cancer-associated fibroblasts or lymphocytes, have improved physiological relevance, but reproducibility and scalability are limited [72,97,98]. Standardized media formulations, batch effects, and stromal cell sourcing continue to confound inter-laboratory comparisons [99].
3. Organoid-on-Chip and Microfluidic Co-Cultures
Engineering efforts recently combined patient-derived organoids (PDOs) with microfluidic “organ-on-chip” devices to control endothelial cells, shear stress, oxygen, and nutrient gradients. These platforms enable direct observation and quantification of invasion, transendothelial migration, and early steps of dissemination, and they can be extended to drug and immune-response testing as well [100-102].
In the context of mCRC, these devices have been used to model specific steps of the metastatic cascade. A CRC-on-chip system combining PDOs with perfused endothelial channels reconstructed the colonic mucosa-submucosa interface and enabled live imaging and quantification of invasion and intravasation under defined stromal and flow conditions [100]. Multiorgan “metastasis-on-a-chip” platforms linking a colon tumor compartment seeded with CRC spheroids to downstream liver-like microtissues have been used to study colon-to-liver extravasation, early hepatic outgrowth, and responses to antiangiogenic or anti-metastatic agents [103,104].
Beyond chip devices, three-dimensional (3D) microfluidic platforms that co-culture organoids with endothelial cells generate self-organized microvascular networks and visualize tumor-vessel interactions. These proofs-of-concept quantify increased angiogenic sprouting, changes in vascular permeability, and chemotactic coupling between tumor cells and endothelium—key dynamics of the pre-seeding phase of metastasis [105]. Broader syntheses emphasize how flow and shear stress modulate endothelial barriers, angiogenesis, and drug distribution in 3D co-cultures [102,106]. Despite these advantages, organoid-on-chip and microfluidic co-culture systems have significant limitations. Matrices and flow regimens are often nonphysiologic or poorly standardized, so readouts can shift with lot-to-lot changes in ECM composition, stiffness, shear stress, or oxygen tension [107]. Stromal and endothelial cells frequently lose their phenotypes over time, and adaptive immune cells rarely maintain stable function, restricting studies of immunoediting and immunotherapy [108-110]. Device materials can adsorb hydrophobic drugs and cytokines, while chip-to-chip and donor-to-donor variability, manufacturing cost, and operator dependency hinder scalability and reproducibility [109,110]. Most platforms also lack lymphatic drainage, innervation, and multiorgan crosstalk [107]. For translational use, careful control and reporting of physical parameters, standardized media/ECM formulations, and side-by-side validation against in vivo benchmarks will therefore be essential [111,112].
4. Patient-Derived Xenografts
PDXs offer higher fidelity in maintaining tissue architecture and inter-patient variability [99,113]. By implanting patient tumor fragments into immunodeficient mice, PDXs preserve clonal heterogeneity and histological features, making them valuable for drug efficacy and resistance modeling [114,115].
In metastasis research, PDXs can recapitulate patient-specific patterns of organotropism and enable evaluation of metastatic outgrowth in a clinically relevant genomic and stromal context [116,117]. Several studies have shown that orthotopic or circulation-based PDX implantation can generate spontaneous liver or lung metastases, allowing functional interrogation of metastatic potential and therapy response [118].
Nevertheless, their dependence on immune-compromised hosts (e.g., nude or severe combined immunodeficient recipient mice) prevents analysis of immune surveillance, tumor-immune crosstalk, and immunotherapy response [114]. Moreover, human and mouse species barriers differ in cytokine signaling, ECM composition, and microbiome, distorting stromal remodeling and metastatic niche formation [94,119,120]. Although PDX models incorporating human immune cells using humanized mice are emerging, they remain technically demanding, expensive, and short-lived due to graft-versus-host reactivity [113,121-123].
5. Genetically Engineered Mouse Models
Early CRC GEMMs, such as ApcMin/+ mice, recapitulate the classical adenoma-carcinoma sequence in the small intestine but rarely progress to frank invasion or distant metastasis, limiting their utility for metastasis research. To promote malignant progression, conditional alleles of Apc, KrasG12D, and Trp53 have been combined with intestine-specific and tamoxifen-inducible Cre drivers (Table 1). Upon tamoxifen administration, Villin-CreERT2; Apcfl/fl; KrasG12D mice generate numerous adenomas throughout the intestinal tract but largely retain a noninvasive phenotype without macroscopic metastases [124], whereas Cdx2-CreERT2-based models restrict recombination to the distal intestine and colon, yielding invasive adenocarcinomas with prominent desmoplastic stroma that more closely resemble human CRC, yet still without significant and consistent distant spread [125-127].
Further pathway engineering has enabled genuine metastatic behavior in a subset of GEMMs. For example, adding biallelic Trp53 loss to Villin-CreERT2; Apcfl/fl; KrasG12D accelerates malignant transformation and produces highly invasive colon tumors with histologically confirmed liver metastases [39]. Similarly, Fabp1-Cre-driven deletion of Apc and Tgfbr2 alleles on a KrasG12D background yields TGF-β-signaling-deficient carcinomas with desmoplastic stroma, of which 10%–20 % give rise to spontaneous liver metastases [128]. These models demonstrate that appropriate combinations of WNT, RAS, p53, and TGF-β pathway alterations drive stepwise progression from adenoma to invasive carcinoma and, in a fraction of animals, clinically relevant hepatic dissemination.
Despite these advances, CRC GEMMs still exhibit several practical limitations. Tumor latency and penetrance are highly variable between strains. Even in “metastatic” models, the frequency and timing of liver lesions remain inconsistent, which complicates adequately powered metastasis studies. Disease progression is also strongly modulated by host-intrinsic variables such as microbiome composition, diet and background inflammation, contributing to substantial inter-animal heterogeneity under nominally identical genotypes [129-132]. Moreover, most GEMMs develop multifocal primary tumors and early intestinal morbidity that restrict the time window available to interrogate premetastatic niches or to impose therapeutic interventions. Thus, while GEMMs provide an immunocompetent setting and faithfully model de novo tumorigenesis, their structural and temporal constraints necessitate complementary platforms—including organoid-based orthotopic and PDX models—to fully dissect the mechanisms of CRC metastasis (Table 1) [34,35,38,39,87-90,124,128,132-147].
6. Genetically Engineered Murine Organoids for Syngeneic Transplantation
Several groups have recently used tumor organoids derived from the intestine of GEMMs and re-implanted them orthotopically into syngeneic hosts [87-89]. KrasG12D Trp53 KO murine intestinal organoids, when transplanted into the distal colon, generate locally invasive adenocarcinomas that remain largely confined to the bowel wall, thus providing a technically tractable platform to interrogate invasion in a colon-restricted microenvironment without consistent distant spread [45,148]. Apc KO KrasG12D Trp53 KO intestinal organoids transplanted into the cecum reproducibly form desmoplastic primary tumors and, in a subset of mice, give rise to liver or lung lesions, capturing early metastatic escape in a genetically well-defined setting [88,89].
Rationally engineered quadruple-mutant organoids harboring Apc KO, KrasG12D, and Trp53 KO along with Smad4 deletion further increase metastatic efficiency. TGF-β signaling plays a well-established, context-dependent role in cancer progression [149]: while it restrains epithelial proliferation in early disease, in advanced tumors it is frequently coopted to drive EMT, immune suppression, and metastatic niche formation across multiple cancer types [135]. In CRC, genetic disruption or pathway rewiring of TGF-β/SMAD signaling is associated with poor prognosis [150], mesenchymal CMS4-like phenotypes, and a higher propensity for liver metastasis [135,151,152]. In line with this, organoids derived from Tgfbr2fl/fl; KrasG12D; Trp53fl/fl GEMMs, when introduced into the cecal wall or via splenic injection, exploit the portal circulation to establish reproducible liver metastases, highlighting the role of TGF-β signaling loss in invasive behavior and hepatic colonization [135,153].
Organoid-based orthotopic models preserve key strengths of GEMMs—tumor growth in an immunocompetent host and within native stromal architecture—while being easier to control experimentally. Defined organoid genotypes and implantation sites allow more synchronized tumor onset, permitting side-by-side imaging and treatment across cohorts. However, engraftment and metastatic yield remain variable, and the immune and microbial environment is still purely murine. These hybrid systems are therefore regarded as a complementary platform rather than a replacement for autochthonous models, well suited to mechanistic studies of the earliest phases of invasion, intravasation, and liver seeding.
7. Orthotopic Transplantation Models in PDX/PDO Systems
In the clinical translation space, most PDX work has relied on transplantation paradigms based on either subcutaneous or orthotopic engraftment of patient-derived colorectal tumor tissues. In conventional flank xenografts, CRC cell lines or small PDX fragments are implanted under the skin of immunodeficient mice, which makes it easy to monitor and quantify in a noninvasive manner [113,154,155]. This ectopic setting, however, provides only a rudimentary stromal and vascular niche and therefore offers limited insight into how colorectal tumors invade, disseminate, and colonize distant organs [37,116,155,156]. While it does not support spontaneous metastasis, noninvasive bioluminescence imaging (in vivo imaging system) can partially compensate for this limitation by enabling longitudinal tracking of tumor burden and early dissemination dynamics.
Orthotopic transplantation protocols instead place PDOs or established CRC cells into the cecum, rectum, or colonic wall of immunocompromised hosts—typically by surgical implantation or intraluminal injection [157,158]. Tumors arising from these procedures grow along the natural mucosal and vascular axes of the intestine and often reproduce the characteristic pattern of colorectal spread, including liver involvement in a subset of animals [87,159-162]. In selected CRC orthotopic models, primary cecal or rectal tumors can be surgically debulked or resected to isolate metastatic outgrowth and extend the observational window for liver metastasis, a strategy that has been adopted in a few recent CRC metastasis protocols [163]. However, routine resection of intracecal or intrarectal primaries is technically demanding, risks disrupting bowel continuity and portal drainage, and can negatively affect animal welfare; consequently, many CRC orthotopic metastasis studies still leave the primary lesions in place and assess metastatic burden in their presence [164,165].
Orthotopic PDX/PDO models are better suited than subcutaneous implants for testing site-specific therapies and for mapping the routes by which human CRC cells reach the portal circulation. That said, they remain technically demanding, with engraftment rates and metastatic yield influenced by injection depth, local stromal compatibility, and operator experience [140,147]. The obligatory use of immunodeficient strains also indicates that adaptive immune surveillance and human-liver crosstalk are only partially captured, so these systems complement rather than replace immunocompetent GEMM-based models in the metastasis toolkit [166,167].
8. Orthotopic Co-Engraftment (Enhanced Models)
Recent studies using orthotopic co-engraftment of CRC organoids with patient-matched fibroblasts or endothelial cells report increased metastatic seeding efficiency, underscoring that stromal cues are rate-limiting for successful colonization. A large matched CRC organoid–stroma biobank further showed that co-culture with patient-matched cancer-associated fibroblasts (CAFs) restores stromal/CMS-related programs, improves transcriptional fidelity, and sharpens functional readouts of drug response and stromal resistance mechanisms [106]. Standardized protocols for simultaneous tumor-plus-stroma orthotopic cecum/rectum implantation enable analysis of growth, invasion, and intravasation, while noting take-rate variability with injection depth, stromal compatibility, and operator experience [168]. In portal-vein models, CRC organoids elicit a fibroblast-rich desmoplastic response that recapitulates human CRC liver metastases stroma, facilitating studies of metastatic seeding and niche-directed therapies [90]. Orthotopic PDXs likewise display spontaneous liver/lung dissemination and reveal associations between metastatic lesions, partial mesenchymal-epithelial transition/stemness programs, and TGF-β signaling —features well suited for probing the dynamics of dissemination and colonization [118]. Collectively, co-culture/co-engraftment with CAFs and endothelial cells supports a functional view that stromal cues govern metastatic seeding efficiency, linking in vitro chips, ex vivo microfluidics, and in vivo orthotopic/portal-vein systems along one mechanistic continuum.
9. Longitudinal Imaging and Metastatic Modeling
Despite these advances, longitudinal monitoring of metastatic progression remains challenging because of anatomical inaccessibility and the need for advanced imaging modalities, such as magnetic resonance imaging, magnetic resonance cholangiopancreatography, micro-computed tomography, positron emission tomography, and in vivo imaging system [169-173]. These modalities have limited sensitivity for detecting submillimeter micrometastases, and optical signals are subject to depth-dependent attenuation, which reduces the quantitative accuracy of longitudinal comparisons [174,175]. Repeated imaging is further constrained by the need for anesthesia or radiation exposure, limiting temporal resolution. Serial sampling of metastatic foci is largely infeasible, preventing direct interrogation of early extravasation, micrometastatic persistence, and early outgrowth stages [175-177].
In addition to orthotopic approaches, experimental metastasis models—notably intrasplenic and portal vein injections—are used to study hepatic colonization. Intrasplenic injection delivers tumor cells into the portal circulation and reproducibly seeds the liver [141,178], whereas direct portal vein injection bypasses the spleen and enables tighter control of metastatic burden and timing [90,139,179]. These methods provide technically consistent and readily quantifiable information for metastatic kinetics, angiogenesis, and therapeutic responses, while they primarily model later stages of metastasis—circulatory survival and colonization—rather than the early steps of local invasion and dissemination. Longitudinal readouts often require advanced imaging [179,180].
Together, orthotopic and experimental metastasis models occupy a critical intermediate position between PDXs and GEMMs. Orthotopic implantation preserves key epithelial–stromal interactions and spontaneous dissemination, whereas splenic and portal vein injections enable reproducible quantification of hepatic seeding. Yet, all remain constrained using immunodeficient hosts and by incomplete reconstruction of immune and stromal complexity. Integrating these models with advanced imaging, immune-competent backgrounds, or humanized microenvironments will be essential for more physiologically faithful investigation of mCRC.
The recent convergence of single-cell transcriptomics, genomics, spatial transcriptomics, and computational modeling has begun to bridge the long-standing divide between molecular characterization and functional metastasis biology. These technologies provide unprecedented resolution to dissect when, where, and how CRC cells acquire metastatic competence—an aspect that classical experimental systems fail to capture. However, widespread adoption of these emerging platforms remains constrained by high costs, specialized instrumentation and bioinformatics expertise, and limited access to high-quality fresh clinical specimens, which can restrict implementation across institutions.
1. Single-Cell and Spatial Transcriptomics: Reconstructing Missing Dynamics
Single-cell RNA-seq atlases of primary CRC and matched liver metastases have revealed marked epithelial and immune heterogeneity, with distinct metastatic ecosystems that differ from primary tumors [181,182]. In liver metastases, integrated single-cell and spatial profiling has identified transcriptional programs associated with EMT and invasive behavior, including BHLHE40-driven EMT programs that promote metastatic spread [183]. Single-cell and spatial mapping of CRC liver metastases further charted immune evolution across treatment and unveiled how tumors respond to neoadjuvant chemotherapy [184]. Spatially resolved analyses of CAFs show that CTHRC1⁺ fibroblast subsets act as major sources of WNT5A, promote EMT, and are linked to poor prognosis. CAF-immune-epithelial crosstalk is topographically organized within tumors [185-187]. Recent work on the premetastatic niche extends these insights, demonstrating that Prok2⁺ neutrophils, tumor-derived small extracellular vesicles, and other systemic cues establish inflammatory and immunosuppressive liver microenvironments that favor CRC seeding [188,189]. Together, single-cell and spatial data give a much more detailed view of which cells and niches drive metastasis than bulk RNA-seq. Nonetheless, single-cell and spatial transcriptomics still miss fragile or deep-lesion cells [190,191] and are difficult to combine consistently across patients and different platforms [192].
2. Multiomics Integration
Multiomics studies that combine genomic, transcriptomic, epigenomic, and proteomic data in primary CRC and liver metastases have begun to systematically link recurrent driver alterations with downstream pathway changes [193,194]. Proteogenomic analyses of matched normal, primary tumor, and liver metastasis triplets integrating whole-exome sequencing, RNAseq, single-nucleotide polymorphism arrays, and quantitative mass spectrometry have identified copy number-mRNA-protein-correlated modules and metastasis-enriched molecules, nominating candidates such as COL1A2, BGN, MYH9, and CCT6A with prognostic relevance [193]. In CRC organoids, integrated analysis of the transcriptome, (phospho)proteome, and secretome has shown that SMAD4 inactivation leads to reduced epithelial differentiation, activation of pro-migratory and proliferative programs, disruption of TGF-β, WNT, and VEGF signaling, and increased secretion of proteins involved in prometastatic processes, illustrating how multi-layer measurements map the consequences of a single driver lesion across regulatory levels [195].
Integrating genomic, transcriptomic, epigenomic, proteomic, and metabolomic data across patients and studies remains technically challenging. Heterogeneous assay performance, missing data, and variation in biospecimen handling, library preparation, and analysis workflows introduce batch effects and other systematic biases. Computational tools such as Harmony, MOFA+, and multimodal Seurat [196-198] help align data from different patients and assays into a shared space and identify common patterns, but batch effects, uneven sampling, and limited proteomic and metabolomic depth still make metastasis-associated signatures noisy and difficult to reproduce [199-202].
3. Computational Modeling
Computational modeling has become central for synthesizing these high-dimensional data into mechanistic hypotheses about metastatic behavior. Hu et al. [203] combined spatial tumor growth modeling with statistical inference of matched primary CRC and metastatic exomes to estimate dissemination timing, showing that metastases are frequently seeded early while the primary lesion remains clinically undetectable, thereby challenging a strictly late-stage linear progression model. Using multiregional whole-genome and exome data across primary tumors, multiple metastases, and PDXs, Dang et al. [204] reconstructed clonal relationships and seeding patterns, revealing therapy-shaped evolutionary branching with both mono- and polyclonal dissemination and instances consistent with parallel or metastasis-to-metastasis spread. These reconstructions align with agent-based and multiscale models that simulate clonal competition, spatial constraints, and microenvironmental feedback to generate testable predictions about metastatic outgrowth, recurrence timing, and treatment resistance; West et al. [205] highlighted how these frameworks translate multiscale data into explicitly mechanistic, hypothesis-driven simulations. Consistent with this view, recent cell lineage-tracing work that couples high-complexity genetic barcoding with single-cell transcriptomics in esophageal preneoplasia quantitatively maps precursor cell dynamics and lineage plasticity, providing ground-truth constraints for evolutionary models of early neoplastic progression [116]. Dynamical systemic approaches that quantify epithelial mesenchymal plasticity and its association with stemness and immune escape provide a useful framework for interpreting the diverse metastatic cell states observed in single-cell datasets [206]. As multiomic and spatial CRC resources expand, iterative cycles between in silico modeling and in vivo or ex vivo perturbation should increasingly shift metastasis research from retrospective description toward predictive modeling of metastatic fitness landscapes and therapeutic vulnerabilities [205,207-210].
Despite remarkable advances in molecular profiling and model development, metastasis remains one of the most challenging biological frontiers. The persistent gap between descriptive genomics and functional understanding stems from both biological complexity and experimental constraints. Nevertheless, combining next-generation profiling tools such as single-cell and spatial transcriptomics with innovative model systems including GEMMs, organoid models and humanized mice, promises to bridge these long-standing divides. A unified framework integrating temporal, spatial, and molecular dimensions may further illuminate how CRC metastasizes—and why it so often resists cure.
Future progress will depend on constructing a multi-layered ecosystem of experimental and computational approaches. Integrating organoid-based co-cultures, lineage-traced GEMMs, and spatial-omics-guided human tissue analysis can help connect molecular alterations to functional outcomes. Additionally, collaborative metastatic biobanks and standardized computational pipelines will be essential to harmonize preclinical and clinical data across institutions. By merging experimental innovation with computational precision, metastasis can finally be reconstructed as a dynamic, evolving ecosystem—one whose vulnerabilities may at last be rendered visible and therapeutically actionable.

Funding Source

This review was supported by grants to the Cancer Prevention and Research Institute of Texas (RP200315 to J.-I.P.), the National Institutes of Health (CA278967 to Park JI), and the American Association for Cancer Research (25-40-60-SEO to Seo Y).

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Data Availability Statement

Data sharing is not applicable as no new data were created or analyzed in this study.

Author Contributions

Conceptualization: Seo Y, Jang J, Park JI. Supervision: Park JI. Writing–original draft: Seo Y, Jang J. Writing–review and editing: Seo Y, Jang J, Park JI. All authors approved the final version for submission.

Additional Contributions

We apologize to those authors whose work was not included. We thank Kyung-Pil Ko and Jieun Ahn (The University of Texas MD Anderson Cancer Center, Houston, TX, USA) for critical comments.

Fig. 1.
Overview of preclinical platforms and integrative technologies to study metastatic colorectal cancer. Patient-derived tumors and genetically engineered mouse models can be used to generate tumors, cell lines, and organoids. These materials are evaluated using transplantation-based in vivo models, including subcutaneous implantation, orthotopic models (cecum/rectum), and intrasplenic/portal vein injection, as well as microphysiological systems such as organ-on-chip. Across these platforms, advanced analytical approaches—singlecell transcriptomics, genomics, spatial transcriptomics, and computational modeling—enable integrated characterization of metastatic progression and the tumor microenvironment. Figure created with BioRender.com (https://BioRender.com/4r040ez).
ir-2025-00315f1.jpg
Table 1.
Representative Preclinical Models for CRC Metastasis
Model type Representative system Metastatic route / target Key features Reference
GEMMs ApcMin/+ No metastasis reported Classic intestinal tumor model; lacks invasive phenotype [35, 132]
Villin-CreERT2; Apcfl/fl; KrasLSLG12D No distant metastasis observed Generates multiple intestinal adenomas; noninvasive phenotype [124]
Cdx2-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/fl Invasive phenotype without distant metastasis Colon-specific genetic recombination that reproduces invasive adenocarcinoma with desmoplastic stroma [88]
Villin-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/fl Liver metastasis observed (macroscopic) Highly invasive adenocarcinomas with confirmed liver metastases [39]
Fabp1-Cre;Apcfl/fl; KrasLSLG12D; Tgfbr2fl/fl Liver metastases detected in subset (10%–20%) TGF-β–signaling-loss-driven invasion and desmoplasia [128]
Orthotopic transplantation of genetically engineered murine organoids Cdx2-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/fl-derived tumor organoids Ex vivo organoid culture and orthotopic colonic injection; no distant metastasis observed; localized invasive growth in colon wall Organoids derived from GEMM generate colon-restricted invasive adenocarcinomas upon orthotopic transplantation; faithfully mimic human CRC architecture and desmoplastic stroma but lack metastatic spread [88]
Villin-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/R172H-derived tumor organoids Orthotopic transplantation into cecum wall; occasional metastasis to liver and lung [87, 133]
Apc-/-; KrasLSLG12D/+; Trp53-/-; Smad4-/- Orthotopic transplantation of genetically engineered intestinal or colonic organoids into cecum/rectum; metastasis to liver and lung Reproducible macroscopic metastases; recapitulates adenoma-carcinoma-metastasis sequence [87, 89, 134]
Apcfl/fl; KrasLSLG12D; Tgfbr2fl/fl; Trp53fl/fl GEMM-derived organoids Orthotopic cecal or splenic injection of in vitro Ad-Cre–recombined tumor organoids; portal dissemination to liver (occasional lung lesions) TGF-β-signaling loss drives invasive adenocarcinoma and reproducible liver metastasis [135]
Orthotopic transplantation of PDOs Human CRC PDOs Subcutaneous and orthotopic into cecal or rectal wall. No distant metastasis reported Human PDOs maintain histological and genetic fidelity to the parental tumor; first demonstration of in vivo tumorigenicity of human CRC organoids [136]
Orthotopic portal vein injection; metastasis to liver Human PDOs reproducibly form hepatic metastatic nodules following portal vein injection; faithfully mimic desmoplastic and fibroblast-rich stroma observed in clinical CRC liver metastases [90]
Human CRC primary- and metastatic-derived PDOs Subcutaneous and orthotopic into cecal or rectal wall; occasional metastasis to liver Occasional liver metastases observed only in mice transplanted with metastatic-origin PDOs; none in primary PDO group [137]
Non-orthotopic transplantation of murine cancer cell lines CT26 (BALB/c) Tail vein injection; metastasis to lung Formation of lung metastases within approximately 2 weeks after injection [138]
Intrasplenic injection or intraportal; metastasis to liver Mimics hematogenous spread via portal circulation; widely used for hepatic metastasis evaluation [34, 139]
MC38 (C57BL/6) Intrasplenic or intraportal vein injection; metastasis to liver and occasionally to lung Highly reproducible hepatic metastasis via portal circulation; mimics hematogenous spread under immunocompetent background. Preferred routes for MC38 due to low orthotopic engraftment efficiency [34, 139-141]
Non-orthotopic transplantation of human CRC cell lines KM20L2, HCT116, HCT15, SW480, SW620, Colo320DM Orthotopic cecal injection; occasional metastasis to liver and lymph nodes SW620: 20% liver metastasis; common nodal metastasis except for SW480, Colo320DM [142]
Co115 Tumor take rate 90%; metastasis to nodal and occasionally to liver [142]
HCC2998 Tumor take rate 88%; metastasis to nodal and rarely to liver [142]
HT29 Tumor take rate 69%; metastasis to nodal and rarely to liver [142]
CaCo2, WiDr, Co205 Tumor take rate 40%; very low metastasis [142]
HCT116 Orthotopic cecal submucosa (micropipette injection) Tumor take rate 75%; metastasis to 100% nodal, 67% liver, and 50% lung [143]
Rectal wall (rectal injection) Tumor take rate 65%; rare metastasis (3.3%) [144]
Intraportal injection 90% developed liver metastasis (the highest hepatic take rate) within 30 days [145]
HT29 Intrasplenic injection (metastasis to liver) 78% developed macroscopic liver metastasis within 6 weeks [146]
SW620 Intrasplenic injection (metastasis to liver) Approximately 80% liver metastasis within 4–6 weeks [147]
Intraportal injection (metastasis to liver) 100% liver metastasis in all injected mice (dose-dependent tumor load) [38]

Tumor take rate (%)=number of animals developing tumors/total number of animals inoculated/transplanted.

CRC, colorectal cancer; GEMMs, genetically engineered mouse models; Min, multiple intestinal neoplasia; Cre, Cre recombinase; CreERT2, Cre recombinase fused with estrogen receptor (ER) conditionally activated by tamoxifen (T2); fl, floxed (flanked by loxP sites, conditionally deleted by Cre recombinase); LSL, a loxP-stop-loxP cassette conditionally removed by Cre recombinase for subsequent expression of gene(s); TGF-β, transforming growth factor β; PDOs, patient-derived organoids.

  • 1. Morgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut 2023;72:338–344.ArticlePubMed
  • 2. Muhammad S, Kaur K, Huang R, et al. MicroRNAs in colorectal cancer: role in metastasis and clinical perspectives. World J Gastroenterol 2014;20:17011–17019.ArticlePubMedPMC
  • 3. Weiss L, Grundmann E, Torhorst J, et al. Haematogenous metastatic patterns in colonic carcinoma: an analysis of 1541 necropsies. J Pathol 1986;150:195–203.PubMed
  • 4. Kalyan A, Kircher S, Shah H, Mulcahy M, Benson A. Updates on immunotherapy for colorectal cancer. J Gastrointest Oncol 2018;9:160–169.ArticlePubMedPMC
  • 5. Ganesh K, Stadler ZK, Cercek A, et al. Immunotherapy in colorectal cancer: rationale, challenges and potential. Nat Rev Gastroenterol Hepatol 2019;16:361–375.ArticlePubMedPMCPDF
  • 6. Steup C, Kennel KB, Neurath MF, Fichtner-Feigl S, Greten FR. Current and emerging concepts for systemic treatment of metastatic colorectal cancer. Gut 2025;74:2070–2095.ArticlePubMedPMC
  • 7. Elez E, Baraibar I. Immunotherapy in colorectal cancer: an unmet need deserving of change. Lancet Oncol 2022;23:830–831.ArticlePubMed
  • 8. Pérez-González A, Bévant K, Blanpain C. Cancer cell plasticity during tumor progression, metastasis and response to therapy. Nat Cancer 2023;4:1063–1082.ArticlePubMedPMCPDF
  • 9. Kennel KB, Greten FR. The immune microenvironment of colorectal cancer. Nat Rev Cancer 2025;25:945–964.ArticlePubMedPDF
  • 10. Liu X, Xin Z, Wang K. Patient-derived xenograft model in colorectal cancer basic and translational research. Animal Model Exp Med 2023;6:26–40.ArticlePubMedPDF
  • 11. Ahmad Zawawi SS, Salleh EA, Musa M. Spheroids and organoids derived from colorectal cancer as tools for in vitro drug screening. Explor Target Antitumor Ther 2024;5:409–431.ArticlePubMedPMC
  • 12. Neto Í, Rocha J, Gaspar MM, Reis CP. Experimental murine models for colorectal cancer research. Cancers (Basel) 2023;15:2570.ArticlePubMedPMC
  • 13. Zhou H, Liu Z, Wang Y, et al. Colorectal liver metastasis: molecular and interventional therapy. Signal Transduct Target Ther 2022;7:70.PubMedPMC
  • 14. Hong SN. Genetic and epigenetic alterations of colorectal cancer. Intest Res 2018;16:327–337.ArticlePubMedPMCPDF
  • 15. Okugawa Y, Grady WM, Goel A. Epigenetic alterations in colorectal cancer: emerging biomarkers. Gastroenterology 2015;149:1204–1225.e12.ArticlePubMedPMC
  • 16. Bardhan K, Liu K. Epigenetics and colorectal cancer pathogenesis. Cancers (Basel) 2013;5:676–713.ArticlePubMedPMC
  • 17. Li QL, Lin X, Yu YL, et al. Genome-wide profiling in colorectal cancer identifies PHF19 and TBC1D16 as oncogenic super enhancers. Nat Commun 2021;12:6407.ArticlePubMedPMCPDF
  • 18. Chen Y, Huang M, Liu X, et al. Alternative splicing of mRNA in colorectal cancer: new strategies for tumor diagnosis and treatment. Cell Death Dis 2021;12:752.ArticlePubMedPMCPDF
  • 19. Nie F, Sun X, Sun J, Zhang J, Wang Y. Epithelial-mesenchymal transition in colorectal cancer metastasis and progression: molecular mechanisms and therapeutic strategies. Cell Death Discov 2025;11:336.ArticlePubMedPMCPDF
  • 20. Lakatos E, Gunasri V, Zapata L, et al. Epigenetically driven and early immune evasion in colorectal cancer evolution. Nat Genet 2025;57:3039–3049.ArticlePubMedPMCPDF
  • 21. Ge T, Gu X, Jia R, et al. Crosstalk between metabolic reprogramming and epigenetics in cancer: updates on mechanisms and therapeutic opportunities. Cancer Commun (Lond) 2022;42:1049–1082.ArticlePubMedPMCPDF
  • 22. Teng S, Li YE, Yang M, et al. Tissue-specific transcription reprogramming promotes liver metastasis of colorectal cancer. Cell Res 2020;30:34–49.ArticlePubMedPDF
  • 23. Guinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med 2015;21:1350–1356.PubMedPMC
  • 24. Dienstmann R, Vermeulen L, Guinney J, Kopetz S, Tejpar S, Tabernero J. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat Rev Cancer 2017;17:79–92.ArticlePubMedPDF
  • 25. Eide PW, Moosavi SH, Eilertsen IA, et al. Metastatic heterogeneity of the consensus molecular subtypes of colorectal cancer. NPJ Genom Med 2021;6:59.ArticlePubMedPMCPDF
  • 26. Le DT, Uram JN, Wang H, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 2015;372:2509–2520.PubMedPMC
  • 27. Ten Hoorn S, de Back TR, Sommeijer DW, Vermeulen L. Clinical value of consensus molecular subtypes in colorectal cancer: a systematic review and meta-analysis. J Natl Cancer Inst 2022;114:503–516.ArticlePubMedPMCPDF
  • 28. Rebersek M. Consensus molecular subtypes (CMS) in metastatic colorectal cancer: personalized medicine decision. Radiol Oncol 2020;54:272–277.ArticlePubMedPMC
  • 29. Thanki K, Nicholls ME, Gajjar A, et al. Consensus molecular subtypes of colorectal cancer and their clinical implications. Int Biol Biomed J 2017;3:105–111.PubMedPMC
  • 30. Pulaski BA, Ostrand-Rosenberg S. Mouse 4T1 breast tumor model. Curr Protoc Immunol 2000;39:20.2.1–20.2.16.ArticlePDF
  • 31. Bobek V, Kolostova K, Pinterova D, et al. A clinically relevant, syngeneic model of spontaneous, highly metastatic B16 mouse melanoma. Anticancer Res 2010;30:4799–4803.PubMed
  • 32. Liu H, Patel MR, Prescher JA, et al. Cancer stem cells from human breast tumors are involved in spontaneous metastases in orthotopic mouse models. Proc Natl Acad Sci U S A 2010;107:18115–18120.ArticlePubMedPMC
  • 33. Oh BY, Hong HK, Lee WY, Cho YB. Animal models of colorectal cancer with liver metastasis. Cancer Lett 2017;387:114–120.ArticlePubMed
  • 34. Romano G, Chagani S, Kwong LN. The path to metastatic mouse models of colorectal cancer. Oncogene 2018;37:2481–2489.ArticlePubMedPDF
  • 35. Moser AR, Pitot HC, Dove WF. A dominant mutation that predisposes to multiple intestinal neoplasia in the mouse. Science 1990;247:322–324.ArticlePubMed
  • 36. Su LK, Kinzler KW, Vogelstein B, et al. Multiple intestinal neoplasia caused by a mutation in the murine homolog of the APC gene. Science 1992;256:668–670.ArticlePubMed
  • 37. Bürtin F, Mullins CS, Linnebacher M. Mouse models of colorectal cancer: past, present and future perspectives. World J Gastroenterol 2020;26:1394–1426.ArticlePubMedPMC
  • 38. Rosenberg DW, Giardina C, Tanaka T. Mouse models for the study of colon carcinogenesis. Carcinogenesis 2009;30:183–196.ArticlePubMed
  • 39. Boutin AT, Liao WT, Wang M, et al. Oncogenic Kras drives invasion and maintains metastases in colorectal cancer. Genes Dev 2017;31:370–382.ArticlePubMedPMC
  • 40. Gröschel C, Prinz-Wohlgenannt M, Mesteri I, et al. Switching to a healthy diet prevents the detrimental effects of western diet in a colitis-associated colorectal cancer model. Nutrients 2019;12:45.ArticlePubMedPMC
  • 41. Kucherlapati MH. Mouse models in colon cancer, inferences, and implications. iScience 2023;26:106958.ArticlePubMedPMC
  • 42. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012;487:330–337.ArticlePubMedPMCPDF
  • 43. Tsilimigras DI, Ntanasis-Stathopoulos I, Pawlik TM. Molecular mechanisms of colorectal liver metastases. Cells 2023;12:1657.ArticlePubMedPMC
  • 44. Abdel Hamid M, Pammer LM, Oberparleiter S, et al. Multidimensional differences of right- and left-sided colorectal cancer and their impact on targeted therapies. NPJ Precis Oncol 2025;9:116.PubMedPMC
  • 45. Xue Y, Johnson R, Desmet M, Snyder PW, Fleet JC. Generation of a transgenic mouse for colorectal cancer research with intestinal Cre expression limited to the large intestine. Mol Cancer Res 2010;8:1095–1104.ArticlePubMedPMCPDF
  • 46. Feng Y, Sentani K, Wiese A, et al. Sox9 induction, ectopic Paneth cells, and mitotic spindle axis defects in mouse colon adenomatous epithelium arising from conditional biallelic Apc inactivation. Am J Pathol 2013;183:493–503.ArticlePubMedPMC
  • 47. el Marjou F, Janssen KP, Chang BH, et al. Tissue-specific and inducible Cre-mediated recombination in the gut epithelium. Genesis 2004;39:186–193.ArticlePubMed
  • 48. Álvarez-Aznar A, Martínez-Corral I, Daubel N, Betsholtz C, Mäkinen T, Gaengel K. Tamoxifen-independent recombination of reporter genes limits lineage tracing and mosaic analysis using CreERT2 lines. Transgenic Res 2020;29:53–68.ArticlePubMedPDF
  • 49. Srinivas S, Watanabe T, Lin CS, et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev Biol 2001;1:4.ArticlePubMedPMCPDF
  • 50. Vermeulen L, Morrissey E, van der Heijden M, et al. Defining stem cell dynamics in models of intestinal tumor initiation. Science 2013;342:995–998.ArticlePubMed
  • 51. Robanus-Maandag EC, Koelink PJ, Breukel C, et al. A new conditional Apc-mutant mouse model for colorectal cancer. Carcinogenesis 2010;31:946–952.ArticlePubMed
  • 52. Barker N, van Es JH, Kuipers J, et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 2007;449:1003–1007.ArticlePubMedPDF
  • 53. Sweetser DA, Hauft SM, Hoppe PC, Birkenmeier EH, Gordon JI. Transgenic mice containing intestinal fatty acid-binding protein-human growth hormone fusion genes exhibit correct regional and cell-specific expression of the reporter gene in their small intestine. Proc Natl Acad Sci U S A 1988;85:9611–9615.ArticlePubMedPMC
  • 54. Magnuson MA, Osipovich AB. Pancreas-specific Cre driver lines and considerations for their prudent use. Cell Metab 2013;18:9–20.ArticlePubMedPMC
  • 55. Rakhilin N, Garrett A, Eom CY, et al. An intravital window to image the colon in real time. Nat Commun 2019;10:5647.ArticlePubMedPMCPDF
  • 56. Kolesnikov M, Farache J, Shakhar G. Intravital two-photon imaging of the gastrointestinal tract. J Immunol Methods 2015;421:73–80.ArticlePubMed
  • 57. Tanaka K, Morimoto Y, Toiyama Y, et al. In vivo time-course imaging of tumor angiogenesis in colorectal liver metastases in the same living mice using two-photon laser scanning microscopy. J Oncol 2012;2012:265487.ArticlePubMedPDF
  • 58. Jang WH, Park A, Wang T, et al. Two-photon microscopy of Paneth cells in the small intestine of live mice. Sci Rep 2018;8:14174.ArticlePubMedPMCPDF
  • 59. de Medeiros G, Ortiz R, Strnad P, et al. Multiscale light-sheet organoid imaging framework. Nat Commun 2022;13:4864.PubMedPMC
  • 60. Frenkel N, Poghosyan S, van Wijnbergen JW, et al. Tissue clearing and immunostaining to visualize the spatial organization of vasculature and tumor cells in mouse liver. Front Oncol 2023;13:1062926.ArticlePubMedPMC
  • 61. Smid M, Wang Y, Zhang Y, et al. Subtypes of breast cancer show preferential site of relapse. Cancer Res 2008;68:3108–3114.ArticlePubMedPDF
  • 62. Kennecke H, Yerushalmi R, Woods R, et al. Metastatic behavior of breast cancer subtypes. J Clin Oncol 2010;28:3271–3277.ArticlePubMed
  • 63. Fowlkes N, Clemons K, Rider PJ, et al. Factors affecting growth kinetics and spontaneous metastasis in the B16F10 syngeneic murine melanoma model. Comp Med 2019;69:48–54.ArticlePubMed
  • 64. Svendsen C, Alexander J, Knutsen HK, Husøy T. The min mouse on FVB background: susceptibility to spontaneous and carcinogen-induced intestinal tumourigenesis. Anticancer Res 2011;31:785–788.PubMed
  • 65. Stastna M, Janeckova L, Hrckulak D, Kriz V, Korinek V. Human colorectal cancer from the perspective of mouse models. Genes (Basel) 2019;10:788.ArticlePubMedPMC
  • 66. Amos-Landgraf JM, Heijmans J, Wielenga MC, et al. Sex disparity in colonic adenomagenesis involves promotion by male hormones, not protection by female hormones. Proc Natl Acad Sci U S A 2014;111:16514–16519.ArticlePubMedPMC
  • 67. Cooper HS, Chang WC, Coudry R, et al. Generation of a unique strain of multiple intestinal neoplasia (Apc(+/Min-FCCC)) mice with significantly increased numbers of colorectal adenomas. Mol Carcinog 2005;44:31–41.ArticlePubMed
  • 68. Taketo MM, Edelmann W. Mouse models of colon cancer. Gastroenterology 2009;136:780–798.ArticlePubMed
  • 69. Li C, Lau HC, Zhang X, Yu J. Mouse models for application in colorectal cancer: understanding the pathogenesis and relevance to the human condition. Biomedicines 2022;10:1710.ArticlePubMedPMC
  • 70. Song M, Chan AT, Sun J. Influence of the gut microbiome, diet, and environment on risk of colorectal cancer. Gastroenterology 2020;158:322–340.ArticlePubMed
  • 71. Choi Y, Lee S, Kim K, Kim SH, Chung YJ, Lee C. Studying cancer immunotherapy using patient-derived xenografts (PDXs) in humanized mice. Exp Mol Med 2018;50:1–9.ArticlePDF
  • 72. Neal JT, Li X, Zhu J, et al. Organoid modeling of the tumor immune microenvironment. Cell 2018;175:1972–1988.e16.ArticlePubMedPMC
  • 73. Yang C, Xiao W, Wang R, et al. Tumor organoid model of colorectal cancer (Review). Oncol Lett 2023;26:328.ArticlePubMedPMC
  • 74. Rizzo G, Bertotti A, Leto SM, Vetrano S. Patient-derived tumor models: a more suitable tool for pre-clinical studies in colorectal cancer. J Exp Clin Cancer Res 2021;40:178.ArticlePubMedPMCPDF
  • 75. de Sousa e Melo F, Kurtova AV, Harnoss JM, et al. A distinct role for Lgr5+ stem cells in primary and metastatic colon cancer. Nature 2017;543:676–680.ArticlePubMedPDF
  • 76. Fumagalli A, Oost KC, Kester L, et al. Plasticity of Lgr5-negative cancer cells drives metastasis in colorectal cancer. Cell Stem Cell 2020;26:569–578.e7.ArticlePubMedPMC
  • 77. Medico E, Russo M, Picco G, et al. The molecular landscape of colorectal cancer cell lines unveils clinically actionable kinase targets. Nat Commun 2015;6:7002.ArticlePubMedPDF
  • 78. Kubens BS, Zänker KS. Differences in the migration capacity of primary human colon carcinoma cells (SW480) and their lymph node metastatic derivatives (SW620). Cancer Lett 1998;131:55–64.ArticlePubMed
  • 79. Castle JC, Loewer M, Boegel S, et al. Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma. BMC Genomics 2014;15:190.ArticlePubMedPMC
  • 80. Maamer-Azzabi A, Ndozangue-Touriguine O, Bréard J. Metastatic SW620 colon cancer cells are primed for death when detached and can be sensitized to anoikis by the BH3-mimetic ABT-737. Cell Death Dis 2013;4:e801.ArticlePubMedPMCPDF
  • 81. Hewitt RE, McMarlin A, Kleiner D, et al. Validation of a model of colon cancer progression. J Pathol 2000;192:446–454.ArticlePubMed
  • 82. Ben-David U, Siranosian B, Ha G, et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature 2018;560:325–330.ArticlePubMedPMCPDF
  • 83. Gillet JP, Varma S, Gottesman MM. The clinical relevance of cancer cell lines. J Natl Cancer Inst 2013;105:452–458.ArticlePubMedPMC
  • 84. Ronen J, Hayat S, Akalin A. Evaluation of colorectal cancer subtypes and cell lines using deep learning. Life Sci Alliance 2019;2:e201900517.ArticlePubMedPMC
  • 85. van de Wetering M, Francies HE, Francis JM, et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 2015;161:933–945.ArticlePubMedPMC
  • 86. Ooft SN, Weeber F, Dijkstra KK, et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci Transl Med 2019;11:eaay2574.ArticlePubMed
  • 87. Fumagalli A, Drost J, Suijkerbuijk SJ, et al. Genetic dissection of colorectal cancer progression by orthotopic transplantation of engineered cancer organoids. Proc Natl Acad Sci U S A 2017;114:E2357–E2364.ArticlePubMedPMC
  • 88. Roper J, Tammela T, Cetinbas NM, et al. In vivo genome editing and organoid transplantation models of colorectal cancer and metastasis. Nat Biotechnol 2017;35:569–576.ArticlePubMedPMCPDF
  • 89. O’Rourke KP, Loizou E, Livshits G, et al. Transplantation of engineered organoids enables rapid generation of metastatic mouse models of colorectal cancer. Nat Biotechnol 2017;35:577–582.ArticlePubMedPMCPDF
  • 90. Kobayashi H, Gieniec KA, Ng JQ, et al. Portal vein injection of colorectal cancer organoids to study the liver metastasis stroma. J Vis Exp 2021;(175): e62630.Article
  • 91. Kobayashi H, Gieniec KA, Wright JA, et al. The balance of stromal BMP signaling mediated by GREM1 and ISLR drives colorectal carcinogenesis. Gastroenterology 2021;160:1224–1239.e30.ArticlePubMed
  • 92. Wang X, Cramer Z, Leu NA, et al. Forward genetic screening in engineered colorectal cancer organoids identifies regulators of metastasis. Proc Natl Acad Sci U S A 2025;122:e2510910122.ArticlePubMedPMC
  • 93. Zhu J, Ji L, Chen Y, et al. Organoids and organs-on-chips: insights into predicting the efficacy of systemic treatment in colorectal cancer. Cell Death Discov 2023;9:72.ArticlePubMedPMCPDF
  • 94. Heo JH, Kang D, Seo SJ, Jin Y. Engineering the extracellular matrix for organoid culture. Int J Stem Cells 2022;15:60–69.ArticlePubMedPMC
  • 95. Kozlowski MT, Crook CJ, Ku HT. Towards organoid culture without Matrigel. Commun Biol 2021;4:1387.ArticlePubMedPMCPDF
  • 96. Lumibao JC, Okhovat SR, Peck KL, et al. The effect of extracellular matrix on the precision medicine utility of pancreatic cancer patient-derived organoids. JCI Insight 2024;9:e172419.ArticlePubMedPMC
  • 97. Dijkstra KK, Cattaneo CM, Weeber F, et al. Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 2018;174:1586–1598.e12.ArticlePubMedPMC
  • 98. Farin HF, Mosa MH, Ndreshkjana B, et al. Colorectal cancer organoid-stroma biobank allows subtype-specific assessment of individualized therapy responses. Cancer Discov 2023;13:2192–2211.ArticlePubMedPMCPDF
  • 99. Bleijs M, van de Wetering M, Clevers H, Drost J. Xenograft and organoid model systems in cancer research. EMBO J 2019;38:e101654.ArticlePubMedPMCPDF
  • 100. Strelez C, Chilakala S, Ghaffarian K, et al. Human colorectal cancer-on-chip model to study the microenvironmental influence on early metastatic spread. iScience 2021;24:102509.ArticlePubMedPMC
  • 101. Strelez C, Perez R, Chlystek JS, et al. Integration of patient-derived organoids and organ-on-chip systems: investigating colorectal cancer invasion within the mechanical and GABAergic tumor microenvironment [Preprint]. Posted 2023;Sep 17 https://doi.org/10.1101/2023.09.14.557797.Article
  • 102. Lim J, Ching H, Yoon JK, Jeon NL, Kim Y. Microvascularized tumor organoids-on-chips: advancing preclinical drug screening with pathophysiological relevance. Nano Converg 2021;8:12.ArticlePubMedPMCPDF
  • 103. Aleman J, Skardal A. A multi-site metastasis-on-a-chip microphysiological system for assessing metastatic preference of cancer cells. Biotechnol Bioeng 2019;116:936–944.ArticlePubMedPDF
  • 104. Sarvestani SK, DeHaan RK, Miller PG, et al. A tissue engineering approach to metastatic colon cancer. iScience 2020;23:101719.ArticlePubMedPMC
  • 105. Schulla LS, Alupoaie ED, De Silva L, et al. Development of a novel microfluidic co-culture model to study organoid vascularization [Preprint]. Posted 2022;Mar 27 https://doi.org/10.1101/2022.03.25.485813.Article
  • 106. Cauli E, Polidoro MA, Marzorati S, Bernardi C, Rasponi M, Lleo A. Cancer-on-chip: a 3D model for the study of the tumor microenvironment. J Biol Eng 2023;17:53.ArticlePubMedPMCPDF
  • 107. Leung CM, De Haan P, Ronaldson-Bouchard K, et al. A guide to the organ-on-a-chip. Nat Rev Methods Primers 2022;2:33.ArticlePDF
  • 108. Dabbagh Moghaddam F, Anvar A, Ilkhani E, et al. Advances in engineering immune-tumor microenvironments on-a-chip: integrative microfluidic platforms for immunotherapy and drug discovery. Mol Cancer 2025;24:271.PubMedPMC
  • 109. Chernyavska M, Masoudnia M, Valerius T, Verdurmen WP. Organ-on-a-chip models for development of cancer immunotherapies. Cancer Immunol Immunother 2023;72:3971–3983.ArticlePubMedPMCPDF
  • 110. Srivastava SK, Foo GW, Aggarwal N, Chang MW. Organ-on-chip technology: opportunities and challenges. Biotechnol Notes 2024;5:8–12.ArticlePubMedPMC
  • 111. Piergiovanni M, Cangar O, Leite SB, et al. Putting Science into Standards workshop on standards for organ-on-chip. Stem Cell Reports 2021;16:2076–2077.ArticlePubMedPMC
  • 112. Piergiovanni M, Leite SB, Corvi R, Whelan M. Standardisation needs for organ on chip devices. Lab Chip 2021;21:2857–2868.ArticlePubMed
  • 113. Liu Y, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduct Target Ther 2023;8:160.ArticlePubMedPMCPDF
  • 114. Hidalgo M, Amant F, Biankin AV, et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov 2014;4:998–1013.ArticlePubMedPMCPDF
  • 115. Byrne AT, Alférez DG, Amant F, et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat Rev Cancer 2017;17:254–268.ArticlePubMedPDF
  • 116. Brown KM, Xue A, Mittal A, Samra JS, Smith R, Hugh TJ. Patient- derived xenograft models of colorectal cancer in pre clinical research: a systematic review. Oncotarget 2016;7:66212–66225.ArticlePubMedPMC
  • 117. Lai Y, Wei X, Lin S, Qin L, Cheng L, Li P. Current status and perspectives of patient-derived xenograft models in cancer research. J Hematol Oncol 2017;10:106.ArticlePubMedPMCPDF
  • 118. De Angelis ML, Francescangeli F, Nicolazzo C, et al. An orthotopic patient-derived xenograft (PDX) model allows the analysis of metastasis-associated features in colorectal cancer. Front Oncol 2022;12:869485.PubMedPMC
  • 119. Morton JJ, Bird G, Refaeli Y, Jimeno A. Humanized mouse xenograft models: narrowing the tumor-microenvironment gap. Cancer Res 2016;76:6153–6158.ArticlePubMedPMCPDF
  • 120. Leystra AA, Clapper ML. Gut microbiota influences experimental outcomes in mouse models of colorectal cancer. Genes (Basel) 2019;10:900.ArticlePubMedPMC
  • 121. Yin L, Wang XJ, Chen DX, Liu XN, Wang XJ. Humanized mouse model: a review on preclinical applications for cancer immunotherapy. Am J Cancer Res 2020;10:4568–4584.PubMedPMC
  • 122. Park CK, Khalil M, Pham NA, et al. Humanized mouse models for immuno-oncology research: a review and implications in lung cancer research. JTO Clin Res Rep 2025;6:100781.ArticlePubMed
  • 123. Yang Y, Li J, Li D, Zhou W, Yan F, Wang W. Humanized mouse models: a valuable platform for preclinical evaluation of human cancer. Biotechnol Bioeng 2024;121:835–852.ArticlePubMed
  • 124. Sansom OJ, Meniel V, Wilkins JA, et al. Loss of Apc allows phenotypic manifestation of the transforming properties of an endogenous K-ras oncogene in vivo. Proc Natl Acad Sci U S A 2006;103:14122–14127.ArticlePubMedPMC
  • 125. Tetteh PW, Kretzschmar K, Begthel H, et al. Generation of an inducible colon-specific Cre enzyme mouse line for colon cancer research. Proc Natl Acad Sci U S A 2016;113:11859–11864.ArticlePubMedPMC
  • 126. Washington MK, Powell AE, Sullivan R, et al. Pathology of rodent models of intestinal cancer: progress report and recommendations. Gastroenterology 2013;144:705–717.ArticlePubMedPMC
  • 127. Byun AJ, Hung KE, Fleet JC, et al. Colon-specific tumorigenesis in mice driven by Cre-mediated inactivation of Apc and activation of mutant Kras. Cancer Lett 2014;347:191–195.ArticlePubMedPMC
  • 128. Muñoz NM, Upton M, Rojas A, et al. Transforming growth factor beta receptor type II inactivation induces the malignant transformation of intestinal neoplasms initiated by Apc mutation. Cancer Res 2006;66:9837–9844.PubMed
  • 129. Tomkovich S, Yang Y, Winglee K, et al. Locoregional effects of microbiota in a preclinical model of colon carcinogenesis. Cancer Res 2017;77:2620–2632.ArticlePubMedPMCPDF
  • 130. Mai V, Colbert LH, Berrigan D, et al. Calorie restriction and diet composition modulate spontaneous intestinal tumorigenesis in Apc(Min) mice through different mechanisms. Cancer Res 2003;63:1752–1755.PubMed
  • 131. Li L, Li X, Zhong W, et al. Gut microbiota from colorectal cancer patients enhances the progression of intestinal adenoma in Apcmin/+ mice. EBioMedicine 2019;48:301–315.ArticlePubMedPMC
  • 132. Paoni NF, Feldman MW, Gutierrez LS, Ploplis VA, Castellino FJ. Transcriptional profiling of the transition from normal intestinal epithelia to adenomas and carcinomas in the APCMin/+ mouse. Physiol Genomics 2003;15:228–235.ArticlePubMed
  • 133. Fumagalli A, Suijkerbuijk SJE, Begthel H, et al. A surgical orthotopic organoid transplantation approach in mice to visualize and study colorectal cancer progression. Nat Protoc 2018;13:235–247.ArticlePubMedPDF
  • 134. Tian Y, Wang X, Cramer Z, et al. APC and P53 mutations synergise to create a therapeutic vulnerability to NOTUM inhibition in advanced colorectal cancer. Gut 2023;72:2294–2306.ArticlePubMedPMC
  • 135. Tauriello DV, Palomo-Ponce S, Stork D, et al. TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis. Nature 2018;554:538–543.ArticlePubMedPDF
  • 136. Matano M, Date S, Shimokawa M, et al. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nat Med 2015;21:256–262.ArticlePubMedPDF
  • 137. Li H, Dai W, Xia X, et al. Modeling tumor development and metastasis using paired organoids derived from patients with colorectal cancer liver metastases. J Hematol Oncol 2020;13:119.ArticlePubMedPMCPDF
  • 138. Smith JJ, Deane NG, Wu F, et al. Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer. Gastroenterology 2010;138:958–968.ArticlePubMed
  • 139. Limani P, Borgeaud N, Linecker M, et al. Selective portal vein injection for the design of syngeneic models of liver malignancy. Am J Physiol Gastrointest Liver Physiol 2016;310:G682–G688.ArticlePubMed
  • 140. Greenlee JD, King MR. A syngeneic MC38 orthotopic mouse model of colorectal cancer metastasis. Biol Methods Protoc 2022;7–bpac024.ArticlePDF
  • 141. Giannou AD, Kempski J, Shiri AM, et al. Tissue resident iNKT17 cells facilitate cancer cell extravasation in liver metastasis via interleukin-22. Immunity 2023;56:125–142.e12.ArticlePubMedPMC
  • 142. Flatmark K, Maelandsmo GM, Martinsen M, Rasmussen H, Fodstad Ø. Twelve colorectal cancer cell lines exhibit highly variable growth and metastatic capacities in an orthotopic model in nude mice. Eur J Cancer 2004;40:1593–1598.ArticlePubMed
  • 143. Céspedes MV, Espina C, García-Cabezas MA, et al. Orthotopic microinjection of human colon cancer cells in nude mice induces tumor foci in all clinically relevant metastatic sites. Am J Pathol 2007;170:1077–1085.ArticlePubMedPMC
  • 144. Donigan M, Loh BD, Norcross LS, et al. A metastatic colon cancer model using nonoperative transanal rectal injection. Surg Endosc 2010;24:642–647.ArticlePubMedPDF
  • 145. Thalheimer A, Korb D, Bönicke L, et al. Noninvasive visualization of tumor growth in a human colorectal liver metastases xenograft model using bioluminescence in vivo imaging. J Surg Res 2013;185:143–151.ArticlePubMed
  • 146. Garofalo A, Chirivi RG, Scanziani E, Mayo JG, Vecchi A, Giavazzi R. Comparative study on the metastatic behavior of human tumors in nude, beige/nude/xid and severe combined immunodeficient mice. Invasion Metastasis 1993;13:82–91.PubMed
  • 147. Thalheimer A, Otto C, Bueter M, et al. The intraportal injection model: a practical animal model for hepatic metastases and tumor cell dissemination in human colon cancer. BMC Cancer 2009;9:29.ArticlePubMedPMCPDF
  • 148. Hinoi T, Akyol A, Theisen BK, et al. Mouse model of colonic adenoma-carcinoma progression based on somatic Apc inactivation. Cancer Res 2007;67:9721–9730.ArticlePubMedPDF
  • 149. Seoane J, Gomis RR. TGF-β family signaling in tumor suppression and cancer progression. Cold Spring Harb Perspect Biol 2017;9:a022277.ArticlePubMedPMC
  • 150. Zhang B, Halder SK, Kashikar ND, et al. Antimetastatic role of Smad4 signaling in colorectal cancer. Gastroenterology 2010;138:969–980.e1-e3.ArticlePubMed
  • 151. Lenos KJ, Bach S, Ferreira Moreno L, et al. Molecular characterization of colorectal cancer related peritoneal metastatic disease. Nat Commun 2022;13:4443.PubMedPMC
  • 152. Ai X, Wu Y, Zhang W, et al. Targeting the ERK pathway reduces liver metastasis of Smad4-inactivated colorectal cancer. Cancer Biol Ther 2013;14:1059–1067.ArticlePubMedPMC
  • 153. Sakai E, Nakayama M, Oshima H, et al. Combined mutation of Apc, Kras, and Tgfbr2 effectively drives metastasis of intestinal cancer. Cancer Res 2018;78:1334–1346.ArticlePubMed
  • 154. Tentler JJ, Tan AC, Weekes CD, et al. Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol 2012;9:338–350.ArticlePubMedPMCPDF
  • 155. Pocard M, Tsukui H, Salmon RJ, Dutrillaux B, Poupon MF. Efficiency of orthotopic xenograft models for human colon cancers. In Vivo 1996;10:463–469.PubMed
  • 156. Jang J, Seo Y, Ko KP, Zhang J, Jun S, Park JI. Comparative single-cell transcriptomics of orthotopic and subcutaneous gastric tumors reveal immune and stromal heterogeneity [Preprint]. Posted 2025;Oct 06 https://doi.org/10.1101/2025.10.03.680400.Article
  • 157. Tseng W, Leong X, Engleman E. Orthotopic mouse model of colorectal cancer. J Vis Exp 2007;(10): 484.ArticlePubMedPMC
  • 158. Seguin J, Doan BT, Latorre Ossa H, et al. Evaluation of nonradiative clinical imaging techniques for the longitudinal assessment of tumour growth in murine CT26 colon carcinoma. Int J Mol Imaging 2013;2013:983534.ArticlePubMedPMCPDF
  • 159. Jian M, Ren L, He G, et al. A novel patient-derived organoids-based xenografts model for preclinical drug response testing in patients with colorectal liver metastases. J Transl Med 2020;18:234.ArticlePubMedPMCPDF
  • 160. Fu XY, Besterman JM, Monosov A, Hoffman RM. Models of human metastatic colon cancer in nude mice orthotopically constructed by using histologically intact patient specimens. Proc Natl Acad Sci U S A 1991;88:9345–9349.ArticlePubMedPMC
  • 161. Rajput A, Agarwal E, Leiphrakpam P, Brattain MG, Chowdhury S. Establishment and validation of an orthotopic metastatic mouse model of colorectal cancer. ISRN Hepatol 2013;2013:206875.ArticlePubMedPMCPDF
  • 162. Kubota T. Metastatic models of human cancer xenografted in the nude mouse: the importance of orthotopic transplantation. J Cell Biochem 1994;56:4–8.ArticlePubMed
  • 163. Lücke J, Mercanoglu B, Zhang T, et al. Mouse models of spontaneous liver and lung metastasis for colorectal cancer. STAR Protoc 2024;5:102811.ArticlePubMedPMC
  • 164. Donigan M, Norcross LS, Aversa J, et al. Novel murine model for colon cancer: non-operative trans-anal rectal injection. J Surg Res 2009;154:299–303.ArticlePubMed
  • 165. Nascimento-Gonçalves E, Mendes BAL, Silva-Reis R, Faustino-Rocha AI, Gama A, Oliveira PA. Animal models of colorectal cancer: from spontaneous to genetically engineered models and their applications. Vet Sci 2021;8:59.ArticlePubMedPMC
  • 166. Zitvogel L, Pitt JM, Daillère R, Smyth MJ, Kroemer G. Mouse models in oncoimmunology. Nat Rev Cancer 2016;16:759–773.ArticlePubMedPDF
  • 167. Carlini MJ, De Lorenzo MS, Puricelli L. Cross-talk between tumor cells and the microenvironment at the metastatic niche. Curr Pharm Biotechnol 2011;12:1900–1908.ArticlePubMed
  • 168. Kasashima H, Duran A, Cid-Diaz T, et al. Mouse model of colorectal cancer: orthotopic co-implantation of tumor and stroma cells in cecum and rectum. STAR Protoc 2021;2:100297.ArticlePubMedPMC
  • 169. Dubey P, Su H, Adonai N, et al. Quantitative imaging of the T cell antitumor response by positron-emission tomography. Proc Natl Acad Sci U S A 2003;100:1232–1237.ArticlePubMedPMC
  • 170. Lewis JS, Achilefu S, Garbow JR, Laforest R, Welch MJ. Small animal imaging: current technology and perspectives for oncological imaging. Eur J Cancer 2002;38:2173–2188.PubMed
  • 171. Rapic S, Vangestel C, Verhaeghe J, et al. Characterization of an orthotopic colorectal cancer mouse model and its feasibility for accurate quantification in positron emission tomography. Mol Imaging Biol 2017;19:762–771.ArticlePubMedPDF
  • 172. Ding Q, Chang CJ, Xie X, et al. APOBEC3G promotes liver metastasis in an orthotopic mouse model of colorectal cancer and predicts human hepatic metastasis. J Clin Invest 2011;121:4526–4536.ArticlePubMedPMC
  • 173. Chae YJ, Kim J, Heo H, et al. Magnetic resonance colonography enables the efficacy assessment of immune checkpoint inhibitors in an orthotopic colorectal cancer mouse model. Transl Oncol 2019;12:1264–1270.ArticlePubMedPMC
  • 174. Close DM, Xu T, Sayler GS, Ripp S. In vivo bioluminescent imaging (BLI): noninvasive visualization and interrogation of biological processes in living animals. Sensors (Basel) 2011;11:180–206.ArticlePubMed
  • 175. Khanna C, Hunter K. Modeling metastasis in vivo. Carcinogenesis 2005;26:513–523.ArticlePubMed
  • 176. Weissleder R, Pittet MJ. Imaging in the era of molecular oncology. Nature 2008;452:580–589.ArticlePubMedPMCPDF
  • 177. Kircher MF, Hricak H, Larson SM. Molecular imaging for personalized cancer care. Mol Oncol 2012;6:182–195.ArticlePubMedPMCPDF
  • 178. Oshima G, Stack ME, Wightman SC, et al. Advanced animal model of colorectal metastasis in liver: imaging techniques and properties of metastatic clones. J Vis Exp 2016;(117): 54657.ArticlePubMedPMC
  • 179. Goddard ET, Fischer J, Schedin P. A portal vein injection model to study liver metastasis of breast cancer. J Vis Exp 2016;(118): 54903.ArticlePubMedPMC
  • 180. Stribbling SM, Beach C, Ryan AJ. Orthotopic and metastatic tumour models in preclinical cancer research. Pharmacol Ther 2024;257:108631.ArticlePubMedPMC
  • 181. Che LH, Liu JW, Huo JP, et al. A single-cell atlas of liver metastases of colorectal cancer reveals reprogramming of the tumor microenvironment in response to preoperative chemotherapy. Cell Discov 2021;7:80.ArticlePubMedPMCPDF
  • 182. Wang F, Long J, Li L, et al. Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer. Sci Adv 2023;9:eadf5464.ArticlePubMedPMC
  • 183. Yang S, Zhang D, Sun Q, et al. Single-cell and spatial transcriptome profiling identifies the transcription factor BHLHE40 as a driver of EMT in metastatic colorectal cancer. Cancer Res 2024;84:2202–2217.ArticlePubMedPDF
  • 184. Wu Y, Yang S, Ma J, et al. Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov 2022;12:134–153.ArticlePubMedPDF
  • 185. Lu Y, Chen Y, Wang Z, et al. Single-cell and spatial transcriptome profiling reveal CTHRC1+ fibroblasts promote EMT through WNT5A signaling in colorectal cancer. J Transl Med 2025;23:282.ArticlePubMedPMCPDF
  • 186. Hu S, Qin J, Gao R, et al. Integrated analysis of single cell and bulk RNA sequencing identifies CTHRC1+ INHBA+ CAF as drivers of colorectal cancer progression. Mol Carcinog 2023;62:1787–1802.ArticlePubMed
  • 187. Peng Z, Ye M, Ding H, Feng Z, Hu K. Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer. J Transl Med 2022;20:302.ArticlePubMedPMCPDF
  • 188. Jiang Y, Long G, Huang X, Wang W, Cheng B, Pan W. Single-cell transcriptomic analysis reveals dynamic changes in the liver microenvironment during colorectal cancer metastatic progression. J Transl Med 2025;23:336.ArticlePubMedPMCPDF
  • 189. Li Y, Wang H, Mao D, Che X, Chen Y, Liu Y. Understanding pre-metastatic niche formation: implications for colorectal cancer liver metastasis. J Transl Med 2025;23:340.ArticlePubMedPMCPDF
  • 190. Denisenko E, Guo BB, Jones M, et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol 2020;21:130.ArticlePubMedPMCPDF
  • 191. Schroeder A, Loth ML, Luo C, et al. Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE. Nat Methods 2025;22:1911–1922.ArticlePubMedPMCPDF
  • 192. Andreatta M, Hérault L, Gueguen P, Gfeller D, Berenstein AJ, Carmona SJ. Semi-supervised integration of single-cell transcriptomics data. Nat Commun 2024;15:872.ArticlePubMedPMCPDF
  • 193. Ma YS, Huang T, Zhong XM, et al. Proteogenomic characterization and comprehensive integrative genomic analysis of human colorectal cancer liver metastasis. Mol Cancer 2018;17:139.ArticlePubMedPMCPDF
  • 194. Tanaka A, Ogawa M, Zhou Y, et al. Proteogenomic characterization of primary colorectal cancer and metastatic progression identifies proteome-based subtypes and signatures. Cell Rep 2024;43:113810.ArticlePubMedPMC
  • 195. Dijkstra JJ, Neikes HK, Rezaeifard S, et al. Multiomics of colorectal cancer organoids reveals putative mediators of cancer progression resulting from SMAD4 inactivation. J Proteome Res 2023;22:138–151.PubMed
  • 196. Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods 2019;16:1289–1296.ArticlePubMedPMCPDF
  • 197. Argelaguet R, Arnol D, Bredikhin D, et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol 2020;21:111.ArticlePubMedPMCPDF
  • 198. Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell 2021;184:3573–3587.e29.ArticlePubMedPMC
  • 199. Liu Y, Gao F, Cheng Y, Qi L, Yu H. Applications and advances of multi-omics technologies in gastrointestinal tumors. Front Med (Lausanne) 2025;12:1630788.ArticlePubMedPMC
  • 200. Kirsher DY, Chand S, Phong A, Nguyen B, Szoke BG, Ahadi S. Current landscape of plasma proteomics from technical innovations to biological insights and biomarker discovery. Commun Chem 2025;8:279.ArticlePubMedPMCPDF
  • 201. Toyota N, Konno R, Iwata S, et al. Identification of cancer-associated proteins in colorectal cancer using mass spectrometry. Proteomes 2025;13:38.ArticlePubMedPMC
  • 202. Roberts LD, Souza AL, Gerszten RE, Clish CB. Targeted metabolomics. Curr Protoc Mol Biol 2012;98:30.2.1–20.2.24.ArticlePDF
  • 203. Hu Z, Ding J, Ma Z, et al. Quantitative evidence for early metastatic seeding in colorectal cancer. Nat Genet 2019;51:1113–1122.ArticlePubMedPMCPDF
  • 204. Dang HX, Krasnick BA, White BS, et al. The clonal evolution of metastatic colorectal cancer. Sci Adv 2020;6:eaay9691.ArticlePubMedPMC
  • 205. West J, Robertson-Tessi M, Anderson AR. Agent-based methods facilitate integrative science in cancer. Trends Cell Biol 2023;33:300–311.ArticlePubMed
  • 206. Jia D, Li X, Bocci F, et al. Quantifying cancer epithelial-mesenchymal plasticity and its association with stemness and immune response. J Clin Med 2019;8:725.ArticlePubMedPMC
  • 207. Sommariva S, Berra S, Biddau G, Caviglia G, Benvenuto F, Piana M. In-silico modelling of the mitogen-activated protein kinase (MAPK) pathway in colorectal cancer: mutations and targeted therapy. Front Syst Biol 2023;3:1207898.ArticlePubMedPMC
  • 208. Li B, Xiao M, Zeng R, Zhang L. Developing a multiomics databased mathematical model to predict colorectal cancer recurrence and metastasis. BMC Med Inform Decis Mak 2025;25(Suppl 2): 188.ArticlePubMedPMCPDF
  • 209. Anaya DA, Dogra P, Wang Z, et al. A mathematical model to estimate chemotherapy concentration at the tumor-site and predict therapy response in colorectal cancer patients with liver metastases. Cancers (Basel) 2021;13:444.ArticlePubMedPMC
  • 210. Trebo M, Maurer T, Krendl FJ, et al. Ex vivo modelling of human colorectal cancer liver metastasis by normothermic machine perfusion. Mol Cancer 2025;24:264.ArticlePubMedPMCPDF

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      Biology and preclinical models of colorectal cancer metastasis
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      Fig. 1. Overview of preclinical platforms and integrative technologies to study metastatic colorectal cancer. Patient-derived tumors and genetically engineered mouse models can be used to generate tumors, cell lines, and organoids. These materials are evaluated using transplantation-based in vivo models, including subcutaneous implantation, orthotopic models (cecum/rectum), and intrasplenic/portal vein injection, as well as microphysiological systems such as organ-on-chip. Across these platforms, advanced analytical approaches—singlecell transcriptomics, genomics, spatial transcriptomics, and computational modeling—enable integrated characterization of metastatic progression and the tumor microenvironment. Figure created with BioRender.com (https://BioRender.com/4r040ez).

      Fig. 1.

      Biology and preclinical models of colorectal cancer metastasis
      Model type Representative system Metastatic route / target Key features Reference
      GEMMs ApcMin/+ No metastasis reported Classic intestinal tumor model; lacks invasive phenotype [35, 132]
      Villin-CreERT2; Apcfl/fl; KrasLSLG12D No distant metastasis observed Generates multiple intestinal adenomas; noninvasive phenotype [124]
      Cdx2-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/fl Invasive phenotype without distant metastasis Colon-specific genetic recombination that reproduces invasive adenocarcinoma with desmoplastic stroma [88]
      Villin-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/fl Liver metastasis observed (macroscopic) Highly invasive adenocarcinomas with confirmed liver metastases [39]
      Fabp1-Cre;Apcfl/fl; KrasLSLG12D; Tgfbr2fl/fl Liver metastases detected in subset (10%–20%) TGF-β–signaling-loss-driven invasion and desmoplasia [128]
      Orthotopic transplantation of genetically engineered murine organoids Cdx2-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/fl-derived tumor organoids Ex vivo organoid culture and orthotopic colonic injection; no distant metastasis observed; localized invasive growth in colon wall Organoids derived from GEMM generate colon-restricted invasive adenocarcinomas upon orthotopic transplantation; faithfully mimic human CRC architecture and desmoplastic stroma but lack metastatic spread [88]
      Villin-CreERT2; Apcfl/fl; KrasLSLG12D; Trp53fl/R172H-derived tumor organoids Orthotopic transplantation into cecum wall; occasional metastasis to liver and lung [87, 133]
      Apc-/-; KrasLSLG12D/+; Trp53-/-; Smad4-/- Orthotopic transplantation of genetically engineered intestinal or colonic organoids into cecum/rectum; metastasis to liver and lung Reproducible macroscopic metastases; recapitulates adenoma-carcinoma-metastasis sequence [87, 89, 134]
      Apcfl/fl; KrasLSLG12D; Tgfbr2fl/fl; Trp53fl/fl GEMM-derived organoids Orthotopic cecal or splenic injection of in vitro Ad-Cre–recombined tumor organoids; portal dissemination to liver (occasional lung lesions) TGF-β-signaling loss drives invasive adenocarcinoma and reproducible liver metastasis [135]
      Orthotopic transplantation of PDOs Human CRC PDOs Subcutaneous and orthotopic into cecal or rectal wall. No distant metastasis reported Human PDOs maintain histological and genetic fidelity to the parental tumor; first demonstration of in vivo tumorigenicity of human CRC organoids [136]
      Orthotopic portal vein injection; metastasis to liver Human PDOs reproducibly form hepatic metastatic nodules following portal vein injection; faithfully mimic desmoplastic and fibroblast-rich stroma observed in clinical CRC liver metastases [90]
      Human CRC primary- and metastatic-derived PDOs Subcutaneous and orthotopic into cecal or rectal wall; occasional metastasis to liver Occasional liver metastases observed only in mice transplanted with metastatic-origin PDOs; none in primary PDO group [137]
      Non-orthotopic transplantation of murine cancer cell lines CT26 (BALB/c) Tail vein injection; metastasis to lung Formation of lung metastases within approximately 2 weeks after injection [138]
      Intrasplenic injection or intraportal; metastasis to liver Mimics hematogenous spread via portal circulation; widely used for hepatic metastasis evaluation [34, 139]
      MC38 (C57BL/6) Intrasplenic or intraportal vein injection; metastasis to liver and occasionally to lung Highly reproducible hepatic metastasis via portal circulation; mimics hematogenous spread under immunocompetent background. Preferred routes for MC38 due to low orthotopic engraftment efficiency [34, 139-141]
      Non-orthotopic transplantation of human CRC cell lines KM20L2, HCT116, HCT15, SW480, SW620, Colo320DM Orthotopic cecal injection; occasional metastasis to liver and lymph nodes SW620: 20% liver metastasis; common nodal metastasis except for SW480, Colo320DM [142]
      Co115 Tumor take rate 90%; metastasis to nodal and occasionally to liver [142]
      HCC2998 Tumor take rate 88%; metastasis to nodal and rarely to liver [142]
      HT29 Tumor take rate 69%; metastasis to nodal and rarely to liver [142]
      CaCo2, WiDr, Co205 Tumor take rate 40%; very low metastasis [142]
      HCT116 Orthotopic cecal submucosa (micropipette injection) Tumor take rate 75%; metastasis to 100% nodal, 67% liver, and 50% lung [143]
      Rectal wall (rectal injection) Tumor take rate 65%; rare metastasis (3.3%) [144]
      Intraportal injection 90% developed liver metastasis (the highest hepatic take rate) within 30 days [145]
      HT29 Intrasplenic injection (metastasis to liver) 78% developed macroscopic liver metastasis within 6 weeks [146]
      SW620 Intrasplenic injection (metastasis to liver) Approximately 80% liver metastasis within 4–6 weeks [147]
      Intraportal injection (metastasis to liver) 100% liver metastasis in all injected mice (dose-dependent tumor load) [38]
      Table 1. Representative Preclinical Models for CRC Metastasis

      Tumor take rate (%)=number of animals developing tumors/total number of animals inoculated/transplanted.

      CRC, colorectal cancer; GEMMs, genetically engineered mouse models; Min, multiple intestinal neoplasia; Cre, Cre recombinase; CreERT2, Cre recombinase fused with estrogen receptor (ER) conditionally activated by tamoxifen (T2); fl, floxed (flanked by loxP sites, conditionally deleted by Cre recombinase); LSL, a loxP-stop-loxP cassette conditionally removed by Cre recombinase for subsequent expression of gene(s); TGF-β, transforming growth factor β; PDOs, patient-derived organoids.

      Table 1.

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