Colorectal cancer stands as the third most prevalent malignancy worldwide and remains a formidable cause of cancer-related mortality despite significant advances in therapeutic interventions. This dismal clinical reality is largely attributed to the disease’s remarkable genetic diversity and capacity for adaptive evolution, which consistently undermine the durability of current treatment regimens. Established preclinical platforms, such as immortalized cell lines or genetically engineered mouse models, frequently fall short in recapitulating the intricate tumor-stroma interactions and the clonal complexity inherent to patient tumors. PDX models effectively bridge this gap by maintaining key genetic, histologic, and molecular hallmarks of the primary tumors, providing a robust platform for translational cancer research.

The creation and validation of colorectal cancer PDX models involve a meticulous process beginning with the procurement of viable tumor tissue during surgical resections or biopsies. This tissue is promptly engrafted into immunodeficient mice, typically strains lacking functional T, B, and natural killer cells, which ensures successful tumor take and growth without immune rejection. Subsequent tumor propagation in these hosts mirrors human disease progression, allowing longitudinal studies that unveil the mechanisms governing tumor growth, metastasis, and treatment response. By retaining the tumor microenvironment components, including cancer-associated fibroblasts and extracellular matrix elements, PDX models provide an invaluable microcosm for preclinical evaluation.

One of the most impactful applications of colorectal cancer PDX models lies in drug efficacy testing and therapeutic development. High-throughput drug screening conducted on these models enables correlation of distinct genetic and epigenetic tumor profiles with treatment outcomes, furnishing predictive biomarkers that can guide clinical decision-making. This genotype-phenotype linkage accelerates the identification of patient subgroups likely to benefit from particular drugs, thereby enhancing the precision medicine paradigm. Furthermore, PDX models facilitate the exploration of novel drug combinations, dose optimization, and resistance mechanisms, providing a rigorous preclinical assessment that better forecasts clinical responses.

Drug resistance remains a critical challenge in managing colorectal cancer patients, often leading to relapse and poor prognosis. PDX models are instrumental in elucidating the molecular pathways that underpin resistance to standard chemotherapies, targeted agents, and emerging immunotherapies. Through serial transplantation and drug adaptation studies, researchers can dissect the evolutionary trajectories that cancer cells undertake under therapeutic pressure. These insights have led to the identification of actionable genetic alterations, signaling cascades, and phenotypic plasticity phenomena that contribute to treatment failure, ultimately guiding the development of next-generation inhibitors designed to overcome resistance.

Despite their transformative potential, the establishment and maintenance of PDX models are not without significant hurdles. The process is inherently resource-intensive, requiring careful selection of high-quality tumor specimens and sophisticated technical expertise for successful engraftment. Tumor latency periods may vary, with some samples exhibiting slow or failed growth kinetics. Moreover, genetic drift and clonal selection can occur over successive passages in mice, potentially diverging from the original tumor’s molecular landscape and complicating longitudinal studies. Researchers must therefore implement stringent quality controls and molecular fidelity assessments to preserve model integrity.

Recent advancements in humanized mouse models have begun to address some limitations inherent to conventional PDX platforms. By reconstituting human immune components within these mice, it is now possible to study complex interactions between colorectal tumors and the immune system, which are crucial for exploring immunotherapy efficacy and tumor immune evasion strategies. This innovation enhances the translational relevance of PDX models, particularly in the context of checkpoint inhibitors, adoptive cell transfer therapies, and vaccine development, where immune competence is paramount.

The integration of PDX models into co-clinical trials represents an exciting frontier in colorectal cancer research. These translational studies involve parallel testing of therapeutic agents in both patients and their corresponding PDX models, enabling real-time evaluation of drug responses and resistance development. This approach provides an invaluable feedback loop between bench and bedside, accelerating biomarker validation and facilitating dynamic treatment adaptation tailored to individual patient tumors. The ability to capture tumor evolution under therapeutic selection in vivo enhances clinical trial design and ultimately improves patient outcomes.

From a molecular perspective, colorectal cancer PDX models have illuminated key oncogenic drivers and signaling networks integral to tumor progression, such as aberrations in the Wnt/β-catenin pathway, EGFR signaling, and mismatch repair deficiencies. These insights support biomarker-driven stratification and empower the testing of novel molecularly targeted agents. Moreover, PDX systems facilitate exploration of tumor-stroma crosstalk, angiogenesis, and metabolic reprogramming within the tumor niche, fostering a comprehensive understanding of cancer biology that transcends isolated cellular studies.

As CRC PDX models continue to mature, advances in omics technologies such as single-cell sequencing, proteomics, and spatial transcriptomics are being integrated to dissect tumor heterogeneity at unparalleled resolution. These multidimensional datasets enrich the interpretative power of PDX studies, enabling researchers to track clonal evolution, identify rare subpopulations with aggressive phenotypes, and map niche-specific microenvironmental influences. This synergy between PDX modeling and cutting-edge molecular profiling heralds a new epoch in cancer research with profound implications for diagnostics and therapy.

Despite the undeniable promise of PDX models, ethical considerations and logistical constraints necessitate judicious application and continued refinement. The use of immunodeficient animals demands strict adherence to welfare standards and the search for alternative in vitro systems remains important. Nonetheless, the unique biological insights offered by PDX models firmly establish them as indispensable tools in the fight against colorectal cancer, driving innovation across translational research pipelines.

In sum, colorectal cancer PDX models are reshaping the landscape of cancer biology and treatment. By faithfully capturing the complexity of human tumors within a living system, these models enable precision oncology efforts that strive to overcome therapeutic resistance and improve patient prognosis. Their evolving integration with humanized immune platforms and co-clinical trial designs promises to accelerate the translation of laboratory discoveries into effective, individualized therapies. As the scientific community continues to harness the power of PDX models, a new horizon emerges—one where colorectal cancer is not only better understood but more effectively conquered.

Subject of Research: Colorectal cancer patient-derived xenograft mouse models in translational cancer research

Article Title: Advancing cancer research: Cutting-edge insights from colorectal cancer patient-derived xenograft mouse models

Web References: DOI link

References:
Yalan Lu, Xiaokang Lei, Yanfeng Xu, Yanhong Li, Ruolin Wang, Siyuan Wang, Aiwen Wu, Chuan Qin, “Advancing cancer research: Cutting-edge insights from colorectal cancer patient-derived xenograft mouse models,” Genes & Diseases, Volume 13, Issue 1, 2026, 101634.

Image Credits: Genes & Diseases

Keywords: colorectal cancer, patient-derived xenograft, PDX models, immunodeficient mice, tumor microenvironment, drug resistance, precision medicine, co-clinical trials, humanized mouse models, tumor heterogeneity, molecular profiling, cancer biology

A groundbreaking study led by the Berman Lab at the CHEO Research Institute and the University of Ottawa, in close collaboration with national precision oncology networks across Canada and Australia, has demonstrated the power of pre-clinical zebrafish models in real-time clinical decision-making. This research marks a pivotal step forward by establishing that larval zebrafish patient-derived xenograft (PDX) models can reliably replicate and predict drug responses observed in actual pediatric cancer patients. Unlike traditional mouse models, which have dominated the pre-clinical landscape, zebrafish offer unparalleled speed, cost-effectiveness, and sensitivity that could revolutionize precision pediatric oncology.

Dr. Jason Berman, pediatric oncologist and CEO of CHEO Research Institute, underscores the personal impact of this innovation. “When delivering difficult news to families, having the ability to offer hope based on a concrete understanding of how a child might respond to treatment is invaluable,” he explains. Zebrafish offer that window into personalized therapy, illuminating effective drug regimens well ahead of conventional models. Their unique biology—small size, rapid development, and optical transparency—enables researchers to graft human tumor tissues and observe therapeutic effects in a live organism within days, vastly accelerating the treatment selection process.

In terms of technical specifics, the larval zebrafish PDX approach involves transplanting tumor cells derived from pediatric patients directly into transparent larvae. These xenografts allow for direct observation of tumor drug responses in real-time, facilitating precise evaluation of efficacy and resistance patterns. This approach is particularly advantageous in pediatric oncology, where sample sizes from biopsies are limited, and treatment windows are narrow. Zebrafish models require only minute quantities of tumor tissue, a remarkable advantage over mouse models that often demand larger samples and longer engraftment periods.

The study published in Cancer Research Communications represents the first direct comparison between zebrafish PDX models, traditional mouse PDX models, and actual patient clinical outcomes. By retrospectively analyzing samples from ten children enrolled in the Zero Childhood Cancer program in Australia, researchers were able to assess the fidelity of zebrafish drug response patterns against the backdrop of real-world therapeutic results. Remarkably, the zebrafish PDX models predicted responses accurately in 11 out of 12 treatment regimens, surpassing mouse models in terms of speed and, in several cases, feasibility.

Significantly, for three of the high-risk patients whose tumor tissues failed to establish viable mouse PDX models, zebrafish larvae successfully generated robust drug response data. This finding highlights the zebrafish model’s superior adaptability and its potential to fill critical gaps in pediatric cancer research, especially for aggressive cancers where time-sensitive treatment decisions are paramount. By delivering reliable predictions in a fraction of the time, zebrafish models could effectively serve as frontline bioassays guiding personalized therapies in clinical settings.

The implications of this study stretch beyond model validation, touching on the broader paradigm of precision medicine for childhood cancers. Dr. David Malkin, co-chair of ACCESS and senior staff oncologist at SickKids, elaborates on this bridge between bench and bedside. “Precision tumor modeling with zebrafish is not merely an experimental tool; it’s a transformative clinical instrument that ensures children receive not just care, but the right care, tuned finely to their cancer’s unique biology.” Such advances are crucial because even with extensive genomic sequencing, many pediatric cancers remain without identifiable druggable mutations, leaving clinicians with few targeted treatment strategies.

Technically and ethically, zebrafish offer additional advantages that augment their value in preclinical oncology. Their rapid breeding cycles and transparent embryos permit high-throughput drug screening while minimizing ethical concerns associated with mammalian testing. The external development of embryos allows continuous real-time visualization without invasive procedures, providing unparalleled access to tumor microenvironment dynamics and drug interactions within the living organism. This system empowers researchers to iterate therapeutic testing quickly and identify promising drug candidates or combinations before advancing to more resource-intensive mammalian models or clinical trials.

Another key dimension of this research is its alignment with international collaborative networks such as Canada’s PROFYLE and Australia’s ZERO programs. These networks emphasize molecular profiling and precision medicine tailored to children and young adults with cancer, leveraging multi-institutional expertise and data-sharing. The integration of zebrafish PDX modeling with extensive genomic analyses promises a holistic approach, combining molecular insights with functional testing to optimize treatment plans. This convergence of technologies accelerates personalized therapy pipelines with the overarching goal of improving survival and quality of life for patients facing otherwise grim prognoses.

Importantly, co-senior author Dr. Michelle Haber from the Children’s Cancer Institute in Sydney highlights the clinical utility of zebrafish PDX modeling in cases where molecular profiling alone falls short. She points out that when actionable genomic targets cannot be identified, observing how patient-derived tumors respond dynamically to available drugs in zebrafish becomes a valuable alternative to guide therapeutic decisions. This innovation enhances the traditional precision medicine toolkit, ensuring more children receive hope and tailored care even in challenging diagnostic scenarios.

Beyond treatment selection, this study sets the stage for future prospective use of zebrafish models in clinical oncology. By embedding functional assays within clinical workflows, physicians could potentially receive timely, empirically supported guidance to adjust therapeutic regimens on the fly, responding to tumor evolutions and resistance mechanisms as they arise. The rapid turnaround offered by zebrafish PDX allows for such nimble clinical adaptations, potentially reducing trial-and-error approaches and sparing patients from ineffective treatments and attendant toxicities.

As the landscape of pediatric cancer therapy evolves, the promise of zebrafish models encapsulates a broader shift toward adaptive, precise, and patient-centered oncology. This model system’s success illustrates an elegant marriage of basic science and translational medicine, where organismal biology informs human healthcare. Energetic ongoing collaborations across borders exemplify the commitment to leverage these insights for tangible patient benefit, accelerating not just the pace of research but the very hope entrusted to families confronting pediatric cancer.

In sum, the zebrafish larval PDX model heralds a transformative advance in pediatric cancer precision therapy. Through its rapid, accurate, and scalable drug response profiling, it addresses crucial limitations of existing preclinical models, enabling clinicians to craft personalized therapeutic strategies with greater confidence and speed. The impact of this innovation will ripple through research, clinical protocols, and ultimately patient outcomes—offering a beacon of hope for children with some of the most aggressive and difficult-to-treat cancers.

Subject of Research: Human tissue samples

Article Title: Modeling High-Risk Pediatric Cancers in Zebrafish to Inform Precision Therapy

News Publication Date: 25-Jul-2025

Web References:

• CHEO Research Institute: https://www.cheoresearch.ca/
• ACCESS: https://www.accessforkidscancer.ca/
• PROFYLE: https://www.profyle.ca/
• ZERO: https://www.zerochildhoodcancer.org.au/
• Children’s Cancer Institute: http://ccia.org.au

References:
Azzam, N., Fletcher, J. I., Melong, N., Lau, L. M. S., Dolman, E. M., Mao, J., Tax, G., Cadiz, R., Tuzi, L., Kamili, A., Dumevska, B., Xie, J., Chan, J. A., Senger, D. L., Grover, S. A., Malkin, D., Haber, M., & Berman, J. N. (2025). Modeling High-Risk Pediatric Cancers in Zebrafish to Inform Precision Therapy. Cancer Research Communications, 5(7), 1215–1227. DOI: 10.1158/2767-9764.CRC-25-0080

Image Credits: CHEO

Over the past decade, PDX models have emerged as powerful surrogates for patient tumors, prized for preserving histological and genetic features. Yet, the metabolic landscape—an orchestra of biochemical reactions underpinning tumor growth and survival—has remained less clearly characterized. Metabolism is intimately tied to the cancer phenotype, influencing everything from proliferation to drug resistance. Hence, understanding how metabolic profiles evolve or stabilize during xenotransplantation is vital for ensuring experimental results mirror clinical reality. Rao et al. deliver a comprehensive comparative analysis, marrying metabolomics with transcriptomics, to illuminate this obscure frontier.

Central to the study is the use of matched pairs of patient tumors and respective PDXs, sourced from diverse cancer types. By leveraging mass spectrometry-based metabolite profiling alongside gene expression data, the researchers delineate the metabolic fingerprints of original tumors and their xenografted counterparts. This dual-omics approach enables a multidimensional understanding of metabolic regulation, extending beyond static metabolite measurements to encompass the dynamic control exerted by metabolic genes. The team employs sophisticated bioinformatic pipelines to discern patterns of metabolic conservation and divergence, setting a new standard for rigor in metabolic phenotyping.

One of the pivotal revelations from this work is that while a core subset of metabolic phenotypes remains remarkably conserved between patient tumors and PDX models, significant differences also emerge. Conserved pathways notably include central carbon metabolism aspects such as glycolysis and tricarboxylic acid (TCA) cycle activity, underscoring fundamental energetic programs essential for tumor viability. This conservation validates the continued use of PDX models for studying certain metabolic vulnerabilities—pathways universally co-opted by tumors regardless of microenvironmental context.

Contrastingly, the study reveals divergence in pathways linked to amino acid metabolism, lipid biosynthesis, and redox balance. These variations are hypothesized to stem from the distinct tumor microenvironment in the murine host, which differs drastically from human physiology in factors such as oxygen tension, nutrient availability, and stromal interactions. For example, alterations in cysteine and glutathione metabolism indicate shifts in oxidative stress responses, potentially reflecting adaptive rewiring to the xenograft’s niche. Such metabolic shifts complicate extrapolations from PDX data to the human clinical setting, signaling caution in interpreting results pertaining to metabolic drug targets.

The authors further delineate the influence of tumor intrinsic properties and external factors on metabolic fidelity. Tumors originating from different tissues exhibit variable degrees of metabolic stability post-engraftment, suggesting tissue-specific constraints and plasticity. Moreover, engraftment site and passage number impact metabolic phenotypes, with later PDX passages showing increased divergence likely due to clonal selection and ongoing adaptation. This insight underscores the dynamic nature of metabolic phenotypes and demands thoughtful experimental design when employing PDX models for metabolic investigations.

Intriguingly, although the immune-compromised murine environment simplifies immune-mediated confounders, it simultaneously removes complex human immune-tumor metabolic crosstalk. This absence likely contributes to the metabolic discrepancies observed, particularly in pathways involved in immune modulation and inflammation. Hence, the study raises critical questions about the limitations of existing PDX platforms for immunometabolic research and encourages the development of humanized models that better recapitulate tumor-immune dialogues.

The ramifications of this research extend into therapeutic realms. Metabolic reprogramming is a hallmark of many emerging anticancer strategies, yet if PDX models do not entirely mirror the original tumor’s metabolism, predictions of drug efficacy may be misleading. By identifying specific metabolic pathways that reliably translate between patient and model, the study offers a roadmap for prioritizing targets with higher translational fidelity. Conversely, pathways prone to divergence warrant validation in orthogonal systems before clinical extrapolation.

Technically, Rao et al. push the envelope by integrating high-resolution metabolomics with transcriptomic data in a paired-sample design—a strategy rarely implemented at this scale. Their statistical frameworks correct for batch effects and normalize for inter-sample variability, enhancing confidence in identified differences. This methodological rigor sets a precedent for future metabolic phenotype studies, emphasizing the necessity of multidimensional data integration to unravel complex biological phenomena.

The study also touches upon the potential influence of the host microbiome, an often-overlooked variable in PDX metabolism. While not the central focus, the authors speculate that interactions between murine gut flora and tumor metabolism could subtly shape observed phenotypes. This presents an intriguing extension for future research, as the microbiome’s role in modulating systemic metabolism and therapeutic responses gains broader recognition across oncology disciplines.

Furthermore, the findings invite reevaluation of the widely held dogma that PDX models fully capture patient tumor biology. While invaluable, the recognized metabolic remodeling suggests that PDX models represent a facet, rather than the entirety, of tumor metabolic reality. This reframing encourages complementary use of alternative models such as organoids, genetically engineered mouse models, and ultimately, patient-based clinical studies to triangulate tumor metabolism comprehensively.

Importantly, this work exemplifies the need for metabolic context awareness when interpreting experimental data. Simply put, the tumor ecosystem does not operate in isolation; it engages in continuous, reciprocal interactions with its environment. By highlighting environmental and evolutionary factors influencing metabolic phenotypes post-engraftment, the study underscores that metabolic traits are not immutable identifiers but plastic features subject to selective pressures.

The researchers also emphasize that their findings could influence biomarker discovery pipelines. Metabolites or gene signatures showing stable conservation across patient and PDX contexts represent promising biomarker candidates with higher predictive utility. Conversely, markers with inconsistent presence may reflect experimental artifacts or environmental adaptations, warranting cautious consideration.

Finally, Rao and colleagues’ work paves the way for refining PDX-based therapeutic screening by incorporating metabolic profiling as a standard evaluative layer. Such integrative approaches could enhance the predictive power of preclinical models, accelerating the translation of metabolic-targeted therapies from bench to bedside. It is a compelling call for the cancer research community to broaden their toolkit and adopt more holistic, systems-level assessments.

In sum, this landmark study charts new territory by systematically dissecting metabolic conservation and divergence in patient tumors and matched PDX models. It reveals a nuanced metabolic landscape shaped by both inherent tumor properties and extrinsic environmental factors. These insights provoke a paradigm shift, challenging assumptions about xenograft model fidelity and urging the field towards more sophisticated frameworks that appreciate tumor metabolism’s dynamic and context-dependent nature. As cancer metabolism continues to be a fertile ground for therapeutic innovation, studies like this will be indispensable guides for precision oncology’s future trajectory.

Subject of Research: Metabolic comparison between patient tumors and matched xenograft models.

Article Title: Conservation and divergence of metabolic phenotypes between patient tumours and matched xenografts.

Article References:
Rao, A.D., Cai, L., Snyman, M. et al. Conservation and divergence of metabolic phenotypes between patient tumours and matched xenografts. Nat Metab (2025). https://doi.org/10.1038/s42255-025-01338-2

Image Credits: AI Generated

PDX models originate by engrafting freshly resected human tumor specimens directly into immunodeficient murine hosts. This method preserves the heterogeneity of tumor genetics, the intricate tumor microenvironment, and dynamic drug responsiveness that traditional cell line models fail to capture. Cell lines often lose critical tumor-specific features through prolonged in vitro culture, but PDXs maintain the malignant phenotype in vivo, providing a more faithful and clinically relevant experimental framework. Consequently, PDX models have emerged as an indispensable asset for investigating novel therapeutic strategies prior to clinical trials.

Crucially, PDX models permit the conduct of co-clinical trials, a revolutionary approach where patients receive treatment concurrently with their personalized PDX avatars. This parallel testing enables real-time assessment of therapeutic efficacy, facilitating rapid adaptation of clinical interventions tailored for individual patients. By integrating clinical decision-making with rigorous preclinical validation, this strategy advances the precision medicine vision from concept to clinical application. Notably, PDX models have already yielded critical insights in breast, lung, colorectal, and ovarian cancers, among other malignancies.

Despite their promise, PDX models confront substantial hurdles that have limited their widespread adoption. The complexity of establishing such models demands high costs and extended engraftment periods, requiring access to specialized animal facilities and skilled personnel. Additionally, genetic and epigenetic drift can occur within the murine host, potentially diverging from the evolution observed in patient tumors over time. This discrepancy poses challenges for modeling long-term disease progression and resistance mechanisms, necessitating ongoing efforts to refine system fidelity.

To transcend current limitations, innovative next-generation PDX platforms are under development. Integrating cutting-edge technologies like CRISPR-Cas9 gene editing allows for precise manipulation of tumor genomes within PDXs, enabling in-depth functional studies of oncogenic drivers and resistance pathways. Coupling PDX models with organoid co-cultures offers a hybrid system to examine tumor-stroma interactions and drug responses ex vivo while maintaining physiological relevance. Furthermore, humanized mouse models, equipped with reconstituted human immune systems, provide powerful tools for evaluating immunotherapy responses within the PDX framework.

Biobanking of patient-derived tumors coupled with artificial intelligence-driven analytics is accelerating PDX model utility. High-throughput sequencing and machine learning algorithms facilitate comprehensive characterization of PDX molecular profiles, predicting therapeutic vulnerabilities with unprecedented accuracy. These advances not only expedite drug discovery and validation but also enable stratified medicine approaches that select optimal therapies based on tumor-specific signatures captured by PDX models. Such integration is poised to reshape oncological drug development paradigms fundamentally.

Another advantage of PDX systems lies in their ability to test combination therapies and adaptive dosing regimens in a highly personalized context. By recapitulating patient-specific tumor biology, PDXs allow researchers to dissect mechanistic pathways driving therapeutic synergy or resistance. This capability is invaluable for developing next-generation regimens that circumvent resistance mechanisms and enhance durable responses. The fine-tuned modeling of interpatient variability enhances the translational relevance of PDX-derived data, informing clinical trial design more effectively.

However, ethical considerations and logistical constraints still pose barriers to PDX model scalability. The reliance on immunodeficient rodents warrants careful consideration of welfare and reduction strategies in animal research. Advances in three-dimensional culture systems and in silico modeling may eventually complement or, in part, replace PDX usage, but for now, PDXs remain unparalleled in their predictive power for human oncological applications. Continued investment in infrastructure and collaborative frameworks is essential to democratize access to these powerful models in the research community.

Furthermore, the heterogeneity of tumor microenvironments within PDXs underscores the importance of careful experimental design and interpretation. Infiltrating stromal cells and vasculature components derive from host murine tissue, which can influence tumor behavior and therapeutic responses differently from the native human microenvironment. Addressing this issue through humanization protocols or co-implantation strategies is a fertile area of ongoing research, aiming to recreate a more authentic tumor niche and improve translational validity.

In light of mounting evidence, the role of PDX models as a cornerstone of precision oncology is increasingly apparent. As cancer biology research confronts the multifaceted nature of malignancies, PDX systems offer unparalleled opportunities for dissecting tumor complexity and tailoring therapeutic interventions. Given their ability to bridge experimental findings with clinical realities, these models are set to become standard tools in oncological research, drug development pipelines, and personalized patient care algorithms worldwide.

The convergence of emerging genomic editing technologies, immune-oncology advancements, and computational biology ensures that PDX models will evolve rapidly to meet future challenges. By embracing these multifaceted innovations, researchers are positioning PDX platforms not only as experimental stand-ins but as predictive engines fueling next-generation cancer therapies. Through this lens, the dynamic landscape of cancer precision medicine will be sharpened significantly, ultimately improving patient outcomes and survival rates.

As the oncology community moves forward, continued collaboration between clinicians, basic researchers, and biotechnology developers will be critical in harnessing the full potential of PDX models. Investing in the optimization, standardization, and dissemination of these models globally will accelerate translational breakthroughs. Together, these coordinated efforts herald a new era where cancer treatment becomes increasingly personalized, efficient, and successful—a testament to the power of patient-derived xenograft models in revolutionizing cancer therapeutics.

Subject of Research:
Patient-derived xenograft (PDX) models in cancer research and their role in precision oncology.

Article Title:
Patient-derived xenograft models: Current status, challenges, and innovations in cancer research

News Publication Date:
2025

References:
Minqi Liu, Xiaoping Yang, Patient-derived xenograft models: Current status, challenges, and innovations in cancer research, Genes & Diseases, Volume 12, Issue 5, 2025, 101520.

Image Credits:
Genes & Diseases

Keywords:
Cancer genetics, patient-derived xenograft models, precision medicine, drug resistance, tumor microenvironment, CRISPR gene editing, humanized mouse models, organoid co-cultures, co-clinical trials, biobanking, artificial intelligence in drug discovery, immuno-oncology.