The platform relies on patient-derived organoids, three-dimensional clusters of tumor cells grown in a supportive gel that recapitulate key architectural and molecular features of the original cancer. Organoids have generated enormous enthusiasm in precision oncology because they allow clinicians to screen multiple drugs against a living surrogate of a patient’s tumor before committing that patient to a regimen. Yet the conventional route to building them, which begins with surgically resected or biopsied tissue, carries substantial drawbacks. Tissue acquisition is invasive, often requires a procedure that may not be clinically justified, and yields samples with low tumor cellularity. The resulting cultures can be contaminated with stromal and immune cells that dilute the tumor-specific signal, and establishment rates for pancreatic cancer organoids have historically been frustratingly low.

The Yonsei University team, led by researchers from the Division of Gastroenterology in collaboration with the Departments of Pathology and Hepatobiliary and Pancreatic Surgery at Severance Hospital, took a different route entirely. Rather than solid tissue, they started with malignant effusions, the pleural fluid that accumulates around the lungs and the ascitic fluid that pools in the abdomen of patients with advanced pancreatic ductal adenocarcinoma. These fluids are collected routinely for symptom management through minimally invasive drainage procedures, meaning that the raw material for organoid culture is essentially a clinical byproduct. Because the fluid already contains free-floating tumor cells shed from metastatic deposits, the researchers reasoned that it could serve as a rich, relatively pure starting inoculum.

Their reasoning proved correct. Fluid-derived organoids, or FDOs, established from these effusions grew faster than organoids generated from matched tissue samples, showed a higher establishment success rate, and carried markedly less non-tumor contamination. The comparison was not simply a matter of convenience. The team performed extensive quality control to demonstrate that FDOs faithfully mirror the biology of the parental tumors. Histopathological examination of hematoxylin and eosin stained sections showed that the organoids retained the glandular architecture characteristic of pancreatic ductal adenocarcinoma. Immunostaining for cytokeratin 7, an epithelial marker expressed in pancreatic ductal cells, confirmed ductal origin. Critically, mutation analysis confirmed that the organoids carried the same KRAS driver mutations as the original tumors. Since activating mutations in KRAS, most commonly at codon 12, occur in the vast majority of pancreatic cancers and anchor much of the field’s targeted drug development, this genetic concordance is essential for the model to have any translational value.

To characterize organoid morphology and drug response in fine detail without destructive processing, the researchers turned to holotomography, a label-free imaging technique that uses coherent light to reconstruct three-dimensional refractive index maps of living cells. This allowed quantitative measurement of cellular and organoid morphology and of how the structures changed in response to drug exposure, complementing conventional viability assays.

One of the most clinically significant demonstrations involved MRTX1133, a selective inhibitor of the KRAS G12D mutant protein. KRAS G12D is among the most common KRAS variants in pancreatic cancer, and MRTX1133 has emerged as a preclinical benchmark for direct KRAS targeting in this tumor type. In the study, FDOs harboring the KRAS G12D mutation showed marked sensitivity to the inhibitor, confirming that the fluid-derived platform can reproduce the drug-response behavior expected of a genetically defined tumor. The result establishes a proof of concept that FDOs can serve as a rapid and scalable test bed for emerging targeted agents, potentially shortening the path from genetic diagnosis to an individualized treatment decision.

The second major contribution of the study goes beyond the platform itself and into the molecular roots of chemotherapy failure. Gemcitabine, a nucleoside analog that has anchored pancreatic cancer chemotherapy for years, frequently stops working as tumors evolve resistance. To understand why, the team performed transcriptomic profiling, comparing gene expression in FDOs that responded to chemotherapy with expression in those that did not. Gene set enrichment and differential expression analysis converged on three genes that were consistently upregulated in the resistant cultures: CEMIP, which encodes cell migration inducing hyaluronidase 1; CALB2, which encodes calbindin 2, also known as the heart and neural crest derivatives expressed protein; and LY6D, a member of the lymphocyte antigen 6 family of glycosylphosphatidylinositol-anchored cell surface proteins.

Expression alone does not prove causation, so the researchers moved to functional validation. When they manipulated the activity of these genes in pancreatic cancer cell lines, the results were unambiguous: elevated CEMIP, CALB2, and LY6D suppressed apoptosis, the programmed cell death pathway that gemcitabine is designed to trigger, and thereby conferred resistance to the drug. CEMIP in particular has been previously implicated in hyaluronic acid metabolism and epithelial-mesenchymal transition, processes that pancreatic tumors exploit to remodel their microenvironment and escape cytotoxic stress. The new findings place all three genes squarely in the mechanistic chain linking cellular stress to survival.

The clinical implications of the three-gene signature were reinforced by outcome data. In analyses of patient cohorts, high expression of the CEMIP, CALB2, and LY6D signature correlated with worse progression-free survival and worse overall survival, indicating that the same genes that protect organoids from gemcitabine in a dish are associated with poorer outcomes in patients. This dual role, as both a mechanistic driver and a prognostic marker, is what gives the finding its translational weight. A test measuring the three-gene signature could in principle identify patients unlikely to benefit from standard chemotherapy, steering them toward alternative regimens or clinical trials of targeted and resistance-overcoming strategies. The genes themselves also represent candidate therapeutic targets, since interfering with their activity might restore sensitivity to apoptosis-inducing drugs.

The work also carries broader implications for how organoid models are built across oncology. Effusions are not unique to pancreatic cancer; malignant pleural and peritoneal effusions arise in ovarian, gastric, lung, and breast cancers, among others. A methodology that converts a routine drainage procedure into a high-fidelity drug-screening platform within days rather than weeks could be adapted widely, particularly for patients with advanced disease for whom tissue biopsy is impractical or unsafe. The scalability of the approach addresses one of the persistent bottlenecks of precision oncology: the sheer logistics of generating a personalized model quickly enough for it to influence a treatment decision made under time pressure.

The study was conducted under ethical approval from the Institutional Review Board of Yonsei University with written informed consent from all patients, and it was supported by grants from the National Research Foundation of Korea and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute. The research article was published as an accepted, citable open-access version carrying a permanent digital object identifier, with the final version of record to follow.

Taken together, the findings advance pancreatic cancer research on two fronts simultaneously. They provide a minimally invasive, rapid, and genetically faithful organoid platform derived from malignant effusions, validated against a state-of-the-art KRAS targeted inhibitor. And they expose a concrete molecular mechanism of chemotherapy resistance, distilled into a three-gene signature with demonstrated prognostic power. For a disease in which treatment options remain scarce and clinical timelines are unforgiving, tools that accelerate both drug selection and biomarker discovery are welcome indeed. The next steps, which the researchers and the field more broadly will be watching closely, involve prospective validation of the gene signature in larger patient cohorts and exploration of whether targeting CEMIP, CALB2, or LY6D can resensitize resistant tumors to gemcitabine and other cytotoxic agents.

The models were developed through the Human Cancer Models Initiative, an international effort involving the National Cancer Institute, Cancer Research UK, the Wellcome Sanger Institute and Hubrecht Organoid Technology. The initiative aims to create 1,000 patient-derived models that accurately reproduce the biology of human tumors in laboratory systems. Approximately 2,800 patients from the United States, the United Kingdom, Italy and the Netherlands consented to provide tumor tissue and associated clinical information for the project.

Unlike many traditional laboratory cancer models, the new collection was designed to preserve the biological features of the tumors from which they originated. Patient-derived models can include three-dimensional organoids and spheroids, as well as two-dimensional cell lines. These systems are grown under conditions tailored to the specific cancer type, helping maintain the genetic, molecular and cellular characteristics of the original tumor. The models that passed rigorous quality-control procedures were subjected to standardized genomic sequencing and molecular profiling.

This validation process addresses a major weakness of earlier cancer models. Cells grown in laboratories can gradually acquire genetic or biological changes, a phenomenon often described as “drift,” which may make them increasingly different from the patient’s tumor. Such changes can undermine experiments designed to predict how a cancer will respond to a drug or how a genetic alteration contributes to disease. The HCMI models were selected for their ability to remain faithful to the original samples and to retain stable biological behavior over extended periods.

The collection includes cancers affecting both adults and children, with examples ranging from colorectal, pancreatic, lung and brain cancers to much rarer malignancies. More than 20 percent of the models represent rare cancer types, some of which previously had only one or two experimental models available to researchers worldwide. Expanding representation of these diseases could be particularly important because rare cancers often lack the large patient populations and research infrastructure that support studies of more common tumors.

Clinical context is another defining feature of the resource. Among the models are 168 derived from patients who had already received treatment, including immunotherapy, targeted therapy, chemotherapy and radiotherapy. Another 318 models were generated from samples collected before treatment. Linking the laboratory models to treatment history and patient outcomes may allow researchers to investigate why some tumors resist therapy, identify molecular features associated with response and test potential combinations of drugs in systems that reflect real-world disease.

The models and their associated data are being distributed through the American Type Culture Collection. Researchers will be able to access not only the physical biological materials but also information such as genomic sequencing results, clinical annotations and molecular measurements generated using consistent methods. According to the investigators, this unified structure is essential because it allows findings from different laboratories to be compared more reliably than when researchers use unrelated models created under different conditions.

The resource has already contributed to the expansion of the Cancer Dependency Map, or DepMap, a large-scale effort managed by the Broad Institute that uses CRISPR gene-editing technology to identify genes on which cancer cells depend. By incorporating the HCMI models, investigators have broadened DepMap’s coverage of genetic and molecular cancer subtypes. The new models also include gene-expression patterns and cellular states that were not consistently represented in earlier patient-derived systems, potentially revealing vulnerabilities that had remained invisible in previous screens.

The scientific importance of the collection extends beyond the immediate experiments it enables. With hundreds of carefully characterized models connected to clinical and genomic data, researchers can perform large-scale studies of tumor evolution, drug resistance, cancer dependencies and interactions between genetic alterations. The dataset may also provide valuable training material for computational tools and artificial-intelligence systems designed to predict treatment response or prioritize drug targets. The investigators describe the release as a major change in the experimental infrastructure available to cancer biology, particularly because it combines standardized models with deep patient-level information.

The Nature study, titled “A Compendium of Next-Generation Patient-Derived Models for Diverse Cancers,” was led by investigators including Keith Ligon of Dana-Farber Cancer Institute, Jesse Boehm of the Massachusetts Institute of Technology, Mathew Garnett of the Wellcome Sanger Institute, David Tuveson of Cold Spring Harbor Laboratory and collaborators from institutions across the United States and Europe. The HCMI was funded primarily by the National Cancer Institute and the Wellcome Trust. By making the models broadly accessible, the initiative aims to give researchers the experimental systems needed to translate cancer genome discoveries into new therapies more quickly.

Subject of Research:
Next-generation patient-derived cancer models, including organoids, spheroids and cell lines, for studying tumor biology, treatment response and therapeutic vulnerabilities.

Article Title:
A Compendium of Next-Generation Patient-Derived Models for Diverse Cancers

Web References:
https://www.atcc.org/hcmi
https://depmap.org/portal/
https://doi.org/10.1038/s41586-026-10806-y

References:
Nature article, DOI: 10.1038/s41586-026-10806-y

Keywords:
Cancer research, patient-derived models, organoids, cancer biology, precision medicine, drug discovery, tumor modeling, Cancer Dependency Map, CRISPR screening, genomics, rare cancers, Dana-Farber Cancer Institute, Human Cancer Models Initiative

The international effort was conducted through the Human Cancer Models Initiative, a 10-year program supported primarily by the U.S. National Cancer Institute and the Wellcome Trust. Led by scientists at MIT’s Koch Institute, the Broad Institute, Dana-Farber Cancer Institute, the National Cancer Institute, and partner institutions worldwide, the initiative collected more than 2,700 tumor samples from consenting patients in the United States, the United Kingdom, and the Netherlands. Approximately one-third of the samples were successfully converted into models capable of surviving and multiplying in laboratory conditions.

Most of the resulting models are organoids, three-dimensional structures made from tumor cells and grown in specialized culture media. Unlike conventional cancer cell lines, which typically form a flat layer on the surface of a laboratory dish, organoids develop within a supportive, gelatin-like scaffold that allows cells to organize into tissue-like structures. This three-dimensional environment can reproduce aspects of tumor architecture and cell behavior that are often lost when cancer cells are adapted to traditional two-dimensional culture.

The need for such models became clear after the Cancer Genome Atlas revealed the extraordinary genetic diversity of human tumors. Although thousands of patient samples had been sequenced, researchers had only about 1,000 established patient-derived cancer cell lines available for experiments. Those models were also disproportionately derived from patients of European or Southeast Asian ancestry, while many rare cancers and genetically unusual tumors were poorly represented. A limited model collection makes it difficult to determine whether a potential drug target is broadly relevant or applies only to a narrow subset of patients.

To create the new models, scientists developed tissue-specific culture conditions that provide cancer cells with the nutrients, signaling molecules, and physical support needed for long-term growth. Establishing a stable organoid or cell line can take as long as a year. Once a model was established, researchers compared it with the original tumor using several layers of molecular analysis, including DNA sequencing, RNA-expression profiling, and examination of epigenomic modifications. These tests helped determine whether the cultured cells retained the mutations, gene-activity patterns, and chemical changes that influence how cancer cells behave.

The collection includes models from common cancers such as lung, liver, and pancreatic tumors, as well as roughly 150 rare cancer types, including gallbladder and small-intestinal tumors. Each model has been deposited at the American Type Culture Collection, a nonprofit organization that distributes biological research materials. In addition to the cancer cells themselves, the associated data include information about the patient’s inherited genetic variants, known as germline mutations, and the treatments the patient received. This clinical context may allow researchers to investigate why tumors respond to certain therapies, develop resistance, or recur after treatment.

The models have already been incorporated into large-scale functional studies. In a companion Nature study, researchers at the Broad Institute analyzed more than 300 models using high-throughput DNA sequencing and RNA sequencing. They also performed CRISPR loss-of-function screens on more than 100 models. In these experiments, individual genes are systematically disrupted to reveal which ones cancer cells depend on for survival. If disabling a gene selectively kills cancer cells while leaving normal cells less affected, that gene may represent a potential therapeutic vulnerability.

The resulting data have been added to the Cancer Dependency Map, or DepMap, a research platform that connects cancer genotypes with cellular dependencies and possible drug targets. The resource now contains information on more than 2,000 cancer models. A separate companion study from the Wellcome Sanger Institute characterized another 256 organoids generated through the initiative, expanding the molecular and functional information available to researchers investigating tumor biology.

Scientists involved in the project emphasize that the collection is a major step rather than a final catalog of human cancer diversity. The formal HCMI program is winding down, but participating researchers hope to continue producing models from additional patient samples, especially pediatric and rare cancers. Because tumors can evolve during treatment and differ substantially between patients, even a collection of thousands of models cannot represent every clinically relevant cancer state. The researchers argue that continued tissue donation and international collaboration will be essential for building experimental systems that more accurately reflect the people who ultimately need new therapies.

Subject of Research: Patient-derived cancer models, cancer organoids, cancer genomics, drug discovery, and therapeutic vulnerabilities

Article Title: A compendium of next-generation patient-derived models for diverse cancers

News Publication Date: 5-Aug-2026

Web References: https://doi.org/10.1038/s41586-026-10806-y

References: Nature article, “A compendium of next-generation patient-derived models for diverse cancers”; Human Cancer Models Initiative; Cancer Dependency Map

Keywords: Cancer research, patient-derived models, organoids, cancer cell lines, drug development, drug discovery, genomics, CRISPR, Cancer Dependency Map, tumor biology, precision medicine, rare cancers

Organoids are three-dimensional clusters of living cells grown from tissue taken from patients. Unlike conventional cancer cell lines, which can acquire extensive genetic and behavioral changes after prolonged culture, patient-derived organoids are designed to preserve key characteristics of the original tumor. They can reproduce aspects of tumor architecture, genetic variation, cell behavior and treatment response, allowing scientists to investigate cancer biology in a controlled laboratory environment.

The HCMI collection includes organoids derived from pancreatic, breast, endometrial, colorectal, bladder, ovarian, head and neck, and lung cancers, among other malignancies. Scientists at Cold Spring Harbor Laboratory (CSHL) and Northwell Health led one of the largest contributing teams, providing more than 150 models to the international compendium. Collaborators at the University of Verona in Italy and Hubrecht Institute in the Netherlands also played major roles, including the development and distribution of dozens of pancreatic and colorectal cancer models.

The resource goes beyond living organoid cultures. It links many models to matched tissue from the original tumors, detailed clinical information and molecular data, including DNA and RNA sequencing, transcriptomic profiles and epigenetic measurements. Of the 665 models, 522 include comprehensive clinical data, while 153 represent rare cancers. The collection also contains 71 models from participants of non-European ancestry, addressing a long-standing problem in biomedical research: the underrepresentation of diverse patient populations in preclinical studies.

Researchers created 43 models from pediatric or adolescent patients, and approximately 23 percent of successful models came from rare cancer types. This breadth could make the collection especially valuable for studying cancers that are difficult to obtain in sufficient numbers for conventional experiments. Rare tumor subtypes often have few established cell lines and limited clinical trial data, meaning that organoids may provide an important experimental bridge between individual patient samples and broader biological discoveries.

To ensure that the organoids remained faithful to their source tumors, the HCMI teams established standardized production and quality-control procedures. At CSHL’s Genome Center, researchers used cancer hotspot sequencing to monitor key genetic alterations and assess the identity and quality of developing models. Organoids can also be expanded and cryopreserved, enabling laboratories to perform repeated experiments on the same tumor-derived material without requiring a new biopsy or continuously collecting fresh tissue.

The models are already being incorporated into drug-discovery research. Because organoids can be grown in multiwell plates, scientists can expose them to many drugs or drug combinations at different concentrations and measure effects on cell survival, proliferation and morphology. This approach could help identify therapies that are more likely to work against a particular tumor while revealing resistance mechanisms. Researchers emphasize, however, that organoid drug testing is not yet a substitute for clinical trials and must be interpreted alongside patient data and other biological models.

The HCMI organoids have also expanded the Cancer Dependency Map, or DepMap, a major research program designed to identify genetic and molecular vulnerabilities that cancer cells depend on for survival. By testing diverse organoid models, investigators can examine whether a potential dependency is shared across tumors or limited to a particular genetic background, tissue type or patient population. Such information may help researchers prioritize therapeutic targets and develop more precise strategies for tumors that do not respond to existing treatments.

The initiative was built around close coordination between clinical teams, hospital biospecimen repositories and laboratory scientists. Northwell Health, which treats more than 19,000 cancer patients annually, helped provide access to patient samples and established a pipeline connecting clinical care with experimental research. The project’s leaders say that patient consent and participation were fundamental to creating the resource. By making these models and their associated data available worldwide, the HCMI aims to accelerate cancer research, improve the reproducibility of preclinical experiments and move the field closer to personalized treatment decisions.

Subject of Research: Patient-derived organoid models for cancer research, precision medicine and therapeutic discovery

Article Title: A compendium of next-generation patient-derived models for diverse cancers

News Publication Date: 5 August 2026

Web References: Cold Spring Harbor Laboratory: https://www.cshl.edu/ ; Northwell Health: https://www.northwell.edu/ ; Nature article: https://www.nature.com/articles/s41586-026-10843-7

References: Nature, “A compendium of next-generation patient-derived models for diverse cancers.” DOI: 10.1038/s41586-026-10806-y

Image Credits: Hardik Patel/Cold Spring Harbor Laboratory

Keywords: Organoids, cancer research, patient-derived models, personalized medicine, cancer genomics, translational research, transcriptomics, epigenomics, drug screening, tumor biology

Traditional models used to study RCC and evaluate treatment efficacy have long suffered from significant drawbacks. Conventional two-dimensional cell cultures and animal models often fail to replicate the intricate heterogeneity and microarchitecture of human tumors, which critically influences therapy responses and disease progression. Tumors are not homogenous masses but complex ecosystems composed of varied cell populations and extracellular matrix interactions, factors that contribute to intratumoral diversity and adaptive resistance mechanisms. This complexity underlies the high variability in patient responses to chemotherapy and targeted drugs, rendering generalized treatment protocols often ineffective.

The innovative 3D bioprinting methodology developed by the Tsinghua team builds upon advances in biomaterial science, tissue engineering, and cellular biology. Utilizing patient-derived tumor cells as bioinks, the researchers were able to engineer multi-cellular constructs that incorporate endothelial-like networks, simulating the blood vessels that nourish tumors in the human body. This replication of vasculature is crucial, as it influences tumor metabolism, growth, and the delivery of therapeutic agents, factors typically absent or poorly modeled in traditional systems. These organoids thus serve as robust, physiologically relevant platforms that reflect tumor heterogeneity and microenvironmental dynamics with exceptional fidelity.

The significance of these organoids extends beyond biological fidelity; they represent a scalable and reproducible solution that mitigates labor-intensive manual methodologies predominant in current research workflows. The precise spatial control afforded by 3D bioprinting enables consistent production of tumor models, significantly expediting the process of preclinical drug screening. Researchers can now rapidly assess the efficacy of multiple therapeutic candidates in parallel, tailoring treatment strategies to the unique genetic and phenotypic profile of an individual’s tumor. This personalized approach promises to transform how nephrologists and oncologists devise treatment regimens, potentially improving clinical outcomes and reducing adverse effects associated with ineffective therapies.

Renal cell carcinoma remains a formidable clinical challenge due to its rising incidence and notorious heterogeneity. Its pathogenesis involves a multitude of genetic aberrations that evolve over time, fostering resistance to chemotherapy and targeted agents, heightening the risk of recurrence and metastasis. Conventional laboratory models struggle to capture this evolving complexity, constraining efforts to develop precision medicine protocols. By contrast, the patient-derived organoids created through this bioprinting platform faithfully preserve mutational landscapes and phenotypic traits, enabling longitudinal studies of tumor evolution and drug resistance mechanisms.

At the heart of this innovation is the meticulous integration of multidisciplinary expertise encompassing mechanical engineering, chemical system engineering, and molecular oncology. Dr. Yuan Pang, Associate Professor at Tsinghua University and co-author of this study, highlights that the ability to mass-produce heterogeneous tumor models “could greatly accelerate the discovery of effective, patient-specific treatments.” The combination of engineering precision and biological authenticity in these organoids provides an essential bridge between bench research and bedside application, epitomizing the ideals of translational medicine.

The implications of this research resonate well beyond RCC, offering a versatile framework applicable to other malignancies characterized by cellular heterogeneity and microenvironmental complexity. The capacity to bioprint organoids maintaining phenotypic fidelity opens new avenues for studying tumor-stroma interactions, immunotherapy responses, and the role of the extracellular matrix in cancer progression. Furthermore, the reduced reliance on animal testing aligns with ethical imperatives, marking progress toward more humane and efficient research methodologies.

This breakthrough also promises to influence pharmaceutical development pipelines. By enabling high-throughput screening of drug candidates on patient-specific tumor constructs, pharmaceutical companies can refine lead compounds earlier in the development process, reducing costs and attrition rates traditionally associated with oncology therapeutics. Additionally, clinicians could leverage such organoids to predict resistance patterns and adapt treatment plans dynamically, a feat previously unattainable with static biopsy samples or generic cell lines.

Moreover, the vascular-like structures incorporated into these bioprinted tumors provide a unique vantage point for studying angiogenesis—the formation of new blood vessels—a hallmark of cancer progression. Understanding how these neovessels interact with cancer cells and facilitate metastasis could inform the development of novel anti-angiogenic therapies that disrupt tumor sustenance and dissemination. This integrated modeling approach thus serves as a powerful investigative tool across multiple dimensions of tumor biology.

Despite these promising advancements, challenges remain. Scaling bioprinting techniques for routine clinical application requires further refinement to ensure reproducibility, cost-effectiveness, and regulatory compliance. Additionally, comprehensive molecular characterization of the printed organoids across diverse RCC subtypes will be essential to validate their utility broadly. Nonetheless, the current progress heralds a new era in personalized oncology research, emphasizing precision, fidelity, and translational relevance.

The study exemplifies the synergy achievable when engineering innovation meets medical necessity, charting a transformative course for kidney cancer research and therapy. As these patient-derived, bioprinted organoids become more integrated into clinical and pharmaceutical workflows, they hold the promise of enabling truly personalized medicine—where treatments are not just designed based on population averages but intricately woven around the unique biological signature of each patient’s tumor.

Subject of Research: Cells

Article Title: Bioprinting of Patient-Derived Heterogeneous Renal Cell Carcinoma Organoids for Personalized Therapy

News Publication Date: 12-Aug-2025

Web References:
https://iopscience.iop.org/article/10.1088/1758-5090/adecc5

References:
Pang, Y., Shou, J., et al. (2025). Bioprinting of Patient-Derived Heterogeneous Renal Cell Carcinoma Organoids for Personalized Therapy. Biofabrication. DOI: 10.1088/1758-5090/adecc5

Image Credits: J-VAR / IOP Publishing

Keywords: Diseases and disorders, Renal Cell Carcinoma, 3D Bioprinting, Personalized Medicine, Tumor Organoids, Cancer Research