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A Comprehensive Human Gastric Cancer Organoid Biobank Captures Tumor Subtype Heterogeneity and Enables Therapeutic Screening
Abstract
Gastric cancer displays marked molecular heterogeneity with aggressive behavior and treatment resistance. Therefore, good in vitro models that encompass unique subtypes are urgently needed for precision medicine development. Here, we have established a primary gastric cancer organoid (GCO) biobank that comprises normal, dysplastic, cancer, and lymph node metastases (n = 63) from 34 patients, including detailed whole-exome and transcriptome analysis. The cohort encompasses most known molecular subtypes (including EBV, MSI, intestinal/CIN, and diffuse/GS, with CLDN18-ARHGAP6 or CTNND1-ARHGAP26 fusions or RHOA mutations), capturing regional heterogeneity and subclonal architecture, while their morphology, transcriptome, and genomic profiles remain closely similar to in vivo tumors, even after long-term culture. Large-scale drug screening revealed sensitivity to unexpected drugs that were recently approved or in clinical trials, including Napabucasin, Abemaciclib, and the ATR inhibitor VE-822. Overall, this new GCO biobank, with linked genomic data, provides a useful resource for studying both cancer cell biology and precision cancer therapy.
... Subsequent transplantation of bone marrow metastatic tumor organoids into immunodeficient mice showed the same histological and immunological features as in vitro organoids. Moreover, tumor organoids retained the histopathological features and corresponding functions of parental tumors in PDOs based on glioblastoma [ 62 ], primary liver cancer [ 48 ], esophageal adenocarcinoma [ 63 ], bladder cancer [ 64 ], and gastric cancer [ 65 ]. The growth and metastatic abilities of the parental tumor are retained following the transplantation of tumor organoids into animal models [ 48 , 62 , 66 ]. ...
... Multi-omics has also contributed to further high-throughput drug screening and drug testing in clinical development. Yan et al. [ 65 ] established a comprehensive and well-characterized organoid biobank of primary gastric cancer, which contained detailed whole-exome and transcriptome analyses. Drugs with good antitumor effects (napabucasin, abemaciclib, and the ATR inhibitor VE-822) were tested using this biobank. ...
... Microscopic imaging is the basis for observing and analyzing the morphology of tumor organoids. Although traditional microscopic imaging methods, such as light microscopy and electron microscopy, can visualize images of tumor cells and tissue structures, these methods have limitations in resolution, contrast, and imaging depth [ 65 ]. ...
Precision medicine is a personalized medical model based on the individual’s genome, phenotype, and lifestyle that provides tailored treatment plans for patients. In this context, tumor organoids, a 3-dimensional preclinical model based on patient-derived tumor cell self-organization, combined with digital analysis methods, such as high-throughput sequencing and image processing technology, can be used to analyze the genome, transcriptome, and cellular heterogeneity of tumors, so as to accurately track and assess the growth process, genetic characteristics, and drug responsiveness of tumor organoids, thereby facilitating the implementation of precision medicine. This interdisciplinary approach is expected to promote the innovation of cancer diagnosis and enhance personalized treatment. In this review, the characteristics and culture methods of tumor organoids are summarized, and the application of multi-omics, such as bioinformatics and artificial intelligence, and the digital methods of organoids in precision medicine research are discussed. Finally, this review explores the main causes and potential solutions for the bottleneck in the clinical translation of digital tumor organoids, proposes the prospects of multidisciplinary cooperation and clinical transformation to narrow the gap between laboratory and clinical settings, and provides references for research and development in this field.
... In recent years, patient-derived organoids (PDOs) have emerged as promising ex vivo models to study cellular and molecular processes underlying human gastric disorders 27,28 . Particularly, PDOs have been a valuable tool for studying H. pylori infection in the context of gastric pathology [29][30][31] and for understanding GC response to drug treatment [32][33][34] . However, despite the importance of glycosylation and the potential for PDOs to offer insights into gastric disorders, the role of this dynamic cellular process within gastric PDOs remains poorly explored. ...
... We generated a gastric PDO biobank comprising 56 PDOs derived from non-malignant, pre-malignant, and tumoral tissues including early and later cancer stages of different GC subtypes which can be subject to further studies of GC biology and tumor progression. Other GC PDO biobanks have been reported, however, only one described PDOs derived from cancer-free individuals 32,33,[44][45][46] . Indeed, one of the key observations of this study relates to the morphological heterogeneity exhibited by gastric PDOs derived from cancer patient's adjacent mucosa. ...
... the T-PDO glycophenotypes are preserved when subjected to biobanking, extended culture periods, and after xenografting procedures. Despite the observed loss of sTn and sLe x expression in tumor PDOs upon 5 months of continuous culture, our findings support previous studies demonstrating genomic and transcriptomic shifts in PDOs after long-term culture (3 months for non-tumoral PDOs and 6 months for T-PDOs) 27,32 . This loss of heterogeneity may be a result of the selective processes over passage, and raise the the possibility of certain subpopulations of tumor cells with distinct glycomic profiles and/or best survival capabilities being favored during long-term culture conditions. ...
BACKGROUND AND AIMS
Aberrant cellular glycosylation remains a key event that accompanies and actively sustains gastric neoplastic transformation. Patient-derived organoids (PDOs) have recently emerged as a promising ex vivo model to study human gastric disorders. Since the PDOs glycosylation landscape remains unknown, this study aims to evaluate PDOs as potential avatars of in vivo tissue glycosylation profiles in the gastric context.
METHODS
Fresh gastric mucosa samples derived from non-tumoral obese patients (n=11), adjacent tumor mucosa samples (n=29), and tumor tissue samples derived from gastric cancer (GC) patients (n=30) were used to establish a biobank of gastric PDOs (n=56). The N - and O- glycophenotypes of normal, adjacent, and tumor PDOs and respective in vivo tissues were thoroughly characterized by immunostaining. Additionally, a comparative glycan analysis was performed over time, upon PDO biobanking and xenografting in mice. The binding of two Helicobacter pylori ( H. pylori ) isogenic strains with distinct glycan-binding affinities was assessed in parental gastric mucosa tissues and compared with the respective PDOs before and after modulation of their glycan landscape.
RESULTS
Our results show that PDOs mimic different phenotypes of the carcinogenic cascade and recapitulate parental gastric tissues’ glycosylation profile. Tumor PDOs recapitulate the inter- and intra-heterogeneity features observed in GC, which is maintained over time, upon biobanking and xenografting. We demonstrated that the expression of type I and type II Lewis antigens is dynamically controlled by PDOs differentiation status, which results in differential binding to H. pylori strains displaying distinct glycan-binding adhesins, mirroring the gastric epithelium tissue interactions.
CONCLUSIONS
This study established PDOs as invaluable ex vivo tools to study the complex glycan dynamics in both gastric physiological and pathological settings.
... Organoids that develop from gastric tumor cells of patients pathologically diagnosed with gastric cancer, and can simulate the characteristics of original gastric cancer tissues (Fujii et al. 2019;Seidlitz et al. 2019;Yan et al. 2018;Wang et al. 2024). ...
... Human gastric cancer organoids display a variety of morphological characteristics under optical microscopy, including vesicular, compact, and mixed types, among others (Yan et al. 2018;Seidlitz et al. 2019Seidlitz et al. , 2020Togasaki et al. 2021;Wang et al. 2024;). ...
... Cells within the organoids should maintain the atypical characteristics of tumor cells found in the original tumor tissue, such as hyperchromatic nuclei, abnormal mitotic figures, and disrupted nuclear-cytoplasmic ratio, etc. (Yan et al. 2018;Seidlitz et al. 2019). ...
Gastric cancer is one of the most common malignancies with poor prognosis. The use of organoids to simulate gastric cancer has rapidly developed over the past several years. Patient-derived gastric cancer organoids serve as in vitro models that closely mimics donor characteristics, offering new opportunities for both basic and applied research. The “Human Gastric Cancer Organoid” is part of a series of guidelines for human gastric cancer organoids in China, jointly drafted by experts from the Chinese Society for Cell Biology and its branches, and initially released on October 29, 2024. This standard outlines terminology, technical requirements, assessment protocols, and applies to production, evaluation procedures, and quality control for human gastric cancer organoids. The publication of this guideline aims to assist institutions in endorsing, establishing, and applying best practices, advancing the international standardization of human gastric cancer organoids for clinical development and therapeutic application.
... They demonstrated that culturing the tumour cell pellet with a moderate amount of the supernatant resulted in increased organoid growth and size in comparison to completely removing the supernatant according to the standard method [52]. Yan et al. cultured 46 GC organoid lines from 34 patients, achieving a 50% culture success rate, and uniquely cultured multiple biopsies from the same patient [53]. This enabled an analysis of subclones found within primary cancer and matched organoids using Superfreq, which identified varying degrees of heterogeneity across the tumour and metastatic lymph nodes. ...
... A primary challenge in culturing GC organoids is controlling the overgrowth of nonmalignant organoids that occurs when identical media is used to culture both normal and neoplastic tissue organoids [43]. Yan et al. used two methods to prevent the overgrowth of normal organoids, including the microscopic selection of tumour organoids and the use of Nutlin3a in growth media, to select against normal cells containing wild-type TP53, as many GCs have mutant TP53 [53]. Nanki et al. cultured 37 GC organoid lines and tested four alternate growth media to prevent normal organoid overgrowth in slow-growing tumour lines. ...
... While most GC organoid studies collected tumour samples during surgical resection, successful organoid establishment has also been achieved from tissue biopsies and ascites fluid [52]. A few studies have recruited treatment-naïve patients to allow assessment of organoid drug responses in the absence of acquired resistance to chemotherapeutics [49,50,53,56,57], with the largest of these generating 53 organoid lines from treatment-naïve patients [50]. ...
Gastric cancer (GC) presents a significant health challenge and ranks as the fifth most common cancer in the world. Unfortunately, most patients with GC exhaust standard care treatment options due to late diagnosis and tumour heterogeneity that leads to drug resistance, resulting in poor survival outcomes. Potentially, this situation can be improved by personalising treatment choice. Organoids are an emerging cell model system that recapitulates tumour heterogeneity and drug responses. Coupled with genomic analysis, organoid culture can be used to guide personalised medicine. The GC organoid field, however, lacks standardised methodologies for assessing organoid drug sensitivities. Comparing results across different GC organoid studies and correlating organoid drug responses with patient outcomes is challenging. Hence, we aim to summarise the methodologies used in GC organoid drug testing and correlation with clinical outcomes and discuss design considerations and limitations to enhance the robustness of such studies in the future.
... In vitro, they form cell clusters capable of self-assembly and self-renewal, maintaining the genomic stability and tumor heterogeneity characteristics of the original organ [15]. Organoids provide valuable insights into organ development, disease modeling, cancer progression, drug response, and personalized treatments [16][17][18]. Compared to xenograft models, organoid cultures require less time and tissue, are easier to handle, and crucially tumor organoids reliably retain the essential features of the primary tumor, even after prolonged passaging [19,20]. ...
... Researchers conducted large-scale drug screening, testing both FDA-approved therapies and drugs in clinical trials using this biobank. Notably, a stemness STAT3-target inhibitor-Napabucasin, mTOR inhibitor-Vistusertib and the ATR inhibitor-VE-822 showed significant sensitivity in organoid models, demonstrating the utility of organoid biobanks for identifying targeted therapies, guiding clinical drug selection, and accelerating anticancer drug development [17]. Expanding on this work, Wang and colleagues developed a gastric tumor organoid (GTO) biobank to conduct high-throughput drug screening while incorporating clinical prognosis insights. ...
Gastric cancer (GC) is a prevalent digestive system tumor, the fifth most diagnosed cancer worldwide, and a leading cause of cancer deaths. GC is distinguished by its pronounced heterogeneity and a dynamically evolving tumor microenvironment (TME). The lack of accurate disease models complicates the understanding of its mechanisms and impedes the discovery of novel drugs. A growing body of evidence suggests that GC organoids, developed using organoid culture technology, preserve the genetic, phenotypic, and behavioral characteristics. GC organoids hold significant potential for predicting treatment responses in individual patients. This review provides a comprehensive overview of the current clinical treatment strategies for GC, as well as the history, construction and clinical applications of organoids. The focus is on the role of organoids in simulating the TME to explore mechanisms of immune evasion and intratumoral microbiota in GC, as well as their applications in guiding clinical drug therapy and facilitating novel drug screening. Furthermore, we summarize the limitations of GC organoid models and underscore the need for continued technological advancements to benefit both basic and translational oncological research.
... While we believe that the reproducibility and ease-of-use of the OncoPro medium system offers substantial advantages to homebrew approaches, 17 of the 18 NCI PDMR tumoroid models that we tested also expanded in homebrew media (Fig. 6, Supplementary Figure S10, Supplementary Table S6), which remains an alternative option to culture tumoroid models. Additional homebrew media recipes have been used and described for tumoroid derivation and culture in the literature 10,11,13,21,25,27,29,35,[41][42][43][45][46][47][48][49][50][51][52] , and these could also serve as potential alternatives to OncoPro medium. ...
... Molecular subtypes of colorectal and triple-negative breast cancers were maintained during tumoroid culture, suggesting that key clinical aspects of gene expression signatures are stable during culture using OncoPro medium. Such maintenance of clinical subtype during tumoroid establishment has been named as critically important to demonstrate the physiological relevance of in vitro cancer models, especially with regard to drug response 21,47,53 , and previous publications have mixed claims regarding the ability of tumoroids to adequately maintain subtypes representative of the tissue from which the tumoroid was derived or generate biobanks with tumoroid lines representing certain clinical subtypes. In line with this result, we observed some shift of hormone-receptor positive lines towards a HER2 subtype in OncoPro medium, with concomitant decreases in estrogen receptor and progesterone receptor gene expression levels compared to cryopreserved tumoroids received from the NCI PDMR ( Supplementary Fig. S13). ...
Tumoroids, sometimes referred to as cancer organoids, are patient-derived cancer cells grown as 3D, self-organized multicellular structures that maintain key characteristics (e.g., genotype, gene expression levels) of the tumor from which they originated. These models have emerged as valuable tools for studying tumor biology, cytotoxicity, and response of patient-derived cells to cancer therapies. However, the establishment and maintenance of tumoroids has historically been challenging, labor intensive, and highly variable from lab to lab, hindering their widespread use. Here, we characterize the establishment and/or expansion of colorectal, lung, head and neck, breast, pancreas, and endometrial tumoroids using the standardized, serum-free Gibco OncoPro Tumoroid Culture Medium. Newly derived tumoroid lines (n = 20) were analyzed by targeted genomic profiling and RNA sequencing and were representative of tumor tissue samples. Tumoroid lines were stable for over 250 days in culture and freeze-thaw competent. Previously established tumoroid lines were also transitioned to OncoPro medium and exhibited, on average, similar growth rates and conserved donor-specific characteristics when compared to original media systems. Additionally, OncoPro medium was compatible with both embedded culture in extracellular matrix and growth in a suspension format for facile culture and scale up. An example application of these models for assessing the cytotoxicity of a natural killer cell line and primary natural killer cells over time and at various doses demonstrated the compatibility of these models with assays used in compound and cell therapy development. We anticipate that the standardization and versatility of this approach will have important benefits for basic cancer research, drug discovery, and personalized medicine and help make tumoroid models more accessible to the cancer research community.
... Moreover, PDTOs is distinct from PDTXs as it's more efficient and cost-effective in model establishment and expansion, without ethical concerns [6]. So far, PDTOs have been built as living bio-banks for several tumor types, such as colorectal [7], breast [8], lung [9], stomach [10] and neuroendocrine [11] cancers. It's also applied as a preferred preclinical model to capture patient-individualized characteristics including mutational landscape and response to anticancer agents of the primary tumor [12]. ...
... In reference to the complete culture medium (CCM) for primary LC [13,14], the CCM for LC-BoM organoids was formulated by Advanced DMEM/F12 adding with 2 mM GlutaMax (Gibco, Catalog# C35050-061), 10 For the solidified Matrigel dome per well of the 24-well plate, 500 µl CMM was added. The 24-well plate was transferred to 37 ℃ and 5% CO 2 incubators for culture. ...
Patient-derived tumor organoids (PDTOs) models have been widely used to investigate the response of primary cancer tissues to anti-cancer agents. Nonetheless, only few case study tried to establish PDTOs and test treatment response based on bone metastasis (BoM) tissues. Fresh BoM tissues were obtained from lung cancer (LC) patients who underwent spinal metastatic tumor surgery for PDTOs culture. Morphology of LC-BoM-PDTOs were characterized during the process: they were high-efficient in self-assembly and regeneration, forming mature 3D-multicellular structures in 2–3 weeks. To be more specific, organoids of BoM derived from patients with EGFR mutation tended to be follicular conglomeration and resembled “a bunch of grapes”, while organoids of BoM derived from patients without driver gene mutation were featured with full sphere and “a ripe sunflower”. PDTOs of BoM retained good consistencies of HE morphology and immunohistochemical markers expression with their parental BoM tissues. Down-regulation of receptor activator of nuclear factor kappa-B ligand (RANKL) expression in LC-BoM-PDTOs after in vitro DMAb intervention was associated with earlier clinical ossification efficacy of DMAb on BoM (median time: 5 vs. 8 months, P = 0.049). Accordingly, BoM-PDTOs can be expected to be a preferred model for predicting treatment response of bone metastatic tumors, considering its high-efficient expansion and good biological consistency with parental bone tumor tissues.
... These advantages render such models highly effective as a tool to link in vitro experiments with in vivo tumor characteristics, showing great potential for research and application. At present, tumor organoids have been successfully established for various cancer types, including gastric cancer [14], colorectal cancer [15], liver cancer [16], breast cancer [17], lung cancer [18], etc. These patient-derived tumor organoids (PDTOs) have been utilized to establish biobanks and to explore tumor onset and progression, simulate the tumor microenvironment (TME), personalize treatment approaches, and integrate multidimensional data ( Table 1). ...
... This not only elucidates the consistency between the histological and molecular characteristics of the tumor organoids and their corresponding parental tumors but also gathers various tumor subtypes within the library. Moreover, it demonstrates the gene expression patterns of cells in the tumor organoids and enhances our understanding of their functional status in specific environments [14,73,80]. Combined with high-throughput drug screening, pharmacogenomic analysis can reveal the different response patterns and potential drug genomes of different molecular subtypes of tumor organoids under the gene-drug association mode. ...
The complexity of tumors and the challenges associated with treatment often stem from the limitations of existing models in accurately replicating authentic tumors. Recently, organoid technology has emerged as an innovative platform for tumor research. This bioengineering approach enables researchers to simulate, in vitro, the interactions between tumors and their microenvironment, thereby enhancing the intricate interplay between tumor cells and their surroundings. Organoids also integrate multidimensional data, providing a novel paradigm for understanding tumor development and progression while facilitating precision therapy. Furthermore, advancements in imaging and genetic editing techniques have significantly augmented the potential of organoids in tumor research. This review explores the application of organoid technology for more precise tumor simulations and its specific contributions to cancer research advancements. Additionally, we discuss the challenges and evolving trends in developing comprehensive tumor models utilizing organoid technology.
... In ALI culture, these supplemented culture media are also applied [18]. The PDTOs cultured by submerged Matrigel not only can recapitulate the histological, genetic, and phenotypic characteristics of original tumors, but also can model the patient response to clinical treatment, thus promoting disease modeling and drug screening [9,11,17,27]. However, conventional submerged Matrigel culture primarily supports the long-term proliferation of epithelial cells. ...
Cancer immunotherapy has revolutionized tumor treatment. However, robust and effective testing platforms remain lacking, especially for the selection of the optimized therapy at the patient-specific level. Unlike conventional treatment evaluations, testing platforms for cancer immunotherapy must incorporate not only tumor cells but also the tumor microenvironment (TME), including immune components. Recently, emergence of patient-derived tumor organoids (PDTOs), an in vitro preclinical model, has provided a novel approach for studying tumor evolution and assessing treatment responses, and shows great potential when coculturing with immune cells to study the mechanisms of immunotherapy efficacy and resistance. However, traditional organoid technology is limited in capturing the full impact of the TME on tumor behaviors due to the absence of stromal components. To circumvent these restrictions, complex organoid cocultures with immune cells, cancer-associated fibroblasts and vasculatures are developed. In this review, we summarized recent advances in PDTO culture techniques for modeling the TME and explored the application of complex tumor organoids in cancer immunotherapy.
... PDOs allow the culture of 3D organ-like structures from patient tissues that retain the genetic and phenotypic characteristics of the primary tumor, providing a more accurate model for studying tumor biology and detecting response to therapy, and representing a major advance in cancer research (Yan et al., 2018). PDOs can be categorized on the basis of their origin, such as tumor-derived organoids from cancerous tissue for tumorderived organoids, and from healthy tissue for normal tissuederived organoids. ...
The persistently high mortality rates associated with cancer underscore the imperative need for innovative, efficacious, and safer therapeutic agents, as well as a more nuanced understanding of tumor biology. Patient-derived organoids (PDOs) have emerged as innovative preclinical models with significant translational potential, capable of accurately recapitulating the structural, functional, and heterogeneous characteristics of primary tumors. When integrated with cutting-edge genomic tools such as CRISPR, PDOs provide a powerful platform for identifying cancer driver genes and novel therapeutic targets. This comprehensive review delves into recent advancements in CRISPR-mediated functional screens leveraging PDOs across diverse cancer types, highlighting their pivotal role in high-throughput functional genomics and tumor microenvironment (TME) modeling. Furthermore, this review highlights the synergistic potential of integrating PDOs with CRISPR screens in cancer immunotherapy, focusing on uncovering immune evasion mechanisms and improving the efficacy of immunotherapeutic approaches. Together, these cutting-edge technologies offer significant promise for advancing precision oncology.
... Additionally, the efficacy of GDC-0980 (a mammalian target of rapamycin (mTOR)/PI3K inhibitor) in PIK3CA mutant organoids varied, highlighting the importance of genetic context in determining drug responses. Yan et al. [64] established nine organoid lines and tested 37 drugs, revealing that ARID1A mutant organoids responded well to the ATR inhibitor VE-822. Additionally, the study showed that organoids can not only maintain patient-specific genetic mutations but also acquire rare genetic mutations, providing a stable genetic background for studying drug targets and facilitating personalized medicine approaches. ...
Tumor drug resistance presents a growing challenge in medical practice, particularly during anti-cancer therapies, where the emergence of drug-resistant cancer cells significantly complicates clinical treatment. In recent years, three-dimensional (3D) tumor culture technology, which more effectively simulates the in vivo physiological environment, has gained increasing attention in tumor drug resistance research and clinical applications. By mimicking the in vivo cellular microenvironment, 3D tumor culture technology not only recapitulates cell-cell interactions but also more faithfully reproduces the biological effects of therapeutic agents. Consequently, 3D tumor culture technology is emerging as a crucial tool in biomedical and clinical research. We summarize the benefits of 3D culture models and organoid technology, explore their application in the realm of drug resistance, drug screening, and personalized therapy, and discuss their potential application prospects and challenges in clinical transformation, with the aim of providing insights for optimizing cancer treatment strategies and advancing precision therapy.
... In contrast, patient-derived tumor organoids (PDOs) are emerging as an invaluable system for studying immune-cancer cell interactions across several solid tumor types and optimizing combinatorial immunotherapy strategies 27,[30][31][32][33][34][35][36][37][38] . PDOs maintain the complex 3D tissue architecture of primary tumors and recapitulate individual tumor phenotypes, including inter-and intratumor drug response heterogeneity [39][40][41][42][43] and patient responses to chemotherapies 41,[44][45][46][47][48][49][50] . In a recent study, PDOs in co-culture with tumor-infiltrating lymphocytes were established as a scalable immunotherapeutic modeling system suitable for combinatorial therapy testing 37 . ...
Intratumoral heterogeneity drives therapy resistance and relapses in advanced stage cancers, such as ovarian cancer. Here, we present a live cell imaging assay using patient-derived ovarian cancer organoids for real time capture and quantification of natural killer cell-mediated apoptotic events in >500 organoids simultaneously. Our assay revealed significant inter- and intratumor response heterogeneity and identified a rare resistant organoid population, opening avenues to test immunomodulatory strategies that overcome resistance.
... Recently, culture conditions for HNSCC organoids were published that accurately preserved the patient-specific molecular and phenotypic tumor characteristics [37]. PDTO biobanks can serve as a valuable resource to study personalized responses to treatments, validate potential biomarkers and model disease progression by targeted genetic modifications [30,31,34,[37][38][39][40][41][42][43][44]. Here, we have established the head and neck organoid biobank (HNOB) to address two key aspects: first, to characterize individual and cancer driver-specific tumor characteristics and second, to generate genetically defined TP53/HPV models to address driver-specific consequences on the tumor phenotype and response to radiation therapy. ...
Background
Head and neck cancers (HNC) represent an extremely heterogeneous group of diseases with a poorly predictable therapy outcome. Patient-derived tumor organoids (PDTO) offer enormous potential for individualized therapy testing and a better mechanistic understanding of the main HNC drivers.
Methods
Here, we have established a comprehensive molecularly and functionally characterized head and neck organoid biobank (HNOB) recapitulating the clinically relevant subtypes of TP53 mutant and human papillomavirus type 16 (HPV 16) infection-driven HNC. Organoids were exposed to radiotherapy, and responses were correlated with clinical data. Genetically engineered normal and tumor organoids were used for testing the direct functional consequences of TP53-loss and HPV infection.
Results
The HNOB consisting of 18 organoid models, including 15 tumor models, was generated. We identified subtype-associated transcriptomic signatures and pathological features, including sensitivity to TP53 stabilization by the MDM2 inhibitor Nutlin-3. Furthermore, we describe an in vitro radio response assay revealing phenotypic heterogeneity linked to the individual patient’s treatment outcome, including relapse probability. Using genetically engineered organoids, the possibility of co-existence of both cancer drivers was confirmed. TP53 loss, as well as HPV, increased growth in normal and tumor organoids. TP53 loss-of-function alone was insufficient to promote radiation resistance, whereas HPV 16 oncogenes E6/E7 mediated radiosensitivity via induction of cell cycle arrest.
Conclusion
Our results highlight the translational value of the head and neck organoid models not only for patient stratification but also for mechanistic validation of therapy responsiveness of specific cancer drivers.
... Moreover, organoids have emerged as invaluable tools in cancer research, enabling the in-depth study of a variety of tumors while preserving their inherent heterogeneity and complex tissue architecture. Their application spans across multiple cancer types, including colorectal [169][170][171] , liver 18,172,173 , pancreatic 174 , ovarian 175 , and gastric [176][177][178] cancers. In these contexts, organoids facilitate the exploration of molecular mechanisms underlying tumor biology, provide a platform for assessing drug sensitivity, and aid in the development of personalized treatment strategies. ...
Organoids are three-dimensional (3D) cell cultures derived from human pluripotent stem cells or adult stem cells that recapitulate the cellular heterogeneity, structure, and function of human organs. These microstructures are invaluable for biomedical research due to their ability to closely mimic the complexity of native tissues while retaining human genetic material. This fidelity to native organ systems positions organoids as a powerful tool for advancing our understanding of human biology and for enhancing preclinical drug testing. Recent advancements have led to the successful development of a variety of organoid types, reflecting a broad range of human organs and tissues. This progress has expanded their application across several domains, including regenerative medicine, where organoids offer potential for tissue replacement and repair; disease modeling, which allows for the study of disease mechanisms and progression in a controlled environment; drug discovery and evaluation, where organoids provide a more accurate platform for testing drug efficacy and safety; and microecological research, where they contribute to understanding the interactions between microbes and host tissues. This review provides a comprehensive overview of the historical development of organoid technology, highlights the key achievements and ongoing challenges in the field, and discusses the current and emerging applications of organoids in both laboratory research and clinical practice.
... This makes them an exciting alternative or complement to organoids. It is certainly feasible to compare, for instance, digital twins of cancer cell lines 142,143 with organoids synthesized for the same cell lines 144 . As discussed next, in this type of scenario, digital twins provide various advantages that need to be considered. ...
Digital twins (DTs) in precision medicine are increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. We argue that including mechanistic simulations that produce behavior based on explicitly defined biological hypotheses and multiscale mechanisms is beneficial. It enables the exploration of diverse therapeutic strategies and supports dynamic clinical decision-making through insights from network science, quantitative biology, and digital medicine.
... A tumor organoid, a 3D culture of cancer cells, has been regarded as an excellent drug screening platform for precision medicine (Walsh et al., 2017;Weeber et al., 2017). Many studies have reported that cancer organoids can recapitulate the heterogeneity of the original tumor (Lee et al., 2018;Sachs et al., 2018;Yan et al., 2018;Boretto et al., 2019). Among the current disease models, cancer cell lines are utilized widely with the lowest cost. ...
Background
Sigmoid colon cancer with spinal metastases is rare in distant metastasis. In addition, the prognosis of colon cancer patients with spinal metastases is extremely poor. In order to find effective therapeutic agents, we need to know the biological characteristics of such patients from related models.
Methods
We collected sigmoid colon cancer tissue from a young female subject who was diagnosed with sigmoid colon cancer with multiple spinal metastases. We successfully established a sigmoid colon cancer organoid using this tissue and investigated drug screening in the patient. HE staining, immunohistochemistry, and DNA sequencing were utilized to compare the biological characteristics between the original tumor and the organoid. Furthermore, we investigated the drug screening of the sigmoid colon cancer organoid in vitro.
Results
A colon cancer organoid from sigmoid colon cancer with spinal metastases was successfully established. The organoid culture maintained the morphological features, histological features, and genomic landscape of the corresponding sigmoid colon cancer cells. Moreover, we performed drug screening tests to evaluate the effects of chemotherapeutic drugs and targeted drugs.
Conclusion
The sigmoid colon cancer organoid with spinal metastases was a favorable preclinical model to explore the clinicopathologic characteristics of colon cancer patients with spinal metastases.
... intestine,133,134 lung,135,136 stomach,13 esophagus,137 bone,138,139 skin,140,141 retina,142 cochlea,143 etc. Regarding regulation of cell microenvironment, biochemical and physical factors, especially cytokines and environmental stress, exert greatest influence on cell positioning and cell assembly (Figure 5a,b).Hydrogel microsphere, by virtue of biofriendly and easy-to-mold material, is an ideal scaffold for cell assembly, providing sufficient space for cell clustering. Confined within small spaces, cells tend to proliferate to fill intercellular gaps, which is proved efficient for cell assembly,ultimately leading to the formation of desired structures by control of microenvironment. ...
Research on cells and organ‐like tissues is critical in the fields of molecular biology, genetic analysis, proteomics analysis, tissue engineering, and others. In recent years, advancements in precise cell manipulation technologies have made precise positioning and batch processing of cells feasible. Various methods are used for cell recognition, positioning, manipulation, and assembly, often introducing external fields such as electric, magnetic, acoustic, or optical fields into the liquid environment to interact with cells, applying forces to induce cell movement and rearrangement. Alternatively, three‐dimensional (3D) bioprinting technology is employed for precise cell positioning and assembly. This review will comprehensively assess the status, principles, advantages, disadvantages, and prospects of these precise cell manipulation technologies, covering single‐cell manipulation, multicellular assembly, and biological 3D printing techniques.
... The organoids are cultivated under precise conditions that preserve the histological architecture, genetic variations, and sensitivity to drugs that resemble those found in the original tumor [1][2][3][4][5][6]. These models derived from patients serve as a system for the rapid screening of prospective treatments, providing scientists with the means to test new drug efficacy and safety in models that closely resemble actual tumors found in vivo [7][8][9][10]. Furthermore, PD-LCOs encapsulate the diverse cell populations found in lung cancer, which is crucial for investigating the fundamental mechanisms of disease evolution and the emergence of resistance to treatments [11][12][13]. ...
Background
Patient-derived lung cancer organoids (PD-LCOs) demonstrate exceptional potential in preclinical testing and serve as a promising model for the multimodal management of lung cancer. However, certain lung cancer cells derived from patients exhibit limited capacity to generate organoids due to inter-tumor or intra-tumor variability. To overcome this limitation, we have created an in vitro system that employs mesenchymal stromal cells (MSCs) or fibroblasts to serve as a supportive scaffold for lung cancer cells that do not form organoids.
Methods
We successfully established an MSCs/fibroblast co-culture system to form LCOs. We analyzed the morphological and histological similarities between LCOs co-cultured with fibroblast and primary lung cancer lesions through HE and IF staining. We evaluated whether LCOs co-cultured with fibroblast retained the original genetic mutations of their source tumors based on WES. RNA sequencing was used to analyze the differences in gene expression profiles between LCOs co-cultured with fibroblast and paracancerous organoids (POs). Importantly, we have successfully validated the impact of Kindlin-2 on the regulation of MSCs in organoid formation through lentiviral vector-mediated interference or overexpression of kindlin-2.
Results
Our findings demonstrate that the addition of MSCs/fibroblasts to three tumor samples, initially incapable of forming organoids by traditional methods, successfully facilitated the cultivation of tumor organoids. Importantly, these organoids co-cultured with fibroblast faithfully recapitulate the tissue morphology of original lung tumors and replicate the genetic profile observed in the parental tumors even after prolonged in vitro culture. Moreover, drug responses exhibited by these organoids co-cultured with MSCs/fibroblasts are consistent with those observed in the original tumors. Mechanistically, we have also identified kindlin-2 as a crucial regulator linking extracellular matrix (ECM) and mitochondria that influence MSC/fibroblast-mediated support for tumor organoid formation.
Conclusion
The results obtained from our research enhance the understanding of the mechanisms implicated in the formation of tumor organoids and aid in creating stronger patient-specific tumor organoid models. This advancement supports the refinement of personalized drug response assessments for use in clinical settings.
Graphical Abstract
... Following the establishment of protocols for generating non-tumor organoids [10,11], tumor organoids were generated in 2011 from mouse intestinal adenoma and endoscopic biopsy samples of colon cancer patients [12]. Since then, a wide range of tumor organoids have been developed from patient-derived tissue, mouse tumors, cancer cell lines, or stem cells with oncogenic mutations [3,13] to model multiple tumors, including breast [14], pancreatic [15], prostate [16], liver [17], brain [18], lung [19], gastric [20], ovarian [21], and bladder [22]. As a relatively new technology, tumor organoid models are still rapidly developing, for example, in terms of capturing more complex tumor physiology that includes tumor heterogeneity and microenvironment [3]. ...
Background: Tumor organoid and tumor-on-chip (ToC) platforms replicate aspects of the anatomical and physiological states of tumors. They, therefore, serve as models for investigating tumor microenvironments, metastasis, and immune interactions, especially for precision drug testing. To map the changing research diversity and focus in this field, we performed a quality-controlled text analysis of categorized academic publications and clinical studies. Methods: Previously, we collected metadata of academic publications on organoids or organ-on-chip platforms from PubMed, Web of Science, Scopus, EMBASE, and bioRxiv, published between January 2011 and June 2023. Here, we selected documents from this metadata corpus that were computationally determined as relevant to tumor research and analyzed them using an in-house text analysis algorithm. Additionally, we collected and analyzed metadata from ClinicalTrials.gov of clinical studies related to tumor organoids or ToC as of March 2023. Results and Discussion: From 3551 academic publications and 139 clinical trials, we identified 55 and 24 tumor classes modeled as tumor organoids and ToC models, respectively. The research was particularly active in neural and hepatic/pancreatic tumor organoids, as well as gastrointestinal, neural, and reproductive ToC models. Comparative analysis with cancer statistics showed that lung, lymphatic, and cervical tumors were under-represented in tumor organoid research. Our findings also illustrate varied research topics, including tumor physiology, therapeutic approaches, immune cell involvement, and analytical techniques. Mapping the research geographically highlighted the focus on colorectal cancer research in the Netherlands, though overall the specific research focus of countries did not reflect regional cancer prevalence. These insights not only map the current research landscape but also indicate potential new directions in tumor model research.
... Παράλληλα, τα οργανοειδή παρέχουν πληροφορίες για την παροχή εξατομικευμένης θεραπείας καθώς επαληθεύουν την ανταπόκριση του ασθενή σε αυτές. 32,33 Η σύσταση και η λειτουργία βιοτραπεζών, επομένως, ενισχύει το ακαδημαϊκό και το ερευνητικό έργο που σχετίζεται με την τεχνολογία οργανοειδών. Γεγονός που οδηγεί, τα τελευταία χρόνια, στην αύξηση των εγκαταστάσεων βιοτραπεζών οργανοειδών ώστε να παρέχουν ένα σύνολο πλεονεκτημάτων για τον τομέα της υγείας. ...
Η τεχνολογία των οργανοειδών έχει αποτελέσει αντικείμενο ενδιαφέροντος για την ακαδημαϊκή και επιστημονική κοινότητα. Με την αποθήκευση, ειδικότερα, των οργανοειδών σε βιοτράπεζες παρουσιάστηκαν νέες ερευνητικές δυνατότητες στους τομείς της βιοϊατρικής και της βιοτεχνολογίας. Παράλληλα, όμως, με τη συμβολή των βιοτραπεζών στην επιστημονική διερεύνηση της φυσιολογίας και της λειτουργίας των οργανοειδών, τίθενται και άλλες, πιο σύνθετες ηθικές και νομικές προκλήσεις. Το παρόν άρθρο αποσκοπεί στην ανάλυση των πολύπλευρων ηθικών και νομικών ζητημάτων που εντοπίζονται στη λειτουργία των βιοτραπεζών οργανοειδών και στη σύσταση μέτρων που πρέπει να ληφθούν σε διάφορα επίπεδα έτσι, ώστε να διασφαλιστεί, ηθικά και νομικά, η συμβολή τους στην επιστήμη. Τα ζητήματα αυτά αφορούν στη συγκατάθεση μετά από ενημέρωση και στην προστασία της ιδιωτικής ζωής των δωρητών κυττάρων, στην εμπορευματοποίηση των οργανοειδών και στην ενδεχόμενη παύση λειτουργίας της βιοτράπεζας. Αναφέρονται, επιπροσθέτως, οι υφιστάμενες κατευθυντήριες γραμμές και τα κανονιστικά πλαίσια που ισχύουν για τα οργανοειδή και τις βιοτράπεζες. Συμπεραίνεται ότι, παρά τις σοβαρές ηθικές και νομικές προκλήσεις που θέτει η χρήση των βιοτραπεζών οργανοειδών, απαιτείται να διασφαλίσουμε τη λειτουργία των βιοτραπεζών ώστε, κατ’ επέκταση, να αξιοποιήσουμε τα δυνητικά οφέλη τους. Προς αυτή την κατεύθυνση, συνιστάται η τυποποίηση διαδικασιών, η σύνταξη πρωτοκόλλων διασφάλισης και η εξασφάλιση ελέγχου ποιότητας. Υπό αυτή την οπτική, οι βιοτράπεζες οργανοειδών μπορούν να συνεισφέρουν στη μεγιστοποίηση των βιοϊατρικών και κοινωνικών οφελών της τεχνολογίας των οργανοειδών αλλά και να συμβάλλουν στη μελλοντική διακυβέρνηση της τεχνολογίας των οργανοειδών.
... • Mechanically dissociating mouse stomach with micro-dissecting scissors and fine forceps; cutting into pieces < 5 mm²; shaking in DPBS (without Ca 2+ and Mg 2+ , with EDTA); followed by pipetting up and down [109] • Cutting human samples into ~ 5 mm pieces; incubating with chelating solution; pipetting up and down to extract glands [16,110] • Mincing epithelial tissue with surgical razors; digesting fragments with collagenase type I [111] • Mincing gastric cancer specimens following enzymatic digestion with dispase II and collagenase XI [112,113] , or with EDTA and TrypLE [13] , or Liberase TH and TrypLE Express [114] , or collagenase and hyaluronidase [115] ++ ...
... The PDO model has fast modeling speed and a high success rate and allows for the recapitulation of key attributes such as tissue structure, heterogeneity and physiological characteristics of the original tumor, providing an ideal model for mimicking the parental tumor in vitro. A broad spectrum of cancer PDO models have been successfully established, allowing a better understanding of cancer development patterns and providing an ideal model for exploring how drugs interact with tumors [7][8][9][10][11][12][13][14][15][16]. The results of existing studies have shown that PDO drug susceptibility testing can not only reflect the previous clinical treatment response of patients but also predict effective individualized treatment options for patients, providing a new method for drug research and development [10,[17][18][19][20][21][22]. ...
... As a result, coupling molecular and therapeutic profiling of living PDOs can aid in clinical outcome prediction and lead to tailored cancer treatment and drug development [75] (Fig. 2b). Several studies have shown different living biobanks from organoids, such as cancer tissues, including those of colorectal cancer [76][77][78][79], gastric [80,81], prostate [82], lung [83,84], liver [85,86] ovary [87], bladder [88], breast [9] and pancreatic cancer [89]. ...
One of the most famous and heterogeneous cancers worldwide is breast cancer (BC). Owing to differences in the gene expression profiles and clinical features of distinct BC subtypes, different treatments are prescribed for patients. However, even with more thorough pathological evaluations of tumors than in the past, available treatments do not perform equally well for all individuals. Precision medicine is a new approach that considers the effects of patients’ genes, lifestyle, and environment to choose the right treatment for an individual patient. As a powerful tool, the organoid culture system can maintain the morphological and genetic characteristics of patients’ tumors. Evidence also shows that organoids have high predictive value for patient treatment. In this review, a variety of BC studies performed on organoid culture systems are evaluated. Additionally, the potential of using organoid models in BC translational research, especially in immunotherapy, drug screening, and precision medicine, has been reported.
... We found that CD8-GZMH, CD8-PDCD1 and NK-FGFBP2 cells were distributed predominantly in the TME (Fig. 5a). With respect to reported T cell function-associated gene sets ( Fig. 5b and Extended Data Fig. 5a) 23,24 , we annotated the identities of several clusters of CD8 + T cells (GZMH-effector, GZMK-effector memory, ZNF683-resident memory and PDCD1-exhausted), conventional CD4 + T cells (CCR7-naive, IL7R-central memory and IL21-follicular helper) as well as FOXP3-resting and CTLA4-immunosuppressive populations of CD4 + regulatory T cells (T reg ) 25 . Pseudotime trajectory analysis revealed a dichotomous structure of both CD4 + and CD8 + T cell differentiation, which reflected the classical perspective on T cell developmental dynamics proposed by previous studies (Fig. 5c,d) 23,26,27 . ...
Olfactory neuroblastoma (ONB) is a rare malignancy known to originate from the olfactory epithelium. The complex tumor ecosystem of this pathology remains unclear. Here, we explored the cellular components within ten ONB tumors and one olfactory mucosa sample based on single-cell RNA profiles. We showed the intra-tumoral heterogeneity by identifying five unique expression programs among malignant epithelial cells. A distinct three-classification system (neural, basal, mesenchymal) for ONB was established according to the distinguished gene expression patterns. Biomarkers for categorizing bulk tumors into uncharacterized subtypes were elucidated. Different responses towards certain chemotherapy regimens could be cautiously inferred according to the molecular features representing the three tumor types, thus helping with precision chemotherapy. We also analyzed subclusters of the tumor microenvironment (TME) and the interactions among different cell types within the TME. The relative abundance of immunosuppressive tumor-associated macrophages suggests potential benefits of immunotherapies targeting macrophages.
... 17 Evaluating the effects of drugs on tumor organoids requires a multifaceted approach, encompassing drug preparation, drug screening, and data analysis. 18 The drug preparation process involves selecting, dissolving, and diluting the drug to ensure that its quality and concentration meet the experimental requirements. Drug screening involves treating tumor cells with a drug and observing its efficacy and toxicity, among other parameters. ...
Tumor organoids have emerged as an ideal in vitro model for patient-derived tissues, as they recapitulate the characteristics of the source tumor tissue to a certain extent, offering the potential for personalized tumor therapy and demonstrating significant promise in pharmaceutical research and development. However, establishing and applying this model involves multiple labor-intensive and time-consuming experimental steps and lacks standardized protocols and uniform identification criteria. Thus, high-throughput solutions are essential for the widespread adoption of tumor organoid models. This review provides a comprehensive overview of current high-throughput solutions across the entire workflow of tumor organoids, from sampling and culture to drug screening. Furthermore, we explore various technologies that can control and optimize single-cell preparation, organoid culture, and drug screening with the ultimate goal of ensuring the automation and high efficiency of the culture system and identifying more effective tumor therapeutics.
... 119 Another study highlighted a high concordance, even after long-term culture, in terms of genomic and transcriptomic profiles between gastric tumoroids, encompassing different subtypes, and the corresponding in vivo tumor tissues. 120 In addition to the genomic signature, tumoroids faithfully conserve the specific epigenomic features of the modeled tumor type. 111 The analysis of the DNA methylation profile in colon organoids cultured in vitro for a prolonged period (12-14 months) revealed the occurrence of spontaneous promoter hyper-methylation, reminiscent of an aging-like process. ...
Cancer represents one of the diseases with the highest mortality rate worldwide. The burden of cancer continues to increase, not only affecting the health-related quality of life of patients but also causing an elevated global financial impact. The complexity and heterogeneity of cancer pose significant challenges in research and clinical practice, contributing to increase the failure rate of clinical trials for antitumoral drugs. This is partially due to the fact that preclinical models still present important limitations in faithfully recapitulating human tumors to serve as reliable indicators of drug effectiveness. Up to now, research and development strategies employ expensive animal models (including the so-called “humanized mice”) that not only raise ethical concerns, but also frequently fail to accurately predict responses to anticancer drugs because they do not faithfully replicate human physiology as well as the patient's tumor microenvironment. On the other side, traditional two-dimensional (2D) cell cultures fail to adequately reproduce the structural organization of tumor and the cellular heterogeneity found in vivo. The growing necessity to develop more accurate cancer models has increasingly emphasized the importance of three-dimensional (3D) in vitro cell cultures, such as cancer-derived spheroids and organoids, as promising alternatives to bridge the gap between 2D and animal models. In this review, we provide a brief overview focusing on 3D in vitro cell cultures as preclinical models capable of properly reproducing the tissue organization, biological composition, and complexity of in vivo tumors in a fine-tuned microenvironment. Despite their limitations, these models collectively enhance our understanding of the mechanisms underlying cancer and may offer the potential for a more reliable assessment of drug efficacy before clinical testing and, consequently, improve therapeutic outcomes for cancer patients.
... Complementary to existing breast cancer models, PDOs accurately preserve the complexity of human tissue, maintaining the inter-tumor and inter-patient heterogeneity that is often not represented in cell lines and spheroids cultures. Additionally, PDOs enable the study of dynamic biological processes that cannot be captured by static, fixated tissue samples (36,39,(49)(50)(51). ...
Cellular plasticity is enhanced by dedifferentiation processes such as epithelial-mesenchymal transition (EMT). The dynamic and transient nature of EMT-like processes challenges the investigation of cell plasticity in patient-derived breast cancer models. Here, we utilized patient-derived organoids (PDOs) as a model to study the susceptibility of primary breast cancer cells to EMT. Upon induction with TGF-β, PDOs exhibited EMT-like features, including morphological changes, E-cadherin downregulation and cytoskeletal reorganization, leading to an invasive phenotype. Image analysis and the integration of deep learning algorithms enabled the implantation of microscopy-based quantifications demonstrating repetitive results between organoid lines from different breast cancer patients. Interestingly, epithelial plasticity was also expressed in terms of alterations in luminal and myoepithelial distribution upon TGF-β induction. The effective modeling of dynamic processes such as EMT in organoids and their characteristic spatial diversity highlight their potential to advance research on cancer cell plasticity in cancer patients.
... 48 One of the most important advantages of the organoid cultures is that the characteristics of the original tumour tissue such as mutation signatures, phenotype and genetic diversity are well preserved in organoids, as evidenced by the analysis of multiple long-term organoid cultures. 20 24 28 34 38 44 49-51 Moreover, it has been shown that organoids derived from tumour samples preserve the histopathological characteristics of the original tumour both in vitro and following xenotransplantation. 35 Therefore, xenotransplantation can be used to validate drug responses in a more representative in vivo environment. ...
Introduction
Organoids have been successfully used in several areas of cancer research and large living biobanks of patient-derived organoids (PDOs) have been developed from various malignancies. The characteristics of the original tumour tissue such as mutation signatures, phenotype and genetic diversity are well preserved in organoids, thus showing promising results for the use of this model in translational research. In this study, we aim to assess whether we can generate PDOs from head and neck squamous cell carcinoma (HNSCC) samples and whether PDOs can be used to predict treatment sensitivity in HNSCC patients as well as to explore potential biomarkers.
Methods and analysis
This is a prospective observational study at a single centre (Guy’s and St Thomas’ NHS Foundation Trust) to generate PDOs from patients’ samples to assess treatment response and to correlate with patients’ treatment outcomes. Patients will be included if they are diagnosed with HNSCC undergoing curative treatment (primary surgery or radiotherapy) or presenting with recurrent or metastatic cancers and they will be categorised into three groups (cohort 1: primary surgery, cohort 2: primary radiotherapy and cohort 3: recurrent/metastatic disease). Research tumour samples will be collected and processed into PDOs and chemosensitivity/radiosensitivity will be assessed using established methods. Moreover, blood and other biological samples (eg, saliva) will be collected at different time intervals during treatment and will be processed in the laboratory for plasma and peripheral blood mononuclear cell (PBMC) isolation. Plasma and saliva will be used for circulating tumour DNA analysis and PBMC will be stored for assessment of the peripheral immune characteristics of the patients as well as to perform co-culture experiments with PDOs. SOTO study (correlation of the treatment Sensitivity of patient-derived Organoids with Treatment Outcomes in patients with head and neck cancer) uses the collaboration of several specialties in head and neck cancer and has the potential to explore multiple areas of research with the aim of offering a valid and effective approach to personalised medicine for cancer patients.
Ethics and dissemination
This study was approved by North West-Greater Manchester South Research Ethics Committee (REC Ref: 22/NW/0023) on 21 March 2022. An informed consent will be obtained from all participants prior to inclusion in the study. Results will be disseminated via peer-reviewed publications and presentations at international conferences.
Trial registration number
NCT05400239 .
To longitudinally evaluate pathologic response outcomes after neoadjuvant immuno-chemotherapy (NICT) for patients with locally advanced gastric cancer (LAGC) using pre- and post-treatment dual-energy CT (DECT).
Between Jan 2021 and Dec 2023, 115 patients who underwent NICT plus gastrectomy and triple-phase enhanced DECT scans before and after NICT were retrospectively enrolled. Pathologic tumor regression grade (TRG) was the reference standard, patients were labelled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) accordingly. A two-dimensional free-hand region of interest method was adopted to obtain the iodine concentration (IC) values. Pre- and post-NICT IC and normalized IC (nIC) were measured at arterial/venous/delay phase (AP/VP/DP), respectively; their changes [ΔIC (%)] defined as (IC_post-IC_pre)⁄IC_pre × 100% were calculated. Pre- and post-NICT imaging parameters and their changes were compared between different response groups. Non-responders' associated parameters were selected using multivariable logistic regression analysis. Their performances were analyzed by the area under the receiver operating characteristic curve (AUC). Their associations with patient survival were explored by using Kaplan–Meier survival analysis.
ICDP-pre, ΔICAP, thickness-post with cut-off value of > 2.306 mg/mL, ≤ 26.70%, > 18.5 mm, respectively, indicates non-responders with equivalent AUC being 0.616 (95% CI: 0.521–0.705), 0.625 (95% CI: 0.529–0.713), and 0.660 (95% CI: 0.565–0.745). Their combination demonstrated an improved AUC of 0.774 (95% CI: 0.686–0.846) and was associated with patient disease-free survival (DFS) with a hazard ratio being 2.239 (95% CI: 1.004–4.991) (p = 0.026).
Pre- and post-NICT DECT-based quantifications are useful for longitudinal assessment of pathologic response outcomes after NICT in LAGC. ICDP-pre, ΔICAP, and thickness-post are equally useful, their combination demonstrated incremental benefit.
Question Accurate evaluation of the efficacy of NICT in patients with LAGC remains challenging due to the lack of effective biomarkers.
Findings Sequential DECT-based ICDP-pre, ΔICAP, and tumor thickness-post were predictive of TRG status. Their combination demonstrated enhanced performance and was associated with patient DFS.
Clinical relevance DECT represents a promising imaging technique with added advantages for longitudinal assessment of pathologic response to NICT in LAGC, potentially facilitating more personalized treatment strategies among this population.
Organoids represent a significant advancement in disease modeling, demonstrated by their capacity to mimic the physiological/pathological structure and functional characteristics of the native tissue. Recently CRISPR/Cas9 technology has emerged as a powerful tool in combination with organoids for the development of novel therapies in preclinical settings. This review explores the current literature on applications of pooled CRISPR screening in organoids and the emerging role of these models in understanding cancer. We highlight the evolution of genome‐wide CRISPR gRNA library screens in organoids, noting their increasing adoption in the field over the past decade. Noteworthy studies utilizing these screens to investigate oncogenic vulnerabilities and developmental pathways in various organoid systems are discussed. Despite the promise organoids hold, challenges such as standardization, reproducibility, and the complexity of data interpretation remain. The review also addresses the ideas of assessing tumor organoids (tumoroids) against established cancer hallmarks and the potential of studying intercellular cooperation within these models. Ultimately, we propose that organoids, particularly when personalized for patient‐specific applications, could revolutionize drug screening and therapeutic approaches, minimizing the reliance on traditional animal models and enhancing the precision of clinical interventions.
BACKGROUND
The advent of organoid culture systems has revolutionized our ability to model and study complex tissues in vitro. The placenta is one of the last human organs to have a functional organoid model developed: trophoblast organoids. These 3-dimensional structures, derived from placental tissue, offer researchers a valuable tool for studying previously inaccessible processes that occur within the womb and play a significant role in determining the health of the offspring. While primarily used for research, trophoblast organoids hold promise for clinical applications, including prenatal diagnostics and therapeutic interventions, both of which may have commercial interest. However, to ensure that research with organoid models derived from the placenta is conducted responsibly, the relevant ethics of these models need to be addressed.
OBJECTIVE AND RATIONALE
Ethical considerations related to organoid models derived from the placenta, such as trophoblast organoids are important but remain unexplored in literature. Therefore, the goal of this review is to explore the ethical considerations related to trophoblast organoids.
SEARCH METHODS
Since there is no ethical research specifically addressing organoid models of the placenta to date, we have based our findings on discussions related to other organoid models and research involving fetal tissue, placenta, or umbilical cord blood. We employed a scoping review method to search PubMed, Embase, Medline (all), Bioethics Research Library, and Google Scholar for research articles, books, or other correspondence on ethical issues regarding these indicated topics, with no date limits.
OUTCOMES
Ethical considerations related to trophoblast organoids can be divided into three distinct categories. First, there is a need to assess the moral value of trophoblast organoids, including their potential relational and symbolic dimensions. Second, it is important to understand ethical issues associated with ownership and commercialization of trophoblast organoids. Last, there are considerations related to appropriate informed consent procedures. It is worth noting that these three categories are interconnected, with the second and third being largely dependent on the moral value attributed to trophoblast organoids. Future research should assess the perspectives of various stakeholders, including parents who may donate placental tissue for organoid research.
WIDER IMPLICATIONS
This review offers valuable insights into the ethical landscape surrounding the derivation of tissues or products from pregnancies, and their further application, highlighting areas that require attention and discussion within both the scientific community and the broader society.
REGISTRATION NUMBER
N/A.
Bone and cartilage tissues are essential for movement and structure, yet diseases like osteoarthritis affect millions. Traditional therapies have limitations, necessitating innovative approaches. Organoid technology, leveraging stem cells’ regenerative potential, offers a novel platform for disease modelling and therapy. This review focuses on advancements in bone/cartilage organoid technology, highlighting the role of stem cells, biomaterials, and external factors in organoid development. We discuss the implications of these organoids for regenerative medicine, disease research, and personalised treatment strategies, presenting organoids as a promising avenue for enhancing cartilage repair and bone regeneration. Bone/cartilage organoids will play a greater role in the treatment of bone/cartilage diseases in the future, and promote the progress of biological tissue engineering.
The field of cancer biology and therapeutics has soared in the past several decades with new therapeutic modalities and options for patients, such as chemoradiotherapy, immunotherapy, and combination therapy. This dramatic success in expanding patient options is primarily attributed to the development of various model systems to elucidate drivers of oncogenesis, tumor maturation and evolution, and response to therapeutics. While mouse models have been a workhorse of cancer research, technological progress in ex vivo patient‐derived tumor models has afforded more tunable and scrutable systems for patient‐predictive platforms and mechanistic study. This review explores the technological innovations in 3D solid tumor models and their applicability to various aspects of cancer biology and the identification of therapeutics. Features of the tumor and tumor microenvironment like spatial heterogeneity, multicellular populations, and genomic variations are addressed and elaborated through the establishment of new in vitro models. The integration of perfusable vasculature with 3D tumor models and the potentially wide‐ranging applications of these more complex platforms in precision medicine and cancer immunotherapy are further addressed. Finally, an outlook on the future of experimental cancer models for both biological investigation and bench‐to‐bedside pipeline development is provided.
Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored. This study applied multiomics approaches, integrating transcriptomic, epigenetic, and somatic mutation data, with consensus clustering algorithms to classify GC molecular subtypes and assess their biological and immunological features. A machine learning model was developed to create the Gastric Cancer Multi-Omics Programmed Cell Death Signature (GMPS), targeting PCD-related genes. We verified the expression of the GMPS hub genes using the RT-qPCR method. The prognostic influence of GMPS on GC was then evaluated. Single-cell analysis was performed to examine the heterogeneity of PCD characteristics in GC. Findings indicate that GMPS notably correlates with patient survival rates, tumor mutational burden (TMB), and copy number variations (CNV), demonstrating substantial prognostic predictive power. Moreover, GMPS is closely associated with the tumor microenvironment (TME) and immune therapy response. This research elucidates the molecular subtypes of GC, highlighting PCD’s critical role in prognosis assessment. The relationship between GMPS and immune therapy response, alongside gastric cancer’s microenvironmental features, provides insights for personalized treatment.
Highlights
•PDTOs represent a significant advancement in cancer research and personalized medicine, retaining genetic and molecular characteristics of original tumors.
•The success rates of establishing PDTOs vary widely, influenced by factors such as cancer type, tissue quality, and media composition.
•Recent studies have supported the correlation between PDTOs and corresponding patient responses, promoting their integration in clinical trials.
•PDTOs preserve oncogenic signatures of their cancer type, maintaining disrupted transcriptional pathways associated with specific cancers.
•Genomic evolution can occur in PDTOs during extended culture periods, potentially causing drift from the original tumors.
•PDTOs can be engineered using CRISPR technology to model cancer development and progression.
•Challenges in PDTO research include standardization of protocols, addressing tumor heterogeneity, and improving culture conditions for certain cancer types.
•The development of organoid biobanks has facilitated large-scale studies and drug screening efforts, contributing to advancements in cancer research and drug discovery.
Background
Gastric intestinal metaplasia (IM) is a precancerous stage spanning a morphological spectrum that is poorly represented by human cell line models.
Objective
We aim to establish and characterise human IM cell models to better understand IM progression along the cancer spectrum.
Design
A large human gastric IM organoid (IMO) cohort (n=28), their clonal derivatives and normal gastric organoids (n=42) for comparison were established. Comprehensive multi-omics profiling and functional characterisation were performed.
Results
Single-cell transcriptomes revealed IMO cells spanning a spectrum from hybrid gastric/intestinal to advanced intestinal differentiation. Their lineage trajectories connected different cycling and quiescent stem and progenitors, highlighting differences in gastric to IM transition and the potential origin of IM from STMN1 cycling isthmus stem cells. Hybrid IMOs showed impaired differentiation potential, high lineage plasticity beyond gastric or intestinal fates and reactivation of a fetal gene programme.Cell populations in gastric IM and cancer tissues were highly similar to those derived from IMOs and exhibited a fetal signature. Genomically, IMOs showed elevated mutation burden, frequent chromosome 20 gain and epigenetic deregulation of many intestinal and gastric genes. Functionally, IMOs were FGF10 independent and showed downregulated FGFR2. Several IMOs exhibited a cell-matrix adhesion independent subpopulation that displayed chromosome 20 gain but lacked key cancer driver mutations, potentially representing the earliest neoplastic precursor of IM-induced gastric cancer.
Conclusions
Overall, our IMO biobank captured the heterogeneous nature of IM, revealing mechanistic insights on IM pathogenesis and progression, offering an ideal platform for studying early gastric neoplastic transformation and chemoprevention.
5-Fluorouracil (5-FU) chemoresistance contributes to poor therapeutic response and prognosis of gastric cancer (GC), for which effective strategies to overcome chemoresistance are limited. Here, using a CRISPR-Cas9 system, we identified that nitrilase family member 2 (NIT2) reverses chemoresistance independent of its metabolic function. Depletion or low expression of NIT2 led to 5-FU resistance in GC cell lines, patient-derived organoids, and xenografted tumors. Mechanistically, NIT2 interacted with bromodomain-containing protein 1 (BRD1) to inhibit HBO1-mediated acetylation of histone H3 at lysine-14 (H3K14ac) and RELA-targeted oxidative phosphorylation (OXPHOS) gene expression. Upon 5-FU stimulation, NIT2 phosphorylation by Src at Y49 promoted the dissociation of NIT2 from BRD1, followed by binding to E3 ligase CCNB1IP1, causing autophagic degradation of NIT2. Consequently, reduced NIT2 protein resulted in BRD1 forming phase separation and binding to histone H3, as well as increased RELA stability due to suppression of inhibitor of growth family member 4–mediated RELA ubiquitination. In addition, NIT2 expression negatively correlated with H3K14ac and OXPHOS and positively correlated with the chemotherapeutic responses and prognosis of patients with GC. Our findings reveal the moonlighting function of NIT2 in chemoresistance and underscore that OXPHOS blockade by metformin enhances 5-FU chemosensitivity upon NIT2 loss.
The application of three-dimensional (3D) spheroids or organoids in drug testing and precision medicine has led to significant advancements in how cancer and other diseases are treated. Not only can these 3D structures mimic the architecture and structure of tumors, but organoids formed from primary patient samples are able to recapitulate the molecular and functional characteristics of the original patient tumors. These clinically and physiologically relevant organoids can therefore be used to address questions related to drug efficacy and resistance and can even be used to predict patient-specific drug responses. However, despite such evident advantages, the lack of a patient-specific tumor microenvironment (TME), or even the basic TME that includes sufficient immune cells and other cell types, limits the potential of these organoids in immunotherapy drug testing. As such, co-culture models of patient-derived organoids with immune cells have since been developed to explore cancer-immune interactions, monotherapy or combinatorial immunotherapy drug testing, and variable patient drug responses. Moreover, when coupled with artificial intelligence-driven platforms, these organoid models can be more efficiently utilized to identify better therapeutic options and improve health outcomes through precision medicine. This review aims to highlight the use of organoids and the broader implementations of such organoid models in functional precision medicine, particularly in the context of immunotherapy.
Background/aim:
Biomarkers indicating sensitivity to poly ADP-ribose polymerase (PARP) inhibitors have not yet been identified in gastric cancer. PARP inhibitors target homologous recombination deficiency (HRD); however, homologous recombination (HR) induces complex changes in gene expression, which makes it difficult to identify reliable biomarkers. In this study, we identified a multi-gene expression signature as a marker of PARP inhibitor sensitivity in gastric cancer.
Materials and methods:
Seven gastric cancer cell lines were evaluated for susceptibility to PARP inhibitors using a growth inhibition assay. Gene expression profiling (GEP) was used to identify differentially expressed genes between PARP inhibitor-sensitive and -resistant cell lines. The resulting gene set was subjected to cluster analysis using tumor samples from 250 patients who underwent gastrectomy for primary gastroesophageal junction and gastric adenocarcinoma. HRD was defined as a truncating mutation in one or more of 22 HR-related genes and HRD scores were calculated using whole-exome sequencing data.
Results:
In the growth inhibition assays, the HGC27 and HSC39 cell lines were sensitive to the PARP inhibitors, olaparib, and rucaparib, and were significantly correlated with the GEP results. Seven (2.8%) patients harbored truncating mutations in HR-related genes. A gene expression signature based on the top 100 high and low differentially expressed genes between sensitive and resistant cell lines revealed a patient cluster with a high prevalence of HR-related gene mutations and high HRD scores.
Conclusion:
The 100-gene expression signature identified in this study may serve as a valuable predictive biomarker for PARP inhibitor sensitivity in gastric cancer.
Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.
A new era of cancer management is underway in which treatments are being developed for the entire continuum of the disease process. The availability of genetically engineered and naturally occurring preclinical models serve as instructive platforms for evaluating therapeutic mechanisms. However, a major clinical challenge is that the entire malignancy process occurs across multiple scales including genetic mutations, malignant changes in cell behavior, dysregulated tumor microenvironments, and systemic adaptations in the host. A multi-disciplinary group of investigators coalesced at the National Cancer Institute Oncology Models Forum (NCI-OMF) with the overall goal to provide updates on the use of precision preclinical models of cancer. The benefits and limitations of preclinical models were discussed in order to identify strategies for maximizing opportunities in modeling that could inform future cancer prevention and treatment approaches. Our shared perspective is that the continuum of single cell, multi-cell, organoid, and in situ models are remarkable resources for the clinical challenges ahead. We provide a roadmap for parsing already available models and include preliminary recommendations for the application of next generation preclinical modeling in cancer intervention.
Analysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples.
SuperFreq is implemented in R and available on github at https://github.com/ChristofferFlensburg/SuperFreq .
Objective
Gastric cancer is the second leading cause of cancer-related deaths and the fifth most common malignancy worldwide. In this study, human and mouse gastric cancer organoids were generated to model the disease and perform drug testing to delineate treatment strategies.
Design
Human gastric cancer organoid cultures were established, samples classified according to their molecular profile and their response to conventional chemotherapeutics tested. Targeted treatment was performed according to specific druggable mutations. Mouse gastric cancer organoid cultures were generated carrying molecular subtype-specific alterations.
Results
Twenty human gastric cancer organoid cultures were established and four selected for a comprehensive in-depth analysis. Organoids demonstrated divergent growth characteristics and morphologies. Immunohistochemistry showed similar characteristics to the corresponding primary tissue. A divergent response to 5-fluoruracil, oxaliplatin, irinotecan, epirubicin and docetaxel treatment was observed. Whole genome sequencing revealed a mutational spectrum that corresponded to the previously identified microsatellite instable, genomic stable and chromosomal instable subtypes of gastric cancer. The mutational landscape allowed targeted therapy with trastuzumab for ERBB2 alterations and palbociclib for CDKN2A loss. Mouse cancer organoids carrying Kras and Tp53 or Apc and Cdh1 mutations were characterised and serve as model system to study the signalling of induced pathways.
Conclusion
We generated human and mouse gastric cancer organoids modelling typical characteristics and altered pathways of human gastric cancer. Successful interference with activated pathways demonstrates their potential usefulness as living biomarkers for therapy response testing.
Cancer organoids to model therapy response
Cancer organoids are miniature, three-dimensional cell culture models that can be made from primary patient tumors and studied in the laboratory. Vlachogiannis et al. asked whether such “tumor-in-a-dish” approaches can be used to predict drug responses in the clinic. They generated a live organoid biobank from patients with metastatic gastrointestinal cancer who had previously been enrolled in phase I or II clinical trials. This allowed the authors to compare organoid drug responses with how the patient actually responded in the clinic. Encouragingly, the organoids had similar molecular profiles to those of the patient tumor, reinforcing their value as a platform for drug screening and development.
Science , this issue p. 920
Background
Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease.
Methods and Findings
Three patients with oestrogen-receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program (CASCADE). Cases represented a range of management problems in breast cancer, including late relapse after early stage disease, de novo metastatic disease, discordant disease response, and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism (SNP) arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing.
Between cases, there were significant differences in mutational burden, driver mutations, mutational processes, and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross-seeding was found in each case, and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the role of mutational processes in subclonal evolution.
Limitations of this study include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with fresh specimens and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed.
Conclusions
This study highlights various mechanisms that shape the genome of metastatic breast cancer and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.
Identifying genetic biomarkers of synthetic lethal drug sensitivity effects provides one approach to the development of targeted cancer therapies. Mutations in ARID1A represent one of the most common molecular alterations in human cancer, but therapeutic approaches that target these defects are not yet clinically available. We demonstrate that defects in ARID1A sensitize tumour cells to clinical inhibitors of the DNA damage checkpoint kinase, ATR, both in vitro and in vivo. Mechanistically, ARID1A deficiency results in topoisomerase 2A and cell cycle defects, which cause an increased reliance on ATR checkpoint activity. In ARID1A mutant tumour cells, inhibition of ATR triggers premature mitotic entry, genomic instability and apoptosis. The data presented here provide the pre-clinical and mechanistic rationale for assessing ARID1A defects as a biomarker of single-agent ATR inhibitor response and represents a novel synthetic lethal approach to targeting tumour cells.
COSMIC, the Catalogue of Somatic Mutations in Cancer (http://cancer.sanger.ac.uk) is a high-resolution resource for exploring targets and trends in the genetics of human cancer. Currently the broadest database of mutations in cancer, the information in COSMIC is curated by expert scientists, primarily by scrutinizing large numbers of scientific publications. Over 4 million coding mutations are described in v78 (September 2016), combining genome-wide sequencing results from 28 366 tumours with complete manual curation of 23 489 individual publications focused on 186 key genes and 286 key fusion pairs across all cancers. Molecular profiling of large tumour numbers has also allowed the annotation of more than 13 million non-coding mutations, 18 029 gene fusions, 187 429 genome rearrangements, 1 271 436 abnormal copy number segments, 9 175 462 abnormal expression variants and 7 879 142 differentially methylated CpG dinucleotides. COSMIC now details the genetics of drug resistance, novel somatic gene mutations which allow a tumour to evade therapeutic cancer drugs. Focusing initially on highly characterized drugs and genes, COSMIC v78 contains wide resistance mutation profiles across 20 drugs, detailing the recurrence of 301 unique resistance alleles across 1934 drug-resistant tumours. All information from the COSMIC database is available freely on the COSMIC website.
Drug sensitivity testing utilizing preclinical disease models such as cancer cell lines is an important and widely used tool for drug development. Importantly, when combined with molecular data such as gene copy number variation or somatic coding mutations, associations between drug sensitivity and molecular data can be used to develop markers to guide patient therapies. The use of organoids as a preclinical cancer model has become possible following recent work demonstrating that organoid cultures can be derived from patient tumors with a high rate of success. A genetic analysis of colon cancer organoids found that these models encompassed the majority of the somatic variants present within the tumor from which it was derived, and capture much of the genetic diversity of colon cancer observed in patients. Importantly, the systematic sensitivity testing of organoid cultures to anticancer drugs identified clinical gene–drug interactions, suggestive of their potential as preclinical models for testing anticancer drug sensitivity. In this chapter, we describe how to perform medium/high-throughput drug sensitivity screens using 3D organoid cell cultures.
The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of “hallmark” gene sets as part of MSigDB. Each hallmark in this collection consists of a “refined” gene set, derived from multiple “founder” sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
Genome rearrangements, a hallmark of cancer, can result in gene fusions with oncogenic properties. Using DNA paired-end-tag (DNA-PET) whole-genome sequencing, we analyzed 15 gastric cancers (GCs) from Southeast Asians. Rearrangements were enriched in open chromatin and shaped by chromatin structure. We identified seven rearrangement hot spots and 136 gene fusions. In three out of 100 GC cases, we found recurrent fusions between CLDN18, a tight junction gene, and ARHGAP26, a gene encoding a RHOA inhibitor. Epithelial cell lines expressing CLDN18-ARHGAP26 displayed a dramatic loss of epithelial phenotype and long protrusions indicative of epithelial-mesenchymal transition (EMT). Fusion-positive cell lines showed impaired barrier properties, reduced cell-cell and cell-extracellular matrix adhesion, retarded wound healing, and inhibition of RHOA. Gain of invasion was seen in cancer cell lines expressing the fusion. Thus, CLDN18-ARHGAP26 mediates epithelial disintegration, possibly leading to stomach H(+) leakage, and the fusion might contribute to invasiveness once a cell is transformed.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.
Despite the enormous replication potential of the human liver, there are currently no culture systems available that sustain hepatocyte replication and/or function in vitro. We have shown previously that single mouse Lgr5+ liver stem cells can be expanded as epithelial organoids in vitro and can be differentiated into functional hepatocytes in vitro and in vivo. We now describe conditions allowing long-term expansion of adult bile duct-derived bipotent progenitor cells from human liver. The expanded cells are highly stable at the chromosome and structural level, while single base changes occur at very low rates. The cells can readily be converted into functional hepatocytes in vitro and upon transplantation in vivo. Organoids from α1-antitrypsin deficiency and Alagille syndrome patients mirror the in vivo pathology. Clonal long-term expansion of primary adult liver stem cells opens up experimental avenues for disease modeling, toxicology studies, regenerative medicine, and gene therapy.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Background & aims:
We previously established long-term, 3-dimensional culture of organoids from mouse tissues (intestine, stomach, pancreas, and liver) and human intestine and pancreas. Here we describe conditions required for long-term 3-dimensional culture of human gastric stem cells. The technology can be applied to study the epithelial response to infection with Helicobacter pylori.
Methods:
We generated organoids from surgical samples of human gastric corpus. Culture conditions were developed based on those for the mouse gastric and human intestinal systems. We used microinjection to infect the organoids with H pylori. Epithelial responses were measured using microarray and quantitative polymerase chain reaction analyses.
Results:
Human gastric cells were expanded indefinitely in 3-dimensional cultures. We cultured cells from healthy gastric tissues, single-sorted stem cells, or tumor tissues. Organoids maintained many characteristics of their respective tissues based on their histology, expression of markers, and euploidy. Organoids from healthy tissue expressed markers of 4 lineages of the stomach and self-organized into gland and pit domains. They could be directed to specifically express either lineages of the gastric gland, or the gastric pit, by addition of nicotinamide and withdrawal of WNT. Although gastric pit lineages had only marginal reactions to bacterial infection, gastric gland lineages mounted a strong inflammatory response.
Conclusions:
We developed a system to culture human gastric organoids. This system can be used to study H pylori infection and other gastric pathologies.
Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour-normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer.
Motivation: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs.
Results: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform.
Availability: BMIQ is freely available from http://code.google.com/p/bmiq/.
Contact:
a.teschendorff@ucl.ac.uk
Supplementary information:
Supplementary data are available at Bioinformatics online
Motivation:
Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.
Results:
To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy.
Availability and implementation:
STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
Objective:
Gastric adenocarcinoma (gastric cancer, GC) is a major cause of global cancer mortality. Identifying molecular programmes contributing to GC patient survival may improve our understanding of GC pathogenesis, highlight new prognostic factors and reveal novel therapeutic targets. The authors aimed to produce a comprehensive inventory of gene expression programmes expressed in primary GCs, and to identify those expression programmes significantly associated with patient survival.
Design:
Using a network-modelling approach, the authors performed a large-scale meta-analysis of GC transcriptome data integrating 940 gastric transcriptomes from multiple independent patient cohorts. The authors analysed a training set of 428 GCs and 163 non-malignant gastric samples, and a validation set of 288 GCs and 61 non-malignant gastric samples.
Results:
The authors identified 178 gene expression programmes ('modules') expressed in primary GCs, which were associated with distinct biological processes, chromosomal location patterns, cis-regulatory motifs and clinicopathological parameters. Expression of a transforming growth factor β (TGF-β) signalling associated 'super-module' of stroma-related genes consistently predicted patient survival in multiple GC validation cohorts. The proportion of intra-tumoural stroma, quantified by morphometry in tissue sections from gastrectomy specimens, was also significantly associated with stromal super-module expression and GC patient survival.
Conclusion:
Stromal gene expression predicts GC patient survival in multiple independent cohorts, and may be closely related to the intra-tumoural stroma proportion, a specific morphological GC phenotype. These findings suggest that therapeutic approaches targeting the GC stroma may merit evaluation.
We describe a computational method that infers tumor purity and malignant cell ploidy directly from analysis of somatic DNA alterations. The method, named ABSOLUTE, can detect subclonal heterogeneity and somatic homozygosity, and it can calculate statistical sensitivity for detection of specific aberrations. We used ABSOLUTE to analyze exome sequencing data from 214 ovarian carcinoma tumor-normal pairs. This analysis identified both pervasive subclonal somatic point-mutations and a small subset of predominantly clonal and homozygous mutations, which were overrepresented in the tumor suppressor genes TP53 and NF1 and in a candidate tumor suppressor gene CDK12. We also used ABSOLUTE to infer absolute allelic copy-number profiles from 3,155 diverse cancer specimens, revealing that genome-doubling events are common in human cancer, likely occur in cells that are already aneuploid, and influence pathways of tumor progression (for example, with recessive inactivation of NF1 being less common after genome doubling). ABSOLUTE will facilitate the design of clinical sequencing studies and studies of cancer genome evolution and intra-tumor heterogeneity.
The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.
Gastric cancer is a heterogeneous disease with multiple environmental etiologies and alternative pathways of carcinogenesis. Beyond mutations in TP53, alterations in other genes or pathways account for only small subsets of the disease. We performed exome sequencing of 22 gastric cancer samples and identified previously unreported mutated genes and pathway alterations; in particular, we found genes involved in chromatin modification to be commonly mutated. A downstream validation study confirmed frequent inactivating mutations or protein deficiency of ARID1A, which encodes a member of the SWI-SNF chromatin remodeling family, in 83% of gastric cancers with microsatellite instability (MSI), 73% of those with Epstein-Barr virus (EBV) infection and 11% of those that were not infected with EBV and microsatellite stable (MSS). The mutation spectrum for ARID1A differs between molecular subtypes of gastric cancer, and mutation prevalence is negatively associated with mutations in TP53. Clinically, ARID1A alterations were associated with better prognosis in a stage-independent manner. These results reveal the genomic landscape, and highlight the importance of chromatin remodeling, in the molecular taxonomy of gastric cancer.
Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.
High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it
remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed
the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional
consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions,
or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the
UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a
‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller
syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and
identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based
annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human
genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/.
It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data.
Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
Contact: mrobinson@wehi.edu.au
Gastric cancer is the world's second most common cause of cancer death. We analyzed gene expression patterns in 90 primary gastric cancers, 14 metastatic gastric cancers, and 22 nonneoplastic gastric tissues, using cDNA microarrays representing approximately 30,300 genes. Gastric cancers were distinguished from nonneoplastic gastric tissues by characteristic differences in their gene expression patterns. We found a diversity of gene expression patterns in gastric cancer, reflecting variation in intrinsic properties of tumor and normal cells and variation in the cellular composition of these complex tissues. We identified several genes whose expression levels were significantly correlated with patient survival. The variations in gene expression patterns among cancers in different patients suggest differences in pathogenetic pathways and potential therapeutic strategies.
Epstein-Barr virus (EBV), the etiological agent of infectious mononucleosis, has an important role in the oncogenesis of EBV-related malignant diseases. The association of EBV with gastric carcinoma cells has become well known recently, but there are only a few reports concerning its association with surrounding epithelia and infiltrating lymphocytes. In this study, we investigated the association of EBV with gastric carcinoma and surrounding cells.
One hundred and two cases of gastric carcinoma were studied. The specimens were studied for the presence of the EBV genome by polymerase chain reaction (PCR), and then by in situ hybridization (ISH) technique to determine the localization of EBV.
Of 97 informative cases, EBV was detected in 21 cases (21.6%) by the PCR method. ISH studies showed that EBV RNA was expressed in 5 of the 97 cases (5.2%) and was localized to the nuclei of carcinoma cells. All these 5 lesions were found in male patients. In these 5 cases, 3 were diffuse type and 2 were intestinal type, and all cases arose in the proximal region of the stomach. EBV RNA was not detected in non-neoplastic epithelia, but it was detected in 24 of the 97 cases (24.7%) in small lymphocytes.
EBV was detected in 5.2% of gastric carcinomas and in 24.7% of infiltrating lymphocytes by the ISH method. The high positive rate (21.6%) by the PCR method corresponds to the presence of the EBV genome in surrounding lymphocytes.
Epstein-Barr virus (EBV) was discovered 40 years ago from examining electron micrographs of cells cultured from Burkitt's lymphoma, a childhood tumour that is common in sub-Saharan Africa, where its unusual geographical distribution - which matches that of holoendemic malaria -indicated a viral aetiology. However, far from showing a restricted distribution, EBV - a gamma-herpesvirus - was found to be widespread in all human populations and to persist in the vast majority of individuals as a lifelong, asymptomatic infection of the B-lymphocyte pool. Despite such ubiquity, the link between EBV and 'endemic' Burkitt's lymphoma proved consistent and became the first of an unexpectedly wide range of associations discovered between this virus and tumours.
Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available¹. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens².
Recent sequencing analyses have shed light on heterogeneous patterns of genomic aberrations in human gastric cancers (GCs). To explore how individual genetic events translate into cancer phenotypes, we established a biological library consisting of genetically engineered gastric organoids carrying various GC mutations and 37 patient-derived organoid lines, including rare genomically stable GCs. Phenotype analyses of GC organoids revealed divergent genetic and epigenetic routes to gain Wnt and R-spondin niche independency. An unbiased phenotype-based genetic screening identified a significant association between CDH1/TP53 compound mutations and the R-spondin independency that was functionally validated by CRISPR-based knockout. Xenografting of GC organoids further established the feasibility of Wnt-targeting therapy for Wnt-dependent GCs. Our results collectively demonstrate that multifaceted genetic abnormalities render human GCs independent of the stem cell niche and highlight the validity of the genotype-phenotype screening strategy in gaining deeper understanding of human cancers.
The recent advances in in vitro 3D culture technologies, such as organoids, have opened new avenues for the development of novel, more physiological human cancer models. Such preclinical models are essential for more efficient translation of basic cancer research into novel treatment regimens for patients with cancer. Wild-type organoids can be grown from embryonic and adult stem cells and display self-organizing capacities, phenocopying essential aspects of the organs they are derived from. Genetic modification of organoids allows disease modelling in a setting that approaches the physiological environment. Additionally, organoids can be grown with high efficiency from patient-derived healthy and tumour tissues, potentially enabling patient-specific drug testing and the development of individualized treatment regimens. In this Review, we evaluate tumour organoid protocols and how they can be utilized as an alternative model for cancer research.
Objective:
Serrated polyps (hyperplastic polyps, sessile or traditional serrated adenomas), which can arise in a sporadic or polyposis setting, predispose to colorectal cancer (CRC), especially those with microsatellite instability (MSI) due to MLH1 promoter methylation (MLH1(me+)). We investigate genetic alterations in the serrated polyposis pathway.
Design:
We used a combination of exome sequencing and target gene Sanger sequencing to study serrated polyposis families, sporadic serrated polyps and CRCs, with validation by analysis of The Cancer Genome Atlas (TCGA) cohort, followed by organoid-based functional studies.
Results:
In one out of four serrated polyposis families, we identified a germline RNF43 mutation that displayed autosomal dominant cosegregation with the serrated polyposis phenotype, along with second-hit inactivation through loss of heterozygosity or somatic mutations in all serrated polyps (16), adenomas (5) and cancer (1) examined, as well as coincidental BRAF mutation in 62.5% of the serrated polyps. Concurrently, somatic RNF43 mutations were identified in 34% of sporadic sessile/traditional serrated adenomas, but 0% of hyperplastic polyps (p=0.013). Lastly, in MSI CRCs, we found significantly more frequent RNF43 mutations in the MLH1(me+) (85%) versus MLH1(me-) (33.3%) group (p<0.001). These findings were validated in the TCGA MSI CRCs (p=0.005), which further delineated a significant differential involvement of three Wnt pathway genes between these two groups (RNF43 in MLH1(me+); APC and CTNNB1 in MLH1(me-)); and identified significant co-occurrence of BRAF and RNF43 mutations in the MSI (p<0.001), microsatellite stable (MSS) (p=0.002) and MLH1(me+) MSI CRCs (p=0.042). Functionally, organoid culture of serrated adenoma or mouse colon with CRISPR-induced RNF43 mutations had reduced dependency on R-spondin1.
Conclusions:
These results illustrate the importance of RNF43, along with BRAF mutation in the serrated neoplasia pathway (both the sporadic and familial forms), inform genetic diagnosis protocol and raise therapeutic opportunities through Wnt inhibition in different stages of evolution of serrated polyps.
The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs.
The latent expression pattern of Epstein-Barr Virus (EBV) genes in nasopharyngeal carcinoma (NPC) has been extensively investigated, and the expression of several lytic genes in NPC has been reported. However, comprehensive information through EBV transcriptome analysis in NPC is limited. We performed paired-end RNA-seq to systematically and comprehensively characterize the expression of EBV genes in NPC tissue and C666-1 NPC cell line, which consistently carries EBV. In addition to the transcripts restricted to type II latency infection, the type III latency EBNA3s genes and a substantial number of lytic genes, such as BZLF1, BRLF1, and BMRF1, were detected through RNA-seq and were further verified in C666-1 cells and NPC tissue through realtime PCR.We also performed clustering analysis to classify NPC patient groups in terms of EBV gene expression, which presented two subtypes of NPC samples. Results revealed interesting patterns of EBV gene expression in NPC patients. This clustering was correlated with many signaling pathways, such as those related to heterotrimeric G-protein signaling, inflammation mediated by chemokine and cytokine signaling, ribosomes, protein metabolism, influenza infection, and ECM-receptor interaction. Our combined findings suggested that the expression of EBV genes in NPC is restricted not only to type II latency genes but also to type III latency and lytic genes. This study provided further insights into the potential role of EBV in the development of NPC.
In Rspondin-based 3D cultures, Lgr5 stem cells from multiple organs form ever-expanding epithelial organoids that retain their tissue identity. We report the establishment of tumor organoid cultures from 20 consecutive colorectal carcinoma (CRC) patients. For most, organoids were also generated from adjacent normal tissue. Organoids closely recapitulate several properties of the original tumor. The spectrum of genetic changes within the "living biobank" agrees well with previous large-scale mutational analyses of CRC. Gene expression analysis indicates that the major CRC molecular subtypes are represented. Tumor organoids are amenable to high-throughput drug screens allowing detection of gene-drug associations. As an example, a single organoid culture was exquisitely sensitive to Wnt secretion (porcupine) inhibitors and carried a mutation in the negative Wnt feedback regulator RNF43, rather than in APC. Organoid technology may fill the gap between cancer genetics and patient trials, complement cell-line- and xenograft-based drug studies, and allow personalized therapy design. PAPERCLIP.
Copyright © 2015 Elsevier Inc. All rights reserved.
Gastric cancer, a leading cause of cancer-related deaths, is a heterogeneous disease. We aim to establish clinically relevant molecular subtypes that would encompass this heterogeneity and provide useful clinical information. We use gene expression data to describe four molecular subtypes linked to distinct patterns of molecular alterations, disease progression and prognosis. The mesenchymal-like type includes diffuse-subtype tumors with the worst prognosis, the tendency to occur at an earlier age and the highest recurrence frequency (63%) of the four subtypes. Microsatellite-unstable tumors are hyper-mutated intestinal-subtype tumors occurring in the antrum; these have the best overall prognosis and the lowest frequency of recurrence (22%) of the four subtypes. The tumor protein 53 (TP53)-active and TP53-inactive types include patients with intermediate prognosis and recurrence rates (with respect to the other two subtypes), with the TP53-active group showing better prognosis. We describe key molecular alterations in each of the four subtypes using targeted sequencing and genome-wide copy number microarrays. We validate these subtypes in independent cohorts in order to provide a consistent and unified framework for further clinical and preclinical translational research.
Tumor-derived cell lines have served as vital models to advance our understanding of oncogene function and therapeutic responses. Although substantial effort has been made to define the genomic constitution of cancer cell line panels, the transcriptome remains understudied. Here we describe RNA sequencing and single-nucleotide polymorphism (SNP) array analysis of 675 human cancer cell lines. We report comprehensive analyses of transcriptome features including gene expression, mutations, gene fusions and expression of non-human sequences. Of the 2,200 gene fusions catalogued, 1,435 consist of genes not previously found in fusions, providing many leads for further investigation. We combine multiple genome and transcriptome features in a pathway-based approach to enhance prediction of response to targeted therapeutics. Our results provide a valuable resource for studies that use cancer cell lines.
This unit describes how to use BWA and the Genome Analysis Toolkit (GATK) to map genome sequencing data to a reference and produce high-quality variant calls that can be used in downstream analyses. The complete workflow includes the core NGS data processing steps that are necessary to make the raw data suitable for analysis by the GATK, as well as the key methods involved in variant discovery using the GATK.
Diffuse-type gastric carcinoma (DGC) is characterized by a highly malignant phenotype with prominent infiltration and stromal induction. We performed whole-exome sequencing on 30 DGC cases and found recurrent RHOA nonsynonymous mutations. With validation sequencing of an additional 57 cases, RHOA mutation was observed in 25.3% (22/87) of DGCs, with mutational hotspots affecting the Tyr42, Arg5 and Gly17 residues in RHOA protein. These positions are highly conserved among RHO family members, and Tyr42 and Arg5 are located outside the guanine nucleotide-binding pocket. Several lines of functional evidence indicated that mutant RHOA works in a gain-of-function manner. Comparison of mutational profiles for the major gastric cancer subtypes showed that RHOA mutations occur specifically in DGCs, the majority of which were histopathologically characterized by the presence of poorly differentiated adenocarcinomas together with more differentiated components in the gastric mucosa. Our findings identify a potential therapeutic target for this poor-prognosis subtype of gastric cancer with no available molecularly targeted drugs.
Gastric cancer is a heterogeneous disease with diverse molecular and histological subtypes. We performed whole-genome sequencing in 100 tumor-normal pairs, along with DNA copy number, gene expression and methylation profiling, for integrative genomic analysis. We found subtype-specific genetic and epigenetic perturbations and unique mutational signatures. We identified previously known (TP53, ARID1A and CDH1) and new (MUC6, CTNNA2, GLI3, RNF43 and others) significantly mutated driver genes. Specifically, we found RHOA mutations in 14.3% of diffuse-type tumors but not in intestinal-type tumors (P < 0.001). The mutations clustered in recurrent hotspots affecting functional domains and caused defective RHOA signaling, promoting escape from anoikis in organoid cultures. The top perturbed pathways in gastric cancer included adherens junction and focal adhesion, in which RHOA and other mutated genes we identified participate as key players. These findings illustrate a multidimensional and comprehensive genomic landscape that highlights the molecular complexity of gastric cancer and provides a road map to facilitate genome-guided personalized therapy.
Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature.
We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications.
featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
shi@wehi.edu.au.
Unlabelled:
Cancer is a genetic disease with frequent somatic DNA alterations. Studying recurrent copy number aberrations (CNAs) in human cancers would enable the elucidation of disease mechanisms and the prioritization of candidate oncogenic drivers with causal roles in oncogenesis. We have comprehensively and systematically characterized CNAs and the accompanying gene expression changes in tumors and matched nontumor liver tissues from 286 hepatocellular carcinoma (HCC) patients. Our analysis identified 29 recurrently amplified and 22 recurrently deleted regions with a high level of copy number changes. These regions harbor established oncogenes and tumor suppressors, including CCND1 (cyclin D1), MET (hepatocyte growth factor receptor), CDKN2A (cyclin-dependent kinase inhibitor 2A) and CDKN2B (cyclin-dependent kinase inhibitor 2B), as well as many other genes not previously reported to be involved in liver carcinogenesis. Pathway analysis of cis-acting genes in the amplification and deletion peaks implicates alterations of core cancer pathways, including cell-cycle, p53 signaling, phosphoinositide 3-kinase signaling, mitogen-activated protein kinase signaling, Wnt signaling, and transforming growth factor beta signaling, in a large proportion of HCC patients. We further credentialed two candidate driver genes (BCL9 and MTDH) from the recurrent focal amplification peaks and showed that they play a significant role in HCC growth and survival.
Conclusion:
We have demonstrated that characterizing the CNA landscape in HCC will facilitate the understanding of disease mechanisms and the identification of oncogenic drivers that may serve as potential therapeutic targets for the treatment of this devastating disease.
Epstein-Barr virus (EBV) is a herpesvirus associated with lymphomas and carcinomas. While EBV-associated epithelial cell lines are good model systems to investigate the role of EBV in carcinoma, only a few cell lines are available as they are hard to acquire. A greater variety of naturally EBV infected cell lines which are derived from tumor patients are needed to represent various features of EBVaGC. We characterized cell line YCCEL1, established from a Korean EBVaGC patient to ascertain whether it can be used to study the roles of EBV in EBVaGC. The expression of EBV genes and cell surface markers was examined by in situ hybridization, RT-PCR, Western blot, immunofluorescence assay, and Northern blot. EBV episomal status was analyzed by Southern blot and real-time PCR. This cell line expressed EBV nuclear antigen (EBNA)1 and latent membrane protein (LMP) 2A, but not EBNA2, LMP2B, nor LMP1. The majority of the lytic proteins were not detected in YCCEL1 cells both before and after treatment with 12-O-tetradecanoylphorbol-13-acetate (TPA). YCCEL1 cells expressed BART microRNAs (miRNAs) at high level but did not express BHRF1 miRNAs. YCCEL1 cells expressed cytokeratin, but not CD21 and CD19, suggesting CD21-independent EBV infection. The latent EBV gene and EBV miRNA expression pattern of YCCEL1 cells closely resembled that of general EBVaGC cases. Our results support the value of YCCEL1 cells as a good model system to study the role of EBV in gastric carcinogenesis.
Whole genome and exome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. The consequent increased demand for somatic variant analysis of paired samples requires methods specialized to model this problem so as to sensitively call variants at any practical level of tumor impurity.
We describe Strelka, a method for somatic SNV and small indel detection from sequencing data of matched tumor-normal samples. The method uses a novel Bayesian approach which represents continuous allele frequencies for both tumor and normal samples, while leveraging the expected genotype structure of the normal. This is achieved by representing the normal sample as a mixture of germline variation with noise, and representing the tumor sample as a mixture of the normal sample with somatic variation. A natural consequence of the model structure is that sensitivity can be maintained at high tumor impurity without requiring purity estimates. We demonstrate that the method has superior accuracy and sensitivity on impure samples compared with approaches based on either diploid genotype likelihoods or general allele-frequency tests.
The Strelka workflow source code is available at ftp://strelka@ftp.illumina.com/.
csaunders@illumina.com
We report the characterization of six new gastric carcinoma cell lines (designated NCC-19, NCC-20, NCC-24, NCC-59, SNU-1750 and SNU-1967) established from primary tumor samples of Korean patients. Four cell lines grew as adherent monolayers, one as both adherent and floating cell clumps and one as floating cell aggregates. The cell phenotypes, including the histopathology of the primary tumors and in vitro growth characteristics, were determined. We also performed molecular characterization, including DNA fingerprinting analysis and abnormalities of K-ras, p53, β-catenin, and TGF-βRII genes by PCR-SSCP and sequencing analyses. Population doubling times varied from 47-135 h. All cell lines showed relatively high viability, absence of mycoplasma or bacteria contamination and genetic heterogeneity by DNA fingerprinting analysis. Three lines had p53 mutations; one line had mutations in codon 13 (Gly13Asp) in K-ras and no line had a β-catenin mutation. NCC-59 cell line had a -1-bp mutation in 10-bp poly deoxy adenine repeat tract of the TGF-βRII gene. Moreover, NCC-24 gastric cancer cell line was found to be infected with Epstein-Barr virus (EBV). EBV infection was also shown in the original carcinoma tissue of the NCC-24 cell line. These well-characterized six gastric cancer cell lines should serve as useful tools for investigating the biological characteristics of gastric cancer and, in particular, NCC-24 may serve as a valuable model system to clarify the precise role of EBV in gastric carcinogenesis.
The ability to successfully cryopreserve neural cells would represent an important advance with benefits to neural tissue engineering, neural transplantation, and neuroscience research. We have examined key factors responsible for damage to rat embryonic neural cells during cryopreservation using a two-step temperature profile, with an emphasis on the effects of cooling rate and plunge temperature. Our results indicate that the initial addition of 8% dimethyl sulfoxide (DMSO) and seeding of extracellular ice do not significantly decrease viable cell yield. However, subsequent freezing resulted in significant cell losses for all profile parameter combinations examined. A maximum post-thaw survival of 56% (compared to unfrozen controls) was observed after cooling at 2°C/min to -80°C followed by direct immersion in liquid nitrogen. Single-step removal of DMSO after thawing was associated with an additional 40-70% loss of viable cells, and the number of viable cells was further reduced by approximately 70% after 2 days of cell culture (resulting in a net viable cell yield of 9.6±0.4%). Nonetheless, the cryopreserved neurons that did survive displayed a normal morphology, including formation of neurites. Trends in neuronal viability conformed with predictions of existing theoretical models of cell freezing, with reduced survival for rapid cooling rates or high plunge temperatures (attributable to intracellular ice formation), and decreasing viability with increasing profile duration (consistent with the known effects of cell dehydration at suboptimal cooling rates). These observations suggest that neural cells are good candidates for further refinement of freezing profile design using a physics-based approach to parameter optimization.
Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
The study of gastric epithelial homeostasis and cancer has been hampered by the lack of stem cell markers and in vitro culture methods. The Wnt target gene Lgr5 marks stem cells in the small intestine, colon, and hair follicle. Here, we investigated Lgr5 expression in the stomach and assessed the stem cell potential of the Lgr5(+ve) cells by using in vivo lineage tracing. In neonatal stomach, Lgr5 was expressed at the base of prospective corpus and pyloric glands, whereas expression in the adult was predominantly restricted to the base of mature pyloric glands. Lineage tracing revealed these Lgr5(+ve) cells to be self-renewing, multipotent stem cells responsible for the long-term renewal of the gastric epithelium. With an in vitro culture system, single Lgr5(+ve) cells efficiently generated long-lived organoids resembling mature pyloric epithelium. The Lgr5 stem cell marker and culture method described here will be invaluable tools for accelerating research into gastric epithelial renewal, inflammation/infection, and cancer.
Epstein-Barr virus (EBV) may be a cofactor in the development of different malignancies, including several types of carcinomas. In this study, we investigated the presence of EBV in human breast cancers.
We used tissues from 100 consecutive primary invasive breast carcinomas, as well as 30 healthy tissues adjacent to a subset of the tumors. DNA was amplified by use of the polymerase chain reaction (PCR), with the primers covering three different regions of the EBV genome. Southern blot analysis was performed by use of a labeled EBV BamHI W restriction fragment as the probe. Infected cells were identified by means of immunohistochemical staining, using monoclonal antibodies directed against the EBV nuclear protein EBNA-1.
We were able to detect the EBV genome by PCR in 51% of the tumors, whereas, in 90% of the cases studied, the virus was not detected in healthy tissue adjacent to the tumor (P<.001). The presence of the EBV genome in breast tumors was confirmed by Southern blot analysis. The observed EBNA-1 expression was restricted to a fraction (5%-30%) of tumor epithelial cells. Moreover, no immunohistochemical staining was observed in tumors that were negative for EBV by PCR. EBV was detected more frequently in breast tumors that were hormone-receptor negative (P =.01) and those of high histologic grade (P =.03). EBV detection in primary tumors varied by nodal status (P =.01), largely because of the difference between subjects with more than three lymph nodes versus less than or equal to three lymph nodes involved (72% versus 44%).
Our results demonstrated the presence of the EBV genome in a large subset of breast cancers. The virus was restricted to tumor cells and was more frequently associated with the most aggressive tumors. EBV may be a cofactor in the development of some breast cancers.
Open source software encourages innovation by allowing users to extend the functionality of existing applications. Treeview
is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits
both its current utility and suitability as a platform for further development. Java Treeview is an open-source, cross-platform
rewrite that handles very large datasets well, and supports extensions to the file format that allow the results of additional
analysis to be visualized and compared. The combination of a general file format and open source makes Java Treeview an attractive
choice for solving a class of visualization problems. An applet version is also available that can be used on any website
with no special server-side setup.
Availability: http://jtreeview.sourceforge.net under GPL.