Fundamental differences in promoter CpG island DNA hypermethylation between human cancer and genetically engineered mouse models of cancer

Genetic and epigenetic alterations are essential for the initiation and progression of human cancer. We previously reported that primary human medulloblastomas showed extensive cancer-specific CpG island DNA hypermethylation in critical developmental pathways. To determine whether genetically engineered mouse models (GEMMs) of medulloblastoma have comparable epigenetic changes, we assessed genome-wide DNA methylation in three mouse models of medulloblastoma. In contrast to human samples, very few loci with cancer-specific DNA hypermethylation were detected, and in almost all cases the degree of methylation was relatively modest compared with the dense hypermethylation in the human cancers. To determine if this finding was common to other GEMMs, we examined a Burkitt lymphoma and breast cancer model and did not detect promoter CpG island DNA hypermethylation, suggesting that human cancers and at least some GEMMs are fundamentally different with respect to this epigenetic modification. These findings provide an opportunity to both better understand the mechanism of aberrant DNA methylation in human cancer and construct better GEMMs to serve as preclinical platforms for therapy development.     

Rapid validation of cancer genes in chimeras derived from established genetically engineered mouse models

Recent technological advances have opened the door for the fast and cost-effective generation of genetically engineered mouse models (GEMMs) to study cancer. We describe here a conceptually novel approach for the generation of chimeric GEMMs based on the controlled introduction of various genetic elements in embryonic stem cells (ESCs) that are derived from existing mouse strains with a predisposition for cancer. The isolation of GEMM-derived ESC lines is greatly facilitated by the availability of the newly defined culture media containing inhibitors that effectively preserve ESC pluripotency. The feasibility of the GEMM-ESC approach is discussed in light of current literature and placed into the context of existing models. This approach will allow for fast and flexible validation of candidate cancer genes and drug targets and will result in a repository of GEMM-ESC lines and corresponding vector collections that enable easy distribution and use of preclinical models to the wider scientific community.     

Cross-species analysis of genetically engineered mouse models of MAPK-driven colorectal cancer identifies hallmarks of the human disease

Effective treatment options for advanced colorectal cancer (CRC) are limited, survival rates are poor and this disease continues to be a leading cause of cancer-related deaths worldwide. Despite being a highly heterogeneous disease, a large subset of individuals with sporadic CRC typically harbor relatively few established ‘driver’ lesions. Here, we describe a collection of genetically engineered mouse models (GEMMs) of sporadic CRC that combine lesions frequently altered in human patients, including well-characterized tumor suppressors and activators of MAPK signaling. Primary tumors from these models were profiled, and individual GEMM tumors segregated into groups based on their genotypes. Unique allelic and genotypic expression signatures were generated from these GEMMs and applied to clinically annotated human CRC patient samples. We provide evidence that a Kras signature derived from these GEMMs is capable of distinguishing human tumors harboring KRAS mutation, and tracks with poor prognosis in two independent human patient cohorts. Furthermore, the analysis of a panel of human CRC cell lines suggests that high expression of the GEMM Kras signature correlates with sensitivity to targeted pathway inhibitors. Together, these findings implicate GEMMs as powerful preclinical tools with the capacity to recapitulate relevant human disease biology, and support the use of genetic signatures generated in these models to facilitate future drug discovery and validation efforts.KEY WORDS: KRAS, BRAF, MAPK, Colorectal cancer, GEMM, Genomic signatures     

Genetically engineered mouse models of human B-cell precursor leukemias

B-cell precursor acute lymphoblastic leukemias (pB-ALLs) are the most frequent type of malignancies of the childhood, and also affect an important proportion of adult patients. In spite of their apparent homogeneity, pB-ALL comprises a group of diseases very different both clinically and pathologically, and with very diverse outcomes as a consequence of their biology, and underlying molecular alterations. Their understanding (as a prerequisite for their cure) will require a sustained multidisciplinary effort from professionals coming from many different fields. Among all the available tools for pB-ALL research, the use of animal models stands, as of today, as the most powerful approach, not only for the understanding of the origin and evolution of the disease, but also for the development of new therapies. In this review we go over the most relevant (historically, technically or biologically) genetically engineered mouse models (GEMMs) of human pB-ALLs that have been generated over the last 20 years. Our final aim is to outline the most relevant guidelines that should be followed to generate an “ideal” animal model that could become a standard for the study of human pB-ALL leukemia, and which could be shared among research groups and drug development companies in order to unify criteria for studies like drug testing, analysis of the influence of environmental risk factors, or studying the role of both low-penetrance mutations and cancer susceptibility alterations.     

Autophagy is required for mitochondrial function,lipid metabolism,growth, and fate of KRASG12D-driven lung tumors

Evidence suggests that the role of autophagy in tumorigenesis is context dependent. Using genetically engineered mouse models (GEMMs) for human non-small-cell lung cancer (NSCLC), we found that deletion of the essential autophagy gene, Atg7, in KRAS^G12D-driven NSCLC inhibits tumor growth and converts adenomas and adenocarcinomas to benign oncocytomas characterized by the accumulation of respiration-defective mitochondria. Atg7 is required to preserve mitochondrial fatty acid oxidation (FAO) to maintain lipid homeostasis upon additional loss of Trp53 in NSCLC. Furthermore, cell lines derived from autophagy-deficient tumors depend on glutamine to survive starvation. This suggests that autophagy is essential for the metabolism, growth, and fate of NSCLC.     

Quantification of Tumor Burden in a Genetically Engineered Mouse Model of Lung Cancer by Micro-CT and Automated Analysis

Genetically engineered mouse models (GEMMs) of lung cancer closely recapitulate the human disease but suffer from the difficulty of evaluating tumor growth by conventional methods. Herein, a novel automated image analysis method for estimating the lung tumor burden from in vivo micro-computed tomography (micro-CT) data is described. The proposed tumor burden metric is the segmented soft tissue volume contained within a chest space region of interest, excluding an estimate of the heart volume. The method was validated by comparison with previously published manual analysis methods and applied in two therapeutic studies in a mutant K-ras GEMM of non–small cell lung carcinoma. Mice were imaged by micro-CT pre-treatment and stratified into four treatment groups: an antibody inhibiting vascular endothelial growth factor (anti-VEGF), chemotherapy, combination of anti-VEGF and chemotherapy, or control antibody. In the first study, post-treatment imaging was performed 4 weeks later. In the second study, mice were scanned serially on a high-throughput scanner every 2 weeks for 8 weeks during treatment. In both studies, the automated tumor burden estimates were well correlated with manual metrics (r value range: 0.83-0.93, P < .0001) and showed a similar, significant reduction in tumor growth in mice treated with anti-VEGF alone or in combination with chemotherapy. Given the fully automated nature of this technique, the proposed analysis method can provide a valuable tool in preclinical drug research for screening and randomizing animals into treatment groups and evaluating treatment efficacy in mouse models of lung cancer in a highly robust and efficient manner.     

Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer

Kinase inhibitors have limited success in cancer treatment because tumors circumvent their action. Using a quantitative proteomics approach, we assessed kinome activity in response to MEK inhibition in triple-negative breast cancer (TNBC) cells and genetically engineered mice (GEMMs). MEK inhibition caused acute ERK activity loss, resulting in rapid c-Myc degradation that induced expression and activation of several receptor tyrosine kinases (RTKs). RNAi knockdown of ERK or c-Myc mimicked RTK induction by MEK inhibitors, and prevention of proteasomal c-Myc degradation blocked kinome reprogramming. MEK inhibitor-induced RTK stimulation overcame MEK2 inhibition, but not MEK1 inhibition, reactivating ERK and producing drug resistance. The C3Tag GEMM for TNBC similarly induced RTKs in response to MEK inhibition. The inhibitor-induced RTK profile suggested a kinase inhibitor combination?therapy that produced GEMM tumor apoptosis and regression where single agents were ineffective. This approach defines mechanisms of drug resistance, allowing rational design of combination therapies for cancer.     

Towards Post-Meiotic Sperm Production: Genetic Insight into Human Infertility from Mouse Models

Declined quality and quantity of sperm is currently the major cause of patients suffering from infertility. Male germ cell development is spatiotemporally regulated throughout the whole developmental process. While it has been known that exogenous factors, such as environmental exposure, diet and lifestyle, et al, play causative roles in male infertility, recent progress has revealed abundant genetic mutations tightly associated with defective male germline development. In mammals, male germ cells undergo dramatic morphological change (i.e., nuclear condensation) and chromatin remodeling during post-meiotic haploid germline development, a process termed spermiogenesis; However, the molecular machinery players and functional mechanisms have yet to be identified. To date, accumulated evidence suggests that disruption in any step of haploid germline development is likely manifested as fertility issues with low sperm count, poor sperm motility, aberrant sperm morphology or combined. With the continually declined cost of next-generation sequencing and recent progress of CRISPR/Cas9 technology, growing studies have revealed a vast number of disease-causing genetic variants associated with spermiogenic defects in both mice and humans, along with mechanistic insights partially attained and validated through genetically engineered mouse models (GEMMs). In this review, we mainly summarize genes that are functional at post-meiotic stage. Identification and characterization of deleterious genetic variants should aid in our understanding of germline development, and thereby further improve the diagnosis and treatment of male infertility.     

Experimental model systems for preclinical research on Waldenstr?m macroglobulinemia

Waldenström macroglobulinemia (WM) is an incurable low-grade lymphoplasmacytic lymphoma of mature IgM⁺ B-lymphocytes that warrants additional research to increase therapeutic options, enhance quality of life, and improve survival of patients with WM. Here we concluded a miniseries of short reviews on the diagnosis and treatment ^[1], natural history ^[2] and putative cell-of-origin of WM ^[3] with a brief survey of preclinical experimental model systems available for fundamental and translational research studies on this enigmatic neoplasm. The model systems comprise of: ① continuous tumor cell lines, three of which are well authenticated and demonstrated to be derived from the patient's index tumor; ② human-in-mouse xenografts that rely on immunodeficient laboratory mice, adapted to carry small fragments of implanted human bone, to provide a suitable microenvironment for incoming lymphoma cells; and ③ genetically engineered mouse models (GEMMs) of neoplastic B-cell development, in which WM-like tumors arise spontaneously in the presence of fully functional innate and adaptive immune systems. Because none of the models developed thus far are perfect, additional efforts are required to achieve a better preclinical representation of disease characteristics of WM. To achieve that goal, the active involvement of basic and clinical research experts from China is called for, so novel drugs and immunotherapies for WM will reach clinics sooner, thereby ensuring the future of patients with WM will be brighter.     

Transgenic mouse models for the prevention of breast cancer

Breast cancer prevention research has made remarkable progress in the past decade. Much of this progress has come from clinical trials. However, in the future to test the many promising agents that are now available, pre-clinical models of breast cancer are needed. Such models are now available. Useful models include rat and mouse models, particularly, the genetically engineered mice (GEM). Many transgenic mouse models have been generated by manipulating growth factors and their receptors, cell cycle regulators, signal transduction pathways, cellular differentiation, oncogenes and tumor suppressor genes. The transgenes are induced to express in the mouse mammary glands under the control of various transgenic promoters, which have respective characteristics in expression pattern and other biological attributes. These models are providing invaluable insight on the molecular mechanisms of breast tumorigenesis. In this review, we discuss the relative relevance of the most commonly used transgenic mouse models for breast cancer prevention studies, and provide examples of how these transgenic models can be used to conduct cancer prevention research. Due to the multi-factor, multi-step nature of breast cancer, many factors should be incorporated into a valid prevention study. However, many barriers to progress must be overcome, including access to and availability of new cancer preventive drugs, and difficulties in conducting studies of combinations of preventive agents.     

Classical mathematical models for prediction of response to chemotherapy and immunotherapy

Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real world-data of human patients. In this study, we fitted a range of differential equation models to tumor volume measurements of patients undergoing chemotherapy or cancer immunotherapy for solid tumors. We used a large dataset of 1472 patients with three or more measurements per target lesion, of which 652 patients had six or more data points. We show that the early treatment response shows only moderate correlation with the final treatment response, demonstrating the need for nuanced models. We then perform a head-to-head comparison of six classical models which are widely used in the field: the Exponential, Logistic, Classic Bertalanffy, General Bertalanffy, Classic Gompertz and General Gompertz model. Several models provide a good fit to tumor volume measurements, with the Gompertz model providing the best balance between goodness of fit and number of parameters. Similarly, when fitting to early treatment data, the general Bertalanffy and Gompertz models yield the lowest mean absolute error to forecasted data, indicating that these models could potentially be effective at predicting treatment outcome. In summary, we provide a quantitative benchmark for classical textbook models and state-of-the art models of human tumor growth. We publicly release an anonymized version of our original data, providing the first benchmark set of human tumor growth data for evaluation of mathematical models.     

Comparison of Serum and Tissue Levels of Trace Elements in Different Models of Cervical Cancer

Cervical cancer is a leading cause of death by cancer among women worldwide. It is necessary to develop and refine cervical cancer models to more accurately reflect human tumor type. The relevance of cervical cancer to trace element was studied in this paper. By means of quantitative trace element analysis in models and patients with cervical cancer, the tissue and serum levels of trace elements in papillomaviruses-induced cancer models were more similar to that of patients than the levels in models induced by HeLa cell and methylcholanthrene. The results reflect papillomaviruses model most accurately mimic in vivo carcinogenesis of patients with cervical cancer. It will have a superior predictive value over HeLa cell and methylcholanthrene models in pre-clinical trials. The papillomaviruses-induced cervical cancer can provide more reliable models for testing the efficacy of drugs in treating human cancers.     

Personalized In Vitro Cancer Modeling — Fantasy or Reality?

With greater technological advancements and understanding of pathophysiology, “personalized medicine” has become a more realistic goal. In the field of cancer, personalized medicine is the ultimate objective, as each cancer is unique and each tumor is heterogeneous. For many decades, researchers have relied upon studying the histopathology of tumors in the hope that it would provide clues to understanding the pathophysiology of cancer. Current preclinical research relies heavily upon two-dimensional culture models. However, these models have had limited success in recreating the complex interactions between cancer cells and the stroma environment in vivo. Thus, there is increasing impetus to shift to three-dimensional models, which more accurately reflect this phenomenon. With a more accurate in vitro tumor model, drug sensitivity can be tested to determine the best treatment option based on the tumor characteristics. Many methods have been developed to create tumor models or “tumoroids,” each with its advantages and limitations. One significant problem faced is the replication of angiogenesis that is characteristic of tumors in vivo. Nonetheless, if three-dimensional models could be standardized and implemented as a preclinical research tool for therapeutic testing, we would be taking a step towards making personalized cancer medicine a reality.     

Understanding the Roles of FAK in Cancer: Inhibitors,Genetic Models,and New Insights

Focal adhesion kinase (FAK) is a protein tyrosine kinase that regulates cellular adhesion, motility, proliferation and survival in various types of cells. Interestingly, FAK is activated and/or overexpressed in advanced cancers, and promotes cancer progression and metastasis. For this reason, FAK became a potential therapeutic target in cancer, and small molecule FAK inhibitors have been developed and are being tested in clinical phase trials. These inhibitors have demonstrated to be effective by inducing tumor cell apoptosis in addition to reducing metastasis and angiogenesis. Furthermore, several genetic FAK mouse models have made advancements in understanding the specific role of FAK both in tumors and in the tumor environment. In this review, we discuss FAK inhibitors as well as genetic mouse models to provide mechanistic insights into FAK signaling and its potential in cancer therapy.     

Complex segregation analysis reveals a multigene model for lung cancer

Lung cancer risk is largely attributed to tobacco exposure, but genetic predisposition also plays an etiologic role. Several studies have investigated the involvement of genetic predisposition in lung cancer aggregation in affected families, although with inconsistent results. Some studies have provided evidence for Mendelian inheritance, whereas others have suggested that environmental models are most appropriate for lung cancer aggregation in families. To examine the genetic basis of lung cancer, we performed segregation analysis on 14,378 individuals from 1,561 lung cancer case families, allowing for the effects of smoking, sex, and age. Both a Mendelian decreasing model and a Mendelian codominant model were found to be the best fitting models for susceptibility. However, when we modeled age-of-onset, all Mendelian models and the environmental model were rejected suggesting that multiple genetic factors (possibly multiple genetic loci and interactions) contribute to the age-of-onset of lung cancer. The results provide evidence that multiple genetic factors contribute to lung cancer and may act as a guide in further studies to localize susceptibility genes in lung cancer.     

Components of the Mediterranean Diet with chemopreventive activity toward colorectal cancer

Colorectal cancer is one of the leading causes of cancer incidence and death worldwide. For the past 30 years researchers have tried to find drugs suitable for the chemoprevention of the diseases. Epidemiological studies have demonstrated that adherence to the Mediterranean Diet (MD) is protective toward colon cancer development. Key components of this diet are olive oil (OO), fruits and vegetables, legumes and fish. Importantly, many bioactives present in these foods have shown anti-cancer activity in in vitro as well as in animal models of colon carcinogenesis, indicating a possible use as preventive agents. However, when translated to human trials, single bioactives have yielded conflicting results mostly due to irreproducibility of pre-clinical data. Since our preclinical models are usually designed to test single molecules at high concentrations and for very short time in order to provide significant effects, future studies should employ multiple bioactives that would resemble a more natural approach with possible significant synergisms that might be critical for cancer prevention. In this review we will discuss data obtained with components of the MD with a specific focus on OO, omega-3 polyunsaturated fatty acids and apples.     

肝靶向动物模型研究与展望

肝癌动物模型是抗肝癌药物实验及肝靶向给药系统验证的重要方法和手段。本文对用于研究肝靶向制剂的动物模型的种类、特征、不足及应用进行了研究论述,提出了目前较适于应用的模型,应用肝癌动物模型可以提供与肝癌病人相似的肝癌生物学特性,也为肝靶向给药制剂药代动力学指标的可靠性提供了保障。