Using genetically engineered mouse models to validate candidate cancer genes and test new therapeutic approaches

Advances in Making Cancer Mouse Models More Accessible and Informative through Non-Germline Genetic Engineering

Genetically engineered mouse models (GEMMs) allow for modeling of spontaneous tumorigenesis within its native microenvironment in mice and have provided invaluable insights into mechanisms of tumorigenesis and therapeutic strategies to treat human disease. However, as their generation requires germline manipulation and extensive animal breeding that is time-, labor-, and cost-intensive, traditional GEMMs are not accessible to most researchers, and fail to model the full breadth of cancer-associated genetic alterations and therapeutic targets. Recent advances in genome-editing technologies and their implementation in somatic tissues of mice have ushered in a new class of mouse models: non-germline GEMMs (nGEMMs). nGEMM approaches can be leveraged to generate somatic tumors de novo harboring virtually any individual or group of genetic alterations found in human cancer in a mouse through simple procedures that do not require breeding, greatly increasing the accessibility and speed and scale on which GEMMs can be produced. Here we describe the technologies and delivery systems used to create nGEMMs and highlight new biological insights derived from these models that have rapidly informed functional cancer genomics, precision medicine, and immune oncology.

Opinion: more mouse models and more translation needed for ALS

Amyotrophic lateral sclerosis is a complex disorder most of which is 'sporadic' of unknown origin but approximately 10% is familial, arising from single mutations in any of more than 30 genes. Thus, there are more than 30 familial ALS subtypes, with different, often unknown, molecular pathologies leading to a complex constellation of clinical phenotypes. We have mouse models for many genetic forms of the disorder, but these do not, on their own, necessarily show us the key pathological pathways at work in human patients. To date, we have no models for the 90% of ALS that is 'sporadic'. Potential therapies have been developed mainly using a limited set of mouse models, and through lack of alternatives, in the past these have been tested on patients regardless of aetiology. Cancer researchers have undertaken therapy development with similar challenges; they have responded by producing complex mouse models that have transformed understanding of pathological processes, and they have implemented patient stratification in multi-centre trials, leading to the effective translation of basic research findings to the clinic. ALS researchers have successfully adopted this combined approach, and now to increase our understanding of key disease pathologies, and our rate of progress for moving from mouse models to mechanism to ALS therapies we need more, innovative, complex mouse models to address specific questions.

Models used to screen for the treatment of multidrug resistant cancer facilitated by transporter-based efflux

PURPOSE

Efflux transporters of the adenosine triphosphate-binding cassette (ABC)-superfamily play an important role in the development of multidrug resistance (multidrug resistant; MDR) in cancer. The overexpression of these transporters can directly contribute to the failure of chemotherapeutic drugs. Several in vitro and in vivo models exist to screen for the efficacy of chemotherapeutic drugs against MDR cancer, specifically facilitated by efflux transporters.

RESULTS

This article reviews a range of efflux transporter-based MDR models used to test the efficacy of compounds to overcome MDR in cancer. These models are classified as either in vitro or in vivo and are further categorised as the most basic, conventional models or more complex and advanced systems. Each model's origin, advantages and limitations, as well as specific efflux transporter-based MDR applications are discussed. Accordingly, future modifications to existing models or new research approaches are suggested to develop prototypes that closely resemble the true nature of multidrug resistant cancer in the human body.

CONCLUSIONS

It is evident from this review that a combination of both in vitro and in vivo preclinical models can provide a better understanding of cancer itself, than using a single model only. However, there is still a clear lack of progression of these models from basic research to high-throughput clinical practice.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Three-Dimensional Organoids in Cancer Research: The Search for the Holy Grail of Preclinical Cancer Modeling

Most solid tumors become therapy resistant and will relapse, with no durable treatment option available. One major impediment to our understanding of cancer biology and finding innovative approaches to cancer treatment stems from the lack of better preclinical tumor models that address and explain tumor heterogeneity and person-to-person differences in therapeutic and toxic responses. Past cancer research has been driven by inadequate in vitro assays utilizing two-dimensional monolayers of cancer cells and animal models. Additionally, animal models do not truly mimic the original human tumor, are time consuming, and usually costly. New preclinical models are needed for innovation in cancer translational research. Hence, it is time to welcome the three-dimensional (3D) organoids: self-organizing cells grown in 3D culture systems mimicking the parent tissues from which the primary cells originate. The 3D organoids offer deeper insights into the crucial cellular processes in tissue and organ formation and pathological processes. Generation of near-perfect physiological microenvironments allow 3D organoids to couple with gene editing tools, such as the clustered regularly interspersed short palindromic repeat (CRISPR)/CRISPR-associated 9 and the transcription activator-like effector nucleases to model human diseases, offering distinct advantages over current models. We explain in this expert review that through recapitulating patients' normal and tumor tissues, organoid technology can markedly advance personalized medicine and help reveal once hidden aspects of cancers. The use of defined tissue- or organ-specific matrices, among other factors, will likely allow organoid technology to realize its potential in innovating many fields of life sciences.

Models of Models: A Translational Route for Cancer Treatment and Drug Development

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using “models of models” has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.

Mouse models of metastasis: progress and prospects

Metastasis is the spread of cancer cells from a primary tumor to distant sites within the body to establish secondary tumors. Although this is an inefficient process, the consequences are devastating as metastatic disease accounts for >90% of cancer-related deaths. The formation of metastases is the result of a series of events that allow cancer cells to escape from the primary site, survive in the lymphatic system or blood vessels, extravasate and grow at distant sites. The metastatic capacity of a tumor is determined by genetic and epigenetic changes within the cancer cells as well as contributions from cells in the tumor microenvironment. Mouse models have proven to be an important tool for unraveling the complex interactions involved in the metastatic cascade and delineating its many stages. Here, we critically appraise the strengths and weaknesses of the current mouse models and highlight the recent advances that have been made using these models in our understanding of metastasis. We also discuss the use of these models for testing potential therapies and the challenges associated with the translation of these findings into the provision of new and effective treatments for cancer patients.

The landscape of chromosomal aberrations in breast cancer mouse models reveals driver-specific routes to tumorigenesis

Aneuploidy and copy-number alterations (CNAs) are a hallmark of human cancer. Although genetically engineered mouse models (GEMMs) are commonly used to model human cancer, their chromosomal landscapes remain underexplored. Here we use gene expression profiles to infer CNAs in 3,108 samples from 45 mouse models, providing the first comprehensive catalogue of chromosomal aberrations in cancer GEMMs. Mining this resource, we find that most chromosomal aberrations accumulate late during breast tumorigenesis, and observe marked differences in CNA prevalence between mouse mammary tumours initiated with distinct drivers. Some aberrations are recurrent and unique to specific GEMMs, suggesting distinct driver-dependent routes to tumorigenesis. Synteny-based comparison of mouse and human tumours narrows critical regions in CNAs, thereby identifying candidate driver genes. We experimentally validate that loss of Stratifin (SFN) promotes HER2-induced tumorigenesis in human cells. These results demonstrate the power of GEMM CNA analysis to inform the pathogenesis of human cancer.

Genetically engineered mouse models of cancer are useful in identifying oncogenes and tumour suppressors. Here, the authors use gene expression profiles to generate a catalogue of copy number aberrations in 45 mouse models, and compare mouse and human tumours to identify additional drivers of tumorigenesis.

Somatic PIK3CA mutations as a driver of sporadic venous malformations

Venous malformations (VM) are vascular malformations characterized by enlarged and distorted blood vessel channels. VM grow over time and cause substantial morbidity because of disfigurement, bleeding, and pain, representing a clinical challenge in the absence of effective treatments (Nguyenet al, 2014; Uebelhoeret al, 2012). Somatic mutations may act as drivers of these lesions, as suggested by the identification of TEK mutations in a proportion of VM (Limayeet al, 2009). We report that activating PIK3CA mutations gives rise to sporadic VM in mice, which closely resemble the histology of the human disease. Furthermore, we identified mutations in PIK3CA and related genes of the PI3K (phosphatidylinositol 3-kinase)/AKT pathway in about 30% of human VM that lack TEK alterations. PIK3CA mutations promote downstream signaling and proliferation in endothelial cells and impair normal vasculogenesis in embryonic development. We successfully treated VM in mouse models using pharmacological inhibitors of PI3Kα administered either systemically or topically. This study elucidates the etiology of a proportion of VM and proposes a therapeutic approach for this disease.

Use of a genetically engineered mouse model as a preclinical tool for HER2 breast cancer

Resistance to human epidermal growth factor receptor 2 (HER2)-targeted therapies presents a major clinical problem. Although preclinical studies have identified a number of possible mechanisms, clinical validation has been difficult. This is most likely to reflect the reliance on cell-line models that do not recapitulate the complexity and heterogeneity seen in human tumours. Here, we show the utility of a genetically engineered mouse model of HER2-driven breast cancer (MMTV-NIC) to define mechanisms of resistance to the pan-HER family inhibitor AZD8931. Genetic manipulation of MMTV-NIC mice demonstrated that loss of phosphatase and tensin homologue (PTEN) conferred de novo resistance to AZD8931, and a tumour fragment transplantation model was established to assess mechanisms of acquired resistance. Using this approach, 50% of tumours developed resistance to AZD8931. Analysis of the resistant tumours showed two distinct patterns of resistance: tumours in which reduced membranous HER2 expression was associated with an epithelial-to-mesenchymal transition (EMT) and resistant tumours that retained HER2 expression and an epithelial morphology. The plasticity of the EMT phenotype was demonstrated upon re-implantation of resistant tumours that then showed a mixed epithelial and mesenchymal phenotype. Further AZD8931 treatment resulted in the generation of secondary resistant tumours that again had either undergone EMT or retained their original epithelial morphology. The data provide a strong rationale for basing therapeutic decisions on the biology of the individual resistant tumour, which can be very different from that of the primary tumour and will be specific to individual patients.

CRISPR/Cas9 somatic multiplex-mutagenesis for high-throughput functional cancer genomics in mice

Here, we show CRISPR/Cas9-based targeted somatic multiplex-mutagenesis and its application for high-throughput analysis of gene function in mice. Using hepatic single guide RNA (sgRNA) delivery, we targeted large gene sets to induce hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). We observed Darwinian selection of target genes, which suppress tumorigenesis in the respective cellular/tissue context, such as Pten or Cdkn2a, and conversely found low frequency of Brca1/2 alterations, explaining mutational spectra in human ICC/HCC. Our studies show that multiplexed CRISPR/Cas9 can be used for recessive genetic screening or high-throughput cancer gene validation in mice. The analysis of CRISPR/Cas9-induced tumors provided support for a major role of chromatin modifiers in hepatobiliary tumorigenesis, including that of ARID family proteins, which have recently been reported to be mutated in ICC/HCC. We have also comprehensively characterized the frequency and size of chromosomal alterations induced by combinatorial sgRNA delivery and describe related limitations of CRISPR/Cas9 multiplexing, as well as opportunities for chromosome engineering in the context of hepatobiliary tumorigenesis. Our study describes novel approaches to model and study cancer in a high-throughput multiplexed format that will facilitate the functional annotation of cancer genomes.

Using the GEMM-ESC strategy to study gene function in mouse models

Preclinical in vivo validation of target genes for therapeutic intervention requires careful selection and characterization of the most suitable animal model in order to assess the role of these genes in a particular process or disease. To this end, genetically engineered mouse models (GEMMs) are typically used. However, the appropriate engineering of these models is often cumbersome and time consuming. Recently, we and others described a modular approach for fast-track modification of existing GEMMs by re-derivation of embryonic stem cells (ESCs) that can be modified by recombinase-mediated transgene insertion and subsequently used for the production of chimeric mice. This 'GEMM-ESC strategy' allows for rapid in vivo analysis of gene function in the chimeras and their offspring. Moreover, this strategy is compatible with CRISPR/Cas9-mediated genome editing. This protocol describes when and how to use the GEMM-ESC strategy effectively, and it provides a detailed procedure for re-deriving and manipulating GEMM-ESCs under feeder- and serum-free conditions. This strategy produces transgenic mice with the desired complex genotype faster than traditional methods: generation of validated GEMM-ESC clones for controlled transgene integration takes 9-12 months, and recombinase-mediated transgene integration and chimeric cohort production takes 2-3 months. The protocol requires skills in embryology, stem cell biology and molecular biology, and it is ideally performed within, or in close collaboration with, a transgenic facility.

Overview of Genetically Engineered Mouse Models of Breast Cancer Used in Translational Biology and Drug Development

Breast cancer is a heterogeneous condition with no single standard of treatment and no definitive method for determining whether a tumor will respond to therapy. The development of murine models that faithfully mimic specific human breast cancer subtypes is critical for the development of patient-specific treatments. While the artificial nature of traditional in vivo xenograft models used to characterize novel anticancer treatments has limited clinical predictive value, the development of genetically engineered mouse models (GEMMs) makes it possible to study the therapeutic responses in an intact microenvironment. GEMMs have proven to be an experimentally tractable platform for evaluating the efficacy of novel therapeutic combinations and for defining the mechanisms of acquired resistance. Described in this overview are several of the more popular breast cancer GEMMs, including details on their value in elucidating the molecular mechanisms of this disorder.

Selective resistance to the PARP inhibitor olaparib in a mouse model for BRCA1-deficient metaplastic breast cancer

Metaplastic breast carcinoma (MBC) is a rare histological breast cancer subtype characterized by mesenchymal elements and poor clinical outcome. A large fraction of MBCs harbor defects in breast cancer 1 (BRCA1). As BRCA1 deficiency sensitizes tumors to DNA cross-linking agents and poly(ADP-ribose) polymerase (PARP) inhibitors, we sought to investigate the response of BRCA1-deficient MBCs to the PARP inhibitor olaparib. To this end, we established a genetically engineered mouse model (GEMM) for BRCA1-deficient MBC by introducing the MET proto-oncogene into a BRCA1-associated breast cancer model, using our novel female GEMM ES cell (ESC) pipeline. In contrast to carcinomas, BRCA1-deficient mouse carcinosarcomas resembling MBC show intrinsic resistance to olaparib caused by increased P-glycoprotein (Pgp) drug efflux transporter expression. Indeed, resistance could be circumvented by using another PARP inhibitor, AZD2461, which is a poor Pgp substrate. These preclinical findings suggest that patients with BRCA1-associated MBC may show poor response to olaparib and illustrate the value of GEMM-ESC models of human cancer for evaluation of novel therapeutics.

In vivo models of brain tumors: roles of genetically engineered mouse models in understanding tumor biology and use in preclinical studies

Although our knowledge of the biology of brain tumors has increased tremendously over the past decade, progress in treatment of these deadly diseases remains modest. Developing in vivo models that faithfully mirror human diseases is essential for the validation of new therapeutic approaches. Genetically engineered mouse models (GEMMs) provide elaborate temporally and genetically controlled systems to investigate the cellular origins of brain tumors and gene function in tumorigenesis. Furthermore, they can prove to be valuable tools for testing targeted therapies. In this review, we discuss GEMMs of brain tumors, focusing on gliomas and medulloblastomas. We describe how they provide critical insights into the molecular and cellular events involved in the initiation and maintenance of brain tumors, and illustrate their use in preclinical drug testing.

Imaging preclinical tumour models: improving translational power

Recent developments and improvements of multimodal imaging methods for use in animal research have substantially strengthened the options of in vivo visualization of cancer-related processes over time. Moreover, technological developments in probe synthesis and labelling have resulted in imaging probes with the potential for basic research, as well as for translational and clinical applications. In addition, more sophisticated cancer models are available to address cancer-related research questions. This Review gives an overview of developments in these three fields, with a focus on imaging approaches in animal cancer models and how these can help the translation of new therapies into the clinic.

MicroRNAs: master regulators of drug resistance, stemness, and metastasis

MicroRNAs (miRNAs) are 20-22 nucleotides long small non-coding RNAs that regulate gene expression post-transcriptionally. Last decade has witnessed emerging evidences of active roles of miRNAs in tumor development, progression, metastasis, and drug resistance. Many factors contribute to their dysregulation in cancer, such as chromosomal aberrations, differential methylation of their own or host genes' promoters and alterations in miRNA biogenesis pathways. miRNAs have been shown to act as tumor suppressors or oncogenes depending on the targets they regulate and the tissue where they are expressed. Because miRNAs can regulate dozens of genes simultaneously and they can function as tumor suppressors or oncogenes, they have been proposed as promising targets for cancer therapy. In this review, we focus on the role of miRNAs in driving drug resistance and metastasis which are associated with stem cell properties of cancer cells. Furthermore, we discuss systems biology approaches to combine experimental and computational methods to study effects of miRNAs on gene or protein networks regulating these processes. Finally, we describe methods to target oncogenic or replace tumor suppressor miRNAs and current delivery strategies to sensitize refractory cells and to prevent metastasis. A holistic understanding of miRNAs' functions in drug resistance and metastasis, which are major causes of cancer-related deaths, and the development of novel strategies to target them efficiently will pave the way towards better translation of miRNAs into clinics and management of cancer therapy.

Pathogenesis of pancreatic cancer: lessons from animal models

The past several decades have seen great effort devoted to mimicking the key features of pancreatic ductal adenocarcinoma (PDAC) in animals and have produced 2 robust models of this deadly cancer. Carcinogen-treated Syrian hamsters develop PDAC with genetic lesions, which reproduce those of human, including activation of the Kras oncogene, and early studies in this species validated nongenetic risk factors for PDAC including pancreatitis, obesity, and diabetes. More recently, PDAC research has been invigorated by the development of genetically engineered mouse models based on tissue-specific Kras activation and deletion of tumor suppressor genes. Surprisingly, mouse PDAC appears to arise from exocrine acinar rather than ductal cells, via a process of phenotypic reprogramming that is accelerated by inflammation. Studies in both models have uncovered molecular mechanisms by which inflammation promotes and sustains PDAC and identified targets for chemoprevention to suppress PDAC in high-risk individuals. The mouse model, in particular, has also been instrumental in developing new approaches to early detection as well as treatment of advanced disease. Together, animal models enable diverse approaches to basic and preclinical research on pancreatic cancer, the results of which will accelerate progress against this currently intractable cancer.

Mouse models for studying angiogenesis and lymphangiogenesis in cancer

The formation of new blood vessels (angiogenesis) is required for the growth of most tumors. The tumor microenvironment also induces lymphangiogenic factors that promote metastatic spread. Anti-angiogenic therapy targets the mechanisms behind the growth of the tumor vasculature. During the past two decades, several strategies targeting blood and lymphatic vessels in tumors have been developed. The blocking of vascular endothelial growth factor (VEGF)/VEGF receptor-2 (VEGFR-2) signaling has proven effective for inhibition of tumor angiogenesis and growth, and inhibitors of VEGF-C/VEGFR-3 involved in lymphangiogenesis have recently entered clinical trials. However, thus far anti-angiogenic treatments have been less effective in humans than predicted on the basis of pre-clinical tests in mice. Intrinsic and induced resistance against anti-angiogenesis occurs in patients, and thus far the clinical benefit of the treatments has been limited to modest improvements in overall survival in selected tumor types. Our current knowledge of tumor angiogenesis is based mainly on experiments performed in tumor-transplanted mice, and it has become evident that these models are not representative of human cancer. For an improved understanding, angiogenesis research needs models that better recapitulate the multistep tumorigenesis of human cancers, from the initial genetic insults in single cells to malignant progression in a proper tissue environment. To improve anti-angiogenic therapies in cancer patients, it is necessary to identify additional molecular targets important for tumor angiogenesis, and to get mechanistic insight into their interactions for eventual combinatorial targeting. The recent development of techniques for manipulating the mammalian genome in a precise and predictable manner has opened up new possibilities for the generation of more reliable models of human cancer that are essential for the testing of new therapeutic strategies. In addition, new imaging modalities that permit visualization of the entire mouse tumor vasculature down to the resolution of single capillaries have been developed in pre-clinical models and will likely benefit clinical imaging.

Preventive evolutionary medicine of cancers

Evolutionary theory predicts that once an individual reaches an age of sufficiently low Darwinian fitness, (s)he will have reduced chances of keeping cancerous lesions in check. While we clearly need to better understand the emergence of precursor states and early malignancies as well as their mitigation by the microenvironment and tissue architecture, we argue that lifestyle changes and preventive therapies based in an evolutionary framework, applied to identified high-risk populations before incipient neoplasms become clinically detectable and chemoresistant lineages emerge, are currently the most reliable way to control or eliminate early tumours. Specifically, the relatively low levels of (epi)genetic heterogeneity characteristic of many if not most incipient lesions will mean a relatively limited set of possible adaptive traits and associated costs compared to more advanced cancers, and thus a more complete and predictable understanding of treatment options and outcomes. We propose a conceptual model for preventive treatments and discuss the many associated challenges.

The biology of personalized cancer medicine: facing individual complexities underlying hallmark capabilities

It is a time of great promise and expectation for the applications of knowledge about mechanisms of cancer toward more effective and enduring therapies for human disease. Conceptualizations such as the hallmarks of cancer are providing an organizing principle with which to distill and rationalize the abject complexities of cancer phenotypes and genotypes across the spectrum of the human disease. A countervailing reality, however, involves the variable and often transitory responses to most mechanism-based targeted therapies, returning full circle to the complexity, arguing that the unique biology and genetics of a patient's tumor will in the future necessarily need to be incorporated into the decisions about optimal treatment strategies, the frontier of personalized cancer medicine. This perspective highlights considerations, metrics, and methods that may prove instrumental in charting the landscape of evaluating individual tumors so to better inform diagnosis, prognosis, and therapy. Integral to the consideration is remarkable heterogeneity and variability, evidently embedded in cancer cells, but likely also in the cell types composing the supportive and interactive stroma of the tumor microenvironment (e.g., leukocytes and fibroblasts), whose diversity in form, regulation, function, and abundance may prove to rival that of the cancer cells themselves. By comprehensively interrogating both parenchyma and stroma of patients' cancers with a suite of parametric tools, the promise of mechanism-based therapy may truly be realized.

Omics and therapy - a basis for precision medicine

A founding premise of the human genome project was that knowledge of the spectrum of abnormalities that comprise cancers and other human diseases would lead to improved disease management by identifying molecular abnormalities that could guide disease detection and diagnosis, suggest new therapeutic strategies and be developed as markers to predict response to therapy. This project led to elucidation of a reference normal human genome sequence and normal polymorphisms therein against which sequences from diseased tissues can be compared to enable identification of causal abnormalities. It also stimulated development of an array of computational tools for genomic analysis and catalyzed public and private sector development of revolutionary tools for genome analysis that transformed analysis of whole genomes from an enterprise that required international teams and hundreds of millions of dollars to a process that can be carried out in core facilities for only a few thousand dollars per sample. Indeed, the $1000 genome is nearly upon us. Applications of these technologies to human cancers in international cancer genome projects are now revealing the spectra of abnormalities that comprise thousands of individual cancers. Analyses of these data are leading to the promised improvements in disease management. We review several aspects of cancer genomics with emphasis on aspects that are relevant to improving cancer therapy.