Cancer Progression Models Research Articles

PARD6A promotes lung adenocarcinoma cell proliferation and invasion through Serpina3.

Par6α encoded by PARD6A is a member of the PAR6 family and is reported to promote cancer initiation and progression. PARD6A is frequently upregulated in different types of cancers, but its regulatory role in lung cancer progression is yet to be established. In this study, we analyzed the PARD6A expression in biopsies from lung adenocarcinoma (LUAD) patients, and the survival probability using LUAD tissue microarray (TMA) and online datasets from TCGA and GEO. We conducted in vitro and in vivo assays to assess the role of PARD6A in regulating lung cancer progression, including proliferation, wound healing, transwell, RNA-seq, and subcutaneous tumor mice models. Our findings revealed that PARD6A is highly expressed in cancer tissues from LUAD patients and is associated with poor prognosis in LUAD patients. In vitro assays showed that PARD6A promoted cell proliferation, migration, and invasion. The transcriptome sequencing identified Serpina3 as one of the key downstream molecules of PARD6A. Ectopic expression of Serpina3 rescued impaired proliferation, migration, and invasion in PARD6A-knocking down H1299 cells, whereas silencing Serpina3 impeded enhanced proliferation, migration, and invasion in PARD6A-overexpressing H1975 cells. Our findings suggest that PARD6A promotes lung cancer progression by inducing Serpina3, which may be a promising therapeutic target.

Optimal age to stop prostate cancer screening and early detection

BackgroundProstate Cancer screening should be discontinued at older ages because competing mortality risks eventually dominate the risk of Prostate Cancer and harms exceed benefits. We explored the Prostate Cancer screening stopping age from the patient, healthcare system, and social perspectives in Iran. MethodsWe applied Bellman Equations to formulate the net benefits biopsy and “do nothing”. Using difference between the net benefits of two alternatives, we calculated the stopping age. The cancer states were without cancer, undetected cancer, detected cancer, metastatic cancer, and death. To move between states, we applied Markov property. Transition probabilities, rewards, and costs were inferred from the medical literature. The base-case scenario estimated the stopping age from the patient, healthcare system, and social perspectives. A one-way sensitivity used to find the most influential parameters on the stopping age. ResultsOur results suggested that Prostate Cancer screening stopping ages from the patient, healthcare system, and social were 70, 68, and 68 respectively. The univariate sensitivity analysis showed that the stopping ages were sensitive to the disutility of treatment, discount factor, the disutility of metastasis, the annual probability of death from other causes, and the annual probability of developing metastasis from the hidden cancer state. ConclusionsMen should not be screened for Prostate Cancer beyond 70 years old, as this results in the net benefit of "do nothing" above the biopsy. Nevertheless, this finding needs to be further studied with more detailed cancer progression models (considering re-biopsy, comorbidities, and more complicated states transition) and using local utility and willingness to pay value information.

PTHrP Regulates Fatty Acid Metabolism via Novel lncRNA in Breast Cancer Initiation and Progression Models.

Parathyroid hormone-related peptide (PTHrP) is the primary cause of malignancy-associated hypercalcemia (MAH). We previously showed that PTHrP ablation, in the MMTV-PyMT murine model of breast cancer (BC) progression, can dramatically prolong tumor latency, slow tumor growth, and prevent metastatic spread. However, the signaling mechanisms using lineage tracing have not yet been carefully analyzed. Here, we generated Pthrpflox/flox; Cre+ mT/mG mice (KO) and Pthrpwt/wt; Cre+ mT/mG tumor mice (WT) to examine the signaling pathways under the control of PTHrP from the early to late stages of tumorigenesis. GFP+ mammary epithelial cells were further enriched for subsequent RNA sequencing (RNAseq) analyses. We observed significant upregulation of cell cycle signaling and fatty acid metabolism in PTHrP WT tumors, which are linked to tumor initiation and progression. Next, we observed that the expression levels of a novel lncRNA, GM50337, along with stearoyl-Coenzyme A desaturase 1 (Scd1) are significantly upregulated in PTHrP WT but not in KO tumors. We further validated a potential human orthologue lncRNA, OLMALINC, together with SCD1 that can be regulated via PTHrP in human BC cell lines. In conclusion, these novel findings could be used to develop targeted strategies for the treatment of BC and its metastatic complications.

3D Tumor Spheroid and Organoid to Model Tumor Microenvironment for Cancer Immunotherapy

The intricate microenvironment in which malignant cells reside is essential for the progression of tumor growth. Both the physical and biochemical features of the tumor microenvironment (TME) play a critical role in promoting the differentiation, proliferation, invasion, and metastasis of cancer cells. It is therefore essential to understand how malignant cells interact and communicate with an assortment of supportive tumor-associated cells including macrophages, fibroblasts, endothelial cells, and other immune cells. To study the complex mechanisms behind cancer progression, 3D spheroid and organoid models are widely in favor because they replicate the stromal environment and multicellular structure present within an in vivo tumor. It provides more precise data about the cell–cell interactions, tumor characteristics, drug discovery, and metabolic profile of cancer cells compared to oversimplified 2D systems and unrepresentative animal models. This review provides a description of the key elements of the tumor microenvironment as well as early research using cell-line derived, 3D spheroid tumor models that paved the way for the adoption of patient-derived spheroid and organoid models. In particular, 3D spheroid and organoid models provide a method for drug screening with a particular emphasis on influence of the TME in cancer immunotherapy.

EvAM-Tools: tools for evolutionary accumulation and cancer progression models.

EvAM-Tools is an R package and web application that provides a unified interface to state-of-the-art cancer progression models and, more generally, evolutionary models of event accumulation. The output includes, in addition to the fitted models, the transition (and transition rate) matrices between genotypes and the probabilities of evolutionary paths. Generation of random cancer progression models is also available. Using the GUI in the web application, users can easily construct models (modifying directed acyclic graphs of restrictions, matrices of mutual hazards or specifying genotype composition), generate data from them (with user-specified observational/genotyping error) and analyze the data. Implemented in R and C; open source code available under the GNU Affero General Public License v3.0 at https://github.com/rdiaz02/EvAM-Tools. Docker images freely available from https://hub.docker.com/u/rdiaz02. Web app freely accessible at https://iib.uam.es/evamtools. Supplementary data are available at Bioinformatics online.

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question "Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?" or, shortly, "What genotype comes next?". Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting method's use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method's results when key assumptions do not hold.

Analysis of Mammography Screening Schedules under Varying Resource Constraints for Planning Breast Cancer Control Programs in Low- and Middle-Income Countries: A Mathematical Study.

Background. Low-and-middle-income countries (LMICs) have higher mortality-to-incidence ratio for breast cancer compared to high-income countries (HICs) because of late-stage diagnosis. Mammography screening is recommended for early diagnosis, however, the infrastructure capacity in LMICs are far below that needed for adopting current screening guidelines. Current guidelines are extrapolations from HICs, as limited data had restricted model development specific to LMICs, and thus, economic analysis of screening schedules specific to infrastructure capacities are unavailable. Methods. We applied a new Markov process method for developing cancer progression models and a Markov decision process model to identify optimal screening schedules under a varying number of lifetime screenings per person, a proxy for infrastructure capacity. We modeled Peru, a middle-income country, as a case study and the United States, an HIC, for validation. Results. Implementing 2, 5, 10, and 15 lifetime screens would require about 55, 135, 280, and 405 mammography machines, respectively, and would save 31, 62, 95, and 112 life-years per 1000 women, respectively. Current guidelines recommend 15 lifetime screens, but Peru has only 55 mammography machines nationally. With this capacity, the best strategy is 2 lifetime screenings at age 50 and 56 years. As infrastructure is scaled up to accommodate 5 and 10 lifetime screens, screening between the ages of 44-61 and 41-64 years, respectively, would have the best impact. Our results for the United States are consistent with other models and current guidelines. Limitations. The scope of our model is limited to analysis of national-level guidelines. We did not model heterogeneity across the country. Conclusions. Country-specific optimal screening schedules under varying infrastructure capacities can systematically guide development of cancer control programs and planning of health investments.

Every which way? On predicting tumor evolution using cancer progression models.

Successful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.

Modelling cancer progression using Mutual Hazard Networks.

MotivationCancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurrence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap.ResultsHere we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data. MHN model events by their spontaneous rate of fixation and by multiplicative effects they exert on the rates of successive events. MHN compared favourably to acyclic models in cross-validated model fit on four datasets tested. In application to the glioblastoma dataset from The Cancer Genome Atlas, MHN proposed a novel interaction in line with consecutive biopsies: IDH1 mutations are early events that promote subsequent fixation of TP53 mutations.Availability and implementationImplementation and data are available at https://github.com/RudiSchill/MHN.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract P5-18-10: BHLHE40-AS1 is an enhancer associated long noncoding RNA critical to breast cancer progression

Abstract Increased emphasis on breast cancer screening has led to a dramatic increase in diagnosis of ductal carcinoma in situ (DCIS). DCIS lesions are nonobligate precursors of invasive ductal carcinoma (IDC) and thus, the current standard-of-care is aggressive therapy to prevent invasive and metastatic disease. However, only ˜40% of DCIS cases are predicted to progress leading to a current state of overtreatment and overdiagnosis. Thus, there is a critical need to identify functional determinants of progression of DCIS to IDC to allow discrimination between indolent and aggressive breast cancers and refine patient treatment strategies.We propose that long noncoding RNAs (lncRNAs) functionally drive breast cancer progression and their expression can discriminate between innocuous and potentially invasive DCIS. Using biopsies from women with tandem DCIS and IDC lesions, we identified 35 lncRNAs whose expression can distinguish between pre-invasive and invasive disease. From this candidate list, the lncRNA BHLHE40-AS1 is found enriched in multiple breast cancer progression models, in HER2+ cell lines, and HER2+ patient tumors. Functionally, BHLHE40-AS1 is found to support tumor cell cycle and motility and may serve as a clinically relevant biomarker and therapeutic target in invasive breast cancer. Further, BHLHE40-AS1 is expressed from a known super-enhancer that preliminary evidence suggests undergoes altered chromatin looping during breast cancer progression. Interestingly, over half of the candidate lncRNAs are associated with enhancer regions. Current studies are investigating super-enhancers, and associated lncRNAs that become acquired during breast cancer progression. Citation Format: DeVaux RS, Schimjawicz H, Coarfa C, Behbod F, Herschkowitz JI. BHLHE40-AS1 is an enhancer associated long noncoding RNA critical to breast cancer progression [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-18-10.

Predicting Advanced Prostate Cancer from Modeling Early Indications in Biopsy and Prostatectomy Samples via Transductive Semi-Supervised Survival Analysis.

Prostate cancer is the most prevalent form of cancer and the second most common cause of cancer deaths among men in the United States. Accurate prognosis is important as it is the principal factor in determining the treatment plan. Prostate cancer is a complex disease which advances in stages. While clinical failure (including metastasis) is a significant endpoint following a radical prostatectomy, it can often take years to manifest, usually too late to be optimistically treated. In practice, the earlier endpoint of PSA Recurrence is frequently used as a surrogate in prognostic modeling. The central issue in these models is managing censored observations which challenge traditional regression techniques. The true target times of a majority of instances are unknown; what is known is a censored target representing some earlier indeterminate time. In this work we apply a novel transduction approach for semi-supervised survival analysis which has previously been shown to be powerful in medical prognosis. The approach considers censored samples as semi-supervised regression targets leveraging the partial nature of unsupervised information. We explore the use of this approach in building prostate cancer progression models from multimodal characteristics extracted from both biopsy and prostatectomy tissues samples. In this work, the approach leads to a significant increase in performance for predicting advanced prostate cancer from earlier endpoints and may also be useful in other diseases for predicting advanced endpoints from earlier stages of the disease.

Pyruvate carboxylase supports the pulmonary tropism of metastatic breast cancer

BackgroundOvercoming systemic dormancy and initiating secondary tumor grow under unique microenvironmental conditions is a major rate-limiting step in metastatic progression. Disseminated tumor cells encounter major changes in nutrient supplies and oxidative stresses compared to the primary tumor and must demonstrate significant metabolic plasticity to adapt to specific metastatic sites. Recent studies suggest that differential utilization of pyruvate sits as a critical node in determining the organotropism of metastatic breast cancer. Pyruvate carboxylase (PC) is key enzyme that converts pyruvate into oxaloacetate for utilization in gluconeogenesis and replenishment of the TCA cycle.MethodsPatient survival was analyzed with respect to gene copy number alterations and differential mRNA expression levels of PC. Expression of PC was analyzed in the MCF-10A, D2-HAN and the 4 T1 breast cancer progression series under in vitro and in vivo growth conditions. PC expression was depleted via shRNAs and the impact on in vitro cell growth, mammary fat pad tumor growth, and pulmonary and non-pulmonary metastasis was assessed by bioluminescent imaging. Changes in glycolytic capacity, oxygen consumption, and response to oxidative stress were quantified upon PC depletion.ResultsGenomic copy number increases in PC were observed in 16–30% of metastatic breast cancer patients. High expression of PC mRNA was associated with decreased patient survival in the MCTI and METABRIC patient datasets. Enhanced expression of PC was not recapitulated in breast cancer progression models when analyzed under glucose-rich in vitro culture conditions. In contrast, PC expression was dramatically enhanced upon glucose deprivation and in vivo in pulmonary metastases. Depletion of PC led to a dramatic decrease in 4 T1 pulmonary metastasis, but did not affect orthotopic primary tumor growth. Tail vein inoculations confirmed the role of PC in facilitating pulmonary, but not extrapulmonary tumor initiation. PC-depleted cells demonstrated a decrease in glycolytic capacity and oxygen consumption rates and an enhanced sensitivity to oxidative stress.ConclusionsOur studies indicate that PC is specifically required for the growth of breast cancer that has disseminated to the lungs. Overall, these findings point to the potential of targeting PC for the treatment of pulmonary metastatic breast cancer.

Abstract P5-07-11: BHLHE40-AS1 is an enhancer associated noncoding RNA critical to breast cancer progression

Abstract Increased emphasis on breast cancer screening has led to a dramatic increase in diagnosis of ductal carcinoma in situ (DCIS). DCIS lesions are nonobligate precursors of invasive ductal carcinoma (IDC) and thus, the current standard-of-care is aggressive therapy to prevent invasive and metastatic disease. However, only ˜40% of DCIS cases are predicted to progress leading to a current state of overtreatment and overdiagnosis. Thus, there is a critical need to identify functional determinants of progression of DCIS to IDC to allow discrimination between indolent and aggressive breast cancers and refine patient treatment strategies. We propose that long noncoding RNAs (lncRNAs) functionally drive breast cancer progression and their expression can discriminate between innocuous and potentially invasive DCIS. Using biopsies from women with tandem DCIS and IDC lesions, we identified the lncRNA BHLHE40-AS1 as enriched in patient IDC. Furthermore, BHLHE40-AS1 is enriched in multiple breast cancer progression models, in HER2+ cell lines, and can be induced by expression of HER2. BHLHE40-AS1 is transcribed from a known super-enhancer that we find becomes rewired in breast cancer progression. In addition, the lncRNA is found antisense to BHLHE40, a transcription factor critical to many core processes (apoptosis, EMT, circadian rhythm). Depletion of the lncRNA attenuates expression of BHLHE40. Future studies are focused on mechanistically elaborating its function, its impact on the associated enhancer and chromatin looping, and determining the utility of BHLHE40-AS1 as a clinically relevant biomarker and therapeutic target in invasive breast cancer. Citation Format: DeVaux RKS, Schimjawicz H, Coarfa C, Behbod F, Herschkowitz JI. BHLHE40-AS1 is an enhancer associated noncoding RNA critical to breast cancer progression [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-07-11.

Cancer progression models and fitness landscapes: a many-to-many relationship.

MotivationThe identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to identify these constraints, and return Directed Acyclic Graphs (DAGs) of restrictions where arrows indicate dependencies or constraints. On the other hand, fitness landscapes, which map genotypes to fitness, contain all possible paths of tumor progression. Thus, we expect a correspondence between DAGs from CPMs and the fitness landscapes where evolution happened. But many fitness landscapes—e.g. those with reciprocal sign epistasis—cannot be represented by CPMs.ResultsUsing simulated data under 500 fitness landscapes, I show that CPMs’ performance (prediction of genotypes that can exist) degrades with reciprocal sign epistasis. There is large variability in the DAGs inferred from each landscape, which is also affected by mutation rate, detection regime and fitness landscape features, in ways that depend on CPM method. Using three cancer datasets, I show that these problems strongly affect the analysis of empirical data: fitness landscapes that are widely different from each other produce data similar to the empirically observed ones and lead to DAGs that infer very different restrictions. Because reciprocal sign epistasis can be common in cancer, these results question the use and interpretation of CPMs. Availability and implementationCode available from Supplementary Material.Supplementary information Supplementary data are available at Bioinformatics online.

Misleading arrows? Fitness landscapes and cancer progression models

Misleading arrows? Fitness landscapes and cancer progression models

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data.

We introduce TRanslational ONCOlogy (TRONCO), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g. retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g. multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints for uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy. TRONCO is released under the GPL license, is hosted at http://bimib.disco.unimib.it/ (Software section) and archived also at bioconductor.org. tronco@disco.unimib.it Supplementary data are available at Bioinformatics online.

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patiens (cross-sectional data) have been defined in the literature since late 90s. These algorithms differ in the way they extract a graphical model of the events modelling the progression, e.g., somatic mutations or copy-number alterations. TRONCO is an R package for TRanslational ONcology which provides a serie of functions to assist the user in the analysis of cross sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results and visualize the models. Availability. Freely available at http://www.bioconductor.org/ under GPL license; project hosted at http://bimib.disco.unimib.it/ and https://github.com/BIMIB-DISCo/TRONCO. Contact. tronco@disco.unimib.it

Premature aging/senescence in cancer cells facing therapy: good or bad?

Normal and cancer cells facing their demise following exposure to radio-chemotherapy can actively participate in choosing their subsequent fate. These programmed cell fate decisions include true cell death (apoptosis-necroptosis) and therapy-induced cellular senescence (TIS), a permanent "proliferative arrest" commonly portrayed as premature cellular aging. Despite a permanent loss of proliferative potential, senescent cells remain viable and are highly bioactive at the microenvironment level, resulting in a prolonged impact on tissue architecture and functions. Cellular senescence is primarily documented as a tumor suppression mechanism that prevents cellular transformation. In the context of normal tissues, cellular senescence also plays important roles in tissue repair, but contributes to age-associated tissue dysfunction when senescent cells accumulate. Theoretically, in multi-step cancer progression models, cancer cells have already bypassed cellular senescence during their immortalization step (see hallmarks of cancer). It is then perhaps surprising to find that cancer cells often retain the ability to undergo TIS, or premature aging. This occurs because cellular senescence results from multiple signalling pathways, some retained in cancer cells, aiming to prevent cell cycle progression in damaged cells. Since senescent cancer cells persist after therapy and secrete an array of cytokines and growth factors that can modulate the tumor microenvironment, these cells may have beneficial and detrimental effects regarding immune modulation and survival of remaining proliferation-competent cancer cells. Similarly, while normal cells undergoing senescence are believed to remain indefinitely growth arrested, whether this is true for senescent cancer cells remains unclear, raising the possibility that these cells may represent a reservoir for cancer recurrence after treatment. This review discusses our current knowledge on cancer cell senescence and highlight questions that must be addressed to fully understand the beneficial and detrimental impacts of cellular senescence during cancer therapy.

Chimeric Tumor and Organ Transplantation Models.

Mouse models of cancer development and progression provide a means to study tumor response in appropriate physiological contexts. However, mouse cancer progression and therapy models have traditionally suffered from many of the same problems as human clinical cancer research, including genetic heterogeneity and tumor-stage variability at the time of treatment. Additionally, most mouse models are not tractable genetic systems, making it difficult to recapitulate the diverse set of alterations that regularly occur during tumor development. The recent development of chimeric and tumor transplantation techniques address many of the limitations of conventional mouse genetics. These strategies allow for the somatic introduction of complex genetic alterations into a subset of cells in reconstituted tumors or organ systems. Moreover, these different approaches can be combined in such a way that tumors with multiple genotypes are rapidly produced. These matched pairs can be systemically introduced into recipient mice for the rapid ex vivo modification of preestablished malignancies allows the generation of "matched pairs" of tumors differing in a single defined lesion (i.e., aliquots of the same primary malignancy with and without a gene of interest). Thus, treatment studies can be performed (1) in the context of an otherwise normal organ system, (2) on tumors that are in their appropriate anatomical context, and (3) on tumors that are essentially identical besides the presence of defined experimentally introduced alterations. Here, we will introduce procedures for modifying both normal and transformed cells and their adaptation to study in vivo tumor biology.

An algorithm for the efficient inference of cancer progression models

An algorithm for the efficient inference of cancer progression models