• Black/African Americans
• Disparities
• Lung cancer
• Simulation model

• Humans
• Lung Neoplasms/epidemiology
• Lung Neoplasms/ethnology
• Lung Neoplasms/etiology
• Male
• Female
• Incidence
• White People/statistics & numerical data
• Black or African American/statistics & numerical data
• Middle Aged
• Smoking/adverse effects
• Smoking/ethnology
• Aged
• Carcinogenesis
• Cohort Studies
• United States/epidemiology
• Adult
• Risk Factors

• U01 CA253858 NCI NIH HHS
• U01CA253858 NCI NIH HHS
• National Cancer Institute

• Airplane
• Asbestos
• Crane
• Friction Materials
• brake dust
• brakes
• chrysotile
• epidemiology
• mesothelioma
• risk assessment
• vehicle mechanics

• Cancer kinetics
• Early-onset cancer
• Screening impact
• Sojourn time
• Tumor size

• KL2 TR001874 NCATS NIH HHS
• R01 CA257971 NCI NIH HHS
• U01 CA265729 NCI NIH HHS
• U24 CA272897 NCI NIH HHS

• cancer biology
• evolutionary biology
• human
• multi-region tumour samples
• spatial genomics
• tumour samples

• Humans
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/pathology
• Mutation
• Liver Neoplasms/genetics
• Liver Neoplasms/pathology
• Tumor Burden

• SFB1310/2 - 325931972 Deutsche Forschungsgemeinschaft
• CEX2020-001049-S Spanish Ministry of Science and Innovation
• 10.55776/J4366 FWF Austrian Science Fund
• CERCA programme Generalitat de Catalunya
• MCIN/AEI /10.13039/50110001 Spanish Ministry of Science and Innovation
• PGC2018-100941-A-I00 Spanish Ministry of Science and Innovation
• PID2021-128976NB-I001033 Spanish Ministry of Science and Innovation

• Clonal expansion
• Intrahepatic cholangiocarcinoma
• Multistage model
• Mutation
• Occupational cholangiocarcinoma
• Risk estimation

• biologically based mathematical model
• cancer mortality
• human–mouse difference
• radiation effect
• risk estimation

• Animals
• Mice
• Neoplasms, Radiation-Induced/mortality
• Neoplasms, Radiation-Induced/genetics
• Neoplasms, Radiation-Induced/etiology
• Humans
• Carcinogenesis/radiation effects
• Mutation
• Dose-Response Relationship, Radiation
• Models, Theoretical
• Atomic Bomb Survivors
• Species Specificity
• Radiation, Ionizing
• Female
• Male

• Multi-walled carbon nanotubes
• inhalation exposure
• lung cancer
• mice
• tumor promoter

• Mice
• Animals
• Lung/pathology
• Lung Neoplasms/chemically induced
• Lung Neoplasms/pathology
• Mice, Inbred Strains
• Cell Transformation, Neoplastic
• Carcinogenesis/chemically induced
• Carcinogenesis/pathology
• Inhalation Exposure
• Mice, Inbred C57BL

• CC999999 Intramural CDC HHS

• colorectal cancer
• driver mutation
• incidence rate
• mathematical modeling
• radiation exposure

• Humans
• United States
• Radiation Exposure
• Models, Theoretical
• Mutation
• Carcinogenesis/genetics
• Carcinogenesis/pathology
• Colorectal Neoplasms/genetics

• Branching process
• Growth pattern
• Heterogeneity
• Leukemia

• Population Growth
• Cell Cycle
• Cell Proliferation
• Clone Cells
• Phenotype
• Stochastic Processes

• R01 GM135396 NIGMS NIH HHS
• R01 CA226258 NCI NIH HHS

• Humans
• Deuterium/metabolism
• Deuterium/pharmacology
• HT29 Cells
• Water/pharmacology
• Colorectal Neoplasms/drug therapy

• branching process
• growth pattern
• heterogeneity
• leukemia

• R01 CA226258 NCI NIH HHS

• CYP1B1
• NCM460
• Nrf2
• benzo[a]pyrene
• colorectal cancer
• piceatannol

• R01 CA226258 NCI NIH HHS

• atherosclerosis
• biological models
• cardiovascular risk factors
• computer simulation
• myocardial infarction
• survival analysis

• Adolescent
• Atherosclerosis/epidemiology
• Atherosclerosis/etiology
• Dyslipidemias
• Female
• Humans
• Hypertension/complications
• Hypertension/epidemiology
• Male
• Myocardial Infarction/epidemiology
• Myocardial Infarction/etiology
• Risk Factors

The process of colorectal cancer (CRC) formation is considered a typical model of multistage carcinogenesis in which aberrant DNA methylation plays an important role. In this study, 752 methylation-driven genes (MDGs) were identified by the MethylMix package based on methylation and gene expression data of CRC in The Cancer Genome Atlas (TCGA). Iterative recursive feature elimination (iRFE) based on linear discriminant analysis (LDA) was used to determine the minimum MDGs (iRFE MDGs), which could distinguish between cancer and cancer-adjacent tissues. Further analysis indicated that the changes in methylation levels of the four iRFE MDGs, ADHFE1-Cluster1, CNRIP1-Cluster1, MAFB, and TNS4, occurred in adenoma tissues, while changes did not occur until stage IV in cell-free DNA. Furthermore, the methylation levels of iRFE MDGs were correlated with the genes involved in the reprogramming process of somatic cells to pluripotent stem cells, which is considered the common signature of cancer cells and embryonic stem cells. The above results indicated that the four iRFE MDGs may play roles in the early stage of colorectal carcinogenesis and highlighted the complicated relationship between tissue DNA and cell-free DNA (cfDNA).

• National Natural Science Foundation of China

Cancer development is a multistep process often starting with a single cell in which a number of epigenetic and genetic alterations have accumulated thus transforming it into a tumor cell. The progeny of such a single benign tumor cell expands in the tissue and can at some point progress to malignant tumor cells until a detectable tumor is formed. The dynamics from the early phase of a single cell to a detectable tumor with billions of tumor cells are complex and still not fully resolved, not even for the well-known prototype of multistage carcinogenesis, the adenoma-adenocarcinoma sequence of colorectal cancer. Mathematical models of such carcinogenesis are frequently tested and calibrated based on reported age-specific incidence rates of cancer, but they usually require calibration of four or more parameters due to the wide range of processes these models aim to reflect. We present a cell-based model, which focuses on the competition between wild-type and tumor cells in colonic crypts, with which we are able reproduce epidemiological incidence rates of colon cancer. Additionally, the fraction of cancerous tumors with precancerous lesions predicted by the model agree with clinical estimates. The correspondence between model and reported data suggests that the fate of tumor development is majorly determined by the early phase of tumor growth and progression long before a tumor becomes detectable. Due to the focus on the early phase of tumor development, the model has only a single fit parameter, the time scale set by an effective replacement rate of stem cells in the crypt. We find this effective rate to be considerable smaller than the actual replacement rate, which implies that the time scale is limited by the processes succeeding clonal conversion of crypts.

Cancer development is a multistep process often starting with a single cell turning into a tumor cell whose progeny growths via clonal expansion into a macroscopic tumor with billions of cells. While experimental insight exists on the cellular scale and cancer registries provide statistics on detectable tumors, the complex dynamics leading from the microscopic cellular scale to a macroscopic tumor is still not fully resolved. Models of cancer biology are commonly used to explain incidence rates but usually require the fit of several biological parameters due to the complexity of the incorporated processes. We employ a cell-based model based on the competition in colonic crypts, to reproduce epidemiological age-specific incidence rates of colon cancer. Due to the focus on the early stage of tumor development, only the time scale in the model has to be calibrated. The agreement between theoretical prediction and epidemiological observation suggests that the fate of tumor development is dominated by the early phase of tumor development long before a tumor becomes detectable.

• Age Factors
• Carcinogenesis
• Colon
• Colonic Neoplasms/epidemiology
• Colonic Neoplasms/genetics
• Humans
• Incidence

• ESF/SAB

• Astrocytoma
• CIN
• Chromosomal instability
• Chromothripsis
• Copy number burden
• Copy number variation
• Glioblastoma
• Glioma
• Oligodendroglioma

• Adult
• Chromosomal Instability/genetics
• Chromothripsis
• Glioma/genetics
• Humans
• Mutation/genetics
• Prognosis

• S10 OD030463 NIH HHS
• R01 NS106229 NINDS NIH HHS
• P30 AG066546 NIA NIH HHS
• P30 AG044271 NIA NIH HHS
• RF1 DA048810 NIDA NIH HHS
• NINDS
• NIDA
• Friedberg Charitable Foundation
• Making Headway Foundation

Aspirin intake has been shown to lead to significant protection against colorectal cancer, for example with an up to twofold reduction in colorectal adenoma incidence rates at higher doses. The mechanisms contributing to protection are not yet fully understood. While aspirin is an anti-inflammatory drug and can thus influence the tumor microenvironment, in vitro and in vivo experiments have recently shown that aspirin can also have a direct effect on cellular kinetics and fitness. It reduces the rate of tumor cell division and increases the rate of cell death. The question arises whether such changes in cellular fitness are sufficient to significantly contribute to the epidemiologically observed protection. To investigate this, we constructed a class of mathematical models of in vivo evolution of advanced adenomas, parameterized it with available estimates, and calculated population level incidence. Fitting the predictions to age incidence data revealed that only a model that included colonic crypt competition can account for the observed age-incidence curve. This model was then used to predict modified incidence patterns if cellular kinetics were altered as a result of aspirin treatment. We found that changes in cellular fitness that were within the experimentally observed ranges could reduce advanced adenoma incidence by a sufficient amount to account for age incidence data in aspirin-treated patient cohorts. While the mechanisms that contribute to the protective effect of aspirin are likely complex and multi-factorial, our study demonstrates that direct aspirin-induced changes of tumor cell fitness can significantly contribute to epidemiologically observed reduced incidence patterns.

• advanced adenoma
• aspirin
• cancer biology
• colon cancer
• computational biology
• human
• mathematical modeling
• systems biology

• MSH6 gene
• mutants
• nsSNPS
• structural alignment

• Deoxycytidine Monophosphate
• Humans
• Mutant Proteins
• Neoplasms/genetics
• Protein Domains
• Proteome

• Science and Engineering Research Board

It is widely believed that cancers develop upon acquiring a particular number of (epi) mutations in driver genes, but the law governing the kinetics of this process is not known. We have previously shown that the age distribution of incidence for the 20 most prevalent cancers of old age is best approximated by the Erlang probability distribution. The Erlang distribution describes the probability of several successive random events occurring by the given time according to the Poisson process, which allows an estimate for the number of critical driver events.

Here we employ a computational grid search method to find global parameter optima for five probability distributions on the CDC WONDER dataset of the age distribution of childhood and young adulthood cancer incidence.

We show that the Erlang distribution is the only classical probability distribution we found that can adequately model the age distribution of incidence for all studied childhood and young adulthood cancers, in addition to cancers of old age.

This suggests that the Poisson process governs driver accumulation at any age and that the Erlang distribution can be used to determine the number of driver events for any cancer type. The Poisson process implies the fundamentally random timing of driver events and their constant average rate. As waiting times for the occurrence of the required number of driver events are counted in decades, and most cells do not live this long, it suggests that driver mutations accumulate silently in the longest-living dividing cells in the body—the stem cells.

• Driver mutations
• Gamma distribution
• Grid search
• Poisson process
• Probability distribution
• Stem cells

Gut microbiome alteration was closely associated with colorectal cancer (CRC). Previous studies had demonstrated the bacteria composition changes but lacked virome profiles, trans-kindom interactions, and reliable diagnostic model explorations in CRC. Hence, we performed metagenomic sequencing to investigate the gut microbiome and microbial interactions in adenoma and CRC patients. We found the decreased microbial diversity in CRC and revealed the taxonomic alterations of bacteria and viruses were highly associated with CRC at the species level. The relative abundance of oral-derived species, such as Fusobacterium nucleatum, Fusobacterium hwasookii, Porphyromonas gingivalis, and Bacteroides fragilis, increased. At the same time, butyrate-producing and anti-inflammatory microbes decreased in adenoma and CRC by non-parametric Kruskal-Wallis test. Despite that, the relative abundance of Escherichia viruses and Salmonella viruses increased, whereas some phages, including Enterobacteria phages and Uncultured crAssphage, decreased along with CRC development. Gut bacteria was negatively associated with viruses in CRC and healthy control by correlation analysis (P=0.017 and 0.002, respectively). Viruses were much more dynamic than the bacteria as the disease progressed, and the altered microbial interactions were distinctively stage-dependent. The degree centrality of microbial interactions decreased while closeness centrality increased along with the adenoma to cancer development. Uncultured crAssphage was the key bacteriophage that enriched in healthy controls and positively associated with butyrate-producing bacteria. Diagnostic tests based on bacteria by random forest confirmed in independent cohorts showed better performance than viruses for CRC. In conclusion, our study revealed the novel CRC-associated bacteria and viruses that exhibited specific differences and intensive microbial correlations, which provided a reliable diagnostic panel for CRC.

• bacteria
• bacteriophage
• diagnostic test
• gut microbiome
• microbial network
• virus

Patterns of cancer incidence, viewed over extended time periods, reveal important aspects of multistage carcinogenesis. Here we show how a multistage clonal expansion (MSCE) model for cancer can be harnessed to identify biological processes that shape the surprisingly dynamic and disparate incidence patterns of esophageal squamous cell carcinoma (ESCC) in the US population. While the dramatic rise in esophageal adenocarcinoma (EAC) in the US has been largely attributed to reflux related increases in the prevalence of Barrett’s esophagus (BE), the premalignant field in which most EAC are thought to arise, only scant evidence exists for field cancerization contributing to ESCC. Our analyses of incidence patterns suggest that ESCC is associated with a premalignant field that may develop very early in life. Although the risk of ESCC, which is substantially higher in Blacks than Whites, is generally assumed to be associated with late-childhood and adult exposures to carcinogens, such as from tobacco smoking, alcohol consumption and various industrial exposures, the temporal trends we identify for ESCC suggest an onset distribution of field-defects before age 10, most strongly among Blacks. These trends differ significantly in shape and strength from field-defect trends that we estimate for US Whites. Moreover, the rates of ESCC-predisposing field-defects predicted by the model for cohorts of black children are decreasing for more recent birth cohorts (for Blacks born after 1940). These results point to a potential etiologic role of factors acting early in life, perhaps related to nutritional deficiencies, in the development of ESCC and its predisposing field-defect. Such factors may explain some of the striking racial differences seen in ESCC incidence patterns over time in the US.

We used a cell-level carcinogenesis model to analyze incidence patterns of esophageal squamous cell carcinoma (ESCC) in the US. We found an important role of an esophageal field-defect that is predicted to occur predominantly in childhood and predisposes to ESCC in adult life. Age-specific ESCC incidence patterns are also known to differ considerably between Blacks and Whites, and between males and females in the US, but the model consistently predicts early-childhood field-defects in all four groups. The estimated historical field-defect trends appear consistent with possible early childhood nutritional deficiencies.

• Cohort Studies
• Esophageal Neoplasms/epidemiology
• Esophageal Neoplasms/pathology
• Esophageal Squamous Cell Carcinoma/epidemiology
• Esophageal Squamous Cell Carcinoma/pathology
• Humans
• Incidence
• Risk Factors

• P30 CA023100 NCI NIH HHS
• U01 CA152926 NCI NIH HHS
• National Cancer Institute
• Moores Cancer Center Support Grant

Colorectal cancer (CRC), the third most common type of cancer, is the second leading cause of cancer-related mortality rates worldwide. Although modern research was able to shed light on the pathogenesis of CRC and provide enhanced screening strategies, the prevalence of CRC is still on the rise. Studies showed several cellular signaling pathways dysregulated in CRC, leading to the onset of malignant phenotypes. Therefore, analyzing signaling pathways involved in CRC metastasis is necessary to elucidate the underlying mechanism of CRC progression and pharmacotherapy. This review focused on target genes as well as various cellular signaling pathways including Wnt/β-catenin, p53, TGF-β/SMAD, NF-κB, Notch, VEGF, and JAKs/STAT3, which are associated with CRC progression and metastasis. Additionally, alternations in methylation patterns in relation with signaling pathways involved in regulating various cellular mechanisms such as cell cycle, transcription, apoptosis, and angiogenesis as well as invasion and metastasis were also reviewed. To date, understanding the genomic and epigenomic instability has identified candidate biomarkers that are validated for routine clinical use in CRC management. Nevertheless, better understanding of the onset and progression of CRC can aid in the development of early detection molecular markers and risk stratification methods to improve the clinical care of CRC patients.

• Colorectal cancer
• chromosomal instability
• microsatellite instability
• molecular pathways
• therapeutics

• HMGB1
• T-cells
• colon cancer
• data driven mathematical model
• dendritic cells
• gene expression profiles
• immune pattern
• macrophages
• sensitivity analysis
• tumor deconvolution

• R21 CA242933 NCI NIH HHS
• R21CA242933 National Cancer Institute of the National Institutes of Health

Cancer is caused by the lifetime accumulation of multiple somatic deformations of the genome and epigenome. At a very low rate, mistakes occur during genomic replication (e.g., mutations or modified epigenetic marks). Long-lived species, such as elephants, are suggested to have evolved mechanisms to slow down the cancer progression. Recently, the life span of companion dogs has increased considerably than before, owing to the improvement of their environment, which has led to an increase in the fraction of companion dogs developing cancer. These findings suggest that short-term responses of cancer risk to longevity differ from long-term responses. In this study, to clarify the situation, we used a simple multi-step model for cancer. The rates of events leading to malignant cancer are assumed to be proportional to those of genomic replication error. Perfect removal of replication error requires a large cost, resulting in the evolution of a positive rate of genomic replication error. The analysis of the model revealed: that, when the environment suddenly becomes benign, the relative importance of cancer enhances, although the age-dependent cancer risk remains unchanged. However, in the long run, the genomic error rate evolves to become smaller and mitigates the cancer risk.

• aspirin
• cancer prevention
• evolutionary dynamics
• lunchsyndrome
• mathematical modeling of cancer
• mutations
• mutator phenotype

• Aspirin/administration & dosage
• Aspirin/pharmacology
• Cell Cycle Checkpoints/drug effects
• Cell Proliferation/drug effects
• Evolution, Molecular
• Humans
• Models, Theoretical
• Neoplasms/metabolism
• Neoplasms/pathology
• Neoplasms/prevention & control

• U01 CA187956 NCI NIH HHS

• cancer evolution
• driver mutations
• stochastic model

• Carcinogenesis/genetics
• Colonic Neoplasms/genetics
• Colorectal Neoplasms/genetics
• Disease Progression
• Genes, APC/physiology
• Genes, p53/genetics
• Genes, ras
• Humans
• Models, Theoretical
• Mutation
• Mutation Rate
• Oncogenes
• Proto-Oncogene Proteins p21(ras)/genetics
• Tumor Suppressor Protein p53/genetics

Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers.

In this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times.

Our results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.

• Cancer progression
• Fitness
• Mutation
• Selection
• Tumorigenesis
• Waiting time to cancer

• Biologically based models of cancer risk
• adverse outcome pathways
• integrative modeling
• molecular biology
• radiation epidemiology

In cancer research, multistage models are used to assess the multistep process that leads to the onset of cancer. In view of biological and clinical similarities between cancer and dementia, we used these models to study Alzheimer’s disease (AD). From the population-based Rotterdam Study, we included 9,362 non-demented participants, of whom 1,124 developed AD during up to 26 years of follow-up. Under a multistage model, we regressed the logarithm of AD incidence rate against the logarithm of five-year age categories. The slope in this model reflects the number of steps (n–1) required for disease onset before the final step leading to disease manifestation. A linear relationship between log incidence rate and log age was observed, with a slope of 12.82 (95% confidence interval: 9.01-16.62), equivalent to 14 steps. We observed fewer steps for those at high genetically determined risk: 12 steps for APOE-ε4 carriers, and 10 steps for those at highest genetic risk based on APOE and a genetic risk score. The pathogenesis of AD complies with a multistage disease-model, requiring 14 steps before disease manifestation. Genetically predisposed individuals require fewer steps indicating that they already inherited multiple of these steps. Unravelling these steps in AD pathogenesis could benefit the development of intervention strategies.

• APOE
• Alzheimer’s disease
• epidemiology
• genetic risk score
• genetics

• Apoptosis
• Colon cancer
• ERBB2/FUT4 pathway
• Invasion
• Proliferation
• SET and MYND domain-containing protein 2 (SMYD2)

• computational models
• computational science
• computer science

• Algorithms
• Antineoplastic Combined Chemotherapy Protocols/pharmacology
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Biomarkers, Tumor/antagonists & inhibitors
• Biomarkers, Tumor/genetics
• Carcinogenesis/genetics
• Computational Biology/instrumentation
• Computational Biology/methods
• Computer Graphics
• Datasets as Topic
• Humans
• Molecular Targeted Therapy/methods
• Mutation
• Neoplasms/drug therapy
• Neoplasms/genetics
• Oligonucleotide Array Sequence Analysis/instrumentation
• Oligonucleotide Array Sequence Analysis/methods
• Precision Medicine/methods
• Time Factors

• VCOM REAP grant - RA2019

Despite advances in the modeling and understanding of colorectal cancer development, the dynamics of the progression from benign adenomatous polyp to colorectal carcinoma are still not fully resolved. To take advantage of adenoma size and prevalence data in the National Endoscopic Database of the Clinical Outcomes Research Initiative (CORI) as well as colorectal cancer incidence and size data from the Surveillance Epidemiology and End Results (SEER) database, we construct a two-type branching process model with compartments representing adenoma and carcinoma cells. To perform parameter inference we present a new large-size approximation to the size distribution of the cancer compartment and validate our approach on simulated data. By fitting the model to the CORI and SEER data, we learn biologically relevant parameters, including the transition rate from adenoma to cancer. The inferred parameters allow us to predict the individualized risk of the presence of cancer cells for each screened patient. We provide a web application which allows the user to calculate these individual probabilities at https://ccrc-eth.shinyapps.io/CCRC/. For example, we find a 1 in 100 chance of cancer given the presence of an adenoma between 10 and 20mm size in an average risk patient at age 50. We show that our two-type branching process model recapitulates the early growth dynamics of colon adenomas and cancers and can recover epidemiological trends such as adenoma prevalence and cancer incidence while remaining mathematically and computationally tractable.

Colorectal cancer is a major public health burden. The development of colorectal cancer starts with the mutational initiation of non-cancerous growths in the form of benign adenomatous polyps. These adenomas grow over time with the potential to develop into carcinomas. Many mathematical and simulation-based models have been used to gain insight into this process. We aimed to understand rates of adenoma growth and transition into carcinomas, to enable better planning of colorectal cancer screening strategies. To this end, we expand the two-type branching process model, and fit it on data describing the frequency of sizes of adenomas and carcinomas. The results provide new, data-based, estimates of the rates of development for colorectal cancer.

• Adenoma/diagnosis
• Adenoma/pathology
• Colorectal Neoplasms/epidemiology
• Colorectal Neoplasms/pathology
• Computational Biology
• Databases, Factual
• Female
• Humans
• Incidence
• Male
• Models, Biological
• Models, Statistical
• Probability
• Reproducibility of Results
• Risk Factors
• SEER Program

• 609883 European Research Council
• Krebsliga Schweiz (CH)

Increasing evidence has demonstrated that increased expression of cyclin-dependent kinase regulatory subunit 1B (CKS1B) is associated with the pathogenesis of many human cancers, including colorectal cancer (CRC). However, the regulatory mechanisms underlying the expression of CKS1B in CRC are not completely understood. Here, we investigate the role played by microRNAs in the expression of CKS1B and carcinogenesis in CRC. Among the six microRNAs predicted to target CKS1B gene expression, only miR-1258 was revealed to downregulate CKS1B expression through binding to its 3’-UTR region, as ectopic miR-1258 expression suppressed CKS1B expression and vice versa. In CRC, miR-1258 expression also decreased cell proliferation and migration in vitro and tumor growth in vivo, similar to cells with silenced CKS1B expression. Considering the highly increased levels of CKS1B and decreased expression of miR-1258 in tumors from CRC patients, these findings suggest that miR-1258 may play tumor-suppressive roles by targeting CKS1B expression in CRC. However, the therapeutic significance of these findings should be evaluated in clinical settings.

• CKS1B
• colorectal cancer
• miR-1258

• Animals
• CDC2-CDC28 Kinases/genetics
• CDC2-CDC28 Kinases/metabolism
• Cell Line, Tumor
• Cell Movement/genetics
• Cell Proliferation/genetics
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/metabolism
• Colorectal Neoplasms/pathology
• Female
• Gene Expression Regulation, Neoplastic
• HEK293 Cells
• HT29 Cells
• Humans
• Mice
• Mice, Inbred BALB C
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Xenograft Model Antitumor Assays

• Adult
• Age Factors
• Aged
• Aged, 80 and over
• Carcinogenesis
• Female
• Helicobacter Infections/complications
• Helicobacter Infections/epidemiology
• Humans
• Incidence
• Lung Neoplasms/epidemiology
• Lung Neoplasms/etiology
• Lung Neoplasms/prevention & control
• Male
• Middle Aged
• Models, Biological
• Mouth Neoplasms/epidemiology
• Mouth Neoplasms/etiology
• Mouth Neoplasms/prevention & control
• Papillomavirus Infections/complications
• Papillomavirus Infections/epidemiology
• Prevalence
• Risk Factors
• SEER Program/statistics & numerical data
• Smoking/adverse effects
• Smoking/epidemiology
• Stomach Neoplasms/epidemiology
• Stomach Neoplasms/etiology
• Stomach Neoplasms/prevention & control
• United States

• U01 CA182915 NCI NIH HHS
• U01 CA199284 NCI NIH HHS

Complex systems can fail through different routes, often progressing through a series of (rate-limiting) steps and modified by environmental exposures. The onset of disease, cancer in particular, is no different. Multi-stage models provide a simple but very general mathematical framework for studying the failure of complex systems, or equivalently, the onset of disease. They include the Armitage-Doll multi-stage cancer model as a particular case, and have potential to provide new insights into how failures and disease, arise and progress. A method described by E.T. Jaynes is developed to provide an analytical solution for a large class of these models, and highlights connections between the convolution of Laplace transforms, sums of random variables, and Schwinger/Feynman parameterisations. Examples include: exact solutions to the Armitage-Doll model, the sum of Gamma-distributed variables with integer-valued shape parameters, a clonal-growth cancer model, and a model for cascading disasters. Applications and limitations of the approach are discussed in the context of recent cancer research. The model is sufficiently general to be used in many contexts, such as engineering, project management, disease progression, and disaster risk for example, allowing the estimation of failure rates in complex systems and projects. The intended result is a mathematical toolkit for applying multi-stage models to the study of failure rates in complex systems and to the onset of disease, cancer in particular.

Individual instances of cancer are primarily a result of a combination of a small number of genetic mutations (hits). Knowing the number of such mutations is a prerequisite for identifying specific combinations of carcinogenic mutations and understanding the etiology of cancer. We present a mathematical model for estimating the number of hits based on the distribution of somatic mutations. The model is fundamentally different from previous approaches, which are based on cancer incidence by age. Our somatic mutation based model is likely to be more robust than age-based models since it does not require knowing or accounting for the highly variable mutation rate, which can vary by over three orders of magnitude. In fact, we find that the number of somatic mutations at diagnosis is weakly correlated with age at cancer diagnosis, most likely due to the extreme variability in mutation rates between individuals. Comparing the distribution of somatic mutations predicted by our model to the actual distribution from 6904 tumor samples we estimate the number of hits required for carcinogenesis for 17 cancer types. We find that different cancer types exhibit distinct somatic mutational profiles corresponding to different numbers of hits. Why might different cancer types require different numbers of hits for carcinogenesis? The answer may provide insight into the unique etiology of different cancer types.

Cancer is primarily a result of genetic mutations. Each individual instance of cancer is initiated by a specific combination of a small number of mutations (hits). In trying to identify these combinations of mutations, it is important to know how many hits to look for. However, there are conflicting estimates for the number of hits. We present a fundamentally different model for estimating the number of hits. We found that the number hits ranges from two-eight depending on cancer type. These findings may provide insight into the unique characteristics of different cancer types.

• cancer
• computational biology and bioinformatics
• cancer genomics

• Algorithms
• Carcinogenesis/genetics
• Computational Biology
• Databases, Genetic
• Humans
• Models, Genetic
• Mutation
• Neoplasms/genetics

• Bradley engineering foundation - VCOM

As mathematical models and computational tools become more sophisticated and powerful to accurately depict system dynamics, numerical methods that were previously considered computationally impractical started being utilized for large-scale simulations. Methods that characterize a rare event in biochemical systems are part of such phenomenon, as many of them are computationally expensive and require high-performance computing. In this paper, we introduce an enhanced version of the doubly weighted stochastic simulation algorithm (dwSSA) (Daigle et al. in J Chem Phys 134:044110, 2011), called dwSSA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{++}$$\end{document}++, that significantly improves the speed of convergence to the rare event of interest when the conventional multilevel cross-entropy method in dwSSA is either unable to converge or converges very slowly. This achievement is enabled by a novel polynomial leaping method that uses past data to detect slow convergence and attempts to push the system toward the rare event. We demonstrate the performance of dwSSA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{++}$$\end{document}++ on two systems—a susceptible–infectious–recovered–susceptible disease dynamics model and a yeast polarization model—and compare its computational efficiency to that of dwSSA.

• Gillespie algorithm
• Importance sampling
• Rare event probability estimation
• SSA
• Stochastic simulation
• dwSSA

• R01 AI028433 NIAID NIH HHS
• R01 OD011095 NIH HHS