Cell selection as driving force in lung and colon carcinogenesis

Different Mechanisms of Cigarette Smoking-Induced Lung Cancer

The risk of cigarette smoking plays a pivotal role in increasing the incidence rates of lung cancer. This paper sheds new light on modeling the impact of cigarette smoking on lung cancer evolution, especially genetic instability and the number of gene mutations in the genome of stem cells. To handle this issue, we have set up stochastic multi-stage models to fit the data set of the probabilities of current and former smokers from the Nurses' Health Study cohort of females (NHS) and the Health Professionals Follow up Study cohort of men (HPFS). Throughout this paper, we consider both mutation rates and clonal expansion rates as parameters in each compartment. For current and former smokers, three-driver mutations are most likely to take place in the progression of lung cancer under smoking risk. For current smokers, our findings reveal that two to sixteen gene mutations are required to obtain a cancerous cell among men and women in US. Moreover, two to six (eleven) cancer-mutations are available in the pathway to lung cancer among former smokers who have quit smoking for more (less) than ten years for both male and female patients. This highlights that cigarette smoking stimulates the number of driver mutations during lung tumorigenesis in both sexes. It is very crucial to examine the role of cigarette smoking in determining whether genomic instability is an early stage or late stage in the process of lung carcinogenesis.

Identification of Differentially Expressed Genes to Establish New Biomarker for Cancer Prediction

The goal of the human genome project is to integrate genetic information into different clinical therapies. To achieve this goal, different computational algorithms are devised for identifying the biomarker genes, cause of complex diseases. However, most of the methods developed so far using DNA microarray data lack in interpreting biological findings and are less accurate in disease prediction. In the paper, we propose two parameters risk_factor and confusion_factor to identify the biologically significant genes for cancer development. First, we evaluate risk_factor of each gene and the genes with nonzero risk_factor result misclassification of data, therefore removed. Next, we calculate confusion_factor of the remaining genes which determines confusion of a gene in prediction due to closeness of the samples in the cancer and normal classes. We apply nondominated sorting genetic algorithm (NSGA-II) to select the maximally uncorrelated differentially expressed genes in the cancer class with minimum confusion_factor. The proposed Gene Selection Explore (GSE) algorithm is compared to well established feature selection algorithms using 10 microarray data with respect to sensitivity, specificity, and accuracy. The identified genes appear in KEGG pathway and have several biological importance.

Case Studies of Gastric, Lung, and Oral Cancer Connect Etiologic Agent Prevalence to Cancer Incidence

Obtaining detailed individual-level data on both exposure and cancer outcomes is challenging, and it is difficult to understand and characterize how temporal aspects of exposures translate into cancer risk. We show that, in lieu of individual-level information, population-level data on cancer incidence and etiologic agent prevalence can be leveraged to investigate cancer mechanisms and to better characterize and predict cancer trends. We use mechanistic carcinogenesis models [multistage clonal expansion (MSCE) models] and data on smoking, Helicobacter pylori (H. pylori), and HPV infection prevalence to investigate trends of lung, gastric, and HPV-related oropharyngeal cancers. MSCE models are based on the initiation-promotion-malignant conversion paradigm and allow for interpretation of trends in terms of general biological mechanisms. We assumed the rates of initiation depend on the prevalence of the corresponding risk factors. We performed two types of analysis, using the agent prevalence and cancer incidence data to estimate the model parameters and using cancer incidence data to infer the etiologic agent prevalence as well as the model parameters. By including risk factor prevalence, MSCE models with as few as three parameters closely reproduced 40 years of age-specific cancer incidence data. We recovered trends of H. pylori prevalence in the United States and demonstrated that cohort effects can explain the observed bimodal, age-specific pattern of oral HPV prevalence in men. Our results demonstrate the potential for joint analyses of population-level cancer and risk factor data through mechanistic modeling. This approach can be a first step in systematically testing relationships between exposures and cancer risk when individual-level data is lacking.Significance: Analysis of trends in risk-factor prevalence and cancer incidence can shed light on cancer mechanisms and the way that carcinogen exposure through time shapes the risk of cancer at different ages.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/12/3386/F1.large.jpg Cancer Res; 78(12); 3386-96. ©2018 AACR.

Dynamics of cell transformation in culture and its significance for tumor development in animals

NIH 3T3 cells grown in conventional Dulbecco's modification of Eagle's basal medium (DME) produce no transformed foci when grown to confluence in 10% calf serum (CS). A few cultures were transformed by ras oncogenes when transfected with DNA from neoplastic cells, but they failed to do so in 80 to 90% of the transfections. However, when they were grown in a medium [molecular, cellular, and developmental biology 402 (MCDB 402)] optimized for their clonal growth in minimal serum, they produced transformed foci without transfection in 10% CS, but not in 2% CS. The first response to growth in MCDB 402 in 2% CS in successive rounds of contact inhibition was uniform increases in saturation density of the population. This was followed by the appearance of transformed foci. A systematic study was made of the dynamics of neoplastic progression in various concentrations of CS in a single round of confluence at 2 and 3 wk, followed by three sequential rounds of confluence in 2% CS for 2 wk. There was a linear relationship between CS concentration and saturation density in the first-round cultures and continuing differences in subsequent cultures. The hyperplastic field of normal-looking cells surrounding transformed foci became increasingly permissive for transformation with serial culture. The dynamics show that epigenetic selection is the major driving force of neoplastic development. Cells from dense foci produced malignant fibrosarcomas in mice, thereby exhibiting a positive relationship between transformation in culture and the development of tumors.

Parameter estimation for multistage clonal expansion models from cancer incidence data: A practical identifiability analysis

Many cancers are understood to be the product of multiple somatic mutations or other rate-limiting events. Multistage clonal expansion (MSCE) models are a class of continuous-time Markov chain models that capture the multi-hit initiation–promotion–malignant-conversion hypothesis of carcinogenesis. These models have been used broadly to investigate the epidemiology of many cancers, assess the impact of carcinogen exposures on cancer risk, and evaluate the potential impact of cancer prevention and control strategies on cancer rates. Structural identifiability (the analysis of the maximum parametric information available for a model given perfectly measured data) of certain MSCE models has been previously investigated. However, structural identifiability is a theoretical property and does not address the limitations of real data. In this study, we use pancreatic cancer as a case study to examine the practical identifiability of the two-, three-, and four-stage clonal expansion models given age-specific cancer incidence data using a numerical profile-likelihood approach. We demonstrate that, in the case of the three- and four-stage models, several parameters that are theoretically structurally identifiable, are, in practice, unidentifiable. This result means that key parameters such as the intermediate cell mutation rates are not individually identifiable from the data and that estimation of those parameters, even if structurally identifiable, will not be stable. We also show that products of these practically unidentifiable parameters are practically identifiable, and, based on this, we propose new reparameterizations of the model hazards that resolve the parameter estimation problems. Our results highlight the importance of identifiability to the interpretation of model parameter estimates.

Parameter estimation from data is an important part of mathematical modeling, and structural identifiability is the study of what parametric information exists, for a given model, in ideal data. Unfortunately, for a variety of reasons, there is often less information available in our real data sets. The study of these problems is called practical identifiability. In this study, we consider a family of models of cancer biology that are commonly used to explain cancer incidence in terms of underlying biological parameters. Using profile likelihoods, a widely applicable numerical tool, we demonstrate that even though the more complex models we consider have theoretically more identifiable parameters, the data contains only three pieces of practically identifiable information for each model: the product of the initiating mutation rates, the net cell proliferation rate, and the scaled malignant conversion rate. This result can be interpreted biologically: we can determine only the product of cell mutation rates not the intermediate rates themselves. Our result limits the interpretability of previous work, but we propose a novel parameterization to resolve the identifiability issue. Ultimately, our analysis demonstrates the importance of verifying the practical identifiability of parameters before assigning too much weight to the interpretation of their estimated values.

Age Effects and Temporal Trends in HPV-Related and HPV-Unrelated Oral Cancer in the United States: A Multistage Carcinogenesis Modeling Analysis

Differences in prognosis in HPV-positive and HPV-negative oral (oropharyngeal and oral cavity) squamous cell carcinomas (OSCCs) and increasing incidence of HPV-related cancers have spurred interest in demographic and temporal trends in OSCC incidence. We leverage multistage clonal expansion (MSCE) models coupled with age-period-cohort (APC) epidemiological models to analyze OSCC data in the SEER cancer registry (1973-2012). MSCE models are based on the initiation-promotion-malignant conversion paradigm in carcinogenesis and allow for interpretation of trends in terms of biological mechanisms. APC models seek to differentiate between the temporal effects of age, period, and birth cohort on cancer risk. Previous studies have looked at the effect of period and cohort on tumor initiation, and we extend this to compare model fits of period and cohort effects on each of tumor initiation, promotion, and malignant conversion rates. HPV-related, HPV-unrelated except oral tongue, and HPV-unrelated oral tongue sites are best described by placing period and cohort effects on the initiation rate. HPV-related and non-oral-tongue HPV-unrelated cancers have similar promotion rates, suggesting similar tumorigenesis dynamics once initiated. Estimates of promotion rates at oral tongue sites are lower, corresponding to a longer sojourn time; this finding is consistent with the hypothesis of an etiology distinct from HPV or alcohol and tobacco use. Finally, for the three subsite groups, men have higher initiation rates than women of the same race, and black people have higher promotion than white people of the same sex. These differences explain part of the racial and sex differences in OSCC incidence.

Evolution of genetic instability in heterogeneous tumors

Genetic instability is an important characteristic of cancer. While most cancers develop genetic instability at some stage of their progression, sometimes a temporary rise of instability is followed by the return to a relatively stable genome. Neither the reasons for these dynamics, nor, more generally, the role of instability in tumor progression, are well understood. In this paper we develop a class of mathematical models to study the evolutionary competition dynamics among different sub-populations in a heterogeneous tumor. We observe that despite the complexity of this multi-component and multi-process system, there is only a small number of scenarios expected in the context of the evolution of instability. If the penalty incurred by unstable cells (the decrease in the growth due to deleterious mutations) is high compared with the gain (the production rate of advantageous mutations), then instability does not evolve. In the opposite case, instability evolves and comes to dominate the system. In the intermediate parameter regime, instability is generated but later gives way to stable clones. Moreover, the model also informs us of the patterns of instability for cancer lineages corresponding to different stages of progression. It is predicted that mutations causing instability are merely "passengers" in tumors that have undergone only a small number of malignant mutations. Further down the path of carcinogenesis, however, unstable cells are more likely to give rise to the winning clonal wave that takes over the tumor and carries the evolution forward, thus conferring a causal role of the instability in such cases. Further, each individual clonal wave (i.e. cells harboring a fixed number of malignant driver mutations) experiences its own evolutionary history. It can fall under one of three types of temporal behavior: stable throughout, unstable to stable, or unstable throughout. Which scenario is realized depends on the subtle (but predictable) interplay among mutation rates and the death toll associated with the instability. The modeling approach provided here sheds light onto important aspects of the evolutionary dynamics of instability, which may be relevant to treatment scenarios that target instability or damage repair.

Invariant Characteristics of Carcinogenesis

Carcinogenic modeling is aimed at mathematical descriptions of cancer development in aging. In this work, we assumed that a small fraction of individuals in the population is susceptible to cancer, while the rest of the population is resistant to cancer. For individuals susceptible to cancer we adopted methods of conditional survival analyses. We performed computational experiments using data on pancreatic, stomach, gallbladder, colon and rectum, liver, and esophagus cancers from the gastrointestinal system collected for men and women in the SEER registries during 1975-2009. In these experiments, we estimated the time period effects, the birth cohort effects, the age effects and the population (unconditional) cancer hazard rates. We also estimated the individual cancer presentation rates and the individual cancer resistance rates, which are, correspondingly, the hazard and survival rates conditioned on the susceptibility to cancer. The performed experiments showed that for men and women, patterns of the age effects, the individual cancer presentation rates and the individual cancer resistance rates are: (i) intrinsic for each cancer subtype, (ii) invariant to the place of living of the individuals diagnosed with cancer, and (iii) well adjusted for the modifiable variables averaged at a given time period. Such specificity and invariability of the age effects, the individual cancer presentation rates and the individual cancer resistance rates suggest that these carcinogenic characteristics can be useful for predictive carcinogenic studies by methods of inferential statistics and for the development of novel strategies for cancer prevention.

Exposomics: mathematics meets biology

Although 'exposome' research has started to appear, and the concept is fascinating, we still have little proof-of-principle. This issue of Mutagenesis reports a few examples of exposome research, showing that the approach is providing the first results. In this Commentary, I develop the example of epigenome-wide methylation studies related to smoking as a success story, that fits well with previous research in humans and in vitro on mechanisms of carcinogenesis, and also with conceptual models such as Cairns' model based on asymmetric division of stem cells. The field of exposomics merges different disciplines, notably biology and mathematics, but also the evolutionary theory, and can possibly lead to interesting breakthroughs in the next years.

Field cancerisation in colorectal cancer: A new frontier or pastures past?

Despite considerable advances in our understanding of cancer biology, early diagnosis of colorectal cancer remains elusive. Based on the adenoma-carcinoma sequence, cancer develops through the progressive accumulation of mutations in key genes that regulate cell growth. However, recent mathematical modelling suggests that some of these genetic events occur prior to the development of any discernible histological abnormality. Cells acquire pro-tumourigenic mutations that are not able to produce morphological change but predispose to cancer formation. These cells can grow to form large patches of mucosa from which a cancer arises. This process has been termed “field cancerisation”. It has received little attention in the scientific literature until recently. Several studies have now demonstrated cellular, genetic and epigenetic alterations in the macroscopically normal mucosa of colorectal cancer patients. In some reports, these changes were effectively utilised to identify patients with a neoplastic lesion suggesting potential application in the clinical setting. In this article, we present the scientific evidence to support field cancerisation in colorectal cancer and discuss important limitations that require further investigation. Characterisation of the field defect is necessary to enable early diagnosis of colorectal cancer and identify molecular targets for chemoprevention. Field cancerisation offers a promising prospect for experimental cancer research and has potential to improve patient outcomes in the clinical setting.

Evolutionary dynamics of morphological stability in a long-term experiment with Escherichia coli

To investigate the questions in morphological evolution, some biologists seek to carry out evolution experiments owing to the incompleteness and uncontrollability of the fossil record and natural populations. To quantitatively analyse the morphology (cell size) evolution observed from a long-term experiment with Escherichia coli, the authors present three mathematical approximations to the Wright-Fisher model of the morphological evolution. They firstly use a deterministic approximation, which fails to predict evolutionary dynamics of cell size and proves the importance of stochasticity in large populations. Then, they develop a stochastic approximation and derive an analytic expression for the anticipated waiting time to reach the stability of cell size. The results show that the calculation of this waiting time is in good agreement with the experimental data and that the selective advantage plays a prominent role in cell size evolution, with mutation rate and population size having less impact. Finally, they employ a multistep process to approximate the Wright-Fisher model of cell size evolution and acquire an analytical formula for the median waiting time until the stability of cell size. This median time supports the idea that the selective advantage is the dominant force for the morphological evolution in the long-term experiment.

Cancer evolution: mathematical models and computational inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Surface modulation of complex stiffness via layer-by-layer assembly as a facile strategy for selective cell adhesion

A facile approach to achieve selective cell adhesion by modulating surface complex stiffness based on layer-by-layer assembly is reported.

Dynamics of the risk of smoking-induced lung cancer: a compartmental hidden Markov model for longitudinal analysis

BACKGROUND

To account for the dynamic aspects of carcinogenesis, we propose a compartmental hidden Markov model in which each person is healthy, asymptomatically affected, diagnosed, or deceased. Our model is illustrated using the example of smoking-induced lung cancer.

METHODS

The model was fitted on a case-control study nested in the European Prospective Investigation into Cancer and Nutrition study, including 757 incident cases and 1524 matched controls. Estimation was done through a Markov Chain Monte Carlo algorithm, and simulations based on the posterior estimates of the parameters were used to provide measures of model fit. We performed sensitivity analyses to assess robustness of our findings.

RESULTS

After adjusting for its impact on exposure duration, age was not found to independently drive the risk of lung carcinogenesis, whereas age at starting smoking in ever-smokers and time since cessation in former smokers were found to be influential. Our data did not support an age-dependent time to diagnosis. The estimated time between onset of malignancy and clinical diagnosis ranged from 2 to 4 years. Our approach yielded good performance in reconstructing individual trajectories in both cases (sensitivity >90%) and controls (sensitivity >80%).

CONCLUSION

Our compartmental model enabled us to identify time-varying predictors of risk and provided us with insights into the dynamics of smoking-induced lung carcinogenesis. Its flexible and general formulation enables the future incorporation of disease states, as measured by intermediate markers, into the modeling of the natural history of cancer, suggesting a large range of applications in chronic disease epidemiology.

Heuristic modeling of carcinogenesis for the population with dichotomous susceptibility to cancer: a pancreatic cancer example

At present, carcinogenic models imply that all individuals in a population are susceptible to cancer. These models either ignore a fall of the cancer incidence rate at old ages, or use some poorly identifiable parameters for its accounting. In this work, a new heuristic model is proposed. The model assumes that, in a population, only a small fraction (pool) of individuals is susceptible to cancer and decomposes the problem of the carcinogenic modeling on two sequentially solvable problems: (i) determination of the age-specific hazard rate in individuals susceptible to cancer (individual hazard rate) from the observed hazard rate in the population (population hazard rate); and (ii) modelling of the individual hazard rate by a chosen “up” of the theoretical hazard function describing cancer occurrence in individuals in time (age). The model considers carcinogenesis as a failure of individuals susceptible to cancer to resist cancer occurrence in aging and uses, as the theoretical hazard function, the three-parameter Weibull hazard function, often utilized in a failure analysis. The parameters of this function, providing the best fit of the modeled and observed individual hazard rates (determined from the population hazard rates), are the outcomes of the modeling. The model was applied to the pancreatic cancer data. It was shown that, in the populations stratified by gender, race and the geographic area of living, the modeled and observed population hazard rates of pancreatic cancer occurrence have similar turnovers at old ages. The sizes of the pools of individuals susceptible to this cancer: (i) depend on gender, race and the geographic area of living; (ii) proportionally influence the corresponding population hazard rates; and (iii) do not influence the individual hazard rates. The model should be further tested using data on other types of cancer and for the populations stratified by different categorical variables.

Promotion and selection by serum growth factors drive field cancerization, which is anticipated in vivo by type 2 diabetes and obesity

Individuals suffering from type 2 diabetes or obesity exhibit a significant increase in the incidence of various types of cancer. It is generally accepted that those conditions arise from overnutrition and a sedentary lifestyle, which lead to insulin resistance characterized by overproduction of insulin acting as a growth factor. There is a consensus based largely on epidemiological data that chronic overproduction of insulin is responsible for the increased incidence of cancer. A model system in culture of NIH 3T3 cells induces the collective effects of serum growth factors on progression through the stages of field cancerization. It shows that the driving force of progression is promotion of cell growth under selection at high cell density, with no requirement for exogenous carcinogenic agents. The early effect is gradual selection among many preexisting, low-penetrance preneoplastic mutations or stable epigenetic variants, followed by sporadic, high-penetrance transforming variants, all dependent on endogenous processes. The significance of the results for cancer in diabetic and obese individuals is that the initial stages of the process involve multiorgan metabolic interactions that produce a systemic insulin resistance with chronic overproduction of insulin and localized field cancerization. Hypomagnesemia is prevalent in the foregoing metabalo/systemic disorders, and may also provide a selective microenvironment for tumor development.

Basic equations and computing procedures for frailty modeling of carcinogenesis: application to pancreatic cancer data

Modeling of cancer hazards at age t deals with a dichotomous population, a small part of which (the fraction at risk) will get cancer, while the other part will not. Therefore, we conditioned the hazard function, h(t), the probability density function (pdf), f(t), and the survival function, S(t), on frailty α in individuals. Assuming α has the Bernoulli distribution, we obtained equations relating the unconditional (population level) hazard function, h_U(t), cumulative hazard function, H_U(t), and overall cumulative hazard, H₀, with the h(t), f(t), and S(t) for individuals from the fraction at risk. Computing procedures for estimating h(t), f(t), and S(t) were developed and used to fit the pancreatic cancer data collected by SEER9 registries from 1975 through 2004 with the Weibull pdf suggested by the Armitage-Doll model. The parameters of the obtained excellent fit suggest that age of pancreatic cancer presentation has a time shift about 17 years and five mutations are needed for pancreatic cells to become malignant.

Modelling the evolution of genetic instability during tumour progression

The role of genetic instability in driving carcinogenesis remains controversial. Genetic instability should accelerate carcinogenesis by increasing the rate of advantageous driver mutations; however, genetic instability can also potentially retard tumour growth by increasing the rate of deleterious mutation. As such, it is unclear whether genetically unstable clones would tend to be more selectively advantageous than their genetically stable counterparts within a growing tumour. Here, we show the circumstances where genetic instability evolves during tumour progression towards cancer. We employ a Wright-Fisher type model that describes the evolution of tumour subclones. Clones can acquire both advantageous and deleterious mutations, and mutator mutations that increase a cell's intrinsic mutation rate. Within the model, cancers evolve with a mutator phenotype when driver mutations bestow only moderate increases in fitness: very strong or weak selection for driver mutations suppresses the evolution of a mutator phenotype. Genetic instability occurs secondarily to selectively advantageous driver mutations. Deleterious mutations have relatively little effect on the evolution of genetic instability unless selection for additional driver mutations is very weak or if deleterious mutations are very common. Our model provides a framework for studying the evolution of genetic instability in tumour progression. Our analysis highlights the central role of selection in shaping patterns of mutation in carcinogenesis.

Molecular damage in cancer: an argument for mTOR-driven aging

Despite common belief, accumulation of molecular damage does not play a key role in aging. Still, cancer (an age-related disease) is initiated by molecular damage. Cancer and aging share a lot in common including the activation of the TOR pathway. But the role of molecular damage distinguishes cancer and aging. Furthermore, an analysis of the role of both damage and aging in cancer argues against "a decline, caused by accumulation of molecular damage" as a cause of aging. I also discuss how random molecular damage, via rounds of multiplication and selection, brings about non-random hallmarks of cancer.

Melanoma update: diagnostic and prognostic factors that can effectively shape and personalize management

Routine light microscopy remains a powerful tool to diagnose, stage and prognose melanoma. Although it is very economical and efficient, it requires a significant level of expertise and, in difficult cases the final diagnosis is affected by subjective interpretation. Fortunately, new insights into the genomic aberrations characteristic of melanoma, coupled with ancillary studies, are further refining evaluation and management allowing for more confident diagnosis, more accurate staging and the selection of targeted therapy. In this article, we review the standard of care and new updates including four probe FISH, the 2009 American Joint Commission on Cancer staging of melanoma and mutant testing of melanoma, which will be crucial for targeted therapy of metastatic melanoma.

The early history of tumor virology: Rous, RIF, and RAV

One hundred years ago Peyton Rous recovered a virus, now known as the Rous sarcoma virus (RSV), from a chicken sarcoma, which reproduced all aspects of the tumor on injection into closely related chickens. There followed recovery of causal viruses of tumors of different morphology from 4 more of 60 chicken tumors. Subsequent studies in chickens of the biology of the first RSV isolated moved slowly for 45 y until an assay of ectodermal pocks of the chorioallantoic membrane of chicken embryos was introduced. The inadequacies of that assay were resolved with the production of transformed foci in cultures of chicken fibroblasts. There followed a productive period on the dynamics of RSV infection. An avian leukosis virus (ALV) was found in some chicken embryos and named resistance-inducing factor (RIF) because it interferes with RSV. Its epidemiology in chickens is described. Another ALV was found in stocks of RSV and called Rous-associated virus (RAV). Cells preinfected with RAV interfere with RSV infection, but RSV does not produce infectious virus unless RAV is added during or after RSV infection. Intracellular RAV provides the infectious coat for the otherwise defective RSV. The coat determines the antigenicity, host range, and maturation rate of RSV. RSV particles carry reverse transcriptase, an enzyme that converts their RNA into DNA and allows integration into the cell's DNA, where it functions as a cellular gene. This was the bridge that joined the biological era to the molecular era. Its relation to oncogenes and human cancer is discussed.

Stable differences in intrinsic mitochondrial membrane potential of tumor cell subpopulations reflect phenotypic heterogeneity

Heterogeneity among cells that constitute a solid tumor is important in determining disease progression. Our previous work established that, within a population of metastatic colonic tumor cells, there are minor subpopulations of cells with stable differences in their intrinsic mitochondrial membrane potential (ΔΨm), and that these differences in ΔΨm are linked to tumorigenic phenotype. Here we expanded this work to investigate primary mammary, as well as colonic, tumor cell lines. We show that within a primary mammary tumor cell population, and in both primary and metastatic colonic tumor cell populations, there are subpopulations of cells with significant stable variations in intrinsic ΔΨm. In each of these 3 tumor cell populations, cells with relatively higher intrinsic ΔΨm exhibit phenotypic properties consistent with promotion of tumor cell survival and expansion. However, additional properties associated with invasive potential appear in cells with higher intrinsic ΔΨm only from the metastatic colonic tumor cell line. Thus, it is likely that differences in the intrinsic ΔΨm among cells that constitute primary mammary tumor populations, as well as primary and metastatic colonic tumor populations, are markers of an acquired tumor phenotype which, within the context of the tumor, influence the probability that particular cells will contribute to disease progression.

Fields and field cancerization: the preneoplastic origins of cancer: asymptomatic hyperplastic fields are precursors of neoplasia, and their progression to tumors can be tracked by saturation density in culture

Most basic research on cancer concerns genetic changes in benign and malignant tumors. Yet evidence indicates that the majority of the mutations in tumors occur in the preneoplastic field stage of their development. That early stage is represented by grossly invisible, broad regions of "field cancerization" which have not, heretofore, been operationally analyzed in cell culture. Conditions are described for quantitating preneoplasia by increased saturation density followed by progression to transformation. These parameters are driven by Darwinian selection of spontaneously occurring, cumulative mutations, in accordance with recent genomic analyses of human cancer, just as it is in the evolution of species. The cell culture model will allow correlation of the preneoplastic increases in saturation density with genetic changes, and development of methods for demarcating fields during surgery so that they can be excised along with the tumor, thereby reducing the possibility of recurrence at the site.