Molecular biology of breast cancer metastasis. The use of mathematical models to determine relapse and to predict response to chemotherapy in breast cancer

Liquid biopsies and patient-reported outcome measures for integrative monitoring of patients with early-stage breast cancer: a study protocol for the longitudinal observational Prospective Breast Cancer Biobanking (PBCB) study

INTRODUCTION

Breast cancer is still the most common malignancy among women worldwide. The Prospective Breast Cancer Biobank (PBCB) collects blood and urine from patients with breast cancer every 6 or 12 months for 11 years from 2011 to 2030 at two university hospitals in Western Norway. The project aims to identify new biomarkers that enable detection of systemic recurrences at the molecular level. As blood represents the biological interface between the primary tumour, the microenvironment and distant metastases, liquid biopsies represent the ideal medium to monitor the patient's cancer biology for identification of patients at high risk of relapse and for early detection systemic relapse.Including patient-reported outcome measures (PROMs) allows for a vast number of possibilities to compare PROM data with biological information, enabling the study of fatigue and Quality of Life in patients with breast cancer.

METHODS AND ANALYSIS

A total of 1455 patients with early-stage breast cancer are enrolled in the PBCB study, which has a one-armed prospective observational design. Participants consent to contribute liquid biopsies (i.e., peripheral blood and urine samples) every 6 or 12 months for 11 years. The liquid biopsies are the basis for detection of circulating tumour cells, circulating tumour DNA (ctDNA), exosomal micro-RNA (miRNA), miRNA in Tumour Educated Platelet and metabolomic profiles. In addition, participants respond to 10 PROM questionnaires collected annually. Moreover, a control group comprising 200 women without cancer aged 25-70 years will provide the same data.

ETHICS AND DISSEMINATION

The general research biobank PBCB was approved by the Ministry of Health and Care Services in 2007, by the Regional Ethics Committee (REK) in 2010 (#2010/1957). The PROM (#2011/2161) and the biomarker study PerMoBreCan (#2015/2010) were approved by REK in 2011 and 2015 respectively. Results will be published in international peer reviewed journals. Deidentified data will be accessible on request.

TRIAL REGISTRATION NUMBER

NCT04488614.

Estimating latent, dynamic processes of breast cancer tumour growth and distant metastatic spread from mammography screening data

We propose a framework for jointly modelling tumour size at diagnosis and time to distant metastatic spread, from diagnosis, based on latent dynamic sub-models of growth of the primary tumour and of distant metastatic detection. The framework also includes a sub-model for screening sensitivity as a function of latent tumour size. Our approach connects post-diagnosis events to the natural history of cancer and, once refined, may prove useful for evaluating new interventions, such as personalised screening regimes. We evaluate our model-fitting procedure using Monte Carlo simulation, showing that the estimation algorithm can retrieve the correct model parameters, that key patterns in the data can be captured by the model even with misspecification of some structural assumptions, and that, still, with enough data it should be possible to detect strong misspecifications. Furthermore, we fit our model to observational data from an extension of a case-control study of post-menopausal breast cancer in Sweden, providing model-based estimates of the probability of being free from detected distant metastasis as a function of tumour size, mode of detection (of the primary tumour), and screening history. For women with screen-detected cancer and two previous negative screens, the probabilities of being free from detected distant metastases 5 years after detection and removal of the primary tumour are 0.97, 0.89 and 0.59 for tumours of diameter 5, 15 and 35 mm, respectively. We also study the probability of having latent/dormant metastases at detection of the primary tumour, estimating that 33% of patients in our study had such metastases.

A Mathematical Model to Predict Diagnostic Periods for Secondary Distant Metastases in Patients with ER/PR/HER2/Ki-67 Subtypes of Breast Cancer

Previously, a consolidated mathematical model of primary tumor (PT) growth and secondary distant metastasis (sdMTS) growth in breast cancer (BC) (CoMPaS) was presented. The aim was to detect the diagnostic periods for visible sdMTS via CoMPaS in patients with different subtypes ER/PR/HER2/Ki-67 (Estrogen Receptor/Progesterone Receptor/Human Epidermal growth factor Receptor 2/Ki-67 marker) of breast cancer. CoMPaS is based on an exponential growth model and complementing formulas, and the model corresponds to the tumor-node-metastasis (TNM) staging system and BC subtypes (ER/PR/HER2/Ki-67). The CoMPaS model reflects (1) the subtypes of BC, such as ER/PR/HER2/Ki-67, and (2) the growth processes of the PT and sdMTSs in BC patients without or with lymph node metastases (MTSs) in accordance with the eighth edition American Joint Committee on Cancer prognostic staging system for breast cancer. CoMPaS correctly describes the growth of the PT in the ER/PR/HER2/Ki-67 subtypes of BC patients and helps to calculate the different diagnostic periods, depending on the tumor volume doubling time of sdMTS, when sdMTSs might appear. CoMPaS and the corresponding software tool can help (1) to start the early treatment of small sdMTSs in BC patients with different tumor subtypes (ER/PR/HER2/Ki-67), and (2) to consider the patient almost healthy if sdMTSs do not appear during the different diagnostic periods.

A Quasi Birth-and-Death model for tumor recurrence

A major cause of chemoresistance and recurrence in tumors is the presence of dormant tumor foci that survive chemotherapy and can eventually transition to active growth to regenerate the cancer. In this paper, we propose a Quasi Birth-and-Death (QBD) model for the dynamics of tumor growth and recurrence/remission of the cancer. Starting from a discrete-state master equation that describes the time-dependent transition probabilities between states with different numbers of dormant and active tumor foci, we develop a framework based on a continuum-limit approach to determine the time-dependent probability that an undetectable residual tumor will become large enough to be detectable. We derive an exact formula for the probability of recurrence at large times and show that it displays a phase transition as a function of the ratio of the death rate μ_A of an active tumor focus to its doubling rate λ. We also derive forward and backward Kolmogorov equations for the transition probability density in the continuum limit and, using a first-passage time formalism, we obtain a drift-diffusion equation for the mean recurrence time and solve it analytically to leading order for a large detectable tumor size N. We show that simulations of the discrete-state model agree with the analytical results, except for O(1/N) corrections. As an example of the use of our model in a clinical setting, we show that a range of model parameters can fit Kaplan-Meier recurrence-free survival data for ovarian cancer. Finally, we show in simulations that extending the duration of chemotherapy increases both the mean recurrence time and the asymptotic (large-time) probability of no recurrence. Our results should be useful in planning optimized chemotherapy dosing and duration for cancer treatment, especially in cancer types for which no targeted therapy is available.

The natural history of renal cell carcinoma with pulmonary metastases illuminated through mathematical modeling

The goal of this study is to uncover some unobservable aspects of the individual-patient natural history of metastatic renal cell carcinoma (RCC) through mathematical modeling. We analyzed four clear cell RCC patients who at the time of primary tumor resection already had pulmonary metastases. Our description of the natural history of cancer in these patients was based on a parameterized version of a previously proposed very general mathematical model adjusted to these clinical cases. For each patient, identifiable model parameters were estimated by the method of maximum likelihood from the volumes of lung metastases computed from CT scans taken at or around the time of surgery. The model-based distribution of the volumes of lung metastases with likelihood maximizing parameters provided an excellent fit to the data for all patients analyzed. We found that, according to the model, the most likely scenario in all four patients had the following clinically important features: (1) duration of metastatic latency was very small compared to the growth period; (2) seeding of the first lung metastasis occurred before primary tumor reached detectable size, which implies that early cancer detection would not have prevented metastasis; (3) primary tumor contained a relatively fast growing subpopulation of metastasis-producing cells, which is consistent with the observed aggressive course of the disease; and (4) the volume of the primary tumor at the time of metastasis survey does not seem to be correlated with such characteristics of the metastatic burden as the number of detected lung metastases, their total volume, and the volume of the largest detected lung metastasis.

Consolidated mathematical growth model of the primary tumor and secondary distant metastases of breast cancer (CoMPaS)

The goal of this research is to improve the accuracy of predicting the breast cancer (BC) process using the original mathematical model referred to as CoMPaS. The CoMPaS is the original mathematical model and the corresponding software built by modelling the natural history of the primary tumor (PT) and secondary distant metastases (MTS), it reflects the relations between the PT and MTS. The CoMPaS is based on an exponential growth model and consists of a system of determinate nonlinear and linear equations and corresponds to the TNM classification. It allows us to calculate the different growth periods of PT and MTS: 1) a non-visible period for PT, 2) a non-visible period for MTS, and 3) a visible period for MTS. The CoMPaS has been validated using 10-year and 15-year survival clinical data considering tumor stage and PT diameter. The following are calculated by CoMPaS: 1) the number of doublings for the non-visible and visible growth periods of MTS and 2) the tumor volume doubling time (days) for the non-visible and visible growth periods of MTS. The diameters of the PT and secondary distant MTS increased simultaneously. In other words, the non-visible growth period of the secondary distant MTS shrinks, leading to a decrease of the survival of patients with breast cancer. The CoMPaS correctly describes the growth of the PT for patients at the T1aN0M0, T1bN0M0, T1cN0M0, T2N0M0 and T3N0M0 stages, who does not have MTS in the lymph nodes (N0). Additionally, the CoMPaS helps to consider the appearance and evolution period of secondary distant MTS (M1). The CoMPaS correctly describes the growth period of PT corresponding to BC classification (parameter T), the growth period of secondary distant MTS and the 10-15-year survival of BC patients considering the BC stage (parameter M).

Identification of optimal strategies for state transition of complex biological networks

Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor. In addition, the minimum time for the state transition was determined by defining and solving a time-optimal control problem. Moreover, time-dependent variable profiles for state transitions were achieved rather than constant values commonly adopted in previous studies. Furthermore, the optimization method allows multiple controls to be simultaneously adjusted to drive the system out of an undesired attractor. Optimization improved the results of the parameter perturbation method, thus providing a valuable guidance for experimental design.

Metformin inhibited esophageal squamous cell carcinoma proliferation in vitro and in vivo and enhanced the anti-cancer effect of cisplatin

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with poor prognosis in China. Chemotherapy now is one of the most frequently used treatments for patients with ESCC in middle or late stage, however the effects were often limited by increased chemoresistance or treatment toxicity. So it is urgent to find new drugs to treat ESCC patients. Metformin with low cost and toxicity has proved to have anti-cancer effects in a numerous cancers, while its role and mechanism in ESCC has seldom been studied. In the present study, we found that metformin exhibited not only an anti-proliferation ability in a dose and time dependent manner but also a proapoptosis effect in a dose dependent manner in ESCC cell line KYSE450. Our in vivo experiment also showed that metformin markedly inhibited KYSE450 xenograft tumors growth compared to those treated with normal saline. What’s more, no obvious toxic reactions were observed. To further explore the underlying mechanism, we found that metformin treatment could significantly damp the expression of 4EBP1 and S6K1 in KYSE 450 cells in vitro and in vivo, furthermore, the p-4EBP1 and p-S6K1 expression in KYSE 450 cells were also inhibited greatly in vitro and in vivo. During the therapy of cancer, in order to overcome side effects, combination therapy was often used. In this paper, we demonstrated that metformin potentiated the effects of cisplatin via inhibiting cell proliferation and promoting cell apoptosis. Taken together, metformin owned the potential anti-cancer effect on ESCC in monotherapy or was combined with cisplatin and these results laid solid basis for the use of metformin in ESCC.

FINITE DURATION TREATMENT OF CANCER BY USING VACCINE THERAPY AND OPTIMAL CHEMOTHERAPY: STATE-DEPENDENT RICCATI EQUATION CONTROL AND EXTENDED KALMAN FILTER

The main purpose of this paper is to propose an optimal finite duration treatment method for preventing tumor growth. The obtained results show that changing the dynamics of the cancer model is essential for a finite duration treatment. Therefore, vaccine therapy is used for changing the parameters of the system and chemotherapy is applied for pushing the system to the domain of attraction of the healthy state. The state-dependent Riccati equation (SDRE)-based optimal control method is used for optimal chemotherapy. In this method, the special conditions of the patients could be considered by choosing suitable weighting matrices in the cost function and restricting the drug dosage. Also, there are infinite ways to choose these state-dependent matrices. In this paper, these interesting features of this method are used for each patient. Since measuring the states of the system is impossible at each time for states feedback; an extended Kalman filter (EKF) is designed as an observer in the nonlinear system. So, the SDRE method is employed just by measuring the population of normal cells. Numerical simulations show the flexibility and effectiveness of this treatment method.

Modeling drug resistance in a conjoint normal-tumor setting

Background

In this paper, we modify our previously developed conjoint tumor-normal cell model in order to make a distinction between tumor cells that are responsive to chemotherapy and those that may show resistance.

Results

Using this newly developed core model, the evolution of three cell types: normal, tumor, and drug-resistant tumor cells, is studied through a series of numerical simulations. In addition, we illustrate critical factors that cause different dynamical patterns for normal and tumor cells. Among these factors are the co-dependency of the normal and tumor cells, the cells’ response mechanism to a single or multiple chemotherapeutic treatment, the drug administration sequence, and the treatment starting time.

Conclusion

The results provide us with a deeper understanding of the possible evolution of normal, drug-responsive, and drug-resistant tumor cells during the cancer progression, which may contribute to improving the therapeutic strategies.

Simplifying the complexity of resistance heterogeneity in metastasis

The main goal of treatment regimens for metastasis is to control growth rates, not eradicate all cancer cells. Mathematical models offer methodologies that incorporate high-throughput data with dynamic effects on net growth. The ideal approach would simplify, but not over-simplify, a complex problem into meaningful and manageable estimators that predict the response of a patient to specific treatments. We explore here three fundamental approaches with different assumptions concerning resistance mechanisms in which the cells are categorized into either discrete compartments or described by a continuous range of resistance levels. We argue in favor of modeling resistance as a continuum, and demonstrate how integrating cellular growth rates, density-dependent versus exponential growth, and intratumoral heterogeneity improves predictions concerning the resistance heterogeneity of metastases.

The dynamics of drug resistance: a mathematical perspective

Resistance to chemotherapy is a key impediment to successful cancer treatment that has been intensively studied for the last three decades. Several central mechanisms have been identified as contributing to the resistance. In the case of multidrug resistance (MDR), the cell becomes resistant to a variety of structurally and mechanistically unrelated drugs in addition to the drug initially administered. Mathematical models of drug resistance have dealt with many of the known aspects of this field, such as pharmacologic sanctuary and location/diffusion resistance, intrinsic resistance, induced resistance and acquired resistance. In addition, there are mathematical models that take into account the kinetic/phase resistance, and models that investigate intracellular mechanisms based on specific biological functions (such as ABC transporters, apoptosis and repair mechanisms). This review covers aspects of MDR that have been mathematically studied, and explains how, from a methodological perspective, mathematics can be used to study drug resistance. We discuss quantitative approaches of mathematical analysis, and demonstrate how mathematics can be used in combination with other experimental and clinical tools. We emphasize the potential benefits of integrating analytical and mathematical methods into future clinical and experimental studies of drug resistance.

Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition

In the last decades the Lattice Boltzmann method (LB) has been successfully used to simulate a variety of processes. The LB model describes the microscopic processes occurring at the cellular level and the macroscopic processes occurring at the continuum level with a unique function, the probability distribution function. Recently, it has been tried to couple deterministic approaches with probabilistic cellular automata (probabilistic CA) methods with the aim to model temporal evolution of tumor growths and three dimensional spatial evolution, obtaining hybrid methodologies. Despite the good results attained by CA-PDE methods, there is one important issue which has not been completely solved: the intrinsic stochastic nature of the interactions at the interface between cellular (microscopic) and continuum (macroscopic) level. CA methods are able to cope with the stochastic phenomena because of their probabilistic nature, while PDE methods are fully deterministic. Even if the coupling is mathematically correct, there could be important statistical effects that could be missed by the PDE approach. For such a reason, to be able to develop and manage a model that takes into account all these three level of complexity (cellular, molecular and continuum), we believe that PDE should be replaced with a statistic and stochastic model based on the numerical discretization of the Boltzmann equation: The Lattice Boltzmann (LB) method. In this work we introduce a new hybrid method to simulate tumor growth and immune system, by applying Cellular Automata Lattice Boltzmann (CA-LB) approach.

ANTI-TUMOR IMMUNITY AND TUMOR ANTI-IMMUNITY IN A MATHEMATICAL MODEL OF TUMOR IMMUNOTHERAPY

A two-dimensional system of ordinary differential equations is used to characterize the basic types of phase portraits of the immune system — tumor interactions model, and to study the impact of anti-immune activity by tumor on the outcome of immunotherapy. The focus is on specific (acquired) immunity and different forms of immunotherapy as active therapy with in vivo stimulation of the immunity and passive one with infusion of ex vivo produced specific immunity. The analysis is performed for two families of stimulation function, which describes the dynamics of the stimulation of the immune system by tumor antigens: (1) antigen dependent and (2) antigen per one immunity unit dependent functions, with Michaelis-Menten and sigmoid functions in each family. We show that there are no limit cycles in the system and that anti-immune activity by tumor changes all equilibrium points from global to local ones. In the latter case, the immune system has no control over the growth of large tumors. Furthermore, if the immunity is weak, the immune system cannot eradicate even small tumors. The weak immunity and stimulation strength result in unrestricted tumor growth. The patterns of asymptotic behavior of the system do not depend on the type of the stimulation function, but do depend on its parameters. Our results reflect the basic clinical and experimental knowledge about immunotherapy and its effectiveness and yield new suggestions for an efficient immunotherapy.

Bayesian enrichment strategies for randomized discontinuation trials

We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

Evolutionary dynamics of BRCA1 alterations in breast tumorigenesis

Cancer results from the accumulation of alterations in oncogenes and tumor suppressor genes. Tumor suppressors are classically defined as genes which contribute to tumorigenesis if their function is lost. Genetic or epigenetic alterations inactivating such genes may arise during somatic cell divisions or alternatively may be inherited from a parent. One notable exception to this rule is the BRCA1 tumor suppressor that predisposes to hereditary breast cancer when lost. Genetic alterations of this gene are hardly ever observed in sporadic breast cancer, while individuals harboring a germline mutation readily accumulate a second alteration inactivating the remaining allele--a finding which represents a conundrum in cancer genetics. In this paper, we present a novel mathematical framework of sporadic and hereditary breast tumorigenesis. We study the dynamics of genetic alterations driving breast tumorigenesis and explore those scenarios which can explain the absence of somatic BRCA1 alterations while replicating all other disease statistics. Our results support the existence of a heterozygous phenotype of BRCA1 and suggest that the loss of one BRCA1 allele may suppress the fitness advantage caused by the inactivation of other tumor suppressor genes. This paper contributes to the mathematical investigation of breast tumorigenesis.

Perspectives on tissue interactions in development and disease

From the morphogenetic movements of the three germ layers during development to the reactive stromal microenvironment in cancer, tissue interactions are vital to maintaining healthy organ morphologic architecture and function. The stromal compartment is thought to be complicit in tumor progression and, as such, represents an opportune target for disease therapies. However, recent developments in our understanding of the diversity of the stromal compartment and the lack of appropriate models to study its relevance in human disease have limited our further understanding of the role of tissue interactions in tumor progression. The failure any model to fully recapitulate the complexities of systemic biology continue to create a higher imperative for incorporating various perspectives into a broader understanding for the ultimate goal of designing interventional therapies. Understanding this potential, this review examines the biological models used to study stromal-epithelial interactions and includes an attempt to incorporate behavioral terminology to define and mathematically model ecological relationships in stromal-epithelial interactions. In addition, the current attempt to incorporate these diverse ecological perspectives into in silico mathematical models through cross-disciplinary coordination is reviewed, which will provide a fresh perspective on defining cell group behavior and tissue ecology in disease and hopefully lead to the generation of new hypotheses to be empirically validated.

Mathematical modeling as a tool for planning anticancer therapy

We review a large volume of literature concerning mathematical models of cancer therapy, oriented towards optimization of treatment protocols. The review, although partly idiosyncratic, covers such major areas of therapy optimization as phase-specific chemotherapy, antiangiogenic therapy and therapy under drug resistance. We start from early cell cycle progression models, very simple but admitting explicit mathematical solutions, based on methods of control theory. We continue with more complex models involving evolution of drug resistance and pharmacokinetic and pharmacodynamic effects. Then, we consider two more recent areas: angiogenesis of tumors and molecular signaling within and among cells. We discuss biological background and mathematical techniques of this field, which has a large although only partly realized potential for contributing to cancer treatment.

Cancer surgery: risks and opportunities

In the recent past, several papers have pointed to the possibility that tumour removal generates a permissive environment in which tumour growth is potentiated. This phenomenon has been coined "perioperative tumour growth" and whilst it represents a departure in terms of our attitude to the surgical process, this concept was first hinted at by Paget(1) himself. Despite this, the time interval immediately before and after cancer surgery (i.e. the perioperative period) remains an underutilised interval during which chemotherapeutic regimens are rarely implemented. Herein, we present a summarised review of the literature that supports the concept that tumour removal may potentiate the growth of residual neoplastic disease. We also outline current knowledge regarding underlying mechanisms and in this manner highlight potential therapeutic entry points. Finally, we emphasise the urgent need for trials of agents that could protect patients against the harmful host-tumour interactions that may occur during the perioperative period.

Modeling the effect of tumor size in early breast cancer

SUMMARY BACKGROUND DATA

The purpose of this study was to determine the type of relationship between tumor size and mortality in early breast carcinoma.

METHODS

The data was abstracted from 83,686 cases registered in the Surveillance, Epidemiology, and End Results Program of women diagnosed with primary breast carcinoma between 1988 and 1997 presenting with a T1-T2 lesion and no metastasis in whom axillary node dissection was performed: 58,070 women were node-negative (N0) and 25,616 were node-positive (N+). End point was death from any cause. Tumor size was modeled as a continuous variable by proportional hazards using a generalized additive models procedure.

RESULTS

Functionally, a Gompertzian expression exp(-exp(-(size-15)/10)) provided a good fit to the effect of tumor size (in millimeters) on mortality, irrespective of nodal status. Quantitatively, for tumor size between 3 and 50 mm, the increase of crude cumulative death rate (number of observed deaths divided by the number of patients at risk) increased with size from 10% to 25% for N0 and from 20% to 40% for N+.

CONCLUSIONS

The functional relationship of tumor size with mortality is concordant with current knowledge of tumor growth. However, its qualitative and quantitative independence of nodal status is in contradiction with the prevailing concept of sequential disease progression from primary tumor to regional nodes. This argues against the perception that nodal metastases are caused by the primary tumor.

Recent translational research: computational studies of breast cancer

The combination of mathematics – queen of sciences – and the general utility of computers has been used to make important inroads into insight-providing breast cancer research and clinical aids. These developments are in two broad areas. First, they provide useful prognostic guidelines for individual patients based on historic evidence. Second, by suggesting numeric tumor growth laws that are correlated to clinical parameters, they permit development of biologically relevant theories and comparison with patient data to help us understand complex biologic processes. These latter studies have produced many new ideas that are testable in clinical trials. In this review we discuss these developments from a clinical perspective, and ask whether and how they translate into useful tools for patient treatment.

Treatment of breast cancer with chemotherapy in combination with filgrastim: approaches to improving therapeutic outcome

Chemotherapy improves disease-free and overall survival in breast cancer, and its benefit is directly related to the percentage of the planned dose that is actually administered. In all current chemotherapeutic regimens, a substantial proportion of patients have reductions and/or delays in dosage due to side effects. In about half such cases, the delays or reductions are related to neutropenia. Overall, approximately 30% of patients have a reduction to less than 85% of the planned dosage. Women aged > or = 50 years are more likely to experience a reduction or delay in dose. Dose-intense regimens (excluding myeloablative high-dose chemotherapy) which increase the dose of chemotherapy or reduce the interval between cycles, or both, are a promising approach now under investigation. The human granulocyte colony-stimulating factor filgrastim reduces the incidence of neutropenia and facilitates adherence to full dose intensity in both standard and dose-intensified regimens. A model based on the first-cycle absolute neutrophil count nadir has been developed and validated to determine which patients should receive filgrastim. A cost benefit associated with the use of filgrastim in patients with breast cancer has been realised. This may lead to a re-evaluation of the current treatment guidelines.