Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions

Metabolic flux analysis (MFA) has emerged as a tool of great significance for metabolic engineering and mammalian physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS and nuclear magnetic resonance (NMR) measurements, is the large number of isotopomer or cumomer equations that need to be solved, especially when multiple isotopic tracers are used for the labeling of the system. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of realistic situations comprising complex bioreaction networks. Here, we present a novel framework for the modeling of isotopic labeling systems that significantly reduces the number of system variables without any loss of information. The elementary metabolite unit (EMU) framework is based on a highly efficient decomposition method that identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using the knowledge of atomic transitions occurring in the network reactions. The functional units generated by the decomposition algorithm, called EMUs, form the new basis for generating system equations that describe the relationship between fluxes and stable isotope measurements. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods, however, require significantly less computation time. For a typical (13)C-labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s isotopomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, analysis of the gluconeogenesis pathway with (2)H, (13)C, and (18)O tracers requires only 354 EMUs, compared to more than two million isotopomers.     

The topology of metabolic isotope labeling networks

Background  

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.     

Anti-hyperglycemic effect of loquat leaf extract is associated with the redistribution of glucose carbon to its metabolites: a <Superscript>13</Superscript>C-tracing study in HepG2 cells

Introduction

Loquat leaf extract (LLE) is commonly used in China for a variety of ailments including diabetes. Several recent reports implicate LLE and a sesquiterpene glycoside, one of its components, as being an anti-hyperglycemic agent. However, the underlying mechanism of action of this anti-hyperglycemic agent has not been reported.

Objective

We have conducted a tracer-based metabolomics study to investigate the effects of sesquiterpene and loquat extract on the balance of flux of central glucose metabolism in HepG2 cells and to compare with those of “insulin sensitizers”, metformin and rosiglitazone.

Methods

Human hepatoma HepG2 cells in confluence culture were incubated in Dulbecco’s modified Eagle’s medium containing 50% [1, 2 ¹³C₂]-glucose in the presence of rosiglitazone, metformin, LLE or pure sesquiterpene. Cells were harvested in 48 h. Mass isotopomers of metabolites (glycogen, ribose, deoxyribose, glutamate and palmitate) were determined.

Results

¹³C labeling in metabolic intermediates were summarized in a mass isotopomer matrix. Treatment with loquat extract/sesquiterpene, metformin and rosiglitazone each produced distinctive mass isotopomer patterns reflecting disparate effects on the contribution of glucose to various metabolites production, and on several metabolic flux ratios. The overall effect of LLE and sesquiterpene on glucose metabolism is clearly different from those of the known “insulin sensitizers”.

Conclusion

Our study demonstrates the utility of isotopomer matrix in summarizing metabolic actions of LLE on the balance of fluxes occurring within the central glucose metabolism in HepG2 cells. ¹³C carbon tracing (tracer-based metabolomics) is a useful systems biology tool to elucidate glucose metabolic pathways affected by diabetes and its treatment.

     

Zinc fluxes into developing barley grains: use of stable Zn isotopes to separate root uptake from remobilization in plants with contrasting Zn status

Background and Aims

Zn imported into developing cereal grains originates from either de novo Zn uptake by the roots or remobilization of Zn from vegetative tissues. The present study was focused on revealing the quantitative importance of the two pathways for grain Zn loading and how their relative contribution varies with the overall plant Zn status.

Methods

The stable isotope ⁶⁷Zn was used to trace Zn uptake and remobilization fluxes in barley (Hordeum vulgare L.) plants growing in hydroponics at 0.1?μM (low Zn), 1.5?μM (medium Zn) or 5?μM Zn (high Zn). When grain development reached 15?days after pollination the Zn source was changed to an enriched ⁶⁷Zn isotope and plants were harvested after 6 to 48?h. Zn concentrations and isotope ratios were determined using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).

Results

Plants with low Zn status absorbed 3-fold more Zn than plants with medium or high Zn status when roots were exposed to an external concentration of 1.5?μM ⁶⁷Zn. Stems and ears were the primary recipients of the de novo incorporated Zn with preferential allocation to the developing grains over time. The leaves received in all cases a very small proportion (<5?%) of the newly absorbed Zn and the proportion did not increase over time. Zn fluxes derived from uptake and remobilization were almost equal in plants with low Zn status, while at high Zn status remobilization delivered 4 times more Zn to the developing grains than did root Zn uptake.

Conclusions

Stable isotopes in combination with ICP-MS provided a strong tool for quantification of Zn fluxes in intact plants. The importance of Zn remobilization compared to de novo root absorption of Zn increased with increasing plant Zn status. Very little de novo absorbed Zn was translocated to the leaves during generative growth stages.     

DeltaMS: a tool to track isotopologues in GC- and LC-MS data

Introduction

Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing.

Objective

To introduce a software tool for the identification of isotopologues from mass spectrometry data.

Methods

DeltaMS relies on XCMS peak detection and X¹³CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X¹³CMS.

Results

To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures.

Conclusion

DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.

Graphical Abstract

Open image in new window

     

Triterpenoid-rich loquat leaf extract induces growth inhibition and apoptosis of pancreatic cancer cells through altering key flux ratios of glucose metabolism

Introduction

Loquat leaf extract (LLE) is a mixture rich in terpenoids, and has broad biological activities including the inhibition of cancer cell growth. The exact metabolic mechanism of this growth inhibiting effect is not known.

Objectives

We investigated the cellular metabolic effect of LLE, and ursolic acid (UA) on pancreatic cancer cells using a ¹³C carbon tracing technology.

Methods

MIA PaCa-2 cells were cultured in medium containing [1, 2 ¹³C₂]-glucose in the presence of either LLE (50 µg/ml), UA (50 µM), or metformin (1 mM). The mass isotopomer distribution of glucose, lactate, ribose, glutamate and palmitate in medium was determined. Based on the mass isotopomer distribution in metabolites we were able to determine individual ¹³C enrichment (∑M?×?n) and the minimum fraction of new synthesis?(1-M0) in each metabolite. Several flux ratios of energy metabolic pathways were calculated from the mass isotopomer ratios of these metabolites.

Results

We found that tumor viability was suppressed by LLE and UA in a dose dependent manner, and the tumor-inhibiting effect was associated with the changes in oxidative/non-oxidative pentose (Ox/Non-ox) and pyruvate dehydrogenase/isocitrate dehydrogenase (PDH/ICDH) flux ratios resulting in decreased new syntheses of ribose and fatty acids.

Conclusion

Metabolic homeostasis (balance of fluxes) in cancer cells is maintained through the regulation of metabolic fluxes by oncogenes and tumor-suppressor genes. Treatment of MIA PaCa-2 cells by LLE, UA and metformin likely altered key metabolic flux ratios affecting metabolic homeostasis required for energy and macromolecular production in tumor growth.

     

Implementing evidence-based interventions in health care: application of the replicating effective programs framework

Background

For people with dementia, patient-centred care should involve timely explanation of the diagnosis and its implications. However, this is not routine. Theoretical models of behaviour change offer a generalisable framework for understanding professional practice and identifying modifiable factors to target with an intervention. Theoretical models and empirical work indicate that behavioural intention represents a modifiable predictor of actual professional behaviour. We identified factors that predict the intentions of members of older people's mental health teams (MHTs) to perform key behaviours involved in the disclosure of dementia.

Design

Postal questionnaire survey.

Participants

Professionals from MHTs in the English National Health Service.

Methods

We selected three behaviours: Determining what patients already know or suspect about their diagnosis; using explicit terminology when talking to patients; and exploring what the diagnosis means to patients. The questionnaire was based upon the Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), and exploratory team variables.

Main outcomes

Behavioural intentions.

Results

Out of 1,269 professionals working in 85 MHTs, 399 (31.4%) returned completed questionnaires. Overall, the TPB best explained behavioural intention. For determining what patients already know, the TPB variables of subjective norm, perceived behavioural control and attitude explained 29.4% of the variance in intention. For the use of explicit terminology, the same variables explained 53.7% of intention. For exploring what the diagnosis means to patients, subjective norm and perceived behavioural control explained 48.6% of intention.

Conclusion

These psychological models can explain up to half of the variation in intention to perform key disclosure behaviours. This provides an empirically-supported, theoretical basis for the design of interventions to improve disclosure practice by targeting relevant predictive factors.

Trial Registration

ISRCTN15871014.     

Metabolic flux analysis in Escherichia coli by integrating isotopic dynamic and isotopic stationary 13C labeling data

The novel concept of isotopic dynamic 13C metabolic flux analysis (ID-13C MFA) enables integrated analysis of isotopomer data from isotopic transient and/or isotopic stationary phase of a 13C labeling experiment, short-time experiments, and an extended range of applications of 13C MFA. In the presented work, an experimental and computational framework consisting of short-time 13C labeling, an integrated rapid sampling procedure, a LC-MS analytical method, numerical integration of the system of isotopomer differential equations, and estimation of metabolic fluxes was developed and applied to determine intracellular fluxes in glycolysis, pentose phosphate pathway (PPP), and citric acid cycle (TCA) in Escherichia coli grown in aerobic, glucose-limited chemostat culture at a dilution rate of D = 0.10 h(-1). Intracellular steady state concentrations were quantified for 12 metabolic intermediates. A total of 90 LC-MS mass isotopomers were quantified at sampling times t = 0, 91, 226, 346, 589 s and at isotopic stationary conditions. Isotopic stationarity was reached within 10 min in glycolytic and PPP metabolites. Consistent flux solutions were obtained by ID-13C MFA using isotopic dynamic and isotopic stationary 13C labeling data and by isotopic stationary 13C MFA (IS-13C MFA) using solely isotopic stationary data. It is demonstrated that integration of dynamic 13C labeling data increases the sensitivity of flux estimation, particularly at the glucose-6-phosphate branch point. The identified split ratio between glycolysis and PPP was 55%:44%. These results were confirmed by IS-13C MFA additionally using labeling data in proteinogenic amino acids (GC-MS) obtained after 5 h from sampled biomass.     

RankAggreg, an R package for weighted rank aggregation

Background

Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems.

Results

In this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters.

Conclusion

The method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out on the constrained optimization problem and yield realistic model parameters that are more likely to hold up in extrapolations with the model.     

Respiratory C fluxes and root exudation differ in two full-sib clones of Pinus taeda (L.) under contrasting fertilizer regimes in a greenhouse

Aims

We investigated whether changes in respiratory C fluxes, soil CO₂ efflux, or root exudate quantity or quality explained differences in growth rates between closely related clones of Pinus taeda (L.).

Methods

A factorial design with two clones, fertilized and control treatments, and four sequential harvests was installed in a greenhouse for 121 days.

Results

The two clones did show significant differences in respiratory C fluxes, soil CO₂ efflux, and root exudation quantity and quality. While the clones also differed in growth rates, the C fluxes assessed in this paper did not explain how seedlings were able to allocate more C to stem growth in the months following fertilizer application. Changes in root exudation were not consistent with reduced heterotrophic soil CO₂ efflux, which does not appear to be a plant-mediated process.

Conclusions

These results indicate that if single genotypes are deployed over large land areas in plantations, dramatic differences between clonal plant-soil interactions may require consideration in ecosystem C budgets. Further, the range of belowground fluxes observed implies that genotype-specific C allocation may make some clones better able to exploit a given resource environment than others.     

INCA 2.0: A tool for integrated,dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state ¹³C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different ¹³C acetate tracers, we show that MFA based on dynamic ¹³C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined ¹³C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.     

A novel method to measure the effect of temperature on diffusion of plant-available nitrogen in soil

Background and aims

Soil temperature influences nitrogen (N) diffusion in soil but until now, such effects have been difficult to quantify. This study aimed at estimating the effect of temperature on the diffusive fluxes of plant-available N forms in two contrasting soils.

Methods

Using a novel technique based on micro-dialysis, we established relationships between diffusive fluxes and temperature in aqueous solutions and in soil samples.

Results

Averaged for all compounds, the decreases of diffusive fluxes from the soil to the microdialysis sampler were 3.8 and 4.7% per degree Celsius in an agricultural and a boreal forest soil, respectively. The temperature-related shift of diffusive flux was, however, significantly dependent on molecular weight of the N compound. In accordance with established functions for temperature effects on diffusive fluxes, the non-linearity of this relationship resulted in a greater temperature response for small N compounds compared to larger compounds.

Conclusions

Our results show that, all other factors being equal, the relative contribution of smaller N compounds to the diffusive flux of total plant-available N increases with increasing soil temperatures.     

Spatial variability and controls over biomass stocks,carbon fluxes,and resource-use efficiencies across forest ecosystems

Key message

Stand age, water availability, and the length of the warm period are the most influencing controls of forest structure, functioning, and efficiency.

Abstract

We aimed to discern the distribution and controls of plant biomass, carbon fluxes, and resource-use efficiencies of forest ecosystems ranging from boreal to tropical forests. We analysed a global forest database containing estimates of stand biomass and carbon fluxes (400 and 111 sites, respectively) from which we calculated resource-use efficiencies (biomass production, carbon sequestration, light, and water-use efficiencies). We used the WorldClim climatic database and remote-sensing data derived from the Moderate Resolution Imaging Spectroradiometer to analyse climatic controls of ecosystem functioning. The influences of forest type, stand age, management, and nitrogen deposition were also explored. Tropical forests exhibited the largest gross carbon fluxes (photosynthesis and ecosystem respiration), but rather low net ecosystem production, which peaks in temperate forests. Stand age, water availability, and length of the warm period were the main factors controlling forest structure (biomass) and functionality (carbon fluxes and efficiencies). The interaction between temperature and precipitation was the main climatic driver of gross primary production and ecosystem respiration. The mean resource-use efficiency varied little among biomes. The spatial variability of biomass stocks and their distribution among ecosystem compartments were strongly correlated with the variability in carbon fluxes, and both were strongly controlled by climate (water availability, temperature) and stand characteristics (age, type of leaf). Gross primary production and ecosystem respiration were strongly correlated with mean annual temperature and precipitation only when precipitation and temperature were not limiting factors. Finally, our results suggest a global convergence in mean resource-use efficiencies.     

Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees

Aims

The aim of this study is on the one hand to identify the most determining variables predicting the site productivity of pedunculate oak, common beech and Scots pine in temperate lowland forests of Flanders; and on the other hand to test whether the accuracy of site productivity models based exclusively on soil or forest floor predictor variables is similar to the accuracy achieved by full ecosystem models, combining all soil, vegetation, humus and litterfall composition related variables.

Methods

Boosted Regression Trees (BRT) were used to model in a climatically homogeneous region the relationship between environmental variables and site productivity. A distinction was made between soil (soil physical and chemical), forest floor (vegetation and humus) and ecosystem (soil, forest floor and litterfall composition jointly) predictors.

Results

Our results have illustrated the strength of BRT to model the non-linear behaviour of ecological processes. The ecosystem models, based on all collected variables, explained most of the variability and were more accurate than those limited to either soil or forest floor variables. Nevertheless, both the soil and forest floor models can serve as good predictive models for many forest management practices.

Conclusions

Soil granulometric fractions and litterfall nitrogen concentrations were the most effective predictors of forest site productivity in Flanders. Although many studies revealed a fertilising effect of increased nitrogen deposition, nitrogen saturation seemed to reduce species’ productivity in this region.     

Field phenotyping of potato to assess root and shoot characteristics associated with drought tolerance

Aims

Potatoes are a globally important source of food whose production requires large inputs of fertiliser and water. Recent research has highlighted the importance of the root system in acquiring resources. Here measurements, previously generated by field phenotyping, tested the effect of root size on maintenance of yield under drought (drought tolerance).

Methods

Twelve potato genotypes, including genotypes with extremes of root size, were grown to maturity in the field under a rain shelter and either irrigated or subjected to drought. Soil moisture, canopy growth, carbon isotope discrimination and final yields were measured. Destructively harvested field phenotype data were used as explanatory variables in a general linear model (GLM) to investigate yield under conditions of drought or irrigation.

Results

Drought severely affected the small rooted genotype Pentland Dell but not the large rooted genotype Cara. More plantlets, longer and more numerous stolons and stolon roots were associated with drought tolerance. Previously measured carbon isotope discrimination did not correlate with the effect of drought.

Conclusions

These data suggest that in-field phenotyping can be used to identify useful characteristics when known genotypes are subjected to an environmental stress. Stolon root traits were associated with drought tolerance in potato and could be used to select genotypes with resilience to drought.     

A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks

Background

We previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm.

Results

We describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.

Conclusions

The DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.     

Dynamic expression of synemin isoforms in mouse embryonic stem cells and neural derivatives

Background

Nucleolus is the most prominent mammalian organelle within the nucleus which is also the site for ribosomal biogenesis. There have been many reports indicating the involvement of nucleolus in the process of aging. Several proteins related to aging have been shown to localize in the nucleolus, which suggests the role of this organelle in senescence.

Results

In this study, we used quantitative mass spectrometry to map the flux of proteins into and out of the nucleolus during the induction of senescence in cultured mammalian cells. Changes in the abundance of 344 nucleolar proteins in sodium butyrate-induced senescence in NIH3T3 cells were studied by SILAC (stable isotope labeling by amino acids in cell culture)-based mass spectrometry. Biochemically, we have validated the proteomic results and confirmed that B23 (nucleophosmin) protein was down-regulated, while poly (ADP-ribose) polymerase (PARP) and nuclear DNA helicase II (NDH II/DHX9/RHA) were up-regulated in the nucleolus upon treatment with sodium butyrate. Accumulation of chromatin in the nucleolus was also observed, by both proteomics and microscopy, in sodium butyrate-treated cells. Similar observations were found in other models of senescence, namely, in mitoxantrone- (MTX) treated cells and primary fibroblasts from the Lamin A knockout mice.

Conclusion

Our data indicate an extensive nuclear organization during senescence and suggest that the redistribution of B23 protein and chromatin can be used as an important marker for senescence.     

Competitive DNA transfection formulation via electroporation for human adipose stem cells and mesenchymal stem cells

Background

Adipose stem cells have a strong potential for use in cell-based therapy, but the current nucleofection technique, which relies on unknown buffers, prevents their use.

Results

We developed an optimal nucleofection formulation for human adipose stem cells by using a three-step method that we had developed previously. This method was designed to determine the optimal formulation for nucleofection that was capable of meeting or surpassing the established commercial buffer (Amaxa), in particular for murine adipose stem cells. By using this same buffer, we determined that the same formulation yields optimal transfection efficiency in human mesenchymal stem cells.

Conclusions

Our findings suggest that transfection efficiency in human stem cells can be boosted with proper formulation.     

Identification of DNA methylation changes associated with human gastric cancer

Background

Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.

Methods

Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.

Results

We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.

Conclusions

Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.