Mariana Díaz-Almirón

Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER(+)) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER(+) and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention.

An Increase of Plasma Advanced Oxidation Protein Products Levels Is Associated with Cardiovascular Risk in Incident Peritoneal Dialysis Patients: A Pilot Study.

Advanced oxidation protein products (AOPPs) are considered as markers and even mediators of the proinflammatory effect of oxidative stress in uremia. We hypothesized that an increase of oxidative stress associated with peritoneal dialysis (PD), estimated by the variation of plasma AOPPs over time, might be associated with cardiovascular (CV) risk and overall prognosis. In 48 PD patients, blood samples were collected on two occasions: the first one in the first six months after starting PD therapy and the second one, one year after. The plasma AOPPs level variation over the first year on PD was significantly associated with CV antecedents and also with CV prognosis. In those patients in whom the AOPPs levels increased more than 50% above the baseline value, a significant association with past and future CV disease was confirmed. These patients had 4.7 times greater risk of suffering later CV disease than those with a smaller increase, even after adjusting for previous CV history. Our data suggest that the increase of AOPPs plasma level over the first year on PD is conditioned by CV antecedents but also independently predicts CV prognosis. AOPPs plasma levels seem to represent the CV status of PD patients with sufficient sensitivity to identify those with a clearly sustained higher CV risk.

Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics

Background Triple-negative breast cancer (TNBC) accounts for 15–20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives. Methods We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data. Results Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients. Conclusions This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.

Functional proteomics outlines the complexity of breast cancer molecular subtypes

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

Fibroblast Growth Factor 21 (FGF-21) in Peritoneal Dialysis Patients: Natural History and Metabolic Implications

Background Human fibroblast growth factor 21 (FGF-21) is an endocrine liver hormone that stimulates adipocyte glucose uptake independently of insulin, suppresses hepatic glucose production and is involved in the regulation of body fat. Peritoneal dialysis (PD) patients suffer potential interference with FGF-21 status with as yet unknown repercussions. Objectives The aim of this study was to define the natural history of FGF-21 in PD patients, to analyze its relationship with glucose homeostasis parameters and to study the influence of residual renal function and peritoneal functional parameters on FGF-21 levels and their variation over time. Methods We studied 48 patients with uremia undergoing PD. Plasma samples were routinely obtained from each patient at baseline and at 1, 2 and 3 years after starting PD therapy. Results Plasma FGF-21 levels substantially increased over the first year and were maintained at high levels during the remainder of the study period (253 pg/ml (59; 685) at baseline; 582 pg/ml (60.5–949) at first year and 647 pg/ml (120.5–1116.6) at third year) (p<0.01). We found a positive correlation between time on dialysis and FGF-21 levels (p<0.001), and also, those patients with residual renal function (RRF) had significantly lower levels of FGF-21 than those without RRF (ρ -0.484, p<0.05). Lastly, there was also a significant association between FGF-21 levels and peritoneal protein losses (PPL), independent of the time on dialysis (ρ 0.410, p<0.05). Conclusion Our study shows that FGF-21 plasma levels in incident PD patients significantly increase during the first 3 years. This increment is dependent on or is associated with RRF and PPL (higher levels in patients with lower RRF and higher PPL). FGF-21 might be an important endocrine agent in PD patients and could act as hormonal signaling to maintain glucose homeostasis and prevent potential insulin resistance. These preliminary results suggest that FGF-21 might play a protective role as against the development of insulin resistance over time in patients undergoing a continuous glucose load.

Directed Bayesian Networks established functional differences between breast cancer subtypes

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which has a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 106 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as metastasis or proliferation between breast cancer subtypes, including our new subtype TN-like. In addition, one of the components, mainly related with metastasis, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context. Author Summary Breast cancer classification in the clinical practice is defined by three biomarkers (estrogen receptor, progesterone receptor and HER2) into hormone receptor positive, HER2+ and triple negative breast cancer (TNBC). Our group recently described a new ER+ subtype with molecular characteristics and prognosis similar to TNBC. In this study we propose a mathematical method, the Bayesian networks, as a useful tool to study protein interactions and differential biological processes in breast cancer subtypes, characterizing differences in relevant processes such as proliferation or metastasis and associated them with patient prognosis.

Probabilistic graphical models and computational metabolic models applied to the analysis of metabolomics data in breast cancer

Abstract Metabolomics has a great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient’s clinical outcome.Metabolomics has great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. A metabolite network was built through the use of probabilistic graphical models. Interestingly, the metabolites were organized into metabolic pathways in this network, thus it was possible to establish differences between breast cancer subtypes at the metabolic pathway level. Additionally, the lipid metabolism node had prognostic value. A second network associating gene expression with metabolites was built. Associations were established between the biological functions of genes and the metabolites included in each node. A third network combined flux activities from Flux Balance Analysis and metabolomics data, showing coherence between the metabolic pathways of the flux activities and the metabolites in each branch. In this study, probabilistic graphical models were valuable for the functional analysis of metabolomics data from a functional point of view, allowing new hypotheses in metabolomics and associating metabolomics data with the patient’s clinical outcome. Author summary Metabolomics is a promising technique to describe new biomarkers in cancer. In this study we proposed computational methods to manage this type of data and associate it with gene expression data. We also employed a metabolic computational model to compare predictions from this model with metabolomics measurements. Finally, we built predictors of relapse based on the integration of those high-dimensional data in breast cancer patients.

PO-137 Computational metabolism modelling predicts risk of relapse in breast cancer patients

Introduction Breast cancer is one of the most prevalent cancers in the world. In previous works we observed differences in glucose metabolism between breast cancer subtypes, suggesting that metabolism plays an important role in this disease. Flux Balance Analysis (FBA) is widely used to study metabolic networks, allowing predicting growth rates or the rate of production of a given metabolite. Material and methods Proteomics data from 96 breast cancer tumours were obtained applying a high-throughput proteomics approach to routinely archive formalin-fixed, paraffin-embedded tumour tissue. Proteomics tumour data were analysed using the human metabolic reconstruction Recon2 and FBA. The tumour growth rate for each tumour was calculated. In order to analyse fluxes from the different metabolic pathways, flux activities were calculated as the sum of the fluxes of each reaction in each pathway defined in the Recon2. Then, flux activities were used to build prognostic models. Results and discussions Using the results obtained from FBA in the proteomics dataset, flux activities were calculated for each pathway. Employing these flux activities, a prognostic signature was built. Flux activities of vitamin A, tetrahydrobiopterin metabolism, and beta-alanine metabolism pathways split our population into a low and a high-risk group (p=0.044). Conclusion Vitamine A, beta-alanine and tetrahydrobiopterin metabolism flux activities could be used to predict relapse risk. Flux activities is a method proposed in a previous work to study response against drugs that now also demonstrated its utility in summarising FBA data and is associated with prognosis.

Molecular characterization of breast cancer cell response to metabolic drugs

Metabolic reprogramming is a hallmark of cancer. It has been described that breast cancer subtypes present metabolism differences and this fact enables the possibility of using metabolic inhibitors as targeted drugs in specific scenarios. In this study, breast cancer cell lines were treated with metformin and rapamycin, showing a heterogeneous response to treatment and leading to cell cycle disruption. The genetic causes and molecular effects of this differential response were characterized by means of SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes such as transcription. Moreover, flux balance analysis using protein expression values showed that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these diverse approaches provide complementary information and allow us to suggest hypotheses about the response to drugs that target metabolism and their mechanisms of action.

Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patient’s immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.