Caracterización molecular del cáncer de mama mediante el análisis del proteoma

Tesis doctoral inédita, leída en la Universidad Autónoma de Madrid. Facultad de Medicina. Departamento de Bioquímica. Fecha de lectura: 24 de julio, 2014El cáncer de mama es el tumor más frecuente en las mujeres occidentales, con una incidencia que ha sufrido un aumento considerable en las dos últimas décadas. En nuestro país, cerca de 6.000 mujeres fallecen anualmente a consecuencia de este tumor, que representa la primera causa de mortalidad por cáncer en las mujeres. Tecnologías tan poderosas como la genómica y la proteómica -en combinación con la bioinformática- han generado una base excelente para la caracterización de nuevos marcadores moleculares en cáncer y otras enfermedades. Estas nuevas tecnologías hacen posible establecer patrones moleculares que pueden correlacionarse con la morfología del tumor, con su comportamiento clínico o con su respuesta al tratamiento. Una alternativa al análisis de los niveles de expresión del RNA mensajero, es la medida de los perfiles de proteínas. Su importancia radica en que son precisamente las proteínas las que, en última instancia, definen la función y controlan la actividad de las células, tejidos y organismos. El fin último de este estudio es identificar patrones moleculares que permitan mejorar la predicción del comportamiento tumoral e identificar nuevas dianas terapéuticas. Para ello se ha estudiado una serie de muestras de tumores de mama, todas ellas con afectación ganglionar al diagnóstico y tratadas con quimioterapia adyuvante. Se ha analizado el proteoma en todas ellas, así como la expresión de miRNAs en una fracción representativa de estas muestras. El análisis de los datos permite establecer diferencias moleculares claras entre los tumores positivos para los receptores hormonales y los tumores triples negativos. Los patrones de expresión de proteínas obtenidos han permitido caracterizar un tercer grupo de tumores de mama, con expresión de receptores hormonales, pero con características moleculares semejantes a los tumores triples negativos. Se ha definido una “Firma de Adhesión” para caracterizar este grupo de tumores, que aporta información adicional a la de los perfiles génicos comerciales con valor pronóstico. La integración de los datos de expresión de miRNAs y proteínas muestra diferencias en cuanto a la actividad de procesos moleculares, como la proliferación y el metabolismo, entre los diferentes subtipos de tumores de cáncer de mama. Estos resultados han sido confirmados sobre una cohorte de validación construida a partir de los datos de expresión génica de dos series de cáncer de mama previamente publicadas.Breast cancer is the most frequent tumor among women in occidental countries, with an incidence that has increased substantially during the last decades. In Spain, close to 6.000 women die each year as a consequence of breast cancer, the first cause of cancer death among women population. Powerful technologies such as genomics and proteomics, with the support of bioinformatics, have generated a basis for the characterization of new molecular markers in cancer and other diseases. These new technologies allow establishing molecular patterns that can be correlated with tumor morphology, its clinical behavior, or its response to treatment. An alternative to analyze mRNA expression levels is the measurement of protein profiles. Its importance stems from the fact that, in the end, proteins define the functioning and control the activity of cells, tissues and organisms. The main goal of our study is to identify molecular profiles that improve prediction of tumor behavior, and allow the identification of new therapeutic targets. Towards this goal, we have analyzed a series of breast tumors samples, all of them from patients with lymph node involvement at the time of diagnosis and treated with adjuvant chemotherapy. Using mass spectrometry we have obtained an overview of these tumors proteome, together with a miRNA expression analysis in a representative fraction of these samples. Data analysis allowed us to establish molecular differences between hormone receptor positive and triple negative tumors. Protein expressions patterns allowed us to characterize a new group of hormone receptor positive breast tumors showing molecular characteristics similar to triple negative tumors. A protein expression-based “Adhesion Signature” has been defined to characterize this type of tumors. This signature has prognosis value, and provides additional information to that derived from profiles used in the clinical setting. Integrating data from miRNA and protein expression allowed us to infer some differences regarding the activity of molecular processes, such as proliferation or metabolism, among the different subtypes of breast cancer tumors. These results have been supported within a validation dataset that was created from breast cancer gene expression data already published

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

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

Clinical characteristics of the patients included in the study.

<p>Clinical characteristics of the patients included in the study.</p

DMFS prediction of the two reduced predictors tested in the publicly available transcriptomics dataset.

<p>DMFS prediction of the two reduced predictors tested in the publicly available transcriptomics dataset.</p

Survival analysis of reduced profile 5 in the PRM validation cohort and in the trasncriptomics orthogonal verification.

<p>Survival analysis of reduced profile 5 in the PRM validation cohort and in the trasncriptomics orthogonal verification.</p

Study design.

<p>Chart of samples included and analysis performed in each cohort.</p

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.ISSN:2045-232

The Long-HER Study: Clinical and Molecular Analysis of Patients with HER2+ Advanced Breast Cancer Who Become Long-Term Survivors with Trastuzumab-Based Therapy

<div><p>Background</p><p>Trastuzumab improves survival outcomes in patients with HER2+ metastatic breast cancer. The Long-Her study was designed to identify clinical and molecular markers that could differentiate long-term survivors from patients having early progression after trastuzumab treatment.</p><p>Methods</p><p>Data were collected from women with HER2-positive metastatic breast cancer treated with trastuzumab that experienced a response or stable disease during at least 3 years. Patients having a progression in the first year of therapy with trastuzumab were used as a control. Genes related with trastuzumab resistance were identified and investigated for network and gene functional interrelation. Models predicting poor response to trastuzumab were constructed and evaluated. Finally, a mutational status analysis of selected genes was performed in HER2 positive breast cancer samples.</p><p>Results</p><p>103 patients were registered in the Long-HER study, of whom 71 had obtained a durable complete response. Median age was 58 years. Metastatic disease was diagnosed after a median of 24.7 months since primary diagnosis. Metastases were present in the liver (25%), lungs (25%), bones (23%) and soft tissues (23%), with 20% of patients having multiple locations of metastases. Median duration of response was 55 months. The molecular analysis included 35 patients from the group with complete response and 18 patients in a control poor-response group. Absence of trastuzumab as part of adjuvant therapy was the only clinical factor associated with long-term survival. Gene ontology analysis demonstrated that PI3K pathway was associated with poor response to trastuzumab-based therapy: tumours in the control group usually had four or five alterations in this pathway, whereas tumours in the Long-HER group had two alterations at most.</p><p>Conclusions</p><p>Trastuzumab may provide a substantial long-term survival benefit in a selected group of patients. Whole genome expression analysis comparing long-term survivors vs. a control group predicted early progression after trastuzumab-based therapy. Multiple alterations in genes related to the PI3K-mTOR pathway seem to be required to confer resistance to this therapy.</p></div