SEOM clinical guidelines in early stage breast cancer (2018)

Breast cancer is the most common cancer in women in our country and it is usually diagnosed in the early and potentially curable stages. Nevertheless, around 20–30% of patients will relapse despite appropriate locoregional and systemic therapies. A better knowledge of this disease is improving our ability to select the most appropriate therapy for each patient with a recent diagnosis of an early stage breast cancer, minimizing unnecessary toxicities and improving long-term efficacy

EuroDia: a beta-cell gene expression resource

Type 2 diabetes mellitus (T2DM) is a major disease affecting nearly 280 million people worldwide. Whilst the pathophysiological mechanisms leading to disease are poorly understood, dysfunction of the insulin-producing pancreatic beta-cells is key event for disease development. Monitoring the gene expression profiles of pancreatic beta-cells under several genetic or chemical perturbations has shed light on genes and pathways involved in T2DM. The EuroDia database has been established to build a unique collection of gene expression measurements performed on beta-cells of three organisms, namely human, mouse and rat. The Gene Expression Data Analysis Interface (GEDAI) has been developed to support this database. The quality of each dataset is assessed by a series of quality control procedures to detect putative hybridization outliers. The system integrates a web interface to several standard analysis functions from R/Bioconductor to identify differentially expressed genes and pathways. It also allows the combination of multiple experiments performed on different array platforms of the same technology. The design of this system enables each user to rapidly design a custom analysis pipeline and thus produce their own list of genes and pathways. Raw and normalized data can be downloaded for each experiment. The flexible engine of this database (GEDAI) is currently used to handle gene expression data from several laboratory-run projects dealing with different organisms and platforms

Health-related quality of life with palbociclib plus endocrine therapy versus capecitabine in postmenopausal patients with hormone receptor–positive metastatic breast cancer: Patient-reported outcomes in the PEARL study

Background: The PEARL study showed that palbociclib plus endocrine therapy (palbociclib/ET) was not superior to capecitabine in improving progression-free survival in postmenopausal patients with metastatic breast cancer resistant to aromatase inhibitors, but was better tolerated. This analysis compared patient-reported outcomes. Patients and methods: The PEARL quality of life (QoL) population comprised 537 patients, 268 randomised to palbociclib/ET (exemestane or fulvestrant) and 269 to capecitabine. Patients completed the European Organisation for Research and Treatment of Cancer QLQ-C30 and QLQ-BR23 and EQ-5D-3L questionnaires. Changes from the baseline and time to deterioration (TTD) were analysed using linear mixed-effect and stratified Cox regression models, respectively. Results: Questionnaire completion rate was high and similar between treatment arms. Significant differences were observed in the mean change in global health status (GHS)/QoL scores from the baseline to cycle 3 (2.9 for palbociclib/ET vs. -2.1 for capecitabine (95% confidence interval [CI], 1.4–8.6; P = 0.007). The median TTD in GHS/QoL was 8.3 months for palbociclib/ET versus 5.3 months for capecitabine (adjusted hazard ratio, 0.70; 95% CI, 0.55–0.89; P = 0.003). Similar improvements for palbociclib/ET were also seen for other scales as physical, role, cognitive, social functioning, fatigue, nausea/vomiting and appetite loss. No differences were observed between the treatment arms in change from the baseline in any item of the EQ-5D-L3 questionnaire as per the overall index score and visual analogue scale. Conclusion: Patients receiving palbociclib/ET experienced a significant delay in deterioration of GHS/QoL and several functional and symptom scales compared with capecitabine, providing additional evidence that palbociclib/ET is better tolerated. Trial registration number: NCT02028507 (ClinTrials.gov). EudraCT study number: 2013-003170-27. © 2021 The Author(s

Epistasis of Transcriptomes Reveals Synergism between Transcriptional Activators Hnf1α and Hnf4α

The transcription of individual genes is determined by combinatorial interactions between DNA–binding transcription factors. The current challenge is to understand how such combinatorial interactions regulate broad genetic programs that underlie cellular functions and disease. The transcription factors Hnf1α and Hnf4α control pancreatic islet β-cell function and growth, and mutations in their genes cause closely related forms of diabetes. We have now exploited genetic epistasis to examine how Hnf1α and Hnf4α functionally interact in pancreatic islets. Expression profiling in islets from either Hnf1a+/− or pancreas-specific Hnf4a mutant mice showed that the two transcription factors regulate a strikingly similar set of genes. We integrated expression and genomic binding studies and show that the shared transcriptional phenotype of these two mutant models is linked to common direct targets, rather than to known effects of Hnf1α on Hnf4a gene transcription. Epistasis analysis with transcriptomes of single- and double-mutant islets revealed that Hnf1α and Hnf4α regulate common targets synergistically. Hnf1α binding in Hnf4a-deficient islets was decreased in selected targets, but remained unaltered in others, thus suggesting that the mechanisms for synergistic regulation are gene-specific. These findings provide an in vivo strategy to study combinatorial gene regulation and reveal how Hnf1α and Hnf4α control a common islet-cell regulatory program that is defective in human monogenic diabetes

Immunohistochemical assessment of Pax8 expression during pancreatic islet development and in human neuroendocrine tumors

The paired box transcription factor Pax8 is critical for development of the eye, thyroid gland as well as the urinary and reproductive organs. In adult, Pax8 overexpression is associated with kidney, ovarian and thyroid tumors and has emerged as a specific marker for these cancers. Recently, Pax8 expression was also reported in human pancreatic islets and in neuroendocrine tumors, identifying Pax8 as a novel member of the Pax family expressed in the pancreas. Herein, we sought to provide a comprehensive analysis of Pax8 expression during pancreogenesis and in adult islets. Immunohistochemical analysis using the most employed Pax8 polyclonal antibody revealed strong nuclear staining in the developing mouse pancreas and in mature human and mouse islets. Astonishingly, Pax8 mRNA in mouse islets was undetectable while human islets exhibited low levels. These discrepancies raised the possibility of antibody cross-reactivity. This premise was confirmed by demonstrating that the polyclonal Pax8 antibody also recognized the islet-enriched Pax6 protein both by Western blotting and immunohistochemistry. Thus, in islets polyclonal Pax8 staining corresponds mainly to Pax6. In order to circumvent this caveat, a novel Pax8 monoclonal antibody was used to re-evaluate whether Pax8 was indeed expressed in islets. Surprisingly, Pax8 was not detected in neither the developing pancreas or in mature islets. Reappraisal of pancreatic neuroendocrine tumors using this Pax8 monoclonal antibody exhibited no immunostaining as compared to the Pax8 polyclonal antibody. In conclusion, Pax8 is not expressed in the pancreas and cast doubts on the value of Pax8 as a pancreatic neuroendocrine tumor marker

Potential for pancreatic maturation of differentiating human embryonic stem cells is sensitive to the specific pathway of definitive endoderm commitment

This study provides a detailed experimental and mathematical analysis of the impact of the initial pathway of definitive endoderm (DE) induction on later stages of pancreatic maturation. Human embryonic stem cells (hESCs) were induced to insulin-producing cells following a directed-differentiation approach. DE was induced following four alternative pathway modulations. DE derivatives obtained from these alternate pathways were subjected to pancreatic progenitor (PP) induction and maturation and analyzed at each stage. Results indicate that late stage maturation is influenced by the initial pathway of DE commitment. Detailed quantitative analysis revealed WNT3A and FGF2 induced DE cells showed highest expression of insulin, are closely aligned in gene expression patterning and have a closer resemblance to pancreatic organogenesis. Conversely, BMP4 at DE induction gave most divergent differentiation dynamics with lowest insulin upregulation, but highest glucagon upregulation. Additionally, we have concluded that early analysis of PP markers is indicative of its potential for pancreatic maturation. © 2014 Jaramillo et al

Predicting Pancreas Cell Fate Decisions and Reprogramming with a Hierarchical Multi-Attractor Model

Cell fate reprogramming, such as the generation of insulin-producing β cells from other pancreas cells, can be achieved by external modulation of key transcription factors. However, the known gene regulatory interactions that form a complex network with multiple feedback loops make it increasingly difficult to design the cell reprogramming scheme because the linear regulatory pathways as schemes of causal influences upon cell lineages are inadequate for predicting the effect of transcriptional perturbation. However, sufficient information on regulatory networks is usually not available for detailed formal models. Here we demonstrate that by using the qualitatively described regulatory interactions as the basis for a coarse-grained dynamical ODE (ordinary differential equation) based model, it is possible to recapitulate the observed attractors of the exocrine and β, δ, α endocrine cells and to predict which gene perturbation can result in desired lineage reprogramming. Our model indicates that the constraints imposed by the incompletely elucidated regulatory network architecture suffice to build a predictive model for making informed decisions in choosing the set of transcription factors that need to be modulated for fate reprogramming

Nuclear expression of Rac1 in cervical premalignant lesions and cervical cancer cells

<p>Abstract</p> <p>Background</p> <p>Abnormal expression of Rho-GTPases has been reported in several human cancers. However, the expression of these proteins in cervical cancer has been poorly investigated. In this study we analyzed the expression of the GTPases Rac1, RhoA, Cdc42, and the Rho-GEFs, Tiam1 and beta-Pix, in cervical pre-malignant lesions and cervical cancer cell lines.</p> <p>Methods</p> <p>Protein expression was analyzed by immunochemistry on 102 cervical paraffin-embedded biopsies: 20 without Squamous Intraepithelial Lesions (SIL), 51 Low- grade SIL, and 31 High-grade SIL; and in cervical cancer cell lines C33A and SiHa, and non-tumorigenic HaCat cells. Nuclear localization of Rac1 in HaCat, C33A and SiHa cells was assessed by cellular fractionation and Western blotting, in the presence or not of a chemical Rac1 inhibitor (NSC23766).</p> <p>Results</p> <p>Immunoreacivity for Rac1, RhoA, Tiam1 and beta-Pix was stronger in L-SIL and H-SIL, compared to samples without SIL, and it was significantly associated with the histological diagnosis. Nuclear expression of Rac1 was observed in 52.9% L-SIL and 48.4% H-SIL, but not in samples without SIL. Rac1 was found in the nucleus of C33A and SiHa cells but not in HaCat cells. Chemical inhibition of Rac1 resulted in reduced cell proliferation in HaCat, C33A and SiHa cells.</p> <p>Conclusion</p> <p>Rac1 is expressed in the nucleus of epithelial cells in SILs and cervical cancer cell lines, and chemical inhibition of Rac1 reduces cellular proliferation. Further studies are needed to better understand the role of Rho-GTPases in cervical cancer progression.</p

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

<p>Abstract</p> <p>Background</p> <p>Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.</p> <p>Results</p> <p>We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.</p> <p>Conclusion</p> <p>our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.</p

Use of non-steroidal anti-inflammatory drugs and risk of breast cancer: The Spanish Multi-Case-control (MCC) study

Background: The relationship between non-steroidal anti-inflammatory drug (NSAID) consumption and breast cancer has been repeatedly studied, although the results remain controversial. Most case-control studies reported that NSAID consumption protected against breast cancer, while most cohort studies did not find this effect. Most studies have dealt with NSAIDs as a whole group or with specific drugs, such aspirin, ibuprofen, or others, but not with NSAID subgroups according to the Anatomical Therapeutic Chemical Classification System; moreover, scarce attention has been paid to their effect on different tumor categories (i.e.: ductal/non-ductal, stage at diagnosis or presence of hormonal receptors). Methods: In this case-control study, we report the NSAID – breast cancer relationship in 1736 breast cancer cases and 1895 healthy controls; results are reported stratifying by the women’s characteristics (i.e.: menopausal status or body mass index category) and by tumor characteristics. Results: In our study, NSAID use was associated with a 24 % reduction in breast cancer risk (Odds ratio [OR] = 0.76; 95 % Confidence Interval [CI]: 0.64–0.89), and similar results were found for acetic acid derivatives, propionic acid derivatives and COXIBs, but not for aspirin. Similar results were found in postmenopausal and premenopausal women. NSAID consumption also protected against hormone + or HER2+ cancers, but not against triple negative breast cancers. The COX-2 selectivity showed an inverse association with breast cancer (i.e. OR < 1), except in advanced clinical stage and triple negative cancers. Conclusion: Most NSAIDs, but not aspirin, showed an inverse association against breast cancer; this effect seems to be restricted to hormone + or HER2+ cancers. Keywords: Breast cancer, Non-steroidal anti-inflammatory drug, Hormone receptor positive breast cancer, HER2 positive breast cancer, Triple negative breast cance