Elmer Andrés Fernández

Predictive outcomes for HER2-enriched cancer using growth and metastasis signatures driven by SPARC.

Understanding the mechanism of metastatic dissemination is crucial for the rational design of novel therapeutics. The secreted protein acidic and rich in cysteine (SPARC) is a matricellular glycoprotein which has been extensively associated with human breast cancer aggressiveness although the underlying mechanisms are still unclear. Here, shRNA-mediated SPARC knockdown greatly reduced primary tumor growth and completely abolished lung colonization of murine 4T1 and LM3 breast malignant cells implanted in syngeneic BALB/c mice. A comprehensive study including global transcriptomic analysis followed by biological validations confirmed that SPARC induces primary tumor growth by enhancing cell cycle and by promoting a COX-2–mediated expansion of myeloid-derived suppressor cells (MDSC). The role of SPARC in metastasis involved a COX-2–independent enhancement of cell disengagement from the primary tumor and adherence to the lungs that fostered metastasis implantation. Interestingly, SPARC-driven gene expression signatures obtained from these murine models predicted the clinical outcome of patients with HER2-enriched breast cancer subtypes. In total, the results reveal that SPARC and its downstream effectors are attractive targets for antimetastatic therapies in breast cancer. Implications: These findings shed light on the prometastatic role of SPARC, a key protein expressed by breast cancer cells and surrounding stroma, with important consequences for disease outcome. Mol Cancer Res; 15(3); 304–16. ©2016 AACR.

Artificial Neural Networks Applications in Dialysis

Artificial Neural Networks are mathematical models resembling the brain behavior. They have the ability to “learn” from the “environment” and produce responses as a consequence of this learning process. They were broadly used in medicine both as a classification model as well as a prediction tool. In hemodialysis they were used for molecular modeling in the estimation of equilibrated urea concentration, as a monitoring strategy for online treatment analysis and also for bed side models for hemodialysis adequacy evaluation. In this chapter the basic concepts of artificial neural models are introduced and a complete application in equilibrated urea estimation in hemodialized patients is presented.

Functional Toll-like Receptor 4 Overexpression in Papillary Thyroid Cancer by MAPK/ERK-induced ETS1 Transcriptional Activity

Emerging evidence suggests that unregulated Toll-like receptor (TLR) signaling promotes tumor survival signals, thus favoring tumor progression. Here, the mechanism underlying TLR4 overexpression in papillary thyroid carcinomas (PTC) mainly harboring the BRAFV600E mutation was studied. TLR4 was overexpressed in PTC compared with nonneoplastic thyroid tissue. Moreover, paired clinical specimens of primary PTC and its lymph node metastasis showed a significant upregulation of TLR4 levels in the metastatic tissues. In agreement, conditional BRAFV600E expression in normal rat thyroid cells and mouse thyroid tissue upregulated TLR4 expression levels. Furthermore, functional TLR4 expression was demonstrated in PTC cells by increased NF-κB transcriptional activity in response to the exogenous TLR4-agonist lipopolysaccharide. Of note, The Cancer Genome Atlas data analysis revealed that BRAFV600E-positive tumors with high TLR4 expression were associated with shorter disease-free survival. Transcriptomic data analysis indicated a positive correlation between TLR4 expression levels and MAPK/ERK signaling activation. Consistently, chemical blockade of MAPK/ERK signaling abrogated BRAFV600E-induced TLR4 expression. A detailed study of the TLR4 promoter revealed a critical MAPK/ERK–sensitive Ets-binding site involved in BRAFV600E responsiveness. Subsequent investigation revealed that the Ets-binding factor ETS1 is critical for BRAFV600E-induced MAPK/ERK signaling-dependent TLR4 gene expression. Together, these data indicate that functional TLR4 overexpression in PTCs is a consequence of thyroid tumor-oncogenic driver dysregulation of MAPK/ERK/ETS1 signaling. Implications: Considering the participation of aberrant NF-κB signaling activation in the promotion of thyroid tumor growth and the association of high TLR4 expression with more aggressive tumors, this study suggests a prooncogenic potential of TLR4 downstream signaling in thyroid tumorigenesis. Mol Cancer Res; 16(5); 833–45. ©2018 AACR.

TarSeqQC: Quality control on targeted sequencing experiments in R

Targeted sequencing (TS) is growing as a screening methodology used in research and medical genetics to identify genomic alterations causing human diseases. In general, a list of possible genomic variants is derived from mapped reads through a variant calling step. This processing step is usually based on variant coverage, although it may be affected by several factors. Therefore, undercovered relevant clinical variants may not be reported, affecting pathology diagnosis or treatment. Thus, a prior quality control of the experiment is critical to determine variant detection accuracy and to avoid erroneous medical conclusions. There are several quality control tools, but they are focused on issues related to whole‐genome sequencing. However, in TS, quality control should assess experiment, gene, and genomic region performances based on achieved coverages. Here, we propose TarSeqQC R package for quality control in TS experiments. The tool is freely available at Bioconductor repository. TarSeqQC was used to analyze two datasets; low‐performance primer pools and features were detected, enhancing the quality of experiment results. Read count profiles were also explored, showing TarSeqQCs effectiveness as an exploration tool. Our proposal may be a valuable bioinformatic tool for routinely TS experiments in both research and medical genetics.

The impact of quality control in RNA-seq experiments

High throughput mRNA sample sequencing, known as RNA-seq, is as a powerful approach to detect differentially expressed genes starting from millions of short sequence reads. Although several workflows have been proposed to analyze RNA-seq data, the experiment quality control as a whole is not usually considered, thus potentially biasing the results and/or causing information lost. Experiment quality control refers to the analysis of the experiment as a whole, prior to any analysis. It not only inspects the presence of technical effects, but also if general biological assumptions are fulfilled. In this sense, multivariate approaches are crucial for this task. Here, a multivariate approach for quality control in RNA-seq experiments is proposed. This approach uses simple and yet effective well-known statistical methodologies. In particular, Principal Component Analysis was successfully applied over real data to detect and remove outlier samples. In addition, traditional multivariate exploration tools were applied in order to asses several controls that can help to ensure the results quality. Based on differential expression and functional enrichment analysis, here is demonstrated that the information retrieval is significantly enhanced through experiment quality control. Results show that the proposed multivariate approach increases the information obtained from RNA-seq data after outlier samples removal.

Analysis of the Dialysis Dose in Different Clinical Situations: A Simulation-Based Approach

This led to development of several HD schedules proposals based on the variation of the session time duration (TD) as well as on its weekly frequency (Fr). However, more fre‐ quent HD schedules require new indexes to measure the delivered dose. In this context, the Equivalent Renal Clearence (EKR) [Casino y Lopez] [3] and Standard Kt/V (stdKt/V) [Gotch] [4] indexes have been proposed to quantify the dialysis dose for different HD frequency schedules.

Bedside Linear Regression Equations to Estimate Equilibrated Blood Urea

Three decades ago Sargent and Gotch established the clinical applicability of Kt/V, a dimensionless ratio which includes clearance of dialyzer (K),duration of treatment(t) and volume of total water of the patient (V), as an index of Hemodialysis (HD) adequacy (Gotch & Keen, 2005). This parameter, derived from single-pool(sp) urea(U) kinetic modelling, has become the gold standard for HD dose monitoring and it is widely used as a predictor of outcome in HD populations (Locatelli et al., 1999; Eknoyan et al., 2002; Locatelli, 2003). However, this spKt/V overestimates the HD dose because it does not take into account the concept of U rebound (UR). UR begins immediately at the end of HD session and it is completed 30-60 minutes after. UR is related to disequilibriums in blood/cell compartments as well as the flow between organs desequilibriums, both produced during HD treatment. Therefore, equilibrated (Eq) Kt/V is the true HD dose and it requires the measurement of a true eqU when UR is completed. A blood sample to obtain an eqU concentration has several drawbacks that make this option impractical (Gotch and Keen,2005). For this reason in the last decade several formulas were developed to predict the eqU and also (Eq) Kt/V eliminating the need of waiting for a equilibrated urea mesurement. For instance, the “rate formula” (Daurgidas et al., 1995) is the most popular and validated equation. It is based in the prediction of (Eq)Kt/V as a linear function of (sp)Kt/V and the rate of dialysis(K/V). Another approach has been proposed by Tattersall, a robust formula based on double–pool analysis (Smye et al.1999). However, spite this eqU prediction approach is conceptually rigorous, it is not accurate (Gotch, 1990; Guh et al., 1999; Fernandez et al., 2001). Consequently, the availability of a model to predict subject-specific equilibrated concentration will be very helpful. Although the behaviour of urea is non-linear since its extraction from blood follows some exponential family model as a function of time, we found that prediction of its equilibrated concentration after the end of the treatment session by means of linear models is accurate. In this study, we have shown how to build linear models to predict equilibrated urea based on two statistical procedures and a machine learning method that can be implemented in hemodialysis centres. The fitted model can be used for daily treatment monitoring and is