MMP11 expression in intratumoral inflammatory cells in breast cancer.

AIMS: It is known that matrix metalloproteinase (MMP)-11 has a role in tumour development and progression, and also that immune cells can influence cancer cells to increase their proliferative and invasive properties. The aim of the present study was to propose the evaluation of MMP11 expression by intratumoral mononuclear inflammatory cells (MICs) as a useful biological marker for breast cancer prognosis. METHODS AND RESULTS: This study comprised 246 women with invasive breast carcinoma, and a long follow-up period. Patients were stratified with regard to nodal status and to the development of metastatic disease. The median follow-up period in patients without metastasis was 146 months and in patients with metastatic disease 31 months. MMP11 was determined by immunohistochemistry. For relapse-free survival (RFS) and overall survival (OS) analysis we used the Cox's univariate method. Cox's regression model was used to examine the interactions between different prognostic factors in a multivariate analysis. CONCLUSIONS: Our results showed that MMP11 expression by stromal cells was significantly associated with prognosis. MMP11 expression by cancer-associated fibroblasts (CAFs) was associated with both shortened RFS and OS, but MMP11 expression by MICs showed a stronger association with both shortened RFS and OS, therefore being the most potent and independent factor to predict RFS and OS.

KCNA4 Gene Variant is Auxiliary in Endurance Running Performance Level.

The present is an observational study following a genetic epidemiology model using a case-control design. We tested the hypothesis of an association between the prevalence of the genotypic and allelic frequencies distribution of the potassium voltage-gated channel of the shaker related subfamily member 4 gene (KCNA4) rs1323860 (C/T transition) and endurance performance level in Hispanic male marathon runners (MR). The subjects (n=1876) were adult Hispanic male MR. Fast-MR (cases; n=938) were finishers in the top 3rd percentile. Slow MR (controls; n=938) were finishers in the lowest 3rd percentile of their respective age. Genomic DNA was purified from a whole blood sample. Polymerase chain reaction was used to amplify a KCNA4 SNP which consists of a C/T (rs1323860) transition. The observed genotype frequencies, in both Cases and Controls, met Hardy-Weinberg equilibrium (X2, P≥0.05). Genotype and allele frequencies were statistically different (P<0.01) between cases and controls. Odds ratio revealed that the C allele was 1.33 times more likely prevalent in the cases than in the controls (95% CI; 1.17, 1.51; P<0.001). The magnitude of the statistical power for the present study was 0.86. In conclusion, the findings strongly suggest that KCNA4 gene rs1323860 (C/T transition) is auxiliary in the complex phenotype of endurance running performance level in Hispanic male marathon runners.

Sensitivity analysis of gene ranking methods in phenotype prediction.

INTRODUCTION: It has become clear that noise generated during the assay and analytical processes has the ability to disrupt accurate interpretation of genomic studies. Not only does such noise impact the scientific validity and costs of studies, but when assessed in the context of clinically translatable indications such as phenotype prediction, it can lead to inaccurate conclusions that could ultimately impact patients. We applied a sequence of ranking methods to damp noise associated with microarray outputs, and then tested the utility of the approach in three disease indications using publically available datasets. MATERIALS AND METHODS: This study was performed in three phases. We first theoretically analyzed the effect of noise in phenotype prediction problems showing that it can be expressed as a modeling error that partially falsifies the pathways. Secondly, via synthetic modeling, we performed the sensitivity analysis for the main gene ranking methods to different types of noise. Finally, we studied the predictive accuracy of the gene lists provided by these ranking methods in synthetic data and in three different datasets related to cancer, rare and neurodegenerative diseases to better understand the translational aspects of our findings. RESULTS AND DISCUSSION: In the case of synthetic modeling, we showed that Fisher's Ratio (FR) was the most robust gene ranking method in terms of precision for all the types of noise at different levels. Significance Analysis of Microarrays (SAM) provided slightly lower performance and the rest of the methods (fold change, entropy and maximum percentile distance) were much less precise and accurate. The predictive accuracy of the smallest set of high discriminatory probes was similar for all the methods in the case of Gaussian and Log-Gaussian noise. In the case of class assignment noise, the predictive accuracy of SAM and FR is higher. Finally, for real datasets (Chronic Lymphocytic Leukemia, Inclusion Body Myositis and Amyotrophic Lateral Sclerosis) we found that FR and SAM provided the highest predictive accuracies with the smallest number of genes. Biological pathways were found with an expanded list of genes whose discriminatory power has been established via FR. CONCLUSIONS: We have shown that noise in expression data and class assignment partially falsifies the sets of discriminatory probes in phenotype prediction problems. FR and SAM better exploit the principle of parsimony and are able to find subsets with less number of high discriminatory genes. The predictive accuracy and the precision are two different metrics to select the important genes, since in the presence of noise the most predictive genes do not completely coincide with those that are related to the phenotype. Based on the synthetic results, FR and SAM are recommended to unravel the biological pathways that are involved in the disease development.

Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems.

INTRODUCTION: Chronic Lymphocytic Leukemia (CLL) is a disease with highly heterogeneous clinical course. A key goal is the prediction of patients with high risk of disease progression, which could benefit from an earlier or more intense treatment. In this work we introduce a simple methodology based on machine learning methods to help physicians in their decision making in different problems related to CLL. MATERIAL AND METHODS: Clinical data belongs to a retrospective study of a cohort of 265 Caucasians who were diagnosed with CLL between 1997 and 2007 in Hospital Cabueñes (Asturias, Spain). Different machine learning methods were applied to find the shortest list of most discriminatory prognostic variables to predict the need of Chemotherapy Treatment and the development of an Autoimmune Disease. RESULTS: Autoimmune disease occurrence was predicted with very high accuracy (>90%). Autoimmune disease development is currently an unpredictable severe complication of CLL. Chemotherapy Treatment has been predicted with a lower accuracy (80%). Risk analysis showed that the number of false positives and false negatives are well balanced. CONCLUSIONS: Our study highlights the importance of prognostic variables associated with the characteristics of platelets, reticulocytes and natural killers, which are the main targets of the autoimmune haemolytic anemia and immune thrombocytopenia for autoimmune disease development, and also, the relevance of some clinical variables related with the immune characteristics of CLL patients that are not taking into account by current prognostic markers for predicting the need of chemotherapy. Because of its simplicity, this methodology could be implemented in spreadsheets.

Sulfatase 2 Is Associated with Steroid Resistance in Childhood Nephrotic Syndrome.

Glucocorticoid (GC) resistance complicates the treatment of ~10-20% of children with nephrotic syndrome (NS), yet the molecular basis for resistance remains unclear. We used RNAseq analysis and in silico algorithm-based approaches on peripheral blood leukocytes from 12 children both at initial NS presentation and after ~7 weeks of GC therapy to identify a 12-gene panel able to differentiate steroid resistant NS (SRNS) from steroid-sensitive NS (SSNS). Among this panel, subsequent validation and analyses of one biologically relevant candidate, sulfatase 2 (SULF2), in up to a total of 66 children, revealed that both SULF2 leukocyte expression and plasma arylsulfatase activity Post/Pre therapy ratios were greater in SSNS vs. SRNS. However, neither plasma SULF2 endosulfatase activity (measured by VEGF binding activity) nor plasma VEGF levels, distinguished SSNS from SRNS, despite VEGF's reported role as a downstream mediator of SULF2's effects in glomeruli. Experimental studies of NS-related injury in both rat glomeruli and cultured podocytes also revealed decreased SULF2 expression, which were partially reversible by GC treatment of podocytes. These findings together suggest that SULF2 levels and activity are associated with GC resistance in NS, and that SULF2 may play a protective role in NS via the modulation of downstream mediators distinct from VEGF.

A Machine Learning Model for Evaluating Imported Disease Screening Strategies in Immigrant Populations.

Given the high prevalence of imported diseases in immigrant populations, it has postulated the need to establish screening programs that allow their early diagnosis and treatment. We present a mathematical model based on machine learning methodologies to contribute to the design of screening programs in this population. We conducted a retrospective cross-sectional screening program of imported diseases in all immigrant patients who attended the Tropical Medicine Unit between January 2009 and December 2016. We designed a mathematical model based on machine learning methodologies to establish the set of most discriminatory prognostic variables to predict the onset of the: HIV infection, malaria, chronic hepatitis B and C, schistosomiasis, and Chagas in immigrant population. We analyzed 759 patients. HIV was predicted with an accuracy of 84.9% and the number of screenings to detect the first HIV-infected person was 26, as in the case of Chagas disease (with a predictive accuracy of 92.9%). For the other diseases the averages were 12 screenings to detect the first case of chronic hepatitis B (85.4%), or schistosomiasis (86.9%), 23 for hepatitis C (85.6%) or malaria (93.3%), and eight for syphilis (79.4%) and strongyloidiasis (88.4%). The use of machine learning methodologies allowed the prediction of the expected disease burden and made it possible to pinpoint with greater precision those immigrants who are likely to benefit from screening programs, thus contributing effectively to their development and design.

The Association of Aquaporin-1 Gene with Marathon Running Performance Level: a Confirmatory Study Conducted in Male Hispanic Marathon Runners.

BACKGROUND: Replication studies are essential for identifying credible associations between alleles and phenotypes. Validation of genotype-phenotype associations in the sports and exercise field is rare. An initial genetic association study suggested that rs1049305 (C > G) in the 3' untranslated region (3'UTR) of the aquaporin-1 (AQP1) gene was associated with marathon running (MR) performance level in Hispanic males. To validate this finding, we conducted a replication analysis in an independent case-control sample of Hispanic male marathon runners (n = 1430; cases n = 713 and controls n = 717). A meta-analysis was utilized to test the extent of the association between the initial results and the present report. It also provided to test the heterogeneity (variation) between the two studies. RESULTS: The replication study showed a statistically significant (p ≤ 0.05) association between rs1049305 (C > G) of the AQP1 gene and MR performance level. Association test results using a fixed effect model for the combined, original study and the present report, yielded an odds ratio = 1.28, 95% confidence interval = 1.13-1.45, p = 0.0001. The extent of the measures of heterogeneity was Tau-squared = 0, H statistic = 1, I2 statistic = 0, and Cochran's Q test (Q = 0.29; p value 0.59), indicated the variation between studies were due to chance and not to differences in heterogeneity between the two studies. Within the limitations of the present replication, contrast of two studies and its effects on meta-analysis, the findings were robust. CONCLUSION: This study successfully replicated the results of Martínez et al. (Med Sportiva 13:251-5, 2009). The meta-analysis provided further epidemiological credibility for the hypothesis of association between the DNA rs1049305 (C > G) variation in the 3'UTR of the AQP1 gene and MR running performance level in Hispanics male marathon runners. It is not precluded that a linked DNA structure in the surrounding molecular neighborhood could be of influence by been part of the overly complex phenotype of MR performance level.

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare. METHOD: We carried out a retrospective analysis of two different microarray datasets, using machine learning techniques to understand the defective pathways involved in this disease. We have modeled two data sets that are publicly accessible. The first was used to establish the list of most discriminatory genes; whereas, the second one was utilized for validation purposes. RESULTS: The analysis provided a list of high discriminatory genes with predictive cross-validation accuracy higher than 95%, both in learning and in blind validation. The results were confirmed via the holdout sampler. The most discriminatory genes were related to the production of Hemoglobin. The biological processes involved were related to T-cell Receptor Signaling and co-stimulation, Interferon-Gamma Signaling and Antigen Processing and Presentation. Drug repositioning via CMAP methodologies highlighted the importance of Trichostatin A and other HDAC inhibitors. CONCLUSIONS: The defective pathways suggest viral or bacterial infections as plausible mechanisms involved in MS development. The pathway analysis also confirmed coincidences with Epstein-Barr virus, Influenza A, Toxoplasmosis, Tuberculosis and Staphylococcus Aureus infections. Th17 Cell differentiation, and CD28 co-stimulation seemed to be crucial in the development of this disease. Furthermore, the additional knowledge provided by this analysis helps to identify new therapeutic targets.

NK-cell Editing Mediates Epithelial-to-Mesenchymal Transition via Phenotypic and Proteomic Changes in Melanoma Cell Lines.

Tumor cell plasticity is a major obstacle for the cure of malignancies as it makes tumor cells highly adaptable to microenvironmental changes, enables their phenotype switching among different forms, and favors the generation of prometastatic tumor cell subsets. Phenotype switching toward more aggressive forms involves different functional, phenotypic, and morphologic changes, which are often related to the process known as epithelial-mesenchymal transition (EMT). In this study, we report natural killer (NK) cells may increase the malignancy of melanoma cells by inducing changes relevant to EMT and, more broadly, to phenotype switching from proliferative to invasive forms. In coculture, NK cells induced effects on tumor cells similar to those induced by EMT-promoting cytokines, including upregulation of stemness and EMT markers, morphologic transition, inhibition of proliferation, and increased capacity for Matrigel invasion. Most changes were dependent on the engagement of NKp30 or NKG2D and the release of cytokines including IFNγ and TNFα. Moreover, EMT induction also favored escape from NK-cell attack. Melanoma cells undergoing EMT either increased NK-protective HLA-I expression on their surface or downregulated several tumor-recognizing activating receptors on NK cells. Mass spectrometry-based proteomic analysis revealed in two different melanoma cell lines a partial overlap between proteomic profiles induced by NK cells or by EMT cytokines, indicating that various processes or pathways related to tumor progression are induced by exposure to NK cells.Significance: NK cells can induce prometastatic properties on melanoma cells that escape from killing, providing important clues to improve the efficacy of NK cells in innovative antitumor therapies. Cancer Res; 78(14); 3913-25. ©2018 AACR.