Cancer evolution and individual susceptibility

El pdf del artículo es la versión de autor.Cancer susceptibility is due to interactions between inherited genetic factors and exposure to environmental carcinogens. The genetic component is constituted mainly by weakly acting low-penetrance genetic variants that interact among themselves, as well as with the environment. These low susceptibility genes can be categorized into two main groups: one includes those that control intrinsic tumor cell activities (i.e. apoptosis, proliferation or DNA repair), and the other contains those that modulate the function of extrinsic tumor cell compartments (i.e. stroma, angiogenesis, or endocrine and immune systems). Genome-Wide Association Studies (GWAS) of human populations have identified numerous genetic loci linked with cancer risk and behavior, but nevertheless the major component of cancer heritability remains to be explained. One reason may be that GWAS cannot readily capture gene–gene or gene–environment interactions. Mouse model approaches offer an alternative or complementary strategy, because of our ability to control both the genetic and environmental components of risk. Recently developed genetic tools, including high-throughput technologies such as SNP, CGH and gene expression microarrays, have led to more powerful strategies for refining quantitative trait loci (QTL) and identifying the critical genes. In particular, the cross-species approaches will help to refine locations of QTLs, and reveal their genetic and environmental interactions. The identification of human tumor susceptibility genes and discovery of their roles in carcinogenesis will ultimately be important for the development of methods for prediction of risk, diagnosis, prevention and therapy for human cancers.J. H. Mao is supported by Office of Biological & Environmental Research, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, by Laboratory Downloaded on 20 February 2012 Published on 24 January 2011 on http://pubs.rsc.org | doi:10.1039/C0IB00094A View Online This journal is c The Royal Society of Chemistry 2011 Integr. Biol., 2011, 3, 316–328 325 Directed Research & Development Program (LDRD), and by the National Institutes of Health, National Cancer Institute grant R01 CA116481. J. Pe´rez-Losada is partially supported by FEDER and MICINN (PLE2009-119), FIS (PI070057; PI10/00328), CSIC (200920I137), Junta de Castilla y Leo´ n (SAN126/SA66/09; SA079A09). A. Castellanos-Martı´n is supported by FEDER and MICINN (PLE2009-119).Peer reviewe

Identifying phenotypes involved in susceptibility to Schistosoma mansoni infection in F1B6CBA mice

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.Schistosomiasis is a disease with a strong genetic component influenced by socioeconomic and ecological factors. Epidemiological studies have identified several genetic regions involved in the schistosomiasis susceptibility. However, it is not well known what physiological traits are predisposing to the disease. The study of experimental infections in inbred mouse strains with variable genetic susceptibility to the disease offers a good opportunity to tackle this question. F1B6CBA hybrid between the most divergent strains was infected in order to characterize the immunophenotypes that correlate with the susceptibility of schistosomiasis disease in mice. Complete blood counts and immunophenotype were determined at 0, 3, 6, and 9 weeks post infection. Nine weeks after cercariae exposure, animals were perfused and worm recovery was assessed. A large number of hepatic lesions, a reduction in the eosinophil and basophil count in the acute phase of infection and the decreased number of monocytes, neutrophils and B-lymphocytes are phenotypes associated with increased susceptibility to S. mansoni infection.The present study was supported by a grant from the Areces Foundation (2010–13) and funding of the Junta de Castilla y Leon (Orden EDU/330/2008).Peer Reviewe

Metabolomic analysis of human astrocytes in lipotoxic condition : potential biomarker identification with machine learning modeling

En la última década se ha estudiado la asociación entre enfermedades neurodegenerativas (EN) y obesidad. Donde la obesidad y los trastornos metabólicos relacionados pueden conducir a fenómenos de neurodegeneración. En este sentido, numerosos estudios han demostrado que los sujetos que padecen obesidad presentan un mayor riesgo de desarrollar diferentes EN. La obesidad se considera un síndrome de etiología multifactorial caracterizado por una acumulación y liberación excesiva de ácidos grasos (FA) en el tejido adiposo y no adiposo. De esta forma, el exceso de FA genera una condición metabólica conocida como lipotoxicidad; que desencadena respuestas patológicas celulares y moleculares que pueden producir una desregulación de la homeostasis y una disminución de la viabilidad celular. Esta condición es un sello distintivo de estas enfermedades, que representan un grupo heterogéneo de trastornos que se caracterizan por una disfunción progresiva de neuronas y astrocitos. Los astrocitos son particularmente sensibles a la lipotoxicidad ya que los efectos de esta condición son más impactantes en estas células dado su papel crucial en la producción de energía y el manejo del estrés oxidativo en el cerebro. Hallazgos recientes sugieren que los astrocitos juegan un papel crítico en la función y protección del sistema nervioso central (SNC). Por esta razón, la pérdida de la función astrocítica normal puede ser un contribuyente principal a la neurodegeneración. Sin embargo, analizar los mecanismos celulares asociados a estas condiciones representa un desafío. En este sentido, la metabolómica es un enfoque que permite el análisis bioquímico desde una perspectiva integral de la fisiología celular. Esta técnica permite determinar perfiles metabólicos celulares en diferentes contextos biológicos como los asociados a EN y agresiones metabólicas específicas como la lipotoxicidad. No obstante, los datos proporcionados por la metabolómica pueden ser complejos y, por tanto, difíciles de interpretar. Por esta razón, las técnicas alternativas de análisis de datos como el Machine Learning han ido creciendo exponencialmente en áreas relacionadas con los datos ómicos. En el presente trabajo, creamos un modelo ML que con resultados del 93% del área bajo la curva ROC, valor de sensibilidad del 80% y valor de especificidad del 93%. En este trabajo el objetivo es analizar los perfiles metabolómicos de los astrocitos en condiciones lipotóxicas con el fin de proporcionar conocimientos novedosos, como posibles biomarcadores para escenarios de lipotoxicidad inducida por ácido palmítico, que hasta donde sabemos no han sido identificados en astrocitos humanos y se proponen como candidatos para una mayor investigación y validaciónMinisterio de Ciencia Tecnología e InnovaciónPontificia Universidad JaverianaIn the last decade, the association between neurodegenerative diseases (NDs) and obesity has been studied. Where obesity and related metabolic disorders can lead to neurodegeneration phenomena. In this sense, many studies have shown that subjects suffering from obesity show a higher risk of developing different NDs. Obesity is considered a syndrome of multifactorial etiology characterized by an excessive accumulation and release of fatty acids (FA) in adipose and non-adipose tissue. In this way, the excess of FA generates a metabolic condition known as lipotoxicity; which triggers pathological cellular and molecular responses that can produce dysregulation of homeostasis and decrease in cell viability. This condition is a hallmark for these diseases, that represent a heterogeneous group of disorders that are characterized by progressive dysfunction of neurons and astrocytes. Astrocytes are particularly sensitive to lipotoxicity since the effects of this condition are more impactful in these cells given their crucial role in energy production and oxidative stress management in the brain. Recent findings suggest that astrocytes play a critical role in the function and protection of the central nervous system (CNS). For this reason, loss of normal astrocytic function may be a primary contributor to neurodegeneration. However, analyzing cellular mechanisms associated to these conditions represents a challenge. In this regard, metabolomics is an approach that allows biochemical analysis from a comprehensive perspective of cell physiology. This technique allows determining cellular metabolic profiles in different biological contexts such as those associated with NDs and specific metabolic insults such as lipotoxicity. Nonetheless, the data provided by metabolomics can be complex and hence hard to interpretate. For this reason, alternative data analysis techniques such as Machine Learning have been growing exponentially in areas related to omics data. Here, we created a ML model that with results of 93% of area under the ROC curve, sensibility value of 80% and specificity value of 93%. In this work the goal is to analyze the metabolomic profiles of astrocytes in lipotoxic conditions in order to provide powerful insights such as potential biomarkers for scenarios of lipotoxicity induced by palmitic acid, that to our knowledge haven´t been identified in human astrocytes and are proposed as candidates for further research and validationBiólogo (a)Pregradohttps://orcid.org/0000-0003-1450-265

Loss of G9a preserves mutation patterns but increases chromatin accessibility, genomic instability and aggressiveness in skin tumours

Mutations in, and the altered expression of, epigenetic modifiers are pervasive in human tumours, making epigenetic factors attractive antitumour targets. The open-versus-closed chromatin state within the cells-of-origin of cancer correlates with the uneven distribution of mutations. However, the long-term effect of targeting epigenetic modifiers on mutability in patients with cancer is unclear. Here, we increased chromatin accessibility by deleting the histone H3 lysine 9 (H3K9) methyltransferase G9a in murine epidermis and show that this does not alter the single nucleotide variant burden or global genomic distribution in chemical mutagen-induced squamous tumours. G9a-depleted tumours develop after a prolonged latency compared with their wild-type counterparts, but are more aggressive and have an expanded cancer progenitor pool, pronounced genomic instability and frequent loss-of-function p53 mutations. Thus, we call for caution when assessing long-term therapeutic benefits of chromatin modifier inhibitors, which may promote more aggressive disease

A new role of SNAI2 in postlactational involution of the mammary gland links it to luminal breast cancer development

PMCID: PMC4560637Breast cancer is a major cause of mortality in women. The transcription factor SNAI2 has been implicated in the pathogenesis of several types of cancer, including breast cancer of basal origin. Here we show that SNAI2 is also important in the development of breast cancer of luminal origin in MMTV-ErbB2 mice. SNAI2 deficiency leads to longer latency and fewer luminal tumors, both of these being characteristics of pretumoral origin. These effects were associated with reduced proliferation and a decreased ability to generate mammospheres in normal mammary glands. However, the capacity to metastasize was not modified. Under conditions of increased ERBB2 oncogenic activity after pregnancy plus SNAI2 deficiency, both pretumoral defects - latency and tumor load - were compensated. However, the incidence of lung metastases was dramatically reduced. Furthermore, SNAI2 was required for proper postlactational involution of the breast. At 3 days post lactational involution, the mammary glands of Snai2-deficient mice exhibited lower levels of pSTAT3 and higher levels of pAKT1, resulting in decreased apoptosis. Abundant noninvoluted ducts were still present at 30 days post lactation, with a greater number of residual ERBB2+ cells. These results suggest that this defect in involution leads to an increase in the number of susceptible target cells for transformation, to the recovery of the capacity to generate mammospheres and to an increase in the number of tumors. Our work demonstrates the participation of SNAI2 in the pathogenesis of luminal breast cancer, and reveals an unexpected connection between the processes of postlactational involution and breast tumorigenesis in Snai2-null mutant mice.JPL was partially supported by FEDER and MICINN (PLE2009-119, SAF2014-56989-R), Instituto de Salud Carlos III (PI07/0057, PI10/00328, PIE14/00066), Junta de Castilla y León (SAN673/SA26/08, SAN126/SA66/09, SA078A09, CSI034U13), the “Fundación Eugenio Rodríguez Pascual”, the “Fundación Inbiomed” (Instituto Oncológico Obra Social de la Caja Guipozcoa-San Sebastian, Kutxa), and the “Fundación Sandra Ibarra de Solidaridad frente al Cáncer”. AC was supported by FIS (PI07/0057) and MICINN (PLE2009-119). SCLL was funded by a JAEdoc Fellowship (CSIC)/FSE. MMSF and ABG are funded by fellowships from the Junta de Castilla y Leon. JHM was supported by the National Institutes of Health, a National Cancer Institute grant (R01 CA116481), and the Low-Dose Scientific Focus Area, Office of Biological & Environmental Research, US Department of Energy (DE-AC02-05CH11231).Peer Reviewe

Aplicación y comparación de métodos univariados para evaluar la estabilidad en maíces del valle Tolucaatlacomulco, México

El objetivo de este estudio fue aplicar 6 métodos univariados para evaluar la estabilidad de 25 genotipos de maíz (Zea mays L.) de los Valles Altos de México. Los genotipos fueron evaluados en 4 ambientes bajo un diseño de bloques completos al azar, con 4 repeticiones por ambiente. Con los datos del rendimiento de grano (RG) se practicó un análisis de varianza combinado. Los índices de estabilidad calculados fueron la desviación estándar (Si) y el coeficiente de variación (CVi) de Francis y Kannenberg, los parámetros de estabilidad (bi y S2di ) de Eberhart y Russell, la ecovalencia (Wi) de Wricke, la varianza de estabilidad ( i 2) de Shukla, los índices no parámetricos (Si (1) y Si (2)) de Huehn, y la medida de superioridad de un cultivar (Pi) de Lin y Binns. Los resultados mostraron que Chalqueño, ETA 13, H-40, San Lucas y VS-46E tuvieron los índices de estabilidad más pequeños. La metodología del biplot identificó a estos 5 genotipos, así como a Ixtlahuaca y HIT-3 como variedades estables y de alto rendimiento (de 5,92 a 7,91 t.ha-1). La metodología del biplot indicó que RG, bi, Pi y CVi, estuvieron relacionados, pero tuvieron poca o ninguna relación con Si, Wi , i 2, Si (1), Si (2) y S2di. Chalqueño y ETA 13 pertenecen a la raza Chalqueño, Ixtlahuaca y San Lucas fueron identificadas en la raza Cónico, mientras que H-40, HIT-3 y VS-46E tienen germoplasma de otras razas, por lo que éstos podrían emplearse en un programa de mejoramiento para incrementar el rendimiento de grano y la estabilidad de los maíces de esta región de México

Strategy for the identification of the tumor intrinsic QTL determining the response to treatment of ERBB2 breast cancer

Resumen del póster presentado al VII Simposium Bases Biológicas del Cáncer y Terapias Personalizadas, celebrado en el Centro de Investigación del Cáncer (CIC-IBMCC) del 21 al 22 de mayo de 2015.-- et al.Este póster ha ganado el 1er premio en el Concurso de Pósters de Oncología Básica y Traslacional en Oncología para Jóvenes Investigadores, celebrado durante el VII Simposium Bases Biológicas del Cáncer y Terapias Personalizadas.An essential aspect of breast cancer is its different evolution among patients with the same histopathological disease. Moreover, cancer is a tissue growing in the context of a complex organism, thus it can be identified two main sources of variability responsible for the disease behavior: intrinsic and extrinsic factors which act, respectively, mainly inside the tumor cells and outside them at local or systemic levels. Our aim is to identify intrinsic factors to the tumor cells responsible for the different responses of breast cancer to chemotherapy with Doxorubicin and Docetaxel. For this purpose, we collected tumors developed in a cohort of genetically heterogeneous mice from a backcross between a resistant strain to breast cancer (C57BL/6) and a susceptible one (FVB) which overexpress the cNeu/ErbB2 protooncogene controlled by the MMTV promoter. The backcross mice were genotyped by SNP analysis. To identify tumor intrinsic factors controlling the response to chemotherapy, we transplanted 125 tumors collected from the backcross mice into singenic F1-C57/FVB mice to remove variability coming from the host compartments. Each tumor was transplanted into two F1 recipient mice; each one was treated with Doxorubicin or Docetaxel, and we studied tumor response to treatment. Linkage analysis permits us to identify QTL (Quantitative Trait Loci) controlling susceptibility to mammary cancer and evolution of the disease in the backcross population, and the specific intrinsic QTL associated with different chemotherapy responses in the F1 mice. Moreover, we are studying molecular and signalling pathways that control chemotherapy responses and the QTL associated with them. The identification of breast cancer susceptibility genes and their pathways associated with different response to chemotherapy will be important for the prediction of human breast cancer evolution during therapy, and to learn about the mechanisms involved in resistance to chemotherapy, thus it would help to develop new preventive and therapeutic strategies.Peer Reviewe

The biological age linked to oxidative stress modifies breast cancer aggressiveness

The incidence of breast cancer increases with age until menopause, and breast cancer is more aggressive in younger women. The existence of epidemiological links between breast cancer and aging indicates that both processes share some common mechanisms of development. Oxidative stress is associated with both cancer susceptibility and aging. Here we observed that ERBB2-positive breast cancer, which developed in genetically heterogeneous ERBB2-positive transgenic mice generated by a backcross, is more aggressive in chronologically younger than in older mice (differentiated by the median survival of the cohort that was 79 weeks), similar to what occurs in humans. In this cohort, we estimated the oxidative biological age using a mathematical model that integrated several subphenotypes directly or indirectly related to oxidative stress. The model selected the serum levels of HDL-cholesterol and magnesium and total AKT1 and glutathione concentrations in the liver. The grade of aging was calculated as the difference between the predicted biological age and the chronological age. This comparison permitted the identification of biologically younger and older mice compared with their chronological age. Interestingly, biologically older mice developed more aggressive breast cancer than the biologically younger mice. Genomic regions on chromosomes 2 and 15 linked to the grade of oxidative aging were identified. The levels of expression of Zbp1 located on chromosome 2, a gene related to necroptosis and inflammation, positively correlated with the grade of aging and tumour aggressiveness. Moreover, the pattern of gene expression of genes linked to the inflammation and the response to infection pathways was enriched in the livers of biologically old mice. This study shows part of the complex interactions between breast cancer and aging.JPL was partially supported by FEDER and the MICINN (SAF2014-56989-R and SAF2017-88854R), the Instituto de Salud Carlos III (PIE14/00066), >Proyectos Integrados IBSAL 2015> (IBY15/00003), the Sandra Ibarra Foundation >de Solidaridad Frente al Cáncer> Foundation and >We can be heroes> Foundation. JHM was supported by the National Institutes of Health, a National Cancer Institute grant (R01 CA116481), and the Low-Dose Scientific Focus Area, Office of Biological & Environmental Research, US Department of Energy (DE-AC02-05CH11231).Peer Reviewe

Unraveling heterogeneous susceptibility and the evolution of breast cancer using a systems biology approach

This is an Open Access article distributed under the terms of the Creative Commons Attribution License.-- et al.[Background]: An essential question in cancer is why individuals with the same disease have different clinical outcomes. Progress toward a more personalized medicine in cancer patients requires taking into account the underlying heterogeneity at different molecular levels. [Results]: Here, we present a model in which there are complex interactions at different cellular and systemic levels that account for the heterogeneity of susceptibility to and evolution of ERBB2-positive breast cancers. Our model is based on our analyses of a cohort of mice that are characterized by heterogeneous susceptibility to ERBB2-positive breast cancers. Our analysis reveals that there are similarities between ERBB2 tumors in humans and those of backcross mice at clinical, genomic, expression, and signaling levels. We also show that mice that have tumors with intrinsically high levels of active AKT and ERK are more resistant to tumor metastasis. Our findings suggest for the first time that a site-specific phosphorylation at the serine 473 residue of AKT1 modifies the capacity for tumors to disseminate. Finally, we present two predictive models that can explain the heterogeneous behavior of the disease in the mouse population when we consider simultaneously certain genetic markers, liver cell signaling and serum biomarkers that are identified before the onset of the disease. [Conclusions]: Considering simultaneously tumor pathophenotypes and several molecular levels, we show the heterogeneous behavior of ERBB2-positive breast cancer in terms of disease progression. This and similar studies should help to better understand disease variability in patient populations.JPL was partially supported by FEDER and MICINN (PLE2009-119), FIS (PI07/0057, PI10/00328, PIE14/00066), the Junta de Castilla y León (SAN673/SA26/08; SAN126/SA66/09, SA078A09, CSI034U13), the “Fundación Eugenio Rodríguez Pascual”, the Fundación Inbiomed (Instituto Oncológico Obra Social de la Caja Guipozcoa-San Sebastian, Kutxa), and the “Fundación Sandra Ibarra de Solidaridad frente al Cáncer”. AC was supported by MICINN (PLE2009-119). SCLL is funded by a JAEdoc Fellowship (CSIC)/FSE. MMSF and ABG are funded by fellowships from the Junta de Castilla y Leon. WR was supported by a Forschungsstipendium of the Deutsche Forschungsgemeinschaft (DFG) [RE 3108/1-1]. TN, BPB and DYL acknowledge support from the US Department of Energy Low-Dose SFA Program at Berkeley Lab [DE-AC02-05CH11231], the National Institutes of Health [RC1NS069177] and the California Breast Cancer Research Program [15IB-0063]. JHM was supported by the National Institutes of Health, a National Cancer Institute grant (R01 CA116481), and the Low-Dose Scientific Focus Area, Office of Biological and Environmental Research, US Department of Energy (DE-AC02-05CH11231).Peer Reviewe

Breast cancer phenotypic variability is affected by the biological age

Resumen del póster presentado al XXXIX Congreso de la Sociedad Española de Bioquímica y Biología Molecular, celebrado en Salamanca del 5 al 8 de septiembre de 2016.Breast cancer incidence rates considerably increase with age and young age at diagnosis correlates with worse prognosis due to a more aggressive breast cancer behaviour. Biological age estimates the functional status of individuals comparing to other individuals of the same chronological age. We defined the biological age integrating phenotypes of oxidative stress, and calculated Δ biological age as the difference between chronological and predicted (biological) age. Mice with predicted ages older than chronological age, were considered biologically older; and mice with predicted ages younger than chronological age, were considered biologically younger. Our main goals were (i) define biological age using processes that are common to cancer and ageing, such as the oxidative stress, and (ii) analyze breast cancer phenotypic variability according to the biological age. We generated a cohort of mice with different susceptibility and evolution to ERBB2-induced breast cancer, using a backcross strategy, and dissected the disease into different pathophenotypes. We also measured intermediate phenotypes of oxidative stress. Linkage analysis was carried out to identify quantitative trait loci (QTL) associated with these phenotypes, and used multivariate models to define biological age. We identified that biologically older mice developed more aggressive disease than biologically younger mice. We also identified QTL simultaneously associated with Δ biological age and tumor pathophenotypes. We identified several genetic and molecular markers that define biological age and observed that ERBB2 breast cancer phenotypic variability is affected by the biological age.Peer reviewe