Avances en el tratamiento del glioblastoma emplando el fármaco Rapamicina

Tesis doctoral inédita. Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 22-10-201

Análisis bioinformático del efecto de la inhibición genética de Ambra1 en fibroblastos embrionarios transformados

Trabajo fin de máster en Bioinformática y Biología ComputacionalTrabajos previos del grupo donde se ha desarrollado el presente TFM han mostrado la importancia de Ambra1 en cáncer. El objetivo principal de este estudio ha sido el análisis bioinformático del efecto de la inhibición genética de Ambra1 en fibroblastos embrionarios transformados. Para lograr este propósito, hemos realizado un código en R para examinar los datos de un array obtenido del análisis de fibroblastos “wild type” y fibroblastos transformados deficientes en Ambra1 (“KO”). Específicamente realizamos un análisis mediante Gene Ontology (GO), para desarrollar una representación computacional de como los genes codifican funciones biológicas a nivel molecular, celular y del sistema tisular; mediante KEGG para comprender las funciones de alto nivel y las utilidades del sistema biológico. Las funciones utilizadas fueron basicProfile, groupGO, enrichGO, enrichKEGG, enrichPathway obteniendo como resultado la confirmación de la hipótesis de partida: la existencia de una asociación entre la inactivación de Ambra1 y un aumento de la adhesión molecular, pérdida de la polaridad apical-basal en el proceso EMT, apoptosis, señalización a través de la vía de fosfatidilinositol. Además, el análisis desarrollado ha permitido identificar la existencia de una correlación entre la inhibición de Ambra1 y la activación de histonas que implica diversos procesos biológicos como ensamblaje de cromatina, ensamblaje de nucleosoma, empaquetamiento de DNA, ensamblaje/desensamblaje de cromatina, recombinación meiótica, expresión de rRNA que no ha sido descrito con anterioridad

Glioma-Parvovirus Interactions: Molecular Insights and Therapeutic Potential

This work was supported by grants from the Spanish Ministerio de Ciencia e Innovación (SAF2008-03238) and Comunidad de Madrid (S-SAL/0185/2006) to the laboratory of J.M.A.The Centro de Biología Molecular "Severo Ochoa" (CSIC-UAM) is in part supported by an institutional grant from Fundación Ramón Areces.Peer reviewe

Evolutionary origins of metabolic reprogramming in cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.This work was supported in JPL’s lab by Grant PID2020-118527RB-I00 funded by MCIN/AEI/10.13039/501100011039; Grant PDC2021-121735-I00 funded by MCIN/AEI/10.13039/501100011039 and by the “European Union Next Generation EU/PRTR.”, the Regional Government of Castile and León (CSI234P18 and CSI144P20). SCLl was the recipient of a Ramón y Cajal research contract from the Spanish Ministry of Economy and Competitiveness and was supported by grant RTI2018-094130-B-100 funded by MCIN/AEI/10.13039/501100011039 and by “ERDF A way of making Europe.” RCC and AJN are funded by fellowships from the Spanish Regional Government of Castile and León. NGS is a recipient of an FPU fellowship (MINECO/FEDER). MJPB is funded by grant PID2020-118527RB-I00 funded by MCIN/AEI/10.13039/501100011039. J.C. is partially supported by grant GRS2139/A/20 (Gerencia Regional de Salud de Castilla y León) and by the Instituto de Salud Carlos III (PI18/00587 and PI21/01207), co-financed by FEDER funds, and by the “Programa de Intensificación” of the ISCIII, grant number INT20/00074. We thank Phil Mason for English language support

From mouse to human: cellular morphometric subtype learned from mouse mammary tumors provides prognostic value in human breast cancer

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.This work was supported by the Department of Defense (DoD)BCRP: BC190820 (J-HM); and the National Cancer Institute (NCI) at the National Institutes of Health (NIH): R01CA184476 (HC). Lawrence Berkeley National Laboratory (LBNL) is a multi-program national laboratory operated by the University of California for the DOE under contract DE AC02-05CH1123

Pathophysiological Integration of Metabolic Reprogramming in Breast Cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. The triggers of these metabolic changes are located in the tumor parenchymal cells, where oncogenic mutations induce an imperative need to proliferate and cause tumor initiation and progression. Cancer cells undergo significant metabolic reorganization during disease progression that is tailored to their energy demands and fluctuating environmental conditions. Oxidative stress plays an essential role as a trigger under such conditions. These metabolic changes are the consequence of the interaction between tumor cells and stromal myofibroblasts. The metabolic changes in tumor cells include protein anabolism and the synthesis of cell membranes and nucleic acids, which all facilitate cell proliferation. They are linked to catabolism and autophagy in stromal myofibroblasts, causing the release of nutrients for the cells of the tumor parenchyma. Metabolic changes lead to an interstitium deficient in nutrients, such as glucose and amino acids, and acidification by lactic acid. Together with hypoxia, they produce functional changes in other cells of the tumor stroma, such as many immune subpopulations and endothelial cells, which lead to tumor growth. Thus, immune cells favor tissue growth through changes in immunosuppression. This review considers some of the metabolic changes described in breast cancer

Image_1_From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer.pdf [Dataset]

Supplementary Figure 1. Representative examples of 256 CMB learned from Trp53-null mouse mammary tumors. Supplementary Figure 2. Consensus clustering on the Trp53-null mouse mammary tumors with different number of clusters (K) and the corresponding Kaplan–Meier curves for tumor growth. A-B. Consensus matrix with 3 and 4 clusters, respectively; C-D Kaplan–Meier curves for 3 and 4 subtypes, respectively. Supplementary Figure 3. Representative example of CMB_13 (A), CMB_249 (D), CMB_120 (G), and CMB_105 (J), and their significant and consistent difference in relative abundance between metastasis ground truth (B, E, H, and K) and low/high metastasis risk groups (i.e., LMRG and HMRG defined by CMS-1 and CMS-2, respectively) (C, F, I, and L). Supplementary Figure 4. BRCA patient subtypes in triple-negative (TNBC) and non-triplenegative (Non-TNBC) groups. A-B. KM curves for representative CMBs show consistent and significant impact on OS in Non-TNBC and TNBC groups, respectively; C. Subtype-specific patients in TCGA-BRCA cohort form distinct clusters in patient-level cellular morphometric context space in Non-TNBC and TNBC groups, respectively; D. Subtype-specific patients in TCGA-BRCA cohort show significant difference in survival in Non-TNBC and TNBC groups, respectively. Supplementary Figure 5. A. BRCA patient heatmap with mouse CMS model on the TCGABRCA cohort; B. BRCA patient heatmap with BC-CMS model on the TCGA-BRCA cohort. C. ROC curves for the prediction of 5-,10-, and 20-year overall survival of BRCA patients using all significant prognostic factors as listed in E; D. Comparison of predictive power between BC-CMS model and mouse CMS model using bootstrapping strategy with 80% sampling rate and 1000 iterations; E. Similar to patient subtype from BC-CMS model as shown in Figure 3F, patient subtype directly predicted from the mouse CMS model is also a significant and independent prognostic factor in the TCGA-BRCA cohort. Supplementary Figure 6. BC-CMS in triple-negative (TNBC) and non-triple-negative (NonTNBC) groups in the TCGA-BRCA cohort show significant difference in tumor microenvironments.Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.Peer reviewe

Table_4_From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer.docx [Dataset]

Supplementary Table 4. Clinical characteristics of patients in TCGA-BRCA cohortMouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.Peer reviewe

The Pseudokinase TRIB3 Negatively Regulates the HER2 Receptor Pathway and Is a Biomarker of Good Prognosis in Luminal Breast Cancer

From MDPI via Jisc Publications RouterHistory: accepted 2021-10-19, pub-electronic 2021-10-22Publication status: PublishedFunder: Instituto de Salud Carlos III; Grant(s): PI18/00442Funder: European Commission; Grant(s): ITN-308 2016 721532Funder: Breast Cancer Now; Grant(s): 2012NovSP033Funder: Ministry of Economy, Industry and Competitiveness; Grant(s): RTI2018-094130-B-100Background: Tribbles pseudokinase 3 (TRIB3) has been proposed to both promote and restrict cancer generation and progression. However, the precise mechanisms that determine this dual role of TRIB3 in cancer remain to be understood. In this study we aimed to investigate the role of TRIB3 in luminal breast cancer, the most frequent subtype of this malignancy. Methods: We genetically manipulated TRIB3 expression in a panel of luminal breast cancer cell lines and analyzed its impact on cell proliferation, and the phosphorylation, levels, or subcellular localization of TRIB3 and other protein regulators of key signaling pathways in luminal breast cancer. We also analyzed TRIB3 protein expression in samples from luminal breast cancer patients and performed bioinformatic analyses in public datasets. Results: TRIB3 enhanced the proliferation and AKT phosphorylation in luminal A (HER2-) but decreased them in luminal B (HER2+) breast cancer cell lines. TRIB3 negatively regulated the stability of HER2 in luminal B breast cancer cell lines. TRIB3 expression was associated with increased disease-free survival and a better response to therapy in luminal breast cancer patients. Conclusions: Our findings support the exploration of TRIB3 as a potential biomarker and therapeutic target in luminal breast cancer

An Off-Target Nucleostemin RNAi Inhibits Growth in Human Glioblastoma-Derived Cancer Stem Cells

Glioblastomas (GBM) may contain a variable proportion of active cancer stem cells (CSCs) capable of self-renewal, of aggregating into CD133+ neurospheres, and to develop intracranial tumors that phenocopy the original ones. We hypothesized that nucleostemin may contribute to cancer stem cell biology as these cells share characteristics with normal stem cells. Here we report that nucleostemin is expressed in GBM-CSCs isolated from patient samples, and that its expression, conversely to what it has been described for ordinary stem cells, does not disappear when cells are differentiated. The significance of nucleostemin expression in CSCs was addressed by targeting the corresponding mRNA using lentivirally transduced short hairpin RNA (shRNA). In doing so, we found an off-target nucleostemin RNAi (shRNA22) that abolishes proliferation and induces apoptosis in GBM-CSCs. Furthermore, in the presence of shRNA22, GBM-CSCs failed to form neurospheres in vitro or grow on soft agar. When these cells are xenotransplanted into the brains of nude rats, tumor development is significantly delayed. Attempts were made to identify the primary target/s of shRNA22, suggesting a transcription factor involved in one of the MAP-kinases signaling-pathways or multiple targets. The use of this shRNA may contribute to develop new therapeutic approaches for this incurable type of brain tumor