Estudio del efecto protector del embarazo y del uso de análogos de la somatostatina frente al cáncer de mama

[ES] El cáncer de mama es el tumor más frecuente. Según los datos de la International Agency for Research on Cancer (IARC), en 2020 se diagnosticaron 2.3 millones de casos nuevos de cáncer de mama en todo el mundo, superando así al cáncer de pulmón como el tumor más diagnosticado. Supuso más de 680.000 muertes, siendo el quinto tipo de cáncer con mayor mortalidad, por detrás del cáncer de pulmón, cáncer colorrectal, cáncer de estómago y cáncer de hígado. En mujeres, el cáncer de mama es el tumor más frecuente y es la principal causa de muerte por cáncer. Según datos recogidos por el programa de Surveillance, Epidemiology and End Results (SEER) del National Cancer Institute (NCI) de Estados Unidos, aproximadamente, una de cada ocho mujeres tiene riesgo de desarrollar cáncer de mama a lo largo de su vida. Alrededor del 80% de todos los cánceres de mama surgen en mujeres mayores de 50 años; y la probabilidad a 10 años de desarrollar cáncer de mama invasivo aumenta de un 2% a los 40 años, a alrededor del 3% a los 50-60 años y hasta el 7% a partir los 70 años, produciendo un riesgo acumulado de por vida del 12.9%2. Las ratios de incidencia son un 88% más altas en los países desarrollados que en los países en vías de desarrollo; sin embargo, mujeres que viven en países en crecimiento tienen tasas de mortalidad un 17% más altas en comparación con las mujeres que viven en países con mayor índice de desarrollo económico. Las elevadas tasas de incidencia en los países desarrollados reflejan una mayor prevalencia de factores de riesgo asociados al cáncer de mama. Entre esos factores está un mayor envejecimiento de la población, factores reproductivos y hormonales (menarquia temprana, menopausia tardía, primer embarazo tardío, menor número de hijos, consumo de terapia hormonal en la menopausia y uso de anticonceptivos orales) y factores de riesgo relacionados con el estilo de vida (ingesta de alcohol, obesidad, sobrepeso y sedentarismo), pero también existe mayor detección mediante mamografías organizadas en programas de prevención3. Además, una prevalencia excepcionalmente elevada de mutaciones en genes de alta penetrancia, como BRCA1 y BRCA2, entre mujeres de ascendencia judía Ashkenazi, explica en parte la alta incidencia en Israel y ciertas subpoblaciones europeas4. La incidencia y la mortalidad por cáncer de mama han aumentado en las tres últimas décadas, aunque las tendencias varían según la región geográfica y el grupo de edad. En los países desarrollados, se produjo un incremento en la incidencia de cáncer de mama durante la década de los 80 y los 90, probablemente, debido al aumento en el consumo de terapia hormonal, al descenso en los ratios de fertilidad y a la incorporación de programas de detección temprana. A partir de los 2000, ese incremento se ha estabilizado e incluso ha disminuido, coincidiendo con un descenso del uso de terapia hormonal de reemplazo y una estabilización en la participación en los programas de detección precoz. Desde 2007, se observa un cambio en la tendencia con un incremento en la incidencia en Estados Unidos del 0.5% anual, e incluso algo mayor en otros países de Europa y Oceanía. Este aumento se observa concretamente en mujeres con tumores positivos para el receptor de estrógenos, lo que se puede deber al incremento en la obesidad que se está produciendo en las últimas décadas y que se relaciona directamente con el desarrollo de tumores de mama positivos para ese receptor. Por el contrario, en los países en vías de desarrollo, se está produciendo un incremento muy rápido en la incidencia de cáncer de mama, debido a la occidentalización de los estilos de vida (embarazos más tardíos, falta de actividad física y dieta deficiente), mejor registro de los casos de cáncer y mejores métodos de detección. En cuanto a la mortalidad por cáncer de mama, en los países desarrollados se ha producido un descenso en las últimas tres décadas, gracias a los avances efectivos en la detección temprana y en el tratamiento del cáncer. Sin embargo, en los países subdesarrollados y en vías de desarrollo, se está produciendo un incremento de la mortalidad por cáncer de mama, debido a una detección del cáncer más tardía y la imposibilidad de aplicar terapias eficaces. Con todo ello, las proyecciones actuales indican que para el año 2050, el número mundial de casos nuevos de cáncer de mama alcanzará los 3.2 millones anuales, y el número de muertes llegará al millón de mujeres, lo que supondrá un gran problema de salud pública. Todos estos datos ponen de manifiesto la necesidad de seguir trabajando para reducir la incidencia de la enfermedad. Por ello, es fundamental conocer los mecanismos implicados en la susceptibilidad y el desarrollo del cáncer de mama para explorar nuevas estrategias de prevención

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

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk.

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.J.P.L.’s lab is sponsored 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 (CSI144P20). J.P.L. and P.L.S. are supported by the Carlos III Health Institute (PIE14/00066). AGN laboratory and human patients’ studies are supported by an ISCIII project grant (PI18/01242). The Human Genotyping unit is a member of CeGen, PRB3, and is supported by grant PT17/0019 of the PE I + D + i 2013–2016, funded by ISCIII and ERDF. SCLl is supported by MINECO/FEDER research grants (RTI2018-094130-B-100). CH was supported by the Department of Defense (DoD) BCRP, No. BC190820; and the National Cancer Institute (NCI) at the National Institutes of Health (NIH), No. R01CA184476. Lawrence Berkeley National Laboratory (LBNL) is a multi-program national laboratory operated by the University of California for the DOE under contract DE AC02-05CH11231. The Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0023 of the PE I + D +i, 2017–2020, funded by ISCIII and FEDER. RCC is funded by fellowships from the Spanish Regional Government of Castile and León. NGS is a recipient of an FPU fellowship (MINECO/FEDER). hiPSC-CM studies were funded in part by the “la Caixa” Banking Foundation under the project code HR18-00304 and a Severo Ochoa CNIC Intramural Project (Exp. 12-2016 IGP) to J.J.S

Cutaneous Squamous Cell Carcinoma: From Biology to Therapy

Cutaneous squamous cell carcinoma (CSCC) is the second most frequent cancer in humans and its incidence continues to rise. Although CSCC usually display a benign clinical behavior, it can be both locally invasive and metastatic. The signaling pathways involved in CSCC development have given rise to targetable molecules in recent decades. In addition, the high mutational burden and increased risk of CSCC in patients under immunosuppression were part of the rationale for developing the immunotherapy for CSCC that has changed the therapeutic landscape. This review focuses on the molecular basis of CSCC and the current biology-based approaches of targeted therapies and immune checkpoint inhibitors. Another purpose of this review is to explore the landscape of drugs that may induce or contribute to the development of CSCC. Beginning with the pathogenetic basis of these drug-induced CSCCs, we move on to consider potential therapeutic opportunities for overcoming this adverse effect

MicroRNA dysregulation in cutaneous squamous cell carcinoma

© 2019 by the authors.Cutaneous squamous cell carcinoma (CSCC) is the second most frequent cancer in humans and it can be locally invasive and metastatic to distant sites. MicroRNAs (miRNAs or miRs) are endogenous, small, non-coding RNAs of 19–25 nucleotides in length, that are involved in regulating gene expression at a post-transcriptional level. MicroRNAs have been implicated in diverse biological functions and diseases. In cancer, miRNAs can proceed either as oncogenic miRNAs (onco-miRs) or as tumor suppressor miRNAs (oncosuppressor-miRs), depending on the pathway in which they are involved. Dysregulation of miRNA expression has been shown in most of the tumors evaluated. MiRNA dysregulation is known to be involved in the development of cutaneous squamous cell carcinoma (CSCC). In this review, we focus on the recent evidence about the role of miRNAs in the development of CSCC and in the prognosis of this form of skin cancer.Javier Cañueto is partially supported by the grants PI18/000587 (Instituto de Salud Carlos III cofinanciado con fondos FEDER) and GRS 1835/A/18 (Gerencia Regional de Salud de Castilla y León), and by the Programa de Intensificación de la Actividad Investigadora de la Gerencia Regional de Salud de Castilla y León (INT/M/10/19), Spai