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Implementing a Black feminist perspective, this ethnographic research examines the seemingly contradictory circumstances in which respectable heterosexual Afrodescendant Creole men of Bluefields are involved in familial relationships based on Christian love and simultaneously engage in intimate partner violence and child sexual abuse. The findings reveal that Creoles’ gender politics are rooted in heteropatriarchal ideology, strongly influenced by Christian respectability. The construction of Creole manhood is based on a complex process characterized by inculcation, emulation, and socialization regulated under strict childrearing practices controlled by men. Regarding their self-perception, within an antiblack multicultural patriarchal context, Creole men define themselves are exceptional benevolent patriarchs. Their perspectives of Creole men’s enactment of intimate partner violence and child sexual abuse connote denial, evasion, minimization, diffusion, and rationalization. Nevertheless, a close examination unveils that Creole men abuse power and enact various forms of gender violence and that they recur to dissemblance to shield the negative aspect of their behavior. Hence, victim-blaming and the belief that perpetrators suffer from mental illness are the primary rationales for explaining perpetrators’ behavior. Nevertheless, it is demonstrated these rationales are rooted in patriarchal ideology. Moreover, patriarchy establishes men to exert power and control over women and children and enables the coexistence of love and gender violence. Notably, Creole men reject all forms of feminist stances and yearn for women to remain submissive; this posture reveals patriarchal nostalgia in the era of women’s liberation. Simultaneously, they advocate for men to practice benevolent patriarchy. To end child sexual abuse, men suggest that both parents and children are to be trained in how to identify sexual predators and avoid them. Lastly, the study reveals that education on gender equality, combined with women’s financial emancipation, is critical to disrupting patriarchy. It also suggests the need to demystify the belief related to benevolent patriarchy, gender complementarity, and love.

Este manual de sustancias químicas es elaborado con el fin de informar y evitar que ocurran accidentes laborales en el área de laboratorio , se da a conocer los diferentes tipos de sustancias peligrosas para la salud de las personas, su reacción e inhalación. INTRODUCCIÓN.............................................................................................................................8 2. OBJETIVOS..................................................................................................................................9 2.2 OBJETIVO GENERAL ................................................................................................................................9 2.3 OBJETIVO ESPECÍFICOS...........................................................................................................................9 3. MARCO REFERENCIAL ......................................................................................................... 10 3.1 MARCO LEGAL...................................................................................................................................10 3.1 .1 ley 9 de 1979 congreso de la república .............................................................................................10 3.1.2 Resolución 2400 de 1979 ministerio de trabajo y seguridad social ....................................................10 3.2 MARCO CONCEPTUAL.............................................................................................................................11 3.2.1 Agentes de riesgos físicos: se consideran agentes de riesgos físicos aquellos que son de factor ambiental es decir que están en la naturaleza. Estos provocan daños y afectaciones a la salud por la intensidad y la concentración que tienen. (SURATEP S.A., 1997)................................................................11 3.2.2 Agentes de riesgos biológicos: son microorganismos que están en el ambiente y que pueden ocasionar enfermedades en las personas cuando tienen contacto con estos. (DISPOSICIONES GENERALES, 1997)..........................................................................................................................................................11 3.2.3 Agentes de riesgos químicos: son todas aquellas sustancias químicas orgánicas que pueden ser manipuladas, almacenadas o transportadas por vías respiratorias y que pueden causar daños en la salud de las personas. Estas sustancias entran en contacto con nuestro cuerpo por medio de la ingestión, inhalación, o absorción. (SURATEP S.A., 1997) ..........................................................................................11 3.2.4 Accidente químico: es un entorno peligroso para la salud humana o el medio ambiente, se da por medio de sucesos como incendios, explosiones, fugas o liberaciones de sustancias toxicas que pueden provocar enfermedad, lesión. Invalidez o muerte en las personas. (SURATEP S.A., 1997)..........................11 3.2.5 Emergencia: se considera una situación la cual no está planeada sino que sucede y se da por un evento no deseado (SURATEP S.A., 1997)..................................................................................................11 3.2.7 Residuo peligroso: se considera aquellos tipos de residuos que pueden ser riesgosos para la salud de las personas y del medio ambiente. (SANCHEZ, 2007)...............................................................................11 4. METODOLOGÍA...................................................................................................................... 20 4.1 ESCENARIO DE ESTUDIO.......................................................................................................................20 4.2 TIPO DE ESTUDIO..................................................................................................................................21 4.3 POBLACIÓN, TIPO DE MUESTREO Y CÁLCULO DEL TAMAÑO DE LA MUESTRA ......................................25 4.4 MÉTODOS E INSTRUMENTOS DE RECOLECCIÓN DE DATOS ..................................................................25 4.5 PROCEDIMIENTO PARA LA RECOLECCIÓN DE INFORMACIÓN...............................................................27 4.6 PLAN PARA LA TABULACIÓN Y ANÁLISIS DE DATOS..............................................................................28 5. RESULTADOS.......................................................................................................................... 29 ETIQUETA...................................................................................................................................................29 PRODUCTOS EXPLOSIVOS ..........................................................................................................................31 6. CONCLUSION........................................................................................................................... 42 BIBLIOGRAFÍA............................................................................................................................ 43 8. ANEXOS..................................................................................................................................... 45 Pregrado Administrador(a) en Salud Práctica Empresarial

BackgroundAround 15–20% of primary breast cancers are characterized by HER2 protein overexpression and/or HER2 gene amplification. Despite the successful development of anti-HER2 drugs, intrinsic and acquired resistance represents a major hurdle. This study was performed to analyze the RANK pathway contribution in HER2-positive breast cancer and anti-HER2 therapy resistance.MethodsRANK and RANKL protein expression was assessed in samples from HER2-positive breast cancer patients resistant to anti-HER2 therapy and treatment-naive patients. RANK and RANKL gene expression was analyzed in paired samples from patients treated with neoadjuvant dual HER2-blockade (lapatinib and trastuzumab) from the SOLTI-1114 PAMELA trial. Additionally, HER2-positive breast cancer cell lines were used to modulate RANK expression and analyze in vitro the contribution of RANK signaling to anti-HER2 resistance and downstream signaling.ResultsRANK and RANKL proteins are more frequently detected in HER2-positive tumors that have acquired resistance to anti-HER2 therapies than in treatment-naive ones. RANK (but not RANKL) gene expression increased after dual anti-HER2 neoadjuvant therapy in the cohort from the SOLTI-1114 PAMELA trial. Results in HER2-positive breast cancer cell lines recapitulate the clinical observations, with increased RANK expression observed after short-term treatment with the HER2 inhibitor lapatinib or dual anti-HER2 therapy and in lapatinib-resistant cells. After RANKL stimulation, lapatinib-resistant cells show increased NF-κB activation compared to their sensitive counterparts, confirming the enhanced functionality of the RANK pathway in anti-HER2-resistant breast cancer. Overactivation of the RANK signaling pathway enhances ERK and NF-κB signaling and increases lapatinib resistance in different HER2-positive breast cancer cell lines, whereas RANK loss sensitizes lapatinib-resistant cells to the drug. Our results indicate that ErbB signaling is required for RANK/RANKL-driven activation of ERK in several HER2-positive cell lines. In contrast, lapatinib is not able to counteract the NF-κB activation elicited after RANKL treatment in RANK-overexpressing cells. Finally, we show that RANK binds to HER2 in breast cancer cells and that enhanced RANK pathway activation alters HER2 phosphorylation status.ConclusionsOur data support a physical and functional link between RANK and HER2 signaling in breast cancer and demonstrate that increased RANK signaling may contribute to the development of lapatinib resistance through NF-κB activation. Whether HER2-positive breast cancer patients with tumoral RANK expression might benefit from dual HER2 and RANK inhibition therapy remains to be elucidated.

Additional file 6: Figure S6. A. Relative number of living (relative survival) SKBR3 RANK cells stimulated with RANKL in the presence or absence of denosumab (DNS) and incubated for 4 days with the indicated concentrations of lapatinib. Cells were seeded in growth media with/without denosumab (1 μg/ml), lapatinib was added after 24 h stimulation with 100 ng/ml RANKL, and cell viability was analyzed with CCK8 as detailed in methods. Determinations were done in triplicates, mean values from n ≥ 2 independent experiments and SD are depicted. B. Western blot analyses of total levels of p65, ERK1/2 and HER2 in whole lysates from SKBR3, BT474 and HCC1954 cells stably transduced with control (empty) or RANK overexpressing (RANK) vectors as depicted in Fig. 5d. Before collecting the cells, they were cultured in media without FBS for 12 h, pretreated with/without lapatinib for 2 h followed by 10 min stimulation with RANKL. Tubulin was used as a loading control. C. Table depicting the relative phospho-protein levels of the indicated proteins from the western blots shown in Fig. 5d and Fig. S6B determined by densitometry analyses with Image J.

Additional file 3: Figure S3. RANK and RANKL expression in breast cancer samples from the PAMELA clinical trial. A. Expression of RANK and RANKL across the intrinsic molecular subtypes from the PAMELA study. P values were calculated by comparing mean values across all groups. B. Scatter plots of RANK and RANKL expression versus ERBB2 expression for baseline samples in the PAMELA study. Solid line in each figure represents the regression line. Pearson correlation coefficient (r) with significance (p value) is presented in each figure. C. Pearson correlation between single genes and gene expression signatures evaluated in baseline samples from the PAMELA study.

Additional file 5: Figure S5. A. Relative number of living (relative survival) SKBR3 and SKLR control cells incubated for 4 days with the indicated concentrations of lapatinib and stimulated with RANKL. Cells were seeded in growth media, 100 ng/ml RANKL were added 24h after seeding, lapatinib was added 24 h later and cells were analyzed with CCK8 as detailed in methods. Determinations were done in triplicates, mean values are depicted from n = 5 independent experiments and SD and p-value (**) calculated by one-way ANOVA is depicted (p ≤ 0.05 for SKBR3 vs SKLR and SKLR +RANKL, SKBR3 +RANKL vs SKLR and SKLR +RANKL; n.s. for SKBR3 vs SKBR3 +RANKL and SKLR vs SKLR +RANKL). Significance of relative survival was calculated for each concentration using two-tailed p values for one sample t test. RANKL significantly increased survival of SKLR cells at 0.018 μM of lapatinib (p = 0.019). B. Western blot showing the total levels of IκB, p65, ERK1/2, AKT and HER2 in SKBR3 control, SKLR control and SKLR sh#3 cells treated with RANKL or lapatinib as depicted in Fig. 4c. Cells were serum starved for 12 h and then treated with lapatinib (2 h) or RANKL (10 min) before processing them. Tubulin was used as a loading control. C. Table depicting the relative phospho-protein levels of the indicated proteins from the western blots shown in Fig. 4c and Fig. S5B determined by densitometry analyses with Image J.

Additional file 7: Figure S7. Western blot analyses of HER2 (p-HER2, p-ERK1/2) and NF-κB (p-p65) pathway activation in SKBR3, BT474 and HCC1954 cells stably transduced with empty or RANK overexpressing (RANK) vectors. Cells were cultured in media without FBS for 12 h, followed by stimulation with EGF (100 ng/ml) (upper panels) or heregulin (HRG 10 ng/ml) (lower panels) for the indicated times. Tubulin was used as a loading control.

Background: A quadrivalent split-virion inactivated influenza vaccine (VaxigripTetre (TM), Sanofi Pasteur; IIV4) containing two A strains (H1N1 and H3N2) and B strains from both lineages (Victoria and Yamagata) was approved in Europe in 2016 for individuals aged >= 3 years. This study examined the efficacy and safety of IIV4 in children aged 6-35 months. Methods: This was a phase III randomised controlled trial conducted in Latin America, Asia, Africa, and Europe during the Northern Hemisphere 2014/2015 and 2015/2016 and Southern Hemisphere 2014 and 2015 influenza seasons. Healthy children aged 6-35 months not previously vaccinated against influenza were randomised to receive two full doses 28 days apart of IIV4, placebo, the licensed trivalent splitvirion inactivated vaccine (IIV3), an investigational IIV3 containing a B strain from the alternate lineage. The primary objective was to demonstrate efficacy against influenza illness caused by any strain or vaccine-similar strains. Results: The study enrolled 5806 participants. Efficacy, assessed in 4980 participants completing the study according to protocol, was demonstrated for IIV4. Vaccine efficacy was 50.98% (97% CI, 37.36-61.86%) against influenza caused by any A or B type and 68.40% (97% CI, 47.07-81.92%) against influenza caused by vaccine-like strains. Safety profiles were similar for IIV4, placebo, and the IIV3s, although injection-site reactions were slightly more frequent for IIV4 than placebo. Conclusions: IIV4 was safe and effective for protecting children aged 6-35 months against influenza illness caused by vaccine-similar or any circulating strains. (C) 2018 The Authors. Published by Elsevier Ltd.

The European Network for Breast Development and Cancer (ENBDC), a worldwide network (http://www.enbdc.org/), celebrated its tenth anniversary with a fantastic meeting last March 15-17, 2018 in Weggis with 76 attendees.

Breast cancer is the most common female cancer, affecting approximately one in eight women during their life-time. Besides environmental triggers and hormones, inherited mutations in the breast cancer 1 (BRCA1) or BRCA2 genes markedly increase the risk for the development of breast cancer. Here, using two different mouse models, we show that genetic inactivation of the key osteoclast differentiation factor RANK in the mammary epithelium markedly delayed onset, reduced incidence, and attenuated progression of Brca1;p53 mutation-driven mammary cancer. Long-term pharmacological inhibition of the RANK ligand RANKL in mice abolished the occurrence of Brca1 mutation-driven pre-neoplastic lesions. Mechanistically, genetic inactivation of Rank or RANKL/RANK blockade impaired proliferation and expansion of both murine Brca1;p53 mutant mammary stem cells and mammary progenitors from human BRCA1 mutation carriers. In addition, genome variations within the RANK locus were significantly associated with risk of developing breast cancer in women with BRCA1 mutations. Thus, RANKL/RANK control progenitor cell expansion and tumorigenesis in inherited breast cancer. These results present a viable strategy for the possible prevention of breast cancer in BRCA1 mutant patients.

The CNA-IC50 correlation differences are not observed when a random, null network model that preserves degree distribution and connectedness is analyzed. (PDF 148 kb)

Biological coherence of the integrated dataset. Graph showing the distributions of expression correlations for random gene pairs or genes that encode for physically interacting proteins. The empirical p value is shown. (PDF 418 kb)

Biological coherence of the integrated dataset. Graph showing the distributions of expression correlations for random gene pairs or genes that encode for physically interacting proteins. The empirical p value is shown. (PDF 418 kb)

The CNA-IC50 correlation differences are not observed when a random, null network model that preserves degree distribution and connectedness is analyzed. (PDF 148 kb)

Corroboration of CNA-IC50 correlations using a high-quality interactome dataset. The PCC distribution difference between drugs that target CTKs or STKs is maintained when the network is based on a high-quality dataset derived from the combination of reliable literature-curated binary interactions and well-verified high-throughput yeast two-hybrid interactions. (PDF 162 kb)

// Antonio Martinez-Aranda 1, 2 , Vanessa Hernandez 1 , Emre Guney 3 , Laia Muixi 1 , Ruben Foj 1, 2 , Nuria Baixeras 4 , Daniel Cuadras 5 , Victor Moreno 5 , Ander Urruticoechea 6 , Miguel Gil 7 , Baldo Oliva 3 , Ferran Moreno 8 , Eva Gonzalez-Suarez 9 , Noemi Vidal 4 , Xavier Andreu 10 , Miquel A. Segui 11 , Rosa Ballester 12 , Eva Castella 13 , Angels Sierra 1, 14 1 Biological Clues of the Invasive and Metastatic Phenotype Group, Molecular Oncology Department, Bellvitge Biomedical Research Institute (IDIBELL), 08907 L’Hospitalet de Llobregat, Barcelona, Spain 2 Universitat Autonoma de Barcelona (UAB), Biochemistry and Molecular Biology Department, Faculty of Biosciences, Campus Bellaterra, Edifici C, Cerdanyola del Valles, 08193 Barcelona, Spain 3 Structural Bioinformatics Laboratory, Experimental Sciences Department, Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine, 08003 Barcelona, Spain 4 Servei d’Anatomia Patologica, Hospital Universitari de Bellvitge, 08907 L’Hospitalet de Llobregat, Barcelona, Spain 5 Biomarkers and Susceptibility Unit, Institut Catala d’Oncologia - IDIBELL, Hospital Duran i Reynals, 08907 L’Hospitalet de Llobregat, Barcelona, Spain 6 Breast Cancer Unit and Neuroncology Unit, Institut Catala d’Oncologia - IDIBELL, Hospital Duran i Reynals, 08907 L’Hospitalet de Llobregat, Barcelona, Spain 7 Oncology Service, Institut Catala d’Oncologia - IDIBELL, Hospital Duran i Reynals, 08907 L’Hospitalet de Llobregat, Barcelona, Spain 8 Radiation Oncology Service, Institut Catala d’Oncologia - IDIBELL, Hospital Duran i Reynals, 08907 L’Hospitalet de Llobregat, Barcelona, Spain 9 Transformation and Metastasis Group, Cancer Epigenetics and Biology Department, IDIBELL, 08907 L’Hospitalet de Llobregat, Barcelona, Spain 10 Pathology Service, Corporacio Sanitaria Parc Tauli, 08208 Sabadell, Spain 11 Oncology Service, Corporacio Sanitaria Parc Tauli, 08208 Sabadell, Spain 12 Radiation Oncology Service, Institut Catala d’Oncologia, Hospital Universitari Germans Trias i Pujol, 08916 Badalona, Spain 13 Pathology Service, Institut Catala d’Oncologia, Hospital Universitari Germans Trias i Pujol, 08916 Badalona, Spain 14 Molecular and Translational Oncology Laboratory, Biomedical Research Center CELLEX-CRBC Institut d’Investigacions Biomediques August Pi i Sunyer-IDIBAPS 08036 Barcelona, Spain Correspondence to: Angels Sierra, e-mail: asierrajim@gmail.com , masierra@clinic.ub.es Keywords: biomarkers, brain metastasis, breast cancer, FN14, GRP94 Received: April 17, 2015 Accepted: October 09, 2015 Published: October 19, 2015 ABSTRACT Brain metastasis is a devastating problem in patients with breast, lung and melanoma tumors. GRP94 and FN14 are predictive biomarkers over-expressed in primary breast carcinomas that metastasized in brain. To further validate these brain metastasis biomarkers, we performed a multicenter study including 318 patients with breast carcinomas. Among these patients, there were 138 patients with metastasis, of whom 84 had brain metastasis. The likelihood of developing brain metastasis increased by 5.24-fold (95%CI 2.83–9.71) and 2.55- (95%CI 1.52–4.3) in the presence of FN14 and GRP94, respectively. Moreover, FN14 was more sensitive than ErbB2 (38.27 vs . 24.68) with similar specificity (89.43 vs . 89.55) to predict brain metastasis and had identical prognostic value than triple negative patients ( p < 0.0001). Furthermore, we used GRP94 and FN14 pathways and GUILD, a network-based disease-gene prioritization program, to pinpoint the genes likely to be therapeutic targets, which resulted in FN14 as the main modulator and thalidomide as the best scored drug. The treatment of mice with brain metastasis improves survival decreasing reactive astrocytes and angiogenesis, and down-regulate FN14 and its ligand TWEAK. In conclusion our results indicate that FN14 and GRP94 are prediction/prognosis markers which open up new possibilities for preventing/treating brain metastasis.

Receptor Activator of NF-kappa B (RANK) pathway controls mammary gland development in mice but its role in mammary stem cell fate remains undefined. We show that constitutive RANK signaling expands luminal and basal mammary compartments including mammary stem and luminal progenitor cell pools and interferes with the generation of CD61+ and Sca1+ luminal cells and Elf5 expression. Impaired mammary cell commitment upon RANK overexpression leads to the accumulation of progenitors including K14+K8+ bipotent cells and the formation of heterogeneous tumors containing hyperplastic basal, luminal, and progenitor cells. RANK expression increases in wild-type mammary epithelia with age and parity, and spontaneous preneoplastic lesions express RANK and accumulate K14+K8+ cells. In human breast tumors, high RANK expression levels are also associated with altered mammary differentiation. These results suggest that increased RANK signaling interferes with mammary cell commitment, contributing to breast carcinogenesis.

Recently, Fong et al reported the antitumor activity of the poly(adenosine diphosphate)-ribose polymerase (PARP) inhibitor olaparib (AZD2281; KU-0059436) in patients with BRCA1/BRCA2 germline mutated ovarian cancer. Female BRCA1 and BRCA2 mutation carriers have a significantly elevated lifetime risk of breast and ovarian cancer. BRCA1 and BRCA2 proteins play major roles in DNA double-strand break repair through homologous recombination, and inhibition of DNA single-strand break repair leads to the accumulation of double-strand breaks. These potentially lethal events in homologous recombination-deficient cells could be exploited for therapeutic purposes. The PARP-1 protein is essential for single-strand break repair, and inhibition of PARP leads to persistence of DNA lesions normally repaired by homologous recombination.

Additional file 4: Figure S4. RANK but not RANKL expression increased after dual anti-HER2 therapy in HR+ and HR- patient samples (n = 151) from the PAMELA trial. A and B. Ladder plots (left panels) showing RANK and RANKL gene expression in PAMELA HER2-positive HR+ (A) and HR- (B) tumors before (baseline) and after (surgery) dual anti-HER2 treatment. An increase in gene expression is represented in red and a decrease in blue. Each line represents a tumor sample from one patient. P values in A were calculated by comparing mean values between both groups and in B were determined by paired two-tailed t-tests. Density plots (right panels) showing RANK and RANKL gene expression in PAMELA HER2-positive HR+ (A) and HR- (B) tumors before (baseline) and after (surgery) treatment.

Additional file 2: Figure S2. RANK and RANKL staining in TMAs. A. Pictures of RANK and RANKL protein expression analyzed by IHC in the TMA cores from the treatment-naïve cohort. B. Pictures of RANK and RANKL protein expression analyzed by IHC in the TMA cores from the anti-HER2 resistant cohort.

Additional file 2: Figure S2. RANK and RANKL staining in TMAs. A. Pictures of RANK and RANKL protein expression analyzed by IHC in the TMA cores from the treatment-naïve cohort. B. Pictures of RANK and RANKL protein expression analyzed by IHC in the TMA cores from the anti-HER2 resistant cohort.

BackgroundAround 15–20% of primary breast cancers are characterized by HER2 protein overexpression and/orHER2gene amplification. Despite the successful development of anti-HER2 drugs, intrinsic and acquired resistance represents a major hurdle. This study was performed to analyze the RANK pathway contribution in HER2-positive breast cancer and anti-HER2 therapy resistance.MethodsRANK and RANKL protein expression was assessed in samples from HER2-positive breast cancer patients resistant to anti-HER2 therapy and treatment-naive patients. RANK and RANKL gene expression was analyzed in paired samples from patients treated with neoadjuvant dual HER2-blockade (lapatinib and trastuzumab) from the SOLTI-1114 PAMELA trial. Additionally, HER2-positive breast cancer cell lines were used to modulate RANK expression and analyze in vitro the contribution of RANK signaling to anti-HER2 resistance and downstream signaling.ResultsRANK and RANKL proteins are more frequently detected in HER2-positive tumors that have acquired resistance to anti-HER2 therapies than in treatment-naive ones.RANK(but notRANKL) gene expression increased after dual anti-HER2 neoadjuvant therapy in the cohort from the SOLTI-1114 PAMELA trial. Results in HER2-positive breast cancer cell lines recapitulate the clinical observations, with increased RANK expression observed after short-term treatment with the HER2 inhibitor lapatinib or dual anti-HER2 therapy and in lapatinib-resistant cells. After RANKL stimulation, lapatinib-resistant cells show increased NF-κB activation compared to their sensitive counterparts, confirming the enhanced functionality of the RANK pathway in anti-HER2-resistant breast cancer. Overactivation of the RANK signaling pathway enhances ERK and NF-κB signaling and increases lapatinib resistance in different HER2-positive breast cancer cell lines, whereas RANK loss sensitizes lapatinib-resistant cells to the drug. Our results indicate that ErbB signaling is required for RANK/RANKL-driven activation of ERK in several HER2-positive cell lines. In contrast, lapatinib is not able to counteract the NF-κB activation elicited after RANKL treatment in RANK-overexpressing cells. Finally, we show that RANK binds to HER2 in breast cancer cells and that enhanced RANK pathway activation alters HER2 phosphorylation status.ConclusionsOur data support a physical and functional link between RANK and HER2 signaling in breast cancer and demonstrate that increased RANK signaling may contribute to the development of lapatinib resistance through NF-κB activation. Whether HER2-positive breast cancer patients with tumoral RANK expression might benefit from dual HER2 and RANK inhibition therapy remains to be elucidated.

Additional file 4: Figure S4. RANK but not RANKL expression increased after dual anti-HER2 therapy in HR+ and HR- patient samples (n = 151) from the PAMELA trial. A and B. Ladder plots (left panels) showing RANK and RANKL gene expression in PAMELA HER2-positive HR+ (A) and HR- (B) tumors before (baseline) and after (surgery) dual anti-HER2 treatment. An increase in gene expression is represented in red and a decrease in blue. Each line represents a tumor sample from one patient. P values in A were calculated by comparing mean values between both groups and in B were determined by paired two-tailed t-tests. Density plots (right panels) showing RANK and RANKL gene expression in PAMELA HER2-positive HR+ (A) and HR- (B) tumors before (baseline) and after (surgery) treatment.

Additional file 1: Figure S1. TMA H-scores and controls. A. RANK and RANKL H-scores in HER2-positive breast cancer samples, treatment-naïve (left panels) or anti-HER2-resistant (right panels). In treatment-naïve TMAs, each number represents a “core” from a single patient. In anti-HER2-resistant TMAs, scored independent tumor cores are numbered for each patient (after the symbol #). B. Representative pictures of human breast tumors from patient-derived xenografts used as positive and negative controls for RANK and RANKL IHC.

Additional file 1: Figure S1. TMA H-scores and controls. A. RANK and RANKL H-scores in HER2-positive breast cancer samples, treatment-naïve (left panels) or anti-HER2-resistant (right panels). In treatment-naïve TMAs, each number represents a “core” from a single patient. In anti-HER2-resistant TMAs, scored independent tumor cores are numbered for each patient (after the symbol #). B. Representative pictures of human breast tumors from patient-derived xenografts used as positive and negative controls for RANK and RANKL IHC.