Mireia Margeli Vila
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mireia Margeli Vila has a total of 385 co-authors. Here are the top co-authors by number of shared publications:
- Montserrat Muñoz-Mateu
Hospital Clinic de Barcelona, Barcelona, Spain
141 shared publications - Eva Ciruelos
Medical Oncology Department, Hospital 12 de Octubre, Madrid, Spain; Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), Madrid, Spain; SOLTI Cancer Research Group, Barcelona, Spain
132 shared publications - Miguel Gil-Gil
GEICAM Spanish Breast Cancer Group, Madrid, Spain
132 shared publications - Antonio Antón
Hospital Universitario Miguel Servet, Zaragoza, Spain
130 shared publications - Begoña Bermejo
Hospital Clínico Universitario de Valencia, Valencia, Spain
130 shared publications - Jesus Herranz
GEICAM, Spanish Breast Cancer Group, Madrid, Spain
130 shared publications
Latest Publications
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S2 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Distribution of clinical and pathological features in the validation cohort of Hospital Universitario Santa Lucía, Cartagena
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S2 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Distribution of clinical and pathological features in the validation cohort of Hospital Universitario Santa Lucía, Cartagena
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Table S4 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Secondary antibodies used for IHC, IF, and WB assays
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Table S4 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Secondary antibodies used for IHC, IF, and WB assays
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S1 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Integration of three scRNA-seq datasets from adult murine mammary gland
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S1 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Integration of three scRNA-seq datasets from adult murine mammary gland
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S6 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Histological distribution of SMA, TAGL and TPM2 in the validation cohorts of TNBC
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S6 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Histological distribution of SMA, TAGL and TPM2 in the validation cohorts of TNBC
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Figure 3 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Histologic expression of basal markers in breast tumor samples. A, Representative sections of tB- and nB-TNBC samples stained with the indicated BaC markers. Scale bar, 50 μm. B, Bar plots representing the percentage of cells expressing each marker in tB- and nB-TNBCs. *, P < 0.05 based on unpaired t test (Holm–Sidak method); ns, not significant. C, Left, representative images showing TAGL staining in tB- and nB-TNBC tumors. Scale bar, 200 and 50 μm in the insets.
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure 3 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Histologic expression of basal markers in breast tumor samples. A, Representative sections of tB- and nB-TNBC samples stained with the indicated BaC markers. Scale bar, 50 μm. B, Bar plots representing the percentage of cells expressing each marker in tB- and nB-TNBCs. *, P < 0.05 based on unpaired t test (Holm–Sidak method); ns, not significant. C, Left, representative images showing TAGL staining in tB- and nB-TNBC tumors. Scale bar, 200 and 50 μm in the insets.
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S9 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Correlation between ACTA2, TAGLN, and TPM2 expression and drug sensitivity of selected compounds of drug screening
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S9 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Correlation between ACTA2, TAGLN, and TPM2 expression and drug sensitivity of selected compounds of drug screening
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Figure 2 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Histologic expression of basal markers in HR-positive breast tumors at different tumor stages. A, Representative sections of HR-positive samples at different tumor stages (LGDCIS, DCIS, and IDC) stained with the indicated basal markers. Scale bar, 50 μm. B, Bar plots representing the percentage of cells expressing each marker in in HR-positive samples across the different tumor stages. Statistics calculated two-way ANOVA. *, P < 0.05; **, P < 0.005; ns, not significant.
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure 2 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Histologic expression of basal markers in HR-positive breast tumors at different tumor stages. A, Representative sections of HR-positive samples at different tumor stages (LGDCIS, DCIS, and IDC) stained with the indicated basal markers. Scale bar, 50 μm. B, Bar plots representing the percentage of cells expressing each marker in in HR-positive samples across the different tumor stages. Statistics calculated two-way ANOVA. *, P < 0.05; **, P < 0.005; ns, not significant.
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S4 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Integration of three scRNA-seq datasets from healthy human mammary gland
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S4 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Integration of three scRNA-seq datasets from healthy human mammary gland
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Figure 6 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Evaluation of dasatinib sensitivity in tB-TNBC models in vitro and in vivo. A, Dose–response curves showing cell viability of indicated cell lines treated with increasing concentrations of dasatinib for 72 hours. Solid lines represent the mean of three biological replicates performed in technical replicates normalized to untreated controls, and IC50 values are indicated. B, Representative IHC images of tB-TNBC PDXs (PDX_01 and PDX_02) showing expression of SMA, TAGL, and TPM2. Scale bars, 100 μm. C, Quantification of positive tumor cells for each tB-marker in PDX_01 and PDX_02. Data are shown as mean ± SEM of different tumor areas. D, Schematic representation of the dasatinib treatment protocol. E, Tumor growth curves of the tB-TNBC PDXs treated with vehicle (black) or dasatinib (red). Error bars, ± SEM. P values calculated by Wilcoxon test. F, Bar plot showing tumor weight at the end point. Data are shown as mean ± SEM. P values calculated by two-tailed Student t test. **, P < 0.01. D, Created in BioRender. Rodilla, V. (2026) https://BioRender.com/qpb7bux.
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure 6 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Evaluation of dasatinib sensitivity in tB-TNBC models in vitro and in vivo. A, Dose–response curves showing cell viability of indicated cell lines treated with increasing concentrations of dasatinib for 72 hours. Solid lines represent the mean of three biological replicates performed in technical replicates normalized to untreated controls, and IC50 values are indicated. B, Representative IHC images of tB-TNBC PDXs (PDX_01 and PDX_02) showing expression of SMA, TAGL, and TPM2. Scale bars, 100 μm. C, Quantification of positive tumor cells for each tB-marker in PDX_01 and PDX_02. Data are shown as mean ± SEM of different tumor areas. D, Schematic representation of the dasatinib treatment protocol. E, Tumor growth curves of the tB-TNBC PDXs treated with vehicle (black) or dasatinib (red). Error bars, ± SEM. P values calculated by Wilcoxon test. F, Bar plot showing tumor weight at the end point. Data are shown as mean ± SEM. P values calculated by two-tailed Student t test. **, P < 0.01. D, Created in BioRender. Rodilla, V. (2026) https://BioRender.com/qpb7bux.
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Supplementary Figure S5 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Immunohistochemical validation of basal markers in TNBC samples
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessSupplementary Figure S5 from Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Immunohistochemical validation of basal markers in TNBC samples