BUSCADOR RECOLECTA

Técnica: Litografía 1922/181, Materia-soporte: Papel, Dimensiones: 56,5 X 52 cm. ; con marco: 85 X 67,5 cm., Dibujo geométrico de cara redonda, Expurgado, Nota: la pieza ya no pertenece al Patrimonio de la Fundación Vital.

Técnica: Litografía, Materia-soporte: Papel, Dimensiones: 76 X 48 cm. ; con marco: 94,5 X 67 cm., Signos abstractos sobre fondos azules., Expurgado, Nota: la pieza ya no pertenece al Patrimonio de la Fundación Vital.

Técnica: Litografía, Materia-soporte: Papel, Dimensiones: 63 X 47 cm. ; con marco: 80 X 64 cm., Niño corriendo, persigue a un ave., Firmado en ángulo superior derecho., Expurgado, Nota: la pieza ya no pertenece al Patrimonio de la Fundación Vital.

Técnica: Litografía, Materia-soporte: Papel, Dimensiones: 61 X 47 cm. ; con marco: 81,5 X 67,5 cm., Trazos figurativos que representan a una niña jugando con un ave., Expurgado, Nota: la pieza ya no pertenece al Patrimonio de la Fundación Vital.

Técnica: Offset, Soporte-Materia: Papel, Dimensiones: 44 X 44 cm. ; con marco: 62 X 62 cm., Obra abstracta. Sucesión de cuadrados de diferentes colores, Expurgado, Nota: la pieza ya no pertenece al Patrimonio de la Fundación Vital.

Técnica: Litografía, Materia-soporte: Papel, Dimensiones: 60 X 44,5 cm. ; con marco: 78,5 X 63 cm., Personaje en primer plano y, a su alrededor, aparecen otros., Expurgado, Nota: la pieza ya no pertenece al Patrimonio de la Fundación Vital.

Técnica: Litografía, Materia-soporte: Papel, Dimensiones: 64,5 X 44 cm. ; con marco: 83,5 X 63 cm., Aspecto de trapo con dibujo de peluche, Expurgado, Nota: la pieza ya no pertenece al Patrimonio de la Fundación Vital.

Supplementary Table 1. Characteristics of samples in Trp53-null mammary tumor cohort., 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 reviewed

Supplementary Table 2. Characteristics of samples in MMTV-Erbb2 mammary tumor cohort., 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 reviewed

Supplementary Table 3. Characteristics of patients in the TCGA-BRCA cohort., 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 reviewed