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1. Pan-cancer molecular patterns and biological implications associated with a tumor-specific molecular signature

Author

Rocha, Darío, García, Iris A., Montoro, Aldana González, Llera, Andrea Sabina, Prato, Laura, Girotti, María Romina, Soria, Gastón, Fernández, Elmer Andrés, Rocha, Darío, García, Iris A., Montoro, Aldana González, Llera, Andrea Sabina, Prato, Laura, Girotti, María Romina, Soria, Gastón, and Fernández, Elmer Andrés

Abstract

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications., Fil: Rocha, Darío. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina, Fil: García, Iris A. Universidad Católica de Córdoba. CONICET. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas; Argentina, Fil: García, Iris A. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Bioquímica Clínica; Argentina, Fil: Montoro, Aldana González. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina, Fil: Montoro, Aldana González. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina, Fil: Llera, Andrea. Fundación Instituto Leloir. CONICET. Laboratorio de Terapia Molecular y Celular; Argentina, Fil: Prato, Laura. Universidad Nacional de Villa María. Instituto Académico Pedagógico de Ciencias Básicas y Aplicadas; Argentina, Fil: Girotti, María R. CONICET. Instituto de Biología y Medicina Experimental. Laboratorio de Inmuno Oncología Traslacional; Argentina, Fil: Soria, Gastón. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Bioquímica Clínica; Argentina, Fil: Soria, Gastón. CONICET. Centro de Investigaciones en Bioquímica Clínica e Inmunología; Agrentina, Fil: Fernández, Elmer A. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina, Fil: Fernández, Elmer A. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Bioquímica Clínica; Argentina

Published
2020

2. Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature.

Author

Rocha, Darío, García, Iris A., González Montoro, Aldana, Llera, Andrea, Prato, Laura, Girotti, María R., Soria, Gastón, and Fernández, Elmer A.

Subjects
BREAST cancer, GENE amplification, GENE expression, UNCERTAINTY, GENES, DNA damage
Abstract

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications. [ABSTRACT FROM AUTHOR]

Published
2021
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3. Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature.

Author

Rocha D, García IA, González Montoro A, Llera A, Prato L, Girotti MR, Soria G, and Fernández EA

Subjects
Algorithms, Biomarkers, Tumor genetics, Breast Neoplasms genetics, Breast Neoplasms metabolism, Cell Differentiation drug effects, Cell Line, Tumor, Cell Proliferation drug effects, Cell Proliferation genetics, Centrosome metabolism, Cohort Studies, Databases, Genetic, Gene Expression Profiling, Humans, Inhibitory Concentration 50, Membrane Proteins genetics, Membrane Proteins metabolism, Mutation, Neoplasms genetics, Neoplasms mortality, Prognosis, Proportional Hazards Models, Receptor, ErbB-2 genetics, Receptor, ErbB-2 metabolism, Retinoblastoma Binding Proteins genetics, Tumor Suppressor Protein p53 genetics, Ubiquitin-Protein Ligases genetics, Biomarkers, Tumor metabolism, Cell Cycle Checkpoints genetics, Cell Differentiation genetics, DNA Damage genetics, Embryonic Stem Cells metabolism, Gene Expression Regulation, Neoplastic genetics, Neoplasms classification, Neoplasms metabolism
Abstract

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.

Published
2020
Full Text
View/download PDF

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