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Are we there yet? A machine learning architecture to predict organotropic metastases.
Skaro, Michael; Hill, Marcus; Zhou, Yi; Quinn, Shannon; Davis, Melissa B; Sboner, Andrea; Murph, Mandi; Arnold, Jonathan.
Afiliación
  • Skaro M; Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. Michael.Skaro@uga.edu.
  • Hill M; Department of Computer Science, University of Georgia, Athens, GA, 30602, USA.
  • Zhou Y; Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
  • Quinn S; Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
  • Davis MB; Department of Computer Science, University of Georgia, Athens, GA, 30602, USA.
  • Sboner A; Department of Cellular Biology, University of Georgia, Athens, GA, 30602, USA.
  • Murph M; Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY, 10065, USA.
  • Arnold J; Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY, 10065, USA.
BMC Med Genomics ; 14(1): 281, 2021 11 24.
Article en En | MEDLINE | ID: mdl-34819069
BACKGROUND & AIMS: Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression. METHODS: We used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression. RESULTS: We developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations. CONCLUSIONS: We have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Neoplasias Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Med Genomics Asunto de la revista: GENETICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Neoplasias Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Med Genomics Asunto de la revista: GENETICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido