Are we there yet? A machine learning architecture to predict organotropic metastases

Abstract 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...

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Autores principales:	Michael Skaro, Marcus Hill, Yi Zhou, Shannon Quinn, Melissa B. Davis, Andrea Sboner, Mandi Murph, Jonathan Arnold
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Cancer Metastatic organotropism Machine learning Transcriptomic profiling Internal medicine RC31-1245 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/fa8ad0a1a41c47debabc6d1cf6c1cc5d
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spelling	oai:doaj.org-article:fa8ad0a1a41c47debabc6d1cf6c1cc5d2021-11-28T12:06:03ZAre we there yet? A machine learning architecture to predict organotropic metastases10.1186/s12920-021-01122-71755-8794https://doaj.org/article/fa8ad0a1a41c47debabc6d1cf6c1cc5d2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12920-021-01122-7https://doaj.org/toc/1755-8794Abstract 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.Michael SkaroMarcus HillYi ZhouShannon QuinnMelissa B. DavisAndrea SbonerMandi MurphJonathan ArnoldBMCarticleCancerMetastatic organotropismMachine learningTranscriptomic profilingInternal medicineRC31-1245GeneticsQH426-470ENBMC Medical Genomics, Vol 14, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cancer Metastatic organotropism Machine learning Transcriptomic profiling Internal medicine RC31-1245 Genetics QH426-470
spellingShingle	Cancer Metastatic organotropism Machine learning Transcriptomic profiling Internal medicine RC31-1245 Genetics QH426-470 Michael Skaro Marcus Hill Yi Zhou Shannon Quinn Melissa B. Davis Andrea Sboner Mandi Murph Jonathan Arnold Are we there yet? A machine learning architecture to predict organotropic metastases
description	Abstract 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.
format	article
author	Michael Skaro Marcus Hill Yi Zhou Shannon Quinn Melissa B. Davis Andrea Sboner Mandi Murph Jonathan Arnold
author_facet	Michael Skaro Marcus Hill Yi Zhou Shannon Quinn Melissa B. Davis Andrea Sboner Mandi Murph Jonathan Arnold
author_sort	Michael Skaro
title	Are we there yet? A machine learning architecture to predict organotropic metastases
title_short	Are we there yet? A machine learning architecture to predict organotropic metastases
title_full	Are we there yet? A machine learning architecture to predict organotropic metastases
title_fullStr	Are we there yet? A machine learning architecture to predict organotropic metastases
title_full_unstemmed	Are we there yet? A machine learning architecture to predict organotropic metastases
title_sort	are we there yet? a machine learning architecture to predict organotropic metastases
publisher	BMC
publishDate	2021
url	https://doaj.org/article/fa8ad0a1a41c47debabc6d1cf6c1cc5d
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Are we there yet? A machine learning architecture to predict organotropic metastases

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