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The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival
SIMPLE SUMMARY: Neuroblastoma is a highly heterogeneous malignancy with a wide range of outcomes from spontaneous regression to fatal chemoresistant disease, as currently treated according to the risk stratification of the Children’s Oncology Group (COG), resulting in some high COG risk patients rec...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220810/ https://www.ncbi.nlm.nih.gov/pubmed/35740540 http://dx.doi.org/10.3390/cancers14122874 |
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author | Li, Xin Wang, Xiaoqi Huang, Ruihao Stucky, Andres Chen, Xuelian Sun, Lan Wen, Qin Zeng, Yunjing Fletcher, Hansel Wang, Charles Xu, Yi Cao, Huynh Sun, Fengzhu Li, Shengwen Calvin Zhang, Xi Zhong, Jiang F. |
author_facet | Li, Xin Wang, Xiaoqi Huang, Ruihao Stucky, Andres Chen, Xuelian Sun, Lan Wen, Qin Zeng, Yunjing Fletcher, Hansel Wang, Charles Xu, Yi Cao, Huynh Sun, Fengzhu Li, Shengwen Calvin Zhang, Xi Zhong, Jiang F. |
author_sort | Li, Xin |
collection | PubMed |
description | SIMPLE SUMMARY: Neuroblastoma is a highly heterogeneous malignancy with a wide range of outcomes from spontaneous regression to fatal chemoresistant disease, as currently treated according to the risk stratification of the Children’s Oncology Group (COG), resulting in some high COG risk patients receiving excessive treatment, due to lacking predictors for treatment response. Here, we sought to complement COG risk classification by using the tumor intracellular microbiome, which is part of the tumor’s molecular signature. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (M(high) and M(low)) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies. ABSTRACT: Currently, most neuroblastoma patients are treated according to the Children’s Oncology Group (COG) risk group assignment; however, neuroblastoma’s heterogeneity renders only a few predictors for treatment response, resulting in excessive treatment. Here, we sought to couple COG risk classification with tumor intracellular microbiome, which is part of the molecular signature of a tumor. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (M(high) and M(low)) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies. Mechanistically, the classification power of M-scores implies the effect of CREB over-activation, which may influence the critical genes involved in cellular proliferation, anti-apoptosis, and angiogenesis, affecting tumor cell proliferation survival and metastasis. Thus, intracellular microbiota abundance in neuroblastoma regulates intracellular signals to affect patients’ survival. |
format | Online Article Text |
id | pubmed-9220810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92208102022-06-24 The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival Li, Xin Wang, Xiaoqi Huang, Ruihao Stucky, Andres Chen, Xuelian Sun, Lan Wen, Qin Zeng, Yunjing Fletcher, Hansel Wang, Charles Xu, Yi Cao, Huynh Sun, Fengzhu Li, Shengwen Calvin Zhang, Xi Zhong, Jiang F. Cancers (Basel) Article SIMPLE SUMMARY: Neuroblastoma is a highly heterogeneous malignancy with a wide range of outcomes from spontaneous regression to fatal chemoresistant disease, as currently treated according to the risk stratification of the Children’s Oncology Group (COG), resulting in some high COG risk patients receiving excessive treatment, due to lacking predictors for treatment response. Here, we sought to complement COG risk classification by using the tumor intracellular microbiome, which is part of the tumor’s molecular signature. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (M(high) and M(low)) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies. ABSTRACT: Currently, most neuroblastoma patients are treated according to the Children’s Oncology Group (COG) risk group assignment; however, neuroblastoma’s heterogeneity renders only a few predictors for treatment response, resulting in excessive treatment. Here, we sought to couple COG risk classification with tumor intracellular microbiome, which is part of the molecular signature of a tumor. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (M(high) and M(low)) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies. Mechanistically, the classification power of M-scores implies the effect of CREB over-activation, which may influence the critical genes involved in cellular proliferation, anti-apoptosis, and angiogenesis, affecting tumor cell proliferation survival and metastasis. Thus, intracellular microbiota abundance in neuroblastoma regulates intracellular signals to affect patients’ survival. MDPI 2022-06-10 /pmc/articles/PMC9220810/ /pubmed/35740540 http://dx.doi.org/10.3390/cancers14122874 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Xin Wang, Xiaoqi Huang, Ruihao Stucky, Andres Chen, Xuelian Sun, Lan Wen, Qin Zeng, Yunjing Fletcher, Hansel Wang, Charles Xu, Yi Cao, Huynh Sun, Fengzhu Li, Shengwen Calvin Zhang, Xi Zhong, Jiang F. The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival |
title | The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival |
title_full | The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival |
title_fullStr | The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival |
title_full_unstemmed | The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival |
title_short | The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival |
title_sort | machine-learning-mediated interface of microbiome and genetic risk stratification in neuroblastoma reveals molecular pathways related to patient survival |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9220810/ https://www.ncbi.nlm.nih.gov/pubmed/35740540 http://dx.doi.org/10.3390/cancers14122874 |
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