Cargando…

MSIFinder: a python package for detecting MSI status using random forest classifier

BACKGROUND: Microsatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree,...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Tao, Chen, Libin, Guo, Jing, Zhang, Mengmeng, Zhang, Yanrui, Cao, Shanbo, Lou, Feng, Wang, Haijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042960/
https://www.ncbi.nlm.nih.gov/pubmed/33845765
http://dx.doi.org/10.1186/s12859-021-03986-z
_version_ 1783678223594291200
author Zhou, Tao
Chen, Libin
Guo, Jing
Zhang, Mengmeng
Zhang, Yanrui
Cao, Shanbo
Lou, Feng
Wang, Haijun
author_facet Zhou, Tao
Chen, Libin
Guo, Jing
Zhang, Mengmeng
Zhang, Yanrui
Cao, Shanbo
Lou, Feng
Wang, Haijun
author_sort Zhou, Tao
collection PubMed
description BACKGROUND: Microsatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size. RESULTS: We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases). CONCLUSION: These results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-03986-z.
format Online
Article
Text
id pubmed-8042960
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-80429602021-04-14 MSIFinder: a python package for detecting MSI status using random forest classifier Zhou, Tao Chen, Libin Guo, Jing Zhang, Mengmeng Zhang, Yanrui Cao, Shanbo Lou, Feng Wang, Haijun BMC Bioinformatics Software BACKGROUND: Microsatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size. RESULTS: We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases). CONCLUSION: These results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-03986-z. BioMed Central 2021-04-12 /pmc/articles/PMC8042960/ /pubmed/33845765 http://dx.doi.org/10.1186/s12859-021-03986-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Zhou, Tao
Chen, Libin
Guo, Jing
Zhang, Mengmeng
Zhang, Yanrui
Cao, Shanbo
Lou, Feng
Wang, Haijun
MSIFinder: a python package for detecting MSI status using random forest classifier
title MSIFinder: a python package for detecting MSI status using random forest classifier
title_full MSIFinder: a python package for detecting MSI status using random forest classifier
title_fullStr MSIFinder: a python package for detecting MSI status using random forest classifier
title_full_unstemmed MSIFinder: a python package for detecting MSI status using random forest classifier
title_short MSIFinder: a python package for detecting MSI status using random forest classifier
title_sort msifinder: a python package for detecting msi status using random forest classifier
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042960/
https://www.ncbi.nlm.nih.gov/pubmed/33845765
http://dx.doi.org/10.1186/s12859-021-03986-z
work_keys_str_mv AT zhoutao msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier
AT chenlibin msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier
AT guojing msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier
AT zhangmengmeng msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier
AT zhangyanrui msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier
AT caoshanbo msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier
AT loufeng msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier
AT wanghaijun msifinderapythonpackagefordetectingmsistatususingrandomforestclassifier