Cargando…
Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma
Previous studies on liquid biopsy-based early detection of advanced colorectal adenoma (advCRA) or adenocarcinoma (CRC) were limited by low sensitivity. We performed a prospective study to establish an integrated model using fragmentomic profiles of plasma cell-free DNA (cfDNA) for accurately and co...
Autores principales: | , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8549237/ https://www.ncbi.nlm.nih.gov/pubmed/34702327 http://dx.doi.org/10.1186/s13045-021-01189-w |
_version_ | 1784590743630774272 |
---|---|
author | Ma, Xiaoji Chen, Yikuan Tang, Wanxiangfu Bao, Hua Mo, Shaobo Liu, Rui Wu, Shuyu Bao, Hairong Li, Yaqi Zhang, Long Wu, Xue Cai, Sanjun Shao, Yang Liu, Fangqi Peng, Junjie |
author_facet | Ma, Xiaoji Chen, Yikuan Tang, Wanxiangfu Bao, Hua Mo, Shaobo Liu, Rui Wu, Shuyu Bao, Hairong Li, Yaqi Zhang, Long Wu, Xue Cai, Sanjun Shao, Yang Liu, Fangqi Peng, Junjie |
author_sort | Ma, Xiaoji |
collection | PubMed |
description | Previous studies on liquid biopsy-based early detection of advanced colorectal adenoma (advCRA) or adenocarcinoma (CRC) were limited by low sensitivity. We performed a prospective study to establish an integrated model using fragmentomic profiles of plasma cell-free DNA (cfDNA) for accurately and cost-effectively detecting early-stage CRC and advCRA. The training cohort enrolled 310 participants, including 149 early-stage CRC patients, 46 advCRA patients and 115 healthy controls. Plasma cfDNA samples were prepared for whole-genome sequencing. An ensemble stacked model differentiating healthy controls from advCRA/early-stage CRC patients was trained using five machine learning models and five cfDNA fragmentomic features based on the training cohort. The model was subsequently validated using an independent test cohort (N = 311; including 149 early-stage CRC, 46 advCRA and 116 healthy controls). Our model showed an area under the curve (AUC) of 0.988 for differentiating advCRA/early-stage CRC patients from healthy individuals in an independent test cohort. The model performed even better for identifying early-stage CRC (AUC 0.990) compared to advCRA (AUC 0.982). At 94.8% specificity, the sensitivities for detecting advCRA and early-stage CRC reached 95.7% and 98.0% (0: 94.1%; I: 98.5%), respectively. Promisingly, the detection sensitivity has reached 100% and 97.6% in early-stage CRC patients with negative fecal occult or CEA blood test results, respectively. Finally, our model maintained promising performances (AUC: 0.982, 94.4% sensitivity at 94.8% specificity) even when sequencing depth was down-sampled to 1X. Our integrated predictive model demonstrated an unprecedented detection sensitivity for advCRA and early-stage CRC, shedding light on more accurate noninvasive CRC screening in clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13045-021-01189-w. |
format | Online Article Text |
id | pubmed-8549237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85492372021-10-27 Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma Ma, Xiaoji Chen, Yikuan Tang, Wanxiangfu Bao, Hua Mo, Shaobo Liu, Rui Wu, Shuyu Bao, Hairong Li, Yaqi Zhang, Long Wu, Xue Cai, Sanjun Shao, Yang Liu, Fangqi Peng, Junjie J Hematol Oncol Letter to the Editor Previous studies on liquid biopsy-based early detection of advanced colorectal adenoma (advCRA) or adenocarcinoma (CRC) were limited by low sensitivity. We performed a prospective study to establish an integrated model using fragmentomic profiles of plasma cell-free DNA (cfDNA) for accurately and cost-effectively detecting early-stage CRC and advCRA. The training cohort enrolled 310 participants, including 149 early-stage CRC patients, 46 advCRA patients and 115 healthy controls. Plasma cfDNA samples were prepared for whole-genome sequencing. An ensemble stacked model differentiating healthy controls from advCRA/early-stage CRC patients was trained using five machine learning models and five cfDNA fragmentomic features based on the training cohort. The model was subsequently validated using an independent test cohort (N = 311; including 149 early-stage CRC, 46 advCRA and 116 healthy controls). Our model showed an area under the curve (AUC) of 0.988 for differentiating advCRA/early-stage CRC patients from healthy individuals in an independent test cohort. The model performed even better for identifying early-stage CRC (AUC 0.990) compared to advCRA (AUC 0.982). At 94.8% specificity, the sensitivities for detecting advCRA and early-stage CRC reached 95.7% and 98.0% (0: 94.1%; I: 98.5%), respectively. Promisingly, the detection sensitivity has reached 100% and 97.6% in early-stage CRC patients with negative fecal occult or CEA blood test results, respectively. Finally, our model maintained promising performances (AUC: 0.982, 94.4% sensitivity at 94.8% specificity) even when sequencing depth was down-sampled to 1X. Our integrated predictive model demonstrated an unprecedented detection sensitivity for advCRA and early-stage CRC, shedding light on more accurate noninvasive CRC screening in clinical practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13045-021-01189-w. BioMed Central 2021-10-26 /pmc/articles/PMC8549237/ /pubmed/34702327 http://dx.doi.org/10.1186/s13045-021-01189-w 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 | Letter to the Editor Ma, Xiaoji Chen, Yikuan Tang, Wanxiangfu Bao, Hua Mo, Shaobo Liu, Rui Wu, Shuyu Bao, Hairong Li, Yaqi Zhang, Long Wu, Xue Cai, Sanjun Shao, Yang Liu, Fangqi Peng, Junjie Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma |
title | Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma |
title_full | Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma |
title_fullStr | Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma |
title_full_unstemmed | Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma |
title_short | Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma |
title_sort | multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma |
topic | Letter to the Editor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8549237/ https://www.ncbi.nlm.nih.gov/pubmed/34702327 http://dx.doi.org/10.1186/s13045-021-01189-w |
work_keys_str_mv | AT maxiaoji multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT chenyikuan multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT tangwanxiangfu multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT baohua multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT moshaobo multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT liurui multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT wushuyu multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT baohairong multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT liyaqi multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT zhanglong multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT wuxue multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT caisanjun multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT shaoyang multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT liufangqi multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma AT pengjunjie multidimensionalfragmentomicassayforultrasensitiveearlydetectionofcolorectaladvancedadenomaandadenocarcinoma |