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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...

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Autores principales: 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
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
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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.
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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
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