Data from Integrating Plasma Cell-Free DNA Fragment End Motif and Size with Genomic Features Enables Lung Cancer Detection Article Swipe
Tae-Rim Lee
,
Jin Mo Ahn
,
Junnam Lee
,
Dasom Kim
,
Juntae Park
,
Byeong‐Ho Jeong
,
Dongryul Oh
,
Sang Man Kim
,
Gyou-Chul Jung
,
Beom Hee Choi
,
Min‐Jung Kwon
,
Mengchi Wang
,
Michael Salmans
,
Andrew D. Carson
,
Bryan Leatham
,
Kristin Fathe
,
Byung In Lee
,
Byoungsok Jung
,
Chang‐Seok Ki
,
Young Sik Park
,
Eun‐Hae Cho
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1158/0008-5472.c.7798996
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1158/0008-5472.c.7798996
Early detection of lung cancer is important for improving patient survival rates. Liquid biopsy using whole-genome sequencing of cell-free DNA (cfDNA) offers a promising avenue for lung cancer screening, providing a potential alternative or complementary approach to current screening modalities. Here, we aimed to develop and validate an approach by integrating fragment and genomic features of cfDNA to enhance lung cancer detection accuracy across diverse populations. Deep learning–based classifiers were trained using comprehensive cfDNA fragmentomic features from participants in multi-institutional studies, including a Korean discovery dataset (218 patients with lung cancer and 2,559 controls), a Korean validation dataset (111 patients with lung cancer and 1,136 controls), and an independent Caucasian validation cohort (50 patients with lung cancer and 50 controls). In the discovery dataset, classifiers using fragment end motif by size, a feature that captures both fragment end motif and size profiles, outperformed standalone fragment end motif and fragment size classifiers, achieving an area under the curve (AUC) of 0.917. The ensemble classifier integrating fragment end motif by size and genomic coverage achieved an improved performance, with an AUC of 0.937. This performance extended to the Korean validation dataset and demonstrated ethnic generalizability in the Caucasian validation cohort. Overall, the development of a deep learning–based classifier integrating cfDNA fragmentomic and genomic features in this study highlights the potential for accurate lung cancer detection across diverse populations.Significance: Evaluating fragment-based features and genomic coverage in cell-free DNA offers an accurate lung cancer screening method, promising improvements in early cancer detection and addressing challenges associated with current screening methods.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1158/0008-5472.c.7798996
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410046918
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410046918Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1158/0008-5472.c.7798996Digital Object Identifier
- Title
-
Data from Integrating Plasma Cell-Free DNA Fragment End Motif and Size with Genomic Features Enables Lung Cancer DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-02Full publication date if available
- Authors
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Tae-Rim Lee, Jin Mo Ahn, Junnam Lee, Dasom Kim, Juntae Park, Byeong‐Ho Jeong, Dongryul Oh, Sang Man Kim, Gyou-Chul Jung, Beom Hee Choi, Min‐Jung Kwon, Mengchi Wang, Michael Salmans, Andrew D. Carson, Bryan Leatham, Kristin Fathe, Byung In Lee, Byoungsok Jung, Chang‐Seok Ki, Young Sik Park, Eun‐Hae ChoList of authors in order
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https://doi.org/10.1158/0008-5472.c.7798996Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1158/0008-5472.c.7798996Direct OA link when available
- Concepts
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Fragment (logic), DNA, genomic DNA, Lung cancer, Computational biology, Motif (music), Biology, Molecular biology, Genetics, Medicine, Computer science, Pathology, Algorithm, Physics, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 7, 25, 218 |
| abstract_inverted_index.the | 121, 155, 185, 194, 199, 216 |
| abstract_inverted_index.(111 | 98 |
| abstract_inverted_index.(218 | 86 |
| abstract_inverted_index.Deep | 66 |
| abstract_inverted_index.This | 181 |
| abstract_inverted_index.area | 153 |
| abstract_inverted_index.both | 135 |
| abstract_inverted_index.deep | 203 |
| abstract_inverted_index.from | 76 |
| abstract_inverted_index.lung | 3, 26, 59, 89, 101, 115, 220, 238 |
| abstract_inverted_index.size | 140, 149, 168 |
| abstract_inverted_index.that | 133 |
| abstract_inverted_index.this | 213 |
| abstract_inverted_index.were | 69 |
| abstract_inverted_index.with | 88, 100, 114, 176, 252 |
| abstract_inverted_index.(AUC) | 157 |
| abstract_inverted_index.1,136 | 104 |
| abstract_inverted_index.2,559 | 92 |
| abstract_inverted_index.Here, | 40 |
| abstract_inverted_index.aimed | 42 |
| abstract_inverted_index.cfDNA | 56, 73, 207 |
| abstract_inverted_index.curve | 156 |
| abstract_inverted_index.early | 245 |
| abstract_inverted_index.motif | 128, 138, 146, 166 |
| abstract_inverted_index.size, | 130 |
| abstract_inverted_index.study | 214 |
| abstract_inverted_index.under | 154 |
| abstract_inverted_index.using | 14, 71, 125 |
| abstract_inverted_index.0.917. | 159 |
| abstract_inverted_index.0.937. | 180 |
| abstract_inverted_index.Korean | 83, 95, 186 |
| abstract_inverted_index.Liquid | 12 |
| abstract_inverted_index.across | 63, 223 |
| abstract_inverted_index.avenue | 24 |
| abstract_inverted_index.biopsy | 13 |
| abstract_inverted_index.cancer | 4, 27, 60, 90, 102, 116, 221, 239, 246 |
| abstract_inverted_index.cohort | 111 |
| abstract_inverted_index.ethnic | 191 |
| abstract_inverted_index.offers | 21, 235 |
| abstract_inverted_index.rates. | 11 |
| abstract_inverted_index.(cfDNA) | 20 |
| abstract_inverted_index.cohort. | 197 |
| abstract_inverted_index.current | 37, 253 |
| abstract_inverted_index.dataset | 85, 97, 188 |
| abstract_inverted_index.develop | 44 |
| abstract_inverted_index.diverse | 64, 224 |
| abstract_inverted_index.enhance | 58 |
| abstract_inverted_index.feature | 132 |
| abstract_inverted_index.genomic | 53, 170, 210, 230 |
| abstract_inverted_index.method, | 241 |
| abstract_inverted_index.patient | 9 |
| abstract_inverted_index.trained | 70 |
| abstract_inverted_index.Overall, | 198 |
| abstract_inverted_index.accuracy | 62 |
| abstract_inverted_index.accurate | 219, 237 |
| abstract_inverted_index.achieved | 172 |
| abstract_inverted_index.approach | 35, 48 |
| abstract_inverted_index.captures | 134 |
| abstract_inverted_index.coverage | 171, 231 |
| abstract_inverted_index.dataset, | 123 |
| abstract_inverted_index.ensemble | 161 |
| abstract_inverted_index.extended | 183 |
| abstract_inverted_index.features | 54, 75, 211, 228 |
| abstract_inverted_index.fragment | 51, 126, 136, 144, 148, 164 |
| abstract_inverted_index.improved | 174 |
| abstract_inverted_index.patients | 87, 99, 113 |
| abstract_inverted_index.studies, | 80 |
| abstract_inverted_index.survival | 10 |
| abstract_inverted_index.validate | 46 |
| abstract_inverted_index.Caucasian | 109, 195 |
| abstract_inverted_index.achieving | 151 |
| abstract_inverted_index.cell-free | 18, 233 |
| abstract_inverted_index.detection | 1, 61, 222, 247 |
| abstract_inverted_index.discovery | 84, 122 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.improving | 8 |
| abstract_inverted_index.including | 81 |
| abstract_inverted_index.potential | 31, 217 |
| abstract_inverted_index.profiles, | 141 |
| abstract_inverted_index.promising | 23, 242 |
| abstract_inverted_index.providing | 29 |
| abstract_inverted_index.screening | 38, 240, 254 |
| abstract_inverted_index.Evaluating | 226 |
| abstract_inverted_index.addressing | 249 |
| abstract_inverted_index.associated | 251 |
| abstract_inverted_index.challenges | 250 |
| abstract_inverted_index.classifier | 162, 205 |
| abstract_inverted_index.controls), | 93, 105 |
| abstract_inverted_index.controls). | 119 |
| abstract_inverted_index.highlights | 215 |
| abstract_inverted_index.screening, | 28 |
| abstract_inverted_index.sequencing | 16 |
| abstract_inverted_index.standalone | 143 |
| abstract_inverted_index.validation | 96, 110, 187, 196 |
| abstract_inverted_index.alternative | 32 |
| abstract_inverted_index.classifiers | 68, 124 |
| abstract_inverted_index.development | 200 |
| abstract_inverted_index.independent | 108 |
| abstract_inverted_index.integrating | 50, 163, 206 |
| abstract_inverted_index.modalities. | 39 |
| abstract_inverted_index.performance | 182 |
| abstract_inverted_index.classifiers, | 150 |
| abstract_inverted_index.demonstrated | 190 |
| abstract_inverted_index.fragmentomic | 74, 208 |
| abstract_inverted_index.improvements | 243 |
| abstract_inverted_index.outperformed | 142 |
| abstract_inverted_index.participants | 77 |
| abstract_inverted_index.performance, | 175 |
| abstract_inverted_index.populations. | 65 |
| abstract_inverted_index.whole-genome | 15 |
| abstract_inverted_index.complementary | 34 |
| abstract_inverted_index.comprehensive | 72 |
| abstract_inverted_index.fragment-based | 227 |
| abstract_inverted_index.generalizability | 192 |
| abstract_inverted_index.learning–based | 67, 204 |
| abstract_inverted_index.multi-institutional | 79 |
| abstract_inverted_index.methods.</p></div> | 255 |
| abstract_inverted_index.<div>Abstract<p>Early | 0 |
| abstract_inverted_index.populations.</p><p><b>Significance:</b> | 225 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 21 |
| citation_normalized_percentile.value | 0.24555451 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |