Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer
Abstract Although neoadjuvant chemotherapy with docetaxel + cisplatin + 5-fluorouracil (CF) has been the standard treatment for stage II and III esophageal cancers, it is associated with severe adverse events caused by docetaxel. Consequently, this study aimed to construct a prognostic system for CF...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11252-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849333280207273984 |
|---|---|
| author | Yutaka Miyawaki Masataka Hirasaki Yasuo Kamakura Tomonori Kawasaki Yasutaka Baba Tetsuya Sato Satoshi Yamasaki Hisayo Fukushima Kousuke Uranishi Yoshinori Makino Hiroshi Sato Tetsuya Hamaguchi |
| author_facet | Yutaka Miyawaki Masataka Hirasaki Yasuo Kamakura Tomonori Kawasaki Yasutaka Baba Tetsuya Sato Satoshi Yamasaki Hisayo Fukushima Kousuke Uranishi Yoshinori Makino Hiroshi Sato Tetsuya Hamaguchi |
| author_sort | Yutaka Miyawaki |
| collection | DOAJ |
| description | Abstract Although neoadjuvant chemotherapy with docetaxel + cisplatin + 5-fluorouracil (CF) has been the standard treatment for stage II and III esophageal cancers, it is associated with severe adverse events caused by docetaxel. Consequently, this study aimed to construct a prognostic system for CF regimens, especially for locally advanced esophageal cancers. Biopsy specimens from 82 patients treated with the CF regimen plus radical surgery were analyzed. Variants in 56 autophagy- and esophageal cancer-related genes were identified using targeted enrichment sequencing. Overall, 13 single-nucleotide variants, including 8 non-synonymous single-nucleotide variants, were identified as significantly associated with esophageal cancer recurrence (p < 0.05). Particularly, variants of ATG2A p.R478C and ULK2 splice-site also showed significant differences in recurrence-free and overall survival. Subsequently, machine learning was used to construct a model for predicting esophageal cancer recurrence based on 21 features, including eight patient characteristics. A Naive Bayes machine-learning model was shown to be highly reliable for predicting esophageal cancer recurrence with an accuracy of 0.88 and an area under the curve of 0.9. We believe that our results provide useful guidance in the selection of neoadjuvant adjuvant chemotherapy, including avoidance of docetaxel. |
| format | Article |
| id | doaj-art-3375b7ec762c4282bb2a33a10e553f21 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3375b7ec762c4282bb2a33a10e553f212025-08-20T03:45:55ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-11252-yDeveloping a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancerYutaka Miyawaki0Masataka Hirasaki1Yasuo Kamakura2Tomonori Kawasaki3Yasutaka Baba4Tetsuya Sato5Satoshi Yamasaki6Hisayo Fukushima7Kousuke Uranishi8Yoshinori Makino9Hiroshi Sato10Tetsuya Hamaguchi11Department of Gastroenterological Surgery, Saitama Medical University International Medical CenterDepartment of Clinical Cancer Genomics, Saitama Medical University International Medical CenterDepartment of Clinical Cancer Genomics, Saitama Medical University International Medical CenterDepartment of Pathology, Saitama Medical University International Medical CenterDepartment of Diagnostic Radiology, Saitama Medical University International Medical CenterBiomedical Research Center, Faculty of Medicine, Saitama Medical UniversityDepartment of Clinical Cancer Genomics, Saitama Medical University International Medical CenterDepartment of Clinical Cancer Genomics, Saitama Medical University International Medical CenterDivision of Biomedical Sciences, Research Center for Genomic Medicine, Saitama Medical UniversityDepartment of Clinical Cancer Genomics, Saitama Medical University International Medical CenterDepartment of Gastroenterological Surgery, Saitama Medical University International Medical CenterDepartment of Clinical Cancer Genomics, Saitama Medical University International Medical CenterAbstract Although neoadjuvant chemotherapy with docetaxel + cisplatin + 5-fluorouracil (CF) has been the standard treatment for stage II and III esophageal cancers, it is associated with severe adverse events caused by docetaxel. Consequently, this study aimed to construct a prognostic system for CF regimens, especially for locally advanced esophageal cancers. Biopsy specimens from 82 patients treated with the CF regimen plus radical surgery were analyzed. Variants in 56 autophagy- and esophageal cancer-related genes were identified using targeted enrichment sequencing. Overall, 13 single-nucleotide variants, including 8 non-synonymous single-nucleotide variants, were identified as significantly associated with esophageal cancer recurrence (p < 0.05). Particularly, variants of ATG2A p.R478C and ULK2 splice-site also showed significant differences in recurrence-free and overall survival. Subsequently, machine learning was used to construct a model for predicting esophageal cancer recurrence based on 21 features, including eight patient characteristics. A Naive Bayes machine-learning model was shown to be highly reliable for predicting esophageal cancer recurrence with an accuracy of 0.88 and an area under the curve of 0.9. We believe that our results provide useful guidance in the selection of neoadjuvant adjuvant chemotherapy, including avoidance of docetaxel.https://doi.org/10.1038/s41598-025-11252-yBiomarkerEsophageal cancerNeoadjuvant chemotherapyMachine-learningTargeted enrichment sequenceRNA sequence |
| spellingShingle | Yutaka Miyawaki Masataka Hirasaki Yasuo Kamakura Tomonori Kawasaki Yasutaka Baba Tetsuya Sato Satoshi Yamasaki Hisayo Fukushima Kousuke Uranishi Yoshinori Makino Hiroshi Sato Tetsuya Hamaguchi Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer Scientific Reports Biomarker Esophageal cancer Neoadjuvant chemotherapy Machine-learning Targeted enrichment sequence RNA sequence |
| title | Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer |
| title_full | Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer |
| title_fullStr | Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer |
| title_full_unstemmed | Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer |
| title_short | Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer |
| title_sort | developing a machine learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer |
| topic | Biomarker Esophageal cancer Neoadjuvant chemotherapy Machine-learning Targeted enrichment sequence RNA sequence |
| url | https://doi.org/10.1038/s41598-025-11252-y |
| work_keys_str_mv | AT yutakamiyawaki developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT masatakahirasaki developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT yasuokamakura developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT tomonorikawasaki developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT yasutakababa developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT tetsuyasato developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT satoshiyamasaki developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT hisayofukushima developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT kousukeuranishi developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT yoshinorimakino developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT hiroshisato developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer AT tetsuyahamaguchi developingamachinelearningmodeltoenabletreatmentselectionforneoadjuvantchemotherapyforesophagealcancer |