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Fu M, Lin Y, Yang J, Cheng J, Lin L, Wang G, Long C, Xu S, Lu J, Li G, Yan J, Chen G, Zhuo S, Chen D. Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer. Gastric Cancer 2024:10.1007/s10120-024-01551-0. [PMID: 39271552 DOI: 10.1007/s10120-024-01551-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Accepted: 09/02/2024] [Indexed: 09/15/2024]
Abstract
BACKGROUND Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). METHODS Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated. RESULTS The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS. CONCLUSIONS The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.
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Affiliation(s)
- Meiting Fu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Guangzhou, 510515, People's Republic of China
- School of Science, Jimei University, Xiamen, 361021, People's Republic of China
| | - Yuyu Lin
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Junyao Yang
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Jiaxin Cheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Liyan Lin
- Department of Pathology, Fujian Key Laboratory of Translational Cancer Medicine, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Guangxing Wang
- School of Science, Jimei University, Xiamen, 361021, People's Republic of China
| | - Chenyan Long
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Shuoyu Xu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Jianping Lu
- Department of Pathology, Fujian Key Laboratory of Translational Cancer Medicine, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Guoxin Li
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Jun Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Gang Chen
- Department of Pathology, Fujian Key Laboratory of Translational Cancer Medicine, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, 361021, People's Republic of China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, 350007, People's Republic of China
| | - Dexin Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China.
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Chen D, Chen H, Chi L, Fu M, Wang G, Wu Z, Xu S, Sun C, Xu X, Lin L, Cheng J, Jiang W, Dong X, Lu J, Zheng J, Chen G, Li G, Zhuo S, Yan J. Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer. JAMA Netw Open 2021; 4:e2136388. [PMID: 34846524 PMCID: PMC8634059 DOI: 10.1001/jamanetworkopen.2021.36388] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
IMPORTANCE The current TNM staging system provides limited information for prognosis prediction and adjuvant chemotherapy benefits for patients with gastric cancer (GC). OBJECTIVE To develop a tumor-associated collagen signature of GC (TACSGC) in the tumor microenvironment to predict prognosis and adjuvant chemotherapy benefits in patients with GC. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included a training cohort of 294 consecutive patients treated between January 1, 2012, and December 31, 2013, from Nanfang Hospital, Southern Medical University, People's Republic of China, and a validation cohort of 225 consecutive patients treated between October 1, 2010, and December 31, 2012, from Fujian Provincial Cancer Hospital, Fujian Medical University, People's Republic of China. In total, 146 collagen features in the tumor microenvironment were extracted with multiphoton imaging. A TACSGC was then constructed using the least absolute shrinkage and selection operator Cox proportional hazards regression model in the training cohort. Data analysis was conducted from October 1, 2020, to April 30, 2021. MAIN OUTCOMES AND MEASURES The association of TACSGC with disease-free survival (DFS) and overall survival (OS) was assessed. An independent external cohort was included to validate the results. Interactions between TACSGC and adjuvant chemotherapy were calculated. RESULTS This study included 519 patients (median age, 57 years [IQR, 49-65 years]; 360 [69.4%] male). A 9 feature-based TACSGC was built. A higher TACSGC level was significantly associated with worse DFS and OS in both the training (DFS: hazard ratio [HR], 3.57 [95% CI, 2.45-5.20]; OS: HR, 3.54 [95% CI, 2.41-5.20]) and validation (DFS: HR, 3.10 [95% CI, 2.26-4.27]; OS: HR, 3.24 [95% CI, 2.33-4.50]) cohorts (continuous variable, P < .001 for all comparisons). Multivariable analyses found that carbohydrate antigen 19-9, depth of invasion, lymph node metastasis, distant metastasis, and TACSGC were independent prognostic predictors of GC, and 2 integrated nomograms that included the 5 predictors were established for predicting DFS and OS. Compared with clinicopathological models that included only the 4 clinicopathological predictors, the integrated nomograms yielded an improved discrimination for prognosis prediction in a C index comparison (training cohort: DFS, 0.80 [95% CI, 0.73-0.88] vs 0.78 [95% CI, 0.71-0.85], P = .03; OS, 0.81 [95% CI, 0.75-0.88] vs 0.80 [95% CI, 0.73-0.86], P = .03; validation cohort: DFS, 0.78 [95% CI, 0.70-0.87] vs 0.76 [95% CI, 0.67-0.84], P = .006; OS, 0.78 [95% CI, 0.69-0.86] vs 0.75 [95% CI, 0.67-0.84], P = .002). Patients with stage II and III GC and low TACSGC levels rather than high TACSGC levels had a favorable response to adjuvant chemotherapy (DFS: HR, 0.65 [95% CI, 0.43-0.96]; P = .03; OS: HR, 0.55 [95% CI, 0.36-0.82]; P = .004; dichotomized variable, P < .001 for interaction for both comparisons). CONCLUSIONS AND RELEVANCE The findings suggest that TACSGC provides additional prognostic information for patients with GC and may distinguish patients with stage II and III disease who are more likely to derive benefits from adjuvant chemotherapy.
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Affiliation(s)
- Dexin Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
- School of Science, Jimei University, Xiamen, People’s Republic of China
| | - Hao Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Liangjie Chi
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, People’s Republic of China
| | - Meiting Fu
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Guangxing Wang
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, People’s Republic of China
| | - Zhida Wu
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Shuoyu Xu
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Caihong Sun
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, People’s Republic of China
| | - Xueqin Xu
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, People’s Republic of China
| | - Liyan Lin
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Jiaxin Cheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Wei Jiang
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiaoyu Dong
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jianping Lu
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Jixiang Zheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Gang Chen
- Department of Pathology, Fujian Medical University Cancer Hospital and Fujian Cancer Hospital, Fuzhou, People’s Republic of China
| | - Guoxin Li
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, People’s Republic of China
| | - Jun Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
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Schneidereit D, Nübler S, Prölß G, Reischl B, Schürmann S, Müller OJ, Friedrich O. Optical prediction of single muscle fiber force production using a combined biomechatronics and second harmonic generation imaging approach. LIGHT, SCIENCE & APPLICATIONS 2018; 7:79. [PMID: 30374401 PMCID: PMC6199289 DOI: 10.1038/s41377-018-0080-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/19/2018] [Accepted: 09/24/2018] [Indexed: 05/22/2023]
Abstract
Skeletal muscle is an archetypal organ whose structure is tuned to match function. The magnitude of order in muscle fibers and myofibrils containing motor protein polymers determines the directed force output of the summed force vectors and, therefore, the muscle's power performance on the structural level. Structure and function can change dramatically during disease states involving chronic remodeling. Cellular remodeling of the cytoarchitecture has been pursued using noninvasive and label-free multiphoton second harmonic generation (SHG) microscopy. Hereby, structure parameters can be extracted as a measure of myofibrillar order and thus are suggestive of the force output that a remodeled structure can still achieve. However, to date, the parameters have only been an indirect measure, and a precise calibration of optical SHG assessment for an exerted force has been elusive as no technology in existence correlates these factors. We engineered a novel, automated, high-precision biomechatronics system into a multiphoton microscope allows simultaneous isometric Ca2+-graded force or passive viscoelasticity measurements and SHG recordings. Using this MechaMorph system, we studied force and SHG in single EDL muscle fibers from wt and mdx mice; the latter serves as a model for compromised force and abnormal myofibrillar structure. We present Ca2+-graded isometric force, pCa-force curves, passive viscoelastic parameters and 3D structure in the same fiber for the first time. Furthermore, we provide a direct calibration of isometric force to morphology, which allows noninvasive prediction of the force output of single fibers from only multiphoton images, suggesting a potential application in the diagnosis of myopathies.
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Affiliation(s)
- Dominik Schneidereit
- Institute of Medical Biotechnology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Paul-Gordan-Str. 3, 91052 Erlangen, Germany
- Erlangen Graduate School in Advanced Optical Technologies (SAOT), FAU Erlangen-Nürnberg, Paul-Gordan-Str. 7, 91052 Erlangen, Germany
| | - Stefanie Nübler
- Institute of Medical Biotechnology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Paul-Gordan-Str. 3, 91052 Erlangen, Germany
- Erlangen Graduate School in Advanced Optical Technologies (SAOT), FAU Erlangen-Nürnberg, Paul-Gordan-Str. 7, 91052 Erlangen, Germany
| | - Gerhard Prölß
- Institute of Medical Biotechnology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Paul-Gordan-Str. 3, 91052 Erlangen, Germany
| | - Barbara Reischl
- Institute of Medical Biotechnology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Paul-Gordan-Str. 3, 91052 Erlangen, Germany
| | - Sebastian Schürmann
- Institute of Medical Biotechnology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Paul-Gordan-Str. 3, 91052 Erlangen, Germany
- Erlangen Graduate School in Advanced Optical Technologies (SAOT), FAU Erlangen-Nürnberg, Paul-Gordan-Str. 7, 91052 Erlangen, Germany
- Muscle Research Center Erlangen (MURCE), Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Oliver J Müller
- Department of Internal Medicine III, University of Kiel, Arnold-Heller-Str. 3, 24105 Kiel, Germany
- DZHK (German Center for Cardiovascular Research) Partner Site Hamburg/Kiel/Lübeck, Kiel, Germany
| | - Oliver Friedrich
- Institute of Medical Biotechnology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Paul-Gordan-Str. 3, 91052 Erlangen, Germany
- Erlangen Graduate School in Advanced Optical Technologies (SAOT), FAU Erlangen-Nürnberg, Paul-Gordan-Str. 7, 91052 Erlangen, Germany
- Muscle Research Center Erlangen (MURCE), Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
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