Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer

Chen, Dexin; Chen, Hao; Chi, Liangjie; Fu, Meiting; Wang, Guangxing; Wu, Zhida; Xu, Shuoyu; Sun, Chao; Xu, Xueqin; Lin, Liyan; Cheng, Jiaxin; Jiang, Wei; Dong, Xiaoyu; Lu, Jianping; Zheng, Jixiang; Chen, Gang; Li, Guoxin; Zhuo, Shuangmu; Yan, Jun

doi:10.1001/jamanetworkopen.2021.36388

Cited by 10 publications

(12 citation statements)

References 50 publications

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“…Similar results were obtained from the subgroup analyses of patients with stage II and III tumours (Supplementary Figs. 18,19). No difference in the performance status of patients with a high PS GC to tolerate the full course of chemotherapy was observed (Supplementary Table 9).…”

Section: Predictive Value Of the Ps Gc For Adjuvant Chemotherapy Resp...mentioning

confidence: 96%

“…Integration of multiple features into a single signature, rather than individual analyses, might improve the performance of the prognostic prediction 14,15 . The least absolute shrinkage and selection operator (LASSO)-Cox regression model is a state-ofthe-art machine learning method for regression analysis of the relationships between high-dimensional features and survival [16][17][18] . Here, we propose a pathomics signature of GC (PS GC ) that was developed with multiple pathomics features extracted from H&Estained sections using a LASSO-Cox regression model.…”

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confidence: 99%

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Prognostic and predictive value of a pathomics signature in gastric cancer

Chen

Chi

et al. 2022

Nat Commun

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The current tumour-node-metastasis (TNM) staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer (GC). Pathomics, which is based on the development of digital pathology, is an emerging field that might improve clinical management. Herein, we propose a pathomics signature (PSGC) that is derived from multiple pathomics features of haematoxylin and eosin-stained slides. We find that the PSGC is an independent predictor of prognosis. A nomogram incorporating the PSGC and TNM staging system shows significantly improved accuracy in predicting the prognosis compared to the TNM staging system alone. Moreover, in stage II and III GC patients with a low PSGC (but not in those with a high PSGC), satisfactory chemotherapy benefits are observed. Therefore, the PSGC could serve as a prognostic predictor in patients with GC and might be a potential predictive indicator for decision-making regarding adjuvant chemotherapy.

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Section: Predictive Value Of the Ps Gc For Adjuvant Chemotherapy Resp...mentioning

confidence: 96%

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confidence: 99%

Prognostic and predictive value of a pathomics signature in gastric cancer

Chen

Chi

et al. 2022

Nat Commun

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“…Chen et al present calibration curves, which, on visual inspection, appear to confirm the model’s ability to make accurate prognoses. The C statistics reported by the authors did show a statistically significant improvement in discrimination with the addition of TACS to the clinicopathological variables.…”

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confidence: 87%

“…The current publication by Chen and colleagues presents a model aimed at predicting overall survival and disease-free survival in patients with gastric cancer. They argue that the current staging system, which uses routinely collected data, is limited in its ability to predict outcomes and, importantly, to predict potential benefits of chemotherapy.…”

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confidence: 99%

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Evaluating the Value of a New Prediction Model for Gastric Cancer

Berlin¹,

Catenacci²

2021

JAMA Netw Open

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The current publication by Chen and colleagues 1 presents a model aimed at predicting overall survival and disease-free survival in patients with gastric cancer. They argue that the current staging system, which uses routinely collected data, is limited in its ability to predict outcomes and, importantly, to predict potential benefits of chemotherapy. They focus on building a tumor-associated collagen signature (TACS) using data from the tumor microenvironment. Collagen is a major component of that tumor microenvironment, which can influence both cancer progression and response to chemotherapy.The authors used a sophisticated statistical modeling approach that allows consideration of large numbers of potential predictor variables, with limited overfitting of the models. (When models are overfit to a specific data set, they can be excellent for prediction in the current sample but not nearly as good at prediction in other populations.

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Tumor stroma ratio, tumor stroma maturity, tumor-infiltrating immune cells in relation to prognosis, and neoadjuvant therapy response in esophagogastric junction adenocarcinoma

Cheng,

Wang,

et al. 2024

Virchows Arch

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Association of Tumor-Associated Collagen Signature With Prognosis and Adjuvant Chemotherapy Benefits in Patients With Gastric Cancer

Cited by 10 publications

References 50 publications

Prognostic and predictive value of a pathomics signature in gastric cancer

Prognostic and predictive value of a pathomics signature in gastric cancer

Evaluating the Value of a New Prediction Model for Gastric Cancer

Tumor stroma ratio, tumor stroma maturity, tumor-infiltrating immune cells in relation to prognosis, and neoadjuvant therapy response in esophagogastric junction adenocarcinoma

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