Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients

Woo, Yanghee; Goldner, Bryan; Son, Taeil; Song, Keun Jeong; Noh, Sung Hoon; Fong, Yuman; Hyung, Woo Jin

doi:10.1016/j.jamcollsurg.2017.12.016

Cited by 15 publications

(11 citation statements)

References 31 publications

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“…Although incidence and death rates are declining (131), advancements in prevention and treatment remain a priority. Five year survival rates drop to 20-30% or less once the cancer moves beyond the lining of the stomach (132). The majority of GC cases are advanced stage (133).…”

Section: Gastric Cancermentioning

confidence: 99%

CD73's Potential as an Immunotherapy Target in Gastrointestinal Cancers

et al. 2020

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CD73, a cell surface 5 ′ nucleotidase that generates adenosine, has emerged as an attractive therapeutic target for reprogramming cancer cells and the tumor microenvironment to dampen antitumor immune cell evasion. Decades of studies have paved the way for these findings, starting with the discovery of adenosine signaling, particularly adenosine A2A receptor (A2AR) signaling, as a potent suppressor of tissue-devastating immune cell responses, and evolving with studies focusing on CD73 in breast cancer, melanoma, and non-small cell lung cancer. Gastrointestinal (GI) cancers are a major cause of cancer-related deaths. Evidence is mounting that shows promise for improving patient outcomes through incorporation of immunomodulatory strategies as single agents or in combination with current treatment options. Recently, several immune checkpoint inhibitors received FDA approval for use in GI cancers; however, clinical benefit is limited. Investigating molecular mechanisms promoting immunosuppression, such as CD73, in GI cancers can aid in current efforts to extend the efficacy of immunotherapy to more patients. In this review, we discuss current clinical and basic research studies on CD73 in GI cancers, including gastric, liver, pancreatic, and colorectal cancer, with special focus on the potential of CD73 as an immunotherapy target in these cancers. We also present a summary of current clinical studies targeting CD73 and/or A2AR and combination of these therapies with immune checkpoint inhibitors.

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Section: Gastric Cancermentioning

confidence: 99%

CD73's Potential as an Immunotherapy Target in Gastrointestinal Cancers

et al. 2020

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“…Woo et al created a novel model to predict prognosis after gastrectomy for gastric cancer; the model had a good C‐statistic of 0.798‐0.868 in four external validation cohorts in three different countries 9 . However, the model showed a decreased C‐statistic of 0.762 when validated using the SEER dataset, but they did not show the calibration plot 43 . A major concern in these prediction models is that they have unsatisfactory generalizability and are thus are difficult to be widely used clinically.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

Gao

et al. 2020

Cancer Medicine

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Background Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN‐based survival prediction model for patients with gastric cancer. Methods Patients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time‐dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models. Results An ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5‐year survival prediction, respectively. In the calibration curve analysis, the ANN‐predicted survival had a high consistency with the actual survival. Comparison of the DCA and time‐dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts. Conclusions The ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

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“…Although a model for the prognoses of patients with metastatic or recurrent GC and good PS who undergo chemotherapy as a first-line treatment has been modified before (7), some issues still need to be solved. Firstly, some important host-or tumor-related prognostic factors, such as coagulation markers and tumor biomarkers, that have been proved in the recent studies were not included in the previous prognostic model (12)(13)(14)(15). Secondly, there is no validation procedure in the previous prognostic model (7).…”

Section: Introductionmentioning

confidence: 99%

Nomogram-based prediction of survival in unresectable or metastatic gastric cancer patients with good performance status who received first-line chemotherapy

Wang¹,

Yang²,

Li³

et al. 2020

Ann Transl Med

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Background: Good performance status (PS) is widely acknowledged to have a high prognostic ability, although the prognostic parameters of cancer patients with good PS are still uncertain. This study was conducted to establish and validate a point-based nomogram to assist with predicting prognosis in unresectable or metastatic gastric cancer (GC) patients who had good PS and underwent first-line chemotherapy.Methods: At random, a total of 309 patients with GC were split into 2 cohorts: a training cohort (n=259) and an internal validation cohort (n=50). An independent external validation cohort comprising 147 patients was also recruited. Both univariate and multivariate Cox regression analyses were used to evaluate patients based on the overall survival (OS) to develop the nomogram, which was subsequently validated using the concordance index (c-index), calibration curve, and decision curve analysis (DCA).Results: The nomogram contained 3 independent prognostic variables in the training cohort: the number of distant metastatic sites (P<0.001), carbohydrate antigen 199 (CA199) level (P=0.002), and fibrinogen (P=0.020). The nomogram predicted an OS with a c-index of 0.623 (95% CI, 0.58-0.67) in the training cohort. The internal validation showed that the nomogram had a c-index of 0.614 (95% CI, 0.51-0.72). For external validation, the c-index was 0.638 (95% CI, 0.58-0.70).Conclusions: A reliable point-based nomogram for predicting the prognosis of patients who had unresectable or metastatic GC and good PS who underwent first-line chemotherapy was developed and validated.

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Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients

Cited by 15 publications

References 31 publications

CD73's Potential as an Immunotherapy Target in Gastrointestinal Cancers

CD73's Potential as an Immunotherapy Target in Gastrointestinal Cancers

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study

Nomogram-based prediction of survival in unresectable or metastatic gastric cancer patients with good performance status who received first-line chemotherapy

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