Immunotherapy for advanced gastric cancer

Leowattana, Wattana; Leowattana, Pathomthep; Leowattana, Tawithep

doi:10.5662/wjm.v13.i3.79

Cited by 5 publications

(3 citation statements)

References 89 publications

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“…In recent years, there has been a notable evolution in immunotherapies, which are now recommended for patients diagnosed with advanced GC/GEJC ( 26 , 27 ). This progress has been driven by the discovery of innovative diagnostic biomarkers, the development of drugs targeting novel molecules, and the emergence of combination therapies, such as immunotherapy combined with chemotherapy, antiangiogenic agents, anti-HER-2 antibodies, and chemotherapy ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer

Wang,

Xu,

Cheng

et al. 2024

Front. Immunol.

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BackgroundSintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions.Materials and methodsA cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions.ResultsThe findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS.ConclusionA ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.

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Section: Discussionmentioning

confidence: 99%

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer

Wang,

Xu,

Cheng

et al. 2024

Front. Immunol.

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“…This is the most frequently used method of cancer immunotherapy in clinical practice; it can enhance the host’s immune response or inhibit tumour development by blocking cancer growth factors. By contrast, active immunotherapy focuses on boosting the body’s immune response against cancer cells (e.g., vaccinations and chimeric antigen receptors) [ 18 ]. The effect of immunotherapy is more selective, offering greater protection of healthy tissues with fewer side effects.…”

Section: Immunotherapy In Gastric Cancermentioning

confidence: 99%

Immunotherapy Based on Immune Checkpoint Molecules and Immune Checkpoint Inhibitors in Gastric Cancer–Narrative Review

Poniewierska-Baran,

Sobolak,

Niedźwiedzka-Rystwej

et al. 2024

IJMS

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Due to its rapid progression to advanced stages and highly metastatic properties, gastric cancer (GC) is one of the most aggressive malignancies and the fourth leading cause of cancer-related deaths worldwide. The metastatic process includes local invasion, metastasis initiation, migration with colonisation at distant sites, and evasion of the immune response. Tumour growth involves the activation of inhibitory signals associated with the immune response, also known as immune checkpoints, including PD-1/PD-L1 (programmed death 1/programmed death ligand 1), CTLA-4 (cytotoxic T cell antigen 4), TIGIT (T cell immunoreceptor with Ig and ITIM domains), and others. Immune checkpoint molecules (ICPMs) are proteins that modulate the innate and adaptive immune responses. While their expression is prominent on immune cells, mainly antigen-presenting cells (APC) and other types of cells, they are also expressed on tumour cells. The engagement of the receptor by the ligand is crucial for inhibiting or stimulating the immune cell, which is an extremely important aspect of cancer immunotherapy. This narrative review explores immunotherapy, focusing on ICPMs and immune checkpoint inhibitors in GC. We also summarise the current clinical trials that are evaluating ICPMs as a target for GC treatment.

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“…Gastric cancer (GC) is believed to be the fifth most common cancer and the third most common cause of death worldwide.Notably, China and Japan are at the forefront, collectively accounting for 75% of Asian cases ( 1 , 2 ). Despite being one of the most common treatment modalities for gastric cancer, surgical intervention alone has failed to elevate the overall 5-year survival rate beyond 50%.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study

Lu,

Liu

et al. 2024

Front. Oncol.

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ObjectiveGastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients.MethodsData of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People’s Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients’ clinical prognosis.ResultsLasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities.ConclusionMachine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.

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Immunotherapy for advanced gastric cancer

Cited by 5 publications

References 89 publications

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer

Immunotherapy Based on Immune Checkpoint Molecules and Immune Checkpoint Inhibitors in Gastric Cancer–Narrative Review

Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study

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