The Present and Future of Neoadjuvant and Adjuvant Therapy for Locally Advanced Gastric Cancer

Koerner, Anna; Moy, Ryan H.; Ryeom, Sandra; Yoon, Sam S.

doi:10.3390/cancers15164114

Cited by 7 publications

(3 citation statements)

References 121 publications

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“…Cases of GC are expected to increase to about 1.8 million by 2040, while the number of deaths due to GC worldwide will reach 1.3 million each year [ 17 ]. Due to the insidious nature of symptoms, patients used to be diagnosed with advanced and unresectable GC [ 18 ]. Therefore, good prognosis largely depends on early diagnosis and effective treatment of GC.…”

Section: Discussionmentioning

confidence: 99%

Unravelling diagnostic clusters and immune landscapes of disulfidptosis patterns in gastric cancer through bioinformatic assay

Zhang,

Chen,

Lin

et al. 2023

Aging

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Disulfidptosis is a novel type of cell death mediated by SLC7A11-induced disulfide stress. Gastric cancer (GC) is a common malignant gastrointestinal tumor. Existing evidence shows that SLC7A11 can regulate cell death and improve the progression of GC, suggesting disulfidptosis may exist in the pathological process of GC. However, the underlying functions of disulfidptosis regulators in GC remain unknown. The dataset of GSE54129 was screened to comprehensively investigate the disulfidptosis-related diagnostic clusters and immune landscapes in GC. Totally 15 significant disulfidptosis regulators were identified via difference analysis between GC samples and controls. Then random forest model was utilized to assess their importance score (mean decrease Gini). Then a nomogram model was constructed, which could offer benefit to patients based on our subsequent decision curve analysis. All the included GC patients were divided into 2 disulfidptosis subgroups (clusterA and clusterB) according to the significant disulfidptosis regulators in virtue of consensus clustering analysis. The disulfidptosis score of each sample was calculated through PCA algorithms to quantify the disulfidptosis subtypes. Patients from clusterB exhibited lower disulfidptosis scores than those of patients in clusterA. In addition, we found that the cases in clusterB were closely associated with the immunity of activated CD4 T cell, etc., while clusterA was linked to immature dendritic cell, mast cell, natural killer T cell, natural killer cell, etc., which has a higher disulfidptosis score. Therefore, disulfidptosis regulators play an important role in the pathological process of GC, providing a promising marker and an immunotherapeutic strategy for future GC therapy.

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Section: Discussionmentioning

confidence: 99%

Unravelling diagnostic clusters and immune landscapes of disulfidptosis patterns in gastric cancer through bioinformatic assay

Zhang,

Chen,

Lin

et al. 2023

Aging

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“…It’s the fifth most common and fourth deadliest cancer worldwide ( 1 ). Locally advanced gastric or gastroesophageal junction adenocarcinoma (LAG/GEJA) are marked by tumors extending beyond the muscle layer or involving lymph node metastases, but without distant spread ( 2 – 4 ). Treatment typically involves perioperative chemotherapy, radiochemotherapy, and surgical intervention ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

“…Locally advanced gastric or gastroesophageal junction adenocarcinoma (LAG/GEJA) are marked by tumors extending beyond the muscle layer or involving lymph node metastases, but without distant spread ( 2 – 4 ). Treatment typically involves perioperative chemotherapy, radiochemotherapy, and surgical intervention ( 4 ). These treatments aim to manage the disease from various perspectives, including reducing the cancer stage to increase surgical removal possibility, eliminating microscopic cancer cell clusters, enhancing complete tumor resection chances, and lowering recurrence risk ( 5 , 6 ).…”

Section: Introductionmentioning

confidence: 99%

Neoadjuvant chemoimmunotherapy in locally advanced gastric or gastroesophageal junction adenocarcinoma

Liu,

Ma,

Zhao

2024

Front. Oncol.

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Patients suffering from locally advanced gastric or gastroesophageal junction adenocarcinoma often face a high postoperative recurrence rate. Despite aggressive treatment, less than 50% survive beyond five years. Ongoing clinical studies are exploring ways to prolong patient survival, revealing that perioperative chemotherapy can extend both the period of recurrence-free survival and overall survival for this group of patients. Currently, combining chemotherapy and immune checkpoint inhibitors has become a critical treatment approach for advanced gastric or gastroesophageal junction adenocarcinoma. However, the effectiveness of this approach in locally advanced patients remains unverified. This article delves into the latest research concerning the use of perioperative chemotherapy coupled with immune checkpoint inhibitors in locally advanced gastric or gastroesophageal junction adenocarcinoma treatment, and highlights prospective challenges and discusses how to best identify patients who may benefit from combined chemotherapy and immune checkpoint inhibitor therapy.

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Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients

Zhang,

Zhao

et al. 2024

Abdom Radiol

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Purpose Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients. Methods This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA). Results The nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793–0.893)], 0.802 (95% CI 0.688–0.889), and 0.751 (95% CI 0.652–0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756–0.895), 0.541 (95% CI 0.329–0.740), and 0.556 (95% CI 0.376–0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models. Conclusion This study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights. Graphical abstract

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The Present and Future of Neoadjuvant and Adjuvant Therapy for Locally Advanced Gastric Cancer

Cited by 7 publications

References 121 publications

Unravelling diagnostic clusters and immune landscapes of disulfidptosis patterns in gastric cancer through bioinformatic assay

Unravelling diagnostic clusters and immune landscapes of disulfidptosis patterns in gastric cancer through bioinformatic assay

Neoadjuvant chemoimmunotherapy in locally advanced gastric or gastroesophageal junction adenocarcinoma

Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients

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