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

Significance of concurrent evaluation of HER2 gene amplification and p53 and Ki67 expression in gastric cancer tissues

OBJECTIVE

The primary objective of this study is to explore the significance of concurrent evaluation of HER2 gene amplification and p53 and Ki67 expression in gastric cancer tissues.

METHODS

Fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) methodologies were used to detect HER2 gene amplification, as well as the expression levels of HER2, p53, and Ki67 proteins, across a group of 78 gastric cancer cases.

RESULTS

The expression rate of the HER2 protein was determined to be 43.6% (34/78), with 17.9% (14/78) categorized as HER2 protein 3 + , 14.1% (11/78) as HER2 protein 2 + , and 11.5% (9/78) as HER2 protein 1 + . Using FISH technology, the HER2 gene amplification rate was identified as 19.2% (15/78), including 3 cases of HER2 gene cluster amplification, 5 cases of large granular amplification, 4 cases of punctate amplification, and 3 cases of high polysomy. The positive rate of p53 in gastric cancer cells was 52.6% (41/78), with 62.8% (49/78) of patients exhibiting a ki67 proliferation index ≤ 30, and 37.2% (29/78) accounting for a ki67 proliferation index > 30. The expression rates of the HER2 gene, p53, and ki67 in gastric cancer tissues were significantly associated with both gastric cancer staging and lymph node metastasis (P < 0.05).

CONCLUSION

The HER2 gene amplification rate and gene copy number exhibit a positive correlation with the expression rates of p53 and ki67. Combining these assessments can provide crucial insights into the assessment of metastatic potential, disease progression, and prognosis of gastric tumor cells. This holds paramount importance in steering the formulation of individualized treatment strategies.

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

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.

Neoadjuvant chemoimmunotherapy in locally advanced gastric or gastroesophageal junction adenocarcinoma

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.

Neoadjuvant therapy with immune checkpoint inhibitors in combination with chemotherapy vs . chemotherapy alone in HER2(-) locally advanced gastric cancer: A propensity score-matched cohort study

BACKGROUND

This study aims to compare the efficacy between neoadjuvant immune checkpoint inhibitors (ICIs) plus chemotherapy vs . chemotherapy, and neoadjuvant triplet vs . doublet chemotherapeutic regimens in locally advanced gastric/esophagogastric junction cancer (LAGC).

METHODS

We included LAGC patients from 47 hospitals in China's National Cancer Information Database (NCID) from January 2019 to December 2022. Using propensity score matching (PSM), we retrospectively analyzed the efficacy between neoadjuvant ICIs plus chemotherapy vs . chemotherapy alone, and neoadjuvant triplet vs . doublet chemotherapeutic regimens. The primary study result was the pathologic complete response (pCR) rate. The secondary study results were disease-free survival (DFS) and overall survival (OS).

RESULTS

A total of 1205 LAGC patients were included. After PSM, the ICIs plus chemotherapy and the chemotherapy cohorts had 184 patients each, while the doublet and triplet chemotherapy cohorts had 246 patients each. The pCR rate (14.13% vs . 7.61%, χ2 = 4.039, P = 0.044), and the 2-year (77.60% vs . 61.02%, HR = 0.67, 95% con-fidence interval [CI] 0.43-0.98, P = 0.048) and 3-year (70.55% vs . 61.02%, HR = 0.58, 95% CI 0.32-0.93, P = 0.048) DFS rates in the ICIs plus chemotherapy cohort were improved compared to those in the chemotherapy cohort. No significant increase was observed in the OS rates at both 1 year and 2 years. The pCR rates, DFS rates at 1-3 years, and OS rates at 1-2 years did not differ significantly between the doublet and triplet cohorts, respectively. No differences were observed in postoperative complications between any of the group comparisons.

CONCLUSIONS

Neoadjuvant ICIs plus chemotherapy improved the pCR rate and 2-3 years DFS rates of LAGC compared to chemotherapy alone, but whether short-term benefit could translate into long-term efficacy is unclear. The triplet regimen was not superior to the doublet regimen in terms of efficacy. The safety after surgery was similar between either ICIs plus chemotherapy and chemotherapy or the triplet and the doublet regimen.

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

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.