Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Biomarker-Driven Approaches to Bone Metastases: From Molecular Mechanisms to Clinical Applications

Bone metastases represent a critical complication in oncology, frequently indicating advanced malignancy and substantially reducing patient quality of life. This review provides a comprehensive analysis of the complex interactions between tumor cells and the bone microenvironment, emphasizing the relevance of the "seed and soil" hypothesis, the RANK/RANKL/OPG signaling axis, and Wnt signaling pathways that collectively drive metastatic progression. The molecular and cellular mechanisms underlying the formation of osteolytic and osteoblastic lesions are examined in detail, with a particular focus on their implications for bone metastases associated with breast, prostate, lung, and other cancers. A central component of this review is the categorization of pathological biomarkers into four types: diagnostic, prognostic, predictive, and monitoring. We provide a comprehensive evaluation of circulating tumor cells (CTCs), bone turnover markers (such as TRACP-5b and CTX), advanced imaging biomarkers (including PET/CT and MRI), and novel genomic signatures. These biomarkers offer valuable insights for early detection, enhanced risk stratification, and optimized therapeutic decision-making. Furthermore, emerging strategies in immunotherapy and bone-targeted treatments are discussed, highlighting the potential of biomarker-guided precision medicine to enhance personalized patient care. The distinctiveness of this review lies in its integrative approach, combining fundamental pathophysiological insights with the latest developments in biomarker discovery and therapeutic innovation. By synthesizing evidence across various cancer types and biomarker categories, we provide a cohesive framework aimed at advancing both the scientific understanding and clinical management of bone metastases.

Retrospective study on the prognostic prediction of inflammatory markers and the C-reactive protein/albumin ratio in first-line immunotherapy for advanced HER2 negative gastric cancer patients

Background

Immune checkpoint inhibitors (ICIs) have shown significant clinical benefits in the treatment of advanced human epidermal growth factor receptor 2 (HER2) negative gastric cancer, and are now widely used in combination with chemotherapy for first-line treatment. However, significant individual variability in the treatment response poses challenges in optimizing therapeutic strategies. Systemic inflammatory biomarkers are gaining attention for their ability to reflect tumor-related inflammation and the immune response balance. These markers could be used to predict treatment outcomes and guide personalized therapy. This study aimed to evaluate the prognostic value of peripheral blood inflammatory markers, such as the Systemic Immune Inflammation Index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and C-reactive protein/albumin ratio (CAR), in patients with HER2 negative advanced gastric cancer undergoing first-line immunotherapy with ICIs.

Methods

The clinical data of advanced gastric cancer patients treated with immunotherapy at Jiangsu Cancer Hospital between January 2018 and December 2023 were retrospectively collected. The patients were categorized into progressive disease (PD) and effective treatment [complete response (CR), partial response (PR), stable disease (SD)] groups based on their response after two cycles of treatment. The changes in the SII, NLR, PLR, and CAR based on the baseline data and post-treatment measurements were evaluated. The optimal cut-off values for these markers were determined using X-tile software based on their distribution characteristics. Kaplan-Meier survival analysis, log-rank tests, and Cox regression models were used to assess the prognostic ability of these markers.

Results

A total of 142 patients with advanced gastric cancer were included in the study, of whom, 22 had PD after first-line immunotherapy, and 120 had SD. No significant differences were observed in the baseline characteristics of the patients between the effective treatment group and the PD group (P>0.05). Using X-tile software, the optimal cut-off values for the SII, NLR, PLR, and CAR were 548.22 [hazard ratio (HR): 2.421; 95% confidence interval (CI): 1.214-3.710], 3.75 (HR: 3.210; 95% CI: 2.030-5.115), 245.65 (HR: 2.137; 95% CI: 1.577-4.240), and 0.56 (HR: 1.846; 95% CI: 1.388-2.245), respectively. After two cycles of immunotherapy, the NLR, PLR, SII, and CAR values of the patients in the effective treatment (CR + PR + SD) group all decreased significantly. The Kaplan-Meier survival curve analysis showed that the patients with high SII, NLR, PLR, and CAR values had a poorer prognosis in terms of progression-free survival (PFS) and overall survival (OS) (P<0.05) than those with low values.

Conclusions

Inflammatory markers (i.e., the SII, NLR, PLR, and CAR) can be used to predict the prognosis of HER2 negative advanced gastric cancer patients undergoing first-line immunotherapy.