A substantial amount patients with cancer will develop bone metastases, with 70% of metastatic prostate and breast cancer patients harboring bone metastasis. Despite advancements in systemic therapies for advanced cancer, survival remains poor for those with bone metastases. The interaction between bone cells and the immune system contributes to a better understanding of the role that the immune system plays in the bone metastasis of cancer. The immune and bone systems share various molecules, including transcription factors, signaling molecules, and membrane receptors, which can stimulate the differentiation and activation of bone‐resorbing osteoclasts. The process of cancer metastasis to bone, which deregulates bone turnover and results in bone loss and skeletal-related events (SREs), is also controlled by primary cancer-related factors that modulate the intratumoral microenvironment as well as cellular immune process. The nuclear factor kappa B ligand (RANKL) and the receptor activator of nuclear factor kappa B (RANK) are key regulators of osteoclast development, bone metabolism, lymph node development, and T-cell/dendritic cell communication. RANKL is an osteoclastogenic cytokine that links the bone and the immune system. In this review, we highlight the role of RANKL and RANK in the immune microenvironment and bone metastases and review data on the role of the regulatory mechanism of immunity in bone metastases, which could be verified through clinical efficacy of RANKL inhibitors for cancer patients with bone metastases. With the discovery of the specific role of RANK signaling in osteoclastogenesis, the humanized monoclonal antibody against RANKL, such as denosumab, was available to prevent bone loss, SREs, and bone metastases, providing a unique opportunity to target RANKL/RANK as a future strategy to prevent bone metastases.

Breast cancer is prone to form bone metastases and subsequent skeletal‐related events (SREs) dramatically decrease patients’ quality of life and survival. Prediction and early management of bone lesions are valuable; however, proper prognostic models are inadequate. In the current study, we reviewed a total of 572 breast cancer patients in three microarray data sets including 191 bone metastases and 381 metastases‐free. Gene set enrichment analysis (GSEA) indicated less aggressive and low‐grade features of patients with bone metastases compared with metastases‐free ones, while luminal subtypes are more prone to form bone metastases. Five bone metastases‐related genes (KRT23, REEP1, SPIB, ALDH3B2, and GLDC) were identified and subjected to construct a gene expression signature‐based nomogram (GESBN) model. The model performed well in both training and testing sets for evaluation of breast cancer bone metastases (BCBM). Clinically, the model may help in prediction of early bone metastases, prevention and management of SREs, and even help to prolong survivals for patients with BCBM. The five‐gene GESBN model showed some implications as molecular diagnostic markers and therapeutic targets. Furthermore, our study also provided a way for analysis of tumor organ‐specific metastases. To the best of our knowledge, this is the first published model focused on tumor organ‐specific metastases.

Objective: To summarize the function and efficacy of immunotherapy as an adjunctive therapy in the treatment of advanced chordoma. Methods: Literature search was conducted by two reviewers independently. Case reports, case series and clinical trials of immunotherapy for chordoma were retrieved systematically from Pubmed, Web of Science, Scoupus and Cochrane Library. Clinical outcome data extracted from the literature included median progression-free survival (PFS), median overall survival (OS), clinical responses and adverse events (AEs). Results: All studies were published between 2015 and 2022. Twenty-two eligible studies were selected for systemic review. PD-1/PD-L1 immune checkpoint inhibitors (ICIs) were the most common used immunotherapy agents in chordoma, among which Pembrolizumab was the most frequently prescribed. CTLA-4 antibody was only used as combination therapy in chordoma. Dose Limiting Toxicity (DLT) was not observed in any vaccine targeting brachyury, and injection site response was the most frequent AV. The response evaluation criteria in solid tumors (RECIST) were the most generally used evaluation standard in chordoma immunotherapy, and none of the included studies employed the Choi criteria. Conclusions: No clinical data have demonstrated that CTLA-4 ICIs combined with PD-1/PD-L1 ICIs is more effective than ICIs monotherapy in treating chordoma, and ICIs in combination with other therapies exhibit more toxicity than monotherapy. PD-1/PD-L1 ICIs monotherapy is recommended as an immunotherapy in patients with advanced chordoma, which may even benefit PD-L1-negative patients. The brachyury vaccine has shown good safety in chordoma patients, and future clinical trials should focus on how to improve its therapeutic efficacy. The use of immunomodulatory agents is a promising therapeutic option, though additional clinical trials are required to evaluate their safety and effectiveness. RECIST does not seem to be an appropriate standard for assessing medications of intratumoral immunotherapy.

Study Design: Retrospective and prospective cohort study. Objectives: Survival estimation is necessary in the decision-making process for treatment in patients with spinal metastasis from cancer of unknown primary (SMCUP). We aimed to develop a novel survival prediction system and compare its accuracy with that of existing survival models. Methods: A retrospective derivation cohort of 268 patients and a prospective validation cohort of 105 patients with SMCUP were performed. Univariate and multivariable survival analysis were used to generate independently prognostic variables. A nomogram model for survival prediction was established by integrating these independent predictors based on the size of the significant variables’ β regression coefficient. Then, the model was subjected to bootstrap validation with calibration curves and concordance index (C-index). Finally, predictive accuracy was compared with Tomita, revised Tokuhashi and SORG score by the receiver-operating characteristic (ROC) curve. Results: The survival prediction model included six independent prognostic factors, including pathology ( P < .001), visceral metastases ( P < .001), Frankel score ( P < .001), weight loss ( P = .005), hemoglobin ( P = .001) and serum tumor markers ( P < .001). Calibration curve of the model showed good agreement between predicted and actual mortality risk in 6-, 12-, and 24-month estimation in derivation and validation cohorts. The C-index was .775 in the derivation cohort and .771 in the validation cohort. ROC curve analysis showed that the current model had the best accuracy for SMCUP survival estimation amongst 4 models. Conclusions: The novel nomogram system can be applied in survival prediction for SMCUP patients, and furtherly be used to give individualized therapeutic suggestions based on patients’ prognosis.

Bone metastasis from cancer of unknown primary (BMCUP) brings poor survival prognosis and its management remains controversial. Sunitinib (SUTENT) proved effective in many sorts of solid tumors but has never been applied for patients with occult primary cancers, and there is no study to identify sensitive or resistant biomarkers for sunitinib therapy in CUP patients. An analysis was carried out to investigate the efficacy of sunitinib by multivariate survival analysis of 286 patients with BMCUP. We further carried out multivariate analysis to identify histological and clinical biomarkers that could predict sensitivity or resistance for sunitinib therapy. Of the 286 patients included from January 2011 to March 2016, sunitinib therapy proved effective to prolong survival in patients with BMCUP. Sensitive and resistant biomarkers were identified in histological specimen of patients receiving sunitinib therapy. Clinical factors were also identified that predict poor survival prognosis for sunitinib therapy. Sunitinib therapy proved effective to prolong survival in patients with BMCUP. Sensitive markers for sunitinib therapy include KDR positivity and early-developed treatment-induced hypertension. Resistance factors for sunitinib include VEGF positivity, CAIX positivity and squamous cell carcinoma pathology type. Prolonged symptom time and severe weight loss before therapy seemed to be associated with poor survival prognosis for sunitinib therapy.