CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis.

BACKGROUND: To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis. METHODS: OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts. RESULTS: A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model. CONCLUSIONS: The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.

DNA Base Pairing-Inspired Supramolecular Nanodrug Camouflaged by Cancer-Cell Membrane for Osteosarcoma Treatment.

Osteosarcoma (OS) is one of the most common bone malignant tumors which mainly develops in adolescents. Although neoadjuvant chemotherapy has improved the prognosis of patients, numerous chemotherapeutic challenges still limit their use. Here, inspired by the Watson-Crick base pairing in nucleic acids, hydrophobic (methotrexate) and hydrophilic (floxuridine) chemo-drugs are mixed and self-assembled into M:F nanoparticles (M:F NPs) through molecular recognition. Then, the obtained NPs are co-extruded with membranes derived from OS cells to form cancer-cell membrane-coated NPs (CCNPs). With protected membranes at the outer layer, CCNPs are highly stable in both physiological and weak acid tumor conditions and possess homologous tumor targeted capability. Furthermore, the proteomic analysis first identifies over 400 proteins reserved in CCNPs, most of them participating in tumor cell targeting and adhesion processes. In vitro studies reveal that CCNPs significantly inhibit the PI3K/AKT/mTOR pathway, which promotes cell apoptosis and cell cycle arrest. More importantly, cell membrane camouflage significantly prolongs the circulation half-life of CCNPs, elevates the drug accumulation at tumor sites, and promotes anti-tumor efficacy in vivo. As a convenient and effective strategy to construct a biomimetic NP with high drug loading ratio, the CCNPs provide new potentials for precise and synergistic antitumor treatment.

Construction and validation of a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis and immune infiltration.

BACKGROUND: Apoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma. METHODS: The differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan-Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry. RESULTS: HSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients' samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas. CONCLUSIONS: The present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.

The Outcome of Discontinuing Tyrosine Kinase Inhibitors in Advanced Sarcoma Following a Favorable Tumor Response to Antiangiogenics Therapy.

(1) Background: The use of antiangiogenic TKIs (AA-TKIs) has recently emerged as a major paradigm shift in the treatment of advanced sarcoma. However, the feasibility of drug holidays for patients demonstrating a very favorable response remains unknown. (2) Methods: We aim to explore the outcomes of patients with advanced sarcoma who discontinued AA-TKIs after a (near-) complete remission or were long-term responders. Patients with advanced disease were included if they had bilateral or multiple lung metastases, extrapulmonary recurrence, a short disease-free interval, etc., at the initiation of AA-TKIs. (3) Results: A total of 22 patients with AA-TKI discontinuation were analyzed, with a median follow-up of 22.3 months post-discontinuation. Prior to discontinuation, there were four drug-induced complete remissions (CRs), twelve surgical CRs, and six long-term responders. Disease progression was observed in 17/22 (77.3%) patients, with a median of 4.2 months. However, since the majority were still sensitive to the original AA-TKIs and amenable to a second surgical remission, 7 out of these 17 patients achieved a second CR after disease progression and were thus considered as relapse-free post-discontinuation (pd-RFS). Therefore, the pd-RFS and post-discontinuation overall survival (pd-OS) in the last follow-up were 12/22 (54.5%) and 16/22 (72.7%), respectively. Remarkably, surgical CR and drug tapering off (versus abrupt stopping) were associated with a greater pd-RFS and pd-OS (p < 0.05). Furthermore, higher necrosis rates (p = 0.040) and lower neutrophil-to-lymphocyte ratios (NLR) (p = 0.060) before discontinuation tend to have a better pd-RFS. (4) Conclusions: Our results suggest that AA-TKI discontinuation with a taper-off strategy might be safe and feasible in highly selected patients with advanced sarcoma. Surgical CR, NLR, and tumor necrosis rates before discontinuation were potential biomarkers for AA-TKI withdrawal.

Exploration and Validation of a Novel Inflammatory Response-Associated Gene Signature to Predict Osteosarcoma Prognosis and Immune Infiltration.

BACKGROUND: Inflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature. METHODS: The inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan-Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry. RESULTS: The IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients' samples revealed the same result. CONCLUSION: The novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.