Search

Background: Plasma and tissue biopsy have both used for targeting actionable driver gene mutations in lung cancer, whose concordance is imperfect. A reliable method to predict the concordance is urgently needed to ease clinical application. Methods: A total of 1012 plasma samples, including 519 with paired‐tissue biopsy samples, derived from lung adenocarcinoma patients were retrospectively enrolled. We assessed the associations of several clinicopathological characteristics and serum tumor markers with the concordance between plasma and tissue biopsies. Results: When carcinoembryonic antigen (CEA) levels were higher than thresholds of 15.01 ng/ml and 51.15 ng/ml, the positive predictive value of concordance reached 90% and 95%, respectively. When CEA levels were lower than thresholds of 5.19 ng/ml and 3.26 ng/mL, the negative predictive value of concordance reached 45% and 50%. The performance of CYFRA21‐1 in predicting concordance was similar but inferior to CEA (AUC: 0.727 vs. 0.741, p = 0.633). The performance of CEA combined with CYFRA21‐1 in predicting the concordance was similar to that of the combination of independent factors derived from the LASSO regression model (AUC: 0.796 vs. 0.818, p = 0.067). CEA (r = 0.47, p < 0.01) and CYFRA21‐1 levels (r = 0.45, p < 0.05) were significantly correlated with the maximum variant allele frequency, respectively. Conclusions: CEA combined with CYFRA21‐1 could effectively predict the concordance between plasma and tissue biopsies, which could be used for evaluating the priority of plasma and tissue biopsies for gene testing to timely guide clinical applications in advanced lung adenocarcinoma patients. [ABSTRACT FROM AUTHOR]

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. (a) Breast cancer bone metastases (BCBM) and subsequent skeletal‐related events (SREs) dramatically decrease patients' quality of life and survival. In the current study, we constructed a five‐gene expression signature‐based nomogram (GESBN) model for early prediction of bone metastases, prevention and management of SREs, and even help to prolong survivals for patients with BCBM. To the best of our knowledge, this is the first published model focusing on tumor organ specific metastases. And also the first attempt for construction such models in the field of bone oncology. (b) The five genes were weighted by the multivariate Cox regression model and further subjected to the nomogram scoring model. Nomograms have been developed and shown to be more accurate than the conventional staging systems for predicting prognosis in some cancers. (c) Our model has great importance for potential clinical evaluation. Based on our model, primary breast cancer patients could be classified as high or low incidence of bone metastasis. Based on the nomogram, even the potential time of occurrence of bone met could be predicted. These information are of great value in management of both primary and bone metastatic breast cancers. [ABSTRACT FROM AUTHOR]