ABSTRACT
BACKGROUND: Patients with advanced non-small cell lung cancer (NSCLC) are a heterogeneous population with short lifespan. We aimed to develop methods to better differentiate patients whose survival was >90 days. METHODS: We evaluated 83 characteristics of 106 treatment-naïve, stage IV NSCLC patients with Eastern Cooperative Oncology Group Performance Status (ECOG-PS) >1. Automated machine learning was used to select a model and optimize hyperparameters. 100-fold bootstrapping was performed for dimensionality reduction for a second ("lite") model. Performance was measured by C-statistic and accuracy metrics in an out-of-sample validation cohort. The "lite" model was validated on a second independent, prospective cohort (N = 42). Network analysis (NA) was performed to evaluate the differences in centrality and connectivity of features. RESULTS: The selected method was ExtraTrees Classifier, with C-statistic of 0.82 (p < 0.01) and accuracy of 0.81 (p = 0.01). The "lite" model had 16 variables and obtained C-statistic of 0.84 (p < 0.01) and accuracy of 0.75 (p = 0.039) in the first cohort, and C-statistic of 0.706 (p < 0.01) and accuracy of 0.714 (p < 0.01) in the second cohort. The networks of patients with lower survival were more interconnected. Features related to cachexia, inflammation, and quality of life had statistically different prestige scores in NA. CONCLUSIONS: Machine learning can assist in the prognostic evaluation of advanced NSCLC. The model generated with a reduced number of features showed high accessibility and reasonable metrics. Features related to quality of life, cachexia, and performance status had increased correlation and importance scores, suggesting that they play a role at later disease stages, in line with the biological rationale already described.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Carcinoma, Non-Small-Cell Lung/pathology , Prospective Studies , Lung Neoplasms/pathology , Cachexia , Quality of LifeABSTRACT
The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control vs. disease). However, biological systems often present more than two biological states (e.g., tumor grades). To compare two or more networks simultaneously, we developed BioNetStat, a Bioconductor package with a user-friendly graphical interface. BioNetStat compares correlation networks based on the probability distribution of a feature of the graph (e.g., centrality measures). The analysis of the structural alterations on the network reveals significant modifications in the system. For example, the analysis of centrality measures provides information about how the relevance of the nodes changes among the biological states. We evaluated the performance of BioNetStat in both, toy models and two case studies. The latter related to gene expression of tumor cells and plant metabolism. Results based on simulated scenarios suggest that the statistical power of BioNetStat is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis (GSCA). Also, besides being able to identify nodes with modified centralities, BioNetStat identified altered networks associated with signaling pathways that were not identified by other methods.
ABSTRACT
Three zygotic developmental stages and two somatic Araucaria angustifolia cell lines with contrasting embryogenic potential were analyzed to identify the carbohydrate-mediated responses associated with embryo formation. Using a comparison between zygotic and somatic embryogenesis systems, the non-structural carbohydrate content, cell wall sugar composition and expression of genes involved in sugar sensing were analyzed, and a network analysis was used to identify coordinated features during embryogenesis. We observed that carbohydrate-mediated responses occur mainly during the early stages of zygotic embryo formation, and that during seed development there are coordinated changes that affect the development of the different structures (embryo and megagametophyte). Furthermore, sucrose and starch accumulation were associated with the responsiveness of the cell lines. This study sheds light on how carbohydrate metabolism is influenced during zygotic and somatic embryogenesis in the endangered conifer species, A. angustifolia.
