AREAS COVERED: This review examines the state of the art in passive (processing and formulation) and active (targeting ligand and receptor binding) technologies in association with the use of nanocarrier to combat lung cancer. It highlights routes to equip nanocarrier with targeting ligands as a function of the chemistry of participating biomolecules and challenges in inhalational nanoproduct development and clinical applications. Both research and review articles were examined using the Scopus, Elsevier, Web of Science, Chemical Abstracts, Medline, CASREACT, CHEMCATS, and CHEMLIST database with the majority of information retrieved between those of 2000-2018.
EXPERT COMMENTARY: The therapeutic efficacy of targeting ligand-decorated nanocarriers needs to be demonstrated in vivo in the form of finished inhalational products. Their inhalation efficiency and medical responses require further examination. Clinical application of inhaled nanocancer chemotherapeutics is premature.
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.
METHODS: Cancer 10-pathway reporter array was performed to screen the pathways affected by Phyllanthus in lung carcinoma cell line (A549) to exert its antimetastatic effects. Results from this array were then confirmed with western blotting, cell cycle analysis, zymography technique, and cell based ELISA assay for human total iNOS. Two-dimensional gel electrophoresis was subsequently carried out to study the differential protein expressions in A549 after treatment with Phyllanthus.
RESULTS: Phyllanthus was observed to cause antimetastatic activities by inhibiting ERK1/2 pathway via suppression of Raf protein. Inhibition of this pathway resulted in the suppression of MMP2, MMP7, and MMP9 expression to stop A549 metastasis. Phyllanthus also inhibits hypoxia pathway via inhibition of HIF-1α that led to reduced VEGF and iNOS expressions. Proteomic analysis revealed a number of proteins downregulated by Phyllanthus that were involved in metastatic processes, including invasion and mobility proteins (cytoskeletal proteins), transcriptional proteins (proliferating cell nuclear antigen; zinc finger protein), antiapoptotic protein (Bcl2) and various glycolytic enzymes. Among the four Phyllanthus species tested, P. urinaria showed the greatest antimetastatic activity.
CONCLUSIONS: Phyllanthus inhibits A549 metastasis by suppressing ERK1/2 and hypoxia pathways that led to suppression of various critical proteins for A549 invasion and migration.
MATERIALS AND METHODS: In this prospective study, EGFR mutations in exons 18, 19, 20 and 21 in formalin-fixed paraffin-embedded biopsy specimens of consecutive NSCLC patients were asessed by real-time polymerase chain reaction.
RESULTS: EGFR mutations were detected in NSCLCs from 55 (36.4%) of a total of 151 patients, being significantly more common in females (62.5%) than in males (17.2%) [odds ratio (OR), 8.00; 95% confidence interval (CI), 3.77-16.98; p<0.001] and in never smokers (62.5%) than in ever smokers (12.7%) (OR, 11.50; 95%CI, 5.08-26.03; p<0.001). Mutations were more common in adenocarcinoma (39.4%) compared to non-adenocarcinoma NSCLCs (15.8%) (p=0.072). The mutation rates in patients of different ethnicities were not significantly different (p=0.08). Never smoking status was the only clinical feature that independently predicted the presence of EGFR mutations (adjusted OR, 5.94; 95%CI, 1.94- 18.17; p=0.002).
CONCLUSIONS: In Malaysian patients with NSCLC, the EGFR mutation rate was similar to that in other Asian populations. EGFR mutations were significantly more common in female patients and in never smokers. Never smoking status was the only independent predictor for the presence of EGFR mutations.