Invasive mucinous lung adenocarcinoma (IMA) is a rare subtype of lung adenocarcinoma with no effective treatment option in advanced disease. KRAS mutations occur in 28–87% of the cases. NRG1 fusions were recently discovered in KRAS‐negative IMA cases and otherwise negative for known driver oncogenes and could represent an attractive therapeutic target. Published data suggest that NRG1 fusions occur essentially in nonsmoking Asian women. From an IMA cohort of 25 French patients of known ethnicity, driver oncogenes EGFR, KRAS, BRAF, ERBB2 mutations, and ALK and ROS1 rearrangements presence were analyzed. In the IMA samples remaining negative for these driver oncogenes, an NRG1 rearrangement detection was performed by FISH. A driver oncogene was identified in 14/25 IMA, namely 12 KRAS mutations (48%), one ROS1 rearrangement (4%), and one ALK rearrangement (4%). The detection of NRG1 rearrangement by FISH was conducted in the 11 pan‐negative IMA. One sample was NRG1 FISH‐positive and 100% of the tumor nuclei analyzed were positive. This NRG1‐positive patient was a 61‐year‐old nonsmoking woman of Vietnamese ethnicity and was the sole patient of Asian ethnicity of the cohort. She died 6 months after the diagnosis with a pulmonary multifocal disease. NRG1 FISH detection should be considered in patients with IMA pan‐negative for known driver oncogenes. These results might suggest that NRG1 fusion is more frequent in IMA from Asian patient. Larger studies are needed.

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.