More Than Meets the Eye: A Case of Breast Cancer Switching from Being Luminal-Androgen-Receptor-Positive to Being Hormone-Receptor-Positive.

Triple-negative breast cancer (TNBC) represents about 15% of all breast cancers and is usually characterized by aggressive clinical behavior and a poor prognosis. Four TNBC subgroups have been previously defined with different molecular profiles: (i) luminal androgen receptor (LAR), (ii) mesenchymal (MES), (iii) basal-like immunosuppressed (BLIS) and (iv) basal-like immune-activated (BLIA). Among these, LAR is characterized by the expression of the androgen receptor (AR), and exhibits genomic characteristics that resemble luminal breast cancers, with a still undefined prognosis and clinical behavior. Here, we report a case of a woman affected by recurring LAR TNBC, which underwent phenotypic changes throughout its natural history. After the initial diagnosis of LAR breast cancer, the patient experienced local recurrence with strong expression of the estrogen receptor. Due to this finding, she started treatment with a CDK4/6-inhibitor and an aromatase inhibitor, followed by oral vinorelbine, both with dismal outcomes. Then, she received everolimus and exemestane, which determined temporary disease stabilization. An extensive NGS analysis of tumor tissue showed PIK3CA and HER2 mutations. Our case is consistent with previous reports of LAR breast cancer and underlines the potential utility of re-biopsy and molecular testing in breast cancer (BC), especially in rare subtypes.

Distributed dynamic strain sensing of very long period and long period events on telecom fiber-optic cables at Vulcano, Italy.

Volcano-seismic signals can help for volcanic hazard estimation and eruption forecasting. However, the underlying mechanism for their low frequency components is still a matter of debate. Here, we show signatures of dynamic strain records from Distributed Acoustic Sensing in the low frequencies of volcanic signals at Vulcano Island, Italy. Signs of unrest have been observed since September 2021, with CO2 degassing and occurrence of long period and very long period events. We interrogated a fiber-optic telecommunication cable on-shore and off-shore linking Vulcano Island to Sicily. We explore various approaches to automatically detect seismo-volcanic events both adapting conventional algorithms and using machine learning techniques. During one month of acquisition, we found 1488 events with a great variety of waveforms composed of two main frequency bands (from 0.1 to 0.2 Hz and from 3 to 5 Hz) with various relative amplitudes. On the basis of spectral signature and family classification, we propose a model in which gas accumulates in the hydrothermal system and is released through a series of resonating fractures until the surface. Our findings demonstrate that fiber optic telecom cables in association with cutting-edge machine learning algorithms contribute to a better understanding and monitoring of volcanic hydrothermal systems.