RESUMO
Primary tumors with similar mutational profiles can progress to vastly different outcomes where transcriptional state, rather than mutational profile, predicts prognosis. A key challenge is to understand how distinct tumor cell states are induced and maintained. In triple negative breast cancer cells, invasive behaviors and aggressive transcriptional signatures linked to poor patient prognosis can emerge in response to contact with collagen type I. Herein, collagen-induced migration heterogeneity within a TNBC cell line was leveraged to identify transcriptional programs associated with invasive versus non-invasive phenotypes and implicate molecular switches. Phenotype-guided sequencing revealed that invasive cells upregulate iron uptake and utilization machinery, anapleurotic TCA cycle genes, actin polymerization promoters, and a distinct signature of Rho GTPase activity and contractility regulating genes. The non-invasive cell state is characterized by actin and iron sequestration modules along with glycolysis gene expression. These unique tumor cell states are evident in patient tumors and predict divergent outcomes for TNBC patients. Glucose tracing confirmed that non-invasive cells are more glycolytic than invasive cells, and functional studies in cell lines and PDO models demonstrated a causal relationship between phenotype and metabolic state. Mechanistically, the OXPHOS dependent invasive state resulted from transient HO-1 upregulation triggered by contact with dense collagen that reduced heme levels and mitochondrial chelatable iron levels. This induced expression of low cytoplasmic iron response genes regulated by ACO1/IRP1. Knockdown or inhibition of HO-1, ACO1/IRP1, MRCK, or OXPHOS abrogated invasion. These findings support an emerging theory that heme and iron flux serve as important regulators of TNBC aggressiveness.
RESUMO
Objective measurement of gaze pattern and eye movement during untethered activity has important applications for neuroscience research and neurological disease detection. Current commercial eye-tracking tools rely on desk-top devices with infrared emitters and conventional frame-based cameras. Although wearable options do exist, the large power-consumption from their conventional cameras limit true long-term mobile usage. The query-driven Dynamic Vision Sensor (qDVS) is a neuromorphic camera which dramatically reduces power consumption by outputting only intensity-change threshold events, as opposed to full frames of intensity data. However, such hardware has not yet been implemented for on-body eye-tracking, but the feasibility can be demonstrated using a mathematical simulator to evaluate the eye-tracking ca-pabilities of the qDVS under controlled conditions. Specifically, a framework utilizing a realistic human eye model in the 3D graphics engine, Unity, is presented to enable the controlled and direct comparison of image-based gaze tracking methods. Eye-tracking based on qDVS frames was compared against two different conventional frame eye-tracking methods - the traditional ellipse pupil-fitting algorithm and a deep learning neural network inference model. Gaze accuracy from qDVS frames achieved an average of 93.2% for movement along the primary horizontal axis (pitch angle) and 93.1 % for movement along the primary vertical axis (yaw angle) under 4 different illumination conditions, demonstrating the feasibility for using qDVS hardware cameras for such applications. The quantitative framework for the direct comparison of eye tracking algorithms presented here is made open-source and can be extended to include other eye parameters, such as pupil dilation, reflection, motion artifact, and more.
