We compared overexpression of the magnetotactic bacterial gene MagA with the modified mammalian ferritin genes HF + LF, in which both heavy and light subunits lack iron response elements. Whereas both expression systems have been proposed for use in non-invasive, magnetic resonance (MR) reporter gene expression, limited information is available regarding their relative potential for providing gene-based contrast. Measurements of MR relaxation rates in these expression systems are important for optimizing cell detection and specificity, for developing quantification methods, and for refinement of gene-based iron contrast using magnetosome associated genes. We measured the total transverse relaxation rate (R2*), its irreversible and reversible components (R2 and R2′, respectively) and the longitudinal relaxation rate (R1) in MDA-MB-435 tumor cells. Clonal lines overexpressing MagA and HF + LF were cultured in the presence and absence of iron supplementation, and mounted in a spherical phantom for relaxation mapping at 3 Tesla. In addition to MR measures, cellular changes in iron and zinc were evaluated by inductively coupled plasma mass spectrometry, in ATP by luciferase bioluminescence and in transferrin receptor by Western blot. Only transverse relaxation rates were significantly higher in iron-supplemented, MagA- and HF + LF-expressing cells compared to non-supplemented cells and the parental control. R2* provided the greatest absolute difference and R2′ showed the greatest relative difference, consistent with the notion that R2′ may be a more specific indicator of iron-based contrast than R2, as observed in brain tissue. Iron supplementation of MagA- and HF + LF-expressing cells increased the iron/zinc ratio approximately 20-fold, while transferrin receptor expression decreased approximately 10-fold. Level of ATP was similar across all cell types and culture conditions. These results highlight the potential of magnetotactic bacterial gene expression for improving MR contrast.

Background This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics. Methods This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty‐two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co‐occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical‐pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross‐validation was performed using a leave‐one‐out cross‐validation method. Results Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K ‐nearest neighbors ( K‐ NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%. Conclusion QUS‐based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.