Using a novel mouse model, a mitochondrial-nuclear exchange model termed MNX, we tested the hypothesis that inherited mitochondrial haplotypes alter primary tumor latency and metastatic efficiency. Male FVB/N-Tg(MMTVneu)202Mul/J (Her2) transgenic mice were bred to female MNX mice having FVB/NJ nuclear DNA with either FVB/NJ, C57BL/6J, or BALB/cJ mtDNA. Pups receiving the C57BL/6J or BALB/cJ mitochondrial genome (i.e., females crossed with Her2 males) showed significantly (P < 0.001) longer tumor latency (262 vs. 293 vs. 225 days), fewer pulmonary metastases (5 vs. 7 vs. 15), and differences in size of lung metastases (1.2 vs. 1.4 vs. 1.0 mm diameter) compared with FVB/NJ mtDNA. Although polyoma virus middle T–driven tumors showed altered primary and metastatic profiles in previous studies, depending upon nuclear and mtDNA haplotype, the magnitude and direction of changes were not the same in the HER2-driven mammary carcinomas. Collectively, these results establish mitochondrial polymorphisms as quantitative trait loci in mammary carcinogenesis, and they implicate distinct interactions between tumor drivers and mitochondria as critical modifiers of tumorigenicity and metastasis.
Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exams or procedures, and low sensitivity. Digital breast tomosynthesis (DBT) improves on conventional mammography by increasing both sensitivity and specificity and is becoming common in clinical settings. However, deep learning (DL) models have been developed mainly on conventional 2D full-field digital mammography (FFDM) or scanned film images. Due to a lack of large annotated DBT datasets, it is difficult to train a model on DBT from scratch. In this work, we present methods to generalize a model trained on FFDM images to DBT images. In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images. In the proposed approach, the differences between the FFDM and DBT domains are reduced via HM and then the base model, which was trained on abundant FFDM images, is fine-tuned. When evaluating on image patches extracted around identified findings, we are able to achieve similar areas under the receiver operating characteristic curve (ROC AUC) of ∼ 0.9 for FFDM and ∼ 0.85 for MIP images, as compared to a ROC AUC of ∼ 0.75 when tested directly on MIP images.
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