The lethal chicken mutation nanomelia leads to severe skeletal defects because of a deficiency of aggrecan, which is the largest aggregating chondroitin sulphate proteoglycan of cartilage. In previous work, we have demonstrated that nanomelic chondrocytes produce a truncated aggrecan precursor that fails to be secreted, and is apparently arrested in the endoplasmic reticulum (ER). In this study, we investigated the biosynthesis and extent of processing of the abnormal aggrecan precursor. The truncated precursor was translated directly in cell-free reactions, indicating that it does not arise post-translationally. Further studies addressed the processing capabilities of the defective precursor. We found that the mutant precursor was modified by N-linked, mannose-rich oligosaccharides and by the addition of xylose, but was not further processed; this is consistent with the conclusion that it moves no further along the secretory pathway than the ER. Using brefeldin A we demonstrated that the defective precursor can function as a substrate for Golgi-mediated glycosaminoglycan chains, but does not do so in the nanomelic chondrocyte because it fails to be translocated to the appropriate membrane compartment. These studies illustrate how combined cell biological/biochemical and molecular investigations may contribute to our understanding of the biological consequences and molecular basis of genetic diseases, particularly those involving errors in large, highly modified molecules such as proteoglycans.
Purpose: For this study, we investigated the computer‐extracted tumor phenotypes from diffusion weighted imaging, dynamic contrast‐enhanced, and T2‐weighted magnetic resonance imaging modalities on a dataset of malignant and benign breast lesions. Methods: The IRB‐approved, retrospectively‐collected dataset included 118 breast lesions with 105 malignant and 13 benign. All images were acquired during clinical breast MRI at both 1.5T and 3.0T magnet strength. Phenotypic categories extracted with each modality included tumor size, shape, margin sharpness, enhancement texture, kinetics, and variance kinetics for DCE, size, shape, margin sharpness, texture for T2w, and ADC features for DWI. Results: In the task of distinguishing between benign and malignant lesions, each modality's performance was analyzed by Round Robin evaluation using Receiver Operating Characteristic (ROC) analysis. DCE alone outperformed DWI and T2w with an AUC value of 0.89 +/−0.06. DWI and T2w yielded AUC values of 0.86 +/−0.05 and 0.84 +/−0.06 respectively. The combination of all three modalities yielded an AUC value of 0.88 +/−0.04 under single‐loop Round Robin evaluation. The contrast phenotype from T2w and the standard deviation phenotype from DWI were found to be statistically different between the malignant and benign multimodality lesion groups. Conclusion: The results obtained from merging radiomic features from multimodality breast MRI (DCE, T2w, and DWI) indicate that the additional benefit of multimodality breast MRI in cancer diagnosis could be significant. This method also has potential to determine the most discriminatory radiomic phenotype from each modality. APPM DREAM Fellowship and the University of Chicago Dean Bridge Fund. M. L. Giger is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a co‐founder and stockholder in Quantitative Insights.
Purpose: For this study, we investigated quantitative radiomics of breast tumors on diffusion weighted imaging and dynamic contrast‐enhanced MRIs in the task of assessing the prognostic status of breast cancers. Methods: Our IRB‐approved, retrospectively‐collected dataset included 316 breast cancers with 235 ER+ and 81 ER‐ cases. All images were acquired during clinical breast MRI incorporating dynamic‐contrast MRI and diffusion‐weighted MRI. Phenotypic categories extracted quantitatively from DCE‐MRI included tumor size, shape, margin sharpness, enhancement texture, kinetics, and variance kinetics, and from DWI‐DCE ADC features (average, range, variation) for DWI. Phenotypes, as well as merged tumor signatures from round robin evaluation, were assessed for the prognostic tasks using area under the ROC curve (AUC) as the index of performance. Results: In the task of distinguishing between ER+ and ER‐ cancers, computer‐extracted phenotypes from DCE and DWI yielded comparable performance levels, however, we found that the phenotypes, as well as the modality‐specific tumor signatures, showed only slight correlation (r=−0.44), thus indicating the promise of multi‐modality signatures. In the tasks of ER+ vs. ER‐. PR+ vs. PR‐, lymph node positive vs negative, we obtained AUC values of 0.66 (0.03), 0.64 (0.03), and 0.64 (0.03) for DCE‐MRI, and AUC values of 0.64 (0.03), 0.61 (0.03), and 0.61 (0.03) for DWI‐MRI, respectively. The combination of the modalities yielded AUC values of 0.67 (0.03), 0.64 (0.03), and 0.62 (0.03), respectively. Conclusion The correlation and performance results obtained from merging radiomic features from DCE‐MRI and DWI‐MRI indicate that the additional benefit of multimodality breast MRI in assessing prognosis is promising. Funded by an NIH (PREP) (R25) Grant and the University of Chicago Dean Bridge Fund. COI: M.L. Giger is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverrain Medical, Mitsubushi, and Toshiba. MLG is a co‐founder and stockholder in Quantitative Insights.
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