PRMT1 Sustains De Novo Fatty Acid Synthesis by Methylating PHGDH to Drive Chemoresistance in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) chemoresistance hampers the ability to effectively treat patients. Identification of mechanisms driving chemoresistance can lead to strategies to improve treatment. Here, we revealed that protein arginine methyltransferase-1 (PRMT1) simultaneously methylates D-3-phosphoglycerate dehydrogenase (PHGDH), a critical enzyme in serine synthesis, and the glycolytic enzymes PFKFB3 and PKM2 in TNBC cells. 13C metabolic flux analyses showed that PRMT1-dependent methylation of these three enzymes diverts glucose toward intermediates in the serine-synthesizing and serine/glycine cleavage pathways, thereby accelerating the production of methyl donors in TNBC cells. Mechanistically, PRMT1-dependent methylation of PHGDH at R54 or R20 activated its enzymatic activity by stabilizing 3-phosphoglycerate binding and suppressing polyubiquitination. PRMT1-mediated PHGDH methylation drove chemoresistance independently of glutathione synthesis. Rather, activation of the serine synthesis pathway supplied α-ketoglutarate and citrate to increase palmitate levels through activation of fatty acid synthase (FASN). Increased palmitate induced protein S-palmitoylation of PHGDH and FASN to further enhance fatty acid synthesis in a PRMT1-dependent manner. Loss of PRMT1 or pharmacologic inhibition of FASN or protein S-palmitoyltransferase reversed chemoresistance in TNBC. Furthermore, IHC coupled with imaging MS in clinical TNBC specimens substantiated that PRMT1-mediated methylation of PHGDH, PFKFB3, and PKM2 correlates with chemoresistance and that metabolites required for methylation and fatty acid synthesis are enriched in TNBC. Together, these results suggest that enhanced de novo fatty acid synthesis mediated by coordinated protein arginine methylation and protein S-palmitoylation is a therapeutic target for overcoming chemoresistance in TNBC. SIGNIFICANCE: PRMT1 promotes chemoresistance in TNBC by methylating metabolic enzymes PFKFB3, PKM2, and PHGDH to augment de novo fatty acid synthesis, indicating that targeting this axis is a potential treatment strategy.

Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.

PURPOSE: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers' experience and data characteristics on the sensitivity and final diagnosis. MATERIALS AND METHODS: The CRs of 453 patients were retrospectively selected from two institutions. Among these CRs, 60 images with abnormal findings (pulmonary nodules, masses, and consolidation) and 140 without abnormal findings were randomly selected for sequential observer-performance testing. In the test, 12 readers (three radiologists, three pulmonologists, three non-pulmonology physicians, and three junior residents) interpreted 200 images with and without CAD, and the findings were compared. Weighted alternative free-response receiver operating characteristic (wAFROC) figure of merit (FOM) was used to analyze observer performance. The lesions that readers initially missed but CAD detected were stratified by anatomic location and degree of subtlety, and the adoption rate was calculated. Fisher's exact test was used for comparison. RESULTS: The mean wAFROC FOM score of the 12 readers significantly improved from 0.746 to 0.810 with software assistance (P = 0.007). In the reader group with < 6 years of experience, the mean FOM score significantly improved from 0.680 to 0.779 (P = 0.011), while that in the reader group with ≥ 6 years of experience increased from 0.811 to 0.841 (P = 0.12). The sensitivity of the CAD software and the adoption rate for the lesions with subtlety level 2 or 3 (obscure) lesions were significantly lower than for level 4 or 5 (distinct) lesions (50% vs. 93%, P < 0.001; and 55% vs. 74%, P = 0.04, respectively). CONCLUSION: CAD software use improved doctors' performance in detecting nodules/masses and consolidation on CRs, particularly for non-expert doctors, by preventing doctors from missing distinct lesions rather than helping them to detect obscure lesions.

Clinical utility of the Bosniak classification version 2019: Diagnostic value of adding magnetic resonance imaging to computed tomography examination.

PURPOSE: To assess the impact of the updated Bosniak classification (BC2019) for cystic renal masses (CRMs) on interobserver agreement between radiologists and urologists and the diagnostic value of adding MRI to CT examination (combined CT/MRI). METHOD: This study included 103 CRMs from 83 consecutive patients assessed using contrast-enhanced CT and MRI between 2010 and 2016. Nine readers in three groups (three radiologists, three radiology residents, and three urologists) reviewed CT alone and the combined CT/MRI using BC2019. Bosniak category was determined by consensus in each group for diagnosing malignancy, with a cut-off category of ≧III. Interobserver agreement was assessed using Fleiss' kappa values. The effect of CT or combined CT/MRI on the diagnosis of malignancy was assessed using McNemar's test. RESULTS: Interobserver agreement of BC2019 for CT alone was substantial for radiologists and residents, moderate for urologists (0.77, 0.63, and 0.58, respectively). Interobserver agreement of BC2019 for combined CT/MRI was substantial for all three groups (radiologists: 0.78; residents: 0.65; and urologists: 0.61). Among residents, the sensitivity/specificity/accuracy rates of combined CT/MRI vs. CT alone were 82.1/74.7/76.7% vs. 75.0/66.7/68.9%, and specificity and accuracy were significantly higher for combined CT/MRI than that for CT alone (p = 0.03 and 0.008, respectively). Similarly, sensitivity/specificity/accuracy values were significantly higher for combined CT/MRI among urologists (78.6/73.3/74.8% vs. 64.3/64.0/64.1%, p = 0.04/0.04/0.008). However, sensitivity/specificity/accuracy did not significantly differ between the two among radiologists (89.3/74.7/78.6% vs. 85.7/73.3/76.7%, p = 0.32/0.56/0.32). CONCLUSIONS: Combined CT/MRI is useful for diagnosing malignancy in patients with CRMs using BC2019, especially for non-expert readers.