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Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy, and its incidence has increased rapidly in recent years. The BRAF inhibitor vemurafenib is effective against BRAFV600E-positive PTC; however, acquired resistance to single agent therapy frequently leads to tumor recurrence and metastasis, underscoring the need to develop tailored treatment strategies. We previously showed that the oncogenic kinase PIM1 was associated with the malignant phenotype and prognosis of PTC. In this study, we showed that sustained expression of the PIM1 protein in PTC was affected by the BRAFV600E mutation. Based on this regulatory mechanism, we tested the synergistic effects of inhibitors of BRAF (BRAFi) and PIM1 in BRAFV600E-positive PTC cell lines and xenograft tumors. LC-MS metabolomics analyses suggested that BRAFi/PIMi therapy acted by restricting the amounts of critical amino acids and nucleotides required by cancer cells as well as modulating DNA methylation. This study elucidates the role of BRAFV600E in the regulation of PIM1 in PTC and demonstrates the synergistic effect of a novel combination, BRAFi/PIMi, for the treatment of PTC. This discovery, along with the pathways that may be involved in the powerful efficacy of BRAFi/PIMi strategy from the perspective of cell metabolism, provides insight into the molecular basis of PTC progression and offers new perspectives for BRAF-resistant PTC treatment. [ABSTRACT FROM AUTHOR]

Fully supervised learning (FSL) and weakly supervised learning based on multiple instance learning (WSLMIL) have become two mainstream paradigms for performing computer-aided pathological diagnosis (CAPD). It is well known that the high-intensity annotation burden of FSL and the performance degradation due to poor training constraints of WSLMIL are stumbling blocks for clinical translation. Even more unfortunate is the lack of comprehensive experimental analysis to help researchers make content-specific trade-offs between FSL and WSLMIL. In this work, we systematically compare the performances of FSL and WSLMIL on lymph node metastasis in breast cancer using a publicly available dataset. By analyzing the results of 16 backbone networks in the FSL paradigm, we find that emerging networks based on transformer (PVTv2-B2) and multi-layer perceptron (CycleMLP-B3) are more advantageous for performing patch-level classification task than convolution-based structure (ResNet50); combining their output with morphological feature extraction can be better used to universally perform slide-level classification task. However, the slight improvement brought by the evolution of the backbone network may be overshadowed by the aggregation operation in 6 WSLMIL algorithms, whereas relying on the in-domain backbone network can achieve a stable and excellent prediction performance in both quantitative analysis and interpretability comparisons. All the experimental results ultimately illustrate that the combination of in-domain backbone network and emergent aggregation operation becomes an economical and efficient technical tool for CAPD, which can be regarded as a compromise between FSL and WSLMIL. [ABSTRACT FROM AUTHOR]