Histomorphological factors in the risk prediction of lymph node metastasis in papillary thyroid carcinoma.

AIMS: Few clinicopathological parameters have been identified as independent predictive factors for lymph node metastasis. This study evaluated the predictive ability of three histological characteristics of PTC in lymph node metastases: hobnail features, loss of cohesiveness/polarity (LOCP) and micropapillary structures. METHODS AND RESULTS: Tissue specimens from 153 patients with histologically confirmed PTC including 112 cases of papillary thyroid microcarcinoma (PTMC) were enrolled in this study. Three histological characteristics (hobnail features, LOCP and micropapillary structures) and several clinicopathological parameters were evaluated for their value in predicting lymph node metastasis. Hobnail features, LOCP and micropapillary structures were each significantly associated with and found to be independent predictive factors for lymph node metastasis (P < 0.05). These three histological characteristics were closely correlated with one another (P < 0.001). Six of the seven possible combinations of these three histological characteristics were independently correlated with lymph node metastasis (P < 0.05). Among these combinations, the coincidence of all three histological parameters represented the strongest independent predictive factor for lymph node metastasis (OR: 3.270, P = 0.006). CONCLUSIONS: Our study demonstrates that hobnail features, LOCP and micropapillary structures, either alone or in combinations, represent strong independent predictive factors for lymph node metastasis in PTC.

Detection of Marker-Free Precision Genome Editing and Genetic Variation through the Capture of Genomic Signatures.

Genome editing technologies have transformed our ability to engineer desired genomic changes within living systems. However, detecting precise genomic modifications often requires sophisticated, expensive, and time-consuming experimental approaches. Here, we describe DTECT (Dinucleotide signaTurE CapTure), a rapid and versatile detection method that relies on the capture of targeted dinucleotide signatures resulting from the digestion of genomic DNA amplicons by the type IIS restriction enzyme AcuI. DTECT enables the accurate quantification of marker-free precision genome editing events introduced by CRISPR-dependent homology-directed repair, base editing, or prime editing in various biological systems, such as mammalian cell lines, organoids, and tissues. Furthermore, DTECT allows the identification of oncogenic mutations in cancer mouse models, patient-derived xenografts, and human cancer patient samples. The ease, speed, and cost efficiency by which DTECT identifies genomic signatures should facilitate the generation of marker-free cellular and animal models of human disease and expedite the detection of human pathogenic variants.