Hepatoblastoma is the most common pediatric liver malignancy and usually occurs within the first 3 years of life. In recent years, the overall incidence of hepatoblastoma has exhibited the greatest increase among all pediatric malignancies worldwide. The diagnosis of hepatoblastoma may be challenging due to the lack of a current consensus classification system. The International Pediatric Liver Tumors Consensus introduced guidelines and a consensus classification for the diagnosis of hepatoblastoma as either epithelial or mixed epithelial and mesenchymal and in the updated 5th edition of the World Health Organization Classification of Digestive System Tumors.

Purpose Tyrosine kinase inhibitors (TKI) targeting vascular endothelial growth factor receptor (VEGFR) signaling pathways have been used for metastatic clear cell renal cell carcinoma (mCCRCC), but resistance to the drug develops in most patients. We aimed to explore the underlying mechanism of the TKI resistance with regard to programmed death-ligand 1 (PD-L1) and to investigate signaling pathway associated with the resistant mechanism.Materials and Methods To determine the mechanism of resistance, 10 mCCRCC patients from whom tumor tissues were harvested at both the pretreatment and the TKI-resistant post-treatment period were included as the discovery cohort, and their global gene expression profiles were compared. A TKI-resistant renal cancer cell line was established by long-term treatment with sunitinib.Results Among differentially expressed genes in the discovery cohort, increased PD-L1 expression in post-treatment tissues was noted in four patients. Pathway analysis showed that PD-L1 expression was positively correlated with the mammalian target of rapamycin (mTOR) signaling pathway. The TKI-resistant renal cancer cells showed increased expression of PD-L1 and mTOR signaling proteins and demonstrated aggressive tumoral behaviour. Treatment with mTOR inhibitors down-regulated PD-L1 expression and suppressed aggressive tumoral behaviour, which was reversed with stimulation of the mTOR pathway.Conclusion These results showed that PD-L1 expression may be increased in a subset of VEGFR-TKI–resistant mCCRCC patients via the mTOR pathway.

Jeong SU, et al. Investig Clin Urol. 2024 Jan;65(1):84-93. https://doi.org/10.4111/icu.20230294

This study aimed investigate the effect of the level of invasion into fibromuscular band (FMB) of prostate on the positive surgical margin (PSM) and biochemical recurrence (BCR) after radical prostatectomy (RP) in patients with organ-confined (pT2) prostate cancer. The clinical and pathological data of 461 consecutive patients with pT2 prostate cancer were evaluated regarding the level of invasion into FMB. The relationship between levels of invasion into FMB and PSM / BCR was assessed. The rate of PSM at an FMB level of at 2 was 18.8%, which was significantly greater than the rates at levels 0 (5.4%) and 1 (7.8%). The level of FMB (p = 0.007) and percentage of tumor volume (p = 0.012) were identified as independent factors predictive of a positive surgical margin in a multivariate analysis. The 5-year BCR-free survival rates for a level 0–1 FMB with negative surgical margin, level 0–1 FMB with positive surgical margin, level 2 FMB with negative surgical margin, and level 2 FMB with positive surgical margin were 96.6%, 86.4%, 85.6%, and 72.9%, respectively (p < 0.001). A level 2 FMB (p = 0.050), positive surgical margin (p = 0.001), and surgical Gleason score (p = 0.001) were identified as independent predictors of a BCR of pT2 prostate cancer. Among patients with negative surgical margins, the surgical Gleason score and level of FMB independently affected the incidence of a BCR of pT2 prostate cancer. The level of FMB was an independent predictor of both a positive surgical margin and a BCR of pT2 disease. Accordingly, the level of FMB might help to further stratify the prognosis of patients with pT2 disease.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions: These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.