Chronological alterations in de novo malignancies after living-donor liver transplantation: A cohort study of 1781 recipients using annual comparisons of standardized incidence ratios.

BACKGROUND: De novo malignancies (DNMs) are a major adverse event after solid organ transplantation; however, their characteristics and recent trends after living-donor liver transplantation (LDLT) remain unclear. METHODS: We retrospectively reviewed 1781 primary LDLT recipients (1990-2020) and annually calculated standardized incidence ratios (SIRs) of DNMs compared to the age-adjusted Japanese general population. RESULTS: After 21 845 person-years follow-up, 153 DNM lesions (8.6%) were identified in 131 patients (7.4%). The incidence was 0.007 person-years. DNMs included 81 post-transplant lymphoproliferative disorders (PTLDs), 14 colorectal, 12 lung, and 12 gastric cancers, and so on. Comorbid DNMs significantly worsened recipient survival than those without (p < .001). The 5- and 10-year recipient survival after DNM diagnosis were 65% and 58%, respectively. Notably, SIR1993-1995: 8.12 (95% CI: 3.71-15.4, p < .001) and SIR1996-1998: 3.11 (1.34-6.12, p = .01) were significantly high, but had decreased time-dependently to SIR2005-2007: 1.31 (0.68-2.29, p = .42) and SIR2008-2010: 1.34 (0.75-2.20, p = .33), indicating no longer significant difference in DNMs development. Currently, however, SIR2014-2016: 2.27 (1.54-3.22, p < .001) and SIR2017-2019: 2.07 (1.40-2.96, p < .001) have become significantly higher again, reflecting recent aging of recipients (>50 years) and resultant increases in non-PTLD DNMs. Furthermore, characteristically in LDLT, the fewer the donor-recipient HLA-mismatches, the less the post-transplant DNMs development. CONCLUSION: DNM development after LDLT was significantly higher than in the general population due to higher PTLD incidence (1993-1998), but once became equivalent (2005-2013), then significantly increased again (2014-2019) due to recent recipient aging and resultant increase in solid cancers.

Neural-net-based cell deconvolution from DNA methylation reveals tumor microenvironment associated with cancer prognosis.

DNA methylation is a pivotal epigenetic modification that defines cellular identity. While cell deconvolution utilizing this information is considered useful for clinical practice, current methods for deconvolution are limited in their accuracy and resolution. In this study, we collected DNA methylation data from 945 human samples derived from various tissues and tumor-infiltrating immune cells and trained a neural network model with them. The model, termed MEnet, predicted abundance of cell population together with the detailed immune cell status from bulk DNA methylation data, and showed consistency to those of flow cytometry and histochemistry. MEnet was superior to the existing methods in the accuracy, speed, and detectable cell diversity, and could be applicable for peripheral blood, tumors, cell-free DNA, and formalin-fixed paraffin-embedded sections. Furthermore, by applying MEnet to 72 intrahepatic cholangiocarcinoma samples, we identified immune cell profiles associated with cancer prognosis. We believe that cell deconvolution by MEnet has the potential for use in clinical settings.