Tian, Shulan

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

ABSTRACT Background & Aims Steatotic liver disease (SLD) is characterised by liver fat accumulation exceeding 5%. Lean body weight (BMI ≤ 25 kg/m 2 ) with non‐alcoholic SLD is a rare phenotype, and the balance of risks related to metabolic conditions or genetic predisposition, or the risk of progressive liver disease, is not known. This study evaluates clinical and genetic predictors of non‐alcoholic SLD and advanced liver disease in lean individuals. Methods We used International Classification of Disease codes, radiology and pathology review to identify 177 lean non‐alcoholic SLD cases (47 cryptogenic, 130 MASLD), 677 matched lean controls, and 3090 overweight/obese SLD cases in the Mayo Clinic Biobank and Tapestry databases. We performed case–control and cross‐sectional comparisons of clinical and genetic factors between these groups, using univariable and multivariable analyses. Results Lean individuals with non‐alcoholic SLD exhibited metabolic and genetic profiles that were intermediate between those of lean controls without SLD and overweight/obese SLD individuals, including intermediate rates of diabetes, hypertension, hyperlipidaemia and of homozygosity for the risk allele of PNPLA3 . Multivariable analysis indicated that diabetes was an independent predictor of SLD among lean individuals. Among lean individuals with non‐alcoholic SLD, those with metabolic‐associated SLD (MASLD), as compared to those with cryptogenic SLD (without metabolic risk factor), were more likely to be homozygous for risk alleles in GCKR . The Fib‐4 score, a tool for screening advanced liver disease in SLD, accurately predicted advanced fibrosis among lean individuals with non‐alcoholic SLD. Conclusion Diabetes serves as a primary predictor of non‐alcoholic SLD in lean individuals. These results support the recommendation to screen for SLD in patients with diabetes, regardless of BMI. The GCKR risk allele is associated with MASLD in lean individuals, and the risk factors for cryptogenic SLD remain unclear.

Abstract Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Abstract Glioblastoma (GBM) remains one of the most therapy-resistant malignancies with frequent local failures despite aggressive surgery, chemotherapy, and ionizing radiation (IR). Small molecule inhibitors of DNA-dependent protein kinase (DNA-PKi) are potent radiosensitizers currently in clinical trials. Determining which patients may benefit from radiosensitization with DNA-PKi is critical to avoid unnecessary increased risk of normal tissue toxicity. In this study, we used GBM patient-derived xenografts (PDX) in orthotopic murine models to study the relationship between molecular features, pharmacokinetics, and the radiosensitizing potential of the DNA-PKi peposertib. We show that peposertib radiosensitizes established and PDX GBM lines in vitro at 300 nmol/L and above, with a significant increase in radiosensitization by maintaining post-IR exposure for >12 hours. Radiosensitization by peposertib is mediated by catalytic inhibition of DNA-PK, and knockdown of DNA-PK by short hairpin RNA (shRNA) largely abolished the radiosensitizing effect. Peposertib decreased auto-phosphorylation of DNA-PKcs after IR in a dose-dependent manner with a delay in resolution of γH2AX foci at 24 hours. The addition of peposertib to IR significantly increased survival in GBM120 orthotopic xenografts, but not in GBM10. There was no difference in plasma or average tumor concentrations of peposertib in the two cohorts. Although the mechanism underpinning this discordant effect in vitro versus in vivo is not clear, there was an association for greater sensitization in TP53 mutant lines. Transfection of a dominant-negative TP53 mutant in baseline TP53 wild-type GBM lines significantly delayed growth and decreased nonhomologous end joining efficiency (but not homologous recombination), after peposertib exposure. See related commentary by Buchsbaum, p. 840