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 Rationale Idiopathic Pulmonary Fibrosis (IPF) is characterized by chronic progressive pulmonary fibrosis and high mortality. Genetic markers, summarized into a polygenic risk score (PRS), associate with IPF in well-phenotyped research cohorts. Objectives To evaluate the performance of the PRS using real-world data from routinely captured electronic healthcare records. Methods We conducted an observational study evaluating the association of a PRS for IPF with electronic healthcare record diagnosis of IPF as well as lung transplant-free survival in four independent cohorts; the Mass General Brigham Biobank (MGBB), Mayo Clinic Biobank (MCBB), Mayo Clinic Tapestry Cohort (Tapestry), and U.K. Biobank (UKBB). We used multivariable logistic regression and multivariable Cox proportional hazards models adjusting for age, gender, and principal components of ancestry. The cohorts then underwent fixed and random effects meta-analysis. Measurements and Main Results Of 37,709; 44,195; 43,202; and 447,422 participants from MGBB, MCBB, Tapestry and UKBB respectively, 1,015 (2.7%) 2,879 (6.5%), 1,310 (3.0%), and 2,742 (0.6%) participants had an IPF diagnosis. Meta-analysis demonstrated a high-risk PRS associated with IPF diagnosis, OR 2.88 (95%CI 2.41-3.44) compared to all other individuals. A high-risk PRS also associated with the composite endpoint of mortality or lung transplant among those with an IPF diagnosis, HR 1.23(95%CI 1.11-1.35) compared to all other individuals. Conclusions A PRS can identify those at risk for an IPF diagnosis and mortality in biobank-scale data, which may have implications for clinical decisions. Further work is necessary to evaluate the utility of adding genetics in clinical settings.

Abstract The rapid progress in tumour genome sequencing has created a need for bioinformatics tools to interpret the clinical significance of detected variants. VarStack² integrates information from several publicly available resources, including the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, cBioPortal, UCSC Genome Browser, and ClinicalTrials.gov, CIViC and presents it through a user-friendly interface. VarStack2 simplifies the process of retrieving data, saving users significant time compared to manually navigating each database individually. Users can input a variant by specifying a gene symbol, amino acid change, and coding sequence change, with the option to search tumour-specific studies in cBioPortal alongside their primary query. Results are organized into separate sections and can be exported in CSV format for further analysis. Additionally, VarStack2 offers a smart search feature that suggests variants for the gene of interest based on its database search results. These features make VarStack2 a useful tool for scientists and clinicians by enhancing the variant interpretation process and integrating somatic variant information into workflows. VarStack² is freely available at http://varstack.brown.edu/

Abstract Cyclin D1, encoded by CCND1, is a cell cycle regulator, overexpressed in ~15% of multiple myeloma (MM) cases via CCND1::IGH super-enhancer juxtaposition through t(11;14). Cyclin D1 also plays a non-canonical role in transcriptional regulation, but in t(11;14) MM its function in gene regulation remains undefined. We investigated the global transcriptional and epigenomic consequences of CCND1overexpression in t(11;14) MM. To define a CCND1 signature, RNA-seq from two independent primary MM patient cohorts, CoMMpass and Mayo, was utilized to compare high CCND1 t(11;14) cases (≥300 transcripts-per-million [TPM], n=271) vs. low CCND1 hyperdiploid cases (<30 TPM, n=233). Two CCND1 knockout (KO) clones in t(11;14) MM U266B1 were also generated by CRISPR/Cas9, introducing a frameshift insertion (A) after chr11:69641478 (hg38) in exon 1. ATAC-seq and ChIP-seq (H3K4me3, H3K4me1, and H3K27ac) were performed in duplicates, and RNA-seq in triplicates in U266B1 wild-type (WT) and KO. Libraries were sequenced on Illumina NextSeq 2000 (2x51 bp for ChIP/ATAC, 2x150 bp for RNA-seq). Reads were mapped to hg38 by BWA (ChIP/ATAC) or STAR (RNA-seq), peaks called by MACS2 (FDR< 0.01), peak and gene raw counts estimated by featureCounts. Differential peaks (fold-change ≥1.5) and differentially expressed genes (fold-change ≥2) were identified by edgeR (FDR<0.05), including peaks detected in ≥2 samples and genes with TPM ≥0.1 in ≥1 sample. Interactions between promoters and distal regulatory regions were identified using publicly available chromatin interaction data in blood cell types. Pathway analysis was performed using Enrichr. Co-immunoprecipitation was performed in t(11;14) KMS12BM cells. Differential analysis comparing CCND1-high t(11;14) and CCND1-low MM cases in the two patient cohorts identified 21 upregulated and 21 downregulated genes associated with CCND1 expression which were also captured in the wild-typevs. CCND1 KO U266 cells. Enriched pathways (p<0.05) included Notch signaling, early/late estrogen response, epithelial-mesenchymal transition and reduced mitotic spindle and myogenesis. Dysregulated genes included those related to apoptosis, ubiquitin-proteosome, extracellular matrix, adhesion, and immune response, demonstrating a broader role for cyclin D1 in MM beyond cell cycle control. To explore the epigenomic basis of this transcriptional regulation, for each of the 42 differentially expressed genes we examined the changes of histone marks and chromatin accessibility in promoter and the interacting distal regulatory regions in the U266 KO model. Ten of the 21 genes upregulated in CCND1-high (downloaded in KO; e.g., argininosuccinate synthase 1 [ASS1], PMAIP1, MYEOV, CCR7, and DTX1) and 12 of the 21 genes downregulated in CCND1-high (upregulated in KO; e.g., UACA, RASAL2, PTPRZ1, PPT2, and IL23R) showed concordant changes in histone marks and/or chromatin accessibility, predominantly in distal regulatory regions, suggesting a chromatin-mediated mechanism of gene regulation by cyclin D1. The most prominent changes involved H3K27ac, a mark defining active enhancers. As H3K27ac is deposited by histone acetyltransferases CBP, we tested for the possible cyclin D1-CBP interaction. Co-IP confirmed this interaction, suggesting a role for this complex in chromatin remodeling and transcriptional control in t(11;14) MM. One gene notably upregulated in association with CCND1 is ASS1, which encodes a urea cycle enzyme responsible for endogenous arginine synthesis. Metabolomic studies demonstrated KO cells had elevated aspartate and citrulline and lowered arginine and ornithine compared to WT cells (p<0.01) demonstrating impaired urea cycle function. Further, KO cells displayed significantly reduced ASS1 protein expression, cell growth and S-phase entry compared to WT cells in arginine-replete conditions, with both WT and KO cell types further inhibited by arginine depletion (p<0.05). Together, a cyclin D1-associated gene signature was identified supporting its role in t(11;14) MM beyond the CDK4/6 mediated cell cycle control, including a role in metabolic plasticity and survival under arginine depleted conditions. These findings highlight cyclin D1 as a therapeutic vulnerability and open new avenues for personalized treatment strategies.

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 Determining the balance between DNA double strand break repair (DSBR) pathways is essential for understanding treatment response in cancer. We report a method for simultaneously measuring non-homologous end joining (NHEJ), homologous recombination (HR), and microhomology-mediated end joining (MMEJ) and show that HR and MMEJ are therapeutically-targetable treatment resistance mechanisms in glioblastoma. Using a targeted screen of clinically relevant small molecules, we identify the ATM kinase inhibitor, AZD1390, as a potent dual HR/MMEJ inhibitor. Mechanistic studies in GBM cell lines show that AZD1390 suppresses DSB end resection and blocks radiation-induced phosphorylation of DSBR proteins including nibrin, Rad50, and Mettl16. Interestingly, AZD1390 enhances the cytotoxic effects of TMZ and radiation in both treatment-naïve and treatment-resistant GBM samples harboring mutations in the TP53 gene. We show that TP53 mutation is associated with upregulation of ATM pathway genes in GBM patient samples along with heightened ATM-dependent HR and MMEJ in GBM cell lines. Finally, we show that TP53-mutant GBMs fail to arrest in G2 phase after radiation/AZD1390 or TMZ/AZD1390 combinations and instead attempt mitosis, leading to mitotic cell death. Expression of the mitotic DSBR factor, RHINO1, is associated with poor prognosis in patients with TP53-mutant GBMs, suggesting that factors that promote efficient mitotic DSBR protect TP53-mutant GBMs from treatment-induced cell death. This report identifies ATM-dependent HR and MMEJ as targetable resistance mechanisms in TP53-mutant GBM and establishes ATM inhibition as a potential strategy for overcoming intrinsic and acquired treatment resistance in TP53-mutant GBMs. Citation Format: Daniel J Laverty, Shiv Gupta, Gary Bradshaw, Jiajia Chen, Brett L Carlson, Nery Matias Calmo, Shulan Tian, Jann N Sarkaria, Zachary D Nagel. ATM inhibitors exploit checkpoint defects and ATM-dependent double strand break repair to induce mitotic cell death in TP53-mutant glioblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A011.

Background and Aims: Metabolic dysfunction–associated steatotic liver disease affects about a third of adults worldwide and is projected soon to be the leading cause of liver cirrhosis. It occurs when fat accumulates in hepatocytes and can progress to metabolic dysfunction–associated steatohepatitis, liver cirrhosis, and HCC. Metabolic dysfunction–associated steatotic liver disease pathogenesis is believed to involve a combination of genetic and environmental risk factors. Single nucleotide polymorphisms have been implicated, but non-syndromic monogenic causes are lacking. Approach and Results: We identified a novel genetic variant in a familial case of metabolic dysfunction–associated steatohepatitis and performed deep variant functional analysis, including protein modeling, dynamics, and cell-based assays to assess molecular mechanisms of dysfunction and altered cellular signaling. We analyzed exome sequencing data of 3904 individuals with steatotic liver disease (SLD) to identify additional cases and establish the link between specific gene variants and SLD diagnosis. We discovered and functionally validated the NM_000245.4:c.3505A>T; p.(Ile1169Phe) variant in the MET (mesenchymal-epithelial transition) kinase domain as a monogenic cause of SLD. Subsequently, we detected additional ultra-rare, previously uninterpreted, and likely deleterious variants in MET from screening sequencing data. Among individuals with confirmed SLD based on electronic record review, 1.1% (45/3904) had rare predicted deleterious MET variants. Eight of 45 (17.7%) individuals had predicted deleterious variants in the MET kinase domain confirmed to be functionally like the familial case variant. Conclusions: We report the first germline nonmalignant rare MET -driven disease, a monogenic form of SLD.

Background: Immune dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), and several studies suggest that hypogammaglobulinemia at the time of CLL diagnosis may predict shorter time to first therapy (TTFT; Parikh et al, 2015). However, it is unclear if hypogammaglobulinemia can predict TTFT independent of the CLL-International Prognostic Index (CLL-IPI) and the tumor mutational load (TML). We sought to determine if serum immunoglobulin (Ig) levels at diagnosis can independently predict outcomes in patients with CLL and its precursor, high-count monoclonal B-cell lymphocytosis (MBL). Methods: We used the Mayo Clinic CLL Database to identify newly diagnosed CLL/MBL consented within 2 years of diagnosis. Next generation sequencing (NGS) was performed using a 59 gene panel (SureSelect) to detect mutated CLL genes. Serum Ig levels were quantitated by radial immunodiffusion (Immunoplates). Serum Ig levels below the lower limit of normal were defined as hypogammaglobulinemia. We used Chi-squared or Fisher's exact tests to compare discrete variables and the Kruskal Wallis test for continuous variables. TTFT was calculated from date of diagnosis to date of first treatment or date last known to be untreated. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for TTFT and overall survival (OS) associations. Results: We identified 895 newly diagnosed patients (588 CLL and 307 MBL) who had undergone NGS for recurrent somatic mutations and had serum Ig levels available at the time of diagnosis. The median absolute B-cell count was 6.7 x109/L; 613 (69%) were male, 375 (42%) had unmutated IGHV genes, and 81 (9%) had TP53 disruption. Based on CLL-IPI, 412 (46%) patients had low risk, 285 (32%) had intermediate risk, and 198 (22%) had high/very high risk. The most commonly mutated genes were NOTCH1 (14%), SF3B1 (11%), TP53 (10%), NFKBIE (8%), ATM (6%), and BIRC3 (6%). Based on the number of genes with high impact or hotspot mutations, we classified 40%, 29%, and 31% patients with TML scores of 0, 1, or 2+, respectively. At CLL/MBL diagnosis, the median serum IgG, IgA, and IgM was 891 mg/dL (111-2620), 121 mg/dL (1-1150), and 44 mg/dL (5-1320), respectively; 284 (32%) patients had low serum IgG, 128 (17%) had low serum IgA, and 322 (42%) had low serum IgM. Low serum IgA was associated with mutations in NOTCH1, SF3B1, ATM, BIRC3 and NFKBIE, low IgM was associated with SF3B1 and BIRC3 mutations (all comparisons, P < .05), and low IgG was associated with BIRC3 mutation (P = .05). The median follow-up for the cohort was 8.4 years. A total of 359 patients progressed requiring therapy (299 CLL and 60 MBL), and 265 patients died (177 CLL and 88 MBL). The TTFT was significantly shorter for patients with low serum IgG compared to normal serum IgG at diagnosis (median 4.4 vs 10.8 years) and similar findings were observed in individuals with low serum IgA (1.7 vs 10.8 years) and those with low serum IgM (4.6 vs 11.1 years). On univariable analysis, the following were associated with a shorter TTFT: low serum IgG (HR: 1.8, 95% CI 1.4-2.2), low serum IgA (HR: 3.5, 95% CI 2.7-4.4), low serum IgM (HR: 1.9, 95% CI 1.5-2.3), TML score of 1 (HR: 1.7, 95% CI 1.3-2.2), TML of 2+ (HR: 4.4, 95% CI 3.4-5.7), CLL-IPI intermediate risk (HR: 3.7, 95% CI 2.8-4.8), and CLL-IPI high/very high risk (HR: 7.6, 95% CI 5.8-10.1) (all comparisons, P < .0001). In multivariable analyses after adjusting for CLL-IPI and TML, low serum levels of IgG (HR: 1.8, 95% CI: 1.5-2.3), IgA (HR: 2.4, 95% CI: 1.8-3.0), and IgM (HR: 1.8, 95% CI: 1.5-2.3) were independently associated with a shorter TTFT (all comparisons, P < .0001). No significant differences in OS were detected between patients with low and normal serum IgG, IgM, and IgA on univariate analysis. Similar results for TTFT and OS were observed after adding clonal B-cell count to the Cox model. Conclusions: Our study shows that low serum Ig levels at the time of initial diagnosis is predictive of TTFT independently of the CLL-IPI and TML, which can further risk stratify individuals with newly diagnosed CLL/MBL. Additionally, low serum IgA, IgM, and IgG levels at diagnosis are significantly associated with specific somatic mutations in individuals with newly diagnosed CLL/MBL.