Liu, Li

ABSTRACT Germline variation shapes cancer risk, yet its influence on the evolutionary dynamics of established tumors remains poorly understood. In multiple myeloma, subclonal diversification drives disease progression and treatment failure, but the heritable factors that modulate this process are unknown. Here, we show that germline variation is associated with tumor evolutionary features, implicating inherited regulation in subclonal expansion. Integrating germline variation with tumor evolutionary parameters identifies variants associated with evolutionary features, with signals enriched in regulatory regions, consistent with a transcriptional basis. We further identify TBKBP1 as a key locus linking germline variation to tumor evolution and clinical outcome. Germline variation at this locus is associated with TBKBP1 expression and subclonal expansion, and TBKBP1 expression correlates with adverse prognosis, consistent across independent cohorts. Functional analyses demonstrate that TBKBP1 promotes proliferation and activates MYC, mTORC1 and non-canonical NF-κB signaling pathway. Together, these findings establish germline regulatory variation as a determinant of tumor evolutionary dynamics and identify TBKBP1 as a mediator linking inherited variation to subclonal expansion and disease progression in multiple myeloma.

Abstract Expression quantitative trait locus (eQTL) mapping is used to identify the functional link between a genomic variant and a gene’s expression. A significant eQTL association does not mean a causal relationship or mechanism, and further investigation is needed to understand how a single-nucleotide polymorphism (SNP) impacts gene expression. One of the most plausible explanations for eQTL is that a genomic variant affects transcription factor (TF) binding and thus impacts its regulation on target genes (TGs). However, the current eQTL model does not provide information on the TF and how its regulation is mediated by the SNP’s genotypes. Here, we propose a new method called differential co-expression QTL (ceQTL) among different alleles using Chow statistics to specifically detect eQTLs that are bound by a particular TF. We start with building a trio of TF, its TG, and related SNP, and then test the significant coefficient difference among different genotypes of the SNP. We applied this ceQTL model to simulated data and the lung tissue datasets from the genotype-tissue expression project. The simulated data results showed that the model was robust to detect true ceQTLs at variable sample sizes and different minor allele frequencies as measured by area under the curve. Our tool also performed a TF binding affinity analysis to add another layer of evidence for functional interpretation. In summary, ceQTL analysis provides a more interpretable and biological insight into the mechanism of eQTL and transcriptomic regulation, which would help us better understand how genomic variants affect phenotypes and diseases.

Abstract Motivation Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. Results We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. Availability and implementation R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

Abstract Intracellular protein transport (ICT) is a tightly regulated process that orchestrates protein localization and expression, ensuring proper cellular function. Dysregulated ICT can lead to aberrant expression of surface proteins involved in cell-cell communication, adhesion, and immune responses, contributing to disease progression and therapeutic resistance. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) enables the simultaneous measurement of mRNA and surface protein levels in the same cell, providing a powerful opportunity to investigate the molecular mechanisms underlying surface protein regulation. In this study, we introduce a novel computational frame for Modeling Protein Expression and Transport (MPET) that evaluates the contribution of ICT activity to differential surface protein expression using CITE-seq data. MPET comprises three modules for identification of ICT– surface protein regulatory circuits across biological scales and their contributions to phenotypic variation. We applied MPET to analyze single-cell data from COVID-19 patients with varying disease severity. Our analysis revealed context-dependent recruitment of ICT genes and pervasive rewiring of ICT pathways throughout the course of disease progression. Notably, we found that even when the transcriptional levels of key immune response proteins remained stable, their expression on cell surface were significantly altered due to dysregulated ICT. MPET provides a valuable new tool for dissecting complex regulatory networks and offers mechanistic insight into post-transcriptional regulation of cell surface proteins in diseases.

ABSTRACT Intratumor heterogeneity arises from ongoing somatic evolution complicating cancer diagnosis, prognosis, and treatment. Here we present TEATIME (es t imating e volution a ry events t hrough s i ngle-ti m epoint s e quencing), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.

Abstract Automated and accurate identification of refugees in healthcare databases is a critical first step to investigate healthcare needs of this vulnerable population and improve health disparities. In this study, we developed a machine‐learning method, named refugee identification system (RIS) to address this need. We curated a data set consisting of 103 refugees and 930 non‐refugees in Arizona. We compiled de‐identified individual‐level information including age, primary language, and noise‐masked home address, state‐level refugee resettlement statistics, and world language statistics. We then performed feature engineering to convert language and masked address into quantitative features. Finally, we built a random forest model to classify refugee and non‐refugees. RIS achieved high classification accuracy (overall accuracy=0.97, specificity=0.99, sensitivity=0.85, positive predictive value=0.88, negative predictive value=0.98, and area under receiver operating characteristic curve=0.98). RIS is customizable for refugee identification outside Arizona. Its application enables large‐scale investigation of refugee healthcare needs and improvement of health disparities.

AbstractUnderstanding intratumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300× depth) to reliably infer subclones. Here, we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30×. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30× and 200×, whereas the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single-nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).

Abstract Motivation Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited. Results We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms. Availability and implementation An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust. Supplementary information Supplementary data are available at Bioinformatics online.