Publications

Integrating results from genome-wide association studies (GWASs) and studies of molecular phenotypes such as gene expressions can improve our understanding of the biological functions of trait-associated variants and can help prioritize candidate genes for downstream analysis. Using reference expression quantitative trait locus (eQTL) studies, several methods have been proposed to identify gene-trait associations, primarily based on gene expression imputation. To increase the statistical power by leveraging substantial eQTL sharing across tissues, meta-analysis methods aggregating such gene-based test results across multiple tissues or contexts have been developed as well. However, most existing meta-analysis methods have limited power to identify associations when the gene has weaker associations in only a few tissues and cannot identify the subset of tissues in which the gene is “activated.” For this, we developed a cross-tissue subset-based transcriptome-wide association study (CSTWAS) meta-analysis method that improves power under such scenarios and can extract the set of potentially associated tissues. To improve applicability, CSTWAS uses only GWAS summary statistics and pre-computed correlation matrices to identify a subset of tissues that have the maximal evidence of gene-trait association. Through numerical simulations, we found that CSTWAS can maintain a well-calibrated type-I error rate, improves power especially when there is a small number of associated tissues for a gene-trait association, and identifies an accurate associated tissue set. By analyzing GWAS summary statistics of three complex traits and diseases, we demonstrate that CSTWAS could identify biological meaningful signals while providing an interpretation of disease etiology by extracting a set of potentially associated tissues.

Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.

Patients with metastatic acral lentiginous melanoma (ALM) suffer worse outcomes relative to patients with other forms of cutaneous melanoma (CM), and do not benefit as well to approved melanoma therapies. Identification of cyclin-dependent kinase 4 and 6 (CDK4/6) pathway gene alterations in >60% of ALMs has led to clinical trials of the CDK4/6 inhibitor (CDK4i/6i) palbociclib for ALM; however, median progression free survival with CDK4i/6i treatment was only 2.2 months, suggesting existence of resistance mechanisms. Therapy resistance in ALM remains poorly understood; here we report hyperactivation of MAPK signaling and elevated cyclin D1 expression serve as a mechanism of intrinsic early/adaptive CDK4i/6i resistance. ALM cells that have acquired CDK4i/6i resistance following chronic treatment exposure also exhibit hyperactivation of the MAPK pathway. MEK and/or ERK inhibition increases CDK4i/6i efficacy against therapy naïve and CDK4i/6i-resistant AM cells in xenograft and patient-derived xenograft (PDX) models and promotes a defective DNA repair, cell cycle arrested and apoptotic program. Notably, gene alterations poorly correlate with protein expression of cell cycle proteins in ALM or efficacy of CDK4i/6i, urging additional strategies when stratifying patients for CDK4i/6i trial inclusion. Concurrent targeting of the MAPK pathway and CDK4/6 represents a new approach for patients with metastatic ALM to improve outcomes.

Activating mutations in PIK3CA are frequently found in estrogen-receptor-positive (ER+) breast cancer, and the combination of the phosphatidylinositol 3-kinase (PI3K) inhibitor alpelisib with anti-ER inhibitors is approved for therapy. We have previously demonstrated that the PI3K pathway regulates ER activity through phosphorylation of the chromatin modifier KMT2D. Here, we discovered a methylation site on KMT2D, at K1330 directly adjacent to S1331, catalyzed by the lysine methyltransferase SMYD2. SMYD2 loss attenuates alpelisib-induced KMT2D chromatin binding and alpelisib-mediated changes in gene expression, including ER-dependent transcription. Knockdown or pharmacological inhibition of SMYD2 sensitizes breast cancer cells, patient-derived organoids, and tumors to PI3K/AKT inhibition and endocrine therapy in part through KMT2D K1330 methylation. Together, our findings uncover a regulatory crosstalk between post-translational modifications that fine-tunes KMT2D function at the chromatin. This provides a rationale for the use of SMYD2 inhibitors in combination with PI3Kα/AKT inhibitors in the treatment of ER+/PIK3CA mutant breast cancer.

We performed a series of integrative analyses including transcriptome-wide association studies (TWASs) and proteome-wide association studies (PWASs) of renal cell carcinoma (RCC) to nominate and prioritize molecular targets for laboratory investigation. On the basis of a genome-wide association study (GWAS) of 29,020 affected individuals and 835,670 control individuals and prediction models trained in transcriptomic reference models, our TWAS across four kidney transcriptomes (GTEx kidney cortex, kidney tubules, TCGA-KIRC [The Cancer Genome Atlas kidney renal clear-cell carcinoma], and TCGA-KIRP [TCGA kidney renal papillary cell carcinoma]) identified 38 gene associations (false-discovery rate <5%) in at least two of four transcriptomic panels and identified 12 genes that were independent of GWAS susceptibility regions. Analyses combining TWAS associations across 48 tissues from GTEx identified associations that were replicable in tumor transcriptomes for 23 additional genes. Analyses by the two major histologic types (clear-cell RCC and papillary RCC) revealed subtype-specific associations, although at least three gene associations were common to both subtypes. PWAS identified 13 associated proteins, all mapping to GWAS-significant loci. TWAS-identified genes were enriched for active enhancer or promoter regions in RCC tumors and hypoxia-inducible factor binding sites in relevant cell lines. Using gene expression correlation, common cancers (breast and prostate) and RCC risk factors (e.g., hypertension and BMI) display genetic contributions shared with RCC. Our work identifies potential molecular targets for RCC susceptibility for downstream functional investigation.

TWe have shown that KRAS–TP53 genomic coalteration is associated with immune-excluded microenvironments, chemoresistance, and poor survival in pancreatic ductal adenocarcinoma (PDAC) patients. By treating KRAS–TP53 cooperativity as a model for high-risk biology, we now identify cell-autonomous Cxcl1 as a key mediator of spatial T-cell restriction via interactions with CXCR2 neutrophilic myeloid-derived suppressor cells in human PDAC using imaging mass cytometry. Silencing of cell-intrinsic Cxcl1 in LSL-Kras;Trp53;Pdx-1(KPC) cells reprograms the trafficking and functional dynamics of neutrophils to overcome T-cell exclusion and controls tumor growth in a T cell–dependent manner. Mechanistically, neutrophil-derived TNF is a central regulator of this immunologic rewiring, instigating feed-forward Cxcl1 overproduction from tumor cells and cancer-associated fibroblasts (CAF), T-cell dysfunction, and inflammatory CAF polarization via transmembrane TNF–TNFR2 interactions. TNFR2 inhibition disrupts this circuitry and improves sensitivity to chemotherapy in vivo. Our results uncover cancer cell–neutrophil cross-talk in which context-dependent TNF signaling amplifies stromal inflammation and immune tolerance to promote therapeutic resistance in PDAC.