Biology subjects

Thornton, A. M.

Publications and source records attributed to Thornton, A. M..

3 recordsLinked to original sources

bioRxiv2024

U2AF1 S34F enhances tumorigenic potential of lung cells by exhibiting synergy with KRAS mutation and altering response to environmental stress

Although U2AF1S34F is a recurrent splicing factor mutation in lung adenocarcinoma (ADC), U2AF1S34F alone is insufficient for producing tumors in previous models. Because lung ADCs with U2AF1S34F frequently have co-occurring KRAS mutations and smoking histories, we hypothesized that tumor-forming potential arises from U2AF1S34F interacting with oncogenic KRAS and environmental stress. To elucidate the effect of U2AF1S34F co-occurring with a second mutation, we generated human bronchial epithelial cells (HBEC3kt) with co-occurring U2AF1S34F and KRASG12V. Transcriptome analysis revealed that co-occurring U2AF1S34F and KRASG12V differentially impacts inflammatory, cell cycle, and KRAS pathways. Subsequent phenotyping found associated suppressed cytokine production, increased proliferation, anchorage-independent growth, and tumors in mouse xenografts. Additionally, HBEC3kts harboring U2AF1S34F alter splicing in stress granule protein genes, show increased stress granule formation, and have increased viability in cigarette smoke concentrate. Our results suggest that U2AF1S34F may potentiate transformation by granting precancerous cells survival advantage in environmental stress, permitting accumulation of additional mutations, like in KRAS, which synergize with U2AF1S34F to transform the cell.

cancer biology↗

bioRxiv2019

eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants

While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659, was, surprisingly, predicted to be a gain-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF alpha via NFKB signaling, KRAS signaling, and hypoxia. To validate these predictions, we performed reporter assays and found that all eVIP2 impactful pathways tested in the assay were activated by expression of RNF43 G659fs, but not by expression of RNF43 WT, supporting that RNF43 G659fs is a gain-of-function mutation and its effect on the identified pathways. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2.

bioinformatics↗

bioRxiv2018

Proteasome inhibitor-induced modulation reveals the spliceosome as a specific therapeutic vulnerability in multiple myeloma

Enhancing the efficacy of proteasome inhibitors is a central goal in myeloma therapy. We proposed that signaling-level responses after PI would reveal new mechanisms of action that could be therapeutically exploited. Unbiased phosphoproteomics after the PI carfilzomib surprisingly demonstrated the most prominent phosphorylation changes on splicing related proteins. Spliceosome modulation was invisible to RNA or protein abundance alone. Transcriptome analysis after PI demonstrated broad-scale intron retention, suggestive of spliceosome interference, as well as specific alternative splicing of protein homeostasis machinery components. These findings led us to evaluate direct spliceosome inhibition in myeloma, which synergized with carfilzomib and showed potent anti-tumor activity. Functional genomics and exome sequencing further supported the spliceosome as a specific vulnerability in myeloma. Our results propose splicing interference as an unrecognized modality of PI mechanism, reveal additional modes of spliceosome modulation, and suggest spliceosome targeting as a promising therapeutic strategy in myeloma. SignificanceNew ways to enhance PI efficacy are of major interest. We combine systems-level analyses to discover that PIs specifically interfere with splicing and that myeloma is selectively vulnerable to spliceosome inhibition. We reveal a new approach to advance myeloma therapy and uncover broader roles of splicing modulation in cancer.

cancer biology↗