Katherine A Fitzgerald | PubMed Health

PubMed2025

PPT1 is a negative regulator of STING signaling in cancer cells and its inhibition reactivates immune surveillance in cold tumors.

Immunotherapy modalities have revolutionized cancer treatment for a number of metastatic and treatment-refractory tumor types. Still, many malignancies that lack T cell infiltration and are termed immunologically "cold" fail to respond to these modalities. One approach to increase tumor immunogenicity has been to induce stimulator of interferon gene (STING) and downstream interferon signaling that is often dysregulated in cold tumors. Despite some early success of STING agonists in preclinical cancer models, these approaches have not been successful in the clinic due to poor tumor penetrance and systemic toxicities. Here, we performed a genome-wide CRISPR screen to uncover therapeutic targets to activate STING expression in human tumors. We identified the lysosomal hydrolase Palmitoyl Protein Thioesterase1 (PPT1) as a negative regulator of STING highly expressed in cold ovarian and prostate tumors. Genetic or pharmacological PPT1 suppression increased STING protein stability and its downstream activation of interferon and inflammatory cytokine signaling to enhance T cell migration. Treatment of preclinical prostate and ovarian cancer models expressing low levels of STING with the small molecule PPT1 inhibitor GNS561 enhanced STING expression and activation, leading to infiltration and activation of cytotoxic T cells that turned these tumors "hot" and reduced tumor growth, fibrosis, and dissemination without toxicity. Further analysis demonstrated that PPT1 is associated with reduced STING expression, CD8+ T cell numbers, overall survival, and immunotherapy outcomes in ovarian and prostate cancer patients. Thus, PPT1 inhibition may be a promising approach to activate STING and potentiate the effects of immunotherapy in cold tumors.

Membrane Proteins↗

PubMed2022

Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data.

Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30-75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software, Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform.

RNA-seq↗