LPA receptor 1 (LPAR1) is a novel interaction partner of Filamin A that promotes Filamin A phosphorylation, MRTF-A transcriptional activity and oncogene-induced senescence.

Myocardin-related transcription factors A and B (MRTFs) are coactivators of Serum Response Factor (SRF), which controls fundamental biological processes such as cell growth, migration, and differentiation. MRTF and SRF transcriptional activity play an important role in hepatocellular carcinoma (HCC) growth, which represents the second leading cause of cancer-related mortality in humans worldwide. We, therefore, searched for druggable targets in HCC that regulate MRTF/SRF transcriptional activity and can be exploited therapeutically for HCC therapy. We identified the G protein-coupled lysophosphatidic acid receptor 1 (LPAR1) as a novel interaction partner of MRTF-A and Filamin A (FLNA) using fluorescence resonance energy transfer-(FRET) and proximity ligation assay (PLA) in vitro in HCC cells and in vivo in organoids. We found that LPAR1 promotes FLNA phosphorylation at S2152 which enhances the complex formation of FLNA and MRTF-A, actin polymerization, and MRTF transcriptional activity. Pharmacological blockade or depletion of LPAR1 prevents FLNA phosphorylation and complex formation with MRTF-A, resulting in reduced MRTF/SRF target gene expression and oncogene-induced senescence. Thus, inhibition of the LPAR1-FLNA-MRTF-A interaction represents a promising strategy for HCC therapy.

DNA Polymerase Theta Plays a Critical Role in Pancreatic Cancer Development and Metastasis.

Pancreatic ductal adenocarcinoma (PDAC), due to its genomic heterogeneity and lack of effective treatment, despite decades of intensive research, will become the second leading cause of cancer-related deaths by 2030. Step-wise acquisition of mutations, due to genomic instability, is considered to drive the development of PDAC; the KRAS mutation occurs in 95 to 100% of human PDAC, and is already detectable in early premalignant lesions designated as pancreatic intraepithelial neoplasia (PanIN). This mutation is possibly the key event leading to genomic instability and PDAC development. Our study aimed to investigate the role of the error-prone DNA double-strand breaks (DSBs) repair pathway, alt-EJ, in the presence of the KRAS G12D mutation in pancreatic cancer development. Our findings show that oncogenic KRAS contributes to increasing the expression of Polθ, Lig3, and Mre11, key components of alt-EJ in both mouse and human PDAC models. We further confirm increased catalytic activity of alt-EJ in a mouse and human model of PDAC bearing the KRAS G12D mutation. Subsequently, we focused on estimating the impact of alt-EJ inactivation by polymerase theta (Polθ) deletion on pancreatic cancer development, and survival in genetically engineered mouse models (GEMMs) and cancer patients. Here, we show that even though Polθ deficiency does not fully prevent the development of pancreatic cancer, it significantly delays the onset of PanIN formation, prolongs the overall survival of experimental mice, and correlates with the overall survival of pancreatic cancer patients in the TCGA database. Our study clearly demonstrates the role of alt-EJ in the development of PDAC, and alt-EJ may be an attractive therapeutic target for pancreatic cancer patients.

HELLS Is Negatively Regulated by Wild-Type P53 in Liver Cancer by a Mechanism Involving P21 and FOXM1.

The major tumor suppressor P53 (TP53) acts primarily as a transcription factor by activating or repressing subsets of its numerous target genes, resulting in different cellular outcomes (e.g., cell cycle arrest, apoptosis and senescence). P53-dependent gene regulation is linked to several aspects of chromatin remodeling; however, regulation of chromatin-modifying enzymes by P53 is poorly understood in hepatocarcinogenesis. Herein, we identified Helicase, lymphoid specific (HELLS), a major epigenetic regulator in liver cancer, as a strong and selective P53 repression target within the SNF2-like helicase family. The underlying regulatory mechanism involved P53-dependent induction of P21 (CDKN1A), leading to repression of Forkhead Box Protein M1 (FOXM1) that in turn resulted in downregulation of HELLS expression. Supporting our in vitro data, we found higher expression of HELLS in murine HCCs arising in a Trp53-/- background compared to Trp53+/+ HCCs as well as a strong and highly significant correlation between HELLS and FOXM1 expression in different HCC patient cohorts. Our data suggest that functional or mutational inactivation of P53 substantially contributes to overexpression of HELLS in HCC patients and indicates a previously unstudied aspect of P53's ability to suppress liver cancer formation.

Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma.

BACKGROUND: Targeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification. METHODS: TCC was analyzed in 120 H&E and thyroid transcription factor 1 (TTF-1) stained high-resolution images by 19 participants with different levels of pathological expertise as well as by applying two semi-automatic digital pathology image analysis tools (HALO and QuPath). RESULTS: Agreement of TCC estimations [intra-class correlation coefficients (ICC)] between the two software tools (H&E: 0.87; TTF-1: 0.93) was higher compared to that between conventional observers (0.48; 0.47). Digital TCC estimations were in good agreement with the average of human TCC estimations (0.78; 0.96). Conventional TCC estimators tended to overestimate TCC, especially in H&E stainings, in tumors with solid patterns and in tumors with an actual TCC close to 50%. CONCLUSIONS: Our results determine factors that influence TCC estimation. Computer-assisted analysis can improve the accuracy of TCC estimates prior to molecular diagnostic workflows. In addition, we provide a free web application to support self-training and quality improvement initiatives at other institutions.