ATX/LPA axis regulates FAK activation, cell proliferation, apoptosis, and motility in human pancreatic cancer cells

Autotaxin-Lysophosphatidate Axis: Promoter of Cancer Development and Possible Therapeutic Implications

Autotaxin (ATX) is a member of the ectonucleotide pyrophosphate/phosphodiesterase (ENPP) family; it is encoded by the ENPP2 gene. ATX is a secreted glycoprotein and catalyzes the hydrolysis of lysophosphatidylcholine to lysophosphatidic acid (LPA). LPA is responsible for the transduction of various signal pathways through the interaction with at least six G protein-coupled receptors, LPA Receptors 1 to 6 (LPAR1-6). The ATX-LPA axis is involved in various physiological and pathological processes, such as angiogenesis, embryonic development, inflammation, fibrosis, and obesity. However, significant research also reported its connection to carcinogenesis, immune escape, metastasis, tumor microenvironment, cancer stem cells, and therapeutic resistance. Moreover, several studies suggested ATX and LPA as relevant biomarkers and/or therapeutic targets. In this review of the literature, we aimed to deepen knowledge about the role of the ATX-LPA axis as a promoter of cancer development, progression and invasion, and therapeutic resistance. Finally, we explored its potential application as a prognostic/predictive biomarker and therapeutic target for tumor treatment.

The Emerging Role of LPA as an Oncometabolite

Lysophosphatidic acid (LPA) is a phospholipid that displays potent signalling activities that are regulated in both an autocrine and paracrine manner. It can be found both extra- and intracellularly, where it interacts with different receptors to activate signalling pathways that regulate a plethora of cellular processes, including mitosis, proliferation and migration. LPA metabolism is complex, and its biosynthesis and catabolism are under tight control to ensure proper LPA levels in the body. In cancer patient specimens, LPA levels are frequently higher compared to those of healthy individuals and often correlate with poor responses and more aggressive disease. Accordingly, LPA, through promoting cancer cell migration and invasion, enhances the metastasis and dissemination of tumour cells. In this review, we summarise the role of LPA in the regulation of critical aspects of tumour biology and further discuss the available pre-clinical and clinical evidence regarding the feasibility and efficacy of targeting LPA metabolism for effective anticancer therapy.

Diagnostic value of serum autotaxin level in colorectal cancer

Background: Autotaxin (ATX) is a nucleotide enzyme linked to cell growth, differentiation and migration. This study investigated serum levels of ATX in colorectal cancer (CRC). Methods: The study involved stage I-III CRC diagnosed between December 2020 and 2021, excluding those with neoadjuvant or adjuvant therapy, or metastasis. Healthy volunteers were controls. Serum ATX levels were measured by ELISA and compared. Results: This study included 129 patients (91 in the patient group and 38 in the control group). The optimal cutoff value of ATX for CRC was 169.98 ng/ml, and sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 91.2% (95% CI: 89.4-96.2), 78.9% (95% CI: 62.7-90.4), 4.33 and 0.11, respectively. Conclusion: The serum ATX level is a useful biomarker for CRC.

Activation of skeletal muscle FAPs by LPA requires the Hippo signaling via the FAK pathway

Lysophosphatidic acid (LPA) is a lysophospholipid that signals through six G-protein coupled receptors (LPARs), LPA₁ to LPA₆. LPA has been described as a potent modulator of fibrosis in different pathologies. In skeletal muscle, LPA increases fibrosis-related proteins and the number of fibro/adipogenic progenitors (FAPs). FAPs are the primary source of ECM-secreting myofibroblasts in acute and chronic damage. However, the effect of LPA on FAPs activation in vitro has not been explored. This study aimed to investigate FAPs' response to LPA and the downstream signaling mediators involved. Here, we demonstrated that LPA mediates FAPs activation by increasing their proliferation, expression of myofibroblasts markers, and upregulation of fibrosis-related proteins. Pretreatment with the LPA₁/LPA₃ antagonist Ki16425 or genetic deletion of LPA₁ attenuated the LPA-induced FAPs activation, resulting in decreased expression of cyclin e1, α-SMA, and fibronectin. We also evaluated the activation of the focal adhesion kinase (FAK) in response to LPA. Our results showed that LPA induces FAK phosphorylation in FAPs. Treatment with the P-FAK inhibitor PF-228 partially prevented the induction of cell responses involved in FAPs activation, suggesting that this pathway mediates LPA signaling. FAK activation controls downstream cell signaling within the cytoplasm, such as the Hippo pathway. LPA induced the dephosphorylation of the transcriptional coactivator YAP (Yes-associated protein) and promoted direct expression of target pathway genes such as Ctgf/Ccn2 and Ccn1. The blockage of YAP transcriptional activity with Super-TDU further confirmed the role of YAP in LPA-induced FAPs activation. Finally, we demonstrated that FAK is required for LPA-dependent YAP dephosphorylation and the induction of Hippo pathway target genes. In conclusion, LPA signals through LPA₁ to regulate FAPs activation by activating FAK to control the Hippo pathway.

Three-Dimensional-QSAR and Relative Binding Affinity Estimation of Focal Adhesion Kinase Inhibitors

Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK-targeting inhibitors using three-dimensional structure-activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure-activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (ΔΔGRBFEA→B) terms of analogous ligands were estimated using alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. Overall, the results suggested that using ML and physics-based hybrid approaches could be useful in synergy for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor.