KDM6A missense variants hamper H3 histone demethylation in lung squamous cell carcinoma

Clinical and Molecular Traits of a Novel SPECC1L-ALK Fusion in a Patient with Advanced Non-Small Cell Lung Cancer

Anaplastic lymphoma kinase (ALK) fusions account for 5-7% of non-small cell lung cancer (NSCLC) patients, the therapeutic approaches for which have significantly evolved in the last few years. However, the response to target therapies remains heterogeneous, partially due to the many different ALK fusion variants reported to date. Rare fusion variants have also been discovered, but their role in influencing responses to ALK inhibitors (ALKis) remains poorly elucidated. Laboratory investigation at both the tissue and protein levels, and a molecular profile by next-generation sequencing (NGS) were performed on a lung biopsy sample from one patient with poorly differentiated adenocarcinoma. An in silico prediction model using ColabFold software v1.5.5 was used to model and predict the entire structure of the chimeric aberrant ALK protein. Here, we report a case of a former smoker, a 60-year-old man, diagnosed with NSCLC and undergoing ALK translocation. He received first-, second- and third-generation ALK protein inhibitors (ALKis), showing a clinical benefit for about 4 years. IHC analysis and the molecular examination of the tissue biopsy indicated a positive staining for ALK and a novel ALK gene fusion variant, involving the sperm antigen with calponin homology and coiled-coil domain 1-like (SPECC1L) gene with an unreported breakpoint in exon 7. The novel SPECCL1::ALK fusion was identified using Anchored Multiplex PCR (AMP)-NGS technology and was predicted to retain the Pkinase_Tyr domain at the carboxy-terminal region of the resulting chimeric protein. To the best of our knowledge, this is the first case of an ALK-positive NSCLC patient carrying the SPECC1L exon 7 fusion breakpoint and one of the few reports about clinical outcomes related to SPECC1L::ALK fusion. The in silico hypothesized biological role of this newly identified fusion variant corroborates the observed clinical response to multiple ALKis. The molecular findings also reinforce the utility of AMP-based NGS technology as a valuable tool for the identification of rare chromosomal events that may be related to the variability of patient outcomes to different ALKis treatments.

In silico discovery of potential PPI inhibitors for anti-lung cancer activity by targeting the CCND1-CDK4 complex via the P21 inhibition mechanism

Non-Small Cell Lung Cancer (NSCLC) is a prevalent and deadly form of lung cancer worldwide with a low 5-year survival rate. Current treatments have limitations, particularly for advanced-stage patients. P21, a protein that inhibits the CCND1-CDK4 complex, plays a crucial role in cell proliferation. Computer-Aided Drug Design (CADD) based on pharmacophores can screen and design PPI inhibitors targeting the CCND1-CDK4 complex. By analyzing known inhibitors, key pharmacophores are identified, and computational methods are used to screen potential PPI inhibitors. Molecular docking, pharmacophore matching, and structure-activity relationship studies optimize the inhibitors. This approach accelerates the discovery of CCND1-CDK4 PPI inhibitors for NSCLC treatment. Molecular dynamics simulations of CCND1-CDK4-P21 and CCND1-CDK4 complexes showed stable behavior, comprehensive sampling, and P21's impact on complex stability and hydrogen bond formation. A pharmacophore model facilitated virtual screening, identifying compounds with favorable binding affinities. Further simulations confirmed the stability and interactions of selected compounds, including 513457. This study demonstrates the potential of CADD in optimizing PPI inhibitors targeting the CCND1-CDK4 complex for NSCLC treatment. Extended simulations and experimental validations are necessary to assess their efficacy and safety.

Exploiting in silico structural analysis to introduce emerging genotype-phenotype correlations in DHCR24-related sterol biosynthesis disorder: a case study

Desmosterolosis is a rare sterol biosynthesis disorder characterized by multiple congenital anomalies, failure to thrive, severe developmental delay, progressive epileptic encephalopathy, and elevated levels of desmosterol caused by biallelic mutations of DHCR24 encoding 3-β-hydroxysterol Δ-24-reductase. DHCR24 is regarded as the key enzyme of cholesterol synthesis in the metabolism of brain cholesterol as it catalyzes the reduction of the Δ-24 double bond of sterol intermediates during cholesterol biosynthesis. To date, 15 DHCR24 variants, detected in 2 related and 14 unrelated patients, have been associated with the desmosterolosis disorder. Here, we describe a proband harboring the never-described DHCR24 homozygous missense variant NM_014762.4:c.506T>C, NP_055577.1:p.M169T, whose functional validation was confirmed through biochemical assay. By using molecular dynamics simulation techniques, we investigated the impact of this variant on the protein stability and interaction network with the flavin adenine dinucleotide cofactor, thereby providing a preliminary assessment of its mechanistic role in comparison to all known pathogenic variants, the wild-type protein, and a known benign DHCR24 variant. This report expands the clinical and molecular spectra of the DHCR24-related disorder, reports on a novel DHCR24 deleterious variant associated with desmosterolosis, and gives new insights into genotype-phenotype correlations.

The role of lysine-specific demethylase 6A (KDM6A) in tumorigenesis and its therapeutic potentials in cancer therapy

Histone demethylation is a key post-translational modification of chromatin, and its dysregulation affects a wide array of nuclear activities including the maintenance of genome integrity, transcriptional regulation, and epigenetic inheritance. Lysine specific demethylase 6A (KDM6A, also known as UTX) is an Fe²⁺- and α-ketoglutarate- dependent oxidase which belongs to KDM6 Jumonji histone demethylase subfamily, and it can remove mono-, di- and tri-methyl groups from methylated lysine 27 of histone H3 (H3K27me1/2/3). Mounting studies indicate that KDM6A is responsible for driving multiple human diseases, particularly cancers and pharmacological inhibition of KDM6A is an effective strategy to treat varieties of KDM6A-amplified cancers in cellulo and in vivo. Although there are several reviews on the roles of KDM6 subfamily in cancer development and therapy, all of them only simply introduce the roles of KDM6A in cancer without systematically summarizing the specific mechanisms of KDM6A in tumorigenesis, which greatly limits the advances on the understanding of roles KDM6A in varieties of cancers, discovering targeting selective KDM6A inhibitors, and exploring the adaptive profiles of KDM6A antagonists. Herein, we present the structure and functions of KDM6A, simply outline the functions of KDM6A in homeostasis and non-cancer diseases, summarize the role of KDM6A and its distinct target genes/ligand proteins in development of varieties of cancers, systematically classify KDM6A inhibitors, sum up the difficulties encountered in the research of KDM6A and the discovery of related drugs, and provide the corresponding solutions, which will contribute to understanding the roles of KDM6A in carcinogenesis and advancing the progression of KDM6A as a drug target in cancer therapy.

Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.