An integrative functional genomics approach reveals EGLN1 as a novel therapeutic target in KRAS mutated lung adenocarcinoma

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database

Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its subsidiaries. Egl nine homolog 1 (EGLN1) inhibition prevents poly ubiquitination-mediated proteosomal destruction HIF-1α. The pharmacological interventions aimed at stabilizing HIF-1α have the potential to be a promising treatment option for a range of human diseases, including ischemic stroke. To unveil a novel EGLN1 inhibitor from marine natural products, a custom-based virtual screening was carried out using a Deep Convolutional Neural Network (DCNN) architecture, docking, and molecular dynamics simulation. The custom DCNN model was optimized and further employed to screen marine natural products from the CMNPD database. The docking was performed as a secondary strategy for screened hits. Molecular dynamics (MD) and molecular mechanics/generalized Born surface area (MM-GBSA) were used to analyze inhibitor binding and identify key interactions. The findings support the claim that deep learning-based virtual screening is a rapid, reliable and accurate method of identifying highly contributing drug candidates (EGLN1 inhibitors). This study demonstrates that deep learning architecture can significantly accelerate drug discovery and development, and provides a solid foundation for using (Z)-2-ethylhex-2-enedioic acid [(Z)-2-ethylhex-2-enedioic acid] as a potential EGLN1 inhibitor for treating various health complications.Communicated by Ramaswamy H. Sarma.

KAP1 is a new non-genetic vulnerability of malignant pleural mesothelioma (MPM)

Malignant pleural mesothelioma (MPM) is a rare and incurable cancer, which incidence is increasing in many countries. MPM escapes the classical genetic model of cancer evolution, lacking a distinctive genetic fingerprint. Omics profiling revealed extensive heterogeneity failing to identify major vulnerabilities and restraining development of MPM-oriented therapies. Here, we performed a multilayered analysis based on a functional genome-wide CRISPR/Cas9 screening integrated with patients molecular and clinical data, to identify new non-genetic vulnerabilities of MPM. We identified a core of 18 functionally-related genes as essential for MPM cells. The chromatin reader KAP1 emerged as a dependency of MPM. We showed that KAP1 supports cell growth by orchestrating the expression of a G2/M-specific program, ensuring mitosis correct execution. Targeting KAP1 transcriptional function, by using CDK9 inhibitors resulted in a dramatic loss of MPM cells viability and shutdown of the KAP1-mediated program. Validation analysis on two independent MPM-patients sets, including a consecutive, retrospective cohort of 97 MPM, confirmed KAP1 as new non-genetic dependency of MPM and proved the association of its dependent gene program with reduced patients’ survival probability. Overall these data: provided new insights into the biology of MPM delineating KAP1 and its target genes as building blocks of its clinical aggressiveness.

The multifaceted role of EGLN family prolyl hydroxylases in cancer: going beyond HIF regulation

EGLN1, EGLN2 and EGLN3 are proline hydroxylase whose main function is the regulation of the HIF factors. They work as oxygen sensors and are the main responsible of HIFα subunits degradation in normoxia. Being their activity strictly oxygen-dependent, when oxygen tension lowers, their control on HIFα is released, leading to activation of systemic and cellular response to hypoxia. However, EGLN family members activity is not limited to HIF modulation, but it includes the regulation of essential mechanisms for cell survival, cell cycle metabolism, proliferation and transcription. This is due to their reported hydroxylase activity on a number of non-HIF targets and sometimes to hydroxylase-independent functions. For these reasons, EGLN enzymes appear fundamental for development and progression of different cancer types, playing either a tumor-suppressive or a tumor-promoting role, according to EGLN isoform and to tumor context. Notably, EGLN1, the most studied isoform, has been shown to have also a central role in tumor micro-environment modulation, mediating CAF activation and impairing HIF1α -related angiogenesis, thus covering an important function in cancer metastasis promotion. Considering the recent knowledge acquired on EGLNs, the possibility to target these enzymes for cancer treatment is emerging. However, due to their multifaceted and controversial roles in different cancer types, the use of EGLN inhibitors as anti-cancer drugs should be carefully evaluated in each context.

Construction of hypoxia-immune-related prognostic model and targeted therapeutic strategies for cervical cancer

Emerging evidence indicates that hypoxia and immunity play important roles in tumorigenesis and development. However, the hypoxia-immune-related prognostic risk model has not been established in cervical cancer (CC). We aimed to construct a hypoxia-immune-based prognostic risk model with potential application in CC patient prognosis and predicting response to targeted therapy. The RNA-seq data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Hypoxia and immune status of CC patients were evaluated using the Consensus Clustering method and single sample gene set enrichment analysis (ssGSEA), respectively. The univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to establish the prognostic risk model of CC. The chemotherapy response for six chemotherapeutic agents of each CC patient was calculated according to the Genomics of Drug Sensitivity in Cancer (GDSC). And the Connectivity Map (CMap) database was performed to screen candidate small molecule drugs. In this study, we identified 7 gene signatures (P4HA2, MSMO1, EGLN1, ZNF316, IKZF3, ISCU, MYO1B) with prognostic values. And the survival time of patients with low-risk was significantly longer than those with high-risk. Meanwhile, CC patients in the high-risk group yielded higher sensitivity to five chemotherapeutic agents. And we listed ten candidate small-molecules drugs that exhibited a high correlation with the prognosis of cervical cancer. Thus, the prognostic model can accurately predict the prognosis of patients with CC and may be helpful for the development of new hypoxia-immune prognostic markers and therapeutic strategies for CC.

Daily Practice Assessment of KRAS Status in NSCLC Patients: A New Challenge for the Thoracic Pathologist Is Right around the Corner

Simple Summary

RAS mutation is the most frequent oncogenic alteration in human cancers and KRAS is the most frequently mutated, notably in non-small cell lung carcinomas (NSCLC). Various attempts to inhibit KRAS in the past were unsuccessful in these latter tumors. However, recently, several small molecules (AMG510, MRTX849, JNJ-74699157, and LY3499446) have been developed to specifically target KRAS G12C-mutated tumors, which seems promising for patient treatment and should soon be administered in daily practice for non-squamous (NS)-NSCLC. In this context, it will be mandatory to systematically assess the KRAS status in routine clinical practice, at least in advanced NS-NSCLC, leading to new challenges for thoracic oncologists.

Abstract

KRAS mutations are among the most frequent genomic alterations identified in non-squamous non-small cell lung carcinomas (NS-NSCLC), notably in lung adenocarcinomas. In most cases, these mutations are mutually exclusive, with different genomic alterations currently known to be sensitive to therapies targeting EGFR, ALK, BRAF, ROS1, and NTRK. Recently, several promising clinical trials targeting KRAS mutations, particularly for KRAS G12C-mutated NSCLC, have established new hope for better treatment of patients. In parallel, other studies have shown that NSCLC harboring co-mutations in KRAS and STK11 or KEAP1 have demonstrated primary resistance to immune checkpoint inhibitors. Thus, the assessment of the KRAS status in advanced-stage NS-NSCLC has become essential to setting up an optimal therapeutic strategy in these patients. This stimulated the development of new algorithms for the management of NSCLC samples in pathology laboratories and conditioned reorganization of optimal health care of lung cancer patients by the thoracic pathologists. This review addresses the recent data concerning the detection of KRAS mutations in NSCLC and focuses on the new challenges facing pathologists in daily practice for KRAS status assessment.

Construction of a Novel Ferroptosis-Related Gene Signature for Predicting Survival of Patients With Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most diagnosed subtype of lung cancer; ferroptosis is widely involved in the pathological cell death associated with various cancers, including lung cancer. However, the comprehensive relationship between ferroptosis and LUAD is little known in molecular levels until now. In the present study, 513 LUAD patients could be aggregated into three clusters by consensus clustering based on RNA sequencing data of 291 ferroptosis-related genes (FRGs) in The Cancer Genome Atlas (TCGA) database; cluster2 had significant survival advantage compared to the other two clusters. A novel prognostic model of 8 differential FRGs was constructed to effectively divide LUAD patients into high- or low-risk group according to the risk scores by the Cox and LASSO regression analyses. The overall survival of LUAD patients in the high-risk group was significantly worse in the TCGA and GEO cohorts. Moreover, patients with radiation therapy or high clinical stage had obviously higher risk scores. We validated the differential mRNA and protein expression of four FRGs in paired tumor and normal samples from our clinical cohort. Our study constructed a novel FRG signature to predict the prognosis of LUAD patients, which might provide a new prognostic tool and potential therapeutic targets for LUAD.

CSNK1A1, KDM2A, and LTB4R2 Are New Druggable Vulnerabilities in Lung Cancer

Simple Summary

The main histological subtypes of lung cancer are small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). NSCLC is further subdivided into squamous-cell carcinoma (SCC) and adenocarcinoma (AD). Despite the recent introduction of innovative therapies, lung cancer is still the first cause of cancer-related human death, indicating that the discovery of new therapeutic targets is still a compelling need for this disease. In the present work, we performed a functional genomics analysis on different lung cancer histotypes, combining data derived from different omics resources with in vitro validation. Through this approach, we identified and validated CSNK1A1, KDMA2, and LTB4R2 as new druggable vulnerabilities in lung cancer. These results open new possibilities for the development of innovative therapies for lung cancer patients.

Abstract

Lung cancer is the leading cause of cancer-related human death. It is a heterogeneous disease, classified in two main histotypes, small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC), which is further subdivided into squamous-cell carcinoma (SCC) and adenocarcinoma (AD) subtypes. Despite the introduction of innovative therapeutics, mainly designed to specifically treat AD patients, the prognosis of lung cancer remains poor. In particular, available treatments for SCLC and SCC patients are currently limited to platinum-based chemotherapy and immune checkpoint inhibitors. In this work, we used an integrative approach to identify novel vulnerabilities in lung cancer. First, we compared the data from a CRISPR/Cas9 dependency screening performed in our laboratory with Cancer Dependency Map Project data, essentiality comprising information on 73 lung cancer cell lines. Next, to identify relevant therapeutic targets, we integrated dependency data with pharmacological data and TCGA gene expression information. Through this analysis, we identified CSNK1A1, KDM2A, and LTB4R2 as relevant druggable essentiality genes in lung cancer. We validated the antiproliferative effect of genetic or pharmacological inhibition of these genes in two lung cancer cell lines. Overall, our results identified new vulnerabilities associated with different lung cancer histotypes, laying the basis for the development of new therapeutic strategies.