1
|
Kenakin T. Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition. Nat Rev Drug Discov 2024; 23:626-644. [PMID: 38890494 DOI: 10.1038/s41573-024-00958-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 06/20/2024]
Abstract
Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.
Collapse
Affiliation(s)
- Terry Kenakin
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
| |
Collapse
|
2
|
Zhang D, Zhao F, Li J, Guo P, Liu H, Lu T, Li S, Li Z, Li Y. Comprehensive single-cell transcriptomic profiling reveals molecular subtypes and prognostic biomarkers with implications for targeted therapy in esophageal squamous cell carcinoma. Transl Oncol 2024; 44:101948. [PMID: 38582059 PMCID: PMC11004200 DOI: 10.1016/j.tranon.2024.101948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/05/2024] [Accepted: 03/26/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Esophageal squamous cell carcinoma (ESCC) is a genetically heterogeneous disease with poor clinical outcomes. Identification of biomarkers linked to DNA replication stress may enable improved prognostic risk stratification and guide therapeutic decision making. We performed integrated single-cell RNA sequencing and computational analyses to define the molecular determinants and subtypes underlying ESCC heterogeneity. METHODS Single-cell RNA sequencing was performed on ESCC samples and analyzed using Seurat. Differential gene expression analysis was used to identify esophageal cell phenotypes. DNA replication stress-related genes were intersected with single-cell differential expression data to identify potential prognostic genes, which were used to generate a DNA replication stress (DRS) score. This score and associated genes were evaluated in survival analysis. Putative prognostic biomarkers were evaluated by Cox regression and consensus clustering. Mendelian randomization analyses assessed the causal role of PRKCB. RESULTS High DRS score associated with poor survival. Four genes (CDKN2A, NUP155, PPP2R2A, PRKCB) displayed prognostic utility. Three molecular subtypes were identified with discrete survival and immune properties. A 12-gene signature displayed robust prognostic performance. PRKCB was overexpressed in ESCC, while PRKCB knockdown reduced ESCC cell migration. CONCLUSIONS This integrated single-cell sequencing analysis provides new insights into the molecular heterogeneity and prognostic determinants underlying ESCC. The findings identify potential prognostic biomarkers and a gene expression signature that may enable improved patient risk stratification in ESCC. Experimental validation of the role of PRKCB substantiates the potential clinical utility of our results.
Collapse
Affiliation(s)
- Dengfeng Zhang
- Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - Fangchao Zhao
- Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - Jing Li
- Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - Pengfei Guo
- Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - Haitao Liu
- College of Life Science, Inner Mongolia University, Hohhot 010000, China
| | - Tianxing Lu
- Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - Shujun Li
- Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China.
| | - Zhirong Li
- Provincial Center for Clinical Laboratories, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China.
| | - Yishuai Li
- Department of Thoracic Surgery, Hebei Chest Hospital, Shijiazhuang 050000, China; Hebei Provincial Key Laboratory of Pulmonary Diseases, Shijiazhuang 050000, China.
| |
Collapse
|
3
|
Gorostiola González M, Rakers PRJ, Jespers W, IJzerman AP, Heitman LH, van Westen GJP. Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities. Int J Mol Sci 2024; 25:3698. [PMID: 38612509 PMCID: PMC11011372 DOI: 10.3390/ijms25073698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.
Collapse
Affiliation(s)
- Marina Gorostiola González
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
- Oncode Institute, 2333 CC Leiden, The Netherlands
| | - Pepijn R. J. Rakers
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
| | - Willem Jespers
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
| | - Adriaan P. IJzerman
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
| | - Laura H. Heitman
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
- Oncode Institute, 2333 CC Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; (M.G.G.); (P.R.J.R.); (W.J.); (A.P.I.); (L.H.H.)
| |
Collapse
|
4
|
Zheng S, He A, Chen C, Gu J, Wei C, Chen Z, Liu J. Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index. Front Immunol 2024; 15:1343425. [PMID: 38571962 PMCID: PMC10987686 DOI: 10.3389/fimmu.2024.1343425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/29/2024] [Indexed: 04/05/2024] Open
Abstract
Introduction Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction. Methods In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment. Results Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature. Discussion Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.
Collapse
Affiliation(s)
- Shaoluan Zheng
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Anqi He
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Chenxi Chen
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Jianying Gu
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- Artificial Intelligence Center for Plastic Surgery and Cutaneous Soft Tissue Cancers, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chuanyuan Wei
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhiwei Chen
- Big Data and Artificial Intelligence Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jiaqi Liu
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- Artificial Intelligence Center for Plastic Surgery and Cutaneous Soft Tissue Cancers, Zhongshan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
5
|
Ding Y, Zhao Z, Cai H, Zhou Y, Chen H, Bai Y, Liu Z, Liu S, Zhou W. Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma. Front Immunol 2023; 14:1304466. [PMID: 38077400 PMCID: PMC10701528 DOI: 10.3389/fimmu.2023.1304466] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
Background We explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood. Methods We integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments. Results Analysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness. Conclusion SRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients.
Collapse
Affiliation(s)
- Yantao Ding
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Zhijie Zhao
- Department of Plastic Surgery, The Ninth Affiliated Hospital of Shanghai Jiaotong University, Shanghai, China
| | - Huabao Cai
- Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yi Zhou
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - He Chen
- Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yun Bai
- Department of Plastic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhenran Liu
- Department of Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shengxiu Liu
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Wenming Zhou
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| |
Collapse
|
6
|
Shen K, Song W, Wang H, Wang L, Yang Y, Hu Q, Ren M, Gao Z, Wang Q, Zheng S, Zhu M, Yang Y, Zhang Y, Wei C, Gu J. Decoding the metastatic potential and optimal postoperative adjuvant therapy of melanoma based on metastasis score. Cell Death Discov 2023; 9:397. [PMID: 37880239 PMCID: PMC10600209 DOI: 10.1038/s41420-023-01678-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/26/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023] Open
Abstract
Metastasis is a formidable challenge in the prognosis of melanoma. Accurately predicting the metastatic potential of non-metastatic melanoma (NMM) and determining effective postoperative adjuvant treatments for inhibiting metastasis remain uncertain. In this study, we conducted comprehensive analyses of melanoma metastases using bulk and single-cell RNA sequencing data, enabling the construction of a metastasis score (MET score) through diverse machine-learning algorithms. The reliability and robustness of the MET score were validated using various in vitro assays and in vivo models. Our findings revealed a distinct molecular landscape in metastatic melanoma characterized by the enrichment of metastasis-related pathways, intricate cell-cell communication, and heightened infiltration of pro-angiogenic tumor-associated macrophages compared to NMM. Importantly, patients in the high MET score group exhibited poorer prognoses and an immunosuppressive microenvironment, featuring increased infiltration of regulatory T cells and decreased infiltration of CD8+ T cells, compared to the low MET score patient group. Expression of PD-1 was markedly higher in patients with low MET scores. Anti-PD-1 (aPD-1) therapy profoundly affected antitumor immunity activation and metastasis inhibition in these patients. In summary, our study demonstrates the effectiveness of the MET score in predicting melanoma metastatic potential. For patients with low MET scores, aPD-1 therapy may be a potential treatment strategy to inhibit metastasis. Patients with high MET scores may benefit from combination therapies.
Collapse
Affiliation(s)
- Kangjie Shen
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wenyu Song
- Department of Cardiovascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hongye Wang
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Lu Wang
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yang Yang
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qianrong Hu
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Min Ren
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zixu Gao
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qiangcheng Wang
- The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China
| | - Shaoluan Zheng
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Xiamen Clinical Research Center for Cancer Therapy, Xiamen, China
| | - Ming Zhu
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanwen Yang
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yong Zhang
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chuanyuan Wei
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Jianying Gu
- Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
- Xiamen Clinical Research Center for Cancer Therapy, Xiamen, China.
| |
Collapse
|