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Cicenas J, Simkus J. CDK Inhibitors and FDA: Approved and Orphan. Cancers (Basel) 2024; 16:1555. [PMID: 38672637 PMCID: PMC11049492 DOI: 10.3390/cancers16081555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The protein kinases are a large family of enzymes which catalyze protein phosphorylation at certain amino acids [...].
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Affiliation(s)
- Jonas Cicenas
- MAP Kinase Resource, Bioinformatics, Melchiorstrasse 9, CH-3027 Bern, Switzerland;
- Secondary School “Varnų sala”, Baltupio g. 14, LT-08304 Vilnius, Lithuania
| | - Jokubas Simkus
- MAP Kinase Resource, Bioinformatics, Melchiorstrasse 9, CH-3027 Bern, Switzerland;
- Faculty of Medicine, Vilnius University, LT-01513 Vilnius, Lithuania
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Csergeová L, Krbušek D, Janoštiak R. CIP/KIP and INK4 families as hostages of oncogenic signaling. Cell Div 2024; 19:11. [PMID: 38561743 PMCID: PMC10985988 DOI: 10.1186/s13008-024-00115-z] [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: 12/12/2023] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
CIP/KIP and INK4 families of Cyclin-dependent kinase inhibitors (CKIs) are well-established cell cycle regulatory proteins whose canonical function is binding to Cyclin-CDK complexes and altering their function. Initial experiments showed that these proteins negatively regulate cell cycle progression and thus are tumor suppressors in the context of molecular oncology. However, expanded research into the functions of these proteins showed that most of them have non-canonical functions, both cell cycle-dependent and independent, and can even act as tumor enhancers depending on their posttranslational modifications, subcellular localization, and cell state context. This review aims to provide an overview of canonical as well as non-canonical functions of CIP/KIP and INK4 families of CKIs, discuss the potential avenues to promote their tumor suppressor functions instead of tumor enhancing ones, and how they could be utilized to design improved treatment regimens for cancer patients.
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Affiliation(s)
- Lucia Csergeová
- BIOCEV-First Faculty of Medicine, Charles University, Prague, Czechia
| | - David Krbušek
- BIOCEV-First Faculty of Medicine, Charles University, Prague, Czechia
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Tufail M, Hu JJ, Liang J, He CY, Wan WD, Huang YQ, Jiang CH, Wu H, Li N. Predictive, preventive, and personalized medicine in breast cancer: targeting the PI3K pathway. J Transl Med 2024; 22:15. [PMID: 38172946 PMCID: PMC10765967 DOI: 10.1186/s12967-023-04841-w] [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: 10/24/2023] [Accepted: 12/27/2023] [Indexed: 01/05/2024] Open
Abstract
Breast cancer (BC) is a multifaceted disease characterized by distinct molecular subtypes and varying responses to treatment. In BC, the phosphatidylinositol 3-kinase (PI3K) pathway has emerged as a crucial contributor to the development, advancement, and resistance to treatment. This review article explores the implications of the PI3K pathway in predictive, preventive, and personalized medicine for BC. It emphasizes the identification of predictive biomarkers, such as PIK3CA mutations, and the utility of molecular profiling in guiding treatment decisions. The review also discusses the potential of targeting the PI3K pathway for preventive strategies and the customization of therapy based on tumor stage, molecular subtypes, and genetic alterations. Overcoming resistance to PI3K inhibitors and exploring combination therapies are addressed as important considerations. While this field holds promise in improving patient outcomes, further research and clinical trials are needed to validate these approaches and translate them into clinical practice.
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Affiliation(s)
- Muhammad Tufail
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Jia-Ju Hu
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Jie Liang
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Cai-Yun He
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Wen-Dong Wan
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Yu-Qi Huang
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Can-Hua Jiang
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China
- Institute of Oral Precancerous Lesions, Central South University, Changsha, China
- Research Center of Oral and Maxillofacial Tumor, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hong Wu
- State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083, China
| | - Ning Li
- Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital, Central South University, Changsha, China.
- Institute of Oral Precancerous Lesions, Central South University, Changsha, China.
- Research Center of Oral and Maxillofacial Tumor, Xiangya Hospital, Central South University, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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Almalki SG. The pathophysiology of the cell cycle in cancer and treatment strategies using various cell cycle checkpoint inhibitors. Pathol Res Pract 2023; 251:154854. [PMID: 37864989 DOI: 10.1016/j.prp.2023.154854] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 09/23/2023] [Accepted: 10/02/2023] [Indexed: 10/23/2023]
Abstract
The cell cycle is the series of events that occur in a cell leading to its division and duplication. It can be divided into two main stages: interphase and mitosis. Interphase is the longest stage of the cell cycle and can be further divided into three phases: G1, S, and G2. During G1, the cell grows and prepares for DNA synthesis. In the S phase, DNA synthesis occurs, leading to the replication of the genetic material. In G2, the cell continues to grow and prepares for mitosis. After mitosis, the cell enters the final stage of the cell cycle, called cytokinesis, during which the cytoplasm is divided, resulting in two separate daughter cells. The cell cycle then begins again with interphase. Cell cycle dysregulation is a hallmark of cancer, and it can have several consequences that contribute to the development and progression of cancer. Cyclin inhibitors and checkpoint activators have shown promise in the treatment of cancer, particularly in combination with other therapies.
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Affiliation(s)
- Sami G Almalki
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia.
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Lennerz JK, Salgado R, Kim GE, Sirintrapun SJ, Thierauf JC, Singh A, Indave I, Bard A, Weissinger SE, Heher YK, de Baca ME, Cree IA, Bennett S, Carobene A, Ozben T, Ritterhouse LL. Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML. Clin Chem Lab Med 2023; 61:544-557. [PMID: 36696602 DOI: 10.1515/cclm-2022-1151] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.
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Affiliation(s)
- Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia
| | - Grace E Kim
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | | | - Julia C Thierauf
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
- Department of Otorhinolaryngology, Head and Neck Surgery, German Cancer Research Center (DKFZ), Heidelberg University Hospital and Research Group Molecular Mechanisms of Head and Neck Tumors, Heidelberg, Germany
| | - Ankit Singh
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | - Iciar Indave
- European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), Lisbon, Portugal
| | - Adam Bard
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Yael K Heher
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
| | | | - Ian A Cree
- International Agency for Research on Cancer (IARC), World Health Organization, Lyon, France
| | - Shannon Bennett
- Department of Laboratory Medicine and Pathology (DLMP), Mayo Clinic, Rochester, MN, USA
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Tomris Ozben
- Medical Faculty, Dept. of Clinical Biochemistry, Akdeniz University, Antalya, Türkiye
- Medical Faculty, Clinical and Experimental Medicine, Ph.D. Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Lauren L Ritterhouse
- Department of Pathology, Massachusetts General Hospital/Harvard Medical, Boston, MA, USA
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Bai JW, Qiu SQ, Zhang GJ. Molecular and functional imaging in cancer-targeted therapy: current applications and future directions. Signal Transduct Target Ther 2023; 8:89. [PMID: 36849435 PMCID: PMC9971190 DOI: 10.1038/s41392-023-01366-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 01/19/2023] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.
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Affiliation(s)
- Jing-Wen Bai
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Department of Medical Oncology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China
| | - Si-Qi Qiu
- Diagnosis and Treatment Center of Breast Diseases, Clinical Research Center, Shantou Central Hospital, 515041, Shantou, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Shantou University Medical College, 515041, Shantou, China
| | - Guo-Jun Zhang
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
- Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, 361100, Xiamen, China.
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