Dataset Information

RET aberrant cancers and RET inhibitor therapies: Current state-of-the-art and future perspectives.

ABSTRACT: Precision oncology informed by genomic information has evolved in leaps and bounds over the last decade. Although non-small cell lung cancer (NSCLC) has moved to center-stage as the poster child of precision oncology, multiple targetable genomic alterations have been identified in various cancer types. RET alterations occur in roughly 2% of all human cancers. The role of RET as oncogenic driver was initially identified in 1985 after the discovery that transfection with human lymphoma DNA transforms NIH-3T3 fibroblasts. Germline RET mutations are causative of multiple endocrine neoplasia type 2 syndrome, and RET fusions are found in 10-20% of papillary thyroid cases and are detected in most patients with advanced sporadic medullary thyroid cancer. RET fusions are oncogenic drivers in 2% of Non-small cell lung cancer. Rapid translation and regulatory approval of selective RET inhibitors, selpercatinib and pralsetinib, have opened up the field of RET precision oncology. This review provides an update on RET precision oncology from bench to bedside and back. We explore the impact of selective RET inhibitor in patients with advanced NSCLC, thyroid cancer, and other cancers in a tissue-agnostic fashion, resistance mechanisms, and future directions.

SUBMITTER: Addeo A

PROVIDER: S-EPMC10141525 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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RET aberrant cancers and RET inhibitor therapies: Current state-of-the-art and future perspectives.

Addeo Alfredo A Miranda-Morales Ernesto E den Hollander Petra P Friedlaender Alex A O Sintim Herman H Wu Jie J Mani Sendurai A SA Subbiah Vivek V

Pharmacology & therapeutics 20230109

Precision oncology informed by genomic information has evolved in leaps and bounds over the last decade. Although non-small cell lung cancer (NSCLC) has moved to center-stage as the poster child of precision oncology, multiple targetable genomic alterations have been identified in various cancer types. RET alterations occur in roughly 2% of all human cancers. The role of RET as oncogenic driver was initially identified in 1985 after the discovery that transfection with human lymphoma DNA transfo ...[more]

PMID: 36632846

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Project description:Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Dataset Information

RET aberrant cancers and RET inhibitor therapies: Current state-of-the-art and future perspectives.

Publications

RET aberrant cancers and RET inhibitor therapies: Current state-of-the-art and future perspectives.

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