We are analyzing https://link.springer.com/article/10.1186/s13039-024-00698-w.

Title:
Precision oncology platforms: practical strategies for genomic database utilization in cancer treatment | Molecular Cytogenetics
Description:
In recent years, the expansion of molecularly targeted cancer therapies has significantly advanced precision oncology. Parallel developments in next-generation sequencing (NGS) technologies have also improved precision oncology applications, making genomic analysis of tumors more affordable and accessible. Targeted NGS panels now enable the rapid identification of diverse actionable mutations, requiring clinicians to efficiently assess the predictive value of cancer biomarkers for specific treatments. The urgency for timely and accurate decision-making in oncology emphasizes the importance of reliable precision oncology software. Online clinical decision-making tools and associated cancer databases have been designed by consolidating genomic data into standardized, accessible formats. These new platforms are highly integrated and crucial for identifying actionable somatic genomic biomarkers essential for tumor survival, determining corresponding drug targets, and selecting appropriate treatments based on the mutational profile of each patient’s tumor. To help oncologists and translational cancer researchers unfamiliar with these tools, we review the utility, accuracy, and comprehensiveness of several commonly used precision medicine software options currently available. Our analysis categorized selected genomic databases based on their primary content, utility, and how well they provide practical guidance for interpreting somatic biomarker data. We identified several comprehensive, mostly open-access platforms that are easy to use for genetic biomarker searches, each with unique features and limitations. Among the precision oncology tools we evaluated, we found MyCancerGenome and OncoKB to be the first choice, offering comprehensive, accurate up-to-date information on the clinical significance of somatic mutations. To illustrate the application of these precision oncology tools in clinical settings, we evaluated three case studies to see how use of the platforms could have influenced treatment planning. Most of the precision oncology software evaluated could be easily streamlined into clinical workflows to provide updated information on approved drugs and clinical trials related the actionable mutations detected. Some platforms were very intuitive and easy to use, while others, often developed in smaller academic settings, were more difficult to navigate and may not be updated consistently. Future enhancements, incorporating artificial intelligence algorithms, are likely to improve integration of the platforms with diverse big data sources, enabling more accurate predictions of potential therapeutic responses.
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28 years and 1 months (reg. 1997-05-29).

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Keywords {🔍}

cancer, clinical, data, platform, information, genomic, platforms, evidence, mutations, pubmed, users, search, gene, drug, article, mutation, tumor, variants, treatment, specific, google, scholar, genes, precision, database, drugs, databases, alterations, response, therapies, trials, including, somatic, studies, updated, research, oncology, types, variant, biomarkers, case, level, list, access, potential, based, researchers, analysis, comprehensive, therapeutic,

Topics {✒️}

/solutions/mmportal-clinical-decision-support multi-omics integration database large-scale genomic cohorts amp/asco/cap somatic guidelines dna copy-number variations circulating tumor dna tumor expressed pd-l1 personalized cancer therapy—leveraging incorporate multi-omics data egfr tyrosine-kinase inhibitors large-scale public repository artificial intelligence support clinical decision-making informed decision-making based pd-l1 expression level ngs-based multiplex assay aid clinical decision-making include egfr tyrosine-kinase dna methylation data memorial sloan kettering thirty-day free trial small-cell lung carcinoma cancer-related biological activities relevant pubmed articles structured clinical-grade mutations personalized cancer treatment suspected deleterious brca-mutation article download pdf braf wild-type status efgr t790m-mutated cases large-scale project personalized cancer therapy platinum-based chemotherapy personalized treatment plans interactive gene/drug heatmap somatic mutational profiles underwent empiric chemotherapy privacy choices/manage cookies drug–gene interaction database potential decreased sensitivity/resistance fda-approved therapies relating leticia frohlich archangelo amp/asco/cap rees mg seashore-ludlow org/research/standards/onc_path_sop/ disease-focused expert groups molecular oncology knowledgebase precision oncology databases natural language processing

Schema {🗺️}

WebPage:
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         headline:Precision oncology platforms: practical strategies for genomic database utilization in cancer treatment
         description:In recent years, the expansion of molecularly targeted cancer therapies has significantly advanced precision oncology. Parallel developments in next-generation sequencing (NGS) technologies have also improved precision oncology applications, making genomic analysis of tumors more affordable and accessible. Targeted NGS panels now enable the rapid identification of diverse actionable mutations, requiring clinicians to efficiently assess the predictive value of cancer biomarkers for specific treatments. The urgency for timely and accurate decision-making in oncology emphasizes the importance of reliable precision oncology software. Online clinical decision-making tools and associated cancer databases have been designed by consolidating genomic data into standardized, accessible formats. These new platforms are highly integrated and crucial for identifying actionable somatic genomic biomarkers essential for tumor survival, determining corresponding drug targets, and selecting appropriate treatments based on the mutational profile of each patient’s tumor. To help oncologists and translational cancer researchers unfamiliar with these tools, we review the utility, accuracy, and comprehensiveness of several commonly used precision medicine software options currently available. Our analysis categorized selected genomic databases based on their primary content, utility, and how well they provide practical guidance for interpreting somatic biomarker data. We identified several comprehensive, mostly open-access platforms that are easy to use for genetic biomarker searches, each with unique features and limitations. Among the precision oncology tools we evaluated, we found MyCancerGenome and OncoKB to be the first choice, offering comprehensive, accurate up-to-date information on the clinical significance of somatic mutations. To illustrate the application of these precision oncology tools in clinical settings, we evaluated three case studies to see how use of the platforms could have influenced treatment planning. Most of the precision oncology software evaluated could be easily streamlined into clinical workflows to provide updated information on approved drugs and clinical trials related the actionable mutations detected. Some platforms were very intuitive and easy to use, while others, often developed in smaller academic settings, were more difficult to navigate and may not be updated consistently. Future enhancements, incorporating artificial intelligence algorithms, are likely to improve integration of the platforms with diverse big data sources, enabling more accurate predictions of potential therapeutic responses.
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      headline:Precision oncology platforms: practical strategies for genomic database utilization in cancer treatment
      description:In recent years, the expansion of molecularly targeted cancer therapies has significantly advanced precision oncology. Parallel developments in next-generation sequencing (NGS) technologies have also improved precision oncology applications, making genomic analysis of tumors more affordable and accessible. Targeted NGS panels now enable the rapid identification of diverse actionable mutations, requiring clinicians to efficiently assess the predictive value of cancer biomarkers for specific treatments. The urgency for timely and accurate decision-making in oncology emphasizes the importance of reliable precision oncology software. Online clinical decision-making tools and associated cancer databases have been designed by consolidating genomic data into standardized, accessible formats. These new platforms are highly integrated and crucial for identifying actionable somatic genomic biomarkers essential for tumor survival, determining corresponding drug targets, and selecting appropriate treatments based on the mutational profile of each patient’s tumor. To help oncologists and translational cancer researchers unfamiliar with these tools, we review the utility, accuracy, and comprehensiveness of several commonly used precision medicine software options currently available. Our analysis categorized selected genomic databases based on their primary content, utility, and how well they provide practical guidance for interpreting somatic biomarker data. We identified several comprehensive, mostly open-access platforms that are easy to use for genetic biomarker searches, each with unique features and limitations. Among the precision oncology tools we evaluated, we found MyCancerGenome and OncoKB to be the first choice, offering comprehensive, accurate up-to-date information on the clinical significance of somatic mutations. To illustrate the application of these precision oncology tools in clinical settings, we evaluated three case studies to see how use of the platforms could have influenced treatment planning. Most of the precision oncology software evaluated could be easily streamlined into clinical workflows to provide updated information on approved drugs and clinical trials related the actionable mutations detected. Some platforms were very intuitive and easy to use, while others, often developed in smaller academic settings, were more difficult to navigate and may not be updated consistently. Future enhancements, incorporating artificial intelligence algorithms, are likely to improve integration of the platforms with diverse big data sources, enabling more accurate predictions of potential therapeutic responses.
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         Oncogenes
         Tumor suppressor genes
         DNA mutational analysis
         Personalized cancer treatment
         Cancer biomarkers
         Chemotherapy
         Actionable mutations
         Artificial intelligence
         Big data integration
         Oncology software
         Cytogenetics
         Molecular Medicine
         Human Genetics
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