Genome Sequencing and Genetic Testing for Cancer

Dear Colleagues,

The landscape of oncology has been fundamentally transformed by the integration of Next-Generation Sequencing (NGS) and comprehensive genetic profiling. As we move further into the era of precision medicine, the ability to decode the cancer genome has transitioned from a discovery tool to a clinical necessity. These advancements allow for a deeper understanding of tumor heterogeneity and the molecular drivers of malignancy, providing a foundation for more personalized and effective therapeutic strategies.

This Special Issue, “Genome Sequencing and Genetic Testing for Cancer”, aims to explore the pivotal role of genomic data in refining diagnosis, predicting therapeutic response, and monitoring disease progression. We seek original research and comprehensive reviews that delve into the clinical utility of liquid biopsies, the identification of novel biomarkers, and the implementation of whole-genome sequencing in routine clinical practice. Furthermore, we encourage submissions focusing on the integration of multi-omics data and artificial intelligence in interpreting complex genetic variants. By bridging the gap between bench-side discovery and bedside application, this Special Issue intends to highlight how genetic testing is steering the future of precision oncology and improving patient outcomes globally.

Topics of interest for publication include, but are not limited to, the following:

• Clinical Implementation of Next-Generation Sequencing (NGS): advancements in whole-genome and exome sequencing for diagnostic accuracy.
• Precision Oncology and Targeted Therapy: genomic-driven selection of therapeutic agents and monitoring treatment resistance.
• Liquid Biopsy and Circulating Tumor DNA (ctDNA): non-invasive monitoring of minimal residual disease (MRD) and clonal evolution.
• Cancer Susceptibility and Germline Testing: identifying hereditary risk factors and their implications for personalized screening.
• Somatic Mutation Profiling: decoding the molecular landscape of primary and metastatic tumors.
• Pharmacogenomics in Oncology: understanding genetic variations that influence drug metabolism and toxicity.
• Multi-omics Data Integration: combining genomic, transcriptomic, and proteomic data for comprehensive tumor profiling.
• Machine Learning and AI in Omics: utilizing computational models for variant interpretation and predictive modeling in cancer.
• Biomarker Discovery: identification of novel genetic and molecular markers for early detection and prognosis.
• Functional Genomics: using gene perturbation (CRISPR/RNAi) to validate therapeutic targets and cancer drivers.

Dr. Kapila Gunasekera
Guest Editor

Manuscript Submission Information

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