Dear Colleague,

Cancer genomics has been a rapidly progressing field in the past few decades and has revolutionized how we diagnose, prognosticate, and treat cancers. As the clinical utility of genomic assays grows, the use of clinical genomics in cancer care has been considered the key to personalized treatment. The new era of precision medicine focuses on the molecular features that each individual patient presents and that distinguish them from other patients. Advances in medicine and technology have led to significant progress in this area. Although the number of clinical, translational, and validation studies is growing, they are often limited by few genetic samples and insights from patients of diverse backgrounds. The lack of cancer genomic data from underrepresented racial and ethnic groups generates a gap and has the potential to hold back the pace of drug discovery and the development of treatments tailored to each patient. Moreover, personalized medicine requires the use of large datasets, which must be processed, analyzed, and integrated before being used for personalized care. Human diversity includes the challenge of studying the complex interplay of genes and the environment, such as diet and education, among different populations. Artificial intelligence (AI) and machine learning (ML) are computational tools that use specific neural networks and algorithms for the analysis of different types of large datasets.

The aim of this Special Issue is to collect and share translational, clinical, and validation studies related, but not limited to, the following topics:

• Cancer genomic diversity data collection (cancer genomic data from underrepresented racial and ethnic groups);
• Clinical validation of genomics testing;
• Application of cancer genomics in cancer diagnostic, prognostic, and therapy selection;
• The application of AI and ML to genomic data;
• The application of AI in developing personalized treatments of cancer and cancer prevention.

Dr. Carmela Paolillo
Guest Editor

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• cancer genomics
• precision medicine
• cancer biomarker
• cancer epidemiology
• cancer prevention
• cancer screening
• artificial intelligence
• machine learning
• personalized medicine
• diversity
• genomics
• precision oncology