Genome-based medicine holds substantial promise for moving beyond the treatment of typical patients to the development and application of evidence-based personalized care. Success will depend upon the broadest possible utilization of phenotypic and molecular data of increasing volume and variety for accelerating disease discovery, translational science, and individualized care to patients.
Age-related macular degeneration (AMD) is a leading cause of blindness in the elderly population of Western countries.
Many women are considered at high risk of developing breast cancer due to genetic mutations and/or family history of breast cancer. High-risk women struggle to make difficult decisions about risk-reducing interventions, such as chemoprevention, mastectomy, or oophorectomy, which all reduce risk, but are also associated with substantial side effects. Such decisions are highly personal, requiring accurate quantification of individual risk and response characteristics to risk-reducing interventions.
Patient engagement in their own care is essential to improve health outcomes and reduce cost. However, patient-engagement cannot be a one-sided issue. To be engaged fully, patients need to be well-informed, and supported by right healthcare providers who meet their needs and preferences. This presentation will discuss projects on enhancing patient engagement in self-management through informatics approach.
Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy.
Cancer is a disease resulting from genome alterations. Contemporary biotechnologies make genome-scale data from individual patients readily available, and personalized precision medicine based on specific genomic alterations of individual tumors presents promises of more effective therapy. However, there are 3 major gaps hinder the translation from genome data to personalized precision therapy.