Getting each patient the right drug at the right dose at the right time is the goal of pharmacogenomics, which involves studying how people's specific DNA sequences influence their responses to medications.
The drugs available today to treat cancer, heart disease and other conditions are powerful agents that work as intended in most patients. Yet, in some people, a particular drug at the standard dose might not work well enough or may even trigger a serious adverse reaction. The reasons for this lie, at least in part, in each person's genes.
By considering a patient's unique genetic makeup, physical status, demographic information and testing results, health care teams can now build more-sophisticated algorithms to help predict drug response. When prescribing drugs, doctors can use the information to maximize treatment effectiveness while avoiding potentially life-threatening side effects.
Pharmacogenomics can help answer a broad range of questions, such as:
- Why does standard chemotherapy eradicate breast cancer in some women but work less effectively in other women?
- What are new treatment options for men with advanced prostate cancer that has resisted all previous therapies?
- What is the right blood thinner drug for patients who get a stent for their coronary artery disease?
- How can rules be added to pharmacy systems to take the patient's genome into consideration for each prescription?
- How can treatment with antidepressants be better individualized?
Patients with major depressive disorder are often treated with selective serotonin reuptake inhibitors (SSRIs), a standard of care for this group of patients. However, only about 50% of patients respond to the treatment. Using current methods, it's only possible to predict patient response with 55% accuracy.
Mayo Clinic researchers used an approach based in artificial intelligence (AI) to develop the Analytics and Machine Learning Framework for Omics and Clinical Big Data (ALMOND) study, creating an algorithm that incorporates clinical symptoms, demographic information and genetic biomarkers. The SSRI response prediction accuracy of the ALMOND algorithm is between 80% and 90%.
Mayo Clinic is now implementing the ALMOND algorithm in routine practice to advance individualized SSRI therapy. AI algorithms for other conditions also are in development.
In the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT 10K) Study, researchers sequenced a set of 77 "pharmacogenes" and inserted the results into the electronic health record (EHR); they also placed interpretive reports in participants' medical records.
Most electronic medical record systems are not equipped to alert the pharmacist or physician to drug-gene interactions. The RIGHT10K Study uses infrastructure built at Mayo Clinic that can alert physicians as they choose prescriptions, so patients get the right drug in the right dose at the right time.
Scientists are conducting many studies to determine how genetic variation in these 77 pharmacogenes might affect individual drug responses. New findings from these studies will lead to better individualized drug therapy.
BEAUTY and BEAUTY 2 studies
The Breast Cancer Genome-Guided Therapy (BEAUTY) study performed whole-exome sequencing on women who were newly diagnosed with breast cancer. The sequencing was carried out before and after participants received pre-surgical drug therapy (neoadjuvant therapy).
Pharmacogenomics Program researchers were able to compare the tumor genome before and after neoadjuvant anti-cancer therapy with DNA sequences in participants' normal, noncancerous tissue (germline genome) to observe variations in response to therapy.
Based on the findings from the BEAUTY Study, clinicians are personalizing therapy to help ensure that women with chemotherapy-resistant breast cancer receive the right combination of drugs, resulting in the highest possible chance of a cure. The BEAUTY 2 Study tests the effectiveness of drugs that are not commonly used to treat one of the most aggressive breast cancer subtypes, triple negative breast cancer.
The goal of the study is that patients experience seamless and effective health care in the treatment of breast cancer during the crucial time between diagnosis and surgery.
Endocrine resistance is common in patients with breast cancer, and while the drug palbociclib (Ibrance) in combination with endocrine therapy has provided substantial improvement in progression free survival in women with metastatic breast cancer, that is not the case for all patients.
A Prospective Study to Evaluate the Role of Tumor Sequencing in Women Receiving Palbociclib for Advanced Hormone Receptor (HR)-Positive Breast Cancer (PROMISE) uses biopsies of participants' metastatic breast cancer to obtain detailed information regarding the genetic makeup of the tumor as well as each participant's germline genome, with the goal of developing personalized treatment approaches to improve patient outcomes.
The Prostate Cancer Medically Optimized Genome Enhanced Therapy (PROMOTE) Study took an approach similar to the BEAUTY breast cancer studies, but for prostate cancer. Pharmacogenomics Program researchers hope to elucidate DNA sequences associated with response to the current first-line therapy of prostate cancer — one of a new generation of androgen deprivation therapies, abiraterone (Zytiga) — to identify additional treatment options for patients with advanced prostate cancer that has resisted all standard therapies.
The Pharmacogenomics Program's breast cancer and prostate cancer studies have also included groundbreaking work with murine "avatars" — tumors that were grown in murine models to make it possible to test drugs in these models rather than in the patient to identify new treatment options.
Patients with coronary artery disease often come into the emergency room requiring placement of one or more coronary artery stents. In the TAILOR-PCI study, researchers studied specific DNA variants that might indicate whether the patient should receive the anticoagulant drug clopidogrel (Plavix) or an alternative drug. This is a question that has vexed cardiologists for years and for which an incorrect decision might result in a clot forming in a patient's heart artery. Safer and more effective treatment decisions will now be possible based on genetic information.
Mayo Clinic and the University of Illinois at Urbana-Champaign jointly established the CCBGM as a National Science Foundation center to develop new and innovative approaches — such as the application of data analytics and artificial intelligence — to facilitate the translation of genomics and other high-dimensional data into clinical care and address other biomedical challenges. The CCBGM invites companies with interest in genome-based challenges in health care discovery to join the center.
The Mayo Clinic and Illinois Alliance for Technology-Based Healthcare was organized in 2010 to advance research, technology and clinical treatment options in health care. The alliance is a framework for collaboration in individualized medicine. It involves innovative educational programs, integrated research activities and projects, and entrepreneurial efforts to deploy and commercialize outcomes of the collaboration.
The Office of Strategic Coordination of the National Institutes of Health (NIH) administers the BD2K program, which funds research and training activities that support the use of big data to advance biomedical research and discovery. This includes efforts to enhance training, resource indexing, methods, tools development, and other data science-related areas.
As part of this large NIH award, Mayo Clinic and the University of Illinois at Urbana-Champaign created the Knowledge Engine for Genomics (KnowEnG, pronounced "knowing") as a center of excellence in big data computing.
Pharmacogenomics: Genes and Drugs
Richard Weinshilboum, M.D., director, Pharmacogenomics Program
Pharmacogenomic Testing — Karen's Story
Pharmacogenomic testing helps a patient and her family members find answers to health-related questions.
Pharmacogenomics Program Animation
The Pharmacogenomics Program investigates how variations in genes affect response to medications, thereby using a patient's genetic profile to predict a drug's efficacy, guide dosage and improve patient safety.
Individualized Medicine — Holly's Story
Sequencing uncovers the genetic makeup of an aggressive tumor.