Table 1.
Types of precision medicine technologies
| Type of technology or service | Relevance to precision medicine | Estimated timescales for use |
|---|---|---|
| Tests for prognostic biomarkers Example: Decipher® tests [13]—indicate risk of disease progression after prostate cancer diagnosis |
Biomarkers indicate disease course and inform the patient treatment pathway | Genomic biomarkers are already in use. Rapid discovery of proteomic and metabolomic biomarkers is expected in the next 5 years |
| Tests for disease susceptibility biomarkers Example: Tests for BRCA1 gene—indicates risk of breast and ovarian cancer [26] |
Biomarkers indicate risk of developing a particular condition and inform the patient treatment pathway | |
| Tests for predictive biomarkers Example: HER2 protein tests—predicts response to breast cancer treatment [3] |
Biomarkers predict treatment response and inform therapy choice | An increasing number are being evaluated by HTA agencies—a review found NICE had evaluated seven by 2014 [8] Expected to expand rapidly in next 5 years |
| Diagnostic services Including genetic, genomic and molecular testing services but also other types of diagnostic support for clinicians, e.g. Computerised Decision Support [27] |
Services inform diagnoses and the patient treatment pathway | Some of these services are already in use |
| Complex algorithms Example: Sapientia [18]—combines genomic sequencing with clinical phenotyping to inform treatment decisions |
Clinical, genomic, behavioural (and more) data are utilised by these algorithms to inform diagnosis, recommendations for patient treatment pathways and therapy choices | Several are being developed and trialled—expected to be in clinical practice within the next decade Expected to be AI-based as the field progresses (e.g. AI Biocomputing [28]) |
| Digital health applications Example: MyHeart Counts [29]—records and analyses data on activity, risk factors and haematology, providing suggestions on improving heart health |
Apps draw on clinical and behavioural data and aim to influence patient behaviour, healthcare use and/or choice of treatment | Apps are already available but numbers are expected to increase dramatically in next decade |
| Risk prediction tools Example: QRISK [30]—static algorithm that determines risk for cardiovascular disease and informs statin prescribing |
Patient histories and characteristics (e.g. BMI, co-morbidities) are used to calculate disease risk, informing the patient treatment pathway | Currently available for a wide range of clinical areas |
| Patient decision aids Example: MAGIC [31]—produces dynamic decision aids that update based on published guidelines |
Instruments support patients in making decisions tailored to their preferences | Currently available for a wide range of clinical areas |
AI artificial intelligence, BMI body mass index, HTA health technology assessment, NICE National Institute for Health and Care Excellence