Tempus assists doctors and other healthcare professionals deliver personalized care for their cancer patients. The Tempus platform analyzes molecular and clinical data to empower physicians to make real-time data-driven decisions. Tempus’ goal is for each patient to benefit from the treatment of patients who came before by providing physicians with tools that learn as we gather more data.
Tempus’ proprietary approach to data analytics utilizes genomic sequencing and deep machine learning to help doctors obtain a better understanding of each patient’s tumor. Tempus augments the current standard of care by arming physicians with molecular, phenotypic and therapeutic insights delivered in a clinical setting.
Our analytic platform allows each physician to gain a comprehensive understanding of a patient’s cancer. Tempus collects and analyzes large amounts of genomic data using proprietary algorithms. By analyzing data in the context of patients with similar molecular profiles, we uncover opportunities to assist a patient’s physician in providing more personalized treatment options, including FDA-approved and actively-recruiting clinical trials.
Actionable results from rigorous statistical analyses can be validated using in vitro and in vivo patient-derived biological models before clinical deployment. Through independent and collaborative biological testing, Tempus is working with physicians and researchers to develop novel automation and cell-culture technology, which allows high throughput screening of therapeutic agents to occur in synthetic and animal models, instead of patients.
Tempus will deliver a detailed report to a patient’s physician that includes information to be used and considered in developing the patient’s care plan and treatment. The report will contain the results of the patient’s genomic sequencing, as well as any actionable targets that may exist and clinical trials that may be available and appropriate, along with other relevant clinical information and a contextual comparison of a patient’s genomic profile across similar patient populations.