Physicians and oncologists who treat cancer today are often forced to rely on limited trial results from very selective populations, traditional treatment modalities and mostly on their own intuition and experience.
Biological and clinical evidence have shown that cancer becomes more complex over the time and that no two cancer patients are alike. Therefore, concepts that reflect the cancer biology in each patient based on therapeutic targets are crucial to match the right treatment and improve outcomes.
Treatment centers around the world are undergoing a digital transformation towards greater access to health information and resources. They are implementing technology that gives them the capability to share data, both structured and unstructured, on a global scale, enabling them to offer the highest quality treatments.
This precision oncology approach is beneficial for everyone—it is an ecosystem topic that affects treatment centers, pharmaceutical companies, insurers and, of course, patients. The goal of precision oncology is very straightforward: give the right drug and treatment to the right patient at the right time. By collecting multiple sources of information, healthcare providers can get a holistic picture of each patient and can align treatment accordingly.
Precision oncology requires the collection of genomic data. The genomic makeup of a tumor is becoming more important when determining treatment options for cancer patients. Cancer is the general name for a group of more than 100 diseases, and tumors can have thousands of mutations. Genomics can help physicians discover what is driving tumor growth and sensitivity. By capturing genomic information and using in-memory computing technologies, researchers can create algorithms that analyze mutations much faster than manual research methods, reducing the time needed for doctors to choose the treatment with the greatest chance of success.
Next-generation sequencing enables for “mutagenome” analytics of the tumors, and other advancements like liquid biopsy of circulating tumor cells allow for analysis primarily in blood. The hope is that, instead of extensive imaging and invasive tissue biopsies, liquid biopsies could be used to guide cancer treatment decisions and even screen for tumors that are not yet visible with imaging. Pharmacogenomics research and open data initiatives provide added new data-driven insights.
Using high-performance in-memory computing technology, researchers are able to analyze and use this genomic data in innovative ways; leading to extraordinary changes in cancer care.
Developing a holistic picture of a patient and the type of cancer involves collecting not only tumor, genomic and biological data, but also physicians’ notes. By collecting structured and unstructured data, caregivers can better understand care variances.
Approximately 80 percent to 90 percent of data used to treat cancer is unstructured. Fortunately, the use of unstructured data as a component in the construction of cancer knowledge bases is increasing. For example, the mention of a tumor marker in a clinical report or discharge letter can now be identified and mapped using natural language processing and text analytics.
By leveraging in-memory computing and visual tools, the data engineering component shrinks, and smart insights are available in real-time. The user interface experience ensures easy usage, so physicians and oncologists at the point of care can facilitate daily treatment based on their own queries.
The shift from individual treatment center silos to larger intelligent platforms shared by the cancer care community is already happening. One example is CancerLinQ, a platform that aims to make unprecedented use of massive amounts of cancer patient data to rapidly improve the quality of care for people with cancer. Through a partnership between SAP and CancerLinQ LLC, a wholly owned nonprofit subsidiary of the American Society of Clinical Oncology, the CancerLinQ® platform runs on SAP Health.
Clinical research is an important component of patient care. While the number of clinical trials is increasing significantly, trials need a sufficient number of participants in order to gather enough evidence. Consequently, it can be very difficult to match sufficient numbers of participants satisfying the required study criteria with the right studies. Approximately one out of three clinical trials fail to meet recruitment targets.
The Austrian Center for Biomarker Research in Medicine (CBmed) GmbH and SAP Health are working on an innovative software application with the aim of helping clinical researchers and physicians find and screen eligible patients for pre-defined clinical trials in a time-efficient way. While the application is still under development, it aims to address the common recruitment challenges. CBmed, together with the Medical University of Graz and the large Styrian care provider KAGes are exploring functionalities that include a trial data model capable of storing all relevant trial information, the ability to create a trial manually or import trial details from external sources like clinicaltrials.gov to reduce manual intervention by automatically matching patient data from electronic medical records, and entering criteria to improve eligible patient screening results.
We do not know everything we need to know to defeat cancer yet. The next generation of precision medicine is already knocking at the door. The shotgun approach of general chemotherapy will disappear. Multidimensional biological, molecular and phenotypical information will be integrated to treat patients with innovative drugs. This will scale-up the amount of data processed and analyzed and computing platforms will help physicians to deliver the right treatment to the right patient at exactly the right time.
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