The advent of precision oncology is marked by many new changes and improvements in diagnostic, prognostic and treatment methods for cancer patients. Researchers looking to bring new drugs to market are now also privy to a vast database of material from previously conducted studies thanks to a push forward in research collaborations and the creation of shared data hubs.
Today, the coronavirus and COVID-19 pandemic has been a major burden upon national health systems across the world and coupled with economic recession, many countries are now making big cuts to cancer research funding. In this way it is essential for oncologists and pharmaceutical companies to find ways of improving the research process including its speed and efficiency – no more so than with the adoption of newly available technologies and methods.
Liquid biopsies are a less invasive and easily repeatable method that use a blood sample to provide healthcare professionals with genetic information about a patient’s tumor. This new method requires only 5ml of blood with the procedure being considerably faster than a surgical biopsy, cutting down weeks to a few days and is also less expensive. Additionally, liquid biopsies can be used in patients where standard biopsies are not possible, for instance due to an inaccessible tumor location.
In August earlier this year the FDA approved the first Liquid Biopsy Next Generation Sequencing Companion Diagnostic. The Guardant360 CDx assay utilizes both liquid biopsy and next generation sequencing (NGS), known as high throughput tumor profiling. Compared to older technologies, NGS requires only one test and allows better assessment of tumor composition.
At the time of approval, Tim Stenzel, Director at the FDA’s centre for Devices and Radiological Health, said: “Approval of a companion diagnostic that uses a liquid biopsy and leverages next-generation sequencing marks a new era for mutation testing. In addition to benefiting from less invasive testing, patients are provided with a simultaneous mapping of multiple biomarkers of genomic alterations, rather than one biomarker at a time, which can translate to decreased wait times for starting treatment and provide insight into possible resistance mechanisms.”
The identification of disease states and treatment options is a major challenge in precision oncology. Today, artificial intelligence (AI) offers unique opportunities for enhancing such predictive capabilities in the lab and the clinic. Most current AI applications involve the use of machine learning (ML) algorithms. As ML algorithms are exposed to more data, they can observe hidden patterns within datasets and be used in a variety of different ways.
AI is also used in cancer imaging, clinical decision making, clinical photographing and radiographic imaging which have all in their own ways considerably accelerated and refined the diagnostic and treatment processes in oncology.
Other Methods of Cutting Research Time
Also known as adaptive clinical trials, smarter trials are currently transforming efficiency in drug development. In contrast to randomized controlled trials, an adaptive trial involves the patients responses being observed and analysed at pre-defined interim points, and pre-determined modifications to study design can be implemented based on what researchers observe.
This trial methodology helps to deliver the right treatment to the right patient faster than ever before.
Finally, sharing more data and further supporting research collaborations is a vital way top oncologists receive and retain access to data hubs and resources, ultimately cutting down on research time and improving diagnostic and treatment efficiency. The past decade has seen some notable collaborations including Project Data Sphere which launched in 2014 with seven pharma giants including AstraZeneca, Janssen and Pfizer agreeing to share data of around 30,000 cancer patients to better inform and identify cohorts who do not benefit from standard chemotherapy.
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