1. What scientific observation led you and your team to explore the possibility of detecting breast cancer through a blood test?
C.R & S.C.: Using experimental cancer models we originally observed that the presence of a breast cancer modifies immune cells circulating in the blood in their numbers but also in their phenotype and gene expression. Subsequently we performed pilot clinical studies to test whether similar modifications were present in women with early and advanced breast cancer. The results confirmed these modifications in patients. Eventually we performed a larger clinical study to identify the biomarkers associated with these changes.
2. For someone without a scientific background, how does Xemperia’s breast cancer test work, from the blood sample to the final result?
C.R & S.C.: After the collection of the blood sample, the level of expression of several markers of a specific immune cell population are analyzed using PCR, a simple technology widely used in medical diagnostic tests. This requires some laboratory process (cellular isolation, RNA extraction). The different gene expression levels are then informatically analyzed with an algorithm, that generates a score for the presence or absence of a breast cancer. The whole procedure takes a few hours, and results can be delivered within two days.
3. What role do molecular biomarkers, data analysis and the algorithm play in distinguishing signals associated with breast cancer?
C.R & S.C.: The biomarkers of our tests are genes expressed in blood immune cells whose expression is altered by the presence of the tumor. They represent the core of the test. Each biomarker expression is analyzed using PCR technology. Put simply, variations in expression of these genes are analyzed by an algorithm and compared with a reference standard to generate a score indicating the presence or absence of the tumor.
4. What have been the biggest technological challenges in turning the initial research discovery into a test that could eventually be used in clinical practice?
C.R & S.C.: The concept of the test was developed over several years through preclinical and early clinical studies. The first challenge has been to perform a multicenter, case-control clinical study involving 120 women in collaboration with oncologists and radiologists to discover and early validate the biomarkers in immune cells associated with breast cancer. The second challenge was to identify the essential biomarkers among all the data and their combination for optimal performance. The third challenge was to translate the discovery into a PCR test compatible with clinical use. However, there is still a big challenge ahead, which is to validate and optimize the test blindly in a larger screening population. If the results of this upcoming study are robust, it will open the way to a first clinical use and the certification process necessary for broad commercialization.
5. How do you see a blood-based test fitting alongside established breast cancer screening and diagnostic methods such as mammography and other imaging?
C.R & S.C.: Our goal is to introduce this test as a first detection test in a screening program. As it is more convenient, faster than mammography, and avoids X-ray exposure, we expect that more women will participate in screening campaigns and more cancers will be detected at an early stage, when they are more likely to be curable. A positive result will call for an imaging test, sonography, mammography or magnetic resonance. Eventually a biopsy may be necessary to establish a diagnosis.
6. Looking ahead, how could advances in molecular diagnostics, data and digital technologies change the way we detect and monitor breast cancer?
C.R & S.C.: There is a clear need to develop new blood tests to detect and monitor cancer, including breast cancer. Some monitoring tests and therapy companion tests are already in clinical use. Advances in molecular diagnostics and data analysis will be key in this effort. For early detection test it will be important to have highly sensitive and specific tests so that can ideally detect all cancer while keeping false positive at a minimum. Another challenge would be to avoid overdiagnosis and be able to differentiate aggressive cancer that should be treated aggressively from indolent cancer that may not require treatment. We expect that artificial intelligence will help to identify biomarkers from large data sets and optimize algorithms.
