Mitigating Leukemia Relapse Risks with DNA-based Test
By MedTech Outlook | Tuesday, February 05, 2019
Recent developments in sequencing cancer genomes have helped doctors understand the complex molecular landscape of Acute Myeloid Leukemia (AML). Age has a significant influence on the management and outcome of AML patients. A report by National Cancer Intelligence Network (NCIN) says, With AML, only around 20 percent of people will survive their leukemia for five years or more after their diagnosis. After risk stratification and standard treatment, the five-year survival is less than 60 percent in adults. The incidence of poor prognostic factors such as high-risk cytogenetics and secondary leukemia is higher for older patients. This results in a well-known dismal forecast for elderly AML patients. Therefore, clinical tests need to be developed to identify patients who have a suboptimal response to therapy and are at high risk of recurrence so that they can be treated with intensive post-remission strategies such as allogeneic bone marrow transplants.
Although the extensive network of cytogenetic abnormalities, molecular heterogeneity, epigenetic, and gene expression profiles in AML has been addressed, prognostic stratification in AML relies primarily on cytogenetic and a few gene mutations.
Even after addressing the extensive network of cytogenetic abnormalities, molecular heterogeneity, epigenetic, and gene expression profiles in AML, the prognostic stratification in AML mainly relies upon only cytogenetic and a handful of gene mutations. The detection of leukemic cells at a threshold below the morphologic limit of detection is called measurable residual disease (MRD). Some people with AML have leukemia cells mutated in the FLT3 gene. New drugs called FLT3 inhibitors target cells change with this gene. Midostaurin (Rydapt) is approved for use with chemotherapy in patients with an AML FLT3 mutation
The researchers then identified DNA mutations found that some initial mutations could still be detected three weeks after the transplant, indicating the presence of cancer cells resistant to treatment despite chemotherapy and bone marrow transplants eliminated most leukemia cells, leading to a reduction in mutation frequency.
Machine learning methods are one of the other designed and validated studies that can be used to improve the accuracy of cancer susceptibility, recurrence, and mortality predictions (15-25 percent). Machine learning also helps doctors to better understand the development of cancer.