Advancing Cancer Remission Assessment: MRD & Emerging Technologies

In an interview with Targeted Oncology^TM, Dong Chen, MD, PhD, hematopathologist and vice chair of practice in pathology in the Department of Laboratory Medicine and Pathology at Mayo Clinic in Rochester, MN, discusses the most recent advancements in posttreatment cancer detection.

With the progress in cancer treatments, we are increasingly achieving complete remission (CR) in more patients. Traditionally, CR has been defined as either a clinical CR, where symptoms and physical evidence of cancer are no longer apparent, or a pathologic CR, where leukemic or other cancer cells are no longer detectable through pathology. However, it has been observed that some patients in CR still relapse, likely due to minimal residual disease (MRD) that remains below the detection limit of standard clinical, imaging, and pathology exams.

This realization has driven the advancement of posttreatment assessment strategies. These can be broadly categorized into more mature and earlier-stage approaches.

The more mature area focuses on liquid biopsy and MRD testing. Liquid biopsy, available at Mayo Clinic and many other centers, is a sensitive method for detecting residual cancer cells or DNA circulating in the blood. MRD testing, primarily used for hematologic malignancies, involves various techniques, from flow cytometry to molecular assays, many of which are offered at Mayo Clinic. These methods aim to identify residual leukemic cells.

The earlier-stage detection methods include:

1. Single-cell sequencing: This technology allows for detailed analysis of individual cells, potentially revealing rare residual cancer cells.
2. Exosome-based detection: This approach focuses on analyzing exosomes, small vesicles released by cells, which can carry cancer-specific markers.
3. Advanced molecular targeted imaging: These studies, with promising results reported for DICER, aim to visualize residual disease with greater sensitivity.
4. Radiomics imaging: This emerging field combines clinical features and artificial intelligence analysis of radiology images to enhance disease assessment sensitivity.

This overview captures the current landscape of advancements in post-treatment disease assessment.