Unveiling the Unseen: The Future of Posttreatment Cancer Detection

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As cancer treatments advance, a new frontier in patient care is emerging: the precise detection of measurable residual disease (MRD) after treatment. This shift from traditional clinical and pathological assessments to highly sensitive molecular and cellular analyses promises to revolutionize how clinicians monitor patients and predict relapse. Dong Chen, MD, PhD, hematopathologist and vice chair of practice in pathology at Mayo Clinic, shed light on these groundbreaking advancements and the challenges that lie ahead in an interview with Targeted Oncology^TM.

Traditionally, complete remission has been defined by the absence of visible cancer symptoms or cells through physical exams, imaging, or routine pathology. However, as Chen explained, "Gradually, we noticed that [relapse] is likely related to the residual disease, which is below the radar of our routine testing." This critical gap in detection has spurred the development of highly sensitive posttreatment assessment methodologies.

Chen categorizes these advancements into mature and early-phase technologies. The mature forms primarily encompass liquid biopsy and MRD testing. Liquid biopsy, widely adopted in academic and commercial centers, offers a sensitive way to detect circulating tumor DNA. For hematologic neoplasms, cell-based MRD testing, utilizing techniques from flow cytometry to molecular assays, is routinely performed.

Beyond these established methods, exciting early-phase technologies are on the horizon, including single-cell sequencing, exosome-based detection, advanced molecular-targeted imaging studies, and a novel entity called "radiomics imaging," which combines clinical features with artificial intelligence (AI)-powered radiology imaging to boost detection sensitivity.

While the advantages of detecting disease at a molecular level are clear, Chen acknowledges several limitations in the widespread adoption of these sophisticated techniques. The first is their inherent complexity, making clinical integration challenging. Second, the advanced nature of MRD testing often places it "a bit ahead of the game" in terms of cost structure and reimbursement policies, leading to a lag in insurance coverage, according to Chen.

Incorporating AI Into Disease Monitoring and Management

AI is playing an increasingly vital role in enhancing these detection methodologies, according to Chen. In the lab, AI algorithms are being implemented to improve case workups and identify rare events in molecular and flow cytometry analyses. For imaging, AI is augmenting studies by incorporating new biomarkers and integrating multi-omic data, thereby enhancing the sensitivity and specificity of detecting subtle findings. Chen likens this to "trying to find a needle in a haystack,” where AI significantly improves accuracy. In the clinic, machine learning is being explored to proactively predict relapse risk based on clinical data, such as complete blood counts recovery or other patient symptoms and biomarkers.

Barriers to Better Monitoring

Addressing accessibility issues is paramount for widespread adoption. Chen outlined several hurdles, beginning with the need for streamlined FDA approval processes for these new assays. Standardization and quality control across different testing platforms are also essential. "There's a vast variety of testing platforms and operations," he noted, emphasizing the importance of proficiency testing to ensure consistency.

Technical barriers, stemming from the complexity of these high-sensitivity tests, are also significant. Analytical complexity, with many manual processes and numerous steps, poses another hurdle. The need for a highly qualified workforce to operate these complex tests contributes to their concentration in larger institutions. Geographic inequity means patients in remote settings may have less access to these crucial monitoring tools.

Chen explained that reference laboratories and outreach programs can help bridge this gap, ensuring that, "Even though the patients are being taken care of in somewhat geographically remote locations, they still have high quality access to the posttreatment monitoring or MRD testing.”

For health care professionals seeking to stay informed, Chen recommends 3 main approaches: continuing medical education, networking with peers and experts, and proactive engagement in clinical trials.

To Chen, there is a crucial need for continued collaboration within the medical community. This collective effort is essential to unlock the full potential of these advanced detection methods, ultimately leading to improved patient outcomes and a future where MRD no longer goes unseen.