Most cancer deaths are due to the metastatic spread and growth of tumor cells at distant sites. Identifying appropriate treatments for patients with metastatic disease is challenging because of limited biomarkers and detection capabilities, and poor characterization of metastatic tumors.
However, these techniques are limited by single timepoints, poor resolution of smaller lesions and an inability to provide information on individual metastases. Scientists are investigating the potential of dynamic biomarkers to overcome the limitations of standard"Dynamic markers base prognosis not on the absolute value of measurement at a single time point, but on the relative change over time.
The researchers performed their study with data from 16 patients treated in a clinical trial of adaptive therapy. During adaptive therapy, patients are given breaks from treatment based on their change in PSA levels. This approach is designed to prevent the development and growth of drug resistant tumors that cause treatment failure.
They identified several key relationships between metastatic disease and the biomarkers, including metastases that were larger in size had longer treatment cycles; metastases with a higher proportion of drug-resistant cells slowed the cycle; and metastases that had a faster cell turnover rate had a faster drug response time and a slower time to regrow.
The team performed additional modeling to compare adaptive therapy to continuous therapy during which no treatment breaks are given. They discovered that differences between metastatic tumor compositions favored continuous treatment, and differences within metastatic tumor compositions favored adaptive schedules.
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