New biosensors facilitate urinary detection of immunotherapy response
Liv Gaskill | | News
Immune checkpoint blockade inhibitors have transformed the standard of care for patients battling a wide range of cancers – but why do less than a quarter of patients respond to these drugs? Currently, there is no quick, effective method of finding out whether a patient is responding to treatment. Testing can be performed by either biopsy or CT scan – but both approaches have disadvantages. Biopsies can be painful, invasive, and slow to yield results; scans can be inaccurate because they examine apparent tumor size rather than directly measuring treatment response.
To address this problem, researchers have designed a synthetic biosensor that could enable doctors to quickly identify whether or not a therapy is working – all via noninvasive analysis of proteases in patients’ urine (1). “We reasoned that, if patients are responding to the drug, it means [the] T cells are making proteases, and if they’re not responding, these proteases are not present, so the T cells are not active,” said senior author Gabe Kwong (2).
The sensors attach to an immune checkpoint blockade inhibitor while it travels toward the tumor. When it arrives, proteases from T cells and tumor cells activate the sensors, releasing fluorescent reporters designed to concentrate in urine. “These signals would be diluted in blood and would be very hard to pick up, but everything from your blood gets filtered through the kidneys,” said Kwong (2). “So, when we look at the urine, we get very concentrated signals which increase or decrease corresponding to whether the patients are responding or not.”
Kwong and his colleagues are also investigating machine learning techniques that can distinguish between different types of intrinsic resistance driven by either B2M or JAK1 gene mutations. Ultimately, they hope to differentiate between types of acquired resistance as well, so that patients who begin to exhibit resistance can quickly be moved to a more effective treatment.
- QD Mac et al., Nat Biomed Eng, 6, 310 (2022). PMID: 35241815.
- Jerry Grillo (2022). Available at: https://b.gatech.edu/3KdUNj3.