Nanotechnology and machine learning enable circulating tumor cells as a reliable biomarker for radiotherapy responses of gastrointestinal cancer patients

A highly sensitive, circulating tumor cell (CTC)-based liquid biopsy was used to monitor gastrointestinal cancer patients during treatment to determine if CTC abundance was predictive of disease recurrence. The approach used a combination of biomimetic cell rolling on recombinant E-selectin and dendrimer-mediated multivalent immunocapture at the nanoscale to purify CTCs from peripheral blood mononuclear cells. Due to the excep-tionally high numbers of CTCs captured, a machine learning algorithm approach was developed to efficiently and reliably quantify abundance of immunocytochemically-labeled cells. A convolutional neural network and logistic regression model achieved 82.9% true-positive identification of CTCs with a false positive rate below 0.1% on a validation set. The approach was then used to quantify CTC abundance in peripheral blood samples from 27 subjects before, during, and following treatments. Samples drawn from the patients either prior to receiving radiotherapy or early in chemotherapy had a median 50 CTC ml-1 whole blood (range 0.6-541.6). We found that the CTC counts drawn 3 months post treatment were predictive of disease progression (p = .045). This approach to quantifying CTC abundance may be a clinically impactful in the timely determination of gastrointestinal cancer progression or response to treatment.

Automated summary

A highly sensitive liquid biopsy using circulating tumor cells (CTCs) was used to monitor gastrointestinal cancer patients and predict disease recurrence. The approach captured CTCs from peripheral blood and employed a machine learning algorithm to quantify their abundance. The results showed that the CTC counts obtained 3 months post-treatment were predictive of disease progression.