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A flexible electronic strain sensor for the real-time monitoring of tumor regression
Alex Abramson1, Carmel T Chan2,3, Yasser Khan1
1Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.
Abstract:
Assessing the efficacy of cancer therapeutics in mouse models is a critical step in treatment development. However, low-resolution measurement tools and small sample sizes make determining drug efficacy in vivo a difficult and time-intensive task. Here, we present a commercially scalable wearable electronic strain sensor that automates the in vivo testing of cancer therapeutics by continuously monitoring the micrometer-scale progression or regression of subcutaneously implanted tumors at the minute time scale. In two in vivo cancer mouse models, our sensor discerned differences in tumor volume dynamics between drug- and vehicle-treated tumors within 5 hours following therapy initiation. These short-term regression measurements were validated through histology, and caliper and bioluminescence measurements taken over weeklong treatment periods demonstrated the correlation with longer-term treatment response. We anticipate that real-time tumor regression datasets could help expedite and automate the process of screening cancer therapies in vivo.
Insights
A new wearable sensor automates cancer drug testing in mice by tracking tumor size changes in real-time. This technology quickly identifies effective cancer therapeutics, speeding up treatment development.
Area of Science:
- Oncology
- Biomedical Engineering
- Materials Science
Background:
- Assessing cancer therapeutic efficacy in mouse models is crucial but hindered by low-resolution tools and small sample sizes.
- Current methods for in vivo drug efficacy assessment are time-intensive and labor-intensive.
- There is a need for automated, high-resolution tools to expedite cancer therapy screening.
Purpose of the Study:
- To develop and validate a commercially scalable wearable electronic strain sensor for automated, real-time monitoring of tumor growth and regression in vivo.
- To assess the sensor's ability to rapidly detect differences in tumor dynamics between drug-treated and vehicle-treated groups.
- To correlate short-term sensor measurements with long-term treatment responses.
Main Methods:
- A wearable electronic strain sensor was designed to continuously monitor micrometer-scale changes in subcutaneously implanted tumors.
- The sensor was tested in two in vivo cancer mouse models.
- Tumor volume dynamics were analyzed at the minute timescale, with comparisons made between drug- and vehicle-treated groups.
- Short-term regression measurements were validated using histology, caliper, and bioluminescence data over weeklong periods.
Main Results:
- The sensor detected significant differences in tumor volume dynamics between drug- and vehicle-treated tumors within 5 hours of therapy initiation.
- Real-time monitoring enabled rapid assessment of cancer therapeutic efficacy.
- Short-term regression data obtained from the sensor correlated well with longer-term treatment responses.
Conclusions:
- The developed wearable electronic strain sensor offers a scalable and automated solution for in vivo cancer therapeutic efficacy assessment.
- This technology can significantly expedite the screening of cancer therapies by providing real-time, high-resolution tumor regression data.
- Automated, minute-timescale monitoring has the potential to revolutionize preclinical cancer drug development.
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