Real-Time Biosensing of Cancer Therapy Response: Dynamic Biomarkers and the Shift Toward Feedback-Driven Precision Oncology
5 affiliations
- 1Division of Applied and Professional Studies, Indiana University Columbus, IN, USA
- 2Department of Biology, University of Virginia, Charlottesville, USA
- 3College of Business, East Carolina University, Greenville, NC, USA
- 4Cancer Clinical Trials Office, University of Colorado Cancer Center, Aurora, CO, USA
- 5Department of Cell Biology and Genetics, University of Lagos, Akoka, Nigeria
Abstract
Cancer therapy response is still judged largely by tools that report late. Imaging and intermittent laboratory tests register treatment effects only once substantial structural change has accumulated, yet the molecular, metabolic, and immunological events that decide whether a therapy succeeds unfold within hours to days of the first dose. This review argues that dynamic biomarkers, monitored in real time or at high frequency by emerging biosensing platforms, supply a missing temporal axis in precision oncology. Two axes are already established: Genomics defines what to target, and imaging defines where disease resides. Biosensing defines when and how a tumor is responding. We synthesize the biological rationale for dynamic monitoring, define what “real-time” should and should not mean in oncology, and weigh the evidence for tumor-derived, metabolic, and immune biomarker sensing, separating what is clinically demonstrated (most notably circulating tumor DNA kinetics) from what remains preclinical. Rather than cataloguing devices, we organize biosensing around the biological questions it answers and the decision points it could inform, then examine four barriers to adoption (sensor stability, biomarker standardization, data interpretability, and regulatory and ethical constraints) and identify the knowledge gaps that most limit progress. The core conclusion is that real-time biosensing is best understood not as a competing modality but as the temporal complement to genomics and imaging; its adoption is constrained less by sensor novelty than by the absence of standardized thresholds and prospective, interventional evidence that acting on dynamic signals improves outcomes.
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BibTeX
@article{adeboye2026realtime,
title = {{Real-Time Biosensing of Cancer Therapy Response: Dynamic Biomarkers and the Shift Toward Feedback-Driven Precision Oncology}},
author = {Hannah Adeboye and Kwesi Akonu Adom Mensah Forson and Precious Esong Sone and Jessica Awingosit Ayamyiya and Dagunduro Emmanuel},
journal = {Journal of Cancer Biomoleculars and Therapeutics},
year = {2026},
month = oct,
doi = {10.62382/jcbt.v3i4.139},
url = {https://doi.org/10.62382/jcbt.v3i4.139},
note = {CC BY},
}
RIS
TY - JOUR
TI - Real-Time Biosensing of Cancer Therapy Response: Dynamic Biomarkers and the Shift Toward Feedback-Driven Precision Oncology
AU - Hannah Adeboye
AU - Kwesi Akonu Adom Mensah Forson
AU - Precious Esong Sone
AU - Jessica Awingosit Ayamyiya
AU - Dagunduro Emmanuel
PY - 2026
DA - 2026/10/24
JO - Journal of Cancer Biomoleculars and Therapeutics
DO - 10.62382/jcbt.v3i4.139
UR - https://doi.org/10.62382/jcbt.v3i4.139
AB - Cancer therapy response is still judged largely by tools that report late. Imaging and intermittent laboratory tests register treatment effects only once substantial structural change has accumulated, yet the molecular, metabolic, and immunological events that decide whether a therapy succeeds unfold within hours to days of the first dose. This review argues that dynamic biomarkers, monitored in real time or at high frequency by emerging biosensing platforms, supply a missing temporal axis in precision oncology. Two axes are already established: Genomics defines what to target, and imaging defines where disease resides. Biosensing defines when and how a tumor is responding. We synthesize the biological rationale for dynamic monitoring, define what “real-time” should and should not mean in oncology, and weigh the evidence for tumor-derived, metabolic, and immune biomarker sensing, separating what is clinically demonstrated (most notably circulating tumor DNA kinetics) from what remains preclinical. Rather than cataloguing devices, we organize biosensing around the biological questions it answers and the decision points it could inform, then examine four barriers to adoption (sensor stability, biomarker standardization, data interpretability, and regulatory and ethical constraints) and identify the knowledge gaps that most limit progress. The core conclusion is that real-time biosensing is best understood not as a competing modality but as the temporal complement to genomics and imaging; its adoption is constrained less by sensor novelty than by the absence of standardized thresholds and prospective, interventional evidence that acting on dynamic signals improves outcomes.
SN - 3060-8856
ER -