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Patients with high-grade serous ovarian carcinoma (HGSC) exhibit poor 5-year survival rates, which may be significantly improved by early-stage detection. The U.S. Food and Drug Administration-approved biomarkers for HGSC-CA-125 (cancer antigen 125) and HE4 (human epididymis protein 4)-do not generally appear at detectable levels in the serum until advanced stages of the disease. An implantable device placed proximal to disease sites, such as in or near the fallopian tube, ovary, uterine cavity, or peritoneal cavity, may constitute a feasible strategy to improve detection of HGSC. We engineered a prototype optical sensor composed of an antibody-functionalized carbon nanotube complex, which responds quantitatively to HE4 via modulation of the nanotube optical bandgap. The complexes measured HE4 with nanomolar sensitivity to differentiate disease from benign patient biofluids. The sensors were implanted into four models of ovarian cancer, within a semipermeable membrane, enabling the optical detection of HE4 within the live animals. We present the first in vivo optical nanosensor capable of noninvasive cancer biomarker detection in orthotopic models of disease.
Conventional molecular recognition elements, such as antibodies, present issues for developing biomolecular assays for use in certain technologies, such as implantable devices. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, often diagnosed at advanced stages, leading to low survival rates. We investigated the detection of protein biomarkers in uterine lavage samples, which are enriched with certain cancer markers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from patients with cancer. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.
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