Fleisher, M. | bioRxiv Science

bioRxiv2021

Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

Conventional molecular recognition elements, such as antibodies, present issues for the development of biomolecular assays for use in point-of-care devices, implantable/wearables, and under-resourced settings. 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, which are often diagnosed at advanced stages, leading to low survival rates. We investigated the platform for detection in uterine lavage samples, which are enriched with cancer biomarkers 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 cancer patients. 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.

bioengineering↗

bioRxiv2019

Serum Alpha-2-Macroglobulin as an intrinsic radioprotective factor in patients undergoing thoracic radiation therapy

ObjectiveTo investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis. Additionally, we establish multifactorial predictive models for pneumonitis and esophagitis.\n\nMaterials/MethodsBaseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearmans correlation (Rs) test was used to correlate A2M levels, smoking status and dosimetric variables with toxicities. Esophagitis and pneumonitis prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1000 bootstrapped datasets. Models were built using 2/3 of the data for training and 1/3 for validation.\n\nResultsThere were 36 (14.0%) patients with grade [≥]2 pneumonitis and 61 (23.6%) with grade [≥]2 esophagitis. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant correlation between baseline A2M levels and esophagitis (Rs=-0.18/p=0.003) and between A2M and smoking status (former or current) (Rs=0.13/p=0.04) but not between A2M and pneumonitis. On univariate analysis, significant parameters for grade [≥]2 esophagitis included number of fractions (Rs=0.47/p<0.0001), treatment days (Rs=0.44/p<0.0001), chemotherapy use (Rs=0.40/p<0.0001), dose per fraction (Rs=-0.34/p<0.0001), total dose (Rs=0.29/p<0.0001), age (Rs=-0.22/p=0.0003), and several dosimetric variables in esophagus with Rs>0.5 (p<0.0001). For pneumonitis, significant clinical parameters were treatment days (Rs=0.24/p=0.0001), chemotherapy use (Rs=0.22/p=0.0004), number of fractions (Rs=0.21/p=0.0007), dose per fraction (Rs=-0.18/p=0.0035), and total dose (Rs=0.15/p=0.013). The most significant dosimetric variable in lung and heart was D70 (Rs=0.28/p<0.0001) and max dose (Rs=0.27/p<0.0001), respectively. LASSO bootstrap logistic regression models on the validation data resulted in the area under the receiver operating characteristic curve of 0.84 and 0.75 for esophagitis and pneumonitis, respectively.\n\nConclusionOur findings show an association of higher A2M values with lower risk of radiation esophagitis and smoking status. Multivariate predictive models also confirmed a role of heart dose in the risk of pneumonitis.

cancer biology↗