e20551 Background: Enzyme activity is at the center of all biological processes. When these activities are misregulated by changes in sequence, expression, or activity, pathologies emerge. Misregulation of protease enzymes such as Matrix Metalloproteinases and Cathepsins play a key role in the pathophysiology of cancer. We describe here a novel class of graphene-based, cost effective biosensors that can detect altered protease activation in a blood sample from early stage lung cancer patients. Methods: The Gene Expression Omnibus (GEO) tool was used to identify proteases differentially expressed in lung cancer and matched normal tissue. Biosensors were assembled on a graphene backbone annotated with one of a panel of fluorescently tagged peptides. The graphene quenches fluorescence until the peptide is either cleaved by active proteases or altered by post-translational modification. 19 protease biosensors were evaluated on 431 commercially collected serum samples from non-lung cancer controls (69%) and pathologically confirmed lung cancer cases (31%) tested over two independent cohorts. Serum was incubated with each of the 19 biosensors and enzyme activity was measured indirectly as a continuous variable by a fluorescence plate reader. Analysis was performed using Emerge, a proprietary predictive and classification modeling system based on massively parallel evolving “Turing machine” algorithms. Each analysis stratified allocation into training and testing sets, and reserved an out-of-sample validation set for reporting. Results: 256 clinical samples were initially evaluated including 35% cancer cases evenly distributed across stages I (29%), II (26%), III (24%) and IV (21%). The case controls included common co-morbidies in the at-risk population such as COPD, chronic bronchitis, and benign nodules (19%). Using the Emerge classification analysis, biosensor biomarkers alone (no clinical factors) demonstrated Sensitivity (Se.) = 92% (CI 82%-99%) and Specificity (Sp.) = 82% (CI 69%-91%) in the out-of-sample set. An independent cohort of 175 clinical cases (age 67±8, 52% male) focused on early detection (26% cancer, 70% Stage I, 30% Stage II/III) were similarly evaluated. Classification showed Se. = 100% (CI 79%-100%) and Sp. = 93% (CI 80%-99%) in the out-of-sample set. For the entire dataset of 175 samples, Se. = 100% (CI 92%-100%) and Sp. = 97% (CI 92%-99%) was observed. Conclusions: Lung cancer can be treated if it is diagnosed when still localized. Despite clear data showing screening for lung cancer by Low Dose Computed Tomography (LDCT) is effective, screening compliance remains very low. Protease biosensors provide a cost effective additional specialized tool with high sensitivity and specificity in detection of early stage lung cancer. A large prospective trial of at-risk smokers with follow up is being conducted to evaluate a commercial version of this assay.
Over the last 6 years, five-year survival rate for pancreatic cancer patients has increased from 6 to 10% after the initial diagnosis, which makes it one of the deadliest cancer types. This disease is known as the “silent killer” because early detection is challenging due to the location of the pancreas in the body and the nonspecific clinical symptoms. The Bossmann group has developed ultrasensitive nanobiosensors for protease/arginase detection comprised of Fe/Fe3O4nanoparticles, cyanine 5.5, and designer peptide sequences linked to TCPP. Initial data obtained from both gene expression analysis and protease/arginase activity detection in serum indicated the feasibility of early pancreatic cancer detection. Several matrix metalloproteinases (MMPs, -1, -3, and -9), cathepsins (CTS) B and E, neutrophil elastase, and urokinase plaminogen activator (uPA) have been identified as candidates for proximal biomarkers. In this study, we have confirmed our initial results from 2018 performing serum sample analysis assays using a larger group sample size (n=159), which included localized (n=33) and metastatic pancreatic cancer (n=50), pancreatitis (n=26), and an age-matched healthy control group (n=50). The data obtained from the eight nanobiosensors capable of ultrasensitive protease and arginase activity measurements were analyzed by means of an optimized information fusion-based hierarchical decision structure. This permits the modeling of early-stage detection of pancreatic cancer as a multi-class classification problem. The most striking result is that this methodology permits the detection of localized pancreatic cancers from serum analyses with 96% accuracy.
Mitochondria are important intracellular organelles because of their key roles in cellular metabolism, proliferation, and programmed cell death. The differences in the structure and function of the mitochondria of healthy and cancerous cells have made mitochondria an interesting target for drug delivery. Mitochondrial targeting is an emerging field as the targeted delivery of cytotoxic payloads and antioxidants to the mitochondrial DNA is capable of overcoming multidrug resistance. This feature has attracted the focus of much research in the field of mitochondrial targeting that is preferred over nuclear targeting. The negative membrane potential of the inner and outer mitochondrial membranes, as well as their lipophilicity are known to be the features that drive the entry of compatible targeting moiety along with anticancer drug conjugates towards mitochondria. The design of such drug nanocarrier conjugates is challenging because they need not only target the specific tumor/cancer site but have to overcome multiple barriers as well, such as the cell membrane and mitochondrial membrane. This review focuses on the use of peptide-based nanocarriers (organic nanostructures such as liposomes, inorganic, carbon-based, and polymers) for mitochondrial targeting at the tumor/cancer. Both in vitro and in vivo key results are reported.
e16273 Background: There is a critical need to develop fast, reliable, and cost-effective methods for the detection of pancreatic cancer (PC) at the earliest stage to maximize the impact of treatment. To-date, early detection of PC is close to impossible due to the location of the pancreas and the absence of characteristic symptoms in early cancer stages. Methods: Our team of clinicians and scientists has established a fast and reliable nanobiosensor technology that comprises iron/iron oxide nanoparticles attached to a protease or arginase activatable FRET pair (tetrakis (4- carboxyphenyl) porphyrin (TCPP) /cyanine 5.5). Arginase and seven proteases (MMP1, 3, and 9, cathepsin B, and E, urokinase plasminogen activator, and neutrophil elastase) were identified using the Gene Expression Omnibus (GEO) web tool based on their different expression pattern in pancreatic cancer patients, pancreatitis and healthy control subjects. Protease/arginase activities were measured in serum after 1h of incubation. Based on this data, a novel engineering approach to improved early stage detection of pancreatic cancer is reported here. This study was funded by American Cancer Society Institutional Research Grant (IRG‐16‐194‐07), awarded to the University of Kansas Medical Center. Results: In our study, 159 patients were enrolled at KU Cancer Center from 2000-2019, 47 with metastatic PC, 36 with localized PC, 26 pancreatitis and 50 healthy controls using KUCC Biospecimen Repository. The problem of early stage detection of pancreatic cancer can be modeled as a multi-class classification problem. Conventional classification approaches provide at most 77% accuracy for the dataset under consideration. A new hierarchical decision structure with specific feature engineering at each step is introduced here to improve the performance of the classifier. The fundamental premise of this information fusion-based framework involves tailoring the statistically most significant features with appropriate weights to execute an efficient binary classification task at each hierarchical step. An overall accuracy of 95% was achieved for the detection of patients with early pancreatic cancer (see table). Conclusions: Because of the dire survival statistics of pancreatic cancer, detection at the earliest possible time by means of a liquid biopsy will offer the greatest benefit. Novel nanobiosensor based protease biomarkers achieved high accuracy in early detection of pancreatic cancers by applying hierarchical decision structure. Our results need validation in a larger cohort. Predicted true class considering the following combination of classification methods: Step1 – kNN*, step2 – kNN*, step3 – RFC* (Accuracy = 94.97%).[Table: see text]
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