The earlier any catastrophic disease (e.g., cancer) is diagnosed, the more likely it can be treated, providing improved patient prognosis, extended survival and better quality of life. In early 2014, we revealed that various types of disease can substantially affect the composition/profile of protein corona (i.e., a layer of biomolecules that forms at the surface of nanoparticles upon their interactions with biological fluids). Here, by combining the concepts of disease-specific protein corona and sensor array technology we developed a platform with disease detection capacity using blood plasma. Our sensor array consists of three cross-reactive liposomes, with distinct lipid composition and surface charge. Rather than detecting a specific biomarker, the sensor array provides pattern recognition of the corona protein composition adsorbed on the liposomes. As a feasibility study, sensor array validation was performed using plasma samples obtained from patients diagnosed with five different cancer types (i.e. lung cancer, glioblastoma, meningioma, myeloma, and pancreatic cancer) and a control group of healthy donors. Although no single corona composition is specific for any one cancer type, overlapping but distinct patterns of the corona composition constitutes a unique “fingerprint” for each type of cancer (with a high classification accuracy, i.e. 99.4%). To finally probe the capacity of this sensor array for early detection of cancers, we used cohort plasma obtained from healthy people who were subsequently diagnosed several years after plasma collection with lung, brain, and pancreatic cancers. Our results suggest that the disease-specific protein corona sensor array will not only be instrumental in the screening, detection, and identification of diseases, but may also help identify novel protein pattern markers whose role in disease development and/or disease biology has not been appreciated so far.

Today, liposomes are an advanced technology of drug carriers with a dozen drugs in clinical practice and many more in clinical trials. A bottleneck associated with the clinical translation of liposomes has long been ‘opsonization’, i.e. the adsorption of plasma proteins at the liposome surface resulting in their rapid clearance from circulation. For decades, the most popular way to avoid opsonization has been grafting polyethylene glycol (PEG) onto the liposome surface. Recent studies have clarified that grafting PEG onto the liposome surface reduces, but does not completely prevent protein binding. In this work, we employed dynamic light scattering, zeta-potential analysis, one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE), semi-quantitative densitometry and cell imaging to explore the bio-nano-interactions between human plasma (HP) and Onivyde, a PEGylated liposomal drug that has recently been approved by the Food and Drug Administration (FDA) for the treatment of metastatic pancreatic ductal adenocarcinoma (PDAC). To properly evaluate the role of PEGylation, an unPEGylated variant of Onivyde was used as a reference. Collectively, our findings suggest that: (i) although PEGylated, Onivyde is not “stealth” in HP; (ii) surface chemistry is more important than PEGylation in controlling the bio-nano-interactions between Onivyde and plasma components. Of note is that the PC was found to boost the cellular uptake of Onivyde in the pancreas ductal adenocarcinoma cell line (PANC-1) thus suggesting its prominent role in its indication for PDAC treatment. Relevant implications for drug delivery and drug design are discussed.

Pancreatic cancer is a very aggressive malignancy that is often diagnosed in the advanced stages, with the implication that long-term survivors are extremely rare. Thus, developing new methods for the early detection of pancreatic cancer is an urgent task for current research. To date, nanotechnology offers unprecedented opportunities for cancer therapeutics and diagnosis. The aim of this study is the development of a new pancreatic cancer diagnostic technology based on the exploitation of the nano-bio-interactions between nanoparticles and blood samples. In this study, blood samples from 20 pancreatic cancer patients and 5 patients without malignancy were allowed to interact with designed lipid nanoparticles, leading to the formation of a hard “protein corona” at the nanoparticle surface. After isolation, the protein patterns were characterized by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS PAGE). We found that the protein corona of pancreatic cancer patients was much more enriched than that of healthy individuals. Statistical analysis of SDS-PAGE results allowed us to discriminate between healthy and pancreatic cancer patients with a total discriminate correctness rate of 88%.

The protein corona (PC) that forms around nanomaterials upon exposure to human biofluids (e.g., serum, plasma, cerebral spinal fluid etc.) is personalized, i.e., it depends on alterations of the human proteome as those occurring in several cancer types. This may relevant for early cancer detection when changes in concentration of typical biomarkers are often too low to be detected by blood tests. Among nanomaterials under development for in vitro diagnostic (IVD) testing, Graphene Oxide (GO) is regarded as one of the most promising ones due to its intrinsic properties and peculiar behavior in biological environments. While recent studies have explored the binding of single proteins to GO nanoflakes, unexplored variables (e.g., GO lateral size and protein concentration) leading to formation of GO-PC in human plasma (HP) have only marginally addressed so far. In this work, we studied the PC that forms around GO nanoflakes of different lateral sizes (100, 300, and 750 nm) upon exposure to HP at several dilution factors which extend over three orders of magnitude from 1 (i.e., undiluted HP) to 10³. HP was collected from 20 subjects, half of them being healthy donors and half of them diagnosed with pancreatic ductal adenocarcinoma (PDAC) a lethal malignancy with poor prognosis and very low 5-year survival rate after diagnosis. By dynamic light scattering (DLS), electrophoretic light scattering (ELS), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and nano liquid chromatography tandem mass spectrometry (nano-LC MS/MS) experiments we show that the lateral size of GO has a minor impact, if any, on PC composition. On the other side, protein concentration strongly affects PC of GO nanoflakes. In particular, we were able to set dilution factor of HP in a way that maximizes the personalization of PC, i.e., the alteration in the protein profile of GO nanoflakes between cancer vs. non-cancer patients. We believe that this study shall contribute to a deeper understanding of the interactions among GO and HP, thus paving the way for the development of IVD tools to be used at every step of the patient pathway, from prognosis, screening, diagnosis to monitoring the progression of disease.

Advances in nanotechnology are introducing the exciting possibility of cancer identification at early stages via analysis of the personalized biomolecular corona (BC), i.e. the dynamic “halo” of proteins that adsorbs onto NPs following exposure to patients' plasma. In this study, we develop a blood test for early cancer detection based on the characterization of the BC that forms around Graphene Oxide (GO) nanoflakes. Among its elective properties, GO binds low amounts of albumin, the most abundant protein in the blood and one of the most enriched proteins in the BC of many nanomaterials. This unique property of GO allows strong adsorption of poorly concentrated plasma proteins without abundant protein depletion. In our study, GO nanometric flakes have been used to analyze BCs from 50 subjects, half of them diagnosed with pancreatic cancer and half of them being healthy volunteers. Pancreatic cancer was chosen as the model of a high mortality disease with poor survival rates due to its delayed diagnosis. The receiver operating characteristic (ROC) curve analysis was applied to measure the diagnostic accuracy of the BC-based test. We obtained an area under the curve (AUC) of 0.96 and the test discriminated cancer patients from healthy subjects with a sensitivity of 92%. Finally, a double-blind validation was made using a second test dataset (10 healthy subjects + 10 pancreatic cancer patients) and it confirmed the results obtained on the first training dataset. Being highly accurate, fast, inexpensive and easy to perform, we believe that the BC-enabled blood test has the potential to become a turning point in early detection of cancer and other diseases.