Validation of Serum Biomarkers That Complement CA19-9 in Detecting Early Pancreatic Cancer Using Electrochemiluminescent-Based Multiplex Immunoassays

Multiplexed immunosensing of cancer biomarkers on a split-float-gate graphene transistor microfluidic biochip

This work reports the development of a novel microfluidic biosensor using a graphene field-effect transistor (GFET) design for the parallel label-free analysis of multiple biomarkers. Overcoming the persistent challenge of constructing μm2-sized FET sensitive interfaces that incorporate multiple receptors, we implement a split-float-gate structure that enables the manipulation of multiplexed biochemical functionalization using microfluidic channels. Immunoaffinity biosensing experiments are conducted using the mixture samples containing three liver cancer biomarkers, carcinoembryonic antigen (CEA), α-fetoprotein (AFP), and parathyroid hormone (PTH). The results demonstrate the capability of our label-free biochip to quantitatively detect multiple target biomarkers simultaneously by observing the kinetics in 10 minutes, with the detection limit levels in the nanomolar range. This microfluidic biosensor provides a valuable analytical tool for rapid multi-target biosensing, which can be potentially utilized for domiciliary tests of cancer screening and prognosis, obviating the need for sophisticated instruments and professional operations in hospitals.

Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning

Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.

Ultrasensitive Detection and Separation of Pancreatic Cancer Biomarker CA 19-9 Using a Multiphoton Laser Wave-Mixing Detector Interfaced to Capillary Electrophoresis

The carbohydrate antigen 19-9 (CA 19-9) is the most commonly used biomarker in the clinical diagnosis of pancreatic cancer. Multiphoton nonlinear laser wave-mixing spectroscopy is presented as an ultrasensitive detection method for CA 19-9. Wave mixing is an optical absorption-based method, and hence, one can detect CA 19-9 without labels in their native form using compact ultraviolet (UV) lasers or labeled samples using a visible laser. The wave-mixing signal exhibits a quadratic dependence on the sample concentration, and hence, it is an ideal sensor to monitor small changes in the sample. Wave mixing has inherent advantages over other absorption-based detection methods, including short optical path length (micrometer-thin samples instead of 1 cm cuvette) and excellent spatial resolution (micrometer probe). Since the laser wave-mixing probe volume is small (picoliter), it is convenient to interface to microfluidics or capillary-based electrophoresis systems to enhance chemical specificity. Our wave-mixing detectors could be configured as portable battery-powered devices suitable for field use. Laser wave-mixing spectroscopy offers enhanced selectivity levels for protein detection when coupled with capillary electrophoresis (CE). We report a concentration detection limit of 0.16 U/mL, and a corresponding mass detection limit of 1.2 × 10-8 U, and these detection limits are better than those of chemiluminescence- or ELISA- based methods.

Mucins as Potential Biomarkers for Early Detection of Cancer

Early detection significantly correlates with improved survival in cancer patients. So far, a limited number of biomarkers have been validated to diagnose cancers at an early stage. Considering the leading cancer types that contribute to more than 50% of deaths in the USA, we discuss the ongoing endeavors toward early detection of lung, breast, ovarian, colon, prostate, liver, and pancreatic cancers to highlight the significance of mucin glycoproteins in cancer diagnosis. As mucin deregulation is one of the earliest events in most epithelial malignancies following oncogenic transformation, these high-molecular-weight glycoproteins are considered potential candidates for biomarker development. The diagnostic potential of mucins is mainly attributed to their deregulated expression, altered glycosylation, splicing, and ability to induce autoantibodies. Secretory and shed mucins are commonly detected in patients' sera, body fluids, and tumor biopsies. For instance, CA125, also called MUC16, is one of the biomarkers implemented for the diagnosis of ovarian cancer and is currently being investigated for other malignancies. Similarly, MUC5AC, a secretory mucin, is a potential biomarker for pancreatic cancer. Moreover, anti-mucin autoantibodies and mucin-packaged exosomes have opened new avenues of biomarker development for early cancer diagnosis. In this review, we discuss the diagnostic potential of mucins in epithelial cancers and provide evidence and a rationale for developing a mucin-based biomarker panel for early cancer detection.

Advanced iron oxide nanotheranostics for multimodal and precision treatment of pancreatic ductal adenocarcinoma

Despite current advances in new approaches for cancer detection and treatment, pancreatic cancer remains one of the most lethal cancer types. Difficult to detect early, aggressive tumor biology, and resistance to chemotherapy, radiotherapy, and immunotherapy result in a poor prognosis of pancreatic cancer patients with a 5-year survival of 10%. With advances in cancer nanotechnology, new imaging and drug delivery approaches that allow the development of multifunctional nanotheranostic agents offer opportunities for improving pancreatic cancer treatment using precision oncology. In this review, we will introduce potential applications of innovative theranostic strategies to address major challenges in the treatment of pancreatic cancer at different disease stages. Several important issues concerning targeted delivery of theranostic nanoparticles and tumor stromal barriers are discussed. We then focus on the development of a magnetic iron oxide nanoparticle platform for multimodal therapy of pancreatic cancer, including MRI monitoring targeted nanoparticle/drug delivery, therapeutic response, and tumor re-staging, activation of tumor immune response by immunoactivating nanoparticle and magnetic hyperthermia therapy, and intraoperative interventions for improving the outcome of targeted therapy. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.