Specific detection of CA242 by antibody-modified chiral symmetric double "N" metasurface biosensor

In this work, a biosensor based on Fano resonance metasurface is proposed for the specific detection of CA242 which is a typical marker of pancreatic cancer. The biosensor consists of a chiral symmetric plasma double "N" structure, which utilises coherent coupling of bright and dark modes to generate Fano resonance, achieving suppression of radiation loss, concentrating and storing energy more efficiently in the structure, and contributing to increased sensitivity to changes in ambient refractive index, resulting in a sensitivity of the sensor of up to 842.8 nm /RIU. After a series of antibody functionalization modifications, the metasurface has become an immune biosensor that can specifically detect the tumor marker CA242 of pancreatic cancer. The detection of mixed and single antigen solutions with different concentrations has verified the high sensitivity, high specificity, and high linear relationship of the biosensor to CA242, and the detection limit is as low as 0.0692 ng/mL. It is superior to other common methods and breaks the traditional disadvantages of lower detection accuracy and greater damage in tumour detection methods. The detection of the wavelength shift of localized surface plasmon resonance in plasma metasurface has been successfully applied to the highly sensitive detection of tumor markers. This study demonstrates the sensitivity and maneuverability of the chiral symmetric double "N" plasmonic metasurface biosensor, suggesting the potential application of metamaterials in biosensing based on environmental refractive index changes.

Sensitive and selective detection of Mucin1 in pancreatic cancer using hybridization chain reaction with the assistance of Fe3O4@polydopamine nanocomposites

Pancreatic cancer is characterized as the worst for diagnosis lacking symptoms at the early stage, which results in a low overall survival rate. The frequently used techniques for pancreatic cancer diagnosis rely on imaging and biopsy, which have limitations in requiring experienced personnel to operate the expensive instruments and analyze the results. Therefore, there is a high demand to develop alternative tools or methods to detect pancreatic cancer. Herein, we propose a new strategy to enhance the detection sensitivity of pancreatic cancer cells both in biofluids and on tissues by combining the unique property of dopamine coated Fe₃O₄ nanoparticles (Fe₃O₄@DOP NPs) to specifically quench and separate free 6-carboxyfluorescein (FAM) labeled DNA (H₁-FAM/H₂-FAM), and the key feature of hybridization chain reaction (HCR) amplification. We have determined the limit of detection (LOD) to be 21 ~ 41 cells/mL for three different pancreatic cancer cell lines. It was also discovered that the fluorescence intensity of pancreatic cancer cells was significantly higher than that of HPDE-C7 and HepG-2 cells (control cell lines), which express lower MUC1 protein. Moreover, the HCR amplification system was used to identify the cancer cells on pancreatic tissue, which indicated the versatility of our strategy in clinical application. Therefore, the presented detection strategy shows good sensitivity, specificity and has great potential for the diagnosis of pancreatic cancer.

Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images

BACKGROUND

Pancreatic cancer is one of the most aggressive cancers with approximate 10% five-year survival rate. To reduce mortality rate, accurate detection and diagnose of suspicious pancreatic tumors at an early stage plays an important role.

OBJECTIVE

To develop and test a new radiomics-based computer-aided diagnosis (CAD) scheme of computed tomography (CT) images to detect and classify suspicious pancreatic tumors.

METHODS

A retrospective dataset consisting of 77 patients who had suspicious pancreatic tumors detected on CT images was assembled in which 33 tumors are malignant. A CAD scheme was developed using the following 5 steps namely, (1) apply an image pre-processing algorithm to filter and reduce image noise, (2) use a deep learning model to detect and segment pancreas region, (3) apply a modified region growing algorithm to segment tumor region, (4) compute and select optimal radiomics features, and (5) train and test a support vector machine (SVM) model to classify the detected pancreatic tumor using a leave-one-case-out cross-validation method.

RESULTS

By using the area under receiver operating characteristic (ROC) curve (AUC) as an evaluation index, SVM model yields AUC = 0.750 with 95% confidence interval [0.624, 0.885] to classify pancreatic tumors.

CONCLUSIONS

Study results indicate that radiomics features computed from CT images contain useful information associated with risk of tumor malignancy. This study also built a foundation to support further effort to develop and optimize CAD schemes with more advanced image processing and machine learning methods to more accurately and robustly detect and classify pancreatic tumors in future.

Circulating tumour-derived KRAS mutations in pancreatic cancer cases are predominantly carried by very short fragments of cell-free DNA

BACKGROUND

The DNA released into the bloodstream by malignant tumours· called circulating tumour DNA (ctDNA), is often a small fraction of total cell-free DNA shed predominantly by hematopoietic cells and is therefore challenging to detect. Understanding the biological properties of ctDNA is key to the investigation of its clinical relevance as a non-invasive marker for cancer detection and monitoring.

METHODS

We selected 40 plasma DNA samples of pancreatic cancer cases previously reported to carry a KRAS mutation at the 'hotspot' codon 12 and re-screened the cell-free DNA using a 4-size amplicons strategy (57 bp, 79 bp, 167 bp and 218 bp) combined with ultra-deep sequencing in order to investigate whether amplicon lengths could impact on the capacity of detection of ctDNA, which in turn could provide inference of ctDNA and non-malignant cell-free DNA size distribution.

FINDINGS

Higher KRAS amplicon size (167 bp and 218 bp) was associated with lower detectable cell-free DNA mutant allelic fractions (p < 0·0001), with up to 4·6-fold (95% CI: 2·6-8·1) difference on average when comparing the 218bp- and the 57bp-amplicons. The proportion of cases with detectable KRAS mutations was also hampered with increased amplicon lengths, with only half of the cases having detectable ctDNA using the 218 bp assay relative to those detected with amplicons less than 80 bp.

INTERPRETATION

Tumour-derived mutations are carried by shorter cell-free DNA fragments than fragments of wild-type allele. Targeting short amplicons increases the sensitivity of cell-free DNA assays for pancreatic cancer and should be taken into account for optimized assay design and for evaluating their clinical performance.

FUNDING

IARC; MH CZ - DRO; MH SK; exchange program between IARC and Sao Paulo medical Sciences; French Cancer League.

Early detection of pancreatic cancers in liquid biopsies by ultrasensitive fluorescence nanobiosensors

Numerous proteases, such as matrix metalloproteinases (MMPs), cathepsins (CTS), and urokinase plasminogen activator (UpA), are dysfunctional (that is, over- or under-expressed) in solid tumors, when compared to healthy human subjects. This offers the opportunity to detect early tumors by liquid biopsies. This approach is of particular advantage for the early detection of pancreatic cancer, which is a "silent killer". We have developed fluorescence nanobiosensors for ultrasensitive (sub-femtomolar) arginase and protease detection, consisting of water-dispersible Fe/Fe₃O₄ core/shell nanoparticles and two tethered fluorescent dyes: TCPP (Tetrakis(4-carboxyphenyl)porphyrin) and cyanine 5.5. Upon posttranslational modification or enzymatic cleavage, the fluorescence of TCPP increases, which enables the detection of proteases at sub-femtomolar activities utilizing conventional plate readers. We have identified an enzymatic signature for the detection of pancreatic adenocarcinomas in serum, consisting of arginase, matrix metalloproteinase-1, -3, and - 9, cathepsin-B and -E, urokinase plasminogen activator, and neutrophil elastase, which is a potential game-changer.