Non-invasive Cancer Diagnosis Research Articles

Methanethiol: A Scent Mark of Dysregulated Sulfur Metabolism in Cancer.

In order to cope with increased demands for energy and metabolites as well as to enhance stress resilience, tumor cells develop various metabolic adaptations, representing a hallmark of cancer. In this regard, the dysregulation of sulfur metabolism that may result in elevated levels of volatile sulfur compounds (VSCs) in body fluids, breath, and/or excretions of cancer patients has recently gained attention. Besides hydrogen sulfide (H2S), methanethiol is the predominant cancer-associated VSC and has been proposed as a promising biomarker for non-invasive cancer diagnosis. Gut bacteria are the major exogenous source of exposure to this foul-smelling toxic gas, with methanethiol-producing strains such as Fusobacterium nucleatum highly abundant in the gut microbiome of colorectal carcinoma (CRC) patients. Physiologically, methanethiol becomes rapidly degraded through the methanethiol oxidase (MTO) activity of selenium-binding protein 1 (SELENBP1). However, SELENBP1, which is considered a tumor suppressor, is often downregulated in tumor tissues, and this has been epidemiologically linked to poor clinical outcomes. In addition to impaired removal, an increase in methanethiol levels may derive from non-enzymatic reactions, such as a Maillard reaction between glucose and methionine, two metabolites enriched in cancer cells. High methionine concentrations in cancer cells may also result in enzymatic methanethiol production in mitochondria. Moreover, enzymatic endogenous methanethiol production may occur through methyltransferase-like protein 7B (METTL7B), which is present at elevated levels in some cancers, including CRC and hepatocellular carcinoma (HCC). In conclusion, methanethiol contributes to the scent of cancer as part of the cancer-associated signature combination of volatile organic compounds (VOCs) that are increasingly being exploited for non-invasive early cancer diagnosis.

Enzyme-free nucleic acid and luminescent material cascade amplifications enable urinary dual miRNAs assay for non-invasive bladder cancer diagnosis

We presented a facile biosensing strategy for the detection of dual microRNAs (miRNA-21 and miRNA-126) in urine samples from bladder cancer (BC) patients. The fluorescent homogeneous detection of miRNAs was achieved by elaborate parallel catalytic hairpin assembly (CHA) integrated with material cascade amplifications. The continuous release of G-quadruplex (G4) sequences and Hg2+ was attributed to the specific recognition between the miRNAs and the corresponding hairpin oligonucleotides. Assisted by the streptavidin-(T-Hg2+-T-biotin)4 structures, more Hg2+ emission was obtained, and the spatial site block effect was used to order its distribution, significantly improving the selective recognition performance of cadmium telluride quantum dots (CdTe QDs) (1.4–2.9 times). Furthermore, the peroxidase-like activity of G4-hemin was used to catalyze the oxidation of dopamine (DA) to fluorescence-enhanced polydopamine (PDA). The generated PDA signal and the Hg2+-quenched CdTe QDs signal completed the synchronous detection to achieve a high-sensitivity analysis of the dual miRNAs. The limit of detection (LOD) of both miRNAs was as low as the fM level. Eventually, 56 clinical urine samples were quantified. The results showed satisfying specificity (100%) and sensitivity (92%) with a strong clinical agreement, including computed tomography (CT) and pathology findings, indicating a rapid non-invasive solution for early diagnosis of BC.

Ultrasensitive determination of surface proteins on tumor-derived small extracellular vesicles for breast cancer identification based on lanthanide-activated signal amplification strategy

Small extracellular vesicles (sEVs) carrying multiple tumor-associated proteins inherited from parental cells play crucial roles in noninvasive breast cancer (BC) diagnosis. However, it is challenging to assess the subtle variations of surface proteins on sEV membranes due to the highly heterogeneous BC. Therefore, a simple and ultrasensitive assay based on lanthanide (Ln3+)-activated luminescence signal amplification was developed to detect multiple surface proteins on BC-derived sEVs. Multiple protein biomarkers on sEVs can be well identified with high sensitivity and specificity through dissolution-amplified luminescence of the NaEuF4 nanoparticle-based nanoprobe. We employ linear discriminant analysis to successfully discriminate triple negative BC cell (MDA-MB-231 cell) derived sEVs from other breast cell lines (MCF-7, SK-BR-3, BT474 and MCF-10A cell). Furthermore, the strategy enables high accuracy for districting the progression stages of BC patients and healthy donors. The simple and sensitive signal amplification strategy exhibits great potential for early clinic diagnosis by precise protein profiling of sEVs.

Aptamer/Peptide-Functionalized Nanoprobe for Detecting Multiple miRNAs in Circulating Malignant Cells to Study Tumor Heterogeneity.

Identification of diverse biomarkers in heterogenic circulating malignant cells (CMCs) such as circulating tumor cells (CTCs) and circulating tumor endothelial cells (CTECs) has crucial significance in tumor diagnosis. However, it remains a substantial challenge to achieve in situ detection of multiple miRNA markers in living cells in blood. Herein, we demonstrate that an aptamer/peptide-functionalized vector can deliver molecular beacons into targeted living CMCs in peripheral blood of patients for in situ detection of multiple cancer biomarkers, including miRNA-21 (miR-21) and miRNA-221 (miR-221). Based on miR-21 and miR-221 levels, heterogenic CMCs are identified for both nondistant metastatic and distant metastatic cancer patients. CMCs from nondistant metastatic and distant metastatic cancer patients exhibit similar miR-21 levels, while the miR-221 level in CMCs of the distant metastatic cancer patient is higher than that of the nondistant metastatic cancer patient. With the capability to realize precise probing of multiple intracellular biomarkers in living CMCs at the single-cell resolution, the nanoprobe can reveal the tumor heterogeneity and provide useful information for diagnosis and prognosis. The nanoprobe we developed would accelerate the progress toward noninvasive precise cancer diagnosis.

Absolute Quantification of Serum Exosomes in Patients with an SERS-Lateral Flow Strip Biosensor for Noninvasive Clinical Cancer Diagnosis.

Exosomes (exos) widely existing in body fluids show great potential for noninvasive cancer diagnosis. Quantitative analysis of exos is traditionally performed by targeting specific exosomal surface proteins, but it is often imprecise due to the common expression of exosomal proteins and subtle expression differences between different cancer subtypes. Herein, we report quantitative surface-enhanced Raman spectroscopy (SERS) of serum exos through a combination of a paper-based lateral flow strip (LFS) biosensor with multivariate spectral unmixing analysis rather than simply quantifying exosomal proteins. Our SERS-LFS biosensor enables absolute quantification of two different serum exos with a limit of detection down to ∼106 particles/mL for both exos. We further exemplify the application of this strategy in quantitative dual-plex detection of serum exos from breast cancer patients. We find that human epidermal growth factor receptor 2+ (HER2+) and luminal A breast cancer patients undergoing no surgery are enriched in serum exos derived from SKBR-3 cells and MCF-7 cells (denoted as SKBR and MCF exos), respectively. The surgical treatment of these breast cancer patients accompanies an obvious decrease of either SKBR or MCF exos in the serum. These results suggest the great potential of the combination of the SERS-LFS biosensor and multivariate spectral unmixing for breast cancer subtyping and therapeutic surveillance with the powerful quantitative capability of exos in clinical samples.

Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional mutation type-specific frequencies in tissue and cell-free DNA from patients with cancer that were associated with replication timing and other chromatin features. A machine-learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of patients with lung cancer, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.

Mutational Signatures in Gastric Cancer and Their Clinical Implications.

Gastric cancer is characterised by high inter- and intratumour heterogeneity. The majority of patients are older than 65 years and the global burden of this disease is increasing due to the aging of the population. The disease is usually diagnosed at advanced stages, which is a consequence of nonspecific symptoms. Few improvements have been made at the level of noninvasive molecular diagnosis of sporadic gastric cancer, and therefore the mortality rate remains high. A new field of mutational signatures has emerged in the past decade with advances in the genome sequencing technology. These distinct mutational patterns in the genome, caused by exogenous and endogenous mutational processes, can be associated with tumour aetiology and disease progression, and could provide novel perception on the treatment possibilities. This review assesses the mutational signatures found in gastric cancer and summarises their potential for use in clinical setting as diagnostic or prognostic biomarkers. Associated treatment options and biomarkers already implemented in clinical use are discussed, together with those that are still being explored or are in clinical studies.

Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.

GSH-Responsive Prodrug Nanoassembly as a Carrier-Free Nanoplatform for Tumor-Targeting Delivery and Chemo-Photothermal Therapy.

Photothermal therapy, combined with chemotherapy, holds promising prospects for the therapeutic outcome of malignant tumors. However, the synergistic therapeutic effect suffers from low coloading capacity and inefficient synchronous tumor-targeting delivery of chemodrug and photothermal photosensitizers. Herein, we designed a versatile carrier-free nanoplatform to seek improvement for chemo-photothermal therapy. An NIR photosensitizer IR-808 was used for noninvasive cancer imaging, diagnosis, and imaging-guided photothermal therapy. A reduction-sensitive paclitaxel prodrug (PTX-SS-PEG2k) was rationally synthesized by covalently linking paclitaxel with polyethylene glycol 2000 via a disulfide bond. Then, the carrier-free nanoassemblies were constructed with an inner core of IR-808 and an amphiphilic paclitaxel prodrug shell. PTX-SS-PEG2k served as a stabilizer and chemodrug and could facilitate the self-assembly of IR-808 nanoparticles with high coloading efficiency and reduction-sensitive drug release. The versatile nanoplatform exhibited multiple advantages, including high drug payload, reduction-sensitive drug release, tumor-targeting drug delivery, and potent synergistic antitumor effect. We provide a versatile theranostic nanoplatform, which improves the effectiveness of synergetic chemo-photothermal therapy and reduces the off-target toxicity.

Visualization test strip reading of urine miRNA via cascade selective recognition reactions for non-invasive diagnosis of bladder cancer

This study proposed a homogeneous enzyme-free and nucleic acid-based catalytic hairpin assembly and cascade recognition reactions strategy that used calcein as the signal reporter for dual visualization (color and length) and fluorescence signal detection of bladder cancer marker miRNA-126 in clinical urine. This strategy was based on the ability of calcein and CuS nanoparticles (NPs) to recognize C-Ag+-C structure/Ag+/Cu2+/CuS NPs and C-Ag+-C structure/Ag+, respectively. Due to the integration of cascade amplification and material cation exchange reaction (CER), the sensitivity of homogeneous enzyme-free miRNA analysis at room temperature was significantly improved. This study achieved the limits of detections (LODs) as low as 0.25 aM of miRNA-126 and single-cell level of T24 cells with fluorescence mode. And the reading mode of color and length changes could detect miRNA and cells with concentrations as low as 5 aM and 1 cell/mL without instruments, respectively. And less reduction of fluorescence signal was observed when target miRNA was replaced with these base-mismatched RNAs. Then miRNA-126 in 66 clinical urine samples was quantified. The experimental results were consistent with the polymerase chain reaction (PCR) kit, computed tomography (CT), and pathological findings. The receiver operating characteristic (ROC) curve exhibited an area under the curve (AUC) value of 0.954, and the assay showed 100% specificity and 90% sensitivity.

Review Insights on Salivary Proteomics Biomarkers in Oral Cancer Detection and Diagnosis.

Early detection is crucial for the treatment and prognosis of oral cancer, a potentially lethal condition. Tumor markers are abnormal biological byproducts produced by malignant cells that may be found and analyzed in a variety of bodily fluids, including saliva. Early detection and appropriate treatment can increase cure rates to 80-90% and considerably improve quality of life by reducing the need for costly, incapacitating medicines. Salivary diagnostics has drawn the interest of many researchers and has been proven to be an effective tool for both medication monitoring and the diagnosis of several systemic diseases. Since researchers are now searching for biomarkers in saliva, an accessible bodily fluid, for noninvasive diagnosis of oral cancer, measuring tumor markers in saliva is an interesting alternative to blood testing for early identification, post-treatment monitoring, and monitoring high-risk lesions. New molecular markers for oral cancer detection, treatment, and prognosis have been found as a result of developments in the fields of molecular biology and salivary proteomics. The numerous salivary tumor biomarkers and how they relate to oral cancer and pre-cancer are covered in this article. We are optimistic that salivary protein biomarkers may one day be discovered for the clinical detection of oral cancer because of the rapid advancement of proteomic technology.

Polymerase-Driven Logic Signal Amplification for the Detection of Small Extracellular Vesicle Surface Proteins and the Identification of Breast Cancer.

Small extracellular vesicles (sEVs) derived from tumors contain a vast amount of cellular information and are regarded as a potential diagnostic biomarker for noninvasive cancer diagnosis. Nevertheless, it remains challenging to accurately measure sEVs from clinical samples due to the low abundance of these vesicles as well as their phenotypic heterogeneity. Herein, a polymerase-driven logic signal amplification system (PLSAS) was developed for the high-sensitivity detection of sEV surface proteins and breast cancer (BC) identification. Aptamers were introduced to serve as sensing modules to specifically recognize target proteins. By changing the input DNA sequences, two polymerase-driven primer exchange reaction systems were rationally designed for DNA logic computing. This allows for autonomous targeting of a limited number of targets using "OR" and "AND" logic, leading to a significant increase in fluorescence signals and enabling the specific and ultrasensitive detection of sEV surface proteins. In this work, we investigated surface proteins of mucin 1 (MUC1) and the epithelial cell adhesion molecule (EpCAM) as model proteins. When MUC1 or EpCAM proteins were used as single signal input in the "OR" DNA logic system, the detection limit of sEVs was 24 or 58 particles/μL, respectively. And MUC1 and EpCAM proteins of sEVs can be simultaneously detected in the AND logic method, which can significantly reduce the effect of phenotypic heterogeneity of sEVs to distinguish the source of sEVs derived from various mammary cell lines, such as MCF-7, MDA MB 231, SKBR3, and MCF-10A. The approach has achieved high discrimination in serologically tested positive BC samples (AUC 98.1%) and holds significant potential in advancing the early diagnosis and prognostic assessments of BC.

Prostate cancer detection and segmentation on MRI using non-local mask R-CNN with histopathological ground truth.

Automatic detection and segmentation of intraprostatic lesions (ILs) on preoperative multiparametric-magnetic resonance images (mp-MRI) can improve clinical workflow efficiency and enhance the diagnostic accuracy of prostate cancer and is an essential step in dominant intraprostatic lesion boost. The goal is to improve the detection and segmentation accuracy of 3D ILs in MRI by a proposed a deep learning (DL)-based algorithm with histopathological ground truth. This retrospective study included 262 patients with in vivo prostate biparametric MRI (bp-MRI) scans and were divided into three cohorts based on their data analysis and annotation. Histopathological ground truth was established by using histopathology images as delineation reference standard on cohort 1, which consisted of 64 patients and was randomly split into 20 training, 12 validation, and 32 testing patients. Cohort 2 consisted of 158 patients with bp-MRI based lesion delineation, and was randomly split into 104 training, 15 validation, and 39 testing patients. Cohort 3 consisted of 40 unannotated patients, used in semi-supervised learning. We proposed a non-local Mask R-CNN and boosted its performance by applying different training techniques. The performance of non-local Mask R-CNN was compared with baseline Mask R-CNN, 3D U-Net and an experienced radiologist's delineation and was evaluated by detection rate, dice similarity coefficient (DSC), sensitivity, and Hausdorff Distance (HD). The independent testing set consists of 32 patients with histopathological ground truth. With the training technique maximizing detection rate, the non-local Mask R-CNN achieved 80.5% and 94.7% detection rate; 0.548 and 0.604 DSC; 5.72 and 6.36 95 HD (mm); 0.613 and 0.580 sensitivity for ILs of all Gleason Grade groups (GGGs) and clinically significant ILs (GGG>2), which outperformed baseline Mask R-CNN and 3D U-Net. For clinically significant ILs, the model segmentation accuracy was significantly higher than that of the experienced radiologist involved in the study, who achieved 0.512 DSC (p=0.04), 8.21 (p=0.041) 95 HD (mm), and 0.398 (p=0.001) sensitivity. The proposed DL model achieved state-of-art performance and has the potential to help improve radiotherapy treatment planning and noninvasive prostate cancer diagnosis.

Catalytic hairpin assembly-assisted dual-signal amplification platform for ultrasensitive detection of tumor markers and intelligent diagnosis of gastric cancer

Quantification of extracellular tumor markers has shown great promise for non-invasive cancer diagnosis. Combined detection of multiple tumor markers instead of a single one is valuable for accurate diagnosis. Here, we integrate CRISPR-Cas12a with DNA catalytic hairpin assembly (CHA) to doubly amplify the output signal for detecting microRNA-182 (miR-182), which is overexpressed by gastric cancer patients. Additionally, we develop a CHA system with self-replicating capacity (SRCHA) to realize dual-signal amplification for the detection of carcinoembryonic antigen (CEA), a broad-spectrum tumor marker. The proposed cascade amplification strategies enable ultrasensitive detection of miR-182 and CEA with low LODs of 0.063 fM and 4.8 pg mL−1, respectively. Moreover, we design a ternary “AND” logic gate using different concentrations of miR-182 and CEA as inputs, which demonstrates intelligent diagnosis of gastric cancer staging with a high accuracy of 93.3% in a clinical cohort of 30 individuals. Overall, our study expands the application of CRISPR-Cas12a in biosensing and provides a new diagnostic strategy for non-invasive liquid biopsy of gastric cancer before resorting to a traumatic tissue biopsy.

Does the last 20years paradigm of clinical research using volatile organic compounds to non-invasively diagnose cancer need to change? Challenges and future direction.

Volatile organic compounds (VOCs) have shown great potential as novel biomarkers for cancer detection; however, comprehensive quantitative analysis is lacking. In this study, we performed a bibliometric analysis of non-invasive cancer diagnosis using VOCs to better characterise international trends and to predict future hotspots in this field, and then we focussed on human studies to analyse clinical characteristics for presenting the current controversies and future perspectives of further clinical work. Publications, from 2002 to 2022, were retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer were used to generate network maps and identify the annual publications, top countries, authors, institutions, journals, references, and keywords. Then, we further screened clinical trials, and the key information was extracted into Microsoft Excel for further systematical analysis. Six hundred and forty-one articles were identified to evaluate research trends, of which 301 clinical trials were selected for further systematical analysis. Overall, the annual publications in this area increased, with an overall upward trend, while the quality of clinical research remains remarkably uneven. The study of non-invasive cancer diagnosis using VOCs would continue to be an active field. However, without stringent clinical design criteria, most suitable acquisition and analysis devices and statistical approaches, a list of exclusive, specific, reliable and reproducible VOCs to identify a disease and these VOCs appearing in a breath at detectable levels at early stage disease, the clinical utility of VOC tests will be difficult to have any breakthroughs.

Early detection of lung cancer using a panel of circulating cell-free DNA methylation biomarkers.

e20614 Background: Lung cancer remains the leading cause of cancer mortality worldwide. Early detection of lung cancer helps improve treatment and survival. Numerous aberrant DNA methylations have been reported in early-stage lung cancer. Here, we sought to identify novel DNA methylation biomarkers that could potentially be used for noninvasive early diagnosis of lung cancers. Methods: This prospective-specimen collection and retrospective-blinded-evaluation trial enrolled a total of 317 participants (198 tissues and 119 plasmas) comprising healthy controls, patients with lung cancer and benign disease from The First Affiliated Hospital of Soochow University between January 2020 and December 2021. Tissue and plasma samples were subjected to targeted bisulfite sequencing with a lung cancer specific panel targeting 9,307 differential methylation regions (DMRs). DMRs associated with lung cancer were identified by comparing the methylation profiles of tissue samples from patients with lung cancer and benign disease. Markers were selected with minimum redundancy and maximum relevance (mRMR) algorithm. A prediction model for lung cancer diagnosis was built through logistic regression algorithm and validated independently in tissue samples. Furthermore, the performance of this developed model was evaluated in a set of plasma cell-free DNA (cfDNA) samples. Results: We identified 7 DMRs corresponding to 7 differentially methylated genes (DMRs, including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1), highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissues. Based on the 7-DMR biomarker panel, we developed a new diagnostic model in tissue samples, termed “7-DMR model”, to distinguish lung cancers from benign diseases, achieving AUCs of 0.966 (95%CI: 0.933-1.000)/0.961 (0.924-1.000), sensitivities of 0.887 (0.824-0.951)/0.922 (0.863-0.980), specificities of 0.938 (0.889-0.986)/1.000 (1.000-1.000), and accuracies of 0.896 (0.835-0.957)/0.938 (0.886-0.991) in the discovery cohort (n = 96) and the independent validation cohort (n = 81), respectively. Furthermore, the 7-DMR model was applied to noninvasive discrimination of lung cancers and non-lung cancers including benign lung diseases and healthy controls in an independent validation cohort of plasma samples (n = 106), yielding an AUC of 0.935 (0.862-1.000), sensitivity of 0.808 (0.733-0.883), specificity of 0.975 (0.945-1.000), and accuracy of 0.934 (0.887-0.981). Conclusions: The 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.

Machine Learning-Based Label-Free SERS Profiling of Exosomes for Accurate Fuzzy Diagnosis of Cancer and Dynamic Monitoring of Drug Therapeutic Processes.

Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.

Nanoarchitectonics-Assisted Simultaneous Fluorescence Detection of Urinary Dual miRNAs for Noninvasive Diagnosis of Prostate Cancer.

Herein, we report a fluorescence strategy for the homogeneous and simultaneous analysis of urine miRNA-375 and miRNA-148a. The target miRNAs in urine bonded the devised dumbbell-shaped "C-Ag+-C" and "T-Hg2+-T" hairpin structures that could trigger cascade enzyme-free amplification. Then, the fluorescent CdTe quantum dots (QDs) and carbon dots (CDs) could selectively recognize Ag+ and Hg2+, to quantify the dual miRNAs concurrently. Under optimized conditions, the linear range was from 0.1 to 1000 fM and the limits of detection (LOD) for dual miRNAs reached 30 and 25 aM, respectively. The practicality was further evaluated with 45 clinical urine samples including prostate cancer (PC) and other patients, and the results were consistent with the clinical polymerase chain reaction (PCR) kit and ultrasonic and pathological findings. The receiver operating characteristic (ROC) curve analysis showed that the estimates of the area under the curve (AUC) were 0.739 for the serum prostate-specific antigen (PSA) and 0.941 for miRNA-375 and 0.946 for miRNA-148a. The sensitivity and specificity reached 75 and 100% for miRNA-375 and 71 and 94% for miRNA-148a, respectively, which was better than serum PSA. This strategy constructed a reliable system for dual miRNA detection in urine samples and proposed new insights into the rapid and noninvasive diagnosis of PC.

Cascaded microfluidic circuits for pulsatile filtration of extracellular vesicles from whole blood for early cancer diagnosis

Tumor-derived extracellular vesicles (EVs) hold the potential to substantially improve noninvasive early diagnosis of cancer. However, analysis of nanosized EVs in blood samples has been hampered by lack of effective, rapid, and standardized methods for isolating and detecting EVs. To address this difficulty, here we use the electric-hydraulic analogy to design cascaded microfluidic circuits for pulsatile filtration of EVs via integration of a cell-removal circuit and an EV-isolation circuit. The microfluidic device is solely driven by a pneumatic clock pulse generator, allowing for preprogrammed, clog-free, gentle, high-yield, and high-purity isolation of EVs directly from blood within 30 minutes. We demonstrate its clinical utility by detecting protein markers of isolated EVs from patient blood using a polyethylene glycol-enhanced thermophoretic aptasensor, with 91% accuracy for diagnosis of early-stage breast cancer. The cascaded microfluidic circuits can have broad applications in the field of EV research.

Analyses of single extracellular vesicles from non-small lung cancer cells to reveal effects of epidermal growth factor receptor inhibitor treatments

Precision cancer medicine has changed the treatment landscape of non-small cell lung cancer (NSCLC) as illustrated by the introduction of tyrosine kinase inhibitors (TKIs) towards mutated epidermal growth factor receptor (EGFR). However, as responses to EGFR-TKIs are heterogenous among NSCLC patients, there is a need for ways to early monitor changes in treatment response in a non-invasive way e.g., in patient's blood samples. Recently, extracellular vesicles (EVs) have been identified as a source of tumor biomarkers which could improve on non-invasive liquid biopsy-based diagnosis of cancer. However, the heterogeneity in EVs is high. Putative biomarker candidates may be hidden in the differential expression of membrane proteins in a subset of EVs hard to identify using bulk techniques. Using a fluorescence-based approach, we demonstrate that a single-EV technique can detect alterations in EV surface protein profiles. We analyzed EVs isolated from an EGFR-mutant NSCLC cell line, which is refractory to EGFR-TKIs erlotinib and responsive to osimertinib, before and after treatment with these drugs and after cisplatin chemotherapy. We studied expression level of five proteins; two tetraspanins (CD9, CD81), and three markers of interest in lung cancer (EGFR, programmed death-ligand 1 (PD-L1), human epidermal growth factor receptor 2 (HER2)). The data reveal alterations induced by the osimertinib treatment compared to the other two treatments. These include the growth of the PD-L1/HER2-positive EV population, with the largest increase in vesicles exclusively expressing one of the two proteins. The expression level per EV decreased for these markers. On the other hand, both the TKIs had a similar effect on the EGFR-positive EV population.