Non-invasive Cancer Diagnosis Research Articles

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.

Abstract 3301: Ultrasensitive compartment-free digital phenotyping of circulating extracellular vesicles for diagnosis of Ewing sarcoma

Abstract Ewing sarcoma (EWS) is the second most common pediatric osseous malignancy in the US. Vague symptoms and high prevalence of metastasis and recurrence present serious obstacles to its clinical management. Current diagnostic and monitoring modalities for EWS, including tissue biopsy, imaging, and cytogenetics have limitations in the early diagnosis and disease monitoring. Thus, EWS patients, as do other solid pediatric cancers, face unique challenges in the lack of non-invasive tests to aid with patient management. Extracellular vesicles (EVs), including exosomes, are emerging as a new paradigm of liquid biopsy for non-invasive cancer diagnosis and monitoring owing to their informative cargoes and imperative biological functions. However, limited progresses have been reported in the field of EWS-related EV diagnostics. To address this gap, we developed a new microfluidics-based digital immunoassay platform, termed μTUNER (microfluidic Topographic modUlation and iNtensification of Enzymatic Reaction), for ultrasensitive single-molecular analysis of EV protein biomarkers and liquid biopsy-based diagnosis of EWS. Through constructing nanoconfinements with tunable sizes, this technology affords programmable patterning and modulation of surface enzymatic reactions in a non-contact manner. Such unique capacity enables us to develop a compartment-free digital immunoassay platform which is conceptually distinct from the existing digital assays. As a proof-of-concept for clinical applications, we adapted the μTUNER digital immunoassay platform to quantitatively evaluate four exo-protein biomarkers (CD99, ENO-2, NGFR, and Ezrin) that we identified by extensive proteomics analysis of EWS cell line-derived EVs (PMID: 32821345). Using protein standards and EWS cell line-derived EVs, we demonstrated that our technology affords sensitive and quantitative detection of exo-protein biomarkers with LODs at the fg/mL level, which warrants subsequent assessment of its adaptability to clinical EWS diagnosis. EV profiling of plasma samples from adult and pediatric EWS patients (n = 16) and matched healthy controls (n = 17) reveals unique expression patterns of the four markers. These exo-protein biomarkers individually afforded excellent diagnostic performance (AUC, CD99: 0.992; ENO-2: 0.985; NGFR: 0.996; Ezrin: 0.978) to differentiate the EWS and control groups. The unweighted SUM signature of the four proteins further improves the diagnostic power to yield an AUC of 1.00. Meanwhile, the distinctive EV signature obtained from the machine learning-based analysis permits further classification of control, adult, and pediatric patients with an overall accuracy of 97%. Based on these results, we envision that our technology combined with the novel exo-protein biomarker signature can be useful clinically to improve the diagnosis of patients with Ewing sarcoma. Citation Format: Yunjie Wen, Vikalp Vishwakarma, Andrew K. Godwin, Yong Zeng. Ultrasensitive compartment-free digital phenotyping of circulating extracellular vesicles for diagnosis of Ewing sarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3301.

Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinoma.

Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN). 220 PTC- and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy. From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity=0.957, specificity=0.708). The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC. This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850).

Engineering the MoS2 /MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens.

High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.

Sensitive Electrochemical Sensor for Glycoprotein Detection Using a Self-Serviced-Track 3D DNA Walker and Catalytic Hairpin Assembly Enzyme-Free Signal Amplification.

Approaches for the detection of targets in the cellular microenvironment have been extensively developed. However, developing a method with sensitive and accurate analysis for noninvasive cancer diagnosis has remained challenging until now. Here, we reported a sensitive and universal electrochemical platform that integrates a self-serviced-track 3D DNA walker and catalytic hairpin assembly (CHA) triggering G-Quadruplex/Hemin DNAzyme assembly signal amplification. In the presence of a target, the aptamer recognition initiated the 3D DNA walker on the cell surface autonomous running and releasing DNA (C) from the triple helix. The released DNA C as the target-triggered CHA moiety, and then G-quadruplex/hemin, was formed on the surface of electrode. Eventually, a large amount of G-quadruplex/hemin was formed on the sensor surface to generate an amplified electrochemical signal. Using N-acetylgalactosamine as a model, benefiting from the high selectivity and sensitivity of the self-serviced-track 3D DNA walker and the CHA, this designed method showed a detection limit of 39 cell/mL and 2.16 nM N-acetylgalactosamine. Furthermore, this detection strategy was enzyme free and exhibited highly sensitive, accurate, and universal detection of a variety of targets by using the corresponding DNA aptamer in clinical sample analysis, showing potential for early and prognostic diagnostic application.

Viscosity-triggered near-infrared fluorescence nanoprobe for in vivo non-invasive diagnosis of cancer

A universal and non-invasive approach for cancer diagnosis is desired to lessen the burden of cancer mortality. In this work, we exploited viscosity in the cancer microenvironment as a diagnosis indicator and designed a universal probe, the CBT-Q (4-(2-(5-(9-(4-aminophenyl)-9H-carbazol-3-yl) thiophen-2-yl)vinyl)-1-ethylquinolin-1-ium) nanoprobe, for detecting viscosity variation in the cells, plasma, and solid tumor-mice to obtain a cancer diagnosis. The near infrared (NIR) fluorescence of CBT-Q was specifically triggered due to a change in its structure from a flexible to a rigid one in the presence of a higher viscosity agent, such as a cancer microenvironment or cancer patient plasma. The Stokes shift of CBT-Q nanoprobe (CBT-Q-N) could be up to 178 nm, enabling the diagnosis of cancer with a low biological fluorescent background. Not only could the CBT-Q-N remain in cells for 72 h, but detect the changes in cell viscosity induced by various models. Also, the CBT-Q-N differentiated early and terminal tumor mice via the fluorescent intensity of plasma viscosity. The CBT-Q-N enables non-invasive recognition of tumors in vivo by fluorescence imaging. Hence, this work is beneficial to increase the understanding of tumor-related viscosity, which will facilitate a new universal strategy for present cancer diagnostics.

High-tech radiation methods in the diagnosis of endometrial cancer – multiparametric MRI, ultrasound tomography, 3D angiography, energy dopplerography

The increase in the incidence of endometrial cancer is noted not only in postmenopausal women, but also in a socially active group of women of reproductive age, starting from 25 years. Ultrasound and magnetic resonance imaging (MRI) are the main methods of non-invasive diagnosis of endometrial cancer. Our study included 69 patients with endometrial cancer stages I–IV aged 30 to 79 years. The diagnostic informativeness of the MRI method in detecting tumor invasion into the myometrium for all groups was: sensitivity – 82.30%, specificity – 6.6%, PPV – 93.30%, NPV – 40%. The diagnostic informativeness of the ultrasound method in detecting tumor invasion into the myometrium for all groups was: sensitivity – 91.3%, specificity – 88.4%. The peculiarity of this study was to evaluate the possibilities of radiation methods (ultrasound, MRI) in the staging of endometrial cancer.

Point-of-care Analysis for Non-invasive Diagnosis of Oral cancer (PANDORA): A technology-development proof of concept diagnostic accuracy study of dielectrophoresis in patients with oral squamous cell carcinoma and dysplasia.

Delays in the identification and referral of oral cancer remain frequent. An accurate and non-invasive diagnostic test to be performed in primary care may help identifying oral cancer at an early stage and reduce mortality. Point-of-care Analysis for Non-invasive Diagnosis of Oral cancer (PANDORA) was a proof-of-concept prospective diagnostic accuracy study aimed at advancing the development of a dielectrophoresis-based diagnostic platform for oral squamous cell carcinoma (OSCC) and epithelial dysplasia (OED) using a novel automated DEPtech 3DEP analyser. The aim of PANDORA was to identify the set-up of the DEPtech 3DEP analyser associated with the highest diagnostic accuracy in identifying OSCC and OED from non-invasive brush biopsy samples, as compared to the gold standard test (histopathology). Measures of accuracy included sensitivity, specificity, positive and negative predictive value. Brush biopsies were collected from individuals with histologically proven OSCC and OED, histologically proven benign mucosal disease, and healthy mucosa (standard test), and analysed via dielectrophoresis (index test). 40 individuals with OSCC/OED and 79 with benign oral mucosal disease/healthy mucosa were recruited. Sensitivity and specificity of the index test was 86.8% (95% confidence interval [CI], 71.9%-95.6%) and 83.6% (95% CI, 73.0%-91.2%). Analysing OSCC samples separately led to higher diagnostic accuracy, with 92.0% (95% CI, 74.0%-99.0%) sensitivity and 94.5% (95% CI, 86.6%-98.5%) specificity. The DEPtech 3DEP analyser has the potential to identify OSCC and OED with notable diagnostic accuracy and warrants further investigation as a potential triage test in the primary care setting for patients who may need to progress along the diagnostic pathway and be offered a surgical biopsy.

The diagnostic performance of 18F-DCFPyL PET in patients with suspected prostate cancer: A systemic review and meta-analysis.

Our meta-analysis aimed to evaluate the diagnostic value of 18F-DCFPyL prostate-specific membrane antigen (PSMA) PET in patients with suspected prostate cancer. We searched for articles that evaluate the diagnostic value of 18F-DCFPyL PSMA PET in patients with suspected prostate cancer in PubMed, Embase, Cochrane Library, and Web of Science until 1 August 2022. Using the QUADAS-2 instrument, two researchers independently assessed the effectiveness of the studies that were included. The four-grid table data were analyzed by Meta-disc1.4 and Stata 16.0 software. The heterogeneity of each study was tested. A total of five studies with 258 patients were included, and the pooled sensitivity and specificity of 18F-DCFPyL PSMA PET for primary prostate cancer were 0.92 (95% confidence interval (CI): 0.85-0.96) and 0.59 (95% CI: 0.08-0.96), respectively. 18F-DCFPyL PSMA PET was successful in detecting primary prostate cancer, with an area under the curve (AUC) of 0.92 (95% CI: 0.89-0.94). 18F-DCFPyL PSMA PET has a strong predictive value for primary prostate cancer and is an effective method for the non-invasive diagnosis of prostate cancer. More prospective articles were needed.

A visualized automatic particle counting strategy for single‐cell level telomerase activity quantification

AbstractThe accurate evaluation of telomerase activity, a typical cancer biomarker, is vital for early cancer screening. In this study, we developed a dark‐field microscopy (DFM) visual single‐particle detection scheme to detect telomerase activity based on automatic counting gold nanoparticles (AuNPs). This method started with attaching the telomerase substrate (TS) primer to the magnetic beads (MBs) through streptavidin‐biotin interaction. In the presence of telomerase and dNTPs, the TS primer was expanded with (TTAGGG)n repeat units to form the telomerase extension product (MBs‐telomerase extension product), which could be hybridized with the complementary DNA (cDNA) modified with AuNPs through Au‐S bonds (AuNPs‐SH‐cDNA). After magnetic separation and DNA double‐strand unwinding, AuNPs were collected from the supernatant, and the telomerase activity was quantitatively measured by visually counting bright spots based on DFM. This strategy achieved a limit of detection as low as 1 HeLa cell and distinguished telomerase activity among different cell lines, thus verifying its excellent sensitivity and specificity. Further, two common telomerase inhibitors (BIBR1532 and curcumin) were screened with the consistent IC50 values with other methods, respectively. It is worth mentioning that this strategy can clearly identify bladder cancer among various urinary diseases. Consequently, the visualized automatic particle counting strategy is potential as a powerful tool in early and noninvasive diagnosis of bladder cancer.

Abstract OT1-23-01: Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE trial

Abstract Background: The detection of breast lesions through self-examination or during screening tests is a frequent finding. Breast biopsy is required in case of radiologically suspect lesions, bestowing a high burden on both patients and national healthcare system, since only one every four biopsies is breast cancer (BC). To date, the assessment of circulating biomarkers failed to demonstrate clinical utility in the early diagnosis of BC, for its suboptimal accuracy and difficult transferability to clinical practice. The combination of novel cutting-edge methods for the assessment of circulating analytes in an integrated multiomic classifier may overcome such limitations, possibly allowing liquid biopsy to become a novel noninvasive procedure for the differential diagnosis of BC. Design: In the RENOVATE trial (NCT04781062), women with suspect (BI-RADS-4/5) breast lesions ≤ 2 cm (cT1) are asked, before biopsy, to donate ~ 35 mL of blood collected in four dedicated tubes and ~ 50 mL of urine at the Diagnostic Senology Unit of Ospedale Policlinico San Martino (Genoa, IT). Plasma cell-free DNA methylation and copy number alterations are assessed in a cohort of patients diagnosed with early BC and a matched set of patients with histologically proven benign lesions through cell-free methylated DNA immunoprecipitation and high throughput sequencing (cfMeDIPseq), as well as ultra-low pass whole genome sequencing (ULP-WGS). Thanks to the volume and quality of our sample set, other experimental techniques will be tested as well. Results from cfMeDIP-seq and ULP-WGS, possibly in combination with other findings, will be integrated in a unique classifier for the noninvasive differential diagnosis of suspect breast lesions. Eligibility criteria: Patients with radiologically suspect breast lesions ≤ 2 cm (i.e. BIRADS 4/5) are eligible. Patients with previous history of cancer, or diagnosed with autoimmune or active allergic diseases, acute or chronic hepatic, renal, or cardiac diseases, or acute or chronic infectious diseases are excluded from the present trial. Specific aims: The primary aim of the present trial is to develop a noninvasive classifier for the differential diagnosis of suspect breast lesions detected through mammography and/or ultrasound. For such purpose we will assess the performance of plasma cfMeDIPseq, ULP-WGS, and other promising techniques for the differential diagnosis of BC. Such techniques will be integrated in a unique classifier in order to reach the maximum possible accuracy. Statistical methods: Sample size was calculated with a semi-parametric simulation-based approach from beta-distributions of PBMC datasets: assuming to test 20,000 CpG regions, with 300 differentially methylated target CpGs, a target maximal difference in DNA methylation of 0.2 between groups and an FDR of 0.05, 1 – beta ~ 0.90 would be achieved with an overall sample size of 150 samples split in a 1:2 ratio. Target accrual and present accrual: Minimum target accrual is set at 49 patients with BC and 98 patients with benign lesions. To date, we have collected plasma samples from 74 eligible patients with BC and 115 eligible patients with benign lesions. A validation cohort accounting for ~30% of our sample set will be recruited at Istituto Nazionale dei Tumori (Milan, IT). Contact information: For further information, please contact Gabriele Zoppoli at gabriele.zoppoli@unige.it. Citation Format: Francesco Ravera, Martina Dameri, Isabella Lombardo, Mario Stabile, Alberto Tagliafico, Massimo Calabrese, Alberto Ballestrero, Lorenzo Ferrando, Gabriele Zoppoli. Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT1-23-01.

Atomic Force Microscopy-based assessment of multimechanical cellular properties for classification of graded bladder cancer cells and cancer early diagnosis using machine learning analysis.

Cellular mechanical properties (CMPs) have been frequently reported as biomarkers for cell cancerization to assist objective cytology, compared to the current subjective method dependent on cytomorphology. However, single or dual CMPs cannot always successfully distinguish every kind of malignant cell from its benign counterpart. In this work, we extract 4 CMPs of four different graded bladder cancer (BC) cell lines by AFM (atomic force microscopy)-based nanoindentation to generate a CMP database, which is used to train a cancerization-grade classifier by machine learning. The classifier is tested on 4 categories of BC cells at different cancer grades. The classification shows split-independent robustness and an accuracy of 91.25% with an AUC-ROC (ROC stands for receiver operating characteristic, and ROC curve is a graphical plot which illustrates the performance of a binary classifier system as its discrimination threshold is varied) value of 97.98%. Finally, we also compare our proposed method with traditional invasive diagnosis and noninvasive cancer diagnosis relying on cytomorphology, in terms of accuracy, sensitivity and specificity. Unlike former studies focusing on the discrimination between normal and cancerous cells, our study fulfills the classification of 4 graded cell lines at different cancerization stages, and thus provides a potential method for early detection of cancerization. STATEMENT OF SIGNIFICANCE: We measured four cellular mechanical properties (CMPs) of 4 graded bladder cancer (BC) cell lines using AFM (atomic force microscopy). We found that single or dual CMPs cannot fulfill the task of BC cell classification. Instead, we employ MLA (Machine Learning Algorithm)-based analysis whose inputs are BC CMPs. Compared with traditional cytomorphology-based prognoses, the non-invasive method proposed in this study has higher accuracy but with many fewer cellular properties as inputs. The proposed non-invasive prognosis is characterized with high sensitivity and specificity, and thus provides a potential tumor-grading means to identify cancer cells with different metastatic potential. Moreover, our study proposes an objective grading method based on quantitative characteristics desirable for avoiding misdiagnosis induced by ambiguous subjectivity.