Detection Of Early-stage Cancer Research Articles

Plasma mutation profile of precursor lesions and colorectal cancer using the Oncomine Colon cfDNA Assay

BackgroundColorectal cancer (CRC) is the second leading cause of cancer death worldwide. Early detection of precursor lesions or early-stage cancer could hamper cancer development or improve survival rates. Liquid biopsy, which detects tumor biomarkers, such as mutations, in blood, is a promising avenue for cancer screening.AimTo assess the presence of genetic variants in plasma cell-free tumor DNA from patients with precursor lesions and colorectal cancer using the commercial Oncomine Colon cfDNA Assay.Material and methodsCell-free DNA (cfDNA) samples from the plasma of 52 Brazilian patients were analyzed. Eight patients did not have any significant lesions (five normal colonoscopies and three hyperplastic polyps), 24 exhibited precursor lesions (13 nonadvanced adenomas, 10 advanced adenomas, and one sessile serrated lesion), and 20 patients with cancer (CRC). The mutation profile of 14 CRC-associated genes were determined by next-generation sequencing (NGS) using the Oncomine Colon cfDNA Assay in the Ion Torrent PGM/S5 sequencer.ResultsThirty-three variants were detected in eight genes (TP53, PIK3CA, FBXW7, APC, BRAF, GNAS, KRAS, and SMAD4). No variants were detected in the AKT1, CTNNB1, EGFR, ERBB2, MAP2K1 and NRAS genes. All variants were considered pathogenic and classified as missense or truncating. The TP53 gene harbored the most variants (48.48%), followed by the KRAS gene (15.15%) and the APC gene (9.09%). It was possible to detect the presence of at least one pathogenic variant in cfDNA in 60% of CRC patients (12/20) and 25% of precursor lesions (6/24), which included variants in three patients with nonadvanced adenoma (3/13 – 23.08%) and three with advanced adenomas (3/10 – 30%). No variants were detected in the eight patients with normal findings during colonoscopy. The detection of mutations showed a sensitivity of 60% and a specificity of 100% for detecting CRC and a sensitivity of 50% and a specificity of 100% for detecting advanced lesions.ConclusionThe detection of plasma NGS-identified mutations could assist in early screening and diagnostic of CRC in a noninvasive manner.

Non-invasive detection of early-stage lung cancer through exhaled breath volatile organic compound analysis

Non-invasive detection of early-stage lung cancer through exhaled breath volatile organic compound analysis

Does Colonoscopy as a First Screening Test Still Make Sense?-Counterpoint.

Colonoscopy has been widely regarded as the gold standard for its high diagnostic accuracy and preventive potential. However, its invasive nature, high cost, and suboptimal participation rates limit its utility at the population level. Non-invasive screening tests, notably the fecal immunochemical test (FIT) and multitarget stool DNA tests, present promising alternatives that may improve screening participation and reduce barriers to participation. Among these, FIT has demonstrated a consistent advantage in enhancing participation, which subsequently contributes to better long-term outcomes in CRC prevention. FIT-based two-step screening offers several practical advantages, including cost-effectiveness, non-invasiveness, and greater flexibility. Moreover, the quantitative nature of FIT allows for adjustable sensitivity thresholds and the ability of risk stratification, making it adaptable across diverse populations and scenarios. Through serial testing, FIT can increase cumulative detection rates over time. This approach facilitates the identification of high-risk individuals, allowing for more judicious use of colonoscopy resources and reducing unnecessary invasive procedures, especially among low-risk populations. Notably, evidence indicates that participation to FIT-based screening is consistently higher than to colonoscopy, which enhances the detection of early-stage cancers and advanced adenomas in the long run. Given the constraints of limited endoscopic capacity, the aging population, and the recent lowering of the recommended screening age due to the rising incidence of early-onset CRC, FIT emerges as a practical, flexible solution. The role of two-step FIT screening in improving participation and enabling risk-stratified, personalized approaches to CRC prevention is pivotal, advocating for its expanded integration into future screening paradigms.

BREAST CANCER SCREENING IN WOMEN AGED 45-65 IN SAMARKAND REGION, UZBEKISTAN: A CROSS-SECTIONAL ANALYSIS OF DATA FROM 2023

Objective: Breast cancer is a leading cause of cancer-related mortality among women globally, and early detection through screening significantly reduces morbidity and mortality. This study evaluates the outcomes of breast cancer screening programs in the Samarkand region of Uzbekistan in 2023, focusing on participation rates, detection rates, and stage distribution across 17 districts and cities. Methods: Data were collected on women aged 45–65, including annual screening targets, the number of screenings performed, diagnosed cases, and cancer stage distribution (early stages 1–2 vs. late stages 3–4). Statistical analysis was conducted to determine completion and detection rates, as well as regional disparities in screening outcomes. Results: The screening program achieved promising outcomes in participation and early-stage cancer detection, with substantial variability among districts. A higher proportion of cases were detected at stages 1–2, indicating effective early diagnosis in some areas. However, significant regional disparities in screening completion and detection highlight inequitable access to services. Novelty: This study provides a comprehensive regional analysis of breast cancer screening outcomes in Uzbekistan, emphasizing the importance of addressing disparities to improve equitable access to early diagnosis and treatment. These findings underscore the need for targeted public health interventions to enhance program effectiveness and reduce the burden of late-stage breast cancer

Non-Invasive Detection of Lung Cancer: Integrating Genomic Data with Ensemble Learning for Improved Early Diagnosis

The early and accurate diagnosis of lung cancer, a significant contributor to global cancer-related mortality, remains a paramount challenge in healthcare. Conventional diagnostic methods often lack the sensitivity required for early-stage detection, prompting the exploration of non-invasive alternatives. Leveraging advancements in genomics and bioinformatics, this study investigates the potential of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) analysis for early-stage lung cancer diagnosis. Two distinct datasets are utilized: GSE4115, comprising gene expression data from bronchial airway epithelial cells of smokers, and GSE33356, focusing on genomic alterations in Taiwanese female non-smoking lung cancer patients. The research employs comprehensive data pre-processing and feature reduction techniques, including normalization and Kernel Principal Component Analysis (KPCA). Subsequently, an ensemble of diverse learners, including Random Forests, AdaBoost, Bagging, Support Vector Machines (SVMs), and Neural Networks, is trained on the original datasets. A novel ensemble stacking approach is proposed, wherein initial predictions from the base learners are combined through logistic regression - the meta-learner to enhance predictive performance. The study aims to contribute to advancements in lung cancer detection by providing a more precise and non-invasive diagnostic method. By integrating DNA and RNA analysis with ensemble learning techniques, the research endeavours to enhance medical outcomes and potentially save lives through early-stage lung cancer detection.

Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models.

Collagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective invivo detection techniques to provide a deeper understanding. This study presents photoacoustic spectral analysis improved by machine learning as a promising non-invasive diagnostic method, focusing on exploring collagen as a salient biomarker. Murine model experiments revealed more profound associations of collagen with other cancer components than in normal tissues. Moreover, an optimal set of feature wavelengths was identified by a genetic algorithm for enhanced diagnostic performance, among which 75% were from collagen-dominated absorption wavebands. Using optimal spectra, the diagnostic algorithm achieved 72% accuracy, 66% sensitivity, and 78% specificity, surpassing full-range spectra by 6%, 4%, and 8%, respectively. The proposed photoacoustic methods examine the feasibility of offering valuable biochemical insights into existing techniques, showing great potential for early-stage cancer detection.

A simple colorimetric detection of telomerase exploiting specific cleavage of exonuclease III coupled with telomeric DNA controlled aggregation of nanogold.

Telomerase activity has piqued scientists' interest for the reason that it has the potential to be employed for early-stage cancer detection, anticancer therapy and studies related to cancer progression and metastasis. Several approaches have been developed to detect telomerase activity. However, these approaches were lengthy, challenging to quantify, of limited sensitivity and prone to polymerase chain reaction (PCR)-related artefacts. We herein developed a novel nanoplasmonic sensing platform for colorimetric detection of telomerase activity relying on the telomere elongation of telomerase at the 3' end, structure-specific cleavage activity of exonuclease III that removes mononucleotides from the 3'-hydroxyl termini of double-stranded DNA, and electrostatic interaction of elongated telomeres with plasmonic nanoparticles. Using HeLa cells as a model for colorimetric detection of telomerase activity, this biosensor could detect telomerase activity with a detection limit of ∼100 cells per reaction by visible inspection and ∼5 cells per reaction by spectroscopic measurement and analysis time within about three hours. The proposed sensing method provided a novel tool for simple, rapid, and low-cost detection of telomerase activity, eliminating the necessity for thermal cycling, primers in PCR-based assays, and amplification of telomerase extension products. It exhibits significant potential as a label-free, simple, ultrasensitive strategy for on-site detection of telomerase activity in proteomics and clinical diagnostics.

Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy

This study introduces a novel approach for the early detection of pancreatic cancer through biofluid spectroscopy, leveraging a unique machine learning pipeline comprising class-specific principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) in both real patient and synthetic data. By conducting separate PCA on cancerous and non-cancerous samples and integrating the projections prior to LDA and SVM classification, we demonstrate significantly improved diagnostic accuracy compared to traditional methods. This methodology not only enhances predictive performance but also offers deeper insights into the influence of molecular spectra on model efficacy. Our findings, validated on real patient data, suggest a promising avenue for developing non-invasive, accurate diagnostic tools for early-stage pancreatic cancer detection.

Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer

Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%–98%) at 87% (95% CI: 81%–93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than ~ 30%.

Abstract PR013: Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers

Abstract Introduction: Colorectal cancer (CRC) is one of the most common cancers worldwide, and early detection is critical for successful treatment and improved survival rates. However, precancerous and early-stage CRC present significant diagnostic challenges due to the small size of lesions and the very low expression of tumor-specific biomarkers in the bloodstream. Current genomics-based diagnostic methods struggle to detect these lesions, making it imperative to develop more sensitive and specific approaches. Circulating extracellular vesicles (EVs) are emerging as a promising solution for early-stage CRC detection, since EVs are produced by tumor cells as well as the tumor microenviroment and host immune cells. Thus, EVs may offer the means to detect and monitor small early-stage tumors through both direct detection of tumor- associated biomarkers as well through tumor specific host response and tumor microenvironment biomarkers. Methods: To identify novel early-stage CRC specific biomarkers, we performed proteomics analysis on EVs purified from patient plasma using size exclusion chromatography and a proprietary buffer system that enhances EV and corona protein recovery. TrueDiscovery™ Data-independent acquisition (DIA) mass spectrometry (MS) analysis was conducted on 24 pre- cancer/stage 0 and 25 stage 1 CRC patients and 75 normal patient plasma samples. An in-house developed machine learning pipeline was used to identify differentially expressed proteins and model candidate multiplexes to identify those with extremely high diagnostic accuracy (> 0.99). Results: An average of ∼2,500 proteins were identified per sample and included in bioinformatics analysis. Comparative analysis between control patient EVs and either pre- cancerous/Stage 0, or Stage 1 colon cancer EVs using an in-house Machine Learning pipeline identified 336 and 493 differentially expressed proteins for each group (FDR adjusted p-value < 0.001). Of these, the best 17 pre/stage 0 proteins and 53 stage 1 proteins were trained and tested using Support Vector Machine to assess all potential 3-plexes in order to identify those with mean diagnostic accuracy > 99%. This yielded 5 3-plexes in precancer/stage 0 and 34 3-plexes in stage 1 with near perfect diagnostic accuracy. These plexes are currently being assessed in single analyte and multiplex immunoassays with the goal of translating candidate 3-plexes to the clinical. Conclusions: Key biomarkers from CRC patient EVs indicate the potential to detect and diagnose early-stage and low tumor burden cancers using our methods. Interestingly, the most accurate 3-plexes consist of proteins from immune, inflammatory and metabolic processes, suggesting that for the detection of early stage cancer it may be critical to include both tumor and host specific biomarkers. Citation Format: Poorva Mudgal, Cheryl Bandoski, Zachary Opheim, Martin Mehnert, Valesca Anschau, Issa Isaac, Alan Ezrin, Todd Hembrough. Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR013.

Abstract A063: Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers

Abstract This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR013) of the Conference Program/Proceedings. Citation Format: Poorva Mudgal, Cheryl Bandoski, Zachary Opheim, Martin Mehnert, Valesca Anschau, Issa Isaac, Alan Ezrin, Todd Hembrough. Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A063.

Abstract A041: Detection of early-stage gastrointestinal cancers using micronuclei DNA from erythrocytes

Abstract Background: Gastrointestinal (GI) cancers account for over a third of cancer deaths. However, their early detection remains a challenge. Micronuclei (MN) are cytoplasm-resident subcellular structures, containing damaged chromosome segments that indicative of genomic instability. Increased MN frequency in hematopoietic cells is observed in patients with solid tumors. Our developed technique (WO2021/228246 A1) enables the purification and characterization of micronuclei DNA in erythrocytes from peripheral blood. By comparing MN-DNA from healthy donors (HDs) and GI cancer patients, we identified significant changes in read densities at specific genomic locations in patients, which were termed as tumor-associated MN-DNA (taMN- DNA) features. Here, we evaluated the potential of these features for early-stage GI cancer detection, including colorectal (CC), gastric (GC) and esophageal (EC) cancers. Methods: Peripheral blood (1-2 mL) was collected from healthy donors (HD) and cancer patients MN-DNA isolation and purification from erythrocytes. Participants were randomly divided into training, validation and independent test cohorts in a 7:2:1 ratio, maintaining similar cancer types, gender and age distribution. Distinct MN-DNA features between cancer patients and HDs were identified using sequencing data. Machine learning algorithms were applied using these features for cancer detection. Results: The study enrolled 1753 participants, including 987 HDs, 420 CC, 282 GC and 64 EC cases. Of these, over half were diagnosed with early-stage diseases; TNM stage 0-I accounted for 40.57%, stage II for 23.32%, stage III for 29.25% and stage IV for 6.87%. The pan-cancer model achieved 88.2% sensitivity with an overall specificity of 95.4%. Patients in individual caner types were detected from the tissue of origin classification with an overall accuracy of 91.9% based on pan-cancer at 95.4% specificity. Sensitivity was 90.8% for CC, 91.7% for GC and 100% for EC. For early- stage (stage 0-II) CC, GC and EC, the model demonstrated sensitivities of 97.4%, 94.7% and 100.00%, respectively, at 95.4% specificity. Conclusions: Unlike cell-free DNA, MN-DNA are chromosomal fragments in the cytoplasm. The abundance of erythrocytes in peripheral blood provides an easily accessible source for MN-DNA enrichment. This pilot study illustrates the potential of MN-DNA as a tool for early GI cancer detection, contributing to large-scale efforts towards developing an effective GI cancer screening test. Research sponsor: Timing Biotech. Citation Format: Haobo Sun, Xingyun Yao, Yuehua Han, Yurong Jiao, Xiangxing Kong, Chengcheng Liu, Qingqu Guo, Fei Meng, Honghao Liang, Hongbo Li, Xiuqin Fan, Jun Li, Xiaofei Gao. Detection of early-stage gastrointestinal cancers using micronuclei DNA from erythrocytes [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A041.

Abstract B058: Novel machine learning lung cancer classifier shows significant correlation with cancer-specific fragmentomics features

Abstract Lung cancer is the second most prevalent cancer, and the leading cause of cancer-related deaths, in the US. Since the survival rate of patients diagnosed with lung cancer at earlier stages is dramatically higher than those diagnosed at later stages, technologies that shift lung cancer diagnosis to an earlier stage would be expected to improve survival rates. Fragmentomics, the study of short circulating cell-free DNA (cfDNA) fragments in the blood, is an emerging field in cancer diagnostics that has the potential to catalyze such a shift. Studies have shown that fragmentomics features, such as fragment sizes and end motifs, can be used to discriminate normal cfDNA from circulating tumor DNA (ctDNA) generated through tumor necrosis and apoptosis. Using this knowledge, machine learning tools, trained to identify ctDNA-specific features, are being leveraged in novel liquid biopsy assays to improve early-stage lung cancer detection. To create a machine learning classifier that detects discrete signals of ctDNA in individuals with lung cancer, Genece Health utilized low pass (∼3x coverage) whole genome sequencing (Illumina) of plasma extracted cfDNA from >400 lung cancer samples and >1,000 negative controls. This data was used to generate our novel FEMS (Fragment End Motifs and Sizes) dataframe, containing >30,000 features, by combining fragment lengths with their corresponding 5’ 4bp end motifs. The FEMS dataframe was then used to train a neural network machine learning classifier. To evaluate the performance of our machine learning classifier, we investigated whether the classifier scores correlated with well-known hallmarks of ctDNA. First, consistent with published ctDNA characteristics, we observed significant (p<0.01) enrichment of fragment sizes ≤167bp in lung cancer samples. Concordantly, our classifier scores are significantly correlated with this fragment size enrichment. Also, both fragment size enrichment and our classifier scores are correlated with lung cancer stage, with higher enrichment and higher scores observed in later stages. Next, we show that our classifier scores are correlated with specific end motif enrichments or depletions in lung cancer samples. For example, in lung cancer samples, CCCA end motifs were significantly enriched (p<0.01) in fragment sizes 120-140bp (max at 128bp), while significantly depleted in fragment sizes 190-210bp (min at 204bp). Once again, our classifier scores were significantly correlated with both the enrichment of this end motif in short fragments and its depletion in long fragments. Last, we assessed the classifier’s performance in an independent cohort (46 lung cancer and 132 non-cancer samples) and show 89.2% sensitivity with 90.4% specificity (auROC=0.957). The correlation observed between our classifier scores and published ctDNA characteristics, combined with the strong performance in an independent cohort, suggests that Genece’s novel machine learning classifier is effectively leveraging ctDNA-specific fragmentomics features for the identification of lung cancer in patient samples. Citation Format: Carlos Guzman, Michael L Salmans, Mengchi Wang, Byung In Lee, Andrew R Carson. Novel machine learning lung cancer classifier shows significant correlation with cancer-specific fragmentomics features [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B058.

Abstract A056: Performance of multi-biomarker class reflex testing in a prospectively-collected cohort

Abstract Background: A multi-biomarker blood test has the potential to detect cancers in a broad range of organ types and stages. We previously trained and independently assessed the performance of two biomarker classes (methylation and protein; MP) for multi-cancer early detection (MCED) in a prospectively collected study (Cancer Res,2024;84(7Supp):LB100). Here, we assessed the addition of a somatic mutation reflex approach to samples with initially negative MP-results (MP-reflex; MP-r). Methods: This study included 3,160 samples (739 cancer and 2421 non- cancer) representing 20 organ types and all stages. Samples with negative MP results at a specificity threshold of ≥ 98.5% and detectable MP signals above pre-specified lower thresholds were analyzed for mutations using next generation sequencing. Classifier models and thresholds were developed using an independent training set. Sensitivity of the MP biomarker classifier was determined at a combined specificity of ≥98.5%. We also compared the performance of MP and MP-r configurations with prostate (n=52) and breast (n=88) organ types excluded. Results: At 98.5% specificity, the observed overall sensitivity for MP was 52.9%, with sensitivities of 15.3%, 39.8%, 71.2%, 87.2%, 26.7% and 37.5% for stages I, II, III, IV, I/II and unknown, respectively. At the same specificity of 98.5%, when an MP-r configuration was utilized, overall sensitivity was 55.8%, with sensitivities of 19.1%, 42.2%, 72.3%, 89.9%, 29.9% and 46.9% for stages I, II, III, IV, I/II and unknown, respectively. MP-r demonstrated absolute sensitivity increases of 3.8%, 2.5%, 1.1 %, 2.8%, 3.2%, 9.4%, and 2.8%, for stages I, II, III, IV, I/II, unknown, and overall, respectively. When breast and prostate cancers were excluded, test sensitivity for MP was 59.3%, with sensitivities of 17.2%, 51.9%, 76.4%, 88.3%, 31.9% and 41.4% for stages I, II, III, IV, I/II and unknown, respectively. When an MP-r configuration was utilized, overall sensitivity was 62.3% %, with sensitivities of 22.1%, 54.7%, 76.4%, 91.4%, 35.9% and 51.7% for stages I, II, III, IV, I/II and unknown, respectively. MP-r demonstrated absolute sensitivity increases of 4.8 %, 2.8%, 0.0%, 3.1%, 4.0%, 10.3%, and 3.0% for stages I, II, III, IV, I/II, unknown, and overall, respectively. Conclusions: Compared to MP alone (breast and prostate cancers excluded), an MP-r approach results in a relative sensitivity increase of 28% in Stage I cancers and 12.5% for Stage I/II cancers. MP-r is an efficient strategy to add a third biomarker class and enhance sensitivity of MP for the detection of early-stage cancer. Citation Format: Vladimir Gainullin, Jin Bae, Violeta Beleva Guthrie, Fanglei Zhuang, Chen Ji, Christopher Tyson, Mark Evans, Kevin Arvai, Melissa Gray, Madhav Kumar, Mael Manesse, Xi Chen, Philip Uren, Gustavo C. Cerqueira, Amin Mazloom, Jorge Garces, Tomasz M. Beer, Frank Diehl. Performance of multi-biomarker class reflex testing in a prospectively-collected cohort [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A056.

Abstract A020: Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection

Abstract Introduction: Extracting cfRNA from biological samples is crucial in liquid biopsy applications, particularly for detecting low-frequency mutations like KRAS G12V, essential for early cancer diagnosis and minimal residual disease (MRD) monitoring. Traditional cfRNA extraction methods involve multiple transfer steps, aliquoting, and pooling, leading to cfRNA loss and affecting mutation detection sensitivity and accuracy. The nRichDX Revolution cfTNA Max 20 kit, with its innovative nRicher cartridge, simplifies the extraction process by eliminating these extra steps, potentially enhancing yield and efficiency. This study evaluates the cfRNA recovery efficiency of the nRichDX kit compared to the Qiagen Circulating Total Nucleic Acid kit. Methods: Plasma samples (5 mL each) were spiked with RNA containing KRAS G12V mutation at concentrations ranging from 100 to 2 million copies, mimicking early-stage cancer detection and MRD monitoring, where low copy numbers represent early disease and high copy numbers reflect advanced cancer stages. cfRNA was extracted using the nRichDX and Qiagen kits, with each condition tested in triplicate. cfRNA recovery efficiency was assessed using quantitative reverse transcription PCR (qRT-PCR) to quantify KRAS G12V RNA copies recovered. High-sensitivity RNA ScreenTape Analysis was also used to evaluate cfRNA profile quality. Results: The nRichDX kit recovered significantly higher cfRNA yields than Qiagen across all concentrations. Recovery rates were consistent and reproducible, with over 70% of input RNA recovered, even at lower inputs (100 to 100,000 copies), where recovery rates were approximately 70-85%. High Sensitivity RNA ScreenTape Analysis showed a linear increase in 18S and 28S ribosomal peaks and high RNA Integrity Number (RIN) scores in nRichDX profiles. The ability to robustly recover low copy numbers is significant for early cancer detection and MRD monitoring. In contrast, the kit’s effectiveness in high copy number samples reflects its ability to monitor advanced cancer stages accurately. Conclusion: The nRichDX cfTNA kit demonstrates exceptional efficiency in cfRNA extraction from plasma samples across a broad dynamic range, from as few as 100 copies to as many as 2 million copies of the KRAS G12V mutation. Its versatility makes it suitable for various cancer detection and monitoring applications. The streamlined process offered by the nRicher cartridge minimizes sample handling and reduces cfRNA loss, enhancing performance. This translates to more accurate and sensitive detection of early-stage and high-burden mutations associated with advanced cancer. The high-quality profiles indicated by RNA ScreenTape Analysis further validate the reliability of the nRichDX kit, making it a valuable tool for liquid biopsy applications, with significant advantages in early cancer detection and monitoring disease progression. Citation Format: Nafiseh Jafari, Mayer Saidian, Jason Saenz, Andrew Dunnigan, Carlos Hernandez, Leila Aghili. Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A020.

Exploring the role of graphene-metal hybrid nanomaterials as Raman signal enhancers in early stage cancer detection

Molecular diagnosis plays a significant role in detection of biomolecules linked to early stage cancer since it offers greater sensitivity and reliability for identification of biomarker level changes as the disease progresses. The application of vibrational spectroscopy in biomarker detection is defined by the fingerprint spectrum of a molecule originating from single-molecule vibrations. This characteristic makes surface enhanced Raman spectroscopy (SERS) a promising tool for identification of biomarkers. The performance of the SERS technique largely depends on the material being used as the SERS substrate. Graphene, with its large surface area and abundance of aromatic regions, is considered advantageous as SERS substrate. Combining graphene with metal nanomaterials considerably increases SERS signal intensity, thereby enhancing detection sensitivity. Therefore, this review emphasizes the significance of selecting graphene-metal nanohybrids as suitable SERS substrates for signal amplification. The detail understanding of the mechanism of graphene-metal hybrid in SERS based detection of early stage cancer is also presented. Furthermore, several examples demonstrated the application of graphene-metal hybrid nanomaterials in detecting biomarkers and cancer cell differentiation using SERS imaging.

Gold-Graphene Quantum Dot Hybrid Nanoparticle for Smart Diagnostics of Prostate Cancer.

Prostate cancer is one of the most prevalent cancers afflicting men worldwide, often detected at advanced stages, leading to increased mortality rates. Addressing this challenge, we present an innovative approach employing electrochemical biosensing for early-stage prostate cancer detection. This study used Indium-Tin Oxide (ITO) as a substrate and a deposited gold-graphene quantum dot (Au-GQD) nanohybrid to establish electrochemical sensing platforms for DNA-hybridization assays. A capturing DNA probe, PCA3, was covalently immobilized on the surface of the Au-GQDs and deposited electrochemically onto the ITO electrode surface. The Au-GQDs enabled the capturing of the target PCA3 biomarker probe. The sensor achieved a limit of detection (LoD) of up to 211 fM and presented a linear detection range spanning 1 µM to 100 fM. A rapid 5-min response time was also achieved. The tested shelf life of the pre-immobilized sensor was approximately 19 ± 1 days, with pronounced selectivity for its intended target amidst various interferants. The sensing device has the potential to revolutionize prostate cancer management by facilitating early-stage detection and screening with enhanced treatment efficacy.

Advancing prostate cancer detection: High-accuracy urine test distinguishes between high and low-risk cancers

Prostate cancer remains one of the leading causes of cancer-related deaths among men. The standard method for early detection involves measuring Prostate-specific antigen (PSA) levels in the blood, with elevated PSA levels prompting further diagnostic procedures, such as a prostate biopsy. While effective, biopsies can be painful and carry risks of complications such as fever and urinary tract infections. To reduce unnecessary biopsies, researchers have sought alternative diagnostic methods. In recent years, a urine test was developed for early-stage prostate cancer detection. However, this test struggled to differentiate between aggressive and slow-growing cancers, the latter of which often require minimal treatment and are managed through active surveillance. To address this limitation, scientists at Wunderkind University have significantly enhanced the urine test. By analysing the genomes of thousands of prostate cancer patients, they identified a panel of 16 genes detectable in urine that can effectively distinguish between high-risk and low-risk cancers. When applied to individuals with elevated PSA levels, this improved test demonstrated an impressive 97% accuracy in identifying advanced prostate cancer. This advancement holds great promise for reducing unnecessary biopsies and improving patient outcomes by accurately identifying those needing aggressive treatment versus those suitable for observation. This review paper will explore the development, validation, and clinical implications of this novel urine test, highlighting its potential to transform prostate cancer diagnostics and patient management.

The capture of circulating tumor cells by Labyrinth system as a tool for early stage lung cancer detection.

We focus on utilizing the Labyrinth system for the detection of circulating tumor cells (CTCs) in patients with lung nodules. Our aim is to evaluate CTCs isolated through the Labyrinth system as a biomarker for early-stage lung cancer (LC) detection. 167 patients with low dose computed tomography (LDCT) diagnostic results for lung nodules and 31 healthy volunteers (HV) were enrolled. Blood samples were processed for CTC detection. LDCT positive (LDCT+) patients underwent surgery and were categorized into those with LC and those with benign lung diseases (BLD) based on their biopsy results. BLD Patients, LDCT negative (LDCT-) patients and HV served as controls. The correlation of CTC counts with LC, BLD, LDCT- and HV was investigated. Receiver operating characteristic (ROC) curves were used to assess the Labyrinth system's diagnostic potential for early-stage LC. Median CTC counts for LC, BLD, LDCT- and HV were 2.7 CTC/mL, 0.6 CTC/mL, 0.4 CTC/mL, 0 CTC/mL, respectively. Statistical analysis indicated CTC counts could distinguish LC from BLD, LDCT- and HV (p-Values < 0.001). Using a cut-off of 1 CTC/mL, the study showed 84.4% sensitivity and 82.4% specificity for LDCT+ patients. Specificity increased to 85.9% for patients with lung nodules and 88.2% for all participants. In conclusion, CTCs detected by the Labyrinth system can serve as a biomarker for early-stage LC detection for patients with lung nodules. CTCs identified by the Labyrinth system are a promising biomarker for early-stage LC detection in clinical practice.

AN APPROACH FOR LUNG CANCER DETECTION AND CLASSIFICATION USING LENET-DENSENET

Lung cancer is a malicious disorder that affects millions of people around the globe. Premature detection and classification of malignant cells can gradually improve the rate of survival of the patients. The study proposed an innovative strategy for lung cancer identification and classification by utilizing the hybrid deep learning (DL) technique. The presented strategy contains two major parts, segmentation and classification. Attention gated (AG) network is used for segmenting the affected regions. AGs guide a method’s attention to significant regions while repressing the activation of features in non-related fields. This augments the model’s representative influence without significantly increasing computing cost or the number of model parameters. For the lung cancer classification, by combining the architectures of LeNet and Dense Convolutional Network (DenseNet), the proposed LeNet–DenseNet was developed. LeNet and DenseNet are two popular CNN architectures known for the classification of images. LeNet comprises alternating and convolutional layers, followed by fully connected layers. As per the idea of densely connected layers, each layer in DenseNet, which is a recent CNN architecture, is associated with other layers in a feed-forward fashion. This allows for better feature reuse and gradient flow and lessens the parameter count. The findings stated that the proposed method outperforms when compared to other traditional strategies corresponding to specificity, sensitivity and accuracy. The presented approach assists in early-stage detection of high-accuracy lung cancer, which is considered crucial for the treatment procedures in time. The findings stipulated that the presented LeNet–DenseNet model procured accuracy of 94.8%, sensitivity of 96.8% and specificity of 93.5%.