Biomarkers For Early Cancer Detection Research Articles

Electrochemical Sensor for the Detection and Accurate Early Diagnosis of Ovarian Cancer.

Ovarian cancer (OC) has the highest mortality rate among malignant tumors, primarily because it is difficult to diagnose early. Exosomes, a type of extracellular vesicle rich in parental information, have garnered significant attention in the field of cancer diagnosis and treatment. They play an important regulatory role in the occurrence, development, and metastasis of OC. Consequently, exosomes have emerged as noninvasive biomarkers for early cancer detection. Therefore, identifying cancer-derived exosomes may offer a novel biomarker for the early detection of OC. In this study, we developed a metal-organic frameworks assembled "double hook"-type aptamer electrochemical sensor, which enables accurate early diagnosis of OC. Under optimal experimental conditions, electrochemical impedance spectroscopy technology demonstrated a good linear relationship within the concentration range of 31-3.1 × 106 particles per microliter, with a detection limit as low as 12 particles per microliter. The universal exosome detection platform is constructed, and this platform can not only differentiate between high-grade serous ovarian cancer (HGSOC) patients and healthy individuals but also distinguish between HGSOC patients and nonhigh-grade serous OC (non-HGSOC). Consequently, it provides a novel strategy for the early diagnosis of OC and holds great significance in clinical differential diagnosis.

Role of MicroRNA in Hypoxic Tumours and their Potential as Biomarkers for Early Detection of Cancer.

Hypoxia is a pathophysiological condition characterized by oxygen deficiency in tissues, which negatively affects normal biological functions. It is a typical microenvironment character of almost all solid tumours. Noncoding RNA are small functional RNA molecules that regulate gene expression at chromatin and posttranscriptional levels. Micro-RNAs (miRNAs) are a type of noncoding RNA and are ~12-22 nucleotides long that are crucial in regulating gene expression by partnering with the mRNAs of protein-coding genes. It is widely reported that miRs play an important role in various key processes and pathways during tumour formation, as well as advancement in hypoxic tumors by influencing the HIF pathway. The role of miRNAs in hypoxic tumours, namely in pancreatic, kidney, breast, lung and colorectal, are described. These miRNAs have immense potential as diagnostic and prognostic biomarkers for early cancer detection.

Circulating tumor DNA: from discovery to clinical application in breast cancer.

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

Abstract LB102: Triaging oral premalignant lesions: Plasma ultrashort cell-free DNA-based liquid biopsy approach

Abstract Background: Rapid advancements in cell-free DNA (cfDNA) based liquid biopsy (LB), has opened avenues of critical clinical importance. Recent discovery of a novel class of cell-free DNA 40-70 bps in length, ultrashort cfDNA (uscfDNA) is demonstrating immense potential as a cancer early detection biomarker. Globally, oral cavity squamous cell carcinomas (OCSCC) make up a significant proportion of head and neck squamous cell cancers. OCSCC commonly arises after the occurrence of oral premalignant lesions (OPLs), making it an ideal target for plasma-based LB screening initiatives. Methods: Current work focuses on preferential extraction of low molecular weight nucleic acids and single-stranded library preparation and sequencing, Broad Range cell-free DNA- Sequencing (BRcfDNA-Seq), to obtain uscf- & 167 bps mononucleosomal cfDNA (mncfDNA), for disease detection. Sequencing data was bioinformatically processed: paired-end reads merged (BBmerge), aligned with BWA-mem, deduplicated using SRSLY UMI, and blacklisted portions removed. This was followed by analysis for non-mutational features like fragmentomics, occurrence of G-Quad structures, 4-mer Motif sequences at the end of cfDNA sequences, functional genomic element peak formation by uscfDNA. These features have been recently described non-mutation metrics which have demonstrated evidence of clinical relevance. With non-mutational multi-modal analysis of uscfDNA, obtained using BRcfDNA-Seq, we were able to categorize oral premalignant disorders into two cohorts: one which progressed into OCSCC (PG-OPL) (n=19) & one which did not (nPG-OPL) (n=19). Results: Bioinformatic processing for fragmentomics, of the relative proportion of 40-53bps fragments to 54-70bps fragments in the uscfDNA population demonstrated a significant difference across 1700 chromosomal bins between PG & nPG-OPL (multiple t test, FDR 10%, p value = 0.044, AUROC = 0.7258). The occurrence of G-quadruplex structures in uscfDNA was also significantly different between the two groups (student t test, p= 0.02, AUROC= 0.6953). Examination of the end-motifs profiles of uscfDNA between PG-OPL & nPG-OPL samples showed the randomness of the motifs as revealed by the Shannon entropy score is significant between the two groups (p = 0.046, AUROC = 0.6482). We further evaluated individual motifs that could discriminate between the two groups and identified CCCC, CCCT, TGGG, etc. are highly discriminatory. uscfDNA contained a substantially higher proportion of functional genomic element sequences in the PG-OPL group than the nPG-OPL group like exon (p = 0.00067, AUROC = 0.7147) & 5’ UTR (p= 0.000006, AUROC= 0.7673) are different between the groups. Conclusion: Results demonstrated clinical relevance of BRcfDNA-Seq for plasma-derived cfDNA in assessment of malignancy progression of OPLs. These non-mutational multi-modal features of cfDNA maybe indicative of local and systemic factors associated with disease affecting cfDNA. Citation Format: Neeti Swarup, Jordan C. Cheng, Irene Choi, Mohammad Arshad Aziz, Kelly Y. Liu, Fang Wei, Feng Li, Yong Kim, Catherine Poh, David T. Wong. Triaging oral premalignant lesions: Plasma ultrashort cell-free DNA-based liquid biopsy approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB102.

ZnO Nanocages Decorated with Au@AgAu Yolk-Shell Nanomaterials for SERS-Based Detection of Hyperuricemia.

Surface-enhanced Raman scattering (SERS) is widely recognized as a highly sensitive technology for chemical detection and biological sensing. In SERS-based biomedical applications, developing highly efficient sensing platforms based on SERS plays a pivotal role in monitoring disease biomarker levels and facilitating the early detection of cancer biomarkers. Hyperuricemia, characterized by abnormally high concentrations of uric acid (UA) in the blood, was associated with a range of diseases, such as gouty arthritis, heart disease, and acute kidney injury. Recent reports have demonstrated the correlation between UA concentrations in blood and tears. In this work, we report the fabrication of SERS substrates utilizing ZnO nanocages and yolk-shell-structured plasmonic nanomaterials for the noninvasive detection of UA in tears. This innovative SERS substrate enables noninvasive and sensitive detection of UA to prevent hyperuricemia-related diseases.

Downregulation of praja2 restrains endocytosis and boosts tyrosine kinase receptors in kidney cancer

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer in the adult population. Late diagnosis, resistance to therapeutics and recurrence of metastatic lesions account for the highest mortality rate among kidney cancer patients. Identifying novel biomarkers for early cancer detection and elucidating the mechanisms underlying ccRCC will provide clues to treat this aggressive malignant tumor. Here, we report that the ubiquitin ligase praja2 forms a complex with-and ubiquitylates the AP2 adapter complex, contributing to receptor endocytosis and clearance. In human RCC tissues and cells, downregulation of praja2 by oncogenic miRNAs (oncomiRs) and the proteasome markedly impairs endocytosis and clearance of the epidermal growth factor receptor (EGFR), and amplifies downstream mitogenic and proliferative signaling. Restoring praja2 levels in RCC cells downregulates EGFR, rewires cancer cell metabolism and ultimately inhibits tumor cell growth and metastasis. Accordingly, genetic ablation of praja2 in mice upregulates RTKs (i.e. EGFR and VEGFR) and induces epithelial and vascular alterations in the kidney tissue.In summary, our findings identify a regulatory loop between oncomiRs and the ubiquitin proteasome system that finely controls RTKs endocytosis and clearance, positively impacting mitogenic signaling and kidney cancer growth.

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.

Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

Advances and Challenges in Nanomaterial-Based Electrochemical Immunosensors for Small Cell Lung Cancer Biomarker Neuron-Specific Enolase.

Early and rapid detection of neuron-specific enolase (NSE) is highly significant, as it is putative biomarker for small-cell lung cancer as well as COVID-19. Electrochemical techniques have attracted substantial attention for the early detection of cancer biomarkers due to the important properties of simplicity, high sensitivity, specificity, low cost, and point-of-care detection. This work reviews the clinically relevant labeled and label-free electrochemical immunosensors developed so far for the analysis of NSE. The prevailing role of nanostructured materials as electrode matrices is thoroughly discussed. Subsequently, the key performances of various immunoassays are critically evaluated in terms of limit of detection, linear ranges, and incubation time for clinical translation. Electrochemical techniques coupled with screen-printed electrodes developing market level commercialization of NSE sensors is also discussed. Finally, the review concludes with the current challenges associated with available methods and provides a future outlook toward commercialization opportunities for easy detection of NSE.

CRISPR/Cas13a-mediated dual-modal biosensing platform based on the Zn0.5Cd0.5S/Ti3C2 Schottky heterojunction for the sensitive detection of miRNAs-21

MicroRNAs-21 (miRNAs-21), which promote tumor development and invasion by facilitating tumor cell proliferation and inhibiting apoptosis, is identified as a reliable biomarker for early cancer detection and prognosis. Herein, a CRISPR/Cas13a-mediated dual-modal biosensing platform was designed based on the Zn0.5Cd0.5S/Ti3C2 (ZCS/TC) Schottky heterojunction and implemented for monitoring miRNAs-21. Specifically, two-dimensional TC nanosheets with a large surface-to-volume ratio produced ample growth sites for the ZCS nanosphere. Moreover, the formation of Schottky heterojunctions at the ZCS/TC interface, validated by density functional theory calculations, can enhance their photoelectric conversion efficiency, providing a satisfactory initial signal for the subsequent implementation of a photoelectrochemical (PEC) biosensor. Furthermore, with the assistance of the MnO2-mediated CRISPR-Cas13a strategy, the dual-mode detection of miRNAs-21 was initiated via visualization and the use of the PEC biosensor. Under the optimized conditions, the linear range of the biosensor for detecting miRNAs-21 was 0.5 fM-1.0 nM and the detection limit was 0.21 fM. The developed platform can be used for the early detection and monitoring of various diseases, specifically cancer, and help create more research opportunities in this field.

Ultra-short cell-free DNA fragments enhance cancer early detection in a multi-analyte blood test combining mutation, protein and fragmentomics.

Cancer morbidity and mortality can be reduced if the cancer is detected early. Cell-free DNA (cfDNA) fragmentomics emerged as a novel epigenetic biomarker for early cancer detection, however, it is still at its infancy and requires technical improvement. We sought to apply a single-strand DNA sequencing technology, for measuring genetic and fragmentomic features of cfDNA and evaluate the performance in detecting multiple cancers. Blood samples of 364 patients from six cancer types (colorectal, esophageal, gastric, liver, lung, and ovarian cancers) and 675 healthy individuals were included in this study. Circulating tumor DNA mutations, cfDNA fragmentomic features and a set of protein biomarkers were assayed. Sensitivity and specificity were reported by cancer types and stages. Circular Ligation Amplification and sequencing (CLAmp-seq), a single-strand DNA sequencing technology, yielded a population of ultra-short fragments (<100 bp) than double-strand DNA preparation protocols and reveals a more significant size difference between cancer and healthy cfDNA fragments (25.84 bp vs. 16.05 bp). Analysis of the subnucleosomal peaks in ultra-short cfDNA fragments indicates that these peaks are regulatory element "footprints" and correlates with gene expression and cancer stages. At 98 % specificity, a prediction model using ctDNA mutations alone showed an overall sensitivity of 46 %; sensitivity reaches 60 % when protein is added, sensitivity further increases to 66 % when fragmentomics is also integrated. More improvements observed for samples representing earlier cancer stages than later ones. These results suggest synergistic properties of protein, genetic and fragmentomics features in the identification of early-stage cancers.

Breast cancer: miRNAs monitoring chemoresistance and systemic therapy.

With a high mortality rate that accounts for millions of cancer-related deaths each year, breast cancer is the second most common malignancy in women. Chemotherapy has significant potential in the prevention and spreading of breast cancer; however, drug resistance often hinders therapy in breast cancer patients. The identification and the use of novel molecular biomarkers, which can predict response to chemotherapy, might lead to tailoring breast cancer treatment. In this context, accumulating research has reported microRNAs (miRNAs) as potential biomarkers for early cancer detection, and are conducive to designing a more specific treatment plan by helping analyze drug resistance and sensitivity in breast cancer treatment. In this review, miRNAs are discussed in two alternative ways-as tumor suppressors to be used in miRNA replacement therapy to reduce oncogenesis and as oncomirs to lessen the translation of the target miRNA. Different miRNAs like miR-638, miR-17, miR-20b, miR-342, miR-484, miR-21, miR-24, miR-27, miR-23 and miR-200 are involved in the regulation of chemoresistance through diverse genetic targets. For instance, tumor-suppressing miRNAs like miR-342, miR-16, miR-214, and miR-128 and tumor-promoting miRNAs like miR101 and miR-106-25 cluster regulate the cell cycle, apoptosis, epithelial to mesenchymal transition and other pathways to impart breast cancer drug resistance. Hence, in this review, we have discussed the significance of miRNA biomarkers that could assist in providing novel therapeutic targets to overcome potential chemotherapy resistance to systemic therapy and further facilitate the design of tailored therapy for enhanced efficacy against breast cancer.

Pan-cancer detection and tissue-of-origin identification by whole-genome sequencing of plasma cell-free DNA.

3055 Background: Despite notable advances in cancer therapeutics, much of the mortality of human cancers results from late diagnosis. The early detection of cancer remains an unmet need that can benefit the effective treatments and better prognosis. Cell-free DNA (cfDNA) in the circulation provides an emerging diagnostic method for non-invasive cancer screening. The fragmentation signatures of cfDNA originating from different cell types or malignancies display distinct patterns, suggesting the potential as cancer biomarkers. This study aims to investigate the fragmentation profiles of cfDNA in several cancer types, and to develop a new approach for pan-cancer detection and tissue-of-origin identification. Methods: Plasma samples from 739 patients with lung (N=577), colorectal (N=98), breast (N=51) and liver (N=13) cancers, as well as 716 healthy individuals were collected in this study. Cell-free DNA was isolated and used to generate the whole-genome sequencing data. We developed an approach called PatternWGS (Pan-cancer detection and tissue-of-origin identification by whole-genome sequencing) to analyze the DNA fragmentation patterns comprehensively. Fragmentation features integrating cfDNA fragment size distribution, frequency of end motif sequence, and copy number variation were utilized to construct the optimal prediction model. We presented a multi-cancer detection model for distinguishing individuals with and without cancer using Generalized Linear Models (glmnet), and further built a cancer-origin model to predict cancer types using Random Forest algorithm. Training and testing occurred on randomly selected sampling splits of 60% and 40% of the data, respectively. Features selection and model construction were performed by only training cohort, and the test set was solely used for performance evaluation. Results: A larger proportion of shorter cfDNA fragments in cancers was observed. We proposed a fragment score indicating the degree of short fragmentation, and this fragment score was significantly higher in cancers (p&lt;0.001). Several end motifs were identified to be specifically enriched in cancer patients. The multi-cancer detection model reached an area under the curve (AUC) of 0.98, with the sensitivity of 93% at 95% specificity. The sensitivity of detecting lung, colorectal, breast, and liver cancers reached 94.2%, 87%, 100% and 100% respectively, at 95% specificity. The cancer-origin model demonstrated an overall accuracy of 81.7% for the identification of tissue of origin. Conclusions: Our prediction model demonstrated a superior performance for multi-cancer detection and cancer origin prediction, suggesting the potential of plasma cfDNA fragmentation signatures as non-invasive biomarkers for early cancer detection in clinical. The distinct fragmentation patterns in cancer provide new insight to understand the mechanism of tumorigenesis.

Circulating tumor DNA (ctDNA) methylation-based linkage regions to predict recurrence in early-stage lung cancer.

e20513 Background: Lung cancer is the most incident worldwide, and surveillance of recurrence remains a clinically unmet need. Non-invasive early detection is essential to improve prognosis of lung cancer. DNA methylation-based biomarkers for early cancer detection is promising. This study aims to investigate the methylation biomarkers of ctDNA that could predict the postoperative recurrence in early-stage lung cancer. Methods: The HM450K DNA methylation microarray data of lung adenocarcinoma (LUAD) (492 tumor tissues and 32 normal tissues) and lung squamous cell carcinoma (LUSC) (415 tumor tissues and 43 normal tissues) were downloaded from TCGA Data Portal ( https://portal.gdc.cancer.gov/ ), and healthy individuals were obtained from GEO database (656 whole blood from GSE40279) ( https://www.ncbi.nlm.nih.gov/geo/ ). After processing missing values with impute.knn function in CHAMP package, differences &gt; 0.1 between tumor tissues and control samples (normal tissues or whole blood), standard deviation (SD) &lt; 0.15 for tumor tissues and SD &lt; 0.1 for control samples were nominated as differentially methylated sites (DMS). The adjacent ( &lt; 250 bp) methylation sites were stitched into peaks. MCBs (methylation-correlated blocks) were defined as regions with more than or equal to 3 CpG sites within 100 bp and with DNA methylation sites correlation &gt; 0.5. Results: In TCGA LUAD and LUSC, 658 and 551 DMS were identified between tumor and normal tissues; 999 and 912 DMS were generated by comparing tumor tissues and whole blood. The intersection of DMS located in the methylation canyon created a 203 kbp DNA methylation panel, containing 2521 DMS and 1417 methylation peaks. 37 MCBs were detected in tumor tissues of 30 lung cancer with DNA methylation panel sequencing. In term of 37 MCBs, 4 MCBs located in SHOX2, PTGER4, RASSF1A and ARL8B were identified with significant differences (p &lt; 0.001) between plasma of 50 lung cancer patients and 50 healthy individuals. Furthermore, Real-time Quantitative PCR (qPCR) of the 4 MCB regions were performed in plasma of 30 lung cancer patients and 25 healthy individuals, and a cutoff of 0.414 distinguishing lung cancer from the healthy was calculated by the generalized linear model (GLM) based on the cycle threshold (CT) values of qPCR. The GLM incorporated the 4 MCBs showed robust performances in which the sensitivity, specificity and area under the curve (AUC) were 100%, 95% and 0.95, respectively. Postoperative plasma within 3 months were retrospectively collected from 32 lung cancer patients, and 30 patients with GLM scored less than 0.414 (Maximum: 0.327) had no recurrence within 1 year, while 2 patients with GLM scored more than 0.414 (0.480 and 0.557) were recurred within 1 year. Conclusions: The generalized linear model incorporated the 4 MCBs showed robust performances, and predicted the recurrence of early-stage lung cancer within 1 year after surgery.

Targeting altered glycosylation in secreted tumor glycoproteins for broad cancer detection.

There is an urgent need to develop new tumor biomarkers for early cancer detection, but the variability of tumor-derived antigens has been a limitation. Here we demonstrate a novel anti-Tn antibody microarray platform to detect Tn+glycoproteins, a near universal antigen in carcinoma-derived glycoproteins, for broad detection of cancer. The platform uses a specific recombinant IgG1 to the Tn antigen (CD175) as a capture reagent and a recombinant IgM to the Tn antigen as a detecting reagent. These reagents were validated by immunohistochemistry in recognizing the Tn antigen using hundreds of human tumor specimens. Using this approach, we could detect Tn+glycoproteins at subnanogram levels using cell lines and culture media, serum, and stool samples from mice engineered to express the Tn antigen in intestinal epithelial cells. The development of a general cancer detection platform using recombinant antibodies for detection of altered tumor glycoproteins expressing a unique antigen could have a significant impact on cancer detection and monitoring.

Metabolomic investigation of urinary extracellular vesicles for early detection and screening of lung cancer

Lung cancer is a prevalent cancer type worldwide that often remains asymptomatic in its early stages and is frequently diagnosed at an advanced stage with a poor prognosis due to the lack of effective diagnostic techniques and molecular biomarkers. However, emerging evidence suggests that extracellular vesicles (EVs) may promote lung cancer cell proliferation and metastasis, and modulate the anti-tumor immune response in lung cancer carcinogenesis, making them potential biomarkers for early cancer detection. To investigate the potential of urinary EVs for non-invasive detection and screening of patients at early stages, we studied metabolomic signatures of lung cancer. Specifically, we conducted metabolomic analysis of 102 EV samples and identified metabolome profiles of urinary EVs, including organic acids and derivatives, lipids and lipid-like molecules, organheterocyclic compounds, and benzenoids. Using machine learning with a random forest model, we screened for potential markers of lung cancer and identified a marker panel consisting of Kanzonol Z, Xanthosine, Nervonyl carnitine, and 3,4-Dihydroxybenzaldehyde, which exhibited a diagnostic potency of 96% for the testing cohort (AUC value). Importantly, this marker panel also demonstrated effective prediction for the validation set, with an AUC value of 84%, indicating the reliability of the marker screening process. Our findings suggest that the metabolomic analysis of urinary EVs provides a promising source of non-invasive markers for lung cancer diagnostics. We believe that the EV metabolic signatures could be used to develop clinical applications for the early detection and screening of lung cancer, potentially improving patient outcomes.

Abstract 6009: An end-to-end workflow for improved methylation detection

Abstract Introduction: DNA methylation at CpG nucleotide sites in eukaryotes is a key epigenetic mark that can help regulate gene expression. Specific changes in CpG methylation occur in many human cancers, making them a promising biomarker for early cancer detection. However, existing assays can be costly, lack specificity to regions of interest and often provide only semi-quantitative estimates of the methylation fraction. Here, we present a targeted methylome panel to decrease costs associated with next generation sequencing (NGS), methylation controls to calibrate quantitative assays and UMIs for accurate deduplication in low-diversity samples. Experimental Procedures: Genomic DNA (gDNA) was prepared for sequencing using the Twist Methylation Detection System consisting of enzymatic methylation conversion and hybrid capture using the Twist Human Methylome Panel. Twist’s synthetic CpG methylation level controls were spiked-in to gDNA and taken through the Twist Methylation Detection System to demonstrate their utility in calibrating methylation assays. Additionally, libraries were generated using cell free DNA (cfDNA) and either conventional or UMI-containing adapters to investigate the impact on quantitative detection and total unique coverage. Results: Using the Twist Human Methylome Panel at 150x raw coverage achieves uniform coverage with low off-bait Picard metrics. 6.59 million CpG sites were detected using a minimum depth of 10X. Target capture with the Human Methylome Panel allows for informative CpG calling of up to 82 samples on a single Illumina Novaseq S4 flowcell, compared to 3 samples per flowcell with a tradition whole-genome bisulfite sequencing (WGBS) assay. The Twist CpG methylation specific controls are constructed of 48 unique contrived sequences that contain a total of 8 different levels of methylation, ranging from 100% to 0%. Including these controls allows for quantitation of methylation levels in the experimental samples and qualification of the enzymatic conversion process. Conclusions: Our study leverages the Twist methylation detection portfolio to interrogate genome-wide methylation patterns for various applications. In combination, these tools can be used to design cost effective end-to-end assays. *For Research Use Only. Not for use in diagnostic procedures. Citation Format: Lydia Bonar, Kristin Butcher, Michael Bocek, Holly Corbitt, Bryan Hoglund, Cibelle Nassif, Patrick Cherry, Derek Murphy, Jean Challacombe, Esteban Toro. An end-to-end workflow for improved methylation detection. [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 6009.

Abstract 5908: Specific gut microbial community is associated with specific cancer types: A strategy for cancer detection and prevention

Abstract Background: Cancer is the second leading cause of death in the world. Unfortunately, survival rates for cancer patients have stayed poor for many decades. Thus, there is an unmet need to identify novel strategies for early cancer detection and prevention. Recent studies suggest that the gut microbiome including bacteria may have a role in cancer initiation and progression. These bacteria play a vital role in maintaining homeostasis in the body. An imbalance in the bacterial composition may cause diseases including cancer. This evidence led us to hypothesize that the changes in the composition of the gut microbiome can be used as a biomarker for early cancer detection. To test this hypothesis, we focused on analyzing the microbial community of colorectal and blood cancers, which have poor survival rates. Methods and Results: To analyze the association of cancer with the microbiome, we used a published microbiome dataset of leukemic and healthy individuals that were collected by sequencing stool samples (Oliver et al., 2022). We identified that specific bacteria are either abundant, such as Faecalibacterium (1.17 fold), or reduced, such as Clostridium (0.32 fold, p value=0.0029), in leukemia patients compared to healthy controls. We observed similar correlations by analyzing the published colorectal cancer data (Flemer et al., 2017) in which Prevotella increased by 3.75 fold and Escherichia/Shigella increased by 4.0 fold in colorectal cancer while Blautia is reduced compared to healthy controls. Faecalibacterium, which is abundant in leukemia, is reduced in colorectal cancer. Further, we found that treatment of leukemia patients normalized the abundance of these bacteria to the level of healthy patients. To improve analysis efficiency, we applied machine learning algorithms to predict the presence of cancer in patients based on the composition of bacteria, age, gender, and symptoms. Among the tested algorithms, the decision tree and Random Forest both had very high false-negative rates, with 45.5% and 40% respectively, as well as having low sensitivities, with 50% and 60% respectively. The support Vector Machine had a suboptimal false-negative rate of 28.6% and good sensitivity of 80%. Finally, the Neural network had a good false-negative rate of 16.7% and great sensitivity of 90%. Overall, our results suggest that different cancers have an abundance of specific bacteria that can be used as biomarkers for cancer detection. Conclusion: We demonstrate that the increased abundance of certain bacteria in the microbiome can be used as a biomarker for cancer detection. Our data set was small for this project, but the machine learning approaches can be used to further predict cancer These are clinically relevant findings as a microbiome test would be non-invasive and easily accessible that can be performed at a routine checkup. Citation Format: Ekansh Mittal, Andrew Oliver, Kenza El Alaoui, Carolyn Haunschild, Julio Avelar-Barragan, Laura F. Mendez Luque, Katrine Whiteson, Angela G. Fleischman. Specific gut microbial community is associated with specific cancer types: A strategy for cancer detection and prevention [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 5908.

HCC EV ECG score: An extracellular vesicle-based protein assay for detection of early-stage hepatocellular carcinoma.

The sensitivity of current surveillance methods for detecting early-stage hepatocellular carcinoma (HCC) is suboptimal. Extracellular vesicles (EVs) are promising circulating biomarkers for early cancer detection. In this study, we aim to develop an HCC EV-based surface protein assay for early detection of HCC. Tissue microarray was used to evaluate four potential HCC-associated protein markers. An HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, was developed and optimized for quantifying subpopulations of EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations ( E pCAM + CD63 + , C D147 + CD63 + , and G PC3 + CD63 + HCC EVs), was established for detecting early-stage HCC. A phase 2 biomarker study was conducted to evaluate the performance of ECG score in a training cohort ( n =106) and an independent validation cohort ( n =72).Overall, 99.7% of tissue microarray stained positive for at least one of the four HCC-associated protein markers (EpCAM, CD147, GPC3, and ASGPR1) that were subsequently validated in HCC EVs. In the training cohort, HCC EV ECG score demonstrated an area under the receiver operating curve (AUROC) of 0.95 (95% confidence interval [CI], 0.90-0.99) for distinguishing early-stage HCC from cirrhosis with a sensitivity of 91% and a specificity of 90%. The AUROCs of the HCC EV ECG score remained excellent in the validation cohort (0.93; 95% CI,0.87-0.99) and in the subgroups by etiology (viral: 0.95; 95% CI,0.90-1.00; nonviral: 0.94; 95% CI,0.88-0.99). HCC EV ECG score demonstrated great potential for detecting early-stage HCC. It could augment current surveillance methods and improve patients' outcomes.

Dual-Aptamer-Assisted Ratiometric SERS Biosensor for Ultrasensitive and Precise Identification of Breast Cancer Exosomes.

Due to the heterogeneity of breast cancer, its early accurate diagnosis remains a challenge. Exosomes carry abundant genetic materials and proteins and are ideal biomarkers for early cancer detection. Herein, a ratiometric surface-enhanced Raman scattering (SERS) biosensor for exosome detection was constructed using a regularly arranged Au@Ag nanoparticles/graphene oxide (Au@Ag NPs/GO) substrate with 4-nitrothiophenol (4-NTP) molecules as an internal standard. Aptamers of two overexpressed proteins (epithelial cell adhesion molecule and human epidermal growth factor receptor 2) were linked by a short complementary DNA with rhodamine X modified at the 3'-terminal to form V-shaped double-stranded DNA, which attached to the surface of Au@Ag NPs/GO substrate for the selective recognition of breast cancer cell-derived exosomes. In the presence of exosomes, a competitive reaction occurred, resulting in the formation of the V-shaped double-stranded DNA/exosomes complex, and the V-shaped double-stranded DNA separated from the SERS substrate. The SERS signal of rhodamine X on the V-shaped double-stranded DNA decreased with the concentration of exosomes increasing, whereas the SERS signal of 4-NTP on the substrate remained stable. The ratiometric SERS strategy provides huge electromagnetic enhancement and abundant DNA adsorbing sites on the GO layer, achieving a wide detection range of 2.7 × 102 to 2.7 × 108 particles/mL and an ultralow limit of detection down to 1.5 × 102 particles/mL, without the requirement of any nucleic acid amplification. Particularly, the proposed method has significant applications in early cancer diagnosis as it can accurately identify breast cancer cell-derived exosomes in clinical serum samples and can differentiate pancreatic cancer patients and healthy individuals.

Advances in biomarker discovery using circulating cell-free DNA for early detection of hepatocellular carcinoma.

The past several decades have witnessed unprecedented progress in basic and clinical cancer research, and our understanding of the molecular mechanisms and pathogenesis of cancers have been greatly improved. More recently, with the availability of high-throughput sequencing and profiling platforms as well as sophisticated analytical tools and high-performance computing capacity, there have been tremendous advances in the development of diagnostic approaches in clinical oncology, especially the discovery of novel biomarkers for cancer early detection. Although tissue biopsy-based pathology has been the "gold standard" for cancer diagnosis, notable limitations such as the risk due to invasiveness and the bias due to intra-tumoral heterogeneity have limited its broader applications in oncology (e.g., screening, regular disease monitoring). Liquid biopsy analysis that exploits the genetic and epigenetic information contained in DNA/RNA materials from body fluids, particularly circulating cell-free DNA (cfDNA) in the blood, has been an intriguing alternative approach because of advantageous features such as sampling convenience and minimal invasiveness. Taking advantage of innovative enabling technologies, cfDNA has been demonstrated for its clinical potential in cancer early detection, including hepatocellular carcinoma (HCC), the most common liver cancer that causes serious healthcare burden globally. Hereby, we reviewed the current advances in cfDNA-based approaches for cancer biomarker discovery, with a focus on recent findings of cfDNA-based early detection of HCC. Future clinical investigations and trials are warranted to further validate these approaches for early detection of HCC, which will contribute to more effective prevention, control, and intervention strategies with the ultimate goal of reducing HCC-associated mortality. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics.