Detection Of Early-stage Cancer Research Articles

Resectability versus Operability in Early-Stage Non-Small Cell Lung Cancer.

With increased detection of early-stage non-small cell lung cancer (NSCLC) owing to screening, determining optimal management increasingly hinges on assessing resectability and operability. Resectability refers to the feasibility of achieving microscopically negative margins based on tumour size, location and degree of local invasion and achieving an anatomical lobar resection. Operability reflects the patient's tolerance for resection based on comorbidities, cardiopulmonary reserve and frailty. Standardized criteria help guide these assessments, but application variability contributes to practice inconsistencies. This review synthesizes a strategic approach to evaluating resectability and operability in contemporary practice. Standardization promises reduced care variability and optimized patient selection to maximize curative outcomes in this new era of early detection. Recent pivotal trials demonstrate equivalency of sublobar resection to lobectomy for small, peripheral, node-negative NSCLC, expanding options for parenchymal preservation in borderline surgical candidates. Furthermore, recent phase 3 trials have highlighted the benefit of chemoimmunotherapy as a neoadjuvant treatment with an excellent pathological response and a down staging of the tumour, improving the resectability of the early-stage NSCLC. A good assessment of the operability and resectability is paramount in order to offer the best course of treatment for our patients. European and American societies have issued recommendations to help clinicians assess the cardiopulmonary function and predict the extension of pulmonary resection that could afford the patient. This operability assessment is closely linked with the evaluated tumour resectability which will determine the extension of pulmonary resection that is needed for the patient in order to achieve a good oncological outcome. Some major progresses have been done recently to improve the operability and resectability of patients. For instance, prehabilitation program allows better postoperative morbidity. Some studies have shown a potential good oncological outcome with sublobar resection expending access to surgery for patient with reduced lung function. Some others have identified the neoadjuvant immunochemotherapy as a potential solution for downstaging tumours. Work-up of early-stage NSCLC is a key moment and has to be done thoroughly and in full knowledge of the recent findings in order to propose the most appropriate treatment for the patient.

Comprehensive Analysis of microRNA Methylation Profiles and Determination of Their Functional Significance in Colorectal Cancer: A Study Protocol

Colorectal cancer (CRC) is a leading cause of cancer-associated fatalities globally. Despite the notable progress in diagnostics and patient care, challenges persist in areas such as early detection, prognostic variable identification, metastatic disease treatment, and personalised treatment options. In CRC, microRNAs, a type of short non-coding RNA, are found to be deregulated and can significantly influence its onset and progression. However, previous microRNA research primarily focused on expression levels to ascertain their biological significance, leaving the microRNA methylation landscapes in CRC patients largely unexplored. This study aims to investigate the genome-wide methylome profiles of microRNA and clarify their roles in CRC. We will analyse the microRNA methylation profiles from our in-house data of more than 100 CRCs and correlate the differentially methylated microRNAs with clinicopathological features. To understand the biological significance of methylated microRNAs, we will perform pathway enrichment analysis and molecular dynamics simulations to examine the binding of argonaut protein to the differentially methylated microRNAs and the structural changes involved. Furthermore, we will conduct functional studies to determine the roles of selected microRNAs. CRC cell lines with hypermethylated microRNA of interest will be treated with a demethylating agent, followed by cell-based assays. Subsequent transcriptome-wide microRNA gene target identification and protein profiling will be performed to interrogate the molecular pathways affected by demethylation of the microRNA of interest. Methylation signifies a major transformation in cancer detection, as widespread epigenetic changes could potentially provide superior early-stage cancer detection and classification compared to somatic mutations. Effective CRC diagnosis ensures timely and appropriate treatment for patients, thereby improving their quality of life. The insights gained from this study could be applied to individualized health diagnostics, disease prognostication, and treatment monitoring.

An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately. The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model.

A non-invasive method for concurrent detection of multiple early-stage cancers in women

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.

Development of a blood-based extracellular vesicle classifier for detection of early-stage pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) has an overall 5-year survival rate of just 12.5% and thus is among the leading causes of cancer deaths. When detected at early stages, PDAC survival rates improve substantially. Testing high-risk patients can increase early-stage cancer detection; however, currently available liquid biopsy approaches lack high sensitivity and may not be easily accessible.MethodsExtracellular vesicles (EVs) were isolated from blood plasma that was collected from a training set of 650 patients (105 PDAC stages I and II, 545 controls). EV proteins were analyzed using a machine learning approach to determine which were the most informative to develop a classifier for early-stage PDAC. The classifier was tested on a validation cohort of 113 patients (30 PDAC stages I and II, 83 controls).ResultsThe training set demonstrates an AUC of 0.971 (95% CI = 0.953–0.986) with 93.3% sensitivity (95% CI: 86.9–96.7) at 91.0% specificity (95% CI: 88.3–93.1). The trained classifier is validated using an independent cohort (30 stage I and II cases, 83 controls) and achieves a sensitivity of 90.0% and a specificity of 92.8%.ConclusionsLiquid biopsy using EVs may provide unique or complementary information that improves early PDAC and other cancer detection. EV protein determinations herein demonstrate that the AC Electrokinetics (ACE) method of EV enrichment provides early-stage detection of cancer distinct from normal or pancreatitis controls.

Air-brush spray coated Ti3C2-MXene-graphene nanohybrid thin film based electrochemical biosensor for cancer biomarker detection

Cancer is a significant health hazard worldwide and poses a greater threat to the quality of human life. Quantifying cancer biomarkers with high sensitivity has demonstrated considerable potential for compelling, quick, cost-effective, and minimally invasive early-stage cancer detection. In line with this, efforts have been made towards developing an f-graphene@Ti3C2-MXene nanohybrid thin-film-based electrochemical biosensing platform for efficient carcinoembryonic antigen (CEA) detection. The air-brush spray coating technique has been utilized for depositing the uniform thin films of amine functionalized graphene (f-graphene) and Ti3C2-MXene nanohybrid on ITO-coated glass substrate. The chemical bonding and morphological studies of the deposited nanohybrid thin films are characterized by advanced analytical tools, including XRD, XPS, and FESEM. The EDC-NHS chemistry is employed to immobilize the deposited thin films with monoclonal anti-CEA antibodies, followed by blocking the non-specific binding sites with BSA. The electrochemical response and optimization of biosensing parameters have been conducted using CV and DPV techniques. The optimized BSA/anti-CEA/f-graphene@Ti3C2-MXene immunoelectrode showed the ability to detect CEA biomarker from 0.01 pg mL−1 to 2000 ng mL−1 having a considerably lower detection limit of 0.30 pg mL−1.

MicroRNA expression as a diagnostic parameter in early endometrial cancer

ObjectivesMicroRNAs (miRNAs) have emerged as biomarkers that showed strong diagnostic potential in various diseases, including cancer. This study aimed to estimate the expression and diagnostic potential of miRNAs (miR-200a, miR-21,...

NIR-II Fluorescence Imaging for the Detection and Resection of Cancerous Foci and Lymph Nodes in Early-Stage Orthotopic and Advanced-Stage Metastatic Ovarian Cancer Models.

The high mortality rate of ovarian cancer can be primarily attributed to late diagnosis and early lymph node (LN) metastasis. The anatomically deep-located ovaries own intricate anatomical structures and lymphatic drainages that compromise the resolution and sensitivity of near-infrared first-window (NIR-I) fluorescence imaging. Reported NIR-II imaging studies of ovarian cancer focused on late-stage metastasis detection via the intraperitoneal xenograft model. However, given the significant improvement in patient survival associated with early-stage cancer detection, locating tumors that are restricted within the ovary is equally crucial. We obtained the polymer nanoparticles with bright near-infrared-II fluorescence (NIR-II NPs) by nanoprecipitation of DSPE-PEG, one of the ingredients of FDA-approved nanoparticle products, and benzobisthiadiazole, an organic NIR-II dye. The one-step synthesis and safe component lay the groundwork for its clinical translation. Benefiting from the NIR-II emission (∼1060 nm), NIR-II NPs enabled a high signal-to-noise (S/N) ratio (13.4) visualization of early-stage orthotopic ovarian tumors with NIR-II fluorescence imaging for the first time. Imaging with orthotopic xenograft allows a more accurate mimic of human ovarian cancer origin, thereby addressing the dilemma of translating existing nanoprobe preclinical research by providing the nano-bio interactions with early local tumor environments. After PEGylation, the desirable-sized probe (∼80 nm) exhibited high lymphophilicity and relatively extended circulation. NIR-II NPs maintained their accurate detection of orthotopic tumors, tumor-regional LNs, and minuscule (<1 mm) disseminated peritoneal metastases simultaneously (with S/N ratios all above 5) in mice with advanced-stage cancer in real time ∼36 h after systematic delivery. With NIR-II fluorescence guidance, we achieved accurate surgical staging in tumor-bearing mice and complete tumor removal comparable to clinical practice, which provides preclinical data for translating NIR-II fluorescence image-guided surgery.

Detection of early-stage cancers using circulating orphan non-coding RNAs in blood.

3051 Background: Orphan non-coding RNAs (oncRNAs) are a novel category of small RNAs (smRNAs) that are present in tumors and largely absent in healthy tissue. We investigated the utility of oncRNAs extracted from serum for early cancer detection across seven cancer types. Methods: We collected 2,882 serum samples from individuals with known cancers of the bladder ( n=152), breast (220), colon and rectum (141), kidney (283), lung (281), pancreas (287), and stomach (280) as well as donors with no history of cancer (1,238). We used 0.5 mL serum aliquots to generate and sequence smRNA libraries at an average depth of 20 million 50-bp single-end reads. Samples were split into age-, sex-, and smoking status-matched training (1,232 cancer; 922 control) and validation (412 cancer; 316 control) cohorts. A large catalog of oncRNAs specific to each cancer was created using tumor and adjacent normal samples from The Cancer Genome Atlas (TCGA) smRNA-seq database. Using TCGA-derived oncRNAs, we trained a machine learning model to predict cancer presence and tissue of origin (TOO) in a 5-fold cross validation setup using our training cohort. For the validation cohort, we averaged the predictions from the five training cohort models. Results: The model ROC-AUC for detecting cancer was 0.95 (95% CI: 0.94–0.95 for training and 0.94–0.97 for validation cohorts). Sensitivities for detecting cancer at 95% specificity were 0.74 (0.70–0.76) for early stage (I/II) and 0.80 (0.76–0.84) for late stage (III/IV) cancers in the training cohort, and 0.77 (0.71–0.81) and 0.81 (0.73–0.87) in the validation cohort. Sensitivities of detection for each cancer type are shown. For samples with cancer and TOO predictions, our top 1 and top 2 TOO accuracy was 0.76 (0.68–0.84) and 0.83 (0.76–0.90) for the validation set. Conclusions: These results demonstrate that oncRNAs detected in serum can be used for accurate, early detection, and localization of multiple cancers. [Table: see text]

A novel three-biomarker signature (CA-62, CEA, CYFRA 21-1) and early-stage NSC lung cancer detection.

3038 Background: A double-blind clinical study was conducted on 304 clinically verified blood serum samples, including 141 non-small cell lung cancer (NSCLC), 133 healthy volunteers and 30 patients with chronic obstructive pulmonary disease (COPD). The objectives of the study were: The assessment of various cancer markers and their possible combinations for detection of early (I-II) stages of non-small cell lung cancer (NSCLC) and evaluation of the best cancer markers panel as a pre-screening tool for NSCLC before Low-Dose CT scan. Methods: Quantitative measurement of various tumor markers for serum samples was carried out using electrochemiluminescent immunoassays Elecsys CA-125, ELECSYS CA 19-9, ELECSYS CYFRA 21-1 and ELECSYS SCC (COBAS, Roche Diagnostics GmbH, Germany, EU), enzyme immunoassay CA15-3-ELISA-BEST, CEA-ELISA-BEST, NSE-ELISA-BEST (JSC "VECTOR-BEST", RF) and immunochemiluminescent analysis CLIA-CA-62 (LLC "JVS Diagnostics", RF). The results were compared to the literature data for other biomarkers and their panels. Results: Unlike other tumor markers, which are expressed proportionally to tumor size, the marker for epithelial carcinomas CA-62 is expressed from Stage I and demonstrated the highest diagnostic results in detecting early stages (I-II) of NSCLC. The biomarkers CYFRA 21-1 and SCC do not have sufficient sensitivity and specificity to diagnose asymptomatic lung cancer. However, the addition of cytokeratin 19 CYFRA 21-1 to glycoproteins CA-62 and CEA increases the specificity by eliminating false positive results, which significantly improves the diagnostic value of the cancer marker signature (CA-62, CEA and CYFRA 21-1): 100% Specificity, 93% Sensitivity, 100% PPV, 94% NPV and 94% test accuracy. Conclusions: The results of the study demonstrated that using the biomarkers signature (CA-62, CEA and CYFRA 21-1) allows increasing the specificity of CT scan for patients with suspicious abnormalities on the tomogram, improving the interpretation of visualized localized lesion, and improving the accuracy of differential diagnosis at detecting early stages of LC up to 94%. In the prospective, adding cancer markers panel signature (CA-62, CEA and CYFRA 21-1) to the current lung cancer risk assessment system as a pre-screening tool for LDCT may improve the quality of early-stage Lung cancer detection by significant increasing the sensitivity and by reducing the proportion of false positive results. [Table: see text]

Multi-dimensional cell-free DNA-based liquid biopsy and early detection of gastric cancer.

4060 Background: Gastric cancer is one of the most common cancer types. Most patients were diagnosed at advanced stages and experienced poor prognosis. A non-invasive assay for the detection of early-stage gastric cancer is highly desirable for reducing the gastric cancer-associated mortality. Methods: We prospectively collected a study cohort of 249 participants including 110 pathologically confirmed with stage I-II gastric cancer and 139 with non-cancerous conditions. A plasma sample was collected from each patient before conventional screening modalities for gastric cancer. We performed whole-genome sequencing with plasma samples and profiled four types of cell-free DNA (cfDNA) characteristics, fragment size pattern, copy number variation, nucleosome coverage pattern, and single nucleotide substitution. We used these differential profiles to develop an ensemble model to identify gastric cancer patients from non-cancer individuals. We further collected a validation cohort consisting of 73 gastric cancer patients and 94 non-cancer individuals to validate the cfDNA-based assay. Additionally, we compared the performance of our assay and conventional gastroscopy in a hypothetical 100,000 screening population by Monte Carlo simulation. Results: The liquid biopsy assay that incorporated four types of cfDNA characteristics was able to identify early-stage gastric cancer patients from non-cancer individuals at an AUROC of 0.962 in the study cohort and 0.972 in the validation cohort. At a specificity of 92.1% (128/139), it achieved a sensitivity of 88.2% (97/110) in the study cohort. With this threshold, in the validation cohort, 91.5% (86/94) of healthy individuals and 91.8% (67/73) of gastric cancer patients were correctly identified. In both study and validation cohorts, the inferred probabilities of cancer showed consistent trends to increase with pathological stages in the cancer group and disease statuses in the non-cancer group. Of note, our approach detected all gastric tumors located in the cardia and fundus (100.0%, 19/19), which could be challenging for gastroscopic examination. Additionally, through in-silico simulations, we showed that our cfDNA-based non-invasive assay may detect 96.3% more gastric cancer cases than conventional gastroscopy owing to higher sensitivity and anticipated better participant compliance in a large hypothetical screening population. Conclusions: We introduced a liquid biopsy assay using multiple dimensions of cfDNA characteristics that could accurately identify early-stage gastric cancer from non-cancerous conditions. As a cost-effective non-invasive approach, it may provide population-wide benefits for the early detection of gastric cancer.

Abstract 1030: Development of a genome-wide methylome enrichment platform for multi-cancer early detection (MCED)

Abstract Background: Plasma cell-free DNA (cfDNA) tests represent a promising approach for cancer screening. cfDNA methylome approaches are well-suited for MCED; however, different methodologies vary in performance and many tests show decreased performance for early-stage or low-shedding tumors. Here we present a retrospective case-control study evaluating the performance of a novel genome-wide methylome enrichment platform for MCED. Methods: The full cohort (N=4,322) includes cancer cases (individuals with newly diagnosed treatment-naïve cancer) and age- and gender-matched non-cancer controls. Samples were analyzed with a bisulfite-free, non-degradative genome-wide DNA methylation enrichment platform using cfDNA isolated from plasma. Samples were split into distinct sets to train and test a machine learning classifier consisting of differentially methylated regions to distinguish cases from controls. Initial training in 1,536 samples across 8 cancer types is reported here (100 iterations of random splits (80:20) for cross-validation). The area under the receiver operating characteristic curves (AUC) and 95% confidence intervals for the median probabilities are reported in the cross-validation. Results: Cancer cases were distinguished from controls with an AUC of 0.94 (0.93, 0.96), with AUCs for individual cancer types ranging from 0.91 to 0.97. The AUC was 0.94 (0.92, 0.95) for stage I/II cancers and 0.95 (0.94, 0.96) for stage III/IV cancers. The AUC was 0.92 (0.91, 0.94) in the subset of cancers typically considered low shedding, with similar performance for stage I/II (0.91; 0.89, 0.93) and stage III/IV (0.93; 0.91, 0.95) in that subset. Conclusions: Initial analysis of case-control data demonstrates feasibility of a genome-wide methylome enrichment platform for MCED. The high detection of low-shedding and early-stage cancers is promising for MCED applications, as this will be critical for screening to identify cancers for which treatment may be more effective. Overall AUC (95% CI) for the full cohort and by cancer type. Cancer Type All Stages* Stage I/II Stage III/IV All Cancers N 931 461 437 AUC (95% CI) 0.94 (0.93, 0.96) 0.94 (0.92, 0.95) 0.95 (0.94, 0.96) Bladder Cancer** N 75 52 16 AUC (95% CI) 0.93 (0.91, 0.96) 0.93 (0.90, 0.96) 0.97 (0.95, 0.99) Breast Cancer** N 131 76 37 AUC (95% CI) 0.94 (0.91, 0.96) 0.92 (0.88, 0.95) 0.96 (0.94, 0.98) Colorectal Cancer N 182 94 88 AUC (95% CI) 0.97 (0.96, 0.98) 0.97 (0.96, 0.98) 0.97 (0.96, 0.99) Head &amp; Neck Cancer N 75 20 55 AUC (95% CI) 0.96 (0.94, 0.98) 0.93 (0.86, 1) 0.97 (0.96, 0.99) Lung Cancer N 147 72 75 AUC (95% CI) 0.96 (0.95, 0.98) 0.96 (0.95, 0.98) 0.96 (0.95, 0.98) Ovarian Cancer N 46 12 34 AUC (95% CI) 0.96 (0.93, 0.98) 0.98 (0.96, 0.99) 0.95 (0.92, 0.98) Prostate Cancer** N 145 84 59 AUC (95% CI) 0.91 (0.88, 0.93) 0.92 (0.89, 0.95) 0.90 (0.87, 0.93) Renal Cancer** N 130 51 73 AUC (95% CI) 0.91 (0.89, 0.94) 0.89 (0.84, 0.94) 0.93 (0.89, 0.96) *Samples without stage information are included in this category **Typically considered low-shedding tumor Citation Format: Ben H. Park, Shu Yi Shen, Jun Min, Neil Fleshner, Jennifer Knox, Taymaa May, Laurie Ailles, Yulia Newton, Junjun Zhang, Rajat Singhania, Morgan Weichert, Justin Burgener, Iulia Cirlan, Jing Zhang, Yarong Wang, Eduardo Sosa, Angelica Polio, Owen Hall, Sarah Goldberg, Peter Mazzone, Brian Rini, Scott Bratman, Brian Allen, Krystal Brown, Abel Licon, Anne-Renee Hartman, Daniel D. De Carvalho, Geoffrey Liu. Development of a genome-wide methylome enrichment platform for multi-cancer early detection (MCED) [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 1030.

European guidelines for the surgical management of pure ground-glass opacities and part-solid nodules: Task Force of the European Association of Cardio-Thoracic Surgery and the European Society of Thoracic Surgeons.

Pulmonary nodules are often encountered by respiratory physicians, radiologists, and thoracic surgeons alike. The European Society of Thoracic Surgery (ESTS) and European Association of Cardiothoracic Surgery (EACTS) established a multidisciplinary collaboration of clinicians with expertise in managing pulmonary nodules with the aim of producing the first joint comprehensive review of the scientific literature, with a specific focus on the management of pure ground glass opacities and part-solid nodules. The scope of the document has been defined by the EACTS and ESTS governing bodies and focuses on six areas of main interest agreed upon by the Task Force. These include: management of solitary and multiple pure ground glass nodules, solitary part-solid nodules, identifying the non-palpable lesion, the role of minimally invasive surgery and the decision making behind sub-lobar versus lobar resection. The literature revealed that with the increasing use of incidental CT scans and CT lung cancer screening programs, the detection of early-stage lung cancer is going to rise with a higher number of potential cancers presenting on the ground glass and part-solid nodule spectrum. Given that the gold standard for improved survival is surgical resection, there is an urgent need for comprehensive characterisation of these nodules and guidelines that are directed towards their surgical management. It is recommended that standard decision-making tools are used to determine the risk of malignancy and thus guide referral for surgical management and decisions for surgical resection are made in a multidisciplinary setting with equitable consideration given to radiological characteristics, the evolution of the lesion, presence of solid component, patient fitness and co-morbidities. Given the recent surge in robust level I data comparing sublobar and lobar resection with the release of JCOG0802 and CALGB140503 data, a global overview of an individual case must be adopted into clinical practice. This set of recommendations are based on the available literature; however, close collaboration in the design and conduct of randomized controlled trials is still of utmost importance to answer further questions in this rapidly evolving field.

Novel positron emission tomography imaging targeting cell surface glycans for pancreatic cancer: 18 F-labeled rBC2LCN lectin.

Advancement in early detection modalities will greatly improve the overall prognoses of pancreatic ductal adenocarcinoma (PDAC). For this purpose, we report a novel class of tumor-specific probes for positron emission tomography (PET) based on targeting cell surface glycans. The PDAC-targeting ability of rBC2LCN lectin, combined with fluorine-18 (18 F) ([18 F]FB-rBC2LCN), resulted in reproducible, high-contrast PET imaging of tumors in a PDAC xenograft mouse model. [18 F]N-succinimidyl-4-fluorobenzoate ([18 F]SFB) was conjugated to rBC2LCN, and [18 F]FB-rBC2LCN was successfully prepared with a radiochemical purity >95%. Cell binding and uptake revealed that [18 F]FB-rBC2LCN binds to H-type-3-positive Capan-1 pancreatic cancer cells. As early as 60 min after [18 F]FB-rBC2LCN (0.34 ± 0.15 MBq) injection into the tail vein of nude mice subcutaneously bearing Capan-1 tumors, tumor uptake was high (6.6 ± 1.8 %ID/g), and the uptake increased over time (8.8 ± 1.9 %ID/g and 11 ± 3.2 %ID/g at 150 and 240 min after injection, respectively). Tumor-to-muscle ratios increased over time, up to 19 ± 1.8 at 360 min. High-contrast PET imaging of tumors relative to background muscle was also achieved as early as 60 min after injection of [18 F]FB-rBC2LCN (0.66 ± 0.12 MBq) and continued to increase up to 240 min. Our 18 F-labeled rBC2LCN lectin warrants further clinical development to improve the accuracy and sensitivity of early-stage pancreatic cancer detection.

National Cancer Institute's early detection research network: a model organization for biomarker research

For many cancers a primary cause of poor survival is that they are detected at a late stage when therapies are less effective. Although screening methods exist to detect some types of cancer at an early stage, there are currently no effective methods to screen for most types of cancer. Biomarkers have the potential to improve detection of early-stage cancers, risk stratification, and prediction of which pre-cancerous lesions are likely to progress and to make screening tests less invasive. Although thousands of research articles on biomarkers for early detection are published every year, few of these biomarkers have been validated and shown to be clinically useful. This reflects both the inherent difficulty in detecting early-stage cancers and a disconnect between the process of discovering biomarkers and their use in the clinic. To overcome this limitation the US National Cancer Institute created the Early Detection Research Network. It is a highly collaborative program that brings together biomarker discoverers, assay developers, and clinicians. It provides an infrastructure that is essential for developing and validating biomarkers and imaging methods for early cancer detection and has successfully completed several multicenter validation studies.

Accurate detection of early-stage lung cancer using a panel of circulating cell-free DNA methylation biomarkers

BackgroundLung 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.MethodsThis 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 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 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.ResultsWe identified 7 DMRs corresponding to 7 differentially methylated genes (DMGs) including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1 that were highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissue. 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 0.97 (95%CI: 0.93-1.00)/0.96 (0.92-1.00), sensitivities of 0.89 (0.82–0.95)/0.92 (0.86–0.98), specificities of 0.94 (0.89–0.99)/1.00 (1.00–1.00), and accuracies of 0.90 (0.84–0.96)/0.94 (0.89–0.99) 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 0.94 (0.86-1.00), sensitivity of 0.81 (0.73–0.88), specificity of 0.98 (0.95-1.00), and accuracy of 0.93 (0.89–0.98).ConclusionThe 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.Graphical abstract

Abstract 3331: Highly sensitive blood-based multi-cancer screening device with tiered specificity based on diagnostic workflow

Abstract Background. Multi-cancer blood-based tests may yield clinical benefit by improving compliance to guideline recommended screening with a more patient-friendly modality, and also by detection of early (stage I/II) tumors in cancer types that lack screening tests, yet early intervention can save lives. A single test with clinically meaningful performance which addresses both opportunities has yet to be developed. Methods. We evaluated a blood-based multi-modal device based on cfDNA epigenomic and targeted protein analysis that enables high performance for early-stage cancer detection in colon, lung, bladder, gastric, liver, ovarian, and pancreas cancers. Blood samples were obtained from multiple case-control cohorts of individuals with colorectal (N &amp;gt; 2,000), lung (N &amp;gt; 300), and other solid tumor cancers (bladder, gastric, liver, ovarian, pancreas (N &amp;gt; 300)) as well as individuals without cancer (N &amp;gt; 3,000). The assay is based on Guardant Shield™ blood-based CRC screening test and the bioinformatic pipeline is augmented with a multi-cancer screening caller for the detection of additional cancers. Sensitivity for CRC and lung cancer detection is calculated at 90% target specificity thresholds, due to availability of guideline recommended screening tests, colonoscopy and LDCT scan to adjudicate blood-based test results. Specificity for all other cancers were targeted at an overall specificity of 98%. The specificity thresholds for CRC, lung, and multi-cancer are selected to yield assay performance tailored for the cancer type and clinical diagnostic pathway. Results. In this study cohort, this integrated, single device, multi-cancer test yielded CRC sensitivity of 91% (stage I/II: 93%) and lung cancer sensitivity of 85% (stage I/II: 75%) at 90% specificity. The overall sensitivity in bladder, gastric, liver, ovarian, and pancreas cancers was 75% (stage I/II: 66%) at 98% overall specificity. Overall prevalence-adjusted sensitivity of this device in the above 7 cancers were 79% overall sensitivity (stage I/II: 78%). This blood-based test could detect 25% of the expected cancer diagnoses in 2022 according to SEER estimates. Conclusions. This highly-sensitive, integrated, blood-based cancer screening device yields performance on par with currently available screening tests for cancers with screening guidelines (CRC and lung) and clinically meaningful early-stage detection in cancer types without screening guidelines where early intervention can bring clinical benefit, highlighting the ability of this technology to yield clinically meaningful results for the detection of early stage cancer. The performance of this device is under investigation in prospective screening trials. Citation Format: Yupeng He, Anton Valouev, Liyang Xiong, William W. Young Greenwald, Victoria M. Raymond, Sven Duenwald, AmirAli Talasaz. Highly sensitive blood-based multi-cancer screening device with tiered specificity based on diagnostic workflow [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 3331.

Abstract 5434: Machine-learning-based epigenetic detection of early-stage lung cancers using the EpiCheck liquid biopsy platform

Abstract Alterations in DNA methylation are one of the earliest, most common signatures of cancer development, making them ideal biomarkers for early detection. Methylation profiling of plasma cell-free DNA (cfDNA) has significant potential to expand the use of liquid biopsies to cancer screening. The EpiCheck platform combines methylation-sensitive restriction endonuclease (MSRE) digestion with whole genome sequencing to comprehensively map genome-wide DNA methylation changes with high fidelity. In this proof-of-concept study, MSRE-NGS was used to interrogate a liquid biopsy atlas focused on early-stage lung cancers to i) identify informative NGS methylation biomarkers in plasma, and ii) develop a machine learning method to discriminate between cancer and high-risk, non-cancer controls. The EpiCheck lung cancer atlas was constructed using biospecimens from academic (UBC, Vanderbilt, Cleveland Clinic) and commercial biobanks, and from a prospective multi-center study (NCT04968548). Plasma samples were collected from 93 high-risk primary lung cancer patients (62% stage I) and 90 high-risk individuals without cancer. Extracted DNA was digested with methylation-sensitive endonucleases and sequenced at an average depth of 600x. Methylation levels of ~6 million genomic loci were rank ordered using Student's t-test. Gene set enrichment analysis (GSEA) was performed on the top ranking 1000 differentially methylated loci. A logistic regression classifier with Lasso regularization was trained on 100,000 loci, and performance was examined by mean AUC using 5-fold cross-validation. A total of 23,130 loci exhibited significant differential methylation patterns between cancers and controls (p&amp;lt;0.01, FDR corrected). Of these, 20,159 and 2,971 lung cancer loci were hypermethylated and hypomethylated, respectively. Biological characterization using GSEA identified enrichments in transcriptional regulation and developmental control. In particular, loci were enriched for Polycomb Repressive Complex regulated genes, suggesting a possible connection to abnormal epigenetic regulation via histone modification in lung cancer. Construction of a machine learning logistic regression model based on the five training folds utilized 224 loci on average, and achieved a mean cross-validation AUC of 0.93 when distinguishing plasma cancer cases vs controls. Our findings demonstrate that the MSRE-NGS EpiCheck platform identified putative biomarkers within the plasma methylome for detecting early-stage lung cancer. A machine learning model trained on methylation targets performed with high accuracy in discriminating lung cancer patients from high-risk healthy individuals. Additional studies are required for defining the strength of the approach and for validating its use in non-invasive lung cancer screening. Citation Format: Dvir Netanely, Stephen Lam, Anna McGuire, Stephen Deppen, Eric Grogan, Fabien Maldonado, Michael Gieske, Joseph Seaman, Kimberly Rieger-Christ, Satish Kalanjeri, Luis Herrera, Nichole Tanner, Garrett B. Sherwood, Orna Savin, Shacade Danan, Sarah Zaouch, Nimrod Axelrad, Revital Knirsh, Ofir Shliefer, Keren Manor, Radha Duttagupta, Aharona Shuali, Peter J. Mazzone, Gerard A. Silvestri, Catherine A. Schnabel, Danny Frumkin, Adam Wasserstrom. Machine-learning-based epigenetic detection of early-stage lung cancers using the EpiCheck liquid biopsy platform. [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 5434.

Abstract 6510: REM-DREAMing: Low-cost digital microfluidic analysis of DNA methylation heterogeneity for enhanced, liquid biopsy-based detection of early-stage lung cancer

Abstract We report the results of a study exploring the ability to exploit molecular heterogeneity in DNA methylation for improving the performance of liquid biopsy-based screening for early-stage (I &amp; II) non-small cell lung cancer (NSCLC). Annual low-dose CT (LDCT) screening is currently recommended for adults aged 50 or older who are at high-risk of developing lung cancer. While this approach has resulted in improvements in survival, the false positive rate of lung nodules detection by LDCT remains over 95%, leading to unnecessary invasive follow-up procedures and further points to the need for new, complementary methods to improve diagnostics performance and reduce patient risk. DNA methylation biomarkers have demonstrated considerable potential as tumor-specific biomarkers for blood-based detection of early-stage NSCLC. Nonetheless, cell-free DNA (cfDNA) assessment techniques, such as methylation-specific PCR (MSP) or bisulfite sequencing, have limited sensitivity to assess epigenetic heterogeneity of rare epiallelic variants in a cost-effective manner. Here we reported a new platform, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which provides a simple, low-cost solution for multiplexed assessment of loci-specific DNA methylation heterogeneity at single molecule sensitivity. The microfluidic nanoarray contains four independent but identical 10,040 nanowell modules. Methylation biomarkers are differentiated by a ratiometric fluorescence scheme and the assessment of individual epiallele species of each locus are achieved through digitization in the nanoarray and precise high-resolution melt (HRM) analysis. In this study, we explore the potential utility of REM-DREAMing as a complementary assay for improving LDCT screening of NSCLC by testing a cohort of 48 clinical samples (28 cancer and 20 control samples) of low-volume liquid biopsy specimens from patients with CT-scan indeterminant pulmonary nodules. A machine learning algorithm incorporating logistic regression models with leave-one-out cross validation was developed to identify a proper methylation density threshold of each biomarker in the panel. Evaluation of the receiver operation characteristic (ROC) curve yielded an area under the curve (AUC) of 0.97 (95% CI, 0.94-1) with 93% sensitivity at 95% specificity for the REM-DREAMing assay, compared with 93% sensitivity at 62% specificity achieved using a traditional, MSP-based approach. These results suggest that the assessment of intermolecular epigenetic heterogeneity can provide superior clinical performance for cfDNA methylation and noninvasive detection of early-stage NSCLC, in particular. Our results warrant further investigation in a larger sample cohort to validate its utility for improving routine screening of NSCLC in high-risk populations. Citation Format: Yang Zhao, Christine M. O’Keefe, James G. Herman, Thomas R. Pisanic, Tza-Huei Wang. REM-DREAMing: Low-cost digital microfluidic analysis of DNA methylation heterogeneity for enhanced, liquid biopsy-based detection of early-stage lung cancer [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 6510.

Abstract 3330: Development of a genome-wide multiomic atlas of early-stage lung cancer enables identification of novel methylation biomarkers for disease detection beyond TCGA

Abstract Lung cancer is the leading cause of cancer mortality, mainly due to diagnosis at advanced stages. Early detection reduces mortality and screening by CT is recommended for a high-risk population, however, uptake is low. Biomarkers for early-stage detection may enhance screening. Liquid biopsy and blood circulating tumor DNA (ctDNA) are established plasma surrogates of tumor tissue but their use in early detection remains challenging due to the high noise level of current techniques. We constructed a novel multiomic atlas through whole genome sequencing (WGS) to map all genetic and epigenetic changes associated with early-stage lung cancer as a tool to identify markers. Biospecimens from 48 early-stage lung cancer cases (58% stage I) and 29 cancer-free controls, all high-risk by USPSTF, were acquired from academic (UBC, Vanderbilt, Cleveland Clinic) and commercial biobanks. Cases had tumor and normal lung tissue, whole blood (WB) and plasma samples. Controls had WB and plasma samples. EpiCheck sequencing (ECS) which combines methylation-sensitive restriction endonuclease digestion to detect differential methylation with WGS for multiomic analyses was performed on each sample. Extracted DNA was digested, underwent standard library preparation and sequenced at an average depth of 600x for cfDNA and 80x for tissue and WB. A subset also underwent WG bisulfite sequencing (BS) and standard WGS for comparison. Data analysis was performed using customized software. ECS outperformed BS. Mapping rate was 99.6%, 99.7% and 85.7% and unique mapping rate was 94.1%, 94.3% and 81.4% for WGS, ECS, and BS samples, respectively. Copy number integrity showed Pearson correlations of 0.9 for ECS and 0.67 for BS. Somatic mutation analysis in tissue identified a subset of cases with relatively high ctDNA shedding in plasma that were associated with larger tumors, older age and squamous cell carcinoma histology. This subset was further used to identify tumor derived plasma-based markers and assess fragmentation with high confidence. Shorter ctDNA fragments were observed in some, but not all cases. Additional analysis of WB and plasma identified host derived blood-based markers using methylation and copy number. This orthogonal approach enabled detection of small, low shedding tumors. A discovery panel of 87 markers showed 100% sensitivity and 97% specificity to discriminate cases vs controls. Only 11% of these markers are represented in the TCGA data. ECS generates high integrity sequencing data superior to BS, enabling genome-wide multiomic analyses (methylation, mutation, copy number, fragmentomics), with methylation signatures beyond the scope of TCGA. Results, which require validation, underscore the potential of the EpiCheck lung cancer atlas as a development platform of novel blood biomarkers for early-stage lung cancer detection. Citation Format: Revital Knirsh, Stephen Lam, Anna McGuire, Peter J. Mazzone, Stephen Deppen, Eric Grogan, Fabien Maldonado, Orna Savin, Shacade Danan, Sarah Zaouch, Nimrod Axelrad, Dvir Netanely, Aharona Shuali, Catherine A. Schnabel, Adam Wasserstrom, Danny Frumkin. Development of a genome-wide multiomic atlas of early-stage lung cancer enables identification of novel methylation biomarkers for disease detection beyond TCGA [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 3330.