Detection Of Early-stage Cancer Research Articles

Abstract IA023: Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers

Abstract Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr IA023.

Abstract P041: Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers

Abstract Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P041.

The Approach to Performance of Quality Upper Endoscopy in Lynch Syndrome (QUELS)

Strong evidence demonstrates the protective benefit of frequent colonoscopy surveillance for colorectal cancer prevention in Lynch Syndrome (LS) and is endorsed by many guidelines. Until recently, the evidence supporting the utility of upper endoscopy [esophagogastroduodenoscopy (EGD)] for upper gastrointestinal (UGI) cancer surveillance was limited. Over the last 3 years, multiple studies have demonstrated that EGD surveillance in LS is associated with the detection of both precancerous lesions and early-stage UGI cancers. On the basis of the emerging favorable evidence derived from EGD surveillance programs, the 2022 National Comprehensive Cancer Network (NCCN) Guidelines for LS recommend UGI surveillance with EGD starting between age 30 and 40 years with repeat EGDs every 2 to 4 years, preferably in conjunction with colonoscopy, in all patients with a germline pathogenic variant (PV) in MLH1, MSH2, EPCAM, and MSH6 and, because of the lack of data, consideration in PMS2. Standardization of the approach to performing EGD surveillance in LS and reporting clinically actionable findings is requisite for both improving quality and understanding the cost efficiency and outcomes of patients undergoing EGD as a surveillance tool. Accordingly, the primary objective of this Quality of Upper Endoscopy in Lynch Syndrome (QUELS) statement is to articulate a framework for standardizing the approach to performing and reporting EGD findings in patients with LS by introducing emerging quality metrics. The recommendations presented herein were developed from available evidence and consensus-based expert opinion and provide a practical approach for clinicians applying EGD surveillance in accordance with the most recent and existing LS guidelines.

Cervical Cancer Screening with DNA-HPV Testing and Precancerous Lesions Detection: A Brazilian Population-based Demonstration Study.

To evaluate the rates of precancerous lesions, colposcopy referral, and positive predictive value (PPV) by age groups of a population-based screening with DNA-HPV testing. The present demonstration study compared 16,384 HPV tests performed in the first 30 months of the program with 19,992 women tested in the cytology screening. The colposcopy referral rate and PPV for CIN2+ and CIN3+ by age group and screening program were compared. The statistical analysis used the chi-squared test and odds ratio (OR) with 95% confidence interval (95%CI). The HPV tests were 3.26% positive for HPV16-HPV18 and 9.92% positive for 12 other HPVs with a 3.7 times higher colposcopy referral rate than the cytology program, which had 1.68% abnormalities. Human Papillomavirus testing detected 103 CIN2, 89 CIN3, and one AIS, compared with 24 CIN2 and 54 CIN3 detected by cytology (p < 0.0001). The age group between 25 and 29 years old screened by HPV testing had 2.4 to 3.0 times more positivity, 13.0% colposcopy referral, twice more than women aged 30 to 39 years old (7.7%; p < 0.0001), and detected 20 CIN3 and 3 early-stage cancer versus 9 CIN3 and no cancer by cytology screening (CIN3 OR= 2.10; 95%CI: 0.91-5.25; p = 0.043). The PPV of colposcopy for CIN2+ ranged from 29.5 to 41.0% in the HPV testing program. There was a significant increase in detections of cervix precancerous lesions in a short period of screening with HPV testing. In women < 30 years old, the HPV testing exhibited more positivity, high colposcopy referral rate, similar colposcopy PPV to older women, and more detection of HSIL and early-stage cervical cancer.

Abstract PR006: Screening under-served populations by mobile low dose computerized tomography (LDCT) scans results in stage shift with potential cures: Time for a change in health policy?

Abstract Background: More than 60% of under-served patients with lung cancer first present with advanced/metastatic disease. Randomized trials have shown that LDCT of chest reduces lung cancer mortality but under-served populations have been excluded from these studies because of financial, geographic, insurance and access problems. We have tested the first mobile LDCT units and demonstrated improved access. We report a shift to earlier stage at diagnosis and potential cures in a large under-served population that includes African American, Hispanic/LatinX and Native American subjects. Methods: We developed two coaches fitted with portable 32 slice low-dose CT scanners to screen uninsured and under-served heavy smokers for lung cancer in an IRB-approved program ("The Oncologist", 2019). All films were reviewed by a central panel (using LUNG RADS protocol). Medicare patients were excluded because their insurance covers LDCT lung screening (but it should be noted that the elderly population is at increased risk for lung cancer, leading to a negative case selection bias). Results: We initially screened 1200 uninsured/under-insured subjects, mean age 61 years (range 55-64), with average pack year history of 47.8 (range 30- &amp;gt;150); 61% male; 18% Black, 3% Hispanic/LatinX, &amp;lt;1% Native American; 78% rural. We found 97 pts with LUNG RADS 4 (very high risk) lesions, 30 lung cancers (2.5%), including 18 at stage I-III treated with curative intent (60%); &amp;gt;50% had intercurrent cardiovascular disease and COPD seen on LDCT. We have additionally screened 574 cases, including 51 Native Americans (in a structured assessment of one tribe). Of the Native Americans, 5 had LUNG RADS 3-4 (high risk) lesions, but no cancers have been identified to date. For the total series of 1774 subjects with age-related negative case selection bias, 42 lung cancers have been identified (2.4%), 26 of which were treated with curative intent (62%) via surgery, radiation, or chemo-radiation. Only one patient of 18 treated with curative intent in the first series of 30 lung cancers has relapsed to date (median follow up 3 years, with 5 without evidence of disease beyond 3.5 years). These data confirm a shift to detection of early-stage lung cancer in this large under-served population, with an apparent potential for cure. Conclusion: Mobile LDCT yields a higher screening rate for under-served patients than prior hallmark trials, with a consequent shift to early-stage detection of lung cancer, with sustained treatment-induced complete remissions beyond 4 years. This approach could be applied to improve national lung cancer survival in the under-served. Citation Format: Derek Raghavan, Darcy Doege, Mellisa Wheeler, Kia Dungan, John Doty III, Glenn Hickman, Kathryn Mileham, Daniel Carrizosa. Screening under-served populations by mobile low dose computerized tomography (LDCT) scans results in stage shift with potential cures: Time for a change in health policy? [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr PR006.

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network.

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

Aptamer-functionalized two-photon SiO2@GQDs hybrid-based signal amplification strategy for targeted cancer imaging.

Targeted imaging is playing an increasingly important role in the early detection and precise diagnosis of cancer. This need has motivated research into sensory nanomaterials that can be constructed into imaging agents to serve as biosensors. Graphene quantum dots (GQDs) as a valuable nanoprobe show great potential for use in two-photon biological imaging. However, most as-prepared GQDs exhibit a low two-photon absorption cross-section, narrow spectral coverage, and "one-to-one" signal conversion mode, which greatly hamper their wide application in sensitive early-stage cancer detection. Herein, a versatile strategy has been employed to fabricate an aptamer Sgc8c-functionalized hybrid as a proof-of-concept of the signal amplification strategy for targeted cancer imaging. In this study, GQDs with two-photon imaging performance, and silica nanoparticles (SiO2 NPs) as nanocarriers to provide amplified recognition events by high loading of GQD signal tags, were adopted to construct a two-photon hybrid-based signal amplification strategy. Thus, the obtained hybrid (denoted SiO2@GQDs) enabled extremely strong fluorescence with a quantum yield up to 0.49, excellent photostability and biocompatibility, and enhanced bright two-photon fluorescence up to 2.7 times that of bare GQDs (excitation at 760 nm; emission at 512 nm). Moreover, further modification with aptamer Sgc8c showed little disruption to the structure of the SiO2@GQDs-hybrid and the corresponding two-photon emission. Hence, SiO2@GQDs-Sgc8c showed specific responses to target cells. Moreover, it could be used as a signal-amplifying two-photon nanoprobe for targeted cancer imaging with high specificity and great efficiency, which exhibits a distinct green fluorescence compared to that of GQDs-Sgc8c or SiO2@GQDs. This signal amplification strategy holds great potential for the accurate early diagnosis of tumors and offers new tools for the detection a wide variety of analytes in clinical application.

Additional Diffusion-Weighted Imaging with Background Body Signal Suppression (DWIBS) Improves Pre-Therapeutical Detection of Early-Stage (pT1a) Glottic Cancer: A Feasibility and Interobserver Reliability Study.

(1) Background: Early-stage glottic cancer is easily missed on magnetic resonance imaging (MRI). Diffusion-weighted imaging (DWI) may improve diagnostic accuracy. Therefore, our aim was to assess the value of adding diffusion-weighted imaging with background body signal suppression (DWIBS) to pre-therapeutic MRI staging. (2) Methods: Two radiologists with 8 and 13 years of experience, blinded to each other's findings, initially interpreted only standard MRI, later DWIBS alone, and afterward, standard MRI + DWIBS in 41 patients with histopathologically proven pT1a laryngeal cancer of the glottis. (3) Results: Detectability rates with standard MRI, DWIBS only, and standard MRI + DWIBS were 68-71%, 63-66%, and 73-76%, respectively. Moreover, interobserver reliability was calculated as good (κ = 0.712), very good (κ = 0.84), and good (κ = 0.69) for standard MRI, DWIBS only, and standard MRI + DWIBS, respectively. (4) Conclusions: Standard MRI, DWIBS alone, and standard MRI + DWIBS showed an encouraging detection rate, as well as distinct interobserver reliability in the diagnosis of early-stage laryngeal cancer when compared to the definitive histopathologic report.

An Overview on Electrochemical Approaches for the Detection of Alpha-Fetoprotein

Cancer biomarkers have a remarkable role for not only early-stage detection of cancer but also in the monitoring therapy efficacy. Alpha-fetoprotein (AFP) is one of the biomarkers which is highly expressed in hepatocellular carcinoma (HCC) patients. Therefore, a high level of AFP can be considered for early diagnosis of HCC. Electrochemical assays can be used as the miniaturized biosensors for real-time detection of the analytes which are mostly categorized into three groups for AFP detection including immunosensors, aptasensors, and molecularly imprinted polymer-based sensors. All of these techniques provide beneficial detection features of fast and cost-effective compared with other analytical methods. This review describes the most recent electrochemical platforms for AFP detection to provide a perspective for future studies.

Postoperative lung cancer surveillance: the highs and lows of computerized tomographic scanning.

Radiation dose reduction has been a topic of great interest in the last decade. In this study, Nakada and colleagues address a very important contributor of radiation exposure in today’s world, i.e. lung cancer surveillance [1]. Since the USPSTF’s recommendations on lung cancer screening, there has been an increase in the detection and treatment of early-stage lung cancers [2]. With the recent screening guidelines including younger patients, we are bound to see an increase in the pool of patients that are now exposed to intensive surveillance with computerized tomographic (CT) scans after lung cancer treatment. Nakada et al. make a case here to balance our surveillance strategy with the radiation exposure that we commit these patients to. They present a retrospective study of 416 patients who underwent lobectomy or segmentectomy for stage I non-small-cell lung cancer (NSCLC) and were surveilled with low-dose CT (LDCT) or conventional CT with a median follow-up time of 57 months. In the propensity score-matched analysis, they did not find a significant difference in the recurrence-free survival or the overall survival between the 2 groups.

Serum protein profiling of lung, pancreatic, and colorectal cancers reveals alcohol consumption-mediated disruptions in early-stage cancer detection

While the link between serum proteins and cancer has been studied in an effort to enable early-stage cancer detection, factors that might perturb this link has been poorly understood. To ask this question, we performed serum protein profiling on a prospective cohort of 601 individuals with or without lung, pancreatic, or colorectal cancers and identified ten distinct serum protein signatures with distinct link to the patient metadata. Importantly, we discovered that a positive history of alcohol consumption is a major factor that diminishes the sensitivity of serum protein-mediated liquid biopsy in early-stage malignancies, resulting in a 44% decline in the sensitivity of detecting American Joint Committee on Cancer (AJCC) stage I malignancies. Our data provide evidence that patient lifestyle can affect the sensitivity of liquid biopsy and suggest the potential need for abstinence from alcohol before measurement during serum protein-based cancer screening.

Evaluation of optical features of fibronectin fibrils by backscattering polarization imaging

Polarization imaging provides an effective and convenient approach for examining the structural and optical features of scattering media. The present study utilizes a backscattering polarization imaging system to evaluate and compare the polarization features of three different fibronectin (FN) samples: (1) fibronectin-free in phosphate-buffered saline (PBS) solution, (2) fibronectin protein (PFN), and (3) fibrillated fibronectin (FFN). It is shown that the three samples can be qualitatively discriminated via a simple naked-eye observation of the Mueller matrix imaging results. Furthermore, a more quantitative evaluation of the polarization properties of the three samples can be obtained from an inspection of the Mueller Matrix Transformation (MMT) parameters and Frequency Distribution Histogram (FDH) statistics for the pixel intensities of the Mueller matrix elements. The results show that the PFN and PBS samples have isotropic properties, while the FFN sample has anisotropic properties. Overall, the results confirm that the proposed backscattering polarization imaging system provides a feasible means of differentiating between different fibronectin-containing biological samples. It thus has significant potential for various biomedical diagnosis applications, including early-stage cancer detection.

Prediction of tissue-of-origin of early stage cancers using serum miRNomes.

Noninvasive detection of early stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively. A serum miRNA profile (miRNomes)-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7931 serum samples from patients with 13 types of solid cancers and 5013 noncancer samples. The validation set consisted of 1990 cancer and 1256 noncancer samples. The contribution of each miRNA to the cancer-type classification was evaluated, and those with a high contribution were identified. Cancer type was predicted with an accuracy of 0.88 (95% confidence interval [CI] = 0.87 to 0.90) in all stages and an accuracy of 0.90 (95% CI = 0.88 to 0.91) in resectable stages (stages 0-II). The F1 score for the discrimination of the 13 cancer types was 0.93. Optimal classification performance was achieved with at least 100 miRNAs that contributed the strongest to accurate prediction of cancer type. Assessment of tissue expression patterns of these miRNAs suggested that miRNAs secreted from the tumor environment could be used to establish cancer type-specific serum miRNomes. This study demonstrates that large-scale serum miRNomics in combination with machine learning could lead to the development of a blood-based cancer classification system. Further investigations of the regulating mechanisms of the miRNAs that contributed strongly to accurate prediction of cancer type could pave the way for the clinical use of circulating miRNA diagnostics.

Clinical Value of Growth Differentiation Factor 15 Detection in the Diagnosis of Early Liver Cancer Based on Data Mining.

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P < 0.01), there was a significant difference (P < 0.01) compared with adjacent tissue (P < 0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients.

LCSCNet: A multi-level approach for lung cancer stage classification using 3D dense convolutional neural networks with concurrent squeeze-and-excitation module

Lung cancer, the deadliest disease worldwide, poses a massive threat to humankind. Various researchers have designed Computer-Aided-Diagnosis systems for the early-stage detection of lung cancer. However, patients are primarily diagnosed in advanced stages when treatment becomes complicated and dependent on multiple factors like size, nature, location of the tumor, and proper cancer staging. TNM (Tumor, Node, and Metastasis) staging provides all this information. This study aims to develop a novel and efficient approach to classify lung cancer stages based on TNM standards. We propose a multi-level 3D deep convolutional neural network, LCSCNet (Lung Cancer Stage Classification Network). The proposed network architecture consists of three similar classifier networks to classify three labels, T, N, and M-labels. First, we pre-process the data, in which the CT images are augmented, and the label files are processed to get the corresponding TNM labels. For the classification network, we implement a dense convolutional neural network with a concurrent squeeze & excitation module and asymmetric convolutions for classifying each label separately. The overall stage is determined by combining all three labels. The concurrent squeeze & excitation module helps the network focus on the essential information of the image, due to which the classification performance is enhanced. The asymmetric convolutions are introduced to reduce the computation complexity of the network. Two publicly available datasets are used for this study. We achieved average accuracies of 96.23% for T-Stage, 97.63% for N-Stage, and 96.92% for M-Stage classification. Furthermore, an overall stage classification accuracy of 97% is achieved.

Multidimensional Cell-Free DNA Fragmentomic Assay for Detection of Early-Stage Lung Cancer.

Rationale: Cell-free DNA (cfDNA) analysis holds promise for early detection of lung cancer and benefits patients with higher survival. However, the detection sensitivity of previous cfDNA-based studies was still low to suffice for clinical use, especially for early-stage tumors. Objectives: Establish an accurate and affordable approach for early-stage lung cancer detection by integrating cfDNA fragmentomics and machine learning models. Methods: This study included 350 participants without cancer and 432 participants with cancer. The participants' plasma cfDNA samples were profiled by whole-genome sequencing. Multiple cfDNA features and machine learning models were compared in the training cohort to achieve an optimal model. Model performance was evaluated in three validation cohorts. Measurements and Main Results: A stacked ensemble model integrating five cfDNA features and five machine learning algorithms constructed in the training cohort (cancer: 113; healthy: 113) outperformed all the models built on individual feature-algorithm combinations. This integrated model yielded superior sensitivities of 91.4% at 95.7% specificity for cohort validation I (area under the curve [AUC], 0.984), 84.7% at 98.6% specificity for validation II (AUC, 0.987), and 92.5% at 94.2% specificity for additional validation (AUC, 0.974), respectively. The model's high performance remained consistent when sequencing depth was down to 0.5× (AUC, 0.966-0.971). Furthermore, our model is sensitive to identifying early pathological features (83.2% sensitivity for stage I, 85.0% sensitivity for <1 cm tumor at the 0.66 cutoff). Conclusions: We have established a stacked ensemble model using cfDNA fragmentomics features and achieved superior sensitivity for detecting early-stage lung cancer, which could promote early diagnosis and benefit more patients.

PSMA1-mediated ultrasmall gold nanoparticles facilitate tumor targeting and MR/CT/NIRF multimodal detection of early-stage prostate cancer

Prostate-specific membrane antigen (PSMA) is a prominent biomarker for prostate cancer (PCa) diagnosis. Safe contrast agents able to render the expression and distribution of PSMA would facilitate early accurate screening and prognostic prediction of PCa. However, current Gd-containing nanoparticles are often limited by nonspecific redistribution in mononuclear phagocyte system (MPS) and inadequate perfusion to target sites. Besides, intrinsic defects of magnetic resonance (MR) equipment also hamper their use for precisely depicting PSMA details. Herein, we devised a novel noninvasive MR/CT/NIRF multimodal contrast agent (AGGP) coordinated to a high-affinity PSMA ligand (PSMA1) to specifically detect and quantify PSMA expression in PCa lesions, which exhibited formidable tripe-modal signal augments, preferential PSMA targeting, effective MPS escaping and profitable renal-clearable behavior in living mice. Biocompatibility and histopathological studies substantiated high security of AGGP in vivo, opening the door to future opportunities for improving early-stage PCa detection and clinical implementation of more effective multifunctional nanotherapeutics.

Peptide-based optical biosensors: A promising approach for early-stage cancer detection

Cancer is the leading cause of 10 million deaths worldwide in 2020, which is exponentially increasing per year. The early detection and effective treatment of cancer can save millions of lives worldwide. Cancer diagnostic methods are scarce due to their high cost, laborious instrumentation, high maintenance, time-consuming, and complex nature. Furthermore, current cancer detection methods are also inconvenient and necessitate the use of technical experts. However, peptide-based optical biosensors are fast, and compact with a low detection limit and high selectivity, hence having a lot of potential for diagnosing different types of cancer. The present work aims to provide an overview of various types of peptide-based optical biosensors (OBS) for the early-stage detection of cancer. Here, the peptides with high specificity, biocompatibility, small size, and numerous conjugation possibilities incorporated with nanomaterials, are reported for the fabrication of optical biosensors to diagnose cancer. Peptide-based optical biosensors are known to overcome the limitations of the conventional method by providing high sensitivity, and portability, with rapid and real-time detection of all types of cancer cells. Moreover, in comparison to conventional methods, cancer can be detected in a quite few million tumor tissues using optical biosensors. This review mainly focused on the optical biosensors such as colorimetric, fluorescence, surface plasmon resonance (SPR), chemiluminescence (CL), and photoelectrochemical derived from the different types of peptides, including their working principle and conjugation with peptides. We have also discussed the advantages and disadvantages of current peptide-based optical biosensors for early-stage cancer detection, as well as future prospects.

Hydroxymethylation profile of cell-free DNA is a biomarker for early colorectal cancer

Early detection of cancer will improve survival rates. The blood biomarker 5-hydroxymethylcytosine has been shown to discriminate cancer. In a large covariate-controlled study of over two thousand individual blood samples, we created, tested and explored the properties of a 5-hydroxymethylcytosine-based classifier to detect colorectal cancer (CRC). In an independent validation sample set, the classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (95% CI [87, 93]). Sensitivity was 55% at 95% specificity. Performance was similar for early stage 1 (AUC 89%; 95% CI [83, 94]) and late stage 4 CRC (AUC 94%; 95% CI [89, 98]). The classifier could detect CRC even when the proportion of tumor DNA in blood was undetectable by other methods. Expanding the classifier to include information about cell-free DNA fragment size and abundance across the genome led to gains in sensitivity (63% at 95% specificity), with similar overall performance (AUC 91%; 95% CI [89, 94]). We confirm that 5-hydroxymethylcytosine can be used to detect CRC, even in early-stage disease. Therefore, the inclusion of 5-hydroxymethylcytosine in multianalyte testing could improve sensitivity for the detection of early-stage cancer.

10P Detection of early-stage lung cancer using 5-hydroxymethylcytosine signatures in circulating cell-free DNA

10P Detection of early-stage lung cancer using 5-hydroxymethylcytosine signatures in circulating cell-free DNA