Non-invasive Cancer Diagnosis Research Articles

Exploring a Desirable Quadr-mRNAs Panel for Non-invasive and Ultrasensitive Bladder Cancer Diagnosis: In-silico and Clinical Studies.

Most patients with non-muscle invasive bladder cancer (NMIBC) have a high direction for recurrence and disease progression, which remains a significant unresolved challenge in bladder cancer patients. Therefore, a constant search is necessary for identifying appropriate and reliable biomarkers for early diagnosis of NMIBC. The current study has aimed to search for valuable diagnostic biomarkers in the tissue and urine specimens of NMIBC patients. The changes of twelve candidate mRNAs in a screening phase (40 tissue samples of NMIBC patients and their corresponding 40 urine specimens) and a subsequent independent validation phase (40 urine specimens) were estimated using real-time polymerase chain reaction (RT-qPCR). The receiver operating characteristic (ROC) analysis was executed to determine the potential diagnostic values of mRNAs. The mRNA levels of seven candidate genes were markedly higher in tissue specimens relative to their neighboring tissues. Among them, four mRNAs, including ERBB2, CCND1, MKI67, and MAGEA6, were differentially expressed in urine samples of NMIBC patients relative to control subjects. Further, the expression of these four mRNAs was validated in the validation step. Combining these biomarkers showed better diagnostic performance than single biomarkers in the urine sample for non-invasive NMIBC detection. The combination of these mRNAs and cytology enhanced the sensitivity of cytology from 37% to 87%. Our findings suggested that a four-mRNA panel may be promising in the non-invasive diagnosis of NMIBC, which deserves further investigation.

Exploring a Desirable Quadr-mRNAs Panel for Non-invasive and Ultrasensitive Bladder Cancer Diagnosis: In-silico and Clinical Studies.

Most patients with non-muscle invasive bladder cancer (NMIBC) have a high direction for recurrence and disease progression, which remains a significant unresolved challenge in bladder cancer patients. Therefore, a constant search is necessary for identifying appropriate and reliable biomarkers for early diagnosis of NMIBC. The current study has aimed to search for valuable diagnostic biomarkers in the tissue and urine specimens of NMIBC patients. The changes of twelve candidate mRNAs in a screening phase (40 tissue samples of NMIBC patients and their corresponding 40 urine specimens) and a subsequent independent validation phase (40 urine specimens) were estimated using real-time polymerase chain reaction (RT-qPCR). The receiver operating characteristic (ROC) analysis was executed to determine the potential diagnostic values of mRNAs. The mRNA levels of seven candidate genes were markedly higher in tissue specimens relative to their neighboring tissues. Among them, four mRNAs, including ERBB2, CCND1, MKI67, and MAGEA6, were differentially expressed in urine samples of NMIBC patients relative to control subjects. Further, the expression of these four mRNAs was validated in the validation step. Combining these biomarkers showed better diagnostic performance than single biomarkers in the urine sample for non-invasive NMIBC detection. The combination of these mRNAs and cytology enhanced the sensitivity of cytology from 37% to 87%. Our findings suggested that a four-mRNA panel may be promising in the non-invasive diagnosis of NMIBC, which deserves further investigation.

The Role of Biomarkers in HPV-Positive Head and Neck Squamous Cell Carcinoma: Towards Precision Medicine.

Head and neck cancer (HNC) represents a significant global health challenge, with squamous cell carcinomas (SCCs) accounting for approximately 90% of all HNC cases. These malignancies, collectively referred to as head and neck squamous cell carcinoma (HNSCC), originate from the mucosal epithelium lining the larynx, pharynx, and oral cavity. The primary risk factors associated with HNSCC in economically disadvantaged nations have been chronic alcohol consumption and tobacco use. However, in more affluent countries, the landscape of HNSCC has shifted with the identification of human papillomavirus (HPV) infection, particularly HPV-16, as a major risk factor, especially among nonsmokers. Understanding the evolving risk factors and the distinct biological behaviors of HPV-positive and HPV-negative HNSCC is critical for developing targeted treatment strategies and improving patient outcomes in this complex and diverse group of cancers. Accurate diagnosis of HPV-positive HNSCC is essential for developing a comprehensive model that integrates the molecular characteristics, immune microenvironment, and clinical outcomes. The aim of this comprehensive review was to summarize the current knowledge and advances in the identification of DNA, RNA, and protein biomarkers in bodily fluids and tissues that have introduced new possibilities for minimally or non-invasive cancer diagnosis, monitoring, and assessment of therapeutic responses.

The Role of Biomarkers in HPV-Positive Head and Neck Squamous Cell Carcinoma: Towards Precision Medicine

Head and neck cancer (HNC) represents a significant global health challenge, with squamous cell carcinomas (SCCs) accounting for approximately 90% of all HNC cases. These malignancies, collectively referred to as head and neck squamous cell carcinoma (HNSCC), originate from the mucosal epithelium lining the larynx, pharynx, and oral cavity. The primary risk factors associated with HNSCC in economically disadvantaged nations have been chronic alcohol consumption and tobacco use. However, in more affluent countries, the landscape of HNSCC has shifted with the identification of human papillomavirus (HPV) infection, particularly HPV-16, as a major risk factor, especially among nonsmokers. Understanding the evolving risk factors and the distinct biological behaviors of HPV-positive and HPV-negative HNSCC is critical for developing targeted treatment strategies and improving patient outcomes in this complex and diverse group of cancers. Accurate diagnosis of HPV-positive HNSCC is essential for developing a comprehensive model that integrates the molecular characteristics, immune microenvironment, and clinical outcomes. The aim of this comprehensive review was to summarize the current knowledge and advances in the identification of DNA, RNA, and protein biomarkers in bodily fluids and tissues that have introduced new possibilities for minimally or non-invasive cancer diagnosis, monitoring, and assessment of therapeutic responses.

Integrated microfluidic chip with a MIP-gated organic electrochemical transistor for continuous, selective, and label-free quantification of tumor marker

Early diagnosis of cancer is crucial to effective treatment of the patients and to higher survival rates. Tumor marker detection as an effective and noninvasive cancer diagnosis method has important clinical significance. N1, N12-diacetylspermine (DAS) is a new tumor marker. The development of its detection methods is an urgent need for medical diagnosis and human health monitoring. The conventional detection methods of DAS are labor-intensive and time-consuming approaches. In this work, a novel electrochemical detection platform was established by integrating an organic electrochemical transistor (OECT) with a microfluidic chip and applied for trace measurement of DAS. The sensing unit is a gate electrode in the OECT modified with molecularly imprinted polymers (MIPs), which can be used as an artificial receptor to realize label-free detection. The high specific recognition ability of MIPs films combined with the amplified function of an OECT yielded a highly sensitive and selective electrochemical detection chip. The integrated microfluidic channel controlledthe exact amount of solution to be delivered and provided real-time detection of DAS. The experimental results indicated that changes in channel current was proportional to DAS concentrations from 1 nM to 10 μM, with the detection limits of 0.8 nM. It is the first demonstration of the integration of a microfluidic system with a MIP-gated OECT for real-time tumor marker detection. The study results of this work helped to develop Point-Of-Care (POC) tools for the early diagnosis of lung cancer, colon cancer and breast cancer.

A pathway for detection of gastric cancer biomarkers via using a layer-by-layer coated D-shaped grinding long-period fiber grating sensor

Gastric cancer significantly contributes to global cancer mortality, often leading to inoperable stages and high recurrence rates post-surgery. Elevated levels of G-17 and G-gly have been identified as potential risk factors, particularly in patients with duodenal ulcers. This study introduces an innovative D-shaped grinding long-period fiber grating sensor (D-LLPFGs) designed for non-invasive detection of the gastrin G-17 antigen, employing a layer-by-layer chemical self-assembly to bond G-17 antibodies onto the fiber surface through hydrogen bonding. The D-LLPFGs sensor demonstrated significant spectral shifts within one minute of antigen-antibody interaction, highlighting its rapid detection capability. At an optimized antibody concentration of 4 μg/ml, antigen testing across different concentrations (10, 12.5, 20, 50 μg/ml) showed peak changes at 12.5 μg/ml antigen, with a 1.186 nm shift and 0.503 dB loss. The sensor exhibited a wavelength sensitivity of 0.095 nm/μg/ml, indicating its high sensitivity and potential in gastric cancer classification, diagnosis, and treatment. This research concludes that the D-shaped fiber sensor is an effective and sensitive tool for detecting G-17 antigen levels, presenting a significant advancement in non-invasive gastric cancer diagnosis. Its quick response time and high sensitivity highlight its potential for broad biomedical applications, offering a new avenue for early cancer detection and improving patient prognosis. The success of this study opens the door to further exploration and implementation of fiber optic sensors in clinical settings, marking a significant step forward in the fight against gastric cancer.

Comprehensive Urinary Proteome Profiling Analysis Identifies Diagnosis and Relapse Surveillance Biomarkers for Bladder Cancer.

Bladder cancer (BCa) is the predominant malignancy of the urinary system. Herein, a comprehensive urine proteomic feature was initially established for the noninvasive diagnosis and recurrence monitoring of bladder cancer. 279 cases (63 primary BCa, 87 nontumor controls (NT), 73 relapsed BCa (BCR), and 56 nonrelapsed BCa (BCNR)) were collected to screen urinary protein biomarkers. 4761 and 3668 proteins were qualified and quantified by DDA and sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis in two discovery sets, respectively. Upregulated proteins were validated by multiple reaction monitoring (MRM) in two independent combined sets. Using the multi-support vector machine-recursive feature elimination (mSVM-RFE) algorithm, a model comprising 13 proteins exhibited good performance between BCa and NT with an AUC of 0.821 (95% CI: 0.675-0.967), 90.9% sensitivity (95% CI: 72.7-100%), and 73.3% specificity (95% CI: 53.3-93.3%) in the diagnosis test set. Meanwhile, an 11-marker classifier significantly distinguished BCR from BCNR with 75.0% sensitivity (95% CI: 50.0-100%), 81.8% specificity (95% CI: 54.5-100%), and an AUC of 0.784 (95% CI: 0.609-0.959) in the test cohort for relapse surveillance. Notably, six proteins (SPR, AK1, CD2AP, ADGRF1, GMPS, and C8A) of 24 markers were newly reported. This paper reveals novel urinary protein biomarkers for BCa and offers new theoretical insights into the pathogenesis of bladder cancer (data identifier PXD044896).

Buoyant Metal-Organic Framework Corona-Driven Fast Isolation and Ultrasensitive Profiling of Circulating Extracellular Vesicles.

Accurately assaying tumor-derived circulating extracellular vesicles (EVs) is fundamental in noninvasive cancer diagnosis and therapeutic monitoring but limited by challenges in efficient EV isolation and profiling. Here, we report a bioinspired buoyancy-driven metal-organic framework (MOF) corona that leverages on-bubble coordination and dual-encoded surface-enhanced Raman scattering (SERS) nanotags to streamline rapid isolation and ultrasensitive profiling of plasma EVs in a single assay for cancer diagnostics. This integrated bubble-MOF-SERS EV assay (IBMsv) allows barnacle-like high-density adhesion of MOFs on a self-floating bubble surface to enable fast isolation (2 min, near 90% capture efficiency) of tumor EVs via enhanced EV-MOF binding. Also, IBMsv harnesses four-plexed SERS nanotags to profile the captured EV surface protein markers at a single-particle level. Such a sensitive assay allows multiplexed profiling of EVs across five cancer types, revealing heterogeneous EV surface expression patterns. Furthermore, the IBMsv assay enables cancer diagnosis in a pilot clinical cohort (n = 55) with accuracies >95%, improves discrimination between cancer and noncancer patients via an algorithm, and monitors the surgical treatment response from hepatocellular carcinoma patients. This assay provides a fast, sensitive, streamlined, multiplexed, and portable blood test tool to enable cancer diagnosis and response monitoring in clinical settings.

Fibronectin-Targeting Dual-Modal MR/NIRF Imaging Contrast Agents for Diagnosis of Gastric Cancer and Peritoneal Metastasis.

The prevalence and fatality rates of gastric cancer (GC) remain elevated, with advanced stages presenting a grim prognosis. Noninvasive diagnosis of GC cancer often proves challenging until the disease has progressed to an advanced stage or metastasized. Initially, the level of fibronectin (FN) in cancer-associated fibroblasts (CAFs) of GC was at least 3.7 times higher than that in normal fibroblasts. Herein, two FN-targeting magnetic resonance/near-infrared fluorescence (MR/NIRF) imaging contrast agents were developed to detect GC and peritoneal metastasis noninvasively. The probes CREKA-Cy7-(Gd-DOTA) and CREKA-Cy7-(Gd-DOTA)3 demonstrated significant FN-targeting capability (with dissociation constants of 1.0 and 2.1 mM) and effective MR imaging performance (with proton relaxivity values of 9.66 and 27.44 mM-1 s-1 at 9.4 T, 37 °C). In vivo imaging revealed a high signal-to-noise ratio and successful visualization of GC metastasis using NIRF imaging as well as successful tumor detection in MR imaging. Therefore, this study highlights the potential of FN-targeting probes for GC diagnosis and aids in the advancement of new diagnostic strategies for the clinical detection of GC.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography

Objective. Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising. Approach. In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs). Main results. We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape. Significance. In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

Circulating tumor DNA in liquid biopsy: Current diagnostic limitation.

With the rapid development of science and technology, cell-free DNA (cfDNA) is rapidly becoming an important biomarker for tumor diagnosis, monitoring and prognosis, and this cfDNA-based liquid biopsy technology has great potential to become an important part of precision medicine. cfDNA is the total amount of free DNA in the systemic circulation, including DNA fragments derived from tumor cells and all other somatic cells. Tumor cells release fragments of DNA into the bloodstream, and this source of cfDNA is called circulating tumor DNA (ctDNA). cfDNA detection has become a major focus in the field of tumor research in recent years, which provides a new opportunity for non-invasive diagnosis and prognosis of cancer. In this paper, we discuss the limitations of the study on the origin and dynamics analysis of ctDNA, and how to solve these problems in the future. Although the future faces major challenges, it also contains great potential.

Potential application of body fluids autofluorescence in the non-invasive diagnosis of endometrial cancer.

Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions. This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.

Available medical imaging modalities for melanoma screening

The prevalence of melanoma of the skin has seen a significant rise in recent decades, constituting approximately one-third of all diagnosed cancer cases. Melanoma, the most fatal variant among cutaneous malignancies, exhibits a 4% probability of occurrence over an individual’s lifetime. The increasing incidence and mortality rates of skin cancer impose a substantial burden on healthcare resources and the economy. In recent years, several optical modalities, including dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography, multiphoton excited fluorescence imaging, and dermatofluorescence, have been extensively studied and utilized to improve the non-invasive diagnosis of skin cancer. This review article provides an analysis of the approach employed in the recently developed optical non-invasive diagnostic technologies. It explores the clinical uses of these techniques, while also examining their respective advantages and disadvantages. Furthermore, the paper explores the possibility for additional advancements in these technologies in the future.

Electrical Properties of Human Lung Nodules In Vitro From 100 Hz to 100 MHz.

The incidence of pulmonary nodules has been increasing over the past 30 years. Different types of nodules are associated with varying degrees of malignancy, and they engender inconsistent treatment approaches. Therefore, correct distinction is essential for the optimal treatment and recovery of the patients. The commonly-used medical imaging methods have limitations in distinguishing lung nodules to date. A new approach to this problem may be provided by electrical properties of lung nodules. Nevertheless, difference identification is the basis of correct distinction. So, this paper aims to investigate the differences in electrical properties between various lung nodules. At variance with existing studies, benign samples were included for analysis. A total of 252 specimens were collected, including 126 normal tissues, 15 benign nodules, 76 adenocarcinomas, and 35 squamous cell carcinomas. The dispersion properties of each tissue were measured over a frequency range of 100 Hz to 100 MHz. And the relaxation mechanism was analyzed by fitting the Cole-Cole plot. The corresponding equivalent circuit was estimated accordingly. Results validated the significant differences between malignant and normal tissue. Significant differences between benign and malignant lesions were observed in conductivity and relative permittivity. Adenocarcinomas and squamous cell carcinomas are significantly different in conductivity, first-order, second-order differences of conductivity, α-band Cole-Cole plot parameters and capacitance of equivalent circuit. The combination of the different features increased the tissue groups' differences measured by Euclidean distance up to 94.7%. In conclusion, the four tissue groups reveal dissimilarity in electrical properties. This characteristic potentially lends itself to future diagnosis of non-invasive lung cancer.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes.

Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.

Abstract 4950: Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection

Abstract The liquid biopsy approach focusing on DNA molecules supports the non-invasive diagnosis of breast cancer. Low-pass whole genome sequencing(lpWGS) for breast cancer detection has been validated for accuracy and cost-effectiveness, especially with copy number variant (CNV) analysis. However, detecting breast cancer with cell-free DNA (cfDNA) CNV was limited by tumor fraction. Utilizing cell-free DNA fragmentation characteristics for cancer detection has shown potential in plasma with high precision and applicability with low tumor fraction. In this study, 217 patients with breast cancer and 10 healthy individuals were enrolled. The cfDNA was extracted and performed using PredicineSCORE, a low-coverage whole genome sequencing assay to identify tumor-specific features. The cfDNA fragmentomics profiles including the coverage at specific regions, length and end-motif of fragment were implemented. We also investigated the copy number variation and the inferred tumor fraction which was adjusted by the Expectation-Maximum (EM) algorithm. Combining, an ensembled machine learning model was constructed for the classification of breast cancer and normal control. We observed a high correlation between tumor fraction and features. The absolute correlation coefficient scores between tumor fraction with the LYL1, MAF, FOXA1, and GRHL2 bonding sites' relative depth were over 0.82, 0.71,0.89,0.87. The insert size ratio of short reads and the CCCA end motif were over 0.77 and 0.72, respectively. We then diluted the tumor samples of tumor fraction at 0.1% to 50% by manually mixing reads from tumor samples into normal samples. By applying the XGBoost algorithm, the performance (F1 score > 0.95) showed a promising ability to distinguish tumors from normal samples. The limit of detection (LOD) was less than 1% which is more sensitive than previously proposed models. Those samples detected mutations with high through panel which can confirm the accuracy of the assay. Our results discovered that profiling the cell-free DNA fragment could facilitate the way of non-invasively detection of breast cancer with high precisions. Citation Format: Yaxin Liu, Shuang Gan, Hang Dong, Haoran Tang, Nan Wang, Xinyu Gui, Anjie Zhu, Ruyan Zhang, Pan Du, Shidong Jia. Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4950.

Abstract 2334: Computational methods for optimizing marker selection, clonal lineage reconstruction, and longitudinal tracking of clonal dynamics via circulating tumor DNA (ctDNA)

Abstract Background: Technologies for blood-based profiling of tumor DNA (“liquid biopsy”) have offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but further advances in computational models and data analysis to optimize monitoring protocols and develop liquid biopsy as a robust quantitative assay of tumor clonal evolution. Methods: We propose new computational methods to improve our ability to characterize tumor clonal dynamics from circulating tumor DNA (ctDNA). Our methods address how to apply ctDNA assays most effectively to two distinct questions in profiling tumor clonal dynamics: 1) How to apply longitudinal ctDNA data to refine phylogenetic tree models of clonal evolution, and 2) how to most effectively characterize changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions computationally in terms of a probabilistic framework for optimally identifying maximum likelihood markers for the preceding tasks and applying measured marker concentrations to their solution. We apply this framework by first evaluating the distribution of plausible clonal lineage models using bootstrap samples over pre-treatment tissue-based genetic variation data, then refining these lineage models and the clonal frequencies we can infer from them over successive longitudinal samples of selected markers. Results: We tested our method on synthetic data under various model assumptions and showed the method to be effective at refining distribution tree models and clonal frequencies over longitudinal samples so as to minimize measures of tree distance relative to the ground truth. We further apply the methods to a real ovarian cancer case and show their ability to develop and refine a clonal lineage model and assess clonal frequencies. Conclusion: Our methods show the power of ctDNA assays in conjunction with computational optimization to lead to improved marker selection, clonal lineage reconstruction, and tracking of clonal dynamics, with potential for more precise and quantitative profiling of tumor progression enabling improved clinical decision-making. Citation Format: Xuecong Fu, Yueqian Deng, Zhicheng Luo, William Laframboise, David Bartlett, Russell Schwartz. Computational methods for optimizing marker selection, clonal lineage reconstruction, and longitudinal tracking of clonal dynamics via circulating tumor DNA (ctDNA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2334.

Insights into the enigma of oral streptococci in carcinogenesis.

SUMMARYThe genus Streptococcus consists of a taxonomically diverse group of Gram-positive bacteria that have earned significant scientific interest due to their physiological and pathogenic characteristics. Within the genus Streptococcus, viridans group streptococci (VGS) play a significant role in the oral ecosystem, constituting approximately 80% of the oral biofilm. Their primary role as pioneering colonizers in the oral cavity with multifaceted interactions like adherence, metabolic signaling, and quorum sensing contributes significantly to the complex dynamics of the oral biofilm, thus shaping oral health and disease outcomes. Perturbations in oral streptococci composition drive oral dysbiosis and therefore impact host-pathogen interactions, resulting in oral inflammation and representing VGS as an opportunistic pathogen. The association of oral streptococci in tumors across distant organs, spanning the esophagus, stomach, pancreas, and colon, illuminates a potential association between oral streptococci, inflammation, and tumorigenesis. This finding emphasizes the need for further investigations into the role of oral streptococci in mucosal homeostasis and their involvement in carcinogenesis. Hence, here, we review the significance of oral streptococci in biofilm dynamics and how the perturbation may impact mucosal immunopathogenesis in the context of cancer, with a vision of exploiting oral streptococci for cancer intervention and for the development of non-invasive cancer diagnosis.