Oncological Diagnostics Research Articles

Administrative Aspects of Molecular Diagnostics—Oversight, Regulatory Approval Process, Clinical and Operational Workflows, and Payment Models

This paper discusses the administrative aspects of molecular diagnostics in oncology, including US Food and Drug Administration (FDA) oversight, the regulatory approval process, clinical, and operational workflows, and payment models. Comprehensive molecular testing is important to deliver optimal oncology care and improve patient outcomes. Despite the potential benefits of testing, utilization remains low. The FDA regulatory approval process is reviewed for in vitro diagnostic products, which includes classification into three regulatory classes on the basis of risk. Companion diagnostic devices are used to guide treatment decisions. The clinical and operational challenges associated with molecular testing in oncology are also discussed, including the rapidly evolving landscape of precision oncology, the wide range of biomarker testing options, and complexities of test ordering, interpretation, and result delivery. There is a need for a multifaceted support approach involving education, technology enhancements, and workflow support to overcome these challenges. In terms of payment models, coverage policies between Medicare and commercial payers are compared with differences in coverage criteria, with Medicare focusing on FDA approval or clearance, whereas commercial payers consider additional factors such as National Comprehensive Cancer Network and ASCO guidelines. Commercial payers tend to cover smaller panels on the basis of guideline-recommended biomarkers, whereas coverage for broad tumor profiling is limited. Several strategies can increase the utilization of molecular testing, including integrating test results into electronic medical record platforms, standardizing billing practices, increasing clinical trials, and primary literature supporting the use of molecular testing, educating physicians, and using tumor boards for result interpretation and treatment discussions.

Comparative Analysis of Transcription Factors TWIST2, GATA3, and HES5 in Glioblastoma Multiforme : Evaluating Biomarker Potential and Therapeutic Targets Using In Silico Methods.

Glioblastoma multiforme (GBM) is characterized by substantial heterogeneity and limited therapeutic options. As molecular approaches to central nervous system (CNS) tumors have gained prominence, this study examined the roles of three genes, TWIST2, GATA3, and HES5, known to be involved in oncogenesis, developmental processes, and maintenance of cancer stem cell properties, which have not yet been extensively studied in GBM. This study is the first to present gene expression data for TWIST2, GATA3, and HES5 specifically within the context of GBM patient survival. Gene expression data for TWIST2, GATA3, and HES5 were collected from GBM and normal brain tissues using datasets from The Cancer Genome Atlas (TCGA) via the Genomic Data Commons (GDC) portal and the Genotype-Tissue Expression (GTEx) database. These data were rigorously analyzed using in silico methods. All three genes were significantly more expressed in GBM tissues than in normal tissues. TWIST2 and GATA3 were linked to lower survival rates in GBM patients. Interestingly, higher HES5 levels were associated with better survival rates, suggesting a complex role that needs more investigation. This study shows that TWIST2, GATA3, and HES5 could help predict outcomes in GBM patients. Our multigene model offers a better understanding of GBM and points to new treatment options, bringing hope for improved therapies and patient outcomes. This research advances our knowledge of GBM and highlights the potential of molecular diagnostics in oncology.

Advancing Precision Oncology: Whole-Exome Sequencing in Endometrial Cancer Liquid-Based Cytology.

Diagnostic strategies for endometrial cancer have been evolving, with cytologic analysis being considered a key method in integrated oncologic diagnostics because of its less invasive nature and adaptability to various assessments. Liquid-based cytology (LBC) has emerged as a promising method for intact DNA preservation; it exhibits improved efficiency in advanced sequencing applications such as next-generation sequencing. However, despite the use of LBC in panel assays, its application in whole-exome sequencing (WES) for comprehensive genomic profiling remains underexplored. To investigate whether molecular classification is possible based on WES using DNA derived from LBC specimens. We combined WES with targeted gene panel analysis to compare genomic findings of LBC and traditional tissue samples obtained from 7 cases of endometrial cancer. We investigated pathogenic mutations, tumor mutational burden, and microsatellite instability, and achieved molecular classification with high accuracy. We found a substantial concordance between LBC and traditional tissue samples in terms of pathogenic mutation detection, with a 95% match in the LBC samples and 94% in the tissue samples. Notably, our results highlight the importance of combining WES with panel-based analysis in identifying the ultramutated status of a case that had been missed during panel analysis. Our findings emphasize the potential of LBC samples in the precise and noninvasive genomic analysis of cases of endometrial cancer and offer a new avenue for developing diagnostic and therapeutic strategies in precision oncology.

Synergistic signal amplification for HER2 biosensing using tetrahedral DNA nanostructures and 2D transition metal carbon/nitride@Au composites via ARGET ATRP

The rising incidence of breast cancer underscores the critical need for accurate and early detection methods, particularly for key biomarkers like human epidermal growth factor receptor 2 (HER2). Here, a synergistic signal amplification electrochemical biosensor was developed for HER2 detection that utilizes tetrahedral DNA nanostructures (TDN), 2D transition metal carbon/nitride@Au (MXene@Au) composites, and activators regenerated by electron transfer atom transfer radical polymerization (ARGET ATRP). Specifically, TDN serves as the substrate, with the aptamer anchored by the MXene@Au composite support matrix. This design facilitates the generation of electrochemical currents through the ARGET ATRP signal amplification strategy. The robustness and high affinity of TDN minimize surface effects on the electrode and enhance target binding. Additionally, the high conductivity of the MXene@Au composites improves the detection sensitivity of the biosensor. The use of the ARGET ATRP strategy further enhances detection sensitivity. The biosensor exhibits a wide detection range from 10 pg·mL−1 to 10 µg·mL−1 with a remarkable detection limit of 0.39 pg·mL−1, verified under optimal experimental conditions. The designed biosensor offers a significant advancement in the field of oncological diagnostics, providing a highly sensitive, low-cost, and simple method for early HER2 detection, thereby potentially improving treatment outcomes for breast cancer patients.

Deep bone oncology Diagnostics: Computed tomography based Machine learning for detection of bone tumors from breast cancer metastasis

PurposeThe objective of this study is to develop a novel diagnostic tool using deep learning and radiomics to distinguish bone tumors on CT images as metastases from breast cancer. By providing a more accurate and reliable method for identifying metastatic bone tumors, this approach aims to significantly improve clinical decision-making and patient management in the context of breast cancer. MethodsThis study utilized CT images of bone tumors from 178 patients, including 78 cases of breast cancer bone metastases and 100 cases of non-breast cancer bone metastases. The dataset was processed using the Medical Image Segmentation via Self-distilling TransUNet (MISSU) model for automated segmentation. Radiomics features were extracted from the segmented tumor regions using the Pyradiomics library, capturing various aspects of tumor phenotype. Feature selection was conducted using LASSO regression to identify the most predictive features. The model’s performance was evaluated using ten-fold cross-validation, with metrics including accuracy, sensitivity, specificity, and the Dice similarity coefficient. ResultsThe developed radiomics model using the SVM algorithm achieved high discriminatory power, with an AUC of 0.936 on the training set and 0.953 on the test set. The model’s performance metrics demonstrated strong accuracy, sensitivity, and specificity. Specifically, the accuracy was 0.864 for the training set and 0.853 for the test set. Sensitivity values were 0.838 and 0.789 for the training and test sets, respectively, while specificity values were 0.896 and 0.933 for the training and test sets, respectively. These results indicate that the SVM model effectively distinguishes between bone metastases originating from breast cancer and other origins. Additionally, the average Dice similarity coefficient for the automated segmentation was 0.915, demonstrating a high level of agreement with manual segmentations. ConclusionThis study demonstrates the potential of combining CT-based radiomics and deep learning for the accurate detection of bone metastases from breast cancer. The high-performance metrics indicate that this approach can significantly enhance diagnostic accuracy, aiding in early detection and improving patient outcomes. Future research should focus on validating these findings on larger datasets, integrating the model into clinical workflows, and exploring its use in personalized treatment planning.

MIBLood-EV: An Online Reporting Tool to Facilitate the Standardized Reporting of Preanalytical Variables and Quality Control of Plasma and Serum to Enhance Rigor and Reproducibility in Liquid Biopsy Research.

Pre-analytical variability significantly impacts the reproducibility of liquid biopsy research, which is critical for precision medicine and biomedical research. This report highlights the challenges and variability in the pre-analytical processes of liquid biopsies, especially regarding extracellular vesicles (EVs), which are crucial for diagnostics in oncology. The MIBlood-EV initiative aims to standardize the reporting of pre-analytical variables and the quality control of plasma and serum samples to enhance reproducibility in EV research. By providing a comprehensive and flexible reporting framework, MIBlood-EV seeks to improve the reliability of EV studies and facilitate the development of evidence-based protocols, ultimately advancing the field of liquid biopsy research.

The Diagnostic Value of Endobronchial Ultrasound-Guided Fine Needle Aspiration (EBUS-FNA) in Diagnosing FDG-PET-Avid Lymph Nodes in Extrapulmonary Malignancies.

Background and objective The accurate diagnosis of extrapulmonary malignancies with mediastinal lymphadenopathy is crucial for effective patient management. Endobronchial ultrasound-guided fine-needle aspiration(EBUS-FNA)has emerged as a valuable tool in assessingfluorodeoxyglucose (FDG)-positron emission tomography(PET)-avid lymph nodes (LNs).In this study, we aimed to evaluate the diagnostic value of EBUS-FNA in patients with mediastinal lymphadenopathy in extrapulmonary malignancies and compare its efficacy with PET-CT. Methodology Thisretrospective, cross-sectional study was conducted at Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, from February 2018 to February 2023. It included patients with extrapulmonary malignancies with mediastinal lymphadenopathy displaying abnormal PET-CT uptake, with LN diameters ≥5 mm, excluding lung cancer cases. Data on demographics, malignancy type, LN involvement, PET-CT findings, and EBUS-FNA histopathology were collected. EBUS-FNA procedures involved a 22-gauge needle, and samples were analyzed cytologically and histologically. SPSS Statistics version 20 (IBM Corp., Armonk, NY) was used to perform the statistical analysis. Results The study analyzed a total of 216 patients. Males comprised 56.3% of the cohort, and females 43.7%. The most common malignancy was lymphoma (33.0%), followed by breast cancer (12.6%). EBUS-FNA exhibited a sensitivity of 90.9% compared to PET-CT's sensitivity of 72.7%. Lymph node morphology on EBUS showed low echogenicity and irregular borders in malignant cases. Subcarinaland right hilar were the most frequently sampled lymph nodes. The study found significant differences in lymph node characteristics between non-malignant and malignant groups, with EBUS-FNA effectively identifying malignancies. Conclusions EBUS-FNA demonstrates high sensitivity and diagnostic utility in identifying malignant lymph nodes in patients with extrapulmonary malignancies. Its effectiveness in detecting true positive cases highlights its importance as a complementary diagnostic tool to PET-CT in oncological diagnostics.

The K9 lymphoma assay allows a genetic subgrouping of canine lymphomas with improved risk classification

We present here the K9 lymphoma assay, a novel 31-gene targeted next-generation sequencing panel designed for genomic profiling of canine lymphoid neoplasms. Addressing the growing demand for advanced diagnostics in veterinary oncology, this assay enables sensitive identification of known and actionable mutations specific to canine lymphomas, while evaluating its prognostic potential to facilitate diagnosis and prognosis. Our analysis, spanning several B- and T-cell lymphoma histotypes, unveiled distinct mutational landscapes distinguishing tumors derived from immature versus mature lymphocytes. Clustering analysis revealed a shared genetic origin between diffuse large B-cell lymphoma and marginal zone lymphoma, aligning with findings in human lymphomas, with TRAF3 emerging as the most frequently mutated gene across B-cell lymphoma subtypes. Significantly, TP53 mutations demonstrated universal adverse prognostic implications across B-cell lymphomas. Additionally, SETD2 mutations contributed to shorter time-to-progression, underscoring the role of epigenetic dysregulation in B-cell tumors. In T-cell lymphomas, SATB1 and FBXW7 were frequently mutated, warranting further investigation in larger cohorts. Our findings advocate for tailored therapeutic approaches based on the genetic profile, impacting treatment decisions and outcomes in canine lymphoma management. This study provides pivotal insights bridging veterinary and human oncology, paving the way for comprehensive genomic diagnostics and therapeutic strategies in comparative oncology.

A scalable 3D spatial multi-omics method for high-plex morpho-molecular imaging of millimeter-thick tissues.

162 Background: Tissues are three-dimensional (3D). However, current histopathology only allows 2D and single-marker tissue views. Methods: We utilized tissue clearing, supramolecular histochemistry, light-sheet microscopy, and image processing algorithms to compile an end-to-end multi-modality pipeline for 3D digital pathology and oncology diagnostics development. Results: Our end-to-end solution allow parallelized, fully automated processing of formalin-fixed or paraffin-embedded tissues of 1cm3 size. With a tissue-to-image time of 6 days, the whole intact tissue block can be non-destructively imaged with our newly developed 3D HnE (Hematoxylin-analogue & Eosin stain) and 4-plex immunohistochemistry (IHC). Deep and homogeneous penetration of staining was achieved such that all stained markers can be quantified as ground truth. The stained and cleared tissue can be recycled for subsequent FFPE 2D histology over multiple cycles for higher-plex imaging, and is also applicable to simultaneous survey of mRNA with multiplexed fluorescent in situ hybridization (FISH), post-translational modifications such as histone modifications via IHC, and glycosylation profiling with lectins. Image analysis readily allows high-dimensional clustering of cells based on their multi-modal biomolecular expression profiles, morphology, spatial locations, and neighborhoods. The method is applicable to a wide variety of human and murine tissues. Conclusions: Our 3D digital pathology platform enables maximal information extracted for precision diagnostics and advanced biomedical research.

Exploring Salivary Biomarkers for Tumor Diagnosis: A Narrative Review.

A promising method for non-invasive cancer diagnosis and prognosis is through salivary biomarkers. By utilizing the distinct characteristics of saliva and the progress made in biomarker studies, these markers provide more accurate diagnoses for a wider range of malignancies. An attempt was made to thoroughly investigate the field of salivary biomarkers for tumor prognosis and diagnosis, with an emphasis on their use in various cancer forms. Predetermined search criteria were utilized for a systematic search across numerous databases for peer-reviewed papers from 2009 to 2021. Studies concentrating on the detection, validation, and clinical use of salivary biomarkers for different types of cancers were included in the inclusion criteria. Initially, 238 articles were found, of which 15 relevant articles satisfied the inclusion requirements. Information on study aims, methodology, findings, and conclusions were gathered for data extraction. We identified recurrent themes, patterns, and contradictions by a thematic analysis. We also assessedstate-of-the-art salivary biomarkers for tumor diagnosis and prognosis. One major finding is the identification of biomolecules in saliva linked to several cancer forms, including pancreatic, oral, breast, lung, and stomach cancers. There is an increasing amount of evidence demonstrating the value of saliva-based diagnostics in oncology. This is due to new detection methodsand developments in salivary proteomics and genomics. Identification of exosomes and microvesicles as salivary biomarker profiles offered molecular understandings of the etiology and evolution of cancer, thereby opening new avenues for diagnosis and treatment. Salivary biomarkers are a non-invasive approach for the early detection and prediction of cancer, thanks to the unique properties of saliva and advancements in biomarker research. This potential revolution could enhance patient outcomes and reduce cancer-related deaths.

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment.

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, this review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

Neural network model development for detecting atypical mitoses in histological slides

Background: Modern computer systems allow digitizing and examining images of histological preparations, which led the authors to the idea of using machine learning tools in digital pathohistology. The ability of neural networks to find sub-visual image features in digitized histological preparations provides the basis for better qualitative and quantitative image analysis. Existing machine learning methods provide good accuracy and speed in recognizing various images, which gives hope for their wide application, including in oncologic diagnostics. AIM: Use methods of mathematical modeling to identify pathological mitoses in histological preparations as the main sign of the difference between malignant and benign tumor growth. MATERIALS AND METHODS: Histological images of the N.N. Priorov National Medical Research Center of Traumatology and Orthopedics were used as a data set for the neural network model. The model was tested using 188 histologic slides from 67 patients treated at the institute. Histological preparations were scanned on a Leica Aperio CS2 microscope with a ×400 resolution and converted into JPEG format with further processing. Next, the test images were analyzed in streaming mode using the created neural network model in order to obtain the coordinates of the desired diagnostic object — pathological mitosis and the probability with which the model found the object of this category. The obtained images were analyzed by a pathologist to determine whether the detected object corresponded to pathological mitosis. RESULTS: The authors have chosen an architecture, developed a methodology for training a neural network, and created a model that can be used to detect pathologic mitoses in histologic preparations. The authors do not attempt to replace the physician, but show the possibility of an integrated approach to data analysis by a computer system and a pathologist. Conclusions: The developed mathematical model of neural network used as a part of technological solution for recognizing pathological mitoses in scanned histological preparations can be used as a tool to reduce the time of research and increase the accuracy of diagnosis by a pathologist.

BIOREALIZATION ENGINEERING TECHNOLOGIES FOR CANCER DIAGNOSIS: IMPACT ON THE HEALTH SYSTEM

This article delves into the analysis of the impact of bio-realization engineering technologies on cancer diagnostic processes within the contemporary healthcare system. The objective of the article is to identify the potential of molecular-genetic testing and other bio-realization methods in enhancing diagnostic capabilities and therapeutic approaches for oncological diseases. To achieve this goal, general scientific methods of analysis and synthesis of existing research in this field are utilized, including a review of scientific literature and a critical analysis of the results of experimental studies. The findings confirm the significant impact of bio-realization engineering technologies on the advancement of cancer diagnostics. Specifically, molecular-genetic testing opens new avenues for early detection and personalized treatment approaches for cancer, based on the genetic characteristics of tumors. These technologies are particularly valuable for countries with limited medical resources, as they offer cost-effective and efficient solutions that provide broader access to quality diagnostic services. They also contribute to improving the overall efficacy of treatment strategies and optimizing medical research, reducing the burden on medical staff. The practical significance of the obtained results lies in the possibility of their application in the development of new diagnostic tools and techniques aimed at enhancing the accuracy, accessibility, and efficiency of cancer treatment. This, in turn, may contribute to reducing mortality from oncological diseases and improving patients' quality of life. Bio-realization engineering technologies, particularly molecular-genetic testing, play a pivotal role in modern oncological diagnostics, offering promising opportunities for the improvement of diagnostic and therapeutic procedures for cancer. Their integration into the healthcare system enhances diagnostic accuracy, treatment accessibility, and the overall level of medical services, opening new horizons in the fight against oncological diseases.

Highly Elevated Alpha-fetoprotein Concentration in a Premature Infant

Alpha-1-fetoprotein (AFP) is a protein produced by the liver. Although its biological function is not fully understood, it is often used as a marker for cancer. High levels of AFP are also observed in premature infants. Reference ranges for AFP levels in premature infants are not specified. The authors present a case of a male newborn born at 25+5/7 weeks of gestational age. The boy underwent oncological diagnostics due to the detection, on the 24th day of life, of a liver lesion in an abdominal ultrasound examination, as well as significantly elevated AFP levels (>430,000 ng/mL). With each passing day, the AFP level gradually decreased, and the suspicious lesion was not visualized on abdominal ultrasound or magnetic resonance imaging on the 59th day of life. Based on the conducted examinations, prematurity and its treatment were considered the cause of high AFP levels. The aim of the study was to present the diagnostic difficulties associated with the lack of AFP norms for premature infants.

Multiparametric MRI in hepatocellular carcinoma, part2 : Diffusion-weighted imaging in the primary diagnostics and treatment monitoring

In addition to morphology and tissue perfusion, diffusion-weighted imaging (DWI) is the third pillar of multiparametric diagnostics in oncology. Due to the strong correlation between the apparent diffusion coefficient (ADC) and cell count in hepatocellular carcinoma (HCC), it can be used as asurrogate marker for tumor cell quantity. Therefore, ADC effectively reflects the effects of cytoreductive treatment, such as transarterial chemoembolization (TACE) and systemic chemotherapy and becomes an important clinical marker for treatment response. The DWI should remain an integral part of a magnetic resonance imaging (MRI) protocol in primary HCC diagnostics and treatment monitoring but is of secondary clinical importance compared to contrast-enhanced MRI perfusion sequences and the use of liver-specific contrast agents. For the future, standardization of DWI sequences for better comparability of various study protocols would be desirable.

Exploring the literature on artificial intelligence use in oncology.

e13642 Background: Artificial Intelligence (AI) allows machines to analyze data in a way that mimics human intelligence. AI has been used in oncology diagnostics such as radiology and pathology. However, the specific processes and tasks that AI can support, especially in oncology, have not been fully explored. Methods: The electronic databases PubMed and MEDLINE were searched from September 1, 2018, to September 1, 2023, for publications related to the use of AI in oncology. Inclusion criteria consisted of English language, article type “study” and keywords Artificial Intelligence, Healthcare, Predictive/Prediction, Fever, Cytokine Release Syndrome, Nursing, Patient Care, Oncology, and Neutropenic Fever applied in a progressive manner to narrow the search. Results: The term Artificial Intelligence yielded over 124,385 studies. This number was reduced to 466 when adding “healthcare, predictive/prediction, and oncology.” Adding “patient care” further reduced the number to 102. Out of the 102, seven studies identified included the term “nursing” and focused on clinician decision support, readmissions, palliative care, and scheduling. One identified study discussed the potential benefits of AI-generated algorithms for risk stratification related to cytokine release syndrome. The keywords “neutropenic fever” did not generate any results. Six studies were identified by adding in the term “fever,” which focused on AI use in specialties outside of oncology. The literature reviewed shows that AI has had limited use in oncology outside of radiology and pathology. Only one study suggested the use of AI would be beneficial in predicting symptoms related to treatment. Small studies were identified showing the value of AI in increasing adherence to schedules, reducing readmissions, and providing precision medicine. Conclusions: AI is a promising tool for the future of oncology. The current availability of AI within oncology practices has been limited. Further studies should be undertaken to identify how AI can reduce provider burnout, care costs, and early identification of symptoms to prevent adverse events.

A novel NIR fluorescent probe for enhanced β-galactosidase detection and tumor imaging in ovarian cancer models

The advancement of biological imaging techniques critically depends on the development of novel near-infrared (NIR) fluorescent probes. In this study, we introduce a designed NIR fluorescent probe, NRO-βgal, which exhibits a unique off–on response mechanism to β-galactosidase (β-gal). Emitting a fluorescence peak at a wavelength of 670 nm, NRO-βgal showcases a significant Stokes shift of 85 nm, which is indicative of its efficient energy transfer and minimized background interference. The probe achieves a remarkably low in vitro detection limit of 0.2 U/L and demonstrates a rapid response within 10 min, thereby underscoring its exceptional sensitivity, selectivity, and operational swiftness. Such superior analytical performance broadens the horizon for its application in intricate biological imaging studies. To validate the practical utility of NRO-βgal in bio-imaging, we employed ovarian cancer cell and mouse models, where the probe's efficacy in accurately delineating tumor cells was examined. The results affirm NRO-βgal's capability to provide sharp, high-contrast images of tumor regions, thereby significantly enhancing the precision of surgical tumor resection. Furthermore, the probe's potential for real-time monitoring of enzymatic activity in living tissues underscores its utility as a powerful tool for diagnostics in oncology and beyond.

A Systematic Review on the Diagnostic Value of Fibroblast Activation Protein Inhibitor PET/CT in Genitourinary Cancers.

In contemporary oncologic diagnostics, molecular imaging modalities are pivotal for precise local and metastatic staging. Recent studies identified fibroblast activation protein as a promising target for molecular imaging across various malignancies. Therefore, we aimed to systematically evaluate the current literature on the utility of fibroblast activation protein inhibitor (FAPI) PET/CT for staging patients with genitourinary malignancies. Methods: A systematic Embase and Medline search was conducted, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process, on August 1, 2023. Relevant publications reporting on the diagnostic value of FAPI PET/CT in genitourinary malignancies were identified and included. Studies were critically reviewed using a modified version of a tool for quality appraisal of case reports. Study results were summarized using a narrative approach. Results: We included 22 retrospective studies with a cumulative total of 69 patients, focusing on prostate cancer, urothelial carcinoma of the bladder and of the upper urinary tract, renal cell carcinoma, and testicular cancer. FAPI PET/CT was able to visualize both local and metastatic disease, including challenging cases such as prostate-specific membrane antigen (PSMA)-negative prostate cancer. Compared with radiolabeled 18F-FDG and PSMA PET/CT, FAPI PET/CT showed heterogeneous performance. In selected cases, FAPI PET/CT demonstrated superior tumor visualization (i.e., better tumor-to-background ratios and visualization of small tumors or metastatic deposits visible in no other way) over 18F-FDG PET/CT in detecting local or metastatic disease, whereas comparisons with PSMA PET/CT showed both superior and inferior performances. Challenges in FAPI PET/CT arise from physiologic urinary excretion of most FAPI radiotracers, hindering primary-lesion visualization in the bladder and upper urinary tract, despite generally providing high tumor-to-background ratios. Conclusion: The current findings suggest that FAPI PET/CT may hold promise as a future tool to aid clinicians in detecting genitourinary malignancies. Given the substantial heterogeneity among the included studies and the limited number of patients, caution in interpreting these findings is warranted. Subsequent prospective and comparative investigations are anticipated to delve more deeply into this innovative imaging modality and elucidate its role in clinical practice.

Abstract 3671: Analytical performance of the Oncomine™ Dx Express Test, a CE-IVD NGS liquid biopsy assay for identification of clinically relevant variants

Abstract For in vitro Diagnostic use only. Not available in all regions including the United States. Introduction Minimally invasive blood-based liquid biopsy using cell-free total nucleic acid (cfTNA) and next generation sequencing (NGS) has substantially evolved in the field of clinical diagnostics for oncology, for detection of molecular therapeutic targets in non-small cell lung cancer (NSCLC). Here we describe analytical validation results for cfTNA using the CE-IVD Oncomine™ Dx Express Test (ODxET) assay, a qualitative in vitro diagnostic test that uses targeted NGS technology, the Ion Torrent Genexus™ Dx Integrated Sequencer to detect SNV, indel and copy number gain present in 42 genes and fusions in 7 genes from cfTNA isolated from NSCLC plasma samples. Methods Plasma samples from NSCLC patients were screened for variants of interest on several important gene loci. Screening involved isolation of cfTNA from K2-EDTA-derived plasma using the Genexus™ Cell-Free Total Nucleic Acid Purification kit on an automated purification system and sequencing was performed on the Genexus™ Dx integrated sequencer using the ODxET assay. Plasma samples having clinically relevant variants from the set of screened plasma samples were used to perform the validation studies. Results Analytical sensitivity and specificity of NSCLC plasma samples were characterized through determination of limit of detection (LOD) and limit of blank (LOB), respectively. The LOD level tested for DNA SNVs, and indels at 5 ng (minimum) input level ranged from 0.62% to 1.82% allelic frequency (AF), while 30 ng (maximum) input shows the LOD level ranging from 0.23% to 0.42% AF. The LOD for the tested RNA fusions ranged from 4.2 to 19.6 molecular counts for 5ng input and 4.9 to 8.0 molecular counts for 30ng input. Furthermore, LOB for the ODxET assay was determined by testing wild type (WT) cfTNA extracted from 30 healthy donor blood plasma samples, which were confirmed to be negative at all variant locations. The analytical accuracy study shows the false positive rate at 0.20% for SNVs, 0% for indel locations and fusion targets. In addition, the analytical reproducibility study shows 99.64% intra-run repeatability call rate for DNA variants and 98.75% for RNA variants. Conclusion In conclusion, we establish that the ODxET assay on Ion Torrent Genexus Dx Integrated Sequencer, is fast and efficient while demonstrating high sensitivity, specificity, and reproducibility for testing NSCLC liquid biopsy samples. Citation Format: Madhuri Jasti, Swasti Raut, Diarra Hassell, Nader Ezzedine, Daniela Garcia, Stephen Wunsch, Nick Siepert, Elliott Martinez, Emilia Ostrowska, Jeff Schagemann, Jian Gu, Thilanka Jayaweera, Luming He, Angie Cheng. Analytical performance of the Oncomine™ Dx Express Test, a CE-IVD NGS liquid biopsy assay for identification of clinically relevant variants [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 3671.

Tumor tissue samples collection for scientific research in morphology and molecular oncology

Purpose of the study. Is to describe the experience of creating a collection of biological images of tumor tissues and biomaterials, which are control samples, for scientific research in morphology and molecular oncology.Materials and methods. We studied the molecular markers of cell cycle regulation, apoptosis, oncogenesis and angiogenesis, the expression of proteins that regulate inflammation and tumor infiltrate cells in biocollections of verified tumors of common localizations: e. g. thyroid cancer, colorectal cancer, breast cancer, prostate adenocarcinoma, endometrioid adenocarcinoma. Also, tissue fragments with normal structure or non-tumor pathology (autoimmune thyroiditis, adenomatous and thyrotoxic goiter, benign formations of the colon, fibrocystic disease of the mammary glands, benign prostatic hyperplasia, endometrial hyperplasia) were used as control samples or comparison groups. The total number of tissue samples is n = 7000.Results. It is reasonable to gather the collection in a pathomorphological laboratory according to the profile of the medical institutions, which has a sufficient volume of incoming target material and specialized morphologists to verify tumors of a given localization. It is necessary to consider the regional and ethnic specifics of the population, which determines the sampling and mutational load. The laboratory must initiate an addition to the informed consent of patients about the possibility of conducting morphological and molecular genetic studies for scientific purposes and publishing their results in a depersonalized form for the development of new elaborations, when signing the contracts with legal entities and individuals and when serving patients within an institution. When working with biocollections, it has to consider having registers of tissue biomaterials of target disorder groups of main localizations with downloading by year from an accessible information system, consider external factors affecting the database (changes in clinical recommendations and classifications, the population of patients served, pandemics and other significant events). The standard of the preanalytical stage, data collection, development of protocols for analytical molecular genetic studies and their evaluation, the utilization of the capabilities of working with reagents for scientific tasks and modeling experiments on laboratory animals are crucial.Conclusion. The formed biocollection made it possible to carry out a number of initiative and funded domestic and international scientific projects at the request of clinicians and fundamental researchers, as well as to improve the quality standards of morphological and molecular genetic oncology diagnostics. Biobanking makes the pathological archive more accessible for review and use, significantly expanding its scientific and practical potential. Scientific and medical research do not conflict and can be used within the same laboratory.