Late-stage Detection Research Articles

Tumor regression after neoadjuvant hormonal therapy in high risk prostate cancer: pathological outcomes from a randomized phase II trial.

High-risk localized prostate cancer (HRLPC) commonly progresses to metastatic disease after local treatment. Neoadjuvant androgen deprivation therapy (nADT) before radical prostatectomy (RP) has recently been suggested to improve early oncological outcomes in HRLPC. We aimed to perform an exploratory analysis of the pathological outcomes from a prospective trial testing nADT before RP. Prospective, single-centered, phase II, randomized trial performed between October 2018 and July 2021. Random assignment (1:1) for nADT modalities: goserelin (10.8mg) plus abiraterone acetate (1000mg/d) plus prednisone (5mg/d), with or without apalutamide (240mg/d) for 12 weeks, followed by RP (within 30 days) and extended lymph node dissection. Baseline clinical and pathological variables were assessed in needle biopsies before nADT. Tumor regression was histologically evaluated in surgical specimens using the residual cancer burden index (RCB). Sixty-two patients reached the surgical phase. Good response (RCB ≤ 0.25cm³) was achieved in 14 patients (22.5%). Overall stage migration rate between baseline status (MRI before nADT) and final status (after surgery) was 27.4%. Late stage detection (high tumor burden, perineural invasion) and altered PTEN/ERG immunostatus showed significant association with poor response in univariate analysis. Higher baseline tumor burden was the only independent factor related to poor response in multivariate analysis. There are subgroups of patients, such as those with low baseline cancer burden and PTEN/ERG wild-type status, more likely to achieve good response with nADT. In the case of long term oncological benefit to be proven, nADT might be an additional therapeutic resource for these patients.

The Role of the Plasminogen Activator Inhibitor 1 (PAI1) in Ovarian Cancer: Mechanisms and Therapeutic Implications

AbstractOvarian cancer (OC) is one among most significantly fatal gynecological cancers, with late-stage detection and an inadequate prognosis. Plasminogen activator inhibitor-1 (PAI1) gene anticipates negative outcomes in many different kinds of malignancies. Several research investigations are currently being done to examine the biological role of PAI1 in OC and the possible benefits of targeted pharmacotherapies. The PAI1 gene has been linked to the emergence and development of cancer in the ovary. PAI1, an inhibitor of serine protease, influences the fibrinolysis and extracellular matrix remodeling, both of which are crucial for tumor expansion and metastatic growth. PAI1 levels have been discovered to be subsequently more elevated in malignant ovarian tissues than in usual ovarian tissue, demonstrating a potential connection among PAI1 overexpression and OC development. PAI1 promotes tumor cell proliferation, movement, and an invasion by influencing the urokinase-plasminogen activators and through interactions with cell surface receptors. In addition, PAI1 gene contributes to angiogenesis and apoptotic cell death, which contribute to the more hostile phenotypes of OC. The prognostic and therapeutic consequences of focusing on PAI1 in OC are explored, demonstrating PAI1's potential to be a biomarker and emphasizing for novel treatment approaches. The PAI1 gene possesses several functions in OC, affecting tumor development, an invasion, and metastatic growth. Comprehending the complicated interactions and mechanisms that regulate PAI1 in OC may lead to more efficient evaluation and treatment strategies and ultimately enhance patient outcomes.

Multimodal Optical Imaging of Ex Vivo Fallopian Tubes to Distinguish Early and Occult Tubo-Ovarian Cancers

Background: There are currently no effective screening measures to detect early or occult tubo-ovarian cancers, resulting in late-stage detection and high mortality. This work explores whether an optical imaging catheter can detect early-stage tubo-ovarian cancers or precursor lesions where they originate in the fallopian tubes. Methods: This device collects co-registered optical coherence tomography (OCT) and autofluorescence imaging (AFI). OCT provides three-dimensional assessment of underlying tissue structures; autofluorescence imaging provides functional contrast of endogenous fluorophores. Ex vivo fallopian tubes (n = 28; n = 7 cancer patients) are imaged; we present methods for the calculation of and analyze eleven imaging biomarkers related to fluorescence, optical attenuation, and OCT texture for their potential to detect tubo-ovarian cancers and other lesions of interest. Results: We visualize folded plicae, vessel-like structures, tissue layering, hemosiderin deposits, and regions of fibrotic change. High-grade serous ovarian carcinoma appears as reduced autofluorescence paired with homogenous OCT and reduced mean optical attenuation. Specimens containing cancerous lesions demonstrate a significant increase in median autofluorescence intensity and decrease in Shannon entropy compared to specimens with no lesion. Non-cancerous specimens demonstrate an increase in optical attenuation in the fimbriae when compared to the isthmus or the ampulla. Conclusions: We conclude that this approach shows promise and merits further investigation of its diagnostic potential.

Identification of autoantibodies as potential non-invasive biomarkers for intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) presents a challenging diagnosis due to its nonspecific early clinical manifestations, often resulting in late-stage detection and high mortality. Diagnosing iCCA is further complicated by its limited accuracy, often necessitating multiple invasive procedures for precise identification. Despite carbohydrate antigen 19–9 (CA19-9) having been investigated and employed for iCCA diagnosis, it demonstrates modest diagnostic performance. Consequently, the identification of novel biomarkers with improved sensitivity and specificity remains an imperative yet formidable task. Autoantibodies, as early indicators of the immune response against cancer, offer a promising avenue for enhancing diagnostic accuracy. Our study aimed to identify non-invasive blood-based autoantibody biomarkers capable of distinguishing iCCA patients from healthy individuals (CTRs). We profiled autoantibodies in 26 serum samples (16 iCCAs and 10 CTRs) using protein microarrays containing 1622 functional proteins. Leveraging machine learning techniques, we identified a signature composed of three autoantibody biomarkers (NDE1, PYCR1, and VIM) in conjunction with CA19-9 for iCCA detection. This combined signature demonstrated superior diagnostic performance with an AUC of 96.9%, outperforming CA19-9 alone (AUC: 83.8%). These results suggest the potential of autoantibody biomarkers to develop a complementary non-invasive diagnostic utility for routine iCCA screening.

Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection.

The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a data analysis protocol leading to a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC. 539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC. The signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9, the only biomarker currently accepted by the FDA and the National Comprehensive Cancer Network guidelines for pancreatic cancer, and other individual biomarkers and combinations in both discovery and held-out validation sets. An AUC of 0.98 (95% CI 0.98-0.99) and sensitivity of 0.99 (95% CI 0.98-1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66-0.91) and sensitivity 0.67 (95% CI 0.50-0.83), also at 90% specificity, for CA19-9, in the discovery set (p = 0.0016 and p = 0.00050, respectively). During ensemble signature validation in the held-out set, an AUC of 0.95 (95% CI 0.91-0.99), sensitivity 0.86 (95% CI 0.68-1), was attained compared to an AUC of 0.80 (95% CI 0.66-0.93), sensitivity 0.65 (95% CI 0.48-0.56) at 90% specificity for CA19-9 alone (p = 0.0082 and p = 0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92-0.99), sensitivity 0.82 (95% CI 0.64-0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64-0.93) and sensitivity of 0.18 (95% CI 0.03-0.69) (p = 0.013 and p = 0.0055, respectively). Our ensemble modelling technique outperformed CA19-9, individual biomarkers and indices developed with prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk.

Deep Learning-Based Ovarian Cancer Subtype Classification Using VGG16 and MobileNetV2 with Squeeze-and-Excitation Blocks

Background: Ovarian carcinoma remains one of the deadliest gynecological malignancies due to its heterogeneity and late-stage detection. Accurate classification of its subtypes—High-Grade Serous Carcinoma (HGSC), Clear-Cell Carcinoma (CC), Endometrioid Carcinoma (EC), Low-Grade Serous Carcinoma (LGSC), and Mucinous Carcinoma (MC)—is essential for tailored treatments, yet traditional histopathological methods often lack precision. This study aimed to develop a deep learning (DL) model to enhance ovarian cancer subtype classification using histopathological images. Methods: Histopathological images were collected from medical repositories, and data augmentation techniques were applied to increase dataset diversity. Two convolutional neural network (CNN) architectures, VGG16 and MobileNetV2, were fine-tuned using transfer learning to classify the subtypes. The models were pre-trained on the ImageNet dataset and evaluated through accuracy, precision, recall, F1-score, and ROC-AUC, with K-fold cross-validation ensuring robustness. Results: Results indicated that VGG16 improved classification over baseline CNN models, while MobileNetV2, with Squeeze-and-Excitation (SE) blocks, achieved the highest performance, offering greater accuracy and computational efficiency. MobileNetV2’s lightweight architecture captured intricate tissue patterns more effectively, making it the superior model. Conclusion: This study highlights the potential of advanced DL models, particularly MobileNetV2 with SE block attention, for improving ovarian cancer subtype classification. These findings offer promising implications for clinical practice and personalized treatment approaches. Future research should focus on larger datasets and integrating multimodal data for further advancements.

Utilizing an Ensemble of Extra Tree Model for Classifying Mesothelioma Cancer

Objectives: Explore the potential of ensemble learning techniques like Bagging Tree, Random Forest, and Ensemble Extra Tree in transforming mesothelioma diagnosis.Overcome challenges associated with late-stage detection and limited treatment options using advanced machine learning algorithms.Enhance predictive power and feature extraction capabilities through the combination of diverse ensemble algorithms.

Cross-sectional imaging of gastric cancer: pearls, pitfalls and lessons learned from multidisciplinary conference.

Gastric cancer is rising in prevalence associated with high mortality, primarily due to late-stage detection, underscoring the imperative for early and precise diagnosis. Etiology involves an interplay of genetic susceptibilities and environmental factors with a prominent role of Helicobacter pylori infection. Due to its often-delayed symptom presentation, prompt and accurate diagnosis is necessary. A multimodal imaging approach, including endoscopic ultrasound (EUS), multi-detector computed tomography (MDCT), and magnetic resonance imaging (MRI) is critical for accurate staging. Each modality contributes unique advantages and limitations, highlighting the importance of integrating diagnostic strategy. Moreover, multidisciplinary conferences offer a vital collaborative platform, bringing together specialists from diverse fields for treatment planning. This synergistic approach not only enhances diagnostic precision but also improves patient outcome. This review highlights the critical role of imaging in diagnosis, staging, and management and advocates for interdisciplinary collaboration in early detection and comprehensive management of gastric cancer, aiming to reduce mortality.

Water-Soluble Small Organic Fluorophores for Oncological Theragnostic Applications: Progress and Development.

Cancer is one of the major noncommunicable diseases, responsible for millions of deaths every year worldwide. Though various cancer detection and treatment modalities are available today, many deaths occur owing to its late-stage detection and metastatic nature. Noninvasive detection using luminescence-based imaging tools is considered one of the promising techniques owing to its low cost, high sensitivity, and brightness. Moreover, these tools are unique and valuable as they can detect even the slightest changes in the cellular microenvironment. To achieve this, a fluorescent probe with strong tumor uptake and high spatial and temporal resolution, especially with highwatersolubility, is highly demanded. Recently, several water-soluble molecules with emission windows in the visible (400-700nm), first near-infrared (NIR-I, 700-1000nm), and second near-infrared (NIR-II, 1000-1700nm) windows have been reported in literature. This review highlights recently reported water-soluble small organic fluorophores/dyes with applications in cancer diagnosis and therapeutics. We systematically highlight and describe the key concepts, structural classes of fluorophores, strategies for imparting water solubility, and applications in cancer therapy and diagnosis, i.e., theragnostics. We discuss examples of water-soluble fluorescent probes based on coumarin, xanthene, boron-dipyrromethene (BODIPY), and cyanine cores. Some other emerging classes of dyes based on carbocyclic and heterocyclic cores are also discussed. Besides, emerging molecular engineering methods to obtain such fluorophores are discussed. Finally, the opportunities and challenges in this research area are also delineated.

Deciphering pancreatic neuroendocrine tumors: Unveiling through circulating small extracellular vesicles

The survival rate over a five-year period for rare pancreatic neuroendocrine tumors (PanNET) is notably lower compared to other neuroendocrine tumors due to late-stage detection, which is a consequence of the absence of suitable diagnostic markers; therefore, there exists a critical need for an early-stage biomarker-specific to PanNETs. This study introduces a novel approach, investigating the impact of small extracellular vesicles (sEV) in PanNET growth and metastasis. As proof of concept, this study shows a correlation between sEV concentration in controls and PanNET. Notably, higher sEV concentrations were observed in PanNETs than in controls (p < 0.0001) with a sensitivity of 100%. Further, apparent differences were observed in the sEV concentrations between controls and grades 1 PanNET (p = 0.005). The expression of sEV markers was confirmed using CD63, TSG101, CD9, Flotillin-1, and GAD65 antibodies. Additionally, the expression of cancer marker BIRC2/cIAP1 (p = 0.002) and autophagy marker Beclin-1 (p = 0.02) were observed in plasma-derived sEVs and PanNET tissue. This study represents the first to indicate the increased secretion of sEV in PanNET patients' blood plasma, proposing potential function of sEV as a new biomarker for early-stage PanNET detection.

Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging

INTRODUCTION: Alzheimer's disease (AD), a complex neurodegenerative condition, presents significant challenges in early and accurate diagnosis. Early prediction of AD severity holds the potential for improved patient care and timely interventions. This research investigates the use of deep learning methodologies to forecast AD severity utilizing data extracted from Magnetic Resonance Imaging (MRI) scans.&#x0D; OBJECTIVES: This study aims to explore the efficacy of deep learning models in predicting the severity of Alzheimer's disease using MRI data. Traditional diagnostic methods for AD, primarily reliant on cognitive assessments, often lead to late-stage detection. MRI scans offer a non-invasive means to examine brain structure and detect pathological changes associated with AD. However, manual interpretation of these scans is labor-intensive and subject to variability.&#x0D; METHODS: Various deep learning models, including Convolutional Neural Networks (CNNs) and advanced architectures like DenseNet, VGG16, ResNet50, MobileNet, AlexNet, and Xception, are explored for MRI scan analysis. The performance of these models in predicting AD severity is assessed and compared. Deep learning models autonomously learn hierarchical features from the data, potentially recognizing intricate patterns associated with different AD stages that may be overlooked in manual analysis.&#x0D; RESULTS: The study evaluates the performance of different deep learning models in predicting AD severity using MRI scans. The results highlight the efficacy of these models in capturing subtle patterns indicative of AD progression. Moreover, the comparison underscores the strengths and limitations of each model, aiding in the selection of appropriate methodologies for AD prognosis.&#x0D; CONCLUSION: This research contributes to the growing field of AI-driven healthcare by showcasing the potential of deep learning in revolutionizing AD diagnosis and prognosis. The findings emphasize the importance of leveraging advanced technologies, such as deep learning, to enhance the accuracy and timeliness of AD diagnosis. However, challenges remain, including the need for large, annotated datasets, model interpretability, and integration into clinical workflows. Continued efforts in this area hold promise for improving the management of AD and ultimately enhancing patient outcomes.

Exploring Deep Learning Models for Accurate Alzheimer's Disease Classification based on MRI Imaging

INTRODUCTION: Alzheimer's disease (AD), a complex neurodegenerative condition, presents significant challenges in early and accurate diagnosis. Early prediction of AD severity holds the potential for improved patient care and timely interventions. This research investigates the use of deep learning methodologies to forecast AD severity utilizing data extracted from Magnetic Resonance Imaging (MRI) scans.&#x0D; OBJECTIVES: This study aims to explore the efficacy of deep learning models in predicting the severity of Alzheimer's disease using MRI data. Traditional diagnostic methods for AD, primarily reliant on cognitive assessments, often lead to late-stage detection. MRI scans offer a non-invasive means to examine brain structure and detect pathological changes associated with AD. However, manual interpretation of these scans is labor-intensive and subject to variability.&#x0D; METHODS: Various deep learning models, including Convolutional Neural Networks (CNNs) and advanced architectures like DenseNet, VGG16, ResNet50, MobileNet, AlexNet, and Xception, are explored for MRI scan analysis. The performance of these models in predicting AD severity is assessed and compared. Deep learning models autonomously learn hierarchical features from the data, potentially recognizing intricate patterns associated with different AD stages that may be overlooked in manual analysis.&#x0D; RESULTS: The study evaluates the performance of different deep learning models in predicting AD severity using MRI scans. The results highlight the efficacy of these models in capturing subtle patterns indicative of AD progression. Moreover, the comparison underscores the strengths and limitations of each model, aiding in the selection of appropriate methodologies for AD prognosis.&#x0D; CONCLUSION: This research contributes to the growing field of AI-driven healthcare by showcasing the potential of deep learning in revolutionizing AD diagnosis and prognosis. The findings emphasize the importance of leveraging advanced technologies, such as deep learning, to enhance the accuracy and timeliness of AD diagnosis. However, challenges remain, including the need for large annotated datasets, model interpretability, and integration into clinical workflows. Continued efforts in this area hold promise for improving the management of AD and ultimately enhancing patient outcomes.

Abstract 3011: The loss of an orphan nuclear receptor NR2E3 augments Wnt/β-Catenin signaling via epigenetic dysregulation that links to the Sp1-β catenin-p300 interactions in hepatocellular carcinoma

Abstract Hepatocellular carcinoma (HCC) stands as the fourth leading cause of global cancer-related mortality due to late-stage detection, tumor heterogeneity, and drug resistance. The pivotal role of epigenetic factors in HCC development and progression necessitates the identification of critical epigenetic regulators. The orphan nuclear receptor NR2E3 (Nuclear receptor subfamily 2 group E, Member 3), primarily associated with retinal development, has emerged as a potential tumor suppressor. Earlier studies showed that the loss of NR2E3 leads to epigenetic repression of the estrogen receptor α (ER) through the involvement of Lysine Specific histone Demethylase 1 (LSD1). NR2E3 also forms a transcriptional complex with Sp1, governing the aryl hydrocarbon receptor (AHR) expression, a crucial factor in liver xenobiotic metabolism. In vivo, NR2E3 ablation results in p53 inactivation and severe liver injuries when exposed to liver toxicants, highlighting its role in activating p53. Nonetheless, the precise tumor suppressive and epigenetic role of NR2E3 in HCC remains unclear. HCC patients expressing low NR2E3 exhibit unfavorable clinical outcomes, aligning with heightened activation of the Wnt/β-catenin signaling pathway. The murine HCC models utilizing NR2E3 knockout mice show accelerated liver tumor formation and progression accompanied by enhanced activation of the Wnt/β-catenin signaling pathway and inactivation of the p53 signaling pathway. At a cellular level, losing NR2E3 increases the acquisition of aggressive cancer cell phenotype and tumorigenicity and upregulates vital genes in the Wnt/β-catenin pathway with enhanced chromatin accessibility. This event is mediated through the increased formation of active transcription complex involving Sp1, β-catenin, and p300, a histone acetyltransferase, on the promoters of target genes. Correspondingly, treatment of p300 inhibitor C646 decreased the expression of critical genes in the Wnt/β-catenin signaling pathway. These findings demonstrate that the loss of NR2E3 promotes Wnt/β-catenin signaling activation at cellular, organismal, and clinical levels. In summary, NR2E3 is a novel tumor suppressor that maintains epigenetic homeostasis, thereby preventing activation of Wnt/β-catenin signaling that promotes HCC formation. Citation Format: Yuet-Kin Leung, Sung-Gwon Lee, Jiang Wang, Shuk-Mei Ho, Nancy Rusch, Chungoo Park, Kyounghyun Kim. The loss of an orphan nuclear receptor NR2E3 augments Wnt/β-Catenin signaling via epigenetic dysregulation that links to the Sp1-β catenin-p300 interactions in hepatocellular carcinoma [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 3011.

Ceiba pentandra ethyl acetate extract improves doxorubicin antitumor outcomes against chemically induced liver cancer in rat model: a study supported by UHPLC-Q-TOF-MS/MS identification of the bioactive phytomolecules.

Hepatocellular carcinoma (HCC) is a prevalent cancer worldwide. Late-stage detection, ineffective treatments, and tumor recurrence contribute to the low survival rate of the HCC. Conventional chemotherapeutic drugs, like doxorubicin (DOX), are associated with severe side effects, limited effectiveness, and tumor resistance. To improve therapeutic outcomes and minimize these drawbacks, combination therapy with natural drugs is being researched. Herein, we assessed the antitumor efficacy of Ceiba pentandra ethyl acetate extract alone and in combination with DOX against diethylnitrosamine (DENA)-induced HCC in rats. Our in vivo study significantly revealed improvement in the liver-function biochemical markers (ALT, AST, GGT, and ALP), the tumor marker (AFP-L3), and the histopathological features of the treated groups. A UHPLC-Q-TOF-MS/MS analysis of the Ceiba pentandra ethyl acetate extract enabled the identification of fifty phytomolecules. Among these are the dietary flavonoids known to have anticancer, anti-inflammatory, and antioxidant qualities: protocatechuic acid, procyanidin B2, epicatechin, rutin, quercitrin, quercetin, kaempferol, naringenin, and apigenin. Our findings highlight C. pentandra as an affordable source of phytochemicals with possible chemosensitizing effects, which could be an intriguing candidate for the development of liver cancer therapy, particularly in combination with chemotherapeutic drugs.

Abstract C012: TIGIT expression correlates to worse overall survival in primary and metastatic pancreatic ductal adenocarcinoma

Abstract Pancreatic Cancer has a 12% 5-year survival rate due to late-stage detection and lack of many chemotherapy or targeted therapy options. TIGIT is an immune checkpoint inhibitor being explored in clinical trials in pancreatic cancer due to the implications of its ligand (CD155) promoting immune evasion. TIGIT is a marker of T cell exhaustion and plays a key role in the inhibition of anti-tumor immune responses. We hypothesize that targeted anti-TIGIT therapy, in conjunction with other therapies targeting the tumor microenvironment, could reverse immune suppression that is characteristic of pancreatic ductal adenocarcinoma (PDAC). As such, here we aim to quantify TIGIT expression using automated RNAscope technology and to correlate it with overall patient survival. We performed RNAscope in situ hybridization and immunohistochemistry on 25 primary and 4 metastatic (liver) formalin-fixed paraffin-embedded (FFPE) tissue sections from patients with histologically confirmed PDAC. A nuclear counterstain combined with an RNAscope probe specific for the human TIGIT mRNA was utilized. Slides were scanned at 40X magnification with an automated slide scanner and quantified using the ISH-IHC module of the HALO-v3.5 software. After cells were segmented based on nuclear recognition, the presence of TIGIT probe within the cytoplasm of each cell was determined and quantified. Percent positive cell values were then exported for statistical analysis in SPSS. De-identified clinical metadata was obtained from REDCap, a cloud-based HIPAA-compliant database used to compile patient data for research purposes. Gender, survival months, ethnicity, race, tumor histology, stage, cancer history, treatment status, and comorbidities were evaluated. We analyzed a cohort of 29 histologically-confirmed surgically resected PDACs, comprised of 25 primary pancreatic tumor samples and 4 metastatic liver core biopsies. The mean percentage of TIGIT positive cells was 63%. From this, the TIGIT high versus low threshold was determined to be 70%. High TIGIT was associated with significantly lower overall survival (p=.013), suggesting that expression of TIGIT and T cell exhaustion may predict overall worse survival. The median overall survival months for the TIGIT low group was 321 months versus 106 months for the TIGIT high group. This data leads us to hypothesize that expression of TIGIT and T cell exhaustion may predict worse overall survival. Anti-TIGIT medications are currently in clinical trials for metastatic PDAC. Here we demonstrate that high TIGIT expression is associated with an overall worse prognosis and present a novel, automated TIGIT detection protocol that may be used to determine if patients are candidates for anti-TIGIT therapy. In a clinical setting, this novel, automated biomarker TIGIT detection protocol could be used to select candidates for anti-TIGIT therapy. This may lead to improved overall patient survival. Citation Format: Madison George, Julie Clark, Kendyll Gartrelle, Georges Nassif, Donald Rempinski, Daniel Long, Hui-Ju Wen, Simone Benitz, Samuel Zwernik, Rupen Shah, Hakmin Park, David Kwon, Philip Philip, Gazala Khan, Howard Crawford, Brian Theisen, Nina Steele. TIGIT expression correlates to worse overall survival in primary and metastatic pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C012.

Toward Precision Medicine: Exploring the Landscape of Biomarkers in Acute Kidney Injury.

Acute kidney injury (AKI) remains a complex challenge with diverse underlying pathological mechanisms and etiologies. Current detection methods predominantly rely on serum creatinine, which exhibits substantial limitations in specificity and poses the issue of late-stage detection of kidney injury. In this review, we propose an up-to-date and comprehensive summary of advancements that identified novel biomarker candidates in blood and urine and ideal criteria for AKI biomarkers such as renal injury specificity, mechanistic insight, prognostic capacity, and affordability. Recently identified biomarkers not only indicate injury location but also offer valuable insights into a range of pathological processes, encompassing reduced glomerular filtration rate, tubular function, inflammation, and adaptive response to injury. The clinical applications of AKI biomarkers are becoming extensive and serving as relevant tools in distinguishing acute tubular necrosis from other acute renal conditions. Also, these biomarkers can offer significant insights into the risk of progression to chronic kidney disease CKD and in the context of kidney transplantation. Integration of these biomarkers into clinical practice has the potential to improve early diagnosis of AKI and revolutionize the design of clinical trials, offering valuable endpoints for therapeutic interventions and enhancing patient care and outcomes.

The role of biomarkers in diagnosing breast cancer: a review

Breast cancer remains a leading cause of cancer-related mortality worldwide, with late-stage detection significantly impacting prognosis. The emergence of novel biomarkers has revolutionized early diagnosis and prognosis, offering hope for improved outcomes. This review comprehensively explores the diverse types of biomarkers and evaluates their pivotal role in breast cancer diagnosis. Our review employed a systematic search strategy across reputable scientific platforms, utilizing varied keywords to ensure comprehensive coverage. Articles were meticulously selected based on predefined inclusion criteria. New emerging biomarkers are thought to be promising in early diagnosis and, thereby, prognosis. The main uses of tumor markers are to track the effectiveness of treatment and to identify recurrences of the disease before they manifest clinically noticeable symptoms.

Abstract C051: Deciphering the role of stress-survival pathway genes and mutations in Hispanic colorectal cancer disparities

Abstract This study aims to determine the role of stress-survival pathway (SSP) genes and the associated mutations in Hispanic colorectal cancer (CRC) disparities, particularly in young and late-stage Hispanic CRC. CRC is the second most-deadliest among all cancers and the third leading cause of cancer in the United States (US). An overall reduction in CRC cases among individuals aged 50 and older has been reported, albeit there is an alarming rise in the incidence of early-onset (patients under the age of 50) CRC. The increase in the early onset of CRC is concerning as these cases tend to appear with higher pathologic grades and have a higher risk of recurrence and metastasis. The incidence of early-onset CRC is almost two times in Hispanics (16.5%) when compared to that in non-Hispanic White (NHWs; 8.7%). Access to health care for Hispanics is limited, as a result, they also tend to present with later-stage CRC when compared to NHWs (32% versus 19% of Stage IV), which likely accounts for their inferior age-adjusted survival after CRC diagnosis. Since Hispanics have the highest percentage of young people and comprise ~18% of the US population, the rising incidence of early-onset and late-stage detection of CRC among Hispanics coupled with the rapid growth in this segment of the population makes it imperative to correctly assess the causes behind the observed disparities in Hispanics with early-onset and late-stage CRC. Our previous study, using in-silico analyses of CRC datasets, helped us identify twenty-eight oxidative stress-response and apoptosis-associated genes that are expressed differently in CRC when compared to normal colon tissues. Expressions of these SSP genes (mRNA and protein levels) were validated in CRC cell lines, cDNA arrays, and human colon cancer tissue microarrays (TMAs). Finally, we have confirmed the differential expression of all twenty-eight transcripts in Hispanic and NHW CRC, and corresponding normal adjacent to the tumor (NAT) tissues. Interestingly, we have identified distinct transcripts that are differentially expressed in Hispanics with early-onset (PDCD2L, CDK1, and PRDX4) and late-stage (CCNB1, CDK4, CHEK1, and MCM10) CRC. We have also identified the mutations associated with the SSP genes by analyzing the ClinVar and NHGRI-EBI GWAS databases. These findings have laid the foundation to characterize further, the roles played by these genes in the molecular basis for Hispanic CRC disparities at early-onset and late stages. Differential Expression of mRNAs in CRC tissues between the two ethnic groups (Hispanics and NHW) and corresponding NATs has been determined by RNA-seq. Ingenuity Pathway Analysis (IPA) has been performed to correlate SSP genes with the canonical CRC pathways to better understand the roles played by these genes, their effects on the downstream cancer-related pathways, and on the early-onset and late-stage Hispanic CRC. These putative ethnicity-specific biomarkers or targets could be used for developing novel prognostic tools or therapeutic interventions for Hispanic CRC patients in the future. Citation Format: Sourav Roy, Brian I. Grajeda, Md Zahirul Islam Khan, Urbashi Basnet, Soumya Nair. Deciphering the role of stress-survival pathway genes and mutations in Hispanic colorectal cancer disparities [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C051.

Diabetes Mellitus across the Arabo-Islamic World: A Revolution.

Mankind continues to suffer from the ever-growing diabetes epidemic and the rapid rise of type 2 diabetes mellitus (T2DM). This metabolic disease has been studied since ancient civilizations. The Arabo-Islamic civilization excelled in establishing some of the most notable discoveries and teachings that remained the blueprint for years to come in the field of diabetology. This article aimed to review the ancient history of diabetes mellitus, with its main focus on the Arabo-Islamic civilization, and to report our subjective views and analysis of some of the past recommendations based on modern-day findings. Discussion. It is natural to have the teachings of medicine dynamically inspired by one civilization to another, as various fields continue to expand and evolve. This also applies to diabetology as the Arabo-Islamic world used the outlines of prior civilizations to revolutionize the understanding of the disease. Al-Razi and Ibn Sina are probably two of the most renowned polymaths in history, and their contributions to diabetology are well documented. Ibn Maymun's postulation about the higher prevalence of diabetes in Egypt as compared to Andalusia is something to be carefully studied. It could be that diabetes mellitus' underdiagnosis and late-stage detection are some of the major reasons for the disparity between the two mentioned regions. Modern-day Arabo-Islamic scholars continue to excel in revolutionizing diabetology. The Arabo-Islamic world houses an impressive bout of scholars who have contributed since the ancient times to diabetology. This scientific locomotion shows no signs of stopping, as it continues to shine during the present day, and likely in the future.

Emerging cell cycle related non-coding RNA biomarkers from saliva and blood for oral squamous cell carcinoma.

The unnotified or undifferentiable early stages of oral squamous cell carcinoma (OSCC) progression are the prime reasons for late-stage detection and poor survival outcomes of oral cancer. This review summarizes the prior research and recent advancements on the influence of dysregulated non-coding RNA (ncRNA) on cell cycle and their employability as diagnostic and prognostic biomarkers of oral cancer. The literature search was performed using the following keywords: 'serum/saliva non-coding RNAs' and 'serum/saliva non-coding RNAs and cell cycle', 'serum/saliva dysregulated ncRNAs and cell cycle', 'Cdk/CKI and ncRNAs', 'tissue ncRNAs' concerning 'oral cancer''. The compiled data focuses mainly on the diagnostic and prognostic significance of MicroRNAs (miRNAs), Circular RNAs (circRNAs), and Long noncoding RNAs (lncRNAs) on oral cancer and all other cancers as well as subject-relevant articles published in languages other than English are beyond the scope of this review and excluded from the study. Moreover, articles focusing on DNA, protein, and metabolite markers are eliminated from the study. While there exist various potential biomolecules such as DNA, RNA, proteins, metabolites, and specific antigens representing predictive biomarkers in body fluids for oral cancer, this review completely focuses on non-coding RNAs restricted to saliva and blood, picking out a few of the reliable ones amongst the recent investigations based on the sophisticated techniques, cohort, and sensitivity as well as specificity, i.e., salivary miR-1307-5p, miR-3928, hsa_circ_0001874 and ENST00000412740, NR_131012, ENST00000588803, NR_038323, miR-21 in circulation. Thus, further studies are required to clinically confirm the usage of these non-invasive biomarkers in oral cancer.