Search Results (583)

In today’s oncology, immunotherapy arises as a potent complement for conventional cancer treatment, allowing for obtaining better patient outcomes. B7-H3 (CD276) is a member of the B7 protein family, which emerged as an attractive target for the treatment of various tumors. The molecule modulates anti-cancer immune responses, acting through diverse signaling pathways and cell populations. It has been implicated in the pathogenesis of numerous malignancies, including melanoma, gliomas, lung cancer, gynecological cancers, renal cancer, gastrointestinal tumors, and others, fostering the immunosuppressive environment and marking worse prognosis for the patients. B7-H3 targeting therapies, such as monoclonal antibodies, antibody–drug conjugates, and CAR T-cells, present promising results in preclinical studies and are the subject of ongoing clinical trials. CAR-T therapies against B7-H3 have demonstrated utility in malignancies such as melanoma, glioblastoma, prostate cancer, and RCC. Moreover, ADCs targeting B7-H3 exerted cytotoxic effects on glioblastoma, neuroblastoma cells, prostate cancer, and craniopharyngioma models. B7-H3-targeting also delivers promising results in combined therapies, enhancing the response to other immune checkpoint inhibitors and giving hope for the development of approaches with minimized adverse effects. However, the strategies of B7-H3 blocking deliver substantial challenges, such as poorly understood molecular mechanisms behind B7-H3 protumor properties or therapy toxicity. In this review, we discuss B7-H3’s role in modulating immune responses, its significance for various malignancies, and clinical trials evaluating anti-B7-H3 immunotherapeutic strategies, focusing on the clinical potential of the molecule. Full article

Transcription factors play crucial roles in regulating gene expression, and any dysregulation in their levels could be involved in cancer progression. The role of androgen receptors (AR) and zinc finger (ZNF) proteins in tumors, like prostate cancer (PC), remains poorly understood. Moreover, due to the multifaceted transcriptional behavior of ARs and ZNFs, their biological role in cancer progression may also depend on the interplay with micro-RNAs (miRNAs). Based on The Cancer Genome Atlas (TCGA) database, we analyzed the expression levels of zinc finger transcripts and ARs in PC. Specifically, exploring their involvement in cancer progression and regulation by miRNAs. The analysis relied on several tools to create a multivariate combination of the original biomarkers to improve their diagnostic efficacy. Multidimensional Scaling (MDS) identified two new dimensions that were entered into a regression analysis to determine the best predictors of overall survival (OS) and disease-free interval (DFI). A combination of both dimensions predicted almost 50% (R² = 0.46) of the original variance of OS. Kaplan–Meier survival analysis also confirmed the significance of these two dimensions regarding the clinical output. This study showed preliminary evidence that several transcription factor expression levels belonging to the zinc family and related miRNAs can effectively predict patients’ overall PC survivability. Full article

Carboxypeptidase A4 (CPA4) is an exopeptidase that cleaves peptide bonds at the C-terminal domain within peptides and proteins. It preferentially cleaves peptides with terminal aromatic or branched chain amino acid residues such as phenylalanine, tryptophan, or leucine. CPA4 was first discovered in prostate cancer cells, but it is now known to be expressed in various tissues throughout the body. Its physiologic expression is governed by latexin, a noncompetitive endogenous inhibitor of CPA4. Nevertheless, the overexpression of CPA4 has been associated with the progression and aggressiveness of many malignancies, including prostate, pancreatic, breast and lung cancer, to name a few. CPA4’s role in cancer has been attributed to its disruption of many cellular signaling pathways, e.g., PI3K-AKT-mTOR, STAT3-ERK, AKT-cMyc, GPCR, and estrogen signaling. The dysregulation of these pathways by CPA4 could be responsible for inducing epithelial--mesenchymal transition (EMT), tumor invasion and drug resistance. Although CPA4 has been found to regulate cancer aggressiveness and poor prognosis, no comprehensive review summarizing the role of CPA4 in cancer is available so far. In this review, we provide a brief description of peptidases, their classification, history of CPA4, mechanism of action of CPA4 as a peptidase, its expression in various tissues, including cancers, its role in various tumor types, the associated molecular pathways and cellular processes. We further discuss the limitations of current literature linking CPA4 to cancers and challenges that prevent using CPA4 as a biomarker for cancer aggressiveness and predicting drug response and highlight a number of future strategies that can help to overcome the limitations. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background/Objectives: Prostate cancer (PCa) remains a major global health issue, associated with significant mortality and morbidity. Despite advances in diagnosis and treatment, predicting biochemical recurrence (BCR) after radical prostatectomy remains challenging, highlighting the need for reliable biomarkers to guide prognosis and therapy. The study aimed to evaluate the prognostic significance of the PTEN and ERG biomarkers in predicting BCR and tumor progression in PCa patients who underwent radical prostatectomy. Methods: This study consisted of a cohort of 91 patients with localized PCa who underwent radical prostatectomy between 2016 and 2022. From this cohort, 77 patients were selected for final analysis. Tissue microarrays (TMAs) were constructed from paraffin blocks, and immunohistochemical (IHC) staining for PTEN and ERG was performed using specific antibodies on the Ventana BenchMark ULTRA system (Roche Diagnostics, Indianapolis, IN, USA). Stained sections were evaluated and correlated with clinical and pathological data. Results: PTEN expression showed a significant negative correlation with BCR (r = −0.301, p = 0.014), indicating that reduced PTEN expression is associated with increased recurrence risk. PTEN was not significantly linked to PSA levels, tumor stage, or lymph node involvement. ERG expression correlated positively with advanced pathological tumor stage (r = 0.315, p = 0.005) but was not associated with BCR or other clinical parameters. Conclusions: PTEN appears to be a valuable prognostic marker for recurrence in PCa, while ERG may indicate tumor progression. These findings support the potential integration of PTEN and ERG into clinical practice to enhance risk stratification and personalized treatment, warranting further validation in larger patient cohorts. Full article

Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated the efficiency of seven ML models to diagnose the different PCa grades, changing the input variables. Our studied sample comprised 214 men who underwent bpMRI in different imaging centers. Seven ML algorithms were compared using radiomic features extracted from T2-weighted (T2W) and diffusion-weighted (DWI) MRI, with and without the inclusion of Prostate-Specific Antigen (PSA) values. The performance of the models was evaluated using the receiver operating characteristic curve analysis. The models’ performance was strongly dependent on the input parameters. Radiomic features derived from T2WI and DWI, whether used independently or in combination, demonstrated limited clinical utility, with AUC values ranging from 0.703 to 0.807. However, incorporating the PSA index significantly improved the models’ efficiency, regardless of lesion location or degree of malignancy, resulting in AUC values ranging from 0.784 to 1.00. There is evidence that ML methods, in combination with radiomic analysis, can contribute to solving differential diagnostic problems of prostate cancers. Also, optimization of the analysis method is critical, according to the results of our study. Full article

Background: Prostate cancer is a common malignancy among men worldwide, with various histopathologic features that influence its progression and prognosis. One such feature is perineural invasion (PNI), which has been associated with aggressive disease. In this retrospective study, we analyzed genomic alterations associated with PNI in patients who underwent radical prostatectomy. Methods: A total of 421 prostate cancer patients who underwent radical prostatectomy without neoadjuvant therapy were identified from The Cancer Genome Atlas. PNI was present in 378 patients (89.8%) and absent in 43 (10.2%). Differentially expressed genes were identified, and mRNA expression levels of key genes were analyzed. The prognostic significance of these genes was evaluated using log-rank tests and Cox proportional hazards models to estimate hazard ratios and 95% confidence intervals. Results: Levels of COL9A3, ASPN, ESR1, MUC1, PIP, SFRP4, KRT19, CLDN1, and COMP were significantly higher in the tumor tissues of patients in the PNI group compared to those in the non-PNI group (q < 0.05), and RYR2, MME, and AZGP1 expression levels were significantly higher in the non-PNI group (q < 0.05). A high mRNA expression level of AZGP1 was associated with longer disease-free survival, whereas high mRNA expressions of ASPN, COMP, RYR2, and SFRP4 were associated with shorter disease-free survival. Conclusions: Prostate cancer patients with genomic alterations associated with PNI may face a higher risk of disease progression after prostatectomy, highlighting the need for further prospective studies to validate these findings. Full article

BRCA1 and BRCA2 are associated with advanced prostate cancer progression and poor prognosis. Copy number variants (CNVs) of these genes play a crucial role in guiding targeted treatments, particularly for patients receiving PARP inhibitors. However, CNV detection using multiplex ligation-dependent probe amplification (MLPA) is often limited by tumor heterogeneity, leading to ambiguous results. This study therefore aimed to evaluate BRCA1/2 CNVs in advanced prostate cancer patients using droplet digital PCR (ddPCR) and compare the results with MLPA. DNA from 11 advanced prostate cancer tissues was analyzed using both methods, in parallel with four cell lines and seven healthy volunteers. Our findings revealed that ddPCR effectively classified normal CNV groups—including normal control cell lines, healthy volunteers, and samples with normal MLPA final ratios—from deletion groups, which included deletion control cell lines, samples with deletion final ratios from MLPA, and cases with previously ambiguous results. Interestingly, two cases involving BRCA1 and one case involving BRCA2 exhibited ambiguous results using MLPA; however, ddPCR enabled more precise classification by applying the Youden Index from ROC analysis and identifying optimal cutoff values of 1.35 for BRCA1 and 1.55 for BRCA2. These optimal thresholds allowed ddPCR to effectively reclassify the ambiguous MLPA cases into the deletion group. Overall, ddPCR could offer a more sensitive and reliable approach for CNV detection in heterogeneous tissue samples and demonstrates strong potential as a biomarker tool for guiding targeted therapy in advanced prostate cancer patients. However, further validation in larger cohorts is necessary to optimize cutoff precision, confirm diagnostic performance, and evaluate the full clinical utility of ddPCR. Full article

Background: Potent androgen receptor pathway inhibitors induce small cell neuroendocrine prostate cancer (SCNC), a highly aggressive subtype of metastatic androgen deprivation-resistant prostate cancer (ARPC) with limited treatment options and poor survival rates. Patients with metastases in the liver have a poor prognosis relative to those with bone metastases alone. The mechanisms that underlie the different behavior of ARPC in bone vs. liver may involve factors intrinsic to the tumor cell, tumor microenvironment, and/or systemic factors, and identifying these factors is critical to improved diagnosis and treatment of SCNC. Metabolic reprogramming is a fundamental strategy of tumor cells to colonize and proliferate in microenvironments distinct from the primary site. Understanding the metabolic plasticity of cancer cells may reveal novel approaches to imaging and treating metastases more effectively. Methods: Using magnetic resonance (MR) imaging and spectroscopy, we interrogated the physiological and metabolic characteristics of SCNC patient-derived xenografts (PDXs) propagated in the bone and liver, and used correlative biochemical, immunohistochemical, and transcriptomic measures to understand the biological underpinnings of the observed imaging metrics. Results: We found that the influence of the microenvironment on physiologic measures using MRI was variable among PDXs. However, the MR measure of glycolytic capacity in the liver using hyperpolarized ¹³C pyruvic acid recapitulated the enzyme activity (lactate dehydrogenase), cofactor (nicotinamide adenine dinucleotide), and stable isotope measures of fractional enrichment of lactate. While in the bone, the congruence of the glycolytic components was lost and potentially weighted by the interaction of cancer cells with osteoclasts/osteoblasts. Conclusion: While there was little impact of microenvironmental factors on metabolism, the physiological measures (cellularity and perfusion) are highly variable and necessitate the use of combined hyperpolarized ¹³C MRI and multiparametric (anatomic, diffusion-, and perfusion- weighted) ¹H MRI to better characterize pre-treatment tumor characteristics, which will be crucial to evaluate treatment response. Full article

Genomic score testing is increasingly being integrated into the management of prostate cancer (PCa) to overcome the limitations of traditional clinical and pathological parameters. Genomic tools will represent essential components of precision medicine, supporting risk stratification, therapeutic decision-making, and personalized screening strategies. Genomic score tests can be broadly classified into two main categories: polygenic risk scores (PRSs) and tumor-derived genomic classifiers (GCs). While not yet standard in routine practice, several international guidelines recommend their selective use when results are likely to impact clinical management. PRSs estimate an individual’s susceptibility to PCa based on the cumulative effect of multiple low-penetrance germline genetic variants. These scores show promise in enhancing early detection strategies and identifying men at higher genetic risk who may benefit from tailored screening protocols. Tumor-based GCs assays provide prognostic information that complements conventional clinical and pathological parameters, and are used to guide treatment decisions, including eligibility for active surveillance (AS) or adjuvant therapy after treatment of the primary tumor. This review summarizes and analyzes the current evidence on genomic testing in PCa, with a focus on the available assays, their clinical applications, and their predictive and prognostic value across the disease spectrum. When integrated with clinical and pathological parameters, these tools have the potential to significantly enhance personalized care and should be increasingly considered in routine clinical practice. Full article

Jumping translocations (JTs) are rare chromosomal abnormalities that play a crucial role in the pathogenesis of various cancer types. These rearrangements, especially those involving chromosome 1q, are frequently associated with tumor progression, therapeutic resistance, and poor prognosis. One gene of particular interest, human Jumping Translocation Breakpoint (JTB), has been identified at the site of translocation breakpoints and exhibits complex, context-dependent roles in cancer biology. JTB protein functions as a pivotal regulator in mitosis, chromosomal segregation, apoptosis, and cellular metabolism. It is functionally linked with the chromosomal passenger complex (CPC) and is implicated in processes such as epithelial–mesenchymal transition (EMT), immune evasion, and therapy resistance, especially in breast and prostate cancers. Advances in genomic, transcriptomic, and proteomic research have highlighted the significant potential of JTB as a diagnostic biomarker and a target for therapeutic interventions. This review underscores the dual role of JTB as both a tumor suppressor and oncogene, depending on the cellular context, and advocates for its continued investigation at the genomic, transcriptomic, and proteomic levels. Understanding JTB’s multifaceted contributions to tumor biology may pave the way for novel biomarkers and targeted treatments in cancer management. Full article

Background/Objectives: Spinal metastatic disease is a life-altering problem for individuals with cancer. Prognostication is key for tailored treatment of spinal metastases. This manuscript provides a comprehensive overview of the genomic profiles of metastatic spine tumors and investigates the potential of mutational data to stratify overall survival (OS) across various histologies. Methods: This is a cohort study of consecutive patients with spine metastatic disease whose tumors were sequenced on a next generation sequencing platform; a machine learning (ML) algorithm was used to stratify OS risk. Results: Targeted sequencing and stratification of OS risk of 282 spine metastases (breast (84), non-small cell lung (56), prostate (49), other (93)) was performed. TP53 (HR 1.80; 95% CI 1.26, 2.56) and KEAP1 (HR 3.95, 95% CI 2.24, 6.98) mutations were associated with poor survival across the entire cohort in univariate Cox proportional hazards models. The ML algorithm categorized breast cancer metastasis into low- and high-risk groups, revealing a median OS of 71 compared to 22 months (HR 3.3, p < 0.001). TP53 mutations and ESR1 mutations conferred poor prognosis. In lung cancer, low- and high-risk groups with median OS of 30 and 6 months (HR 8.3, p < 0.001), respectively, were identified with poor prognosis linked to MET amplification. No significant prognostic associations were identified for spinal prostate metastases. Conclusions: Metastatic spine tumor molecular data allows for the identification of prognostic groups. We present an open-source machine learning algorithm utilizing genomic mutational data that may aid in prognostication and tailored decision making. Full article

Background/Objectives: Pulmonary lymphangitic carcinomatosis (PLC) is a rare, aggressive manifestation of metastatic cancer characterized by lymphatic infiltration of the lungs, typically indicating advanced disease and poor prognosis. Methods: This comprehensive narrative review evaluates the role of [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) imaging in assessing PLC. Results: Current evidence demonstrates that [18F]FDG PET/CT achieves high diagnostic accuracy, with sensitivity and specificity ranging from 86 to 97% and 84 to 100%, respectively, particularly when employing semiquantitative metrics such as peritumoral standardized uptake value (SUVmax) thresholds (e.g., ≥2.1). PET/CT surpasses high-resolution computed tomography (HRCT) in distinguishing PLC from mimics like pulmonary sarcoidosis by identifying distinct metabolic patterns: bronchovascular hypermetabolism in PLC versus subpleural nodular uptake in sarcoidosis. Prognostically, metabolic tumor burden (e.g., SUVmax × involved lobes) and novel cPLC classifications (localized to the ipsilateral or contralateral lung) independently predict progression-free survival. However, challenges persist, including non-specific tracer uptake in inflammatory conditions and variability in SUV measurements due to technical factors. Emerging digital PET/CT systems, with enhanced spatial resolution, may improve the detection of focal PLC and reduce false negatives. While [18F]FDG PET/CT is invaluable for whole-body staging, therapeutic monitoring and biopsy guidance, the standardization of protocols and multicenter validation of prognostic models are critical for clinical integration. Future research should explore novel tracers (e.g., PSMA for prostate cancer-related PLC) and machine learning approaches to refine diagnostic and prognostic accuracy. Conclusions: This review underscores the role and the transformative potential of [18F]FDG PET/CT in PLC management while advocating for rigorous standardization to maximize its clinical utility. Full article

Background/Objectives: Prognostic information for nonmetastatic prostate cancer (nmPC) patients with prevalent vertebral fractures (PVFs) is very limited. Vertebral fractures can impair physical function, limit activities of daily living, and decrease quality of life. Prevention of vertebral fractures may be important to improve patient prognosis. This study aims to investigate the impact of the presence and severity of PVFs on overall survival in patients with nmPC undergoing androgen deprivation therapy (ADT). Methods: A total of 275 men (median age: 73 years) with nmPC who underwent ADT were studied retrospectively. The median observation period was 55 months. Variables included age, body mass index, T classification, N classification, Gleason score, and pretreatment serum prostate-specific antigen levels. PVF was diagnosed from the sagittal computed tomography images of Th1 to L5 before initiating ADT, and the severity was determined by the number of PVFs and the Semiquantitative (SQ) method. Hazard ratios and 95% confidence intervals for overall survival were calculated using the Cox proportional hazards model. Results: During the observation period, 30 patients died from all causes. Multivariate Cox regression analysis identified multiple PVFs and high-grade PVFs, as determined by the SQ method, as significant predictors of overall survival. The analysis utilized two adjustment models: one adjusted for age only and the other adjusted for age, Gleason score, and clinical T stage. Conclusions: Multiple PVFs and high-grade PVF determined by the SQ method prior to ADT initiation were associated with higher all-cause mortality in nmPC patients treated with ADT. Full article

Background: Bisphosphonate-related osteonecrosis of the jaw (BRONJ) represents a severe complication associated with bisphosphonate therapy commonly used in patients with osteoporosis and malignancies. Methods: This retrospective study evaluates the risk factors and clinical outcomes of BRONJ patients treated at the Oral and Maxillofacial Surgery Clinic in Iaşi, Romania, with the goal of optimizing preventive and therapeutic strategies. Data from 72 BRONJ patients treated between January 2013 and December 2023 were analyzed. Results: The majority (83.3%) of patients had underlying malignancies, predominantly breast and prostate cancers. The mandible was most affected, with tooth extraction identified as the primary triggering event. Systemic comorbidities, notably arterial hypertension, diabetes mellitus, and concurrent chemotherapy, were significantly associated with increased BRONJ severity. Surgical intervention was frequently required, with sequestrectomy being the predominant procedure, reflecting advanced disease at the time of diagnosis. Conclusions: The findings underline the critical importance of early identification, preventive dental management, and a collaborative multidisciplinary approach to improve patient prognosis. Full article