Search Results (716)

Prostate cancer (PC) remains a leading cause of malignancy in men worldwide, with current diagnostic methods such as prostate-specific antigen (PSA) testing and tissue biopsies facing limitations in specificity, invasiveness, and ability to capture tumor heterogeneity. Liquid biopsy, especially analysis of circulating tumor DNA (ctDNA), has emerged as a transformative tool for non-invasive detection, real-time monitoring, and treatment selection for PC. This review examines the role of ctDNA in both localized and metastatic PCs, focusing on its utility in early detection, risk stratification, therapy selection, and post-treatment monitoring. In localized PC, ctDNA-based biomarkers, including ctDNA fraction, methylation patterns, fragmentation profiles, and mutations, demonstrate promise in improving diagnostic accuracy and predicting disease recurrence. For metastatic PC, ctDNA analysis provides insights into tumor burden, genomic alterations, and resistance mechanisms, enabling immediate assessment of treatment response and guiding therapeutic decisions. Despite challenges such as the low ctDNA abundance in early-stage disease and the need for standardized protocols, advances in sequencing technologies and multimodal approaches enhance the clinical applicability of ctDNA. Integrating ctDNA with imaging and traditional biomarkers offers a pathway to precision oncology, ultimately improving outcomes. This review underscores the potential of ctDNA to redefine PC management while addressing current limitations and future directions for research and clinical implementation. Full article

Background/Objectives: Neoadjuvant radiochemotherapy and perioperative chemotherapy are both well-established treatment strategies for locally advanced adenocarcinoma of the esophagus (EAC) and the esophagogastric junction (AEGJ). However, recent knowledge controversially discusses whether neoadjuvant radiotherapy or perioperative chemotherapy represents superior therapeutic options to prolong survival or cause less toxicity. Methods: We retrospectively analyzed 76 patients with locally advanced EAC or AEGJ treated at our tertiary cancer center between January 2015 and March 2023. Patients received either perioperative FLOT chemotherapy (n = 36) or neoadjuvant radiochemotherapy following the CROSS protocol (n = 40), followed by surgical resection and standardized follow-up. We compared survival outcomes, toxicity profiles, treatment compliance, and surgical results between the two groups. Results: There were no statistically significant differences between FLOT and CROSS treatments in five-year loco-regional controls (LRC: 61.5% vs. 68.6%; p = 0.81), progression-free survival (PFS: 33.9% vs. 42.8%; p = 0.82), overall survival (OS: 60.2% vs. 63.4%; p = 0.91), or distant controls (DC: 42.1% vs. 56.5%; p = 0.39). High-grade hematologic toxicities did not significantly differ between groups (p > 0.05). Treatment compliance was lower in the FLOT group, with 50% (18/36) not completing all the planned chemotherapy cycles, compared to 17.5% (7/40) in the CROSS group. All the patients in the CROSS group received the full radiotherapy dose. Surgical outcomes and post-surgical tumor status were comparable between the groups. Conclusions: Although perioperative chemotherapy with FLOT has recently become a standard of care for locally advanced EAC and AEGJ, neoadjuvant radiochemotherapy per the CROSS protocol remains a well-tolerated alternative. In appropriately selected patients, both approaches yield comparable oncological outcomes. Full article

The classification of cancer based on gene expression profiles is a central challenge in precision oncology due to the high dimensionality and low sample size inherent in microarray datasets. Effective gene selection is crucial for improving classification accuracy while minimizing computational overhead and model complexity. This study introduces Genetic-Embedded Nuclear Reaction Optimization (GNR), a novel hybrid metaheuristic that enhances the conventional Nuclear Reaction Optimization (NRO) algorithm by embedding a genetic uniform crossover mechanism into its fusion phase. The proposed algorithm leverages a two-stage process: an initial F-score filtering step to reduce dimensionality, followed by GNR-driven optimization to identify compact, informative gene subsets. Evaluations were conducted on six widely used microarray cancer datasets, with Support Vector Machines (SVM) employed as classifiers and performance assessed via Leave-One-Out Cross-Validation (LOOCV). Results show that GNR consistently outperforms the original NRO and several benchmark hybrid algorithms, achieving 100% classification accuracy with significantly smaller gene subsets across all datasets. These findings confirm the efficacy of the genetic-embedded fusion strategy in enhancing local exploitation while preserving the global search capabilities of NRO, thereby offering a robust and interpretable approach for gene selection in cancer classification. Full article

Liposomes (LPs) represent one of the most effective nanoscale platforms for drug delivery in cancer therapy due to their favorable pharmacokinetic and various body tissue compatibility profiles. Building on recent findings showing that carbon dots derived from N-hydroxyphthalimide (CDs-NHF) possess intrinsic antitumor activity, herein, we investigate the possibility of preparing complex nano-platforms composed of LPs encapsulating CDs-NHF and/or doxorubicin (DOX) for breast and lung cancer. Various LP formulations were prepared and characterized using Cryo-TEM and Cryo-SEM for morphological analysis, while zeta potential and fluorescence assessments confirmed their stability and optical properties. Cellular effects were evaluated through immunofluorescence microscopy and proliferation assays. LPs-CDs-NHF significantly reduced cancer cell viability at lower concentrations compared to free CDs-NHF, and this effect was further amplified when combined with doxorubicin. Mechanistically, the liposomal formulations downregulated key signaling molecules including pAKT, pmTOR, and pERK, indicating the disruption of cancer-related pathways. These findings suggest that LPs containing CDs-NHF, either alone or in combination with DOX, exhibit synergistic antitumor activity and hold strong promise as multifunctional nanocarriers for future oncological applications. Full article

Background: Breast cancer is the most common neoplasm in women. Despite effective treatments, sequelae such as decreased muscle strength, upper limb dysfunction, and tissue changes are common, highlighting the need for functional assessments during rehabilitation. This study analysed the morphofunctional profile of the upper limbs in breast cancer survivors, comparing muscle strength and ultrasound findings between groups with and without lymphoedema, as well as between ipsilateral and contralateral limbs. Methods: This cross-sectional study included female breast cancer survivors treated at an oncology physical therapy clinic. Muscle strength was measured using dynamometry (handgrip and arm flexor strength), and ultrasound assessed the thickness of the dermal–epidermal complex (DEC), subcutaneous tissue (SUB), and muscle (MT). Results: The upper limbs of 41 women were evaluated. No significant differences were observed between those with and without breast cancer-related lymphoedema (BCRL). When comparing the ipsilateral and contralateral limbs, significant reductions were observed in arm flexor strength (p < 0.001; 95% CI: −9.77 to −2.50), handgrip strength (p < 0.001; 95% CI: −4.10 to −1.22), and tissue thickness, with increased DEC thickness on the forearm (0.20 mm; p = 0.022) and arm flexors (0.25 mm; p < 0.001) of the ipsilateral limb. Conclusion: Significant differences in muscle strength and tissue structure between ipsilateral and contralateral limbs may reflect surgical and local pathophysiological effects. A trend toward reduced values for these parameters was also noted in limbs with BCRL, reinforcing the importance of future research to elucidate underlying mechanisms and guide more effective therapeutic strategies. Full article

Background/Objectives: Myelodysplastic syndrome (MDS) is a heterogeneous clonal hematopoietic disorder characterized by ineffective hematopoiesis and leukemic transformation risk. Current therapies show limited efficacy, with ~50% of patients failing hypomethylating agents. This review aims to synthesize recent discoveries through an integrated network model and examine translation into precision therapeutic approaches. Methods: We reviewed breakthrough discoveries from the past three years, analyzing single-cell multi-omics technologies, epitranscriptomics, stem cell architecture analysis, and precision medicine approaches. We examined cell-type-specific splicing aberrations, distinct stem cell architectures, epitranscriptomic modifications, and microenvironmental alterations in MDS pathogenesis. Results: Four interconnected mechanisms drive MDS: genetic alterations (splicing factor mutations), aberrant stem cell architecture (CMP-pattern vs. GMP-pattern), epitranscriptomic dysregulation involving pseudouridine-modified tRNA-derived fragments, and microenvironmental changes. Splicing aberrations show cell-type specificity, with SF3B1 mutations preferentially affecting erythroid lineages. Stem cell architectures predict therapeutic responses, with CMP-pattern MDS achieving superior venetoclax response rates (>70%) versus GMP-pattern MDS (<30%). Epitranscriptomic alterations provide independent prognostic information, while microenvironmental changes mediate treatment resistance. Conclusions: These advances represent a paradigm shift toward personalized MDS medicine, moving from single-biomarker to comprehensive molecular profiling guiding multi-target strategies. While challenges remain in standardizing molecular profiling and developing clinical decision algorithms, this systems-level understanding provides a foundation for precision oncology implementation and overcoming current therapeutic 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: Colorectal liver metastasis (CRLM) represents a major clinical challenge in oncology, affecting 25–50% of colorectal cancer patients and significantly impacting survival. While multimodal therapies—including surgical resection, systemic chemotherapy, and local ablative techniques—have improved outcomes, prognosis remains heterogeneous due to variations in tumor biology, patient factors, and institutional practices. Methods: This review synthesizes current evidence on prognostic factors influencing CRLM management, encompassing clinical (e.g., tumor burden, anatomic distribution, timing of metastases), biological (e.g., CEA levels, inflammatory markers), and molecular (e.g., RAS/BRAF mutations, MSI status, HER2 alterations) determinants. Results: Key findings highlight the critical role of molecular profiling in guiding therapeutic decisions, with RAS/BRAF mutations predicting resistance to anti-EGFR therapies and MSI-H status indicating potential responsiveness to immunotherapy. Emerging tools like circulating tumor DNA (ctDNA) and radiomics offer promise for dynamic risk stratification and early recurrence detection, while the gut microbiome is increasingly recognized as a modulator of treatment response. Conclusions: Despite advancements, challenges persist in standardizing resectability criteria and integrating multidisciplinary approaches. Current guidelines (NCCN, ESMO, ASCO) emphasize personalized strategies but lack granularity in terms of incorporating novel biomarkers. This exhaustive review underscores the imperative for the development of a unified, biomarker-integrated framework to refine CRLM management and improve long-term outcomes. Full article

Background/Objectives: Hepatic cancers, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are major global health concerns due to rising incidence and limited therapeutic success. While traditional risk factors include chronic liver disease and environmental exposures, recent evidence underscores the significance of genetic alterations and gut microbiota in liver cancer development and progression. This review aims to integrate emerging knowledge on the interplay between host genomic changes and gut microbial dynamics in the pathogenesis and treatment of hepatic cancers. Methods: We conducted a comprehensive review of current literature on genetic and epigenetic drivers of HCC and CCA, focusing on commonly mutated genes such as TP53, CTNNB1, TERT, IDH1/2, and FGFR2. In parallel, we evaluated studies addressing the gut–liver axis, including the roles of dysbiosis, microbial metabolites, and immune modulation. Key clinical and preclinical findings were synthesized to explore how host–microbe interactions influence tumorigenesis and therapeutic response. Results: HCC and CCA exhibit distinct but overlapping genomic landscapes marked by recurrent mutations and epigenetic reprogramming. Alterations in the gut microbiota contribute to hepatic inflammation, genomic instability, and immune evasion, potentially enhancing oncogenic signaling pathways. Furthermore, microbiota composition appears to affect responses to immune checkpoint inhibitors. Emerging therapeutic strategies such as probiotics, fecal microbiota transplantation, and precision oncology based on mutational profiling demonstrate potential for personalized interventions. Conclusions: The integration of host genomics with microbial ecology provides a promising paradigm for advancing diagnostics and therapies in liver cancer. Targeting the gut–liver axis may complement genome-informed strategies to improve outcomes for patients with HCC and CCA. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid tumors, with a five-year overall survival rate below 10%. While the introduction of multi-agent chemotherapy regimens has improved outcomes marginally, most patients with advanced disease continue to have limited therapeutic options. Molecular profiling has uncovered actionable genomic alterations in select subgroups of PDAC, yet the clinical impact of targeted therapies remains modest. This review aims to provide a clinically oriented synthesis of emerging molecular targets in PDAC, their therapeutic relevance, and practical considerations for biomarker testing, including current FDA and EMA indications. Methods: A narrative review was conducted using data from PubMed, Embase, Scopus, and international guidelines (NCCN, ESMO, ASCO). The selection focused on evidence published between 2020 and 2025, highlighting molecularly defined PDAC subsets and the current status of targeted therapies. Results: Actionable genomic alterations in PDAC include KRAS G12C mutations, BRCA1/2 and PALB2-associated homologous recombination deficiency, MSI-H/dMMR status, and rare gene fusions involving NTRK, RET, and NRG1. While only a minority of patients are eligible for targeted treatments, early-phase trials and real-world data have shown promising results in these subgroups. Testing molecular profiling is increasingly standard in advanced PDAC. Conclusions: Despite the rarity of targetable mutations, systematic molecular profiling is critical in advanced PDAC to guide off-label therapy or clinical trial enrollment. A practical framework for identifying and acting on molecular targets is essential to bridge the gap between precision oncology and clinical management. Full article

The development of scalable, non-invasive tools to assess tumor responsiveness to structurally active immunoformulations remains a critical unmet need in solid tumor immunotherapy. Here, we introduce a real-time, ex vivo functional system to classify tumor cell lines exposed to a phospholipoproteomic platform, without relying on cytotoxicity, co-culture systems, or molecular profiling. Tumor cells were monitored using IncuCyte^® S3 (Sartorius) real-time imaging under ex vivo neutral conditions. No dendritic cell components or immune co-cultures were used in this mode. All results are derived from direct tumor cell responses to structurally active formulations. Using eight human tumor lines, we captured proliferative behavior, cell death rates, and secretomic profiles to assign each case into stimulatory, inhibitory, or neutral categories. A structured decision-tree logic supported the classification, and a Functional Stratification Index (FSI) was computed to quantify the response magnitude. Inhibitory lines showed early divergence and high IFN-γ/IL-10 ratios; stimulatory ones exhibited a proliferative gain under balanced immune signaling. The results were reproducible across independent batches. This system enables quantitative phenotypic screening under standardized, marker-free conditions and offers an adaptable platform for functional evaluation in immuno-oncology pipelines where traditional cytotoxic endpoints are insufficient. This approach has been codified into the STIP (Structured Traceability and Immunophenotypic Platform), supporting reproducible documentation across tumor models. This platform contributes to upstream validation logic in immuno-oncology workflows and supports early-stage regulatory documentation. Full article

Drug repurposing or drug repositioning is the process of identifying new therapeutic uses for approved or investigational drugs beyond the original treatment indication. The discovery of new drugs for cancer therapy needs this cost-effective and time-saving alternative strategy to traditional drug development for a rapid clinical translation in Phase II/III studies, especially for unmet medical needs and rare diseases. Neuroendocrine tumors (NETs) are a heterogeneous group of rare neoplasms arising from cells of the neuroendocrine system that, though often indolent, can be aggressive and metastatic. In this context, drug repurposing has emerged as a promising strategy to improve treatment options due to the limited number of effective treatments and the heterogeneity of the disease. Indeed, a large number of non-oncology drugs have the potential to address more than one target that could be therapeutic for cancer patients. Although many repurposed drugs are used off-label, efficacy for the new use must be demonstrated in clinical trials. Within regulatory frameworks, both the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have procedures to reduce the need for extensive new studies and to expedite the review of drugs for serious conditions when preliminary evidence indicates substantial clinical improvement over available therapy. In spite of several advantages, including reduced development time, lower costs, known safety profiles, and faster regulatory approval, difficulty in obtaining new patents for old drugs with limited protection for intellectual property may reduce commercial returns and disincentivize investments. This review aims to provide comprehensive information on some marketed drugs currently under investigation to be repurposed or used in clinical practice for NETs and to discuss the major clinical challenges. Although drug repurposing is a useful strategy for early access to medicines, the monitoring of the clinical benefit of oncologic drugs during the post-marketing authorization is crucial to support the safety and effectiveness of treatments. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

Background: The profile of germline genetic variants among colorectal cancer patients in Panama has not yet been explored. Methods: We recruited 95 patients with colorectal cancer in an Oncology Reference Hospital Unit in the Azuero region of central Panama, which exhibited the highest prevalence of colorectal cancer in Panama. DNA analysis was performed with a panel of 113 genes with germline mutations for cancer (TruSight^® Cancer Sequencing Panel from Illumina, San Diego, CA, USA). Results: Among the 95 cases, 10 pathogenic/likely pathogenic variants (P/LP) were identified in the MUTYH, TP53, CHEK2, PALB2, ATM, and BARD1 genes, representing 10% of the total. The variant 1103G>A (p.Gly368Asp) in MUTYH was the most prevalent. The variant at c.1675_1676delCAinsTG (p.Gln559Ter) in PALB2 is new and is reported for the first time in this study. Variants were most frequently detected in the MUTYH and CHEK2 genes, affecting four and two patients, respectively. Notably, none of the 95 Panamanian patients in the initial colorectal cancer cohort had mutations in mismatch repair (MMR) genes. These genes are among the most frequently mutated in other cohorts around the world. Conclusions: The atypical profile of germline genetic variants in this population may be related to the unique characteristics of the Azuero population in Panama’s central region. This profile may partly explain the high prevalence of colorectal cancer among its inhabitants. Full article