Search Results (1,129)

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: 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

The c-ErbB receptor family is a fundamental cell signaling system that regulates cell proliferation, motility, apoptosis, differentiation, and other key cellular functions. Overexpressed and mutated in some tumors, c-ErbB receptors play a pivotal role in their progression but are also present in many non-malignant cells, including those that are promising from the point of view of regenerative medicine, such as mesenchymal stromal cells (MSCs). The role of c-ErbB receptors in these cells is not clearly understood, and the data on their expression are sporadic. Therefore, the systemic characterization of c-ErbB receptor family expression in MSCs from a wide range of tissues is of high priority. Here, using RT-qPCR and Western blotting analysis, we evaluated the c-ErbB receptors expression pattern at the mRNA and protein levels in human MSCs isolated from six different tissues. We found that MSCs possess considerable EGFR and HER2 mRNA levels comparable to those in some malignant cells while showing trace HER3 and HER4 expression. However, EGFR but not HER2 was detected in MSCs at the protein level. We also show that the absence of HER2 protein is not associated with its rapid lysosomal degradation. We conclude that c-ErbB signaling in human MSCs is exclusively mediated by EGFR. Full article

Background: Tyrosine kinase inhibitors (TKIs) have significantly improved outcomes in non-small cell lung cancer (NSCLC), especially among patients with actionable genetic mutations. However, the influence of family and personal medical history (FPMH) on clinical and treatment outcomes with TKI therapy remains underexplored. Methods: We conducted a retrospective cohort study involving 136 NSCLC patients receiving TKIs, categorized into two groups based on the presence or absence of documented FPMH. Clinical variables assessed included demographic data, comorbidities, Eastern Cooperative Oncology Group (ECOG) performance status, tumor characteristics, genetic mutations (EGFR, ALK, ROS1), treatment responses, toxicity profiles, and survival outcomes. Statistical analyses included Chi-square tests, t-tests, Mann–Whitney U tests, Spearman correlation, and univariate logistic regression (p < 0.05 threshold for significance). Results: Patients with FPMH (n = 34) had a significantly higher burden of chronic diseases (58.8% vs. 15.7%), poorer ECOG scores (≥3: 8.8% vs. 1.0%), increased recurrence (41.2% vs. 20.6%), and greater chemotherapy-related toxicity (50.0% vs. 28.4%) compared to those without FPMH (n = 102). However, there were no significant differences in survival duration or mutation status between the two groups. Conclusions: FPMH may be a predictive factor for treatment complications and recurrence in NSCLC patients receiving TKIs, although it does not appear to influence survival or genetic mutation status. These findings support the need for personalized clinical monitoring strategies based on medical history. Full article

Most patients with non-small-cell lung cancer (NSCLC) present with advanced/metastatic disease at diagnosis, and molecular profiling is critical in guiding treatment decisions. This retrospective cohort study aimed to characterize EGFR mutations (EGFRm) in advanced/metastatic NSCLC patients, treatment patterns, and real-world outcomes. Adults diagnosed between 2018 and 2021 and treated at a Comprehensive Care Center were included. Time-to-event outcomes were analyzed using the Kaplan–Meier method. A total of 110 patients were included, with a median age of 69.0 years (range, 37–93), 76.4% female, and 83.2% non-smokers. About 97.3% had adenocarcinomas, with 93.6% at stage IV, 40.9% with ≥ three metastatic sites (brain metastases in 24.5%), 33.6% ECOG 2–4, and 58.2% with an EGFR exon-19 deletion. A minority started supportive care or curative-intent treatment, and 81.8% underwent first-line palliative systemic therapy (TKIs, 91.1%; chemotherapy, 8.9%). Median real-world overall survival (rwOS) was 18.9 months (95% CI, 13.8–28.1). Worse rwOS was observed in patients with ECOG 2–4 versus ECOG 0–1 (10.3 vs. 22.8 months; HR 1.82, 95% CI 1.17–2.85; p = 0.008) and in patients with exon-21 L858R versus exon 19 deletions (15.8 vs. 24.2 months; HR 1.59, 95% CI 1.00–2.54; p = 0.048). In patients treated with palliative systemic treatment, median progression-free survival was 10.9 months (95% CI, 8.8–13.6). This study provides important insights regarding real-world characteristics, treatment patterns, and outcomes from a cohort of EGFRm advanced/metastatic NSCLC patients. Full article

Background/Objectives: The resistance mutations EGFR^{L858R/T790M/C797S} in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFR^{L858R/T790M/C797S} mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation ($Q_{LOO}^2$), 5-fold cross validation ($Q_{5-fold}^2$) and concordance correlation coefficient (CCC), $Q_{F1}^2$, and $Q_{F2}^2$. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: $R^2$ and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; $Q_{LOO}^2$, $Q_{5-fold}^2$, CCC, $Q_{F1}^2$, and $Q_{F2}^2$ are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC₅₀ values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFR^{L858R/T790M/C797S} inhibitors. Full article

Central nervous system metastases involving the brain parenchyma and leptomeninges are common in non-small cell lung cancer, especially cases with EGFR mutations. Here, we examine treatment options for EGFR-mutated non-small cell lung cancer patients with central nervous system metastases, highlighting the efficacy of third-generation EGFR-targeted tyrosine kinase inhibitors. Furthermore, we examine the interplay of this modality with chemotherapy or radiation in resistant cases. Full article

Background: This study aimed to develop a nomogram based on the most relevant clinical, CT, and radiomic features comprising 11 key signatures (2 clinical, 2 CT-based, and 7 radiomic) for the non-invasive prediction of the EGFR mutation status and to support the timely initiation of tyrosine kinase inhibitor (TKI) therapy in patients with non-small cell lung cancer (NSCLC) adenocarcinoma. Methods: Retrospective real-world data were collected from 521 patients with histologically confirmed NSCLC adenocarcinoma who underwent CT imaging and either surgical resection or pathological biopsy for EGFR mutation testing. Five Random Forest classification models were developed and trained on various datasets constructed by combining clinical, CT, and radiomic features extracted from CT image regions of interest (ROIs), with and without feature preselection. Results: The model trained exclusively on the most relevant clinical, CT, and radiomic features demonstrated superior predictive performance compared to the other models, with strong discrimination between EGFR-mutant and wild-type cases (AUC = 0.88; macro-average = 0.90; micro-average = 0.89; precision = 0.90; recall = 0.94; F1-score = 0.91; and accuracy = 0.87). Conclusions: A nomogram constructed using a Random Forest model trained solely on the most informative clinical, CT, and radiomic features outperformed alternative approaches in the non-invasive prediction of the EGFR mutation status, offering a promising decision-support tool for precision treatment planning in NSCLC. Full article

Background: Lung cancer has the highest morbidity and mortality of all tumors, and the development of TKI drugs targeting EGFR activating mutations has brought lung cancer treatment into the targeted era. In view of their low efficacy and susceptibility to drug resistance, there is an urgent need to find strategies to increase their efficacy and reduce the incidence of drug resistance. Methods: In this study, we examined the distribution and probability of EGFR mutations in non-small cell lung cancer patients in the cBioPortal database and compared the survival prognosis of patients with normal and abnormal EGFR, NSCLC patients treated with and without TKI, and NSCLC patients with different EGFR gene copy numbers. We established a mouse lung cancer model and examined the histomorphological characteristics of lung tissues via hematoxylin and eosin staining. Additionally, changes in the copy number of the EGFR gene and its protein expression levels were detected using RT-qPCR and Western blotting. Furthermore, we quantified the concentration of the EGFR protein using ELISA. Results: We found no significant advantage of EGFR-TKI therapy over first-line chemotherapeutic agents in patients with EGFR-abnormal NSCLC. The reason for this may be related to the abnormal EGFR gene copy number; the higher the copy number increases, the worse the survival prognosis of the patients. In molecular biology experiments, we demonstrated that ginsenoside Rg3 down-regulated the copy number of 18, 19, 20, and 21 exons and protein expression of EGFR in lung adenocarcinoma cells. The results of in vivo pharmacodynamic assays confirmed that sequential administration of ginsenoside Rg3 with TKI drugs could achieve a gainful complementary effect. Conclusions: Ginsenoside Rg3 down-regulates the copy number of EGFR important exons in EGFR-mutant cells of lung adenocarcinoma and reduces EGFR protein expression, thus providing a high gainful complementary effect in combination with EGFR-TKI. Full article

Lung adenocarcinoma exhibits a heterogeneous molecular landscape shaped by key oncogenic drivers and tumor suppressor gene alterations. Mutation frequencies vary geographically, influenced by genetic ancestry and environmental factors. However, the molecular profile of lung adenocarcinoma in Bulgarian patients remains largely uncharacterized. We conducted a prospective study of 147 Bulgarian patients with metastatic lung adenocarcinoma, analyzing clinicopathologic features and somatic mutation frequencies using next-generation sequencing. Key mutations and their prevalence were assessed and compared with published data from other populations. The cohort included predominantly male patients (68.0%) with a median age of 67 years. TP53 mutations were most frequent (41.5%), followed by EGFR alterations (19.0%) and KRAS c.34G>T (p.Gly12Cys) (17.0%). Over half of the patients (51.0%) harbored two or more gene mutations. Mutation frequencies aligned closely with European cohorts, exhibiting a lower prevalence of EGFR mutations compared to East Asian populations. This study characterizes the molecular landscape of lung adenocarcinoma in Bulgaria, highlighting the predominance of TP53 and KRAS mutations. The findings emphasize the need for comprehensive molecular profiling to inform targeted therapies and support precision oncology approaches tailored to the Bulgarian population. Further research is needed to validate these results and improve clinical outcomes. Full article

Background/Objectives: The role of tertiary lymphoid structures (TLSs) in cancer prognosis is well established, yet their significance in early-stage EGFR-mutant lung adenocarcinoma remains unclear. While outcomes for early-stage lung cancer are generally better than those of late-stage disease, recurrence remains a significant challenge. This study investigates the prognostic value of TLSs and their molecular characteristics in early-stage EGFR-mutant lung adenocarcinoma. Methods: TLSs were identified in tumor samples using multiplex immunohistochemistry (IHC), and their density was quantified. The PD-L1 tumor proportion score (TPS) and TLS density were analyzed for associations with disease-free survival (DFS). Gene expression profiling was performed to compare tumor microenvironment signatures between high- and low-TLS-density groups. Results: High TLS density correlated with significantly longer DFS (43 vs. 20.5 months, p = 0.0082). No relationship was found between TLS density and PD-L1 TPS or EGFR mutation subtype. Transcriptomic analysis revealed upregulated immune response genes in the high-TLS-density group, including those involved in T and B cell activation. Low-TLS-density tumors exhibited gene signatures promoting tumor growth, such as cell cycle and WNT pathway activation. Conclusions: In summary, TLS density is a potential prognostic biomarker for DFS in early-stage EGFR-mutant lung adenocarcinoma, independent of PD-L1 TPS or EGFR mutation subtype. Enhanced immune activation in high-TLS-density tumors highlights TLSs as a potential target for improving outcomes in these patients. Full article

Oral squamous cell carcinoma (OSCC) remains one of the most common malignancies in the head and neck region, often preceded by a spectrum of oral potentially malignant disorders (OPMDs). Despite advances in diagnostic methods, reliable and non-invasive biomarkers for early detection and prognostic stratification are still lacking. In recent years, circulating cell-free DNA (cfDNA) has emerged as a promising liquid biopsy tool in several solid tumors, offering insights into tumor burden, heterogeneity, and molecular dynamics. However, its application in oral oncology remains underexplored. This study aims to review and discuss the current evidence on cfDNA quantification and mutation analysis (including TP53, NOTCH1, and EGFR) in patients with OPMDs and OSCC. Particular attention is given to cfDNA fragmentation patterns, methylation signatures, and tumor-specific mutations as prognostic and predictive biomarkers. Moreover, we highlight the challenges in standardizing pre-analytical and analytical workflows in oral cancer patients and explore the potential role of cfDNA in monitoring oral carcinogenesis. Understanding cfDNA dynamics in the oral cavity might offer a novel, minimally invasive strategy to improve early diagnosis, risk assessment, and treatment decision-making in oral oncology. Full article

Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the major subtype, accounting for more than 85% of all lung cancer cases. Recent advances in precision oncology have allowed NSCLC patients bearing specific oncogenic epidermal growth factor receptor (EGFR) mutations to respond well to EGFR tyrosine kinase inhibitors (TKIs). Due to the high EGFR mutation frequency (up to more than 50%) observed particularly in Asian NSCLC patients, EGFR-TKIs have produced unprecedented clinical responses. Depending on their binding interactions with EGFRs, EGFR-TKIs are classified as reversible (first-generation: gefitinib and erlotinib) or irreversible inhibitors (second-generation: afatinib and dacomitinib; third-generation: osimertinib). While the discovery of osimertinib represents a breakthrough in the treatment of NSCLC, most patients eventually relapse and develop drug resistance. Novel strategies to overcome osimertinib resistance are urgently needed. In Asian countries, the concomitant use of Western medicine and traditional Chinese medicine (TCM) is very common. Ganoderma lucidum (Lingzhi) and Coriolus versicolor (Yunzhi) are popular TCMs that are widely consumed by cancer patients to enhance anticancer efficacy and alleviate the side effects associated with cancer therapy. The bioactive polysaccharides and triterpenes in these medicinal mushrooms are believed to contribute to their anticancer and immunomodulating effects. This review presents the latest update on the beneficial combination of Lingzhi/Yunzhi and EGFR-TKIs to overcome drug resistance. The effects of Lingzhi/Yunzhi on various oncogenic signaling pathways and anticancer immunity, as well as their potential to overcome EGFR-TKI resistance, are highlighted. The potential risk of herb–drug interactions could become critical when cancer patients take Lingzhi/Yunzhi as adjuvants during cancer therapy. The involvement of drug transporters and cytochrome P450 enzymes in these herb–drug interactions is summarized. Finally, we also discuss the opportunities and future prospects regarding the combined use of Lingzhi/Yunzhi and EGFR-TKIs in cancer patients. Full article

Background: Sex differences play a significant role in the epidemiology, biology, and outcomes of many cancers, including glioblastoma (GB), the most common and aggressive primary brain tumor. GB is more frequent in males, while females tend to have longer survival, though the underlying reasons for these differences remain poorly understood. Potential contributors include hormonal influences, sex-specific risk factors, and treatment disparities. Understanding these differences is critical for optimizing personalized treatment strategies. Methods: We conducted a retrospective analysis of patients with gliomas from a neuro-oncological database, with a primary focus on GB cases. Variables collected included sex, age, tumor type, molecular biomarker, and treatment modalities. The primary objective was to assess sex-based differences in tumor characteristics and outcomes, while the secondary objective was to identify predictors of time to progression and mortality. Results: The cohort comprised 125 GB, 48 astrocytomas, and 16 oligodendrogliomas, with no significant sex-based differences in age or tumor type distribution. Among GB patients, multifocality was more prevalent in females (14% vs. 8%; p = 0.01); also, EGFR amplification was more frequent in females (25.5% vs. 52.5%; p = 0.007). Males received chemotherapy (80% vs. 63%; p = 0.04) and radiotherapy (84% vs. 67%; p = 0.03) more frequently than females. Survival was positively associated with MGMT methylation (p = 0.002) and negatively associated with TERT mutation (p = 0.01). Multivariable analysis identified TERT mutation as a predictor of increased mortality (HR = 4.1; 95% CI: 1.2–14; p = 0.025), while multifocality predicted both mortality (HR = 2.3; 95% CI: 1.3–3.9; p = 0.003) and reduced time to progression (HR = 3.3; 95% CI: 1.02–10.6; p = 0.04). Conclusions: This study underscores the importance of sex and molecular profiling in GB management, revealing distinct patterns in tumor characteristics and treatment administration between males and females. Our findings advocate for the integration of sex-specific considerations and molecular profiling into clinical decision-making to improve outcomes for GB patients. Full article

Background/Objectives: Lung cancer is a highly diverse disease, and reliable preclinical models that accurately reflect tumor characteristics are essential for studying lung cancer biology and testing new therapies. This study aimed to establish patient-derived tumor organoids (PDTOs) using small biopsy samples and surgical specimens to create a model system that preserves the genetic and histological features of the original tumors. Methods: PDTOs were generated from 163 lung cancer specimens, including 109 samples obtained using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) or bronchoscopy, 52 surgical specimens, and 2 pleural fluid samples. The organoid establishment rate beyond passage three was assessed, and histological subtypes and genetic profiles were analyzed using immunohistochemical staining and targeted exome sequencing. Results: The overall PDTO establishment rate was 34.4% (56/163), and 44.6% (25/56) of these organoids retained the histological and genetic features of the parental tumors. Genetic analysis identified key mutations, including KRAS G12C, EGFR L858R, MET exon 14 skipping mutation, and ROS1 fusion. PDTOs successfully formed tumors in mice while maintaining the genetic characteristics of the original tumors. Co-culture of PDTOs with cancer-associated fibroblasts (CAFs) resulted in increased resistance to paclitaxel. In the co-culture model of PDTOs with immune cells, dose-dependent growth inhibition of PDTOs was observed in response to immune checkpoint inhibitors. Conclusions: PDTOs established from small biopsy and surgical specimens serve as a valuable model for studying lung cancer biology, tumor microenvironment interactions, and drug response. This model has the potential to improve personalized treatment strategies. Full article