In pancreatic ductal adenocarcinoma (PDAC), clinicians often ask whether young age is associated with more aggressive disease or patients more likely to tolerate curative-intent multimodality treatment. The prognostic meaning of age after surgery remains uncertain. We analyzed a retrospective multicenter surgical cohort of patients with resected non-metastatic PDAC. Candidate clinical variables were evaluated in univariable Cox models. The prespecified primary multivariable model included age, sex, N status, T stage, and CA19-9 status. Adjuvant therapy was evaluated in a sensitivity model. The cohort included 696 surgical patients; 70 (10.1%) were aged < 55 years. Median overall survival was 34.4 months in patients aged < 55 years versus 22.0 months in those aged ≥ 55 years (log-rank p = 0.019). In the primary multivariable model, age < 55 years remained independently favorable (HR 0.66, 95% CI 0.49–0.89; p = 0.007). Elevated CA19-9 value independently predicted worse survival (HR 1.39, 95% CI 1.13–1.71; p = 0.002). The sensitivity model confirmed favorable associations for young age and not elevated CA19-9 value. In surgically managed PDAC patients, young age and normal CA19-9 value carried independent favorable prognostic information. Prognosis was also shaped by nodal status, T stage, and receipt of adjuvant therapy.
I. Garajová, Bing-Zhi Wang, I. Chen et al.· Life· 0 citations
Oncogenic KRAS mutations are a defining feature of pancreatic ductal adenocarcinoma (PDAC), one of the most lethal solid malignancies, characterized by poor responsiveness to conventional chemotherapy. Recent clinical successes of direct KRAS inhibitors in other cancer types have renewed interest in KRAS-directed therapy for PDAC. However, early clinical experience has revealed often short-lived responses, highlighting the rapid emergence of resistance and the need to better understand the mechanisms limiting durable benefit. This review summarizes current knowledge on molecular resistance to RAS-directed inhibitors in PDAC, organized into five categories: intrinsic resistance, KRAS-dependent mechanisms, KRAS-independent bypass signaling, downstream pathway reactivation, and tumor microenvironment (TME)-mediated resistance. Evidence from PDAC models is integrated with insights from other KRAS-driven malignancies, particularly non-small cell lung cancer, where direct KRAS inhibition has been studied more extensively. Collectively, resistance appears to arise from layered adaptive processes rather than single alterations, including secondary KRAS mutations, receptor tyrosine kinase-driven bypass signaling, reactivation of mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) pathways, stromal-mediated protection, and reduced drug exposure. Notably, until recently, most evidence in PDAC has been indirect, while direct investigation of resistance mechanisms in KRAS-targeted settings has expanded rapidly only in recent years. Building on these advances, ongoing clinical trials increasingly explore rational combination strategies targeting upstream regulators, downstream effectors, and the TME. However, critical challenges persist, including optimal patient selection, treatment sequencing, toxicity, and the lack of well-defined pharmacodynamic frameworks. Overcoming resistance will require mechanistically guided combinations, improved disease-specific models, and biomarker-driven adaptive strategies to ultimately achieve durable clinical benefits in PDAC.
M. Pagano Mariano, Enrica Sgarilli, Dirk Mijnlieff et al.· Cancer Drug Resistance· 0 citations
Colorectal-cancer (CRC) is the third leading cause of mortality due to cancer, thus there is a need for innovative-therapeutic-agents to enhance the efficacy of current treatments and improve outcomes. Here we performed different machine learning (MI) approaches (e.g., random forest, support vector machines, convolutional neural networks, CNN,…), with capable of handling the complex relationship between target markers, and CRC were utilized to select an approapriate with higher efficancy agent and then investigated the therapeutic impact of PGP in CRC. RNAseq and the integrative systems biology technique followed by MI algoritisms were applied to identify differentially expressed genes (DEGs) followed by validation in a large cohort of patients and then PGP was selected for in vitro and in vivo studies. Antiproliferative-activity of (Punica granatum var. pleniflora (PGP)) was tested in 2 and 3D cell-culture models. The effect of PGP on migratory-behaviors and apoptosis was determined using a wound-healing-assay and AnnexinV/PI staining, respectively. The expression were assessed using q-RT-PCR. Molecular-Pathology and histopathological-assessment was used followed by evaluation of oxidative-stress-markers. Metabolomics for assessment of chemical and active components of the PGP extract were determined by LC-MS/MS. The result illustrated a total of 856 upregulated/downregulated-genes in patients. Among the high top-score genes, fibrotic/inflammatory pathways were detected and further validated in 65 patients. PGP inhibited cell-growth and migration in cells by modulating CyclinD1, Survivin, and E-cadherin. Furthermore, PGP increased apoptosis. Moreover, PGP significantly decreased tumor-size in an animal-xenograft CRC via perturbation of fibrosis-markers, Col1A/ACTA2. PGP reduced inflammation, and oxidative-stress via modulation of SOD/Cat/total thiol. Phytochemical profiling showed a total of 28 and 43 compounds including Corilagin, Ellagic acid, Gallic Acid and Quercetin-hexoside, which have anti-cancer properties. The results demonstrated the therapeutic potential of PGP in tumor-growth reduction, indicating its potential value as a new approach in the treatment of colorectal-cancer.
Aida Yavari Kondori, Mehrdad Moetamani Ahmadi, Seyede Elnaz Nazari, Fereshteh Asgharzadeh, Elisa Giovannetti, Majid Khazaei, Amir Avan. Drug Development Using Machine Learning Approches: The Therapeutic Impact of Golnar on Inhibition of Tumor Growth in Colorectal Cancer [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A037.
Aida Yavari Kondori, Mehrdad Ahmadi, S. Nazari et al.· Clinical Cancer Research· 0 citations
An AI-based platform that combines multiple screening modalities with AI-driven digital analysis for identification of high-risk individuals to improve cancer screening, support clinical decision-making, reduce cancer risk, and optimize healthcare resources is developed.
Aida Yavari Kondori, Ahmadreza Tavasouli, Mona Maftouh et al.· Clinical Cancer Research· 0 citations
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