Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm caused by the t(9;22)(q34;q11.2) translocation, known as the Philadelphia chromosome (Ph+), which generates the BCR::ABL1 fusion oncogene central to CML pathogenesis. The disease accounts for approximately 15% of adult leukemias, predominantly affects males, and has a median age at diagnosis of around 57 years. CML typically presents in the chronic phase and may progress to advanced disease phases, with prognostic risk stratification guiding treatment decisions. Tyrosine kinase inhibitors (TKIs) are the cornerstone of therapy, aiming to achieve durable molecular disease control and, in selected patients, sustained deep molecular response and treatment-free remission. This study evaluated the demographic, clinical, and molecular response profiles of patients with Ph + CML treated with first- and second-line TKIs at the Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas (HEMOAM). This retrospective, longitudinal study analyzed medical records of 176 patients diagnosed with Ph + CML between 2011 and 2020. Demographic characteristics, clinical data, and laboratory findings were extracted from medical records. Longitudinal molecular response was assessed in patients with available BCR::ABL1 monitoring. Among the 176 patients identified, 122 were included in the analysis of treatment outcomes. The mean age at diagnosis was 49.6 years, with a slight male predominance. Most patients were diagnosed in the chronic phase, and high-risk Sokal score was the most frequent prognostic category among evaluable cases. The e14a2 (b3a2) transcript was the most prevalent molecular subtype. Clinically meaningful molecular responses were observed in both first- and second-line treatment settings, although response assessment was influenced by real-world constraints, including incomplete molecular monitoring and heterogeneous follow-up. These findings provide real-world insights into the epidemiological, clinical, and molecular response characteristics of Ph + CML patients treated in the Amazon region. The study highlights the challenges of implementing guideline-based molecular monitoring in geographically constrained settings and supports the need for adapted strategies to optimize long-term CML management in routine clinical practice.

Gliomas are the most prevalent and aggressive primary tumors of the central nervous system, with glioblastoma (GBM) exhibiting rapid progression, high recurrence, and poor prognosis. Therapeutic resistance, particularly to radiotherapy and chemotherapy, remains a major clinical challenge. RNA-binding proteins and RNA methyltransferases have emerged as regulators of tumor progression and immune modulation. ZCCHC4, a zinc finger CCHC domain-containing RNA methyltransferase, has been implicated in cancer, but its biological role and mechanisms in glioma remain largely unexplored. Gene expression profiles and clinical data from TCGA and CGGA databases were analyzed to assess ZCCHC4 expression and its correlation with clinicopathological features and patient prognosis. Immunohistochemistry, qRT-PCR, and western blotting were performed to evaluate ZCCHC4 expression in glioma tissues and cell lines. Functional assays, including CCK-8, colony formation, wound healing, and transwell migration assays, were conducted to investigate the effects of ZCCHC4 knockdown in vitro. In vivo xenograft models were used to assess tumor growth. RNA sequencing and pathway analyses (GO/KEGG/GSEA) were applied to explore transcriptomic changes, and ssGSEA was used to infer immune-associated signatures. ZCCHC4 expression was elevated in glioma tissues and cell lines and was associated with higher WHO grade, IDH wild-type status, and unfavorable survival. ZCCHC4 knockdown suppressed glioma cell proliferation and motility in vitro and reduced xenograft growth in vivo. Transcriptomic profiling indicated that ZCCHC4 depletion was accompanied by downregulation of DNA replication/cell-cycle–related programs and upregulation of ER-stress/unfolded protein response–related signatures. ssGSEA-based immune deconvolution suggested that higher ZCCHC4 expression was associated with increased inferred Th2-related and macrophage signatures and decreased inferred cytotoxic T-cell and NK-cell signatures. ZCCHC4 is upregulated in glioma and associated with adverse clinicopathological features and survival. ZCCHC4 depletion attenuated proliferative and motility-related phenotypes in vitro and reduced xenograft growth in vivo, while transcriptomic analyses suggested links to DNA replication/cell-cycle and ER-stress/UPR programs.

Based on multiscale computed tomography (CT) radiomics, a better model was established to differentially diagnose benign lesions and lung adenocarcinoma of Lung-RADS 2022 category 4B solid lung nodules. The retrospective study included 178 patients with solid pulmonary nodules were assigned to the training dataset (n = 124) and the testing dataset (n = 54). Gradient boosting decision tree (GBDT) was used to reduce the dimensionality of data and select the best radiomics features. Four models were developed by logistic regression method, namely the clinical and imaging model (CIM), the plain CT radiomics model (PRM), the enhanced CT radiomics model (ERM), and the combined model (CM). Area under the curve (AUC) evaluates the model performance. Net reclassification improvement (NRI) and the integrated discrimination index (IDI) were calculated to compare the performance of different models to determine the best model. In the training dataset, the AUC of CIM, PRM, ERM, and CM were 0.795, 0.791, 0.828, and 0.888. The continuous NRI and IDI of CM was better than that of CIM, PRM, ERM (P < 0.001), CM is optimal. In the testing dataset, the AUC of CIM, PRM, ERM and CM were 0.810, 0.689, 0.864 and 0.881. The continuous NRI of CM was better than that of CIM, PRM, ERM (P < 0.050). The IDI of CM was better than that of CIM and PRM (P < 0.050). The AUC values of the Mayo Clinic (Mayo) model, Veterans Administration (VA) model, Peking University People’s Hospital (PKUPH) model and United Imaging Artificial Intelligence (UI AI) model were 0.419, 0.410, 0.676 and 0.675, CM is still optimal. Radiomics can be used as a non-invasive tool to distinguish between benign lesions and lung adenocarcinoma of Lung-RADS 2022 category 4B solid lung nodules, and CM is the best predictive model.

The purpose of this article is to present the Brazilian Physical Activity (PA) During Cancer Treatment Guidelines(PA) during cancer treatment, and discuss the adaptations made to align the recommendations with the Brazilian context and its distinctive peculiarities. The development of Brazilian Guidelines for recommending PA during cancer treatment consisted of identifying international guidelines and positions and adapting their recommendations and statements for the Brazilian context. For this, an expert working group including scientists, exercise specialists, and oncologists revised the international guidelines, selected outcomes, and created recommendations based on PA safety and efficacy on the selected adverse effects of cancer treatment and the regional context. After review of 14 PA oncology guidelines, two were selected. Drawing from the selected guidelines, the working group formulated the recommendations considering the scientific evidence extracted from the documents and the Brazilian contextualization. Although the guidelines were based on international recommendations, the expert working group introduced adaptations to better reflect the Brazilian context. These adaptations included translating the content into Portuguese and simplifying the language to improve accessibility for healthcare professionals. Safety information and contraindications were added, and recommendations were adjusted not only in terms of intensity, mode, and frequency but also to promote accessibility, motivation, and long-term adherence. Additionally, the guidelines took into consideration professional regulations, access to qualified staff, the structure of the health system, and regional sociocultural and economic disparities, particularly in underserved and rural areas. There is a clear need for context-specific exercise oncology research and policies that reflect the low and middle-income countries’ social, economic, and cultural characteristics. This Brazilian guideline represents a first and essential step toward aligning Brazil with the international agenda for establishing PA standards in oncology.

Neoadjuvant chemo-immunotherapy has shown promise in improving survival outcomes for non-small cell lung cancer (NSCLC) patients, with pathologic response serving as a critical predictor of long-term outcomes. However, manual assessment of pathologic response is labor-intensive and subject to inter-observer variability. This study aimed to develop an automated AI-based solution to address these limitations. We developed an AI-powered patch-based image analysis model to quantify residual viable tumor (RVT) in hematoxylin and eosin (H&E)-stained whole slide images. The model was evaluated on resected specimens from 47 NSCLC patients treated with neoadjuvant chemo-immunotherapy. The AI-derived estimates of RVT were compared with visual assessments by a board-certified pathologist. Statistical analysis included Pearson’s correlation for continuous tumor estimation and Cohen’s Kappa for concordance in major pathologic response (MPR) and pathologic complete response (pCR) classification. The AI model demonstrated a strong correlation with the pathologist’s continuous estimation of RVT (r = 0.77, p < 0.001, confidence interval [CI]: 0.73–0.81). In the assessment of clinical endpoints, the model achieved an 89.36% concordance rate for MPR (Kappa = 0.79, p < 0.001, CI: 0.61–0.96) and 89.36% concordance rate for pCR (Kappa = 0.56, p < 0.001, CI: 0.24–0.89) when compared with the board-certified pathologist. Our AI-powered model demonstrates potential as a decision-support tool for pathologic response assessments in NSCLC patients treated with neoadjuvant chemo-immunotherapy.

Standard of care for non-metastatic colon cancer is surgery followed by stage-guided adjuvant therapy and/or structured follow-up. Upfront surgery in resectable colon cancer is irrespective of local T/N stage, whereas adjuvant systemic therapy is recommended according to pathological staging. The role of neoadjuvant chemotherapy remains unclear. Whereas perioperative systemic therapy in colon cancer seems to be safe and may lead to pathologic downstaging, evidence on improved survival and quality of life remains scarce. The PROTECTOR / FIRE‑10 trial aims to generate evidence that perioperative systemic therapy improves survival without compromising quality of life in locally advanced, mismatch-repair proficient colon cancer patients. Open-label, randomized, controlled, multicenter, phase III study with two parallel arms. Patients with locally advanced colon or upper rectal cancer staged cT3-4 and/or cN+ are randomized in a 2:1 fashion (favoring preoperative therapy) to investigate the efficacy, patient reported quality of life, and safety of preoperative therapy followed by surgery (Arm A) versus direct surgery followed by non-study specific stage-guided adjuvant therapy (Arm B). Stratification during randomization will be performed according to the following parameters: Fit for mFOLFOXIRI vs. mFOLFOX/CAPOX vs. 80%-mFOLFOX/CAPOX, ECOG 0 vs. ECOG 1-2, and left-sided primary vs. right-sided primary tumor. Only patients with confirmed mismatch-repair proficient and/or microsatellite stable tumor can be included. Preoperative treatment in Arm A is performed for a maximum of 6 biweekly cycles of FOLFOX/FOLFOXIRI or for a maximum of 4 triweekly cycles CAPOX (i.e., appr. 12 weeks). Patients in both arms should undergo quality-controlled surgery of the primary tumor, performed as complete mesocolic excision. Patients will be followed up with regard to relapse, survival and if applicable subsequent anti-cancer treatments until death or for at least 5 years after randomization, whichever date is earlier. The PROTECTOR / FIRE‑10 trial compares preoperative systemic therapy to upfront surgery (with stage-guided adjuvant therapy) in patients with locally advanced colon cancer. This study is registered with clinicaltrials.gov (NCT06899477) and EudraCT (2023-508076-11-00).

The status of human epidermal growth factor receptor 2 (HER2) is a critical determinant of breast cancer progression and outcome, but the role of glycosylation in modulating HER2-related pathways remains poorly understood. Here, we examined the relationship between HER2 and the glycosyltransferases ST3GAL1 and GCNT3, combining data mining with experimental and clinicopathological validation. In silico analyses across public BC cohorts showed that higher expression of ST3GAL1, GCNT3, and HER2 was associated with reduced survival. Immunohistochemistry on invasive ductal carcinoma specimens (n = 25) demonstrated increased ST3GAL1 and GCNT3 in advanced stages/grades. Cross-platform correlation analyses revealed a positive association between ST3GAL1 and HER2, whereas GCNT3 showed an inverse association with HER2. Functional assays in HER2-negative cell lines (MCF7, MDA-MB-231, MDA-MB-435) and the HER2-positive line SKBR3 indicated that GCNT3 supports migratory capacity and clonogenicity, consistent with an oncogenic role independent of HER2 status. Gene set enrichment pointed to upregulation of MUC1 and β-catenin; tissue validation of MUC1, β-catenin, and Cyclin D1 confirmed their clinicopathological relevance, with HER2 expression inversely correlated with β-catenin and Cyclin D1. Collectively, these findings suggested a model in which GCNT3-driven O-glycosylation might remodel the MUC1/β-catenin/Cyclin D1 axis in BC, adding a glyco-regulatory layer to HER2-linked pathobiology. From a diagnostic perspective, ST3GAL1 (positive with HER2) and GCNT3 (inverse with HER2) could be considered as candidate biomarkers that might complement HER2 assessment for risk stratification. This work suggests a plausible mechanistic and clinicopathologic foundation for incorporating glycosylation markers into precision pathology workflows in breast cancer.