Future oncology最新文献

Mitochondrial DNA (mtDNA) is integral to cellular function. Alterations in mtDNA can lead to significant disruptions in mitochondrial function, including cellular energy production, metabolism, and apoptosis regulation. Mitochondrial dysfunction has emerged as a crucial factor in the progression of cancer, including head and neck squamous cell carcinoma (HNSCC). Based on a literature search conducted from August 2024 to March 2025 on PubMed and Google Scholar, our review summarizes the relevance of mtDNA alterations in HNSCC development. HNSCC has been found to cause significant variations in mtDNA, including mutations, copy number variations (or known as mtDNA content) and large-scale deletions. Notably, these alterations varied by disease stage, different key aspects of mitochondrial function, such as cellular growth, senescence, metabolism and apoptosis, and cell-specific genetic context. We further suggest that drugs that modulate mitochondrial pathways, mitochondrial transplantation, and gene-editing technologies, are future treatment strategies for HNSCC. Lastly, we discuss the potential of co-roles of nuclear DNA mutations with mtDNA alterations in HNSCC to provide a holistic view of HNSCC pathogenesis and the possibility for combined therapeutic strategies in HNSCC.

Background: Phosphatidylinositol 3-kinase catalytic subunit Alpha (PIK3CA) is closely correlated with colorectal cancer (CRC). However, the role of PIK3CA in colorectal cancer, particularly in non-metastatic disease, remains inconsistent. In this study, the clinicopathological significance of PIK3CA and its common exon mutations in non-mCRC was explored.

Methods: Data from 448 non‑mCRC patients were obtained from The Cancer Genome Atlas (TCGA), and from 655 non‑mCRC patients at our center. Associations of PIK3CA and its common exon mutations with clinicopathological features and overall survival (OS) were analyzed in non‑mCRC.

Results: In the TCGA cohort, the PIK3CA mutation rate was 26.3%, and mutations were associated with tumor site, TNM stage, and regional lymph node metastasis. Exon 9 mutations correlated with tumor site, while exon 20 mutations were linked to tumor site and lymph node metastasis. In our institutional cohort, the mutation rate was 7.8%, with PIK3CA and exon 20 mutations showing correlations with age, tumor site, TNM stage, and lymph node metastasis. However, neither in TCGA nor in our cohort were PIK3CA mutations or common exon mutations associated with overall survival (OS).

Conclusion: PIK3CA mutation is correlated with age, tumor site, TNM stage, and regional lymph node metastasis in non-mCRC. However, PIK3CA and common exon mutations are not associated with OS in patients with non-mCRC.

Aims: This study aims to assess whether pre-chemotherapy endogenous plasma metabolome can offer improved predictive values for Capecitabine chemotherapy-related adverse events (CRAEs).

Research design and methods: Plasma samples were collected from 25 colorectal cancer patients at different time points: 0 hours (before), and 1, 2.5, 4 hours after oral Capecitabine administration, to assess individual variations in exposure levels. Additionally, the endogenous metabolome profile was analyzed using UHPLC/Q-TOF-MS.

Results: Capecitabine and its metabolites can predict two CRAEs, with 5-FU, 5'-DFCR, and FUH2 exposures being associated with diarrhea and thrombocytopenia, respectively. In contrast, identified plasma endogenous biomarker metabolites can predict all seven observed CRAEs. These CRAE-related endogenous plasma metabolites are involved in various physiological functions, including cell proliferation, maintenance, and inflammation. Pre-chemotherapy endogenous plasma metabolites established superior predictive performance for CRAEs (AUROC values ranging from 0.718 to 0.998) compared to conventional drug exposure (AUROC values ranging from 0.737 to 0.773). Additionally, the endogenous plasma metabolome demonstrated a strong correlation with drug exposures.

Conclusions: Our study demonstrates that pre-chemotherapy endogenous plasma metabolome serve as superior biomarkers for predicting CRAEs, outperforming drug exposure levels. However, the limited sample size may impact the generalizability of these findings, and validation in larger patient cohorts is warranted.Trial Registration: NCT03030508 (registered at www.clinicaltrials.gov).

Aims: To describe United States real-world oral mucositis/stomatitis (OM/S) management for patients with non-small cell lung cancer (NSCLC) or breast cancer (BC) and document physician awareness of OM/S guidelines, risk factors, and barriers to care.

Patients & methods: This study included a cross-sectional physician survey and retrospective chart review. Physicians completed an electronic survey and abstracted chart data for patients with advanced/metastatic NSCLC or BC who developed treatment-related OM/S on or after 1 January 2021.

Results: Thirty-one physicians abstracted data for 272 patients (146 NSCLC; 126 BC). Median patient age at OM/S event was 66.2 years (NSCLC) and 61.6 years (BC). Systemic treatments included chemotherapy (NSCLC: 86.3%; BC: 67.5%), immunotherapy (NSCLC: 56.8%; BC: 10.3%), and targeted therapy (NSCLC: 9.6%; BC: 46.8%). OM/S-related treatment changes (reduction/interruption/discontinuation) were reported in 20.5% and 35.7% of patients with NSCLC or BC, respectively. A majority of physicians (61.3%) were unaware of published OM/S management guidelines. Physicians identified poor oral hygiene (80.6%) and limited physician awareness of OM/S guidelines (71.0%) as barriers to OM/S management.

Conclusions: OM/S occurs across cancer treatment regimens and can lead to treatment modification. Improvements in OM/S management at the patient and provider level are needed to enhance care and improve clinical outcomes.

Aims: Guidance and regulation for the use of social media (SM) by healthcare professionals (HCPs) is lacking in some parts of the world. This paper explores the significance and barriers of SM in oncology care in regions beyond Europe and North America.

Methods: A cross-sectional survey facilitated by Sermo to explore the use of SM among oncologists in Argentina, Brazil, India, Mexico, Saudi Arabia, Taiwan, Türkiye, and the United Arab Emirates was conducted between 14 June 2023 and 28 June 2023. A panel discussion involving seven digital opinion leaders (DOLs) was also held.

Results: Of 340 respondents, the survey found strong support for SM in public and HCP education with most preferring mobile phones and 88% accessing SM in their free time. SM has an average-to-great impact on the prescribing habits of 52% of respondents. Sixty-four percent of respondents are concerned about potential conflicts of interest with SM. The panel developed a framework of recommendations providing navigational aids for key information, verifying sources to avoid misinformation, disclosing conflicts of interests, and creating visual and bite-sized content.

Conclusion: Opportunities exist to enhance SM use in regions beyond Europe and North America. DOLs in oncology can enhance SM content quality.

Breast cancer (BC) presents a considerable global health challenge and is characterized by increasing mortality and morbidity rates. Prompt screening and accurate diagnosis are crucial for improving patient outcomes. For the assessment of BC, radiographic imaging modalities such as digital breast tomosynthesis (DBT), ultrasound, digital mammography (DM), magnetic resonance imaging (MRI), and nuclear medicine procedures are commonly used. The gold standard for confirming cancer is histopathology. To effectively support the segmentation, diagnosis, and prognosis of BC. Artificial intelligence (AI) technologies show great promise for the quantitative depiction of medical images.This review explores recent strides in AI applications for BC. The literature search from 2018 to 2025 was performed with the PubMed database. It includes rapid breast lesion detection, segmentation, cancer diagnosis and enhanced imaging quality through data augmentation. It also discusses the biological characterization of BC via AI-based classification tools, including subtyping and staging. Furthermore, this review also explores the use of multiomics data to predict clinical outcomes such as survival, treatment response, and metastasis in BC. Additionally, we recognized the challenges faced by AI in BC in real-world applications, including organizing data, model interpretability, and regulatory compliance.

Aims: Cancer patients face a higher risk of adverse effects from coronavirus disease 2019 (COVID-19) compared to the general population. However, the safety of restarting antitumor therapy following COVID-19 recovery remains unclear.

Methods: In this prospective, multicenter study conducted between January 1 and 30 March 2023, 419 eligible cancer patients who had recovered from COVID-19 were screened across four medical centers. The primary endpoint was the incidence of treatment-emergent adverse events (TEAEs) during the first cycle of antitumor therapy resumed within 3 months after COVID-19 recovery. Changes in clinical laboratory parameters were assessed as secondary endpoints.

Results: A total of 270 eligible participants were included in this study. The common grade 3 or worse TEAEs were fatigue (3.3%), anemia (1.1%), leukopenia (0.7%), and elevated alanine transaminase (0.3%). No severe cardiac toxicity and significant abnormalities on the chest computed tomography (CT) were observed. D-dimer and cardiac troponin I (cTNI) were significantly increased after treatment (p < 0.05). Increased inflammatory cytokines of peripheral blood could be observed after administration of oxaliplatin and trastuzumab.

Conclusions: Restarting systemic antitumor therapy in solid tumor patients after COVID-19 recovery is generally safe. Systemic inflammatory and coagulation function of patients should be monitored during treatment.

Aims: To develop and validate a deep learning radiomics model to predict non-sentinel lymph node (NSLN) metastases in early-stage breast cancer patients with 1-2 positive sentinel lymph node (SLN) metastases.

Methods: This retrospective and prospective study encompassed 1,647 patients. Clinical, pathological information, and axillary ultrasound (AUS) findings, collected. Radiomic features of breast cancer lesions were extracted from the ultrasound images. We developed predictive models based on clinical factors alone (C model), clinical factors coupled with AUS (CA model), and clinical factors integrated with both AUS and radiomic features (CAR model). The predictive performance of each model was evaluated via the area under the curve (AUC), decision curve analysis (DCA), and calibration curve analysis.

Results: The AUC values for the C model, CA model and CAR model in the test cohort were 0.812, 0.850, and 0.994, respectively. Notably, the CAR model exhibited significantly superior predictive capability compared to both the C model and CA model. In subgroups analyses, the CAR model also achieved the optimal predictive performance. The DCA curve confirmed that the CAR model possessed significant clinical implications.

Conclusions: The CAR model had the capability to predict NSLN metastases in early-stage breast cancer with 1-2 positive SLN metastases.