Expert Review of Anticancer Therapy最新文献

The ESMO 2025 Congress delivered practice-defining data across breast cancer. Key results included the first overall survival benefit for an adjuvant CDK4/6 inhibitor in high-risk HR+/HER2- early breast cancer and T-DXd established as standard in high-risk HER2+ disease. Novel antibody-drug conjugates redefined first-line therapy for metastatic TNBC. Updated guidelines mandate broader NGS profiling for PALB2, AKT1, and PTEN alterations. A strong emphasis emerged on biomarker-driven de-escalation, strategic sequencing, and proactive endocrine resistance management. However, benefits must be balanced against cost, equity, and the need for academically derived biomarkers. This report summarizes pivotal presentations reshaping clinical decision-making while reflecting on societal impact.

Introduction: Theranostic radiopharmaceuticals represent a significant advancement in personalized medicine, particularly in oncology. By integrating diagnostic imaging and targeted radionuclide therapy within a single molecular platform, these agents enable selective delivery of cytotoxic radiation to tumor cells. This integrated 'diagnose-then-treat' paradigm enables precise patient selection, individualized treatment planning, and response assessment.

Areas covered: A comprehensive literature search was performed using major biomedical databases, including PubMed, Scopus, and Web of Science. Clinical successes such as peptide receptor radionuclide therapy for neuroendocrine tumors (NETs), and Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) for metastatic castration-resistant prostate cancer underline the promise of radiotheranostics. Rapid innovation is driving personalized dosimetry from research into clinical practice, improving the balance between efficacy and toxicity. Furthermore, improved target selection using PET imaging is becoming essential for optimal patient selection and clinical trial design.

Expert opinion: Despite these advances, critical challenges persist, including toxicity management and heterogeneous target expression; additionally, radionuclide production, supply logistics, and cold-chain requirements remain significant barriers to widespread adoption; finally, standardization issues, harmonized imaging protocols, and clear response criteria further complicate patient selection. The field's advancement will rely on robust randomized clinical trials and innovative combination strategies to address tumor heterogeneity and resistance.

Introduction: Neurofibromatosis type 1 (NF1) is a rare genetic disorder frequently complicated by plexiform neurofibromas (PNs) - benign but often morbid tumors associated with pain, disfigurement, and functional impairment. Mitogen-activated protein kinase (MEK) inhibitors have emerged as an approach for the management of NF1-PNs.

Areas covered: This review summarizes the pharmacology, mechanism of action, preclinical rationale, and clinical development of mirdametinib, a selective MEK1/2 inhibitor approved for the treatment of symptomatic, inoperable NF1-PNs in children and adults. We review key pharmacokinetic and pharmacodynamic properties, preclinical data supporting MAPK/ERK pathway inhibition, and outcomes from mirdametinib clinical trials. Emphasis is placed on efficacy outcomes, patient-reported benefits, durability of response, safety profile, and regulatory milestones. Emerging evidence from indirect treatment comparisons and early combination strategies is also discussed to contextualize mirdametinib within the evolving therapeutic landscape.

Expert opinion: Mirdametinib represents a meaningful advance in the management of NF1-PNs, offering durable tumor volume reduction, improvements in pain and quality of life, and a manageable safety profile across age groups. While head-to-head comparisons with other MEK inhibitors are lacking, available evidence suggests a favorable balance of efficacy and tolerability that may support its use as a first-line systemic option in appropriately selected patients.

Background: Cutaneous malignant melanoma (CMM) is a highly malignant tumor that necessitates early diagnosis and precise survival prediction. The development of accurate prognostic models is essential for improving patient survival.

Research design and methods: This retrospective study analyzed data from 5979 CMM patients in the SEER database (2004-2015), with external validation using the TCGA dataset. Patients were randomly allocated to training and testing sets in a 7:3 ratio. The SMOTE+DeepSurv (DeepSmote) model was compared against seven models, including DeepSurv, XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). Model performance was evaluated using Area Under the Curve (AUC), accuracy, precision, recall, and F1-score.

Results: The DeepSmote model demonstrated superior prognostic performance across both SEER and TCGA datasets. On the SEER test set, it achieved an AUC of 0.96, accuracy of 0.95, and F1-score of 0.95 for 1-year prediction. This strong performance was maintained in the external TCGA cohort (AUC: 0.91, accuracy: 0.88, F1-score: 0.87), and consistent superiority was observed for 3- and 5-year predictions, confirming its robustness and generalizability.

Conclusion: DeepSmote provides an accurate, generalizable prognostic tool for CMM survival prediction, outperforming other models across multiple datasets and evaluation metrics.

Introduction: Cancer research has become increasingly data-intensive, with digital pathology, imaging, and genomic sequencing generating vast, heterogeneous datasets. Artificial intelligence (AI) now plays a growing role across the oncology continuum, offering tools to interpret complex data, streamline workflows, and enhance clinical decision-making. In the context of clinical trials, AI is emerging as a catalyst for more efficient, inclusive, and data-driven research.

Areas covered: This review summarizes how AI, encompassing foundational machine learning (ML) models alongside advanced deep learning (DL) and large language model (LLM) systems, is being applied across the oncology trial lifecycle - from design and recruitment to data management and outcome assessment. Tools such as Trial Pathfinder, TrialGPT, and PRISM demonstrate the ability to emulate trial criteria, accelerate patient matching, and improve eligibility accuracy. The review also highlights key challenges related to algorithmic bias, explainability, accountability, and evolving regulatory oversight by the FDA and EMA.

Expert opinion: AI is transitioning from conceptual promise to operational utility in oncology clinical research. As regulatory frameworks mature, harmonizing innovation with patient protection will be essential. When responsibly implemented, AI can bridge the gap between research and real-world care, transforming oncology trials into faster, fairer, and more reliable engines of discovery.

Introduction: Higher-risk myelodysplastic syndromes (HR-MDS) remain an area of high unmet need in which multiple randomized late-stage trials have failed to improve outcomes beyond hypomethylating agent monotherapy, despite encouraging early-phase signals.

Areas covered: Key randomized late-stage HR-MDS trials that failed to meet primary endpoints are evaluated for recurring design and interpretive challenges. Key themes include endpoint limitations, optimistic effect-size assumptions, underpowering for modest but clinically meaningful hazard ratios, and signal dilution from biological and operational heterogeneity. We discuss adaptive methodologies applicable to HR-MDS to improve trial efficiency and preserve statistical integrity. Regulatory perspectives including contemporary guidance and emerging international principles for confirmatory adaptive trials are considered. Randomized Phase 2/3 HR‑MDS trials and relevant literature were identified through searches of PubMed, ClinicalTrials.gov, and major conference proceedings (2010-2025).

Expert opinion: Many new HR-MDS therapies are likely to deliver incremental survival gains rather than large overall survival improvements, particularly given an apparent survival plateau around 2 years. Future confirmatory programs should, therefore, plan for incremental improvements, prespecify handling of transplantation and post-protocol therapies, and integrate molecular stratification/enrichment to reduce variance. Adaptive designs should be used to stop futile programs earlier and to expand adequately powered trials when emerging data support clinically meaningful benefit.

Background: Patients with advanced EGFR-mutant NSCLC and high PD-L1 expression (TPS ≥50%) typically respond poorly to EGFR-TKI monotherapy. We compared EGFR-TKI plus chemotherapy versus TKI alone in this population.

Research design and methods: This retrospective study included 212 patients receiving first-line osimertinib with chemotherapy (n = 98) or as monotherapy (n = 114). Primary endpoint was progression-free survival (PFS).

Results: Combination therapy significantly improved median PFS overall (22.5 vs 13.8 months; HR = 0.513, 95% CI:0.372-0.803; p < 0.001) and in PD-L1-high patients (18.2 vs 7.9 months; HR = 0.356, 95% CI:0.195-0.641; p = 0.001). ORR was higher with combination therapy (67.3% vs 36.8%; p < 0.001). Grade ≥3 adverse events were more common with combination therapy (45.9% vs 22.8%) but were primarily manageable hematological events.

Conclusions: Osimertinib-chemotherapy combination showed superior efficacy versus monotherapy in EGFR-mutant, PD-L1-high NSCLC, with manageable safety. Our real-world findings validate and extend the FLAURA2 results by highlighting this high-risk subgroup as one that may derive particular benefit from first-line combination therapy.

Background: To explore Ubiquitin D (UBD) and autophagy in hepatocellular carcinoma (HCC) and the key role of Olaparib targeting UBD in treating HCC.

Research design and methods: Bioinformatics analysis was conducted to study UBD expression in HCC tissues. qRT-PCR and Western blot measured UBD mRNA/protein levels, autophagy markers, and Gln metabolism proteins in HCC tissues. Cellular thermal shift assay (CETSA) confirmed Olaparib-UBD interaction. A xenograft tumor model was established to observe tumor growth in mice, with qRT-PCR and western blot used to measure UBD expression levels in tumor tissues and Immunohistochemistry (IHC) used to assess expression of Microtubule-associated protein light chain 3 (LC3), sequestosome 1 (P62), solute carrier family 1 member 5 (SLC1A5), and glutaminase (GLS).

Results: UBD was highly expressed in HCC tissues (p = 7.6e-11). UBD could negatively regulate autophagy levels by activating Gln metabolism. Olaparib could target and downregulate UBD expression, promoting HCC cell autophagy by regulating Gln metabolism pathways. Olaparib treatment in xenograft mice overexpressing UBD significantly reduced tumor growth (p < 0.05), inhibited Gln metabolism pathways, and enhanced HCC cell autophagy.

Conclusions: Olaparib targeted UBD to promote autophagy in HCC by inhibiting Gln metabolism pathways.

Introduction: This review critically appraises recent clinical and translational advances on ropeginterferon alfa-2b, a long-acting, mono-pegylated interferon that has become a key therapeutic option for polycythemia vera (PV). Beyond its cytoreductive efficacy, ropeginterferon exerts immunomodulatory and antiproliferative effects that may modify disease biology. The review outlines its therapeutic rationale, pharmacologic profile, and emerging role as a disease-modifying agent compared with conventional cytoreductive therapies.

Areas covered: This article summarizes the pharmacokinetic properties, clinical efficacy, molecular activity, and safety of ropeginterferon alfa-2b across pivotal trials and real-world studies. Particular attention is given to hematologic responses, reduction of JAK2 V617F allele burden, and long-term tolerability. Data were identified through a systematic search of PubMed, Embase, and ClinicalTrials.gov for English-language studies published up to 2025.

Expert opinion: Ropeginterferon alfa-2b has redefined PV management by combining durable hematologic control with progressive molecular remission. Its favorable efficacy-toxicity balance and convenient dosing support long-term use, particularly in younger or treatment-naïve patients. Future research should refine patient selection, explore predictive biomarkers, and define its role among disease-modifying agents capable of transforming PV into a chronic, potentially controllable disorder.