Advanced Cancer Research
ISSN: 3106-0382 (Online)
Tumor microenvironment responsive nanotherapeutics in cancer treatment: obstacles, opportunities and future prospects
Cancer remains one of the most severe global health challenges. The emergence of nanomedicines has opened new avenues for targeted tumor therapy, with over 15 types currently in clinical use. Although nanomedicines improve drug accumulation through the enhanced permeability and retention effect, challenges such as inefficient and nonspecific drug release limit their therapeutic efficacy. Due to abnormal proliferation and metabolic disorders, the tumor microenvironment (TME) exhibits significant differences from normal tissues, including low pH, hypoxia, and enzyme overexpression. These specific endogenous signals provide opportunities to achieve tumor-specific drug release and enhance the therapeutic effect of cancer treatment. Consequently, TME-responsive nanomedicines have garnered significant attention from researchers. This review systematically summarizes the latest advancements in TME-responsive nanomedicines in recent years. Furthermore, we discuss several emerging challenges and prospects of TME-responsive nanomedicines, aiming to provide novel insights and significant breakthroughs in this field.
Metabolic reprogramming of immune cells and metabolic intervention in cutaneous tumors
Immunotherapy has achieved notable advancements in the treatment of cutaneous malignancies. However, challenges such as low response rates and frequent drug resistance remain. The metabolic characteristics of immune cells are closely linked to their fate, activation, and function, exhibiting remarkable plasticity that enables metabolic networks to finely regulate immune cell responses to external stimuli. Consequently, targeting metabolic networks to modulate immune cell phenotypes and augment antitumor immunity has emerged as a pivotal avenue for clinical translation. This article systematically elucidates the functional roles of glucose, lipid, and amino acid metabolic reprogramming in immune cells. It summarizes ongoing clinical trials of metabolic immunotherapy for treating melanoma, cutaneous squamous cell carcinoma (cSCC), and basal cell carcinoma (BCC), while discussing unresolved challenges in the clinical translation of metabolic therapy. This provides theoretical support and research directions for novel metabolic therapeutic strategies.
Artificial intelligence in oncology drug discovery: from target identification to therapeutic molecule generation
The development of oncology therapeutics is currently impeded by exorbitant costs, protracted timelines, and high clinical attrition rates stemming from the inherent complexity of tumor biology. Artificial Intelligence (AI) is transforming oncology drug discovery, shifting the paradigm from trial-and-error experimentation to one of data-driven rational design. In this paper, we review recent AI advances in four key areas. First, regarding Target Identification, we examine how multi-omics integration and deep learning uncover novel vulnerabilities, such as synthetic lethal pairs and immune checkpoints. Second, we analyze the evolution of Virtual Screening, moving from classical docking to graph neural networks that efficiently explore vast chemical spaces. Third, we highlight the shift toward Generative Molecular Design, where AI models create de novo small molecules, protein binders, and nucleic acid therapeutics with tailored functional properties. Fourth, we discuss AI applications in Preclinical Evaluation for predicting toxicity and efficacy. Finally, we critically assess current challenges—including data standardization deficits and the “black box” nature of deep learning—and propose emerging strategies, such as automated design-make-test workflows, to bridge the gap between computational prediction and clinical reality.
Proteomics-driven precision oncology: from molecular profiling to biomarker discovery
Proteomics enables systematic, context-dependent characterization of the proteome, and has emerged as a cornerstone of precision cancer medicine. Although the systematical analysis of molecular profiling has transformed oncology over the past two decades, substantial heterogeneity in therapeutic responses still persists among patients with similar genetic alterations, highlighting the limitations of static genomic information. By directly interrogating signaling pathways and regulatory networks of proteins and post-translational modifications (PTMs) that drive tumor initiation, progression, and therapy resistance, proteomics bridges the gap between genomic alterations and phenotypic outcomes. Recent advances in mass spectrometry have enabled low-cost, high-throughput, and high-resolution proteomics from bulk to single cells, providing unprecedented insights into tumor heterogeneity. Integrative analysis of multi-omics, including genomics, transcriptomics and proteomics data, facilitates the construction of multidimensional molecular landscapes that reveal novel biomarkers and therapeutic targets. In this review, we summarize recent advances in proteomics-based biomarker discovery, highlight emerging single-cell and spatial proteomics technologies, and discuss future directions for integrating multi-omics, clinical information, and artificial intelligence to accelerate clinical translation.
Malignancies of ISCs and their niche emerge as frontiers in precision therapy for colorectal cancer
Colorectal cancer (CRC) ranks among the leading malignancies globally in both incidence and mortality. Treatment failure and disease recurrence are largely attributable to significant heterogeneity and acquired resistance to current therapies. Recent studies have extensively demonstrated that the initiation, progression, and recurrence of CRC are closely associated with the malignancy of intestinal stem cells (ISCs) and their derived cancer stem cells (CSCs). CSCs possess the characteristics of sustained self-renewal and multi-potent differentiation, constituting a key cellular population that sustains tumor growth, metastasis and drug resistance. Concurrently, CSCs evade the host immune system by reducing tumor antigen presentation, secreting immunosuppressive factors, and remodeling tumor microenvironment, thereby significantly limiting the clinical efficacy of immunotherapy. Consequently, immunotherapeutic strategies targeting ISCs and CSCs have emerged as a pivotal research direction for precision treatment of CRC. This paper systematically reviews recent advances in this field, discussing vaccine strategies based on CSC-specific antigens, bispecific antibodies (BsAbs) and antibody-drug conjugates (ADCs), CAR-T cells, and multimodal therapeutic approaches. Further, this paper summarizes the application of multi-omics technologies, spatial biology, and organoid models in elucidating the plasticity and drug resistance mechanisms of CSCs. We also discuss the potential role of gut microbial regulation in enhancing immunotherapy response. In summary, comprehensive immunotherapy strategies targeting ISCs and their ecological niches hold promise for overcoming current treatment bottlenecks in CRC, providing new theoretical foundations and practical pathways towards achieving long-term disease control.
Individual risk prediction and cancer prevention for the digital health era
Cancer prevention is, in practice, a prediction problem. Decisions about risk reduction, screening, and survivorship depend on forecasts of incident cancer, progression, recurrence, and treatment-related harms, and on whether earlier or more intensive action improves the benefit-harm balance. For prevention-oriented use, prediction tools should provide calibrated absolute-risk estimates over a defined follow-up period, be anchored to local incidence and competing mortality when relevant, and be embedded in explicit clinical or public health pathways in which prespecified thresholds trigger actionable steps. These steps may include intensified lifestyle support, eligibility assessment for chemoprevention, risk-adapted screening decisions regarding start age, interval, or modality, and referral for confirmatory evaluation. Using breast cancer as the most mature archetype, we illustrate how individualized risk estimates can be linked to multiple prevention levers while making the trade-offs of threshold-based strategies explicit, including induced follow-up procedures and the potential for overdiagnosis. We then synthesize requirements for prevention-ready prediction, spanning longitudinal follow-up with registry-linked outcome ascertainment, absolute-risk estimation anchored to local incidence and competing mortality, evaluation of calibration overall and in relevant subgroups, transparent accounting of threshold-induced follow-up procedures and harms, and planned recalibration and updating as baseline risk and practice patterns change. As cohort-enabled illustrations in the Health Examinees (HEXA) infrastructure, we describe a breast cancer prediction benchmark, CancerFree, a questionnaire-first, registry-anchored multi-cancer framework for site-specific risk-to-action specification, alongside an AI-derived diet layer that converts food frequency questionnaire data into interpretable dietary-pattern features for scalable prevention-oriented profiling. Across emerging data layers, added model complexity should be justified not by discrimination alone, but by demonstrable gains in calibrated absolute-risk estimation and in the benefit–harm balance of the pathways they are intended to inform.
