Treatment-refractory rectal cancer liver metastasis represents a major therapeutic challenge, particularly in the absence of actionable genomic biomarkers. Functional precision oncology approaches integrating genomic profiling with patient-derived organoid (PDO) drug testing may provide biologically informed therapeutic prioritization. A 50-year-old female patient with KRAS/TP53-mutant, microsatellite-stable (MSS) rectal adenocarcinoma refractory to FOLFIRINOX was enrolled. A liver metastasis from a treatment-refractory rectal cancer patient was processed to establish three-dimensional patient-derived organoids. Histopathological concordance was assessed using H&E and p53 immunohistochemistry. Comprehensive genomic profiling was performed using a 637-gene targeted next-generation sequencing panel, enabling detection of single-nucleotide variants, indels, copy number variations, microsatellite instability, and tumor mutational burden. Functional drug sensitivity profiling was conducted in parallel 2D and 3D platforms using a customized 17-agent panel, followed by exploratory combinatorial validation. The organoids demonstrated high phenotypic and genomic concordance with the parental tumor, preserving key driver alterations (KRAS^A146T, TP53^R175H, APC frameshifts, CCNE1 amplification), microsatellite stability, and low tumor mutational burden (TMB: 6.37 mut/Mb). Functional screening identified selective sensitivity to bevacizumab (IC₅₀: 0.130 μM), doxorubicin (IC₅₀: 0.570 μM), carboplatin (IC₅₀: 0.950 μM), and topotecan (IC₅₀: 1.600 μM) in the 3D organoid model, with consistent cross-platform validation. An exploratory combination assay further supported enhanced viability suppression under bevacizumab-based regimens. Critically, at the time of manuscript preparation, the patient demonstrated radiological disease stabilization under bevacizumab plus trastuzumab deruxtecan, consistent with the organoid-derived response profile. These findings highlight the capacity of integrated genomic and organoid-based profiling to uncover therapeutic vulnerabilities beyond standard biomarker assessment. This proof-of-concept case report study demonstrates the feasibility and translational relevance of an established organoid-based functional precision oncology platform for therapeutic prioritization in metastatic rectal cancer.

Theranostic nanoparticles, which integrate diagnostic imaging and therapeutic delivery within a single nanoplatform, represent a transformative paradigm in oncological nanomedicine. Despite substantial preclinical progress, the field faces persistent gaps in rational nanoparticle design informed by tumor biology, preclinical model fidelity, and clinical translation. This review critically synthesizes theranostic nanoparticle research across three underexplored domains. First, we examine tumor microenvironment features - reactive oxygen species dynamics, glutathione gradients, hypoxia, and proteasomal dysregulation - as mechanistic drivers of nanoparticle responsiveness. Second, we evaluate redox-responsive and proteasome-targeted nanoplatforms that exploit these cues for stimuli-triggered drug release and simultaneous imaging readout. Third, we address the unmet need for three-dimensional organoid and microfluidic tumor models as predictive preclinical testing environments, given the well-documented limitations of conventional two-dimensional cultures. Cancer subtype-specific applications are discussed for breast cancer, HPV-associated malignancies, colorectal cancer, and prostate cancer. Clinical translation barriers - including pharmacokinetic constraints, protein corona formation, immune clearance, anti-PEG antibodies, complement activation-related pseudoallergy, and FDA/EMA regulatory pathways - are addressed from a clinical oncology perspective. The review concludes with a research roadmap integrating proteomics-guided nanoparticle engineering, patient-derived organoid biobanks, and artificial intelligence-assisted design as priority areas for next-generation oncological theranostics.

Theranostic nanoparticles, which integrate diagnostic imaging and therapeutic delivery within a single nanoplatform, represent a transformative paradigm in oncological nanomedicine. Despite substantial preclinical progress, the field faces persistent gaps in rational nanoparticle design informed by tumor biology, preclinical model fidelity, and clinical translation. This review critically synthesizes theranostic nanoparticle research across three underexplored domains. First, we examine tumor microenvironment features—reactive oxygen species dynamics, glutathione gradients, hypoxia, and proteasomal dysregulation—as mechanistic drivers of nanoparticle responsiveness. Second, we evaluate redox-responsive and proteasome-targeted nanoplatforms that exploit these cues for stimuli-triggered drug release and simultaneous imaging readout. Third, we address the unmet need for three-dimensional organoid and microfluidic tumor models as predictive preclinical testing environments, given the well-documented limitations of conventional two-dimensional cultures. Cancer subtype-specific applications are discussed for breast cancer, HPV-associated malignancies, colorectal cancer, and prostate cancer. Clinical translation barriers—including pharmacokinetic constraints, protein corona formation, immune clearance, anti-PEG antibodies, complement activation-related pseudoallergy, and FDA/EMA regulatory pathways—are addressed from a clinical oncology perspective. The review concludes with a research roadmap integrating proteomics-guided nanoparticle engineering, patient-derived organoid biobanks, and artificial intelligence-assisted design as priority areas for next-generation oncological theranostics.

The public release of ChatGPT on 30 November 2022, constituted an inflection point in the history of artificial intelligence applied to medicine. Within oncology, a specialty defined by the need to synthesize complex, heterogeneous, and rapidly evolving clinical data, large language models [LLMs] have transitioned within a remarkably compressed timeline from objects of academic curiosity to the threshold of institutional deployment. This narrative review maps the trajectory of LLM integration across the oncological care continuum, from patient-facing information tools to multidisciplinary tumor board decision support. The clinical landscape is organized around a three-tier framework of readiness: domains in which LLMs demonstrate consistent supportive performance across predominantly retrospective, benchmark-based, and early implementation studies; domains in which early evidence is promising but prospective validation is lacking; and domains that remain aspirational and require substantial technical and regulatory development before adoption can be responsibly recommended. A dedicated analysis is presented of the mechanistic distinction between static parametric knowledge and dynamic retrieval-augmented generation [RAG], the probabilistic origins of factual inaccuracy in language model outputs, and the role of authoritative guideline grounding in constructing trustworthy AI architectures for oncology. The review examines how multimodal LLMs [MLLMs] are beginning to integrate radiomics, whole-slide pathology imaging [WSI], next-generation sequencing [NGS] data, transcriptomics, and proteomics into unified oncological reasoning workflows and how this convergence informs the precision oncology vision. The structural and linguistic barriers encountered when LLMs are deployed in non-English clinical environments, with particular reference to the healthcare context, are examined as a representative case of the global versus local challenge that confronts any international deployment strategy. The unresolved legal landscape of liability for AI-assisted clinical errors, the regulatory classification of LLMs as software as a medical device [SaMD], and the human-in-the-loop imperative are analyzed as prerequisites for responsible deployment. Our central argument is that LLMs derive their clinical value not from autonomous decision-making but from their capacity to function as perpetually available, rigorously grounded, and cognitively augmentative assistants to the human oncologist.

The public release of ChatGPT on November 30, 2022 constituted an inflection point in the history of artificial intelligence applied to medicine. Within oncology, a specialty defined by the need to synthesize complex, heterogeneous, and rapidly evolving clinical data, large language models (LLMs) have transitioned within a remarkably compressed timeline from objects of academic curiosity to the threshold of institutional deployment. This narrative review maps the trajectory of LLM integration across the oncological care continuum, from patient-facing information tools to multidisciplinary tumor board decision support. The clinical landscape is organized around a three-tier framework of readiness: domains in which LLMs already demonstrate robust, evidence-supported performance; domains in which early evidence is promising but prospective validation is lacking; and domains that remain aspirational and require substantial technical and regulatory development before adoption can be responsibly recommended. A dedicated analysis is presented of the mechanistic distinction between static parametric knowledge and dynamic retrieval-augmented generation (RAG), the probabilistic origins of factual inaccuracy in language model outputs, and the role of authoritative guideline grounding in constructing trustworthy AI architectures for oncology. The review examines how multimodal LLMs (MLLMs) are beginning to integrate radiomics, whole-slide pathology imaging (WSI), next-generation sequencing (NGS) data, transcriptomics, and proteomics into unified oncological reasoning workflows, and how this convergence informs the precision oncology vision. The structural and linguistic barriers encountered when LLMs are deployed in non-English clinical environments, with particular reference to the healthcare context, are examined as a representative case of the global versus local challenge that confronts any international deployment strategy. The unresolved legal landscape of liability for AI-assisted clinical errors, the regulatory classification of LLMs as software as a medical device (SaMD), and the human-in-the-loop imperative are analyzed as prerequisites for responsible deployment. Our central argument is that LLMs derive their clinical value not from autonomous decision-making but from their capacity to function as perpetually available, rigorously grounded, and cognitively augmentative assistants to the human oncologist.

Human papillomavirus (HPV)-associated cancers have traditionally been viewed as focal diseases arising from localized malignant transformation. However, growing molecular evidence suggests that HPV-associated molecular alterations may extend beyond visible lesions into histologically normal epithelium, raising the possibility of a virus-associated field effect. In this review, HPV-induced field cancerization is distinguished from HPV infection alone and is used to describe spatially extended molecular abnormalities demonstrated in histologically normal HPV-positive or tumor-adjacent tissue, whereas findings from premalignant lesions, established tumors, and experimental models are considered supportive evidence of progression or mechanism. This emerging concept challenges conventional models of HPV-driven carcinogenesis and carries important implications for early detection and cancer prevention. Evidence relevant to HPV-associated field effects includes spatially distributed viral, epigenetic, transcriptomic, and immune alterations in selected histologically normal tissues, while findings related to viral integration, metabolic reprogramming, premalignant lesions, tumors, and experimental models provide complementary progression or mechanistic evidence. Such molecular abnormalities have been reported most consistently in cervical disease, with supportive but more limited evidence in the oropharynx and anal canal, whereas the relevance of HPV-associated field effects in the urinary bladder remains controversial. In this review, we aim to synthesize current molecular evidence supporting HPV-induced field cancerization, discuss potential underlying mechanisms, and evaluate emerging methodological approaches for detecting field effects in normal tissues. We further highlight the translational relevance of this phenomenon for molecular-based screening, risk stratification, and preventive strategies aimed at intercepting HPV-associated cancers at their earliest stages.