Citations

Abstract Endocytosis regulates receptor trafficking and signaling, yet its role in colorectal cancer (CRC) remains unclear. We analyzed transcriptomic data from 15,025 CRC tumors to evaluate gene expression in clathrin-mediated endocytosis (CME) and the endosomal sorting complexes required for transport (ESCRT) pathway. Pathway-level signatures were compared across consensus molecular subtypes (CMS) and examined in relation to oncogenic signaling programs, while individual endocytosis-related genes were evaluated for associations with clinical outcomes in an independent randomized trial cohort. Expression of CME and ESCRT pathways varied by CMS, with the highest expression in CMS4 and the lowest in CMS3. Endocytosis signatures correlated with multiple oncogenic signaling pathways, including MAPK, TGF-β, WNT, PI3K, NOTCH, and angiogenesis, suggesting broad links between endocytic activity and tumor biology. In the randomized phase III CALGB/SWOG 80405 trial, low expression of AP2M1 —a core adaptor in the CME complex—was significantly associated with longer overall survival and progression-free survival in patients treated with anti-EGFR agents, but not in those receiving anti-VEGF agents. These findings suggest that endocytic trafficking may be associated with oncogenic signaling and differential therapeutic benefit in patients with metastatic CRC. The endocytosis pathway reflects biologically relevant heterogeneity in CRC and may help guide therapeutic stratification.

Background: Integrating machine learning (ML) with population pharmacokinetic (PPK) modeling may improve therapeutic drug monitoring predictions. Methods: Tacrolimus trough concentrations from lung transplant patients were split into a training data set (1151 concentrations in 80 patients) and a testing data set (224 concentrations in 20 patients). A PPK model was developed using NONMEM, followed by the development of 10 ML models to fit individual pharmacokinetic parameters from the PPK model with Bayesian forecasting. The best performing ML model was selected as the final model. Both the final PPK and ML models were compared for prediction performance. A web-based dashboard was established with R-shiny to recommend dosing regimens based on patient data. Results: In the PPK model, postoperative days, hematocrit, aspartate aminotransferase, tacrolimus daily dose, coadministered voriconazole or posaconazole, and the CYP3A5*3 genotype were identified significant covariates on the clearance. The Cubist ML model outperformed the PPK model, showing lower root mean squared error for tacrolimus concentrations in the testing data set. An online web-based precision dosing dashboard was created, accessible at (https://tac-dose-ml.shinyapps.io/shiny/). Conclusions: Integrating ML with PPK modeling could yield superior tacrolimus concentration predictions for lung transplant patients, offering an efficient alternative to Bayesian forecasting. The online dashboard provides rapid, individualized dosing recommendations.

Ex vivo liver perfusion (EVLP) sustains human or large animal livers outside the body under near-physiological conditions, enabling functional monitoring for lactate clearance, bile production, and oxygen consumption and allowing targeted therapeutic interventions. Originally developed to optimize donor grafts for transplantation, EVLP has evolved into a powerful translational research platform bridging preclinical discovery and early clinical translation. This review discusses EVLP as a platform for gene therapy, immunotherapy, pharmacology, and personalized medicine, with particular emphasis on gene- and immune-based interventions as mechanistically mature exemplars. We consolidate advances in pharmacological testing and toxicity modeling, viral and non-viral gene delivery, genome engineering, and immunomodulation using perfused livers. We further describe emerging applications, including autologous EVLP pathways for organ-confined therapy, ex vivo liver surgery, and bioengineering strategies such as biliary organoid repair, RNA interference, and mitochondrial transfer. We highlight how these applications align with a paradigm shift in biomedical research, including the NIH’s recent initiative to prioritize human-based experimental models over animal-only studies. By leveraging transplant-declined or surgically resected organs that would otherwise be unused, ex vivo perfusion bridges the gap between pre-clinical testing and clinical practice, enabling real-time evaluation of interventions in functional human tissue. We discuss both the scientific opportunities afforded by EVLP and the technical, biosafety, and ethical challenges that must be addressed to enable responsible clinical translation.

Abstract Background and Objectives Cholangiocarcinoma (CCA) is an aggressive, highly lethal cancer of the biliary tract epithelium for which tumor resection is the only curative treatment. However, approximately half of patients present with nonresectable disease and median survival of these patients is just 8-12 months. A significant need exists for tumor-targeted therapeutics for patients with advanced disease who currently have limited treatment options. We aimed to address this need by developing an aptamer-targeted therapy for delivery of siRNAs inhibiting undruggable components of the Hippo pathway, a key driver of cholangiocarcinoma. Method We applied cell-SELEX against human CCA model cell line HuCCTl with negative selection against normal hepatocytes and cholangiocytes to identify an aptamer that selectively bound tumor cells. This aptamer, termed MC7122, was chemically modified to include fluorescent labels that enabled whole-animal biodistribution studies to assess in vivo selectivity. MC7122 was then deployed onto the surface of siRNA-loaded nanovesicles to drive tumor-targeted nucleic acid delivery. Tumor-bearing mice were treated with siRNAs three times per week for two weeks alone or in combination with standard of care chemotherapy. Results Combined treatment with siRNAs and standard of care chemotherapy reduced tumor burden in mice versus vehicle controls and slowed the spread of metastasis. Importantly, we found tumor-specific target gene knockdown without corresponding off-target knockdown in healthy surrounding liver tissue. Further, we demonstrated that sites in the lung, the primary location of metastasis in this model, were reduced not because of global target knockdown in lung but due to tumor-specific knockdown at the site of liver implantation. Conclusions Aptamer-guided vesicles represent a promising platform for targeted therapy delivery for previously undruggable targets. With this platform, tumor-selective therapies can be designed to exhibit exceptionally potent cytotoxicity, as healthy tissues remain unaffected by off-target effects. Future work will focus on refinement of therapeutic siRNA target selection and modification of nanovesicle components for enhanced levels of tumor specificity.

Abstract Background Cholangiocarcinoma (CCA) is a deadly cancer of the hepatic bile duct epithelial cells. Loss-of-function mutations of epigenetic regulator ubiquitin C-terminal hydrolase deubiquitinase BRCA1associated protein 1 (BAP1) occur in 26-32% of human CCA and are associated with worse prognosis and resistance to cytotoxic therapy. The goal of this study is to identify how BAP1 loss promotes cell death evasion through metabolic rewiring. Materials and methods Isogenic wild-type and BAP1-deficient human and murine CCA cell lines were generated with shRNA/siRNA or CRISPR/Cas9 and were used to assess ferroptosis sensitivity by treatment with ferroptosis inducers. In vitro, ferroptosis was assessed by phospholipid peroxidation (C-11BODIPY), mitochondrial phospholipid peroxidation (MitoPeDPP, MitoCLox), iron overload (MitoFerro green) and cell death (CellTiter-Glo). Phospholipidomics and proteomics were performed in isogenic cells by LC-MS/MS. Syngeneic liver orthotopic CCA murine models and patient-derived xenografts (PDX) were used for in vivo pre-clinical studies. Results Our findings indicate that BAP1 loss confers CCA ferroptosis resistance. BAP1 loss protects CCA against mitochondrial iron overload, ROS accumulation, and phospholipid peroxidation. BAP1 loss protects from mitochondrial phospholipid peroxidation and cardiolipin oxidation. Unbiased phospholipidomic profiling revealed that BAP1 loss remodels membrane phospholipid composition, enriching for ferroptosis blocking monounsaturated phospholipids (MUFA-PLs) at the expense of ferroptosis-inducing polyunsaturated (PUFA-PLs). We found that BAP1 loss upregulates TLCD1, a phosphatidylethanolamine acyltransferase that incorporates MUFAs into membrane phospholipids, thereby stabilizing membranes against peroxidation. TLCD1 knockdown in ferroptosis-resistant BAP1-deficient CCA cells, restored ferroptosis sensitivity. Notably, TLCD1 is significantly upregulated in human BAP1-mutant CCA as compared to BAP1-expressing tumors. While not a mitochondrial protein, TLCD1 localizes in the ER and by the mitochondrial outer membrane, as demonstrated by our immunofluorescence analysis in human CCA cells. From a therapeutic perspective, we developed EpCAM (adhesion molecule highly expressed in CCA cells)-aptamer coated lactosomes packaged with siRNA against glutathione peroxidase 4 (GPX4), as a proof-of-concept strategy to safely induce ferroptosis selectively in CCA in vitro and in vivo. Administration of siGPX4-lactosomes in pre-clinical syngeneic liver orthotopic CCA murine models demonstrated a favorable safety profile, achieved robust intratumoral GPX4 knockdown (>50%), exhibited optimal biodistribution, induced significant tumor lipid peroxidation, and resulted in reduced tumor burden. Lastly, ferroptosis inducer JKE-1674, combined with gemcitabine/cisplatin (Gem/Cis) significantly blocked tumor growth of Gem/Cis-resistant patient-derived xenografts. Conclusions Our findings reveal that BAP1 loss reprograms mitochondrial phospholipid metabolism thus conferring CCA resistance to ferroptosis, identifying TLCD1 as a targetable metabolic vulnerability in BAP1-mutant CCA. Additionally, our findings support ferroptosis inducing compounds as promising therapeutic approaches for CCA.

ABSTRACT Prior surveys of chronic automated red blood cell exchange (RCE) for patients with sickle cell disease (SCD) have identified considerable procedural variability, especially with the use of isovolemic hemodilution red blood cell exchange (IHD‐RCE). We conducted a survey of chronic RCE practices among American Society for Apheresis (ASFA) members to identify opportunities for practice harmonization and future studies. The ASFA SCD Research Subcommittee developed a 72‐item survey of chronic RCE practices, with a focus on IHD‐RCE. The survey was validated internally and distributed by email to all ASFA members from September 2024 to February 2025. One survey response from each institution was retained for data analysis. Descriptive statistics were performed. Sixty‐two survey responses, including three from international institutions, were retained for analysis. Sixty‐one percent of respondents (38/62) reported using at least one fixed RCE target, with end hematocrit being the most common fixed parameter (40%, 25/62), followed by fraction of cells remaining (31%, 19/62) and hemoglobin S% (27%, 17/62). Most respondents (60%, 37/61) performed IHD‐RCE at their institution, though other details such as the minimum hematocrit, exclusion criteria, and response to adverse effects were variable. Consistent pre‐ and post‐RCE laboratory monitoring practices were reported. Consistent with earlier surveys, procedural variability in chronic RCE practices for patients with SCD is observed. This highlights opportunities to compare outcomes where practice variability exists. Consensus guidelines based on institutional practices with optimal outcomes should be developed.

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it.

The NCCN Guidelines for Melanoma: Cutaneous provide strategies for the diagnosis, treatment, and follow-up of patients with cutaneous melanoma, which is among the most common cancers in the United States. The outcome of cutaneous melanoma is significantly influenced by stage at initial presentation. Generally, localized disease and smaller primary tumors have been associated with more favorable prognosis. Thus, the select content featured in this issue focuses on risk factors associated with cutaneous melanoma development and which should be considered during an initial assessment. These include (1) phenotypic predispositions, (2) personal medical history or comorbidities, (3) genetic predispositions, and (4) environmental factors.