Citations

Background Stereotactic body radiotherapy (SBRT) is increasingly used for patients with oligometastatic prostate cancer, yet the optimal target definition, dose, and toxicity profile for rib metastases remain uncertain. Objectives We assessed local control (LC), toxicity, and the risk of radiation-induced rib fracture (RF) after SBRT for rib metastases. Design Retrospective multicenter cohort study. Methods We analyzed prostate cancer patients with radiologically diagnosed rib metastases treated with SBRT and >=12 months of follow-up. Outcomes were descriptively compared between gross tumor volume (GTV)-only and GTV plus an intraosseous clinical target volume (CTV) margin. Analyses included Kaplan-Meier survival estimates, cumulative incidence functions, and exploratory univariable Cox and logistic regression; no formal equivalence testing or multivariable validation was attempted because event numbers were very low. Results We included 102 patients with a median follow-up of 20 months. The 1- and 2-year LC rates were 97.9% and 96.3%, respectively. At 3 years, local progression was 2.1% in the GTV-only group, absent in patients treated with >=10 mm margins, and numerically highest with 3-5 mm CTV margins (13.5%, p=0.022). Among 13 symptomatic patients, pain control was excellent, with complete or partial response in all patients within 6 months. No grade >=3 toxicities occurred. RFs were rare (n=3, 2.9%), comprising one symptomatic Grade 2 event (0.9%) and two asymptomatic Grade 1 events (2.0%). Baseline symptoms and extraosseous infiltration were associated with RF in exploratory univariable analysis, but confidence intervals were wide. Higher PTV dose exposure, particularly PTV D50% (36.9 vs. 31.1 Gy, p=0.041), was also observed among RF cases. Conclusions SBRT for rib metastases from prostate cancer was associated with high LC, pain relief, and low observed toxicity in this retrospective real-world cohort. GTV-only contouring may be feasible in carefully selected patients staged and planned with PSMA-PET/CT, but the non-randomized design, baseline imbalances, short follow-up, and very low event count preclude drawing conclusions about equivalence or independent validation. RF predictor analyses should be considered exploratory and hypothesis-generating.

Abstract The use of natural killer (NK) cells for the treatment of solid tumors such as cholangiocarcinoma has been hindered by challenges related to cell production, persistence and trafficking, as well as by reduced efficacy within an immunosuppressive tumor microenvironment. Antitumor immunity can be reduced by the interaction of NK cell expressed programmed cell death protein 1 (PD1) with ligands on tumor and other cells within the tumor microenvironment. The antitumor effect of NK cells is enhanced in cells engineered to express a truncated PD1 without an intracellular domain. The cytolytic potential and therapeutic efficacy of extracellular vesicles (EVs) derived from human NK cells expressing truncated PD1 was evaluated as a cell-free therapeutic to overcome some limitations faced by cell therapies. We demonstrate the feasibility of efficient expansion, production and isolation of therapeutic vesicles from human NK cell lines and from engineered NK92 cells expressing truncated PD1. The cytotoxic efficacy of these therapeutic vesicles was validated in both monolayer tumor cell cultures and in multicellular tumor spheroids. Concomitant administration of gemcitabine upregulated NKG2D ligands on tumor cells and increased susceptibility to NK cell and to NK cell derived EV mediated killing. These findings position engineered NK cell derived-EVs as a scalable and promising cell-free immunotherapeutic for cholangiocarcinoma.

This article describes clinical, serologic, and histopathologic features of the first North American cohort of patients with positive anti-p200 serology identified at Mayo Clinic, Rochester, Minnesota, USA and Beutner Laboratories, Buffalo, New York, USA. Forty-seven patients were identified with positive p200 laminin beta 4 autoantibodies, detected with either immunoglobulin (Ig)G or IgG4 modified indirect immunofluorescence on HEK293 cells transfected with recombinant laminin beta.

Purpose: To measure the computed tomography (CT) attenuation of acute pulmonary embolism (PE) and establish an upper Hounsfield unit (HU) threshold above which it is unlikely to occur. Materials and Methods: This retrospective study analyzed CT pulmonary angiography (CTPA) exams in patients with acute PE (2018–2023) who had a prior negative CTPA in the preceding 2 weeks. Clot and artifact attenuation were measured on the positive and prior negative CTPA respectively. Analysis of variance with post hoc tests and linear regression analysis were performed to determine the effects of technical and other parameters on clot and artifact attenuation. Results: A total of 60 patients were included, each with 1 record or a pair of CTPA exams, yielding 105 clots and 41 artifacts. Mean attenuation for acute PE was 50±40 HU, and for artifact was 110±146 HU. A threshold of 125 HU achieved 97% (102/105) sensitivity for acute PE below it and the same specificity for artifact above it. Specificity for acute PE below this threshold was 37% (15/41). Subsegmental clots had a lower attenuation than all other clots as a group. Kilovoltage peak (kVp) and proximal vessel attenuation were poor predictors of clot attenuation . Conclusions: Most acute PE demonstrated a CT attenuation ≤125 HU on standard CTPA, an upper threshold higher than previously reported, while artifacts demonstrated both lower and higher attenuation. An attenuation >125 HU on standard CTPA, favoring artifact rather than acute PE, may be a useful adjunct to morphologic differentiation between these entities.

Background Despite advances in cervical cancer screening, cervical cancer rates have been increasing in the last decade. Hispanic women have higher cervical cancer incidence and mortality rates compared to non-Hispanic women in the United States. These disparities are primarily due to language barriers experienced in this Spanish-preferred population. Our study aims to improve cervical cancer screening rates among Spanish-preferred patients through a Spanish-language multimedia presentation on cervical cancer screening. Methods In patients not adherent to U.S. cervical cancer screening guidelines, we conducted a non-randomized study comparing standard care in Family Medicine (FM) to standard care plus a multimedia presentation with Spanish voiceover intervention in Internal Medicine (IM). We evaluated whether baseline characteristics differed between the two study arms using the Wilcoxon rank sum test and either the Chi-Square test or Fisher’s exact test. For the primary goal, we examined whether there was a difference in screening scheduling rates between the intervention and standard care groups using the Chi-Square test. Using descriptive statistics, we investigated whether there was a difference in characteristics for those who scheduled screening and those who did not. Results We had 52 patients in IM and 58 in FM, differing in age and insurance status, with median ages of 45.5 and 40.0, and percent insured of 65.4% and 82.8%. Post-study, 25.0% (13/52) in IM and 15.5% (9/58) in FM scheduled cervical screening appointments, which was not significantly different, p=0.21. Patients who scheduled screening had higher rates of insurance coverage, at 95.5% compared to 69.3%. Conclusion No statistically significant difference in scheduling rates among Spanish-preferred patients was observed in this study. Further efforts should include larger samples and randomization.

Abstract SV40 Large T antigen (LT) is essential for viral replication and a key determinant of host range. This host-range function is mediated by the C-terminal domain (LT-C) through binding to the host serine protease FAM111A, but the underlying mechanism has remained unclear. Here, we report the X-ray crystal structure of the FAM111A serine protease domain in complex with LT-C, revealing the structural basis for direct inhibition of FAM111A. LT-C uses a previously unrecognized zinc-binding motif and a P1-like phenylalanine residue to engage the FAM111A active site through a substrate-mimicking mechanism while avoiding proteolytic cleavage and covalent complex formation. Mutations disrupting either feature abolish FAM111A inhibition and impair SV40 propagation in cells. Consistent with this mechanism, SV40 host restriction requires FAM111A protease activity, which must be antagonized by LT-C for productive infection. Together, these findings define a zinc-dependent, cleavage-avoiding mechanism of protease inhibition that highlights an evolutionary arms race between SV40 and host antiviral proteases.

Importance Although adding immune checkpoint inhibitors to neoadjuvant chemotherapy improves outcomes in high-risk early-stage breast cancer, opportunities remain to further enhance response. Dual checkpoint blockade offers a potential strategy to further enhance efficacy. Objective To evaluate the combination of anti−programmed cell death 1 protein (PD-1) cemiplimab and anti−lymphocyte activation gene 3 (LAG-3) added to neoadjuvant therapy in ERBB2 -negative early-stage, high-risk breast cancer. Design, Setting, and Participants The I-SPY2 (Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis 2) is an ongoing randomized clinical platform trial being conducted at multiple US clinical sites including patients with early-stage (II or III) ERBB2 -negative, high-risk breast cancer. Participants, continuously enrolled since 2010, were adaptively randomized from February 2, 2020, to December 9, 2021, to one of several experimental neoadjuvant therapies or control groups based on receptor subtypes defined by hormone receptor (HR), ERBB2 status, and MammaPrint (Agendia Inc) molecular risk, categorized as high (MP1) or ultrahigh (MP2). Data were analyzed from January 1, 2022, to August 5, 2025. Interventions Both groups received weekly paclitaxel for 12 weeks, then doxorubicin and cyclophosphamide followed by surgery; concomitant with paclitaxel, the intervention group also received 4 doses of cemiplimab and fianlimab (PCF) every 3 weeks. Main Outcomes and Measures Pathologic complete response (pCR). Treatments graduated when they achieved 85% bayesian probability of success in a subtype-specific phase 3 trial. Pathway-specific biomarkers were assessed for response prediction. Results A total of 78 participants (mean [SD] age, 47 [39-54] years) were randomized to the intervention group, with 350 participants (mean [SD] age, 48 [39-57] years) randomized to the historical control population. PCF graduated in all clinical signatures, with pCR rates vs control of 44% (95% CI, 34%-53%) vs 21% (95% CI, 17%-25%) in all ERBB2 , 53% (95% CI, 39%-67%) vs 29% (95% CI, 22%-36%) in triple-negative, and 36% (95% CI, 23%-49%) vs 14% (95% CI, 9%-19%) in HR-positive and ERBB2 -negative disease. Among the total participants, 16 (21%) experienced adrenal insufficiency, including hypophysitis (11% grade 3 or 4), mostly occurring after immunotherapy completion. PCF was found to be highly effective in the subset of patients with immune signature positive status (ImPrint positive). Conclusions and Relevance In this randomized clinical trial, the combination of PD-1 and anti−LAG-3 inhibition with standard NAC was effective in early-stage ERBB2 -negative breast cancer, particularly in patients displaying a positive ImPrint immune signature. These results warrant further definitive trials. Trial Registration ClinicalTrials.gov Identifier: NCT01042379

Abstract Cancer outcomes vary widely between individual patients, each accumulating an irregular record of treatments, diagnoses, measurements, and complications. Current prognostic models reduce this complexity into a single snapshot, focus on narrow clinical settings, and rarely generalize across hospitals. Here we introduce Chronicle , an explainable transformer that learns from entire patient trajectories to predict diverse clinical outcomes throughout the disease course while capturing both short- and long-term temporal dependencies. Trained on 53.7 million longitudinal data points from 51,711 patients spanning 67 cancer types, Chronicle operates natively on irregular data without imputation and jointly predicts eight endpoints within a flexible framework adaptable to additional outcomes. Chronicle outperformed cross-sectional models for overall survival prediction (C-index 0.84 vs 0.76-0.79), stratified patients more accurately than established prognostic systems, including TNM stage, and predicted seven adverse event and transfusion endpoints (AUC 0.80-0.92). Applied without retraining to 69,341 patients in Germany, Switzerland, and the United States, Chronicle generalized across healthcare systems and improved further with local fine-tuning. Integrated explainability traced each risk update to patient-specific clinical factors, revealing distinct temporal persistence of prognostic information, with relevance half-lives ranging from weeks for therapies to nearly one year for baseline characteristics. These findings demonstrate that learning from hospital-wide patient trajectories enables interpretable and continuously updated predictions, providing a scalable framework to support individualized treatment decisions.

Abstract Cholangiocarcinoma (CCA) is an aggressive biliary malignancy with poor prognosis and limited treatment options, underscoring the need for targeted RNA-based therapeutic strategies. Though DNA- aptamers could be one of the promising targeted ligands, conventional enrichment-driven selection often fails to reliably identify high-affinity binders due to PCR bias, stochastic drift, and sequencing errors. In this study, we combined cell-SELEX with a data-driven artificial intelligence framework to identify DNA aptamers targeting CCA cells. Eleven rounds of selection were performed on a library exceeding 6×1014 sequences, with iterative negative selection against normal hepatocytes and cholangiocytes to enhance tumor specificity. Given the limitations of enrichment-based ranking in prioritizing functional binders, we developed and evaluated four deep learning architectures—MLP, CNN, BiLSTM, and a hybrid CNN–BiLSTM—using aptamer pools derived from the final SELEX round. These models were built by integrating one-hot sequential features and DNA structural descriptors of the dataset along with their enrichment patterns across SELEX rounds. Among them, the hybrid CNN–BiLSTM model demonstrated superior performance by capturing both local sequence motifs and long-range dependencies. Model performance was evaluated using AUC-ROC and complementary classification metrics on an independent test set. The hybrid CNN–BiLSTM model achieved the best overall performance with an AUC-ROC of 0.97 and a precision of 68%, indicating strong discriminative power under class imbalance (strong: non-binder=1:68). The model exhibited high specificity (99.5%; 29,855/30,005 true negatives) and moderate sensitivity (71.7%; 312/435 true positives), reflecting its ability to reliably filter non-binding sequences while retaining true functional binders. Model-prioritized candidates were experimentally validated using qPCR-based aptamer–cell binding assays. Notably, two low-abundance/high-prediction aptamers (Read counts: 4) exhibited 2–3-fold higher binding than the reference aptamer (Positive Control) derived from traditional SELEX enrichment. A high-read/high-prediction candidate showed ∼2-fold greater binding than the positive control, highlighting the model’s ability to recover overlooked functional binders. To assess translational relevance, selected aptamers were used to functionalize the lactosome-based nanovesicles for siRNA delivery targeting PD-L1, resulting in measurable gene silencing in HuCCT1 cells after 48 hours. Collectively, this integrative framework enables accurate identification and prioritization of high-affinity, functionally relevant aptamers beyond enrichment alone. This approach enhances SELEX efficiency, supports rational ligand design, and provides a scalable strategy for developing aptamer-guided RNA therapeutics for any target. Citation Format: Sourav Pal, Julia Driscoll, Brandon Wilbanks, Ayano Kabashima, Rory Smoot, Tushar Patel. Integrating cell-SELEX and deep learning method for discovery of high-affinity aptamers targeting cholangiocarcinoma [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A033.