Patel, Tushar

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.

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, increasingly arising in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Epigenetic dysregulation, particularly DNA methylation, has been implicated in MASLD-HCC development, yet the roles that the principal DNA methylation precursor metabolites, S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), play in this association are unclear. Objective: We investigated associations of circulating SAM, SAH, the SAM/SAH ratio, with MASLD-HCC. Methods: In a multi-center pilot case–control study, we evaluated 69 MASLD-HCC cases and 136 cancer-free MASLD controls. Plasma SAM and SAH levels were quantified by liquid chromatography–tandem mass spectrometry. Metabolite levels were categorized as greater than or less than the median based on distribution in controls. Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, body mass index, smoking status, and type 2 diabetes. Results: MASLD-HCC cases had significantly higher plasma SAM levels (mean 121 vs. 96 nmol/L; p = 0.001) and SAM/SAH ratios (2.09 vs. 1.48; p = 6.42 × 10−7) than MASLD controls. In multivariable-adjusted models, elevated SAM levels (OR≥median vs. <median = 2.76; 95% CI: 1.38–5.72) and higher SAM/SAH ratio (OR≥median vs. <median = 2.30; 95% CI: 1.15–4.73) were associated with higher odds of MASLD-HCC. SAH alone was associated with MASLD-HCC. Conclusions: Higher plasma SAM levels and SAM/SAH ratios are independently linked to MASLD-HCC development. These metabolites might serve as noninvasive markers for HCC risk stratification in patients with MASLD and improve early detection efforts for MASLD-HCC.

560 Background: Early diagnosis remains a critical challenge in oncology, particularly for the five deadliest cancers: lung, breast, prostate, colorectal, and pancreatic. These cancers often present with nonspecific symptoms, leading to repeated healthcare visits without a timely suspicion of malignancy. Artificial intelligence (AI) offers an opportunity to augment clinical decision-making and support earlier identification of cancer. C the Signs , an AI cancer prediction platform, has been widely adopted across the UK with substantial evidence supporting its ability to identify cancer at earlier stages. Methods: This retrospective study assessed the performance of C the Signs in intercepting cancer earlier than physician diagnosis. De-identified electronic medical records were accessed via the Mayo Data Platform, spanning a 20-year period from January 1, 2002 to December 31, 2021. The study cohort included 55,375 patients diagnosed with lung, breast, prostate, colorectal or pancreatic cancer. The primary outcome was the proportion of cases identified earlier by the AI model, measured in annual increments up to five years prior to physician diagnosis. Additional analyses were conducted across the five target cancer types. Results: The C the Signs platform identified 14,528 patients (26.2%) with cancer earlier than the original physician diagnosis. Early identification occurred across multiple time points: 8.9% in year 1, 4.7% in year 2, 4.4% in year 3, 4.3% in year 4, and 3.9% in year 5. Across cancer types, the highest early interception rate was observed in prostate cancer (31.7%), while breast cancer showed the lowest impact (21.0%). Conclusions: Use of the C the Signs AI cancer prediction platform demonstrated meaningful improvements in early cancer interception, particularly for lung and prostate cancers. These findings highlight the potential of AI tools to enhance early diagnosis and support timely clinical interventions, offering a scalable solution to improve cancer outcomes across populations.

8053 Background: In the US, only 27.4% of lung cancer cases are diagnosed early, with 5-year survival rates of 63% for localized and 27% for late-stage cancers. Despite recommendations since 2012 for screening high-risk individuals with low-dose CT, uptake has been limited, and most lung cancer diagnoses occur after symptoms appear. Similarly, chest x-rays, while commonly used as an initial test to investigate patients with symptomatic suspected lung cancer, have demonstrated limited sensitivities between 50-70% and specificities over 80%. Symptoms often overlap with common conditions, making detection challenging, and studies have identified median delays of 187 days from symptom onset to diagnosis. This prolonged interval presents an opportunity for improvement. This study examines the use of the AI cancer prediction platform, C the Signs, to passively screen for lung cancer by leveraging electronic medical records (EMRs) for early lung cancer detection. Methods: Utilizing the Mayo data platform, we conducted a retrospective analysis of EMR data from 894,409 patients, including 7,395 individuals diagnosed with lung cancer. We assessed the sensitivity and specificity of the AI cancer prediction platform, in identifying patients at risk of lung cancer. Additionally, we compared the timing of lung cancer risk identification by the AI cancer prediction platform with the timing of diagnoses made by physicians to determine whether the platform enabled earlier detection. Results: The AI cancer prediction platform detected 6,749 cases of lung cancer among the 7,395 individuals diagnosed, resulting in an early detection sensitivity of 91.5%. The platform identified 423,249 false positives among the 887,014 patients who did not have lung cancer, leading to a specificity of 52.3%. Additionally, it identified the risk of a lung cancer diagnosis in 26.6% of patients up to five years earlier than the diagnoses made by physicians. Conclusions: This study highlights the potential of leveraging EMR data and AI platforms like C the Signs to enhance early lung cancer detection. Chest X-rays, with their reduced sensitivity for early-stage lesions and reliance on symptom-driven use, remain limited as a screening tool. In contrast, the AI platform achieved a sensitivity of 91.5% and identified 26.6% of cases up to five years earlier than traditional diagnoses. These findings underscore the promise of AI-based platforms as supplementary tools for improving early detection and facilitating timely intervention to enhance patient outcomes.

Abstract Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a fast-rising global public health problem. About 10% of patients with MASLD will develop hepatocellular carcinoma (HCC), but molecular markers for predicting future risk of HCC development in patients with MASLD are lacking. We investigated whether protein biomarkers in pre-malignant plasma samples can predict susceptibility to HCC in the setting of MASLD. Methods: A nested case-control study was performed among patients with MASLD who later developed HCC (cases) and MASLD patients who never progressed to HCC (controls), matched on age at MASLD diagnosis (± 1-year), sex, the year of blood sample collection, and length of follow-up (± 1-year). MASLD at baseline was determined by the presence of hepatic steatosis (assessed by imaging or biopsy) plus at least one cardiometabolic condition (obesity, diabetes mellitus, hypertension, or dyslipidemia). Archival baseline plasma samples stored at -80oC were used to perform the SomaScan 7k v4.0 assay. We also performed genotyping of the well-established HCC susceptibility variant, PNPLA3-rs738409 (I148M), among the cases and controls. The study sample was split into discovery (70%) and validation (30%) sets, and we selected promising protein biomarkers in the discovery set using differential expression analysis with volcano plots and Bonferroni-corrected p-values. A final set of significant proteins was used for prediction modeling in the validation sample, calculating area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The study sample included 54 MASLD to HCC cases and 54 MASLD controls, split into discovery and validation sets. We identified five significantly differentially expressed proteins in the discovery set, four overexpressed (TSP2, GPR56, MIC-1, TIMP-1) and one under-expressed (IGFALS) in the cases versus controls. Fitting the five proteins in the validation sample yielded an AUC=0.88, sensitivity=0.87, specificity=0.75, and accuracy=0.81. Adding PNPLA3-rs738409 to the model improved the model’s specificity and accuracy (AUC=0.88, sensitivity=0.87; specificity=0.81, accuracy=0.84). Conclusions: Our findings show that a five-protein biomarker panel can robustly predict future HCC development in patients with MASLD. Adding a well-established HCC genetic risk variant, PNPLA3-rs738409, slightly improved the model’s performance. These initial findings provide evidence of HCC prediction with baseline plasma biomarkers and genetic risk variants, toward improving risk stratification and enhancing early detection of HCC in the setting of metabolic perturbations. Citation Format: Ampem Darko Jnr Siaw, Sebastian M. Armasu, Jacob A. Frank, Irene K. Yan, Kirk J. Wangensteen, Tushar Patel, Samuel O. Antwi. Protein biomarkers of HCC development in patients with metabolic liver disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2350.

371 Background: Prostate cancer is the most common malignancy in men globally and remains among the top five leading causes of cancer-related deaths. Emerging evidence suggests that early identification of symptoms can detect localized disease thus enabling opportunities for curative intent treatments. This study evaluates the use of the AI-powered prediction model, C the Signs, to passively screen for prostate cancer by leveraging data from electronic medical records (EMRs), offering a novel pathway for identifying high-risk individuals. Methods: A retrospective analysis was conducted using the Mayo Data Platform, encompassing 418,477 male patient records, of which 16,835 were diagnosed with prostate cancer. C the Signs identified patients at risk based solely on their EMR data, utilizing AI-driven pattern recognition. Sensitivity and specificity analyses were performed to assess the prediction model’s accuracy. Additionally, we examined the time-to-diagnosis advantage for patients flagged by the model compared to traditional physician clinical diagnoses. Results: C the Signs demonstrated a sensitivity of 83.3% and a specificity of 52.5% for identifying patients at risk of prostate cancer. Notably, 31.8% of prostate cancer cases were identified at risk up to five years earlier by the model compared to traditional physician clinical diagnosis. Conclusions: The integration of AI-driven prediction models like C the Signs into prostate cancer screening pathways provides an opportunity to enhance early detection, particularly in symptomatic individuals. Compared to prostate-specific antigen (PSA) testing, the model achieved equivalent sensitivity and 38% higher specificity, positioning it as a valuable companion tool for improving patient outcomes.

Background and Aims: Cholangiocarcinoma (CCA) is an aggressive malignancy arising from the biliary epithelium with limited therapeutic options and poor long-term survival rates. To address the limitations of CCA treatment, we investigated cell-targeted nanovesicles as a delivery platform for transcriptome-targeting therapeutics. Approach and Results: Milk-derived nanovesicles were loaded with short interfering RNAs targeting Yes-associated protein (YAP), the downstream effector of the Hippo pathway; LCK, an upstream regulator of YAP; and tafazzin, a protein critical for the integrity of the inner mitochondrial membrane. These transcriptome-targeting nanovesicles were decorated with a lipid-coupled RNA aptamer to epithelial cell adhesion molecule, including a tracking fluorophore. In vitro studies were conducted using multiple CCA cell lines. In vivo studies were performed using C57BL/6 and non-obese diabetic/severe combined immunodeficient mice to evaluate delivery and efficacy in both an immunocompetent syngeneic murine and a patient-derived xenograft model. We demonstrated that transcriptome-targeting nanovesicles were selectively taken up by liver tumor cells, which was augmented by the incorporation of a targeting aptamer, and that milk-derived nanovesicles loaded with short interfering RNA effectively downregulated target gene expression, both in vitro and in vivo. Downstream effects of target gene inhibition were observed, including downregulation of YAP-TEAD target genes and an increase in reactive oxygen species production at the mitochondrial level. Administration of transcriptome-targeting nanovesicles targeting YAP, LCK, and tafazzin inhibited CCA growth and further synergized with chemotherapy in 2 preclinical CCA models. Conclusions: Herein, we show that aptamer-directed, nanovesicle-mediated targeting of YAP, LCK, and tafazzin potentiates chemosensitivity in 2 CCA models when delivered using aptamer-guided milk-derived nanovesicles.

54 Background: The incidence of colorectal cancer is increasing rapidly, particularly amongst younger people. Despite a screening program being widely available, most patients are diagnosed with cancer through symptomatic presentation. Leveraging electronic medical records (EMRs) through clinical decision support platforms offers potential for identifying high-risk individuals before clinical suspicion develops. This study aims to explore the challenges associated with early colorectal cancer diagnosis and evaluate the effectiveness of the cancer case finding feature of a cancer management platform called C the Signs. Methods: A retrospective analysis was conducted using data from the Mayo Data Platform, comprising records of 894,275 patients, including 7,348 diagnosed with colorectal cancer. C the Signs was employed to identify patients at risk of colorectal cancer based on EMR data. Sensitivity and specificity analyses were performed to assess the platform's performance in identifying high-risk patients. Additionally, the study evaluated the temporal discrepancy in colorectal cancer diagnoses between those flagged by C the Signs and those detected by primary care physicians. Results: The C the Signs screening platform had a sensitivity of 93.8% and a specificity of 19.7% in identifying patients at risk of colorectal cancer. Notably, 29.4% of patients with colorectal cancer were identified as being at risk up to 5 years earlier by C the Signs compared to diagnoses made by primary care physicians, highlighting the platform's potential for early detection. Conclusions: Early identification of high-risk individuals holds promise for improving patient outcomes and reducing the burden of colorectal cancer. Whilst the specificity of the C the Signs platform for colorectal cancer is relatively low, the sensitivity is comparable to the most favorable rates reported for colonoscopy. In addition, if C the Signs was used in conjunction with a relatively cheap screening test such as Faecal Immunochemical Testing, this would significantly drive up the specificity whilst maintaining the sensitivity. Further studies are needed to assess its efficacy in conjunction with tests available in primary care.