Citations

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the U.S. mainly because of late-stage diagnosis. Heritable genetic information can improve risk stratification for individuals with moderate to high-risk of PDAC for targeted screening and early detection. We aimed to develop an enhanced polygenic risk score (PRS) for PDAC using a Bayesian approach, LDpred2, that reweights risk estimates attributed to millions of polymorphisms (SNPs) estimated from genome-wide association studies (GWASs). We then compared the performance of LDpred2 with the commonly used genetic risk score (GRS) approach that is based only on genome-wide significant SNPs. Methods: GWAS summary statistics were obtained from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case Control Consortium for 9040 cases and 12496 controls and were used to compute weights for a GRS (17 SNPs) using a p-value threshold of 5e-8 and weights for an enhanced PRS (1.3 million SNPs) using LDpred2. These weights were then used to compute risk scores in an independent validation sample of 2616 cases and 45407 controls from the Mayo Clinic. We used logistic regression to test for associations of the GRS and PRS with PDAC risk, adjusting for age, sex, and the first 10 principal components of ancestry. Results: The validation sample was of mostly European ancestries (>95%). Cases were slightly older than controls (median age: 66 yrs. vs. 63 yrs.) and had a higher frequency of men than controls (57% vs. 42%). Comparisons between the GRS and the PRS are shown in Table 1. DeLong’s test comparing the AUCs between the two genetic scores showed the PRS significantly outperformed the GRS (AUC = 0.68 vs. 0.67; p = 7.6e-7). Conclusions: The LDpred2 PRS model that used a larger set of SNPs significantly improved prediction of PDAC. Comparing the top percentile of the PRS versus the rest showed that the PRS is a clinically significant risk score (OR > 3) for PDAC and could inform clinical decision making and genetic counseling of individuals identified as having a high risk of PDAC. Citation Format: Samuel O. Antwi, Brandon J. Coombes, Nicholas B. Larson, Erin E. Carlson, Kari G. Rabe, Hunter J. Atkinson, Shounak Majumder, Jun Zhong, David McKean, Pancreatic Cancer Cohort & Case Control Consortia (PanScan and PanC4), Brian M. Wolpin, Rachael Z. Stolzenberg-Solomon, Alison P. Klein, Laufey T. Amundadottir, Ann L. Oberg. Enhanced polygenic risk score for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB331.

Abstract Introduction: With over 80, 000 new cases of non-Hodgkin's lymphoma (NHL) diagnosed annually, modifiable risk factors remain unclear. Circadian disruption, linked to cancers like breast and prostate, affects immune cells (e.g., natural killer cells and T-helper cells) by altering their trafficking and proliferation. Some studies (PMID: 30566672, 27611440) suggest long sleep may increase overall NHL risk, including a Mendelian randomization (MR) study (PMID: 32895918). However, no research has examined the causal relationship between sleep traits, including long sleep, and NHL risk, by subtypes. We hypothesize that sleep traits are causally associated with NHL subtypes in European populations, testing this using MR with data from the International Lymphoma Epidemiology Consortium (InterLymph). Methods: We conducted two-sample MR using genome-wide association studies (GWAS) summary data for sleep traits and NHL subtypes. Only independent, genome-wide significant (p < 5×10-8) Single Nucleotide Polymorphisms (SNPs) were selected as valid instruments, sourced from the UK Biobank. Sleep traits included chronotype (153 SNPs), insomnia (48 SNPs), sleep duration (78 SNPs), excessive daytime sleepiness (37 SNPs), short sleep (27 SNPs), and long sleep (8 SNPs). NHL subtype data were from InterLymph: follicular lymphoma (6, 508 cases / 64, 183 controls), mantle cell lymphoma (1, 169 cases / 61, 603 controls), Waldenstrom macroglobulinemia/lymphoplasmacytic lymphoma (WM/LPL: 1, 697 cases / 59, 333 controls), and chronic lymphocytic leukemia (8, 522 cases / 67, 653 controls). The primary analysis used inverse-variance weighted (IVW) random-effects, with MR-Egger for pleiotropy adjustment. Results: Using the IVW method, we did not identify any statistically significant causal association between sleep traits and the risk of NHL subtypes, but did yield several intriguing trends. Long sleep (sleep duration >= 9 h per night) showed a marginally increased NHL risk [Odds ratio (OR) range: 1.51 - 247.9] across each subtype. Short sleep (sleep duration < 7 h per night) showed a similar trend [OR range: 1.25 - 1.81], except for WM/LPL, where we noticed a negative trend. The confidence limit for long sleep was imprecise due to the small number of variants. We did not notice any significant directional horizontal pleiotropy or weak instrument bias. Conclusions: We found no conclusive evidence of a relationship between sleep traits and four NHL subtypes. However, we see suggestive trends of long and short sleep associated with higher risk of some NHL subtypes. These findings align with the previous studies, which showed long sleep was associated with higher risk of NHL overall. Future directions include expanding our analysis to additional NHL subtypes, generating polygenic risk scores, and conducting stratified analyses. Citation Format: Pankhil Shah, Brittany Crawford, Anwar Merchant, Brenda Birmann, Angelica Macauda, Michelle A. Hildebrandt, Aaron Norman, Neil E. Caporaso, Meredith Yeager, Michael Dean, Immaculata De Vivo, Lynn Goldin, Nicola J. Camp, Rosalie Griffin, Delphine Casabonne, Federico Canzian, Pelin Unal, Elad Ziv, Catherine R. Marinac, Alexandra Nieters, Stephen J. Chanock, Mitchell J. Machiela, Michael Conry, Charlie Zhong, Hanla A. Park, Simon Cheah, Jonathan N. Hofmann, Elizabeth E. Brown, Celine Vachon, Susan Slager, Sonja Berndt, Sophia S. Wang, Vijai Joseph, James McKay, Henrik Hjalgrim, Lara Sucheston-Campbell, Karl Smith-Byrne, Alyssa Clay-Gilmour. Association between sleep traits and risk of non-Hodgkin's lymphoma subtypes: a mendelian randomization study in the International Lymphoma Epidemiology (InterLymph) Consortium [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 7415.

Abstract Approximately two-thirds of hypopharyngeal squamous cell carcinoma (HPSCC) are diagnosed in advanced stage, with the worst prognosis among head and neck squamous cell carcinomas (HNSCCs). Identifying biomarkers for high-risk patients who need aggressive treatment is crucial. We present mutational, transcriptomic and proteomic studies of 103 Chinese HPSCC patients, observing a higher prevalence and poorer prognosis in males comparing to females. Genes in estrogen response pathways are up-regulated and proteins phosphorylated by protein kinase C (PKC) and Cyclin-dependent kinases (CDKs) are aberrantly regulated. We identify aberrant copy number regions including SOX2 (3q26.33), FGFR (8p11.23), CCND1 (11q13.3), CDKN2A/2B (9p21.3), and MYC (8q24.21). HPV status combined with highly mutated genes as predictive markers was assessed, including SYNE1 in HPV(-), MUC4 in HPV(+) patients. We confirm the presence of four known gene expression (GE) subtypes of HNSCC in our HPSCC cohort and present the genomic alterations, patient characteristics and survival status of each subtype. A predictive model involving clinical factors and transcriptomic expression of six genes was established and cross-site validated. The findings open new opportunities for stratifying high-risk patients and molecular targets for novel therapeutic strategies. Citation Format: Yanxin Ren, Wei Xiong, Chun Feng, Dan Yu, Xiaoyan Wang, Qing Yang, Siting Yu, Hongjiang Zhang, Bangyun Huo, Honglu Jiang, Zuli Li, Junlin Wang, Yu-Xiong Su, Ping Yang, Yong Liao, Qi Zhong, Junwen Wang. Multi-omics insights into the molecular signature and prognosis of hypopharyngeal squamous cell carcinoma [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 4999.

Abstract Background The ataxia-telangiectasia mutated (ATM) protein coordinates the cellular response to therapeutic radiation, and ATM inhibitors can potentially enhance the efficacy of radiation in otherwise radiation-resistant tumors. Methods The small molecule ATM kinase inhibitor WSD0628 was specifically designed for enhanced distribution across the blood-brain barrier to more effectively treat glioblastoma (GBM) and brain metastases in combination with radiation therapy (RT). GBM and brain metastasis patient-derived xenograft models were used to understand target inhibition, radiosensitization, inhibition of the DNA damage response, and in vivo efficacy. Results Initial in vitro characterization of WSD0628 demonstrates a high level of selectivity across kinase families, limited aldehyde oxidase liability, and low risk of hERG interactions. Consistent with a central role for ATM in radiation response, WSD0628 blocked radiation-induced signaling and enhanced radiosensitivity in U251 glioma cells and brain tumor PDXs GBM120 and M12. In comparison to control or RT alone in orthotopic PDXs, the combination of WSD0628 with RT markedly prolonged median survival—GBM12 (19, 55, and 408 days, respectively); GBM43 (26, 44, and 143 days, respectively); GBM120 (51, 89, and 231 days, respectively); M12 (17, 39, and 190 days, respectively). Pharmacokinetic and pharmacodynamic testing after treatment in orthotopic GBM43 tumors showed inhibitory levels of WSD0628 and a reduction of γH2AX foci in the combination-treated tumors. Conclusions Collectively, these results suggest a promising role for WSD0628 in combination with RT in brain tumors and provide the rationale for an ongoing Phase 0/1A clinical trial testing this combination in recurrent GBM.

INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2. METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) formed an ensemble model. RESULTS: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% confidence interval [CI]: 0.764–0.819), outperformed VCTE alone (0.745, 95% CI 0.717–0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679–0.748, P < 0.0001), and Agile-3 model (0.740, 0.710–0.770, P < 0.0001) regarding the AUC, decision curve analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668–0.749) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores. DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE (https://aihepatology.shinyapps.io/ALADDIN1) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab (https://aihepatology.shinyapps.io/ALADDIN2) offers an effective alternative when VCTE is unavailable.

Abstract Background The mechanisms underlying metastatic latency in renal cell carcinoma (RCC) remain poorly understood. Methods This study evaluated 2 large independent cohorts for differences in tumor biology between patients who developed metastases early (≤1 year after nephrectomy) and those with late onset (&gt;3 years). Results In the discovery cohort (n = 161), late metastatic RCC was associated with clear cell histology (88.9% vs 78.7%), lower pathological stage (pT1-2; 40.3% vs 18.0%), and favorable histopathological features including low grade (40.0% vs 2.3%), less sarcomatoid (5.6% vs 21.8%), and reduced necrosis (37.7% vs 78.3%; all P &lt; .02). Late metastatic RCC tumors exhibited increased angiogenesis (63.5% vs 19.4%) and reduced inflammation (78.8% vs 50.0%; all P &lt; .02) profiles. Genomic driver analyses revealed comparable rates of PBRM1 and SETD2 loss in late and early metastatic RCC, while BAP1 loss was significantly less common in late metastatic RCC (7.5% vs 27.1%; P &lt; .02). In multivariable models, BAP1/PBRM1/SETD2 status and tumor necrosis emerged as key discriminators of late metastatic RCCs. These findings were confirmed in the second cohort (n = 307). Late metastatic RCC was enriched for fatty acid oxidation and angiogenesis pathways, supporting a less aggressive phenotype. This was further evidenced by a lower engraftment rate in murine models (0% vs 36.5%; P &lt; .001) and significantly longer overall survival from the time of metastasis (median survival doubled, P &lt; .001). Interestingly, late metastatic RCC shared genomic and phenotypic features with RCC that metastasizes to the pancreas, suggesting a common underlying biology influencing both metastatic latency and pancreatic tropism. Conclusions Overall, these findings advocate for recognition of late metastatic RCC because of its distinct biology and improved prognosis.

ABSTRACT Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55–76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56–87), hospital length of stay (LOS) of 7 (IQR, 4–12) days, and ICU LOS of 2 (IQR, 1–4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2–15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6–13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0–1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3–8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.