Citations

AbstractINTRODUCTIONHigher male prevalence in sporadic behavioral variant frontotemporal dementia (bvFTD) has been reported. We hypothesized differences in phenotypes between genetic and sporadic bvFTD females resulting in underdiagnosis of sporadic bvFTD females.METHODSWe included genetic and sporadic bvFTD patients from two multicenter cohorts. We compared behavioral and cognitive symptoms, and gray matter volumes, between genetic and sporadic cases in each sex.RESULTSFemales with sporadic bvFTD showed worse compulsive behavior (p = 0.026) and language impairments (p = 0.024) compared to females with genetic bvFTD (n = 152). Genetic bvFTD females had smaller gray matter volumes than sporadic bvFTD females, particularly in the parietal lobe.DISCUSSIONFemales with sporadic bvFTD exhibit a distinct clinical phenotype compared to females with genetic bvFTD. This difference may explain the discrepancy in prevalence between genetic and sporadic cases, as some females without genetic mutations may be misdiagnosed due to atypical bvFTD symptom presentation.Highlights Sex ratio is equal in genetic behavioral variant of frontotemporal dementia (bvFTD), whereas more males are present in sporadic bvFTD. Distinct neuropsychiatric phenotypes exist between sporadic and genetic bvFTD in females. Phenotype might explain the sex ratio difference between sporadic and genetic cases.

Background: Cholangiocarcinoma is a molecularly heterogenous cancer arising from the biliary epithelium. Individual omic techniques have identified clinically relevant, targetable subtypes but most tumors have no targetable alterations based on exome or transcriptomic evaluation. Integrated multiomics approaches incorporating transcriptomic, proteomic, and phosphoproteomic characterization provide deeper understanding and identify non-mutated, activated pathways that can be targeted. To optimize the potential clinical utility of these approaches, minimal classifier features must be defined. Methods: Approximately 60,000 RNA, protein, and phosphoprotein features were extracted from 35 cholangiocarcinomas treated at the primary US study site. 206 patients treated and profiled at an institution in China were also included. Using the Multiomics Factor Analysis clustering method, patients were sorted into 3 distinct molecular profiles. Important molecular features were selected using competing feature selection (FS) methods: Boruta, RreliefF, variable selection using random forests (RF), recursive factor elimination (RFE), median decrease accuracy in RF, and Concrete Autoencoders. Machine learning classification prediction models including Extreme Gradient Boosting, gradient-boosted trees, multinomial logit, and support vector machine (SVM) were tested on each FS. Each combination of FS and predictive model was tested with 10 replicates of 10-fold cross-validation of a randomly-selected balanced test set and an oversampled, balanced training subset. Accuracy, F1 score, AUC, precision, and recall were assessed for each FS and predictive model combination. A custom function of physical assay cost and classification accuracy was optimized to find the best assay. KEGG pathway overrepresentation analysis was used to analyze feature subsets. Results: A consistent set of 50 important RNA and protein features was identified, including previously reported prognostic factors, like SpryD4 protein expression, as well as previously unreported pathway surrogates, such as Slc2A2 or Pipox protein. Pathway overrepresentation analysis found insulin signaling and resistance and amino acid biosynthesis/metabolism pathways were overrepresented in the discriminative feature set. The gradient-boosted trees algorithm combined with the RFE FS method had the best performance, with a multiclass macro average (±sd) cross-validation accuracy 89% (±6%), F1 score 89% (±8%), AUC 0.98 (±0.02), precision 90% (±10%), recall 89% (±6%). Conclusions: Feature selection methods and predictive machine learning models work synchronously to develop accurate molecular subtype assays based on just 50 protein and RNA levels. This strategy has the potential to make molecular subtyping for cholangiocarcinoma and other molecularly complex cancers fast and widely available. Citation Format: Ellen Larson, Erik Jessen, Dong-Gi Mun, Jennifer Tomlinson, Amro Abdelrahman, Danielle Carlson, Hojjat Salehinejad, Rory Smoot. Feature selection and machine learning strategies optimize an affordable molecular assay for cholangiocarcinoma subtype [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 1107.

Abstract Background: Cholangiocarcinoma (CCA) is a diverse type of cancer. For several years, platinum-based chemotherapy (Gemcitabine and Cisplatin) has been the standard treatment and serves as the foundation for current immunotherapy combinations. The typical response rates are generally between 20% and 30%. In this study, we present the results of preclinical efficacy trials conducted on CCA patient-derived xenograft (PDX) models, correlating them with the baseline multiomics profiling of these models to identify molecular characteristics linked to treatment response. Methods: Tumor samples from twenty-eight CCA PDX were used to isolate RNA and protein to be included in multiomics analysis (Phosphoproteomics, Proteomics, and Transcriptomics). A subset of PDX models were treated with various doses of Gemcitabine and Cisplatin. The Median Efficacy Index (MEI) was calculated as the ratio of the difference in median of delta in tumor volume at the last day of the study between the treatment and the control arms divided by the median of delta in tumor volume of the control at the last day of the treatment (positive= growth, negative=inhibition, and 0=no efficacy). A Hierarchical All-Against-All algorithm was utilized to compare multiomics features to the MEI and pathway analysis completed for identified molecular features. Pathway enrichment was calculated using the KEGG database, and random-forest variable selection chose the pathways most influential in predicting sensitivity or resistance. Results: After conducting preclinical efficacy trials, the 11 tested CCA PDX models were classified into sensitive, moderately sensitive, and resistant models based on the MEI on each treatment arm. The top up-regulated genes were identified for Gemcitabine response (SRPK2, GLK2, and RIOK2) and Cisplatin response (NTRK1, PRPF4B, and SRPK2). The top up-regulated genes related to Gemcitabine resistance were NTRK1, PIP5K1C, and CSNK2A2, while those related to Cisplatin resistance were SRPK1, PRPF4B, and CSNK2A1. Downregulation of the anti-folate resistance pathway is a strong predictor of gemcitabine sensitivity, and the lysine degradation pathway is a strong predictor of cisplatin sensitivity. Conclusion: Multiomics signatures of cholangiocarcinoma can forecast responses to standard chemotherapy in preclinical models. New markers of resistance to Gemcitabine and Cisplatin were discovered, linked to NTRK1, SRPK1, and SRPK2. These resistance profiles will be tested using suitable targeted therapies on the resistant CCA patient-derived xenograft (PDX) models. Citation Format: Amro M. Abdelrahman, Danielle M. Carlson, Ellen L. Larson, Erik Jessen, Dong-Gi Mun, Isaac Lynch, Alessandro Fogliati, Stella Konadu Adjei Antwi, Aushinie Abeynayake, Akhilesh Pandey, Mark J. Truty, Gregory J. Gores, Rory L. Smoot. Baseline multiomics and chemotherapy efficacy in preclinical trials using patient-derived xenograft models of cholangiocarcinoma can reveal distinct signatures of chemotherapy response [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 6762.

Pancreatic cancer ranks third in cancer related deaths in the USA, however, no reliable theragnostic biomarker exists for the disease. The community of bacteria within the body can become dysbiotic and promote cancer through inflammation and other means. The objectives of this study were to determine the identity and abundance of bacterial species in the gut of mice before and after development of pancreatic cancer. As there can be significant variation in the genomes of different strains of bacterial species, a metagenome analysis was also performed. We hypothesized that there would be significant variation in species and genes of the gastrointestinal tract of mice before and after xenografting with pancreatic cancer. A decrease in ecological diversity was also predicted. Fifteen female SCID mice were divided into three cages. To develop the SubQ pancreatic cancer model, 5 X 106 Panc1 cells were injected at the flank. Fecal materials were collected prior to tumor cell implantation and after tumor development. DNA was extracted from the fecal material. For phylogenetic analysis the seven variable regions of the 16S rRNA gene were amplified and sequenced. Metagenomic analysis was performed as well. Average tumor size in mice after 40 days was ∼ 32 mm3. Microbial diversity was decreased in two out of the three groups of mice. Principle component analysis showed major differences between all samples. There were at least 15 OTUs in each set of samples specific to pretumor mice and 15 OTUs specific to tumor bearing mice. One genus of bacteria, Alistipes, was specific to each set of tumor bearing mice. Taxa at significantly increased abundances in tumor bearing mice included Prevotellaceae and Porpyromonadaceae. Moreover, there were several gene functions that were enriched in tumor bearing mice. This study produced data that demonstrates that there is a link between health status and the gut microbiome. However, this is not a new idea. This study could serve as foundation to establish a gut microbiota profile of pancreatic cancer. Precision medicine is becoming more common due to the decreasing costs of DNA sequencing. The findings of our investigation strengthens the hypothesis that there is an interaction between gut microbiome and pancreatic cancer development and bacteria or bacterial genes could serve as theragnostic biomarker for pancreatic cancer. Acknowledgement: This research was supported by NIH Grant# T34-GM100831, NSF-NOYCE Graduate student training award, a US Department of Education Graduate student training award to Elizabeth City State University Campus of The University of North Carolina and a University of North Carolina Collaboratory Grant award to Dr. H. Banerjee and Dr. Colby Hunter. Citation Format: Gabrielle Morgan, Colby Hunter, Michael Hall, Narendra Banerjee, Erik Armstrong, Kanisha McNeil, Karington Perry, Santanu Bhattacharya, Hirendra N. Banerjee, Krishna K. Rachamala, Shamit Dutta, Naga M. Nakka. Phylogenetic and metagenomic analysis of the human pancreatic cancer microbiome [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 6526.

Abstract Introduction: Cholangiocarcinoma (CCA) is an aggressive biliary malignancy with poor survival outcomes and multiple mechanisms of resistance to targeted therapies. We previously validated NXP800, a novel GCN2 small-molecule activator, as an effective anti-cancer drug in six CCA cell lines. In vitro testing identified upregulation of phosphorylated eIF2a, as well as upregulation of ATF4, in CCA cells treated with of NXP800 compared to vehicle, suggesting that treated cells led to stimulation of stress-induced genes and subsequently, apoptosis. Furthermore, patient derived xenografts (PDX) models treated with NXP800 were found to have decreased tumor size, particularly in models containing ARID1A, FGFR1, and ATM mutations. Here, we aimed to predict response to NXP800 using sensitivity and resistance signatures from our profiled PDX models. Methods: GCN2 activation in multiple previously profiled PDX models was evaluated. Whole Exome Sequencing (WES) was done on multiple PDX models to characterize the mutational signature and activity in CCA. Two additional CCA xenografts were expanded into the flank of NOD/SCID mice. Tumor bearing mice were treated with NXP800 (35 mg/kg) or vehicle via oral gavage for 28 days once tumors averaged 120 mm3. Tumor volume and animal weight were collected twice weekly. Median Efficacy Index (MEI) scores were generated as drug response rates based on tumor growth. Predicted sensitivity and resistance kinase signatures to predict drug response were produced using multi-omics. Results: Treatment with NXP800 was associated with a statistically significant decrease in tumor size in two additional PDX models, which included ARID1A and BRAF mutants. Preliminary multi-omics on the PDX models revealed that sensitivity mechanisms were upregulated most in EGFR signaling, while resistance signatures were focused around CSNK2A1 signaling. Conclusion: Multi-omics analysis revealed a sensitivity mechanism structured around EGFR signaling, providing insights into which patients should be selected for treatment with NXP800. A Phase 1b clinical trial investigating the effects of NXP800 use in patients with advanced CCA is open and currently accepting patients. Citation Format: Danielle Marie Carlson, Hendrien Kuipers, Amro Abdelrahman, Erik Jessen, Joshua Roldan Kalill, Jennifer Tomlinson, Jack Sample, Akhilesh Pandey, Mark Truty, Rory Smoot. Modeling response prediction to a novel GCN2 kinase activator (NXP800) in patient-derived xenograft cholangiocarcinoma models using a multi-omics approach [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 5592.

Abstract Introduction: Src Family Kinases (SFKs) have a crucial role in tumor proliferation, survival, and metastasis. NXP900 is a highly selective SFK inhibitor a novel mechanism of action that locks SFKs in a closed, inhibited conformation, preventing interaction with substrate proteins, unlike other SFK inhibitors like dasatinib. A phase 1 study of NXP900 in patients with advanced solid tumors was recently initiated. Our preclinical studies showed therapeutic efficacy of NXP900 in cholangiocarcinoma (CCA) through decreased YAP, RAF-MAP and PI3K-AKT signaling. Here, we examined mechanisms of sensitivity and resistance to SFK inhibition. Methods: Secondary resistance was explored by generating a NXP900-resistant human cell line (HuCCT1-R) via prolonged exposure to NXP900, followed by multiomic profiling. Primary resistance was studied in vivo using nine heterogeneous patient-derived xenograft (PDX) tumors implanted in the flanks of NOD/SCID mice. Tumor-bearing mice were treated with vehicle or 40 mg/kg NXP900 daily for up to 4 weeks. To identify factors associated with response, a multiomic approach was used, integrating RNA-Seq, global proteomics, and phosphoproteomics. A hierarchical-all-against-all algorithm (HallA) assessed associations between multiomic features and drug response in tumor growth. Kinase enrichment analysis (KEA3) identified features linked to sensitivity or resistance. Results: HuCCT1-R cells showed increased pTyr abundancy of TNK2, a protein known to increase PIP3 levels leading to AKT activation, and of DYRK3 and LAMTOR1, both of which can sustain AKT phosphorylation via mTORC, indicating acquired resistance through bypassing inhibited PI3K-AKT signaling to rescue AKT. NXP900 reduced tumor growth rates in 5 of 9 PDX models. Kinases associated with sensitivity in the PDX models included SFKs (SRC, LYN, and FYN), and AKT, PTK2, PRKCZ, EGFR, SYK, and GSK3B, suggesting involvement of the PI3K-AKT pathway in responsiveness to NXP900. Based on tumor inhibition responses in the tested models, we estimated NXP900 response for our additional PDX models, predicting 39% (11/28) of patient tumors may respond. Conclusion: This study identified molecular markers linked to response to SFK inhibition, which could be utilized for patient selection for NXP900 treatment. Further studies are validating these findings and exploring combination therapies to address resistance mechanisms. Citation Format: Hendrien Kuipers, Danielle M. Carlson, Jack W. Sample, Erik Jessen, Dong-Gi Mun, Aushinie M. Abeynayake, Jennifer L. Tomlinson, Amro M. Abdelrahman, Nathan W. Werneburg, Binbin Li, Mitesh J. Borad, Mark J. Truty, Sumera I. Ilyas, Gregory J. Gores, Rory L. Smoot. Multiomic profiling identified biomarkers of response to selective Src family kinase inhibition in preclinical models of cholangiocarcinoma [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 5595.

ABSTRACT Objective Although fetal sex chromosome complement prediction by prenatal cell‐free DNA (cfDNA) screening is highly accurate, discordance between the cfDNA‐inferred chromosomal sex and phenotypic sex observed via ultrasonography or at birth, that is, “fetal sex discordance”, does rarely occur. Such discordance may be caused by differences in sex development (DSD) or technical or biological artifacts. This study aims to provide insights into the proportion of fetal sex discordance cases caused by DSD versus non‐DSD artifacts. Method We retrospectively reviewed 122,748 cases that underwent diagnostic testing at the Mayo Clinic Cytogenetics Laboratory between 2015 and 2023. Thirty‐three singleton‐gestation cases tested for fetal sex discordances were identified. Results Fetal sex discordance was attributed to non‐DSD artifacts in 24 (73%) of the 33 patients. In these cases, the phenotypic sex matched the chromosomal sex revealed by diagnostic cytogenetic testing. The remaining 9 patients included one with mosaic 46,X,psu idic(Y)(q11.21)/45,X, 5 with 46,XY DSD, and 3 with 46,XX DSD. Conclusion The majority of fetal sex discordance cases are caused by non‐DSD artifacts rather than DSD. Accurate clinical counseling of fetal sex discordance requires distinguishing between DSD and non‐DSD causes, emphasizing the need for diagnostic cytogenetic testing.