Citations

Abstract INTRODUCTION African American (AA) individuals are underrepresented in biomarker studies for Alzheimer's disease (AD). Biomarkers that reflect the heterogeneity of AD and achieve the greatest accuracy across populations are sorely needed. METHODS Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in AA participants with clinical diagnoses of AD dementia ( n = 181) and cognitively unimpaired (CU, n = 142). Machine learning was used to identify a set of plasma proteins that yielded the best classification accuracy. RESULTS A set of 36 proteins achieved an area under the curve (AUC) of 0.94 to classify AD dementia versus CU, a 16% improvement over age, sex, and apolipoprotein E ( APOE) . This finding was replicated in multiple plasma and brain datasets (AUCs 0.73–0.97). Our findings underscore the importance of matrisome and cerebrovascular dysfunction in AD pathophysiology. DISCUSSION This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning. Highlights Conducted large‐scale plasma proteomics in Alzheimer's disease (AD) versus cognitively unimpaired controls. Machine learning biomarker discovery was replicated in an independent cohort. Novel set of proteins distinguishes AD versus controls with high accuracy (area under the curve [AUC] = 0.94). Achieved reproducibility across multiple replication cohorts (AUC = 0.73–0.97). Network analyses implicates matrisome biology and cerebrovascular dysfunction.

e14545 Background: Next generation sequencing (NGS) identifies genomic alterations in DNA obtained from tissue biopsies and/or blood [circulating tumor (ct)DNA]. We anticipate a lower rate of copy number loss (CNL) in ctDNA compared to tissue, as lower ctDNA tumor fraction may limit the ability to detect a deletion. However, we observed several instances of a CNL in ctDNA that was not identified in a tissue specimen, prompting this systematic evaluation of an institutional dataset. We focused on BRCA1/2 pathogenic variants (PVs) given their therapeutic implications and coverage across testing platforms. Methods: Using an IRB-approved protocol, we evaluated an institutional repository of NGS results performed across the Mayo Clinic Enterprise. We focused on patients (pts) who had both tissue and blood NGS testing performed. We further narrowed this cohort to those with a BRCA1/2 PVs, somatic and/or germline, as determined by testing performed at Guardant Health, Tempus, or Foundation Medicine. We abstracted information about each variant, including type of alteration classification and functional impact. Demographic information, cancer characteristics, and germline testing information was collected. Fisher’s exact test was used to compare alteration frequencies in ctDNA vs tissue using GraphPad Prism v 10.1. Results: From Dec 2015-Sept 2024, we identified 158 unique pts with BRCA1/2 PV who had NGS results for both tissue and ctDNA. The median age at first reporting BRCA1/2 variant was 67 years (range: 29-98 years). 48% of pts were female, and the most common cancer type sites were lung (23%), colorectal (20%), and pancreaticobiliary (20%). We identified 198 unique pathogenic genomic alterations; 71 in BRCA1 & 127 in BRCA2 . Most common alteration was an SNV or indel (127/198), followed by CNL (n = 63), splice site variants (n = 5), fusions (n = 2), and rearrangements (n = 1). Of the 126 SNV or indels, 50 (39.7%) were identified in both ctDNA and tissue, 40 (31.7%) were detected in blood only, 36 (28.6%) identified only in tissue (p < 0.0001). Amongst the 63 CNL, only 3 (4.8%) were identified in both ctDNA and tissue; 41(65.1%) were only identified in ctDNA, and 19 (30.2%) in tissue only (p < 0.0001). In the 19 tissue-only CNL tests, 12/19 noted one or more additional pathogenic SNV/Indel in BRCA1/2. In the 41 ctDNA-only CNL tests, 1/41 noted one or more additional pathogenic SNV/Indel in BRCA 1/2 . Germline testing was available in 41/63 pts with copy number loss. 3/41 deletions were identified in germline (2 in ctDNA, 1 in tissue). Conclusions: We observed a low degree of concordance in CNL between tissue and ctDNA for BRCA1/2 . CNL detection in ctDNA that could not be confirmed in germline or with a second alteration warrants further attention. In particular, clinicians should be cautioned when interpreting ctDNA reports noting BRCA1/2 CNL without clarity if this represents biallelic loss or a synchronous loss of function.

548 Background: TNBC is associated with higher risk of recurrence and poorer survival rates than other breast cancer subtypes. For node-positive TNBC or tumors larger than 0.5 cm, chemotherapy is generally recommended. For stage I TNBC, the benefit from chemotherapy, the optimal regimen, and whether to administer it in the neoadjuvant versus adjuvant setting remain controversial. Here, we report the treatment patterns and clinical outcomes of patients with stage I TNBC treated at Mayo Clinic. Methods: Using the Mayo Clinic Tumor Registry data, we identified patients with stage I TNBC treated between 2010 and 2021. We used the pathologic tumor (T) category for patients treated with upfront surgery and the clinical T category for patients receiving neoadjuvant chemotherapy. We used the Kaplan-Meier method and log-rank test to compare recurrence-free survival (RFS) according to treatment groups, measured from the day of surgery. RFS was reported at a median follow-up of 3.9, 6.2, and 7.3 years for patients who received chemotherapy neoadjuvantly, adjuvantly, and surgery alone with no chemotherapy, respectively. Results: A total of 602 patients with Stage I TNBC were included, with a median age of 62 years. 290 (48%) underwent upfront surgery followed by adjuvant chemotherapy (pT1a: 2%, pT1b: 25%, pT1c: 74%), 127 (21%) received neoadjuvant chemotherapy followed by surgery (cT1a: 2%, cT1b: 11%, cT1c: 87%), and 185 (31%) underwent primary surgery without any chemotherapy (pT1mi/T1a: 33%, pT1b: 32%, pT1c: 35%). Most patients treated with neoadjuvant therapy received anthracycline/cyclophosphamide + taxane (70%), while most patients treated in the adjuvant setting received a non-anthracycline containing regimen (60%). The 5-year RFS was 96% (95% CI: 93-100%) for patients who received chemotherapy neoadjuvantly, 95% (95% CI: 92-98%) for those who received chemotherapy adjuvantly, and 85% (95% CI: 80-91%) for those treated with surgery alone and no chemotherapy ( P < 0.0001). 71 (56%) of the 127 patients who received neoadjuvant therapy achieved a pCR, with only 1 RFS event (at 14 months) in that group, compared to 4 RFS events among those not achieving pCR (at 12, 20, 28 and 75 months, respectively). Among patients not receiving chemotherapy (either adjuvantly or neoadjuvantly), the 5-year RFS rates were 94% for pT1mi/T1a, 92% for pT1b and 72% for pT1c (T1mi/a/b vs T1c, P = 0.009). Conclusions: In this large cohort of stage I TNBC, patients who received chemotherapy (adjuvantly or neoadjuvantly) had better 5-year RFS compared with those treated with locoregional therapy only. Among patients receiving chemotherapy, 5-year RFS was nearly identical regardless of whether chemotherapy was administered before or after surgery. Interestingly, patients undergoing upfront surgery were more likely to receive an anthracycline-sparing chemotherapy regimen.

Abstract Purpose: While an addition of immune checkpoint inhibitor (ICI), pembrolizumab, has improved outcomes of patients with TNBC, early-stage TNBC patients with residual cancer burden (RCB) II/III after neoadjuvant chemotherapy in combination with pembrolizumab (NAP) still have poor outcomes. Previously, we demonstrated that intratumoral HLA-DRA expression and interferon-alpha (IFNα) responses were associated with immune-inflamed phenotype and better outcomes in TNBC patients receiving chemotherapy. This study utilized the CosMx™ SMI to investigate in early-stage TNBC treated with NAP. Methods: Of 151 samples from TNBC patients treated with NAP at Mayo Clinic, 8 pre-treatment biopsy samples (5 pCR and 3 RCB II/III were analyzed using the CosMx™ SMI platform. Linear mixed models to assess differential expression (DE), adjusting for hierarchical data structures. DE analyses were expressed as log2-fold changes (logFC), with the Benjamini-Yekutieli method. GSEA and IPA for signaling pathway exploration. Results: 1.7 million cells were identified across 8 samples after stringent quality control. Immune cell profiling revealed higher B cells, plasma cells, plasmablasts, and T cells (CD4 and CD8) in responders, whereas fibroblasts and myeloid cells were enriched in non-responders. DE analysis showed significantly elevated expression of HLA-DRA (logFC 1.955, p&lt;0.001), other MHC class I and II components, and IFNα in responders. Tumor cells in responders exhibited the highest IFNα expression across cell types. GSEA and IPA analysis revealed enrichment in interferon signaling, antigen presentation, and the cGAS-STING pathway, which drives IFNα production. Spatial analysis categorized cells by their proximity to tumor cells: within 50 µm, 50-100 µm, and over 100 µm. T cells, particularly CD8 T cells, showed a distance-dependent distribution, with a higher abundance near tumor cells in responders. CD8 T cell activation scores declined with increasing distance from IFNα-positive tumor cells but remained consistently low near IFNα-negative tumor cells. IFNγ-positive CD8 T cells exhibited higher activation compared to IFNγ-negative cells, with activation levels influenced by proximity to tumor cells. Lastly, MHC expression in tumor cells was examined relative to CD8 T cell proximity. HLA-DRA and CIITA expression decreased as tumor cells were farther from CD8 T cells. Conclusions: This spatially resolved single-cell analysis highlights the pivotal role of tumor-immune interactions in response to pembrolizumab in TNBC. Our data suggest that tumor cells secrete IFNα through the activation of the cGAS-STING pathway, which activates T cells within close proximity, highlighting the potential role of cGAS-STING agonist as well as IFNα signaling and antigen presentation in augmenting TNBC responses to ICIs. Citation Format: Yi Liu, Saranya Chumsri, Yaohua Ma, Jodi M. Carter, Aziza Nassar, Edith Perez, Roberto A. Leon-Ferre, David Zahrieh, David W. Hillman, Judy C. Boughey, James Ingle, Krishna R. Kalari, Fergus J. Couch, Matthew P. Goetz, Keith L. Knutson, E. Aubrey Thompson. Spatially resolved single-cell analysis reveals cGAS-STING-mediated tumor-immune interactions via IFNα as determinants of pembrolizumab sensitivity in triple-negative breast cancer [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 7130.

Abstract Background: Second generation androgen receptor signaling inhibitors (ARSIs), such as abiraterone, have demonstrated improved survival in metastatic castration-resistant prostate cancer (mCRPC) patients, however, acquired resistance to these agents often arises. Novel and alternative therapies are needed to combat the ARSI resistant tumor. In the last two decades patient-derived xenograft (PDX) and PDX organoids have become pivotal models in anticancer drug development and biomarker discovery due to their ability to faithfully recapitulate the complex and heterogeneous tumor biology. Methods: We analyzed RNAseq data from mCRPC patients and corresponding PDX models (PROMOTE study, NCT #01953640) and employed L1000 Chem Perturbation down/up signatures database to find candidate drugs to overcome abiraterone resistance by reverse signature search. We then performed proliferation assay using 49 compounds in three PROMOTE-PDX organoid models (2 abiraterone-resistant and 1 abiraterone-sensitive) using our semi high-throughput drug screening platform. Results: Overall, we identified a total of 32 compounds with IC50&lt;10µM in at least one of the models, among which, 5 agents are active in all three models and 5 in at least two of the models. (Table 1). Within active compounds we find selective kinase inhibitors (BRAF, JAK2, AKT, FLT3) and histone deacetylase inhibitors (belinostat and pracinostat). Particularly interesting are two drugs that are only active in the abiraterone resistant setting but not the sensitive PDX. Conclusions: In summary, we leveraged RNAseq data from mCRPC patients and PDX models and performed a high-throughput drug screen in PDX-derived organoid models to identify potential drugs that could overcome abiraterone-resistance in lethal advanced prostate cancer. These data highlight potential drug combinations or subsequent therapeutic strategies for these patients. Citation Format: Irene Marín-Goñi, Huanyao Gao, Joachim L. Petit, Shreya Indulkar, Adam M. Kase, Cassandra N. Moore, Krishna R. Kalari, Michael T. Barrett, Richard Weinshilboum, Winston Tan, John A. Copland, Liewei Wang. Overcoming resistance in mCRPC: drug discovery using PDX models and high-throughput drug screening [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 5658.

Abstract Deep learning has advanced digital pathology and multi-omics data analysis. However, there is still a critical need to integrate these modalities to better understand complex diseases. Deep learning models, such as Prov-Gigapath, a state-of-the-art pathology foundation model, have been developed for analyzing whole-slide images (WSI). Our previously published deep-learning framework, OmicsFootPrint (OFP), transforms high-dimensional multi-omics data into intuitive genomically-arranged circular images and utilizes transfer learning to overcome the challenges of high-dimensional, low-sample-size data. In this study, we aimed to combine WSI and multi-omics data to develop a framework capable of distinguishing between invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), which is clinically significant as it may help explain differential responses to therapies, such as tamoxifen versus letrozole. We enhanced the performance of our OFP model by dividing each multi-omics circular image (1024x1024x3 pixels) into 16 equal-sized patches (256x256x3 pixels). Each patch was trained using a separate EfficientNetV2 architecture with pre-trained ImageNet weights. The probability outcomes from these 16 models were subsequently combined through a meta-learner model using the AutoGluon framework. Concurrently, WSI images corresponding to the same patients were independently trained, maintaining consistent train/validation/test splits. The Prov-Gigapath model, which was trained using a dataset from the Providence Health System, was employed to extract patch-level embeddings from the WSI data. These embeddings were fine-tuned using a LongNet-based masked autoencoder to predict outcomes. Finally, predictions from the OFP model and the WSI-based Prov-Gigapath model were integrated through a meta-learner, preserving the original train/validation/test split. Model performances (AUC) were evaluated using the held-out test set. The gold standard used for the analysis was histological identification of IDC versus ILC, against which the performance of omics data alone, WSI data alone, and the multi-modal integration approach were compared. We evaluated our integrated model as a proof-of-concept using The Cancer Genome Atlas (TCGA) breast cancer cohort, comprising 92 ILC and 391 IDC samples. Multi-omics data, including gene expression, microRNA, copy number variation, and RPPA, were analyzed using the OFP framework, while WSI data were processed using the Prov-Gigapath method. The results demonstrated that the multi-modal integration approach significantly outperformed individual modalities, achieving an AUC of 0.98 compared to 0.87 for OFP (original), 0.95 for OFP (16-patches), and 0.92 for WSI. Integrating WSI and multi-omics data improves predictive accuracy, significantly improving the classification of breast cancer subtypes over histology alone. Citation Format: Naresh Prodduturi, Xiaojia Tang, Hamid R. Tizhoosh, Kevin J. Thompson, Richard Weinshilboum, Karthik V. Giridhar, Judy C. Boughey, Eric W. Klee, Liewei Wang, Matthew P. Goetz, Vera Suman, Krishna R. Kalari. A multi-modal learning framework to integrate digital pathology image and multi-omics data in breast cancer [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 2429.

Abstract Alzheimer’s disease (AD) affects 40 million individuals globally and is characterized by the accumulation of amyloid-beta (Aβ) proteins, which aggregate and form plaques. BBB dysfunction drives AD cerebrovascular pathology and BBB integrity is maintained by neurovascular unit (NVU). Specifically, within the NVU, the cerebral endothelial cells maintain vascular homeostasis. In this study, we isolated endothelial-enriched regions of interest (ROIs) using the Nanostring GeoMx digital spatial profiler and employed a deconvolution model to evaluate transcriptomic changes. We observed dysregulation of cellular signaling potentially disrupting the APP+ BBB integrity. Analysis of ligand-receptor pairings that are the foundation of the NVU intercellular signaling indicated that the endothelial vasculature completes a feedback loop with the NVU to the regulating astroctyes. Further, we identified potentially antagonistic signaling roles for opioid receptor species that should be further investigated for potential therapeutic targets. Graphical Abstract

Abstract Cerebrovascular inflammation is prevalent in a majority of Alzheimer’s patients. Inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-alpha), circulating in the plasma have been shown to cause the inflammation of blood-brain barrier (BBB) endothelium lining the cerebral microvasculature. The BBB inflammation has been implicated in the increase of toxic Aβ accumulation within Alzheimer’s disease (AD) brain. TNF-alpha in the peripheral circulation can aggravate the accumulation of amyloid-beta (Aβ) peptides in Alzheimer’s disease brain. In the current study, we have shown that the exposure to TNF-alpha leads to an increase in Aβ42 accumulation in mice and BBB endothelial cells in vitro. Moreover, dynamic SPECT/CT imaging in wild-type (WT) mice infused with TNF-alpha increased the permeability and influx of Aβ42 into the mice brain. In addition, our results show that TNF-alpha modifies the expression of cofilin, actin, and dynamin, which are critical components for Aβ endocytosis by BBB endothelial cells. These results offer a mechanistic understanding of how TNF-alpha may promote Aβ accumulation at the BBB and the underlying interactions between inflammation and Aβ exposure that drives BBB dysfunction. Hence, a therapeutic intervention aimed at addressing cerebrovascular inflammation in Alzheimer’s disease may potentially reduce Aβ induced cerebrovascular toxicity in Alzheimer’s disease brain. Significance statement Increased levels of TNF-alpha circulating in the plasma are considered significant factors in the consequences of Aβ pathology in Alzheimer’s disease, where it can promote cerebrovascular inflammation and BBB dysfunction. However, the role of TNF-alpha, in exacerbating Aβ pathology by increasing Aβ accumulation at the BBB endothelial cells remains only partially understood. In this study, we demonstrated that TNF-alpha enhances Aβ42 accumulation in the BBB endothelium by altering the expression of the BBB endocytosis machinery, specifically cofilin, actin, and dynamin. These findings are anticipated to contribute to the development of therapeutic approaches aimed at addressing elevated cytokine levels in Alzheimer’s disease. Graphical Abstract

ABSTRACTType 2 diabetes mellitus (T2DM), characterized by insulin resistance, is closely associated with Alzheimer's disease (AD). Cerebrovascular dysfunction is manifested in both T2DM and AD, and is often considered as a pathological link between the two diseases. Insulin signaling regulates critical functions of the blood–brain barrier (BBB), and endothelial insulin resistance could lead to BBB dysfunction, aggravating AD pathology. However, insulin signaling is intrinsically dynamic and involves interactions among numerous molecular mediators. Hence, a mechanistic systems biology model is needed to understand how insulin regulates BBB physiology and the consequences of its impairment in T2DM and AD. In this study, we investigated the pharmacodynamic effect of insulin on the expression of vascular cell adhesion molecule 1 (VCAM1), a marker of cerebrovascular inflammation. Intriguingly, normal insulin concentrations selectively activated the PI3K‐AKT pathway, leading to decreased VCAM1 expression. However, exposure to supraphysiological insulin levels, which is present in insulin resistance, activated both PI3K‐AKT and MEK–ERK pathways, and increased VCAM1 expression. We developed a mathematical model that adequately described the dynamics of various insulin signaling nodes and VCAM1 expression. Further, the model was integrated with in vitro proteomics and transcriptomics data from AD patients to simulate VCAM1 expression under pathological conditions. This approach allowed us to establish a quantitative systems pharmacology framework to investigate BBB dysfunction in AD and metabolic syndrome, thereby offering opportunities to identify specific disruptions in molecular networks that will enable us to identify novel therapeutic targets.

Abstract Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significance Integration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.