Kalari, Krishna R.

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<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<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.

Background Clonal hematopoiesis (CH) is defined by somatic mutations in hematopoietic stem cells, often conferring a selection advantage based on clonal selection pressures. CH in DNA Damage Response and Repair (DDR, e.g. TP53, PPM1D) genes are frequent in individuals exposed to chemotherapy and radiation (chemo/XRT). In metastatic prostate cancer (mPC) patients, the prevalence of CH is estimated at 57% and is increased among those with prior exposure to chemo or XRT. In these patients, the use of radiotheranostics is becoming more prevalent and are now being tested in earlier lines of therapy. We have identified an increase in DDR-somatic variants post lutetium-177-PSMA, raising concern for long term hematologic safety. Here, we evaluate the spectrum of CH and CCUS (clonal cytopenias of undetermined significance) in mPC patients, prior to receiving radioisotope therapies. Methods We performed error corrected next-generation sequencing (NGS) on peripheral blood samples from 67 mPC patients enrolled in the Prostate Cancer Medically Optimized Genome Enhanced Therapy Study (PROMOTE) study (NCT #01953640). All patients enrolled also had whole exome sequencing (WES) performed on initial metastatic biopsies and on whole blood for germline genotyping. Somatic variants with a variant allele fraction (VAF) ≥0.5% were considered as CH, using standard bioinformatic variant calling algorithms. Results The median age at NGS was 74.4 years (range, 58.4-92.3). While all patients received androgen deprivation therapy (ADT) +/- androgen receptor pathway inhibitor (ARPI), 67.2% (n=45, pelvic XRT- 35, palliative XRT- 10) received prior XRT and 3% (n=2) received prior docetaxel. No patients in this cohort received prior platinum or PARP inhibitor. Of those treated with XRT or chemo, the median time from first exposure to CH sample collection was 4.1yrs (range, 0.12-29.6) and 2.3yrs (range, 0.6-4.0), respectively. Of the 67 patients, we identified a total of 111 pathogenic variants in 59 (88.1%) patients. We compared our NGS data to the germline data and tumor for each patient (All samples obtained at the same time point; Wang et al 2018). Our analysis excluded 2 ATM variants likely to be germline, and 1 TP53 variant likely to be tumor-derived. Each patient had at least one somatic variant, with a median of 2 variants (range, 1-6The median CH VAF was 1.8% (range, 0.5-50.2) with 91.0% of alterations detected at a VAF of <10%. Specific somatic variants, presence/absence of CH, number of variants, nor VAF was associated with age, prior chemo, or XRT exposures. At time of NGS, 54% of patients were anemic (hgb <13g/dL), 7% were thrombocytopenic (platelets <135 x109), 6% were leukopenic (WBC <3 x 109), and no patients were neutropenic (ANC <1 x109). Persistent cytopenias, lasting >3months, were present in 25% (n=17) of patients; however, bone marrow biopsies (BMB) were performed only in 6% (n=4). Three (75%) of these 4 patients had mPC infiltration in the BM, and 1 (25%) had mild dysplasia not meeting criteria for MDS but meeting criteria for CCUS (CHEK2 somatic variant). At last follow-up, there were no patients formally diagnosed to have myeloid neoplasm (MN). Median overall survival (OS) from prostate cancer diagnosis was 8.6yrs (range, 1.4-28.4). With a median follow up of 2.2yrs (range 0.1-10.8) after CH testing, 3.0% (n=2) were alive, with most deaths occurring due to mPC. The presence of CH did not impact overall survival (OS), however, presence of cytopenias at the time of NGS testing was associated with worse outcomes (median OS 2.5yrs vs 1.2 yrs., p=0.025). Conclusions We define the landscape of CH mutations in mPC patients predominantly treated with ADT/ARPI and XRT, demonstrating that DNMT3A, TET2, PPM1D and ASXL1 are frequently mutated (88%) and largely subclonal (91% with VAF ≤10%). While 25% of these patients had cytopenias, with cytopenias negatively impacting survival, due to inadequate BM biopsy work up, the true prevalence of CCUS and MN could not be estimated. Given the advancement of genotoxic therapies and the high prevalence of subclonal CH in mPC, future studies investigating CH as a predictive biomarker of hematological dysfunction are much needed.

Abstract The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores—0.98 ± 0.02 for lung cancer subtype differentiation and 0.83 ± 0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

e13036 Background: In January 2023, the FDA approved elacestrant for ESR1-mutated, ER+/HER2- MBC based on a companion diagnostic cell free (cf)DNA test (Guardant Health). In this study, we analyze the trends of liquid biopsy utilization at Mayo Clinic (MC) over a 3 year period to understand the impact of this drug approval on testing patterns and identify emergent alterations in patients (pts) with MBC. Methods: We extracted clinically performed Guardant 360 and Guardant 360 CDx ordered at MC sites in Rochester (MN), Florida (FL), Arizona (AZ), and across 9 additional Mayo Clinic Health System (MCHS) sites. Pts with MBC were identified using the EMR and analyzing visit diagnosis codes and staging information. Shifts in testing patterns were analyzed over a 3 year period from January 1, 2021 - December 31, 2023. Data was collected on the number of tests, serial testing, frequency of pathogenic mutations, and emergent mutations in ESR1 and PIK3CA. Chi-square test was used to compare the categorical values. Results: Between 2021-2023, 2,741 pts with MBC were seen across all sites. A total of 408 cfDNA tests were performed in 370 unique patients with MBC. Most pts (90%) had 1 test performed. The proportion of pts with MBC tested was 85/1350 (6.3%) in 2021, 77/1495 (5.2%) in 2022, and 208/1558 (13.4%) in 2023. The increased proportion of pts tested in 2023 was statistically significant compared to prior years (p<0.0001), while no difference was observed between 2021 and 2022 (p 0.19). Testing patterns varied by site, with FL ordering the highest number of tests, followed by MN and AZ. Of the 34 providers practicing during the entire period seeing more than 10 MBC pts, the number of providers ordering Guardant testing increased from 15/34 (2021) and 14/34 (2022) to 20/34 (2023), though not statistically significant (p 0.15). Of the 41% with EMR-documented staging available, testing was highest in the ER+/HER2- (81.3%) subtype, followed by TNBC (13.6%) and then HER2+ (5.2%). The most commonly identified mutations identified in the first testing were PIK3CA (41.1%), TP53 (40.5%), ESR1 (30.5%), and ATM (13.2%). D538G (8.0%), Y537N (4.4%), and Y537S (3.5%) were frequent emergent mutations seen in ESR1. Conclusions: We observed an increase in cfDNA testing at the start of 2023 which aligns with the FDA approval of elacestrant. We observed variation by site and individual providers that warrants further exploration to improve cfDNA testing in MBC pts at Mayo Clinic.

1037 Background: TBCRC041, a randomized phase 2 trial of alisertib (A) ± fulvestrant (F) in women with HR+/HER2- MBC demonstrated promising clinical activity for alisertib (1). Interrogation of baseline (PreC1) and end of cycle 1 (EOC1) cfDNA and CTCs may identify biomarkers associated with response to A±F. Methods: Plasma cfDNA was sequenced using the Guardant INFINITY platform, which includes genomic and methylated tumor fraction (mTF) analysis. Pathogenic variants were determined using publicly available databases and Mayo Clinic annotation pipelines. CTCs were identified as nucleated, EpCAM+/ cytokeratin+/ CD45- cells and assessed for ER/HER2 staining (RareCyte, Seattle). PFS was defined as the time from randomization to progression or death. EOC1-PFS differs from PFS as time starts at C2D1. Stratified log-rank tests and conditional Cox modeling were used to assess whether PFS or EOC1-PFS differed by mutations in ESR1 or PIK3CA, pre-CTC ≥ 5, or mTF>1%. Results: Of the 91 eligible pts, 84 progressed, and 73 were deceased. In the 86 pts with PreC1-cfDNA results, variants were identified in ESR1 (42; 48.8%), PIK3CA (36; 41.9%), AKT1 (7; 8.1%), PTEN(7; 8.1%), BRCA1 (4; 4.7%), and BRCA2 (7; 8.1%). Pts with a PIK3CAmut had significantly decreased PFS (HR 1.8; 95%CI: 1.1 -2.9, p=0.014) but not by ESR1mut status (p=0.583). PreC1 and EOC1-mTF were determined for 78 and 76 pts, respectively. PreC1-mTF was 0-1% in 21; 1.1-10.0% in 27; and 10.1-100% in 30. mTF levels remained between 0-1% in 17 pts fell to 0-1% in 11 pts, and was 0-1% in 5 pts without a preC1-mTF determination. PFS did not differ with respect to PreC1-mTF (p=0.210), but EOC1-PFS was worse in pts with mTF >1% (HR 3.0; 95%CI: 1.6 -5.2, p<0.001). PreC1 and EOC1-CTC were determined for 78 and 76 pts. 36 (46.2%) pts had a PreC1 CTC count ≥ 5. EOC1-CTC levels remained at (n=17) or increased to (n=1) ≥ 5 and was ≥ 5 in 2 pts without a preC1-CTC determination. EOC1-PFS was not significantly different for EOC1-CTC ≥ 5 (p=0.065), but was significantly decreased in pts with PreC1-CTC≥5 (HR=1.8; 95%CI: 1.1-3.0; p=0.018). Broader cfDNA NGS panel and CTC-derived ER/HER2 staining results will be presented at the meeting. Conclusions: Among patients receiving A±F, PFS varied based on the PIK3CAmutation status. cfDNA and CTC assessment PreC1 and EOC1 provided complementary information. Further validation in larger datasets is needed. 1. JAMA Oncol 2023;9:815. Clinical trial information: NCT02860000 .