Nardi, Valentina

Abstract The detection of genetic abnormalities is required during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this relies on invasive bone marrow (BM) biopsies, severely limiting early detection, frequent longitudinal monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. To address these needs, we recently launched GenoPredicta, a CLIA-approved LDT that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively characterizing MM genomes with whole genome sequencing (WGS) from as few as 50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Briefly, tumor cells are isolated from samples using fluorescence-activated cell sorting and subjected to WGS, from which copy number alterations, structural variants, and short variants (SNVs/indels) are identified using a fully automated pipeline generating physician-ready clinical reports from raw sequencing data in ∼6h. Analytical validation of GenoPredicta showed complete concordance with FISH results. Identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding MM immunotherapies is becoming increasingly important. Here, we describe GenoPredicta results from relapsed/refractory MM patients, highlighting resistance-conferring alterations that can only be detected by WGS, such as deletions in the kilo- to megabase scales that are observed in conjunction with SNVs and indels, leading to biallelic loss/inactivation of the gene, including at subclonal levels. In addition to biallelic loss of BCMA and GPRC5D in response to CAR T or T cell engager therapies, we observed similar resistance mechanisms for targets of immunomodulatory drugs, e.g., CRBN. The observation of these resistance mechanisms was consistent with patients’ clinical histories. In summary, we demonstrate that low input WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with CTC-based measurements enabling comprehensive profiling of the MM genome. Crucially, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection and guidance of therapy. The dramatically improved variant calling ability from WGS, especially in low-input CTC applications, extends to other malignancies and will gain wider adoption as sequencing costs continue to decrease. Citation Format: Bruno Paiva, Peter Voorhees, Patricia T. Greipp, Danielle Sookiasian, Julian Hess, Marisa DeMeo, Vicki Pounder, Sarah Calkins, Reid Meyer, Linda B. Baughn, Christine-Ivy Liacos, Meletios-Athanasios Dimopoulos, Alexandra Papadimou, Taouxi Konstantina, Efstathios Kastritis, Jesus Berdeja, Daniel Auclair, Valentina Nardi, Thomas Mullen, Francois Aguet, Shaji Kunnathu Kumar. Whole genome sequencing of multiple myeloma genomes with a novel clinical assay enables identification of genetic alterations underlying immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1067.

Abstract Introduction Detecting genetic abnormalities is mandatory during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this requires invasive bone marrow (BM) biopsies, severely limiting early detection, patient monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. Moreover, the importance of identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding immunotherapies is becoming increasingly recognized. To address this need, we recently launched GenoPredicta, a CLIA-approved Laboratory Developed Test that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively and robustly characterizing MM genomes with whole genome sequencing (WGS) from a minimum of ~50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Here, we describe the characterization of clinical and research samples with GenoPredicta, highlighting resistance-conferring genetic alterations that can only be detected by sequencing and that are consistent with patients' clinical histories, while further demonstrating the complete concordance of GenoPredicta with prior FISH results. Methods Tumor cells were isolated from BM and/or PB samples using fluorescence-activated cell sorting (FACS) (after CD138+ enrichment for PB) and subjected to whole genome sequencing (WGS), from which we identified copy number alterations, structural variants, and single nucleotide variants and indels. For a subset of samples, the BM was also characterized by FISH, using a standard clinical panel for characterizing MM; for other samples prior FISH results were available from patients' clinical records. Results The analytical validation of GenoPredicta, conducted on a retrospective cohort of >60 viably frozen samples from >30 patients, showed complete concordance between BM and PB for detected genomic abnormalities, with all clinically relevant events (11 translocations and 125 CNVs) observed in BM recapitulated in PB (100% recall). All events identified by FISH in 14 samples (8 translocations and 30 CNVs) were also identified in the BM (100% recall), with 50 translocations and 30 CNVs not detected by FISH also absent in the WGS data (100% precision). The abnormalities detected in these samples also included multiple high-risk events not assayed by FISH, including biallelic TP53 loss (Del 17p coupled with a deleterious TP53 mutation, observed in 4 patients) and MYC::IGL translocations (2 patients). Moreover, for all fresh peripheral blood clinical samples with FISH results available from patients' histories, GenoPredicta recalled 100% of reported events. Among these clinical samples, we notably observed a case with both a biallelic GPRC5D deletion and a TNFRSF17 (BCMA) deletion combined with a truncating mutation on the remaining allele. This was consistent with the patient's clinical history showing progression after therapy with talquetamab (anti-GPRC5D bispecific), and previous nonresponse to teclistamab (anti-BCMA bispecific) after prior therapy with Cilta-cel (BCMA-directed CAR T). GenoPredicta also detected other mutations likely to confer resistance to immunotherapies, including loss-of-function mutations in IKZF3 and deletions of FCRL5 in frozen peripheral blood research samples. Conclusion We demonstrate that WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with blood-based measurements enabling comprehensive profiling of the myeloma cancer genome. Importantly, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection of therapy.

Introduction: Carotid artery intraplaque hemorrhage (IPH) is increasingly recognized as a predictive marker of cerebrovascular events. Despite widespread utilization of contrast-enhanced CT angiography (CTA) for the evaluation of carotid atherosclerosis, diagnosis of IPH on CTA is challenging, limiting risk stratification capabilities. We evaluated convolutional neural networks (CNN) for detection of histology-proven IPH in carotid CTA. Methods: The study included carotid endarterectomy patients from our center who had preoperative CTA and available plaque specimens from the Mayo Clinic Carotid Artery Atherosclerosis Trial (MCAT) Biobank. A validated segmentation model localized common and internal carotid arteries (CCA/ICA). CCA bifurcation served as landmark after a large language model, Llama3.0-70B, verified culprit plaque proximity to the bifurcation from CTA reports. A 3×3×4cm box around ipsilateral CCA bifurcation was sampled from CTA volumes and images were labeled IPH by histology study of carotid specimen (Fig 1). Given limited cases with concurrent imaging and histology, we employed a robust cross-validation strategy: 22 cases (20%) held-out for testing, with remaining data 86 cases used for 5-fold cross-validation (stratified by IPH presence). The five resulting models independently generated IPH probability scores, and ensembled predictions calculated by three methods to enhance generalizability: Majority and weighted voting based on binary IPH class and soft voting based on IPH probability scores. Results: Of 418 patients with histological analysis, 130 had CTA studies available. After excluding 22 cases lacking histological consensus, 108 cases were analyzed. In the 22 test cases the mean age was 68.1±8.5 years and 59% were male; In the cross-validation folds age ranged from 67.1±6.9 to 75.3±7.6 years 56-82% were male. Individual models' performance on the test set showed mean ROC-AUC of 0.79 (95% CI: ±0.05) and average precision of 0.85 (±0.05) (Fig 2). The ensemble approach achieved superior performance with soft voting demonstrated 82% ROC-AUC, 77% accuracy, 79% precision, 85% sensitivity, and 82% F1-score for IPH detection, outperforming majority voting and weighted voting (Fig 3). Conclusion: Automated IPH detection in widely available CTA imaging can turn it to a powerful stroke risk assessment tool, supporting personalized prevention strategies and surgical decision-making, ultimately reducing cerebrovascular morbidity and mortality.

Introduction: Atherosclerosis, the underlying cause of cardiovascular disease (CVD), is a lipid-driven chronic inflammatory disease. We have identified the co-stimulatory CD40-CD40L immune checkpoint dyad as a key driver of inflammation during atherosclerosis. CD40 and CD40L exert cell-type divergent functions via different signaling pathways. Hypothesis: We hypothesize that CD40 on endothelial cell (ECs) induces endothelial-to-mesenchymal transition (EndMT)-mediated vascular inflammation in atherosclerosis. Methods & Results: Using single cell RNAseq, we found that CD40 (TNFRSF5) is expressed on PDE3A + GJA5 + and KDR + COL15A1 + ECs of advanced human carotid atherosclerotic plaques. CD40 is highly expressed in the endothelial lining of human and mouse atherosclerotic plaques, and colocalizes with the EndMT markers fibronectin and collagen and adhesion molecule ICAM-1. CD40L activation of human aortic endothelial cells (HAECs) results in expression of the EndMT markers fibronectin, vimentin, sm22α, FSP1 and snail. Activation of CD40 in EC induces filamin A dependent translocation of CD40 into lipid rafts, increases TNF-receptor associated factor (TRAFs) -2, -3 and 6 levels, induces Akt signaling and increases expression of VCAM1 and CCL2. Conclusions: The endothelial cell has been more and more regarded as a cell type that plays a major role in driving inflammation. Our data suggest that the CD40-CD40L axis plays a significant role in EC-mediated inflammation in atherosclerosis by driving EndMT.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Pediatric Acute Lymphoblastic Leukemia (ALL) were developed as a result of meetings convened by a multidisciplinary panel of pediatric ALL experts, with the goal of providing recommendations on standard treatment approaches based on current evidence. The NCCN Guidelines for pediatric ALL focus on risk assessment and stratification of risk-adapted therapy; treatment strategies for BCR::ABL1 (Philadelphia chromosome [Ph])-negative and BCR::ABL1-positive B-cell lineage, T-cell lineage, and infant ALL; and supportive care considerations. This selection from the NCCN Guidelines for pediatric ALL focuses on the diagnosis of and management of pediatric T-ALL.

Background: Previous studies have linked pericoronary artery adipose tissue attenuation to major adverse cardiac event (MACE). However, the association between pericarotid artery fat and prognosis remains underexplored. This study aimed to investigate the association between preoperative pericarotid fat characteristics and the subsequent prognosis in patients undergoing carotid endarterectomy (CEA). Methods: This study included patients with preoperative computed tomography angiography (CTA) followed by CEA. Preoperative neck CTA images were obtained and analyzed with novel methods using 3D Slicer software. We segmented the common and internal carotid arteries. The segment was horizontally trimmed, isolating the region around the bifurcation, extending 15 mm above and 10 mm below. Pericarotid fat was defined as adipose tissue within a 5 mm radial distance from the outer vessel wall, with Hounsfield units (HU) between -190 and -30 (Figure 1, carotid segmentation [green] and pericarotid fat [yellow]) . For each patient, we calculated the HU average and the volume (mm3) of the pericarotid fat. MACE was defined as a composite of all-cause mortality, myocardial infarction, and cerebrovascular events. The relationship between both pericarotid fat characteristics and MACE was investigated. Results: Of the 43 patients initially included, one was excluded due to an inability to perform segmentation caused by chronic total occlusion, leaving 42 patients (age 73 [67-78] years, 33 [76.7%] male). The comparison of mean pericarotid fat HU between the ipsilateral and contralateral sides showed no significant difference (-60.3±11.0 HU vs. -60.9 ±11.3 HU, p=0.437). However, the median ipsilateral fat volume was significantly higher than the contralateral volume (528 [248, 1100] mm3 vs. 328 [184, 697] mm3, p=0.020). When comparing patients with and without MACE, those with MACE had a significantly lower ipsilateral fat density (-59.4±10.2 HU vs. -66.8 ±11.1 HU, p=0.022) and a higher fat volume (468 [233, 746] mm3 vs. 845 [458, 1451] mm3, p=0.044). Kaplan-Meier analysis revealed that higher volume and lower fat density than the median were significant predictors of MACE (log-rank p=0.015 and p=0.042, respectively) (Figure 2). Conclusions: Assessment of pericarotid fat using a novel CTA analytic method in patients undergoing CEA identified ipsilateral lower fat density and higher fat volume as predictors of adverse outcomes.

Background: Previous research has suggested pericarotid fat density to be significantly linked to atherosclerotic plaque components and cerebrovascular events. Inflammatory markers within the plaques are critical in the pathogenesis and progression of carotid artery disease. However, the association between pericarotid fat and inflammatory processes within atherosclerotic plaques remains underexplored. This study hypothesizes that pericarotid fat density is directly associated with the levels of inflammatory markers within atherosclerotic plaques. Methods: Carotid plaques were prospectively collected from patients with carotid artery disease undergoing carotid endarterectomy (CEA). The levels of inflammatory plaque cytokines (MCP-1, IL-8, IFN-γ, IL-10, IL-1β, IL-6, PDGF-AA, PDGF-AB/BB, TNF-α, VEGF) were measured in tissue homogenates using a customized 10-plex Luminex assay. Baseline demographics and clinical data of our population were extracted from electronic health records. We analyzed preoperative neck computed tomography angiography (CTA) images placing regions-of-interest (ROI) in the pericarotid fat on the slice showing maximal stenosis. For each patient, we calculated the average of Hounsfield Units (HU) within the ROI. Spearman correlation test was used to assess the correlation between variables. Results: Among the 43 patients included, the median age was 73 (67, 78) years, and 33 (76.7%) patients were male. Plaque levels of PDGF-AA and VEGF showed significant positive correlations with HU (r=0.330, p=0.030; r=0.380, p=0.011, respectively), as illustrated in Figure 1 . Although plaque levels of IFN-γ demonstrated a significant negative correlation with HU (r=-0.390, p=0.010), the most cases exhibited values close to zero. Conclusions: The assessment of pericarotid fat density on CTA may allow for the estimation of plaque inflammation, suggesting that more fat density is associated with increased inflammatory activity within atherosclerotic plaques.

Introduction: Patients with atherosclerotic carotid artery disease are at high risk of mortality in the long-term follow-up after carotid endarterectomy (CEA), partly due to dysrhythmia. Atrial fibrillation (AF) is a common cardiac dysrhythmia linked to stroke and cardiovascular events. An artificial intelligence-based electrocardiogram (ECG) AF algorithm (AI-AF) can effectively identify silent AF. We aimed to assess the effectiveness of AI-AF in predicting mortality in patients with carotid artery disease undergoing CEA. Methods: Patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 were included if they had >30 days follow-up and an ECG within one year before CEA. The ECG closest to the CEA date was used to calculate AI-AF (probability of AF ranging from 0 to 1). All-cause mortality rates after CEA were obtained from clinical records. The optimal AI-AF cut-off value for predicting mortality was calculated using a receiver operating characteristic curve and the Youden index. Long-term survival after CEA was depicted with a Kaplan Meier plot and compared with the Log-rank test. Univariate and multivariate Cox Regression models were used to assess the predictive value of AI-AF for mortality, adjusting for age, sex, body mass index, diabetes mellitus, hypertension, and hyperlipidemia. Results: A total of 636 patients with a median age of 72 [IQR: 66, 78] years and 414 (65.1%) males were included. The median AI-AF was 0.056 [IQR: <0, 0.171]. Over a median follow-up time of 7.1 [IQR: 2.7, 11.5] years, mortality occurred in 326 (51.3%) patients. AI-AF significantly predicted mortality in univariate and multivariate Cox regression analysis (HR: 3.47, 95%CI: 2.34-5.16, P<0.001; HR: 2.30, 95%CI: 1.44-3.68, P<0.001, respectively). The best cut-off value for AI-AF for mortality prediction was 0.025 (area under the curve: 0.608). High and low AI-AF were defined as AI-AF≥ 0.025 and <0.025, respectively. Mortality was significantly higher in high AI-AF compared to low AI-AF patients (P<0.001, Figure 1A,1B), and high AI-AF was a significant predictor of mortality (univariate: Figure 2; multivariate Cox regression: HR: 2.11, 95%CI: 1.56-2.84, P<0.001). Conclusion: AI-AF can identify patients with a higher risk of mortality after CEA. This finding supports a role for AI algorithms in risk stratification of patients with carotid artery disease following CEA. Future studies are needed to elucidate the contribution of AF to all-cause mortality.

Background: The bovine aortic arch is a vascular variant in which the left common carotid artery originates from the brachiocephalic trunk rather than directly from the aortic arch. The bovine anatomical variation may alter the blood flow dynamics, potentially contributing to the formation and progression of carotid plaques. In this study, we evaluated the association of the bovine aortic arch and the severity of carotid stenosis. Methods: In this retrospective study, we included patients with carotid artery disease undergoing carotid endarterectomy (CEA) and pre-surgical computed tomography angiography (CTA). CTAs were reviewed to assess the carotid stenosis and the anatomy of the aortic arch. The left carotid artery was classified as normal if it originated from the aortic arch and as bovine if it originated from the brachiocephalic trunk (Figure 1) . Unlimited propensity score matching was performed to balance baseline characteristics between patients with bovine and normal anatomies. The Mann-Whitney U test was applied to compare carotid stenosis between bovine and normal arteries. Results: A total of 203 patients (bovine:23, normal:180) were initially enrolled. Using propensity score matching, 20 patients with bovine anatomy were matched to 45 patients with normal anatomy (Figure 2) . Among the 65 patients included, 47 (74.6%) were male, with a median age of 70 [IQR:65.0-76.0] years. Left-sided carotid artery disease was more prevalent in the bovine compared to normal group (N=12, 66.7% vs. N=16, 36.4%; p=0.030). Additionally, the severity of carotid stenosis in CEA-treated side was higher in patients with bovine arch compared to normal controls (82.5% [76.2-88.7] vs. 70.0% [50.0-95.0]; p=0.030) (Figure 3) . Conclusions: The association of a bovine aortic arch with more severe carotid stenosis suggests that this anatomical variant may be a marker for increased cerebrovascular risk.

Introduction: Atrial fibrillation (AF) is prevalent in patients with atherosclerotic coronary and carotid artery disease and is associated with elevated plasma inflammatory markers, such as Interleukin (IL)-6. In carotid artery atherosclerosis, macrophages are considered a local marker of inflammation. Hypothesis: We hypothesize that AF is independently associated with carotid plaque inflammation. Aims: In patients undergoing carotid endarterectomy (CEA), we aimed to determine whether carotid plaques inflammation is associated with AF. Approach: In this prospective cohort study, consecutive patients with carotid artery disease undergoing CEA were enrolled. From the plaques removed during surgery, fixed in formalin and embedded in paraffin blocks, sections were stained with antibodies against CD68 to detect macrophages (Fig.1A) . Digital copies of the slides were created by Motic Easy Scan and histologically annotated with QuPath. The percentage of CD68+ cells was defined as (CD68+ positive cells/total nuclei) x 100. Flash-frozen plaque segments were analyzed by ELISA for IL-6 concentration. Baseline demographics and clinical data, including AF diagnosis, were extracted from the electronic health records. Results: A total of 106 patients undergoing CEA, with a median age of 73 [67-78] years and comprising 83 (78.3%) males, were included in the study. AF was diagnosed prior to CEA in 13 (12.3%) patients. Univariate logistic regression showed that plaque CD68+ cell content was associated with AF (OR 1.048 [95% CI 1.010 – 1.088]; p = 0.013). Additionally, in Mann-Whitney test IL-6 levels were significantly over twofold higher in plaques of patients with AF compared to those without AF (82.4 [64.9-111.7] pg/mg vs 37.0 [18.4-93.3] pg/mg; p=0.020) (Fig. 1B) . Conclusions: The current study demonstrates an association between carotid atherosclerotic plaque macrophage content, IL6 levels and AF, supporting the role of inflammation in the pathogenesis of AF in these patients. Further studies are needed to determine the potential of plaque inflammation as a therapeutic target in AF.