Hu, D

Abstract Background: Treatment for patients with triple-negative breast cancer (TNBC) relies on non-specific chemotherapy, underscoring the need for precision-based approaches. Our lab discovered that MALT1, the effector protein of the CARMA3-BCL10-MALT1 (CBM) signaling complex, is activated downstream of several oncogenic G protein-coupled receptors (GPCRs) implicated in breast cancer, including the thrombin (PAR1), angiotensin II (AGTR1), and lysophosphatidic acid (LPAR) receptors. When activated, MALT1 functions as a protease to cleave select protein substrates. Based on our finding that CBM activity in TNBC cells promotes tumor growth in immunocompetent but not in immunodeficient mouse models, we hypothesized that MALT1 protease activity induces a pro-tumor secretome that creates an immune-suppressed tumor immune microenvironment (TIME). Methods/Results: We confirmed by western that MALT1 protease is active in multiple human and mouse TNBC cell lines. Next, to identify MALT1-dependent secreted factors, we performed transcriptomic and proteomic analyses comparing TNBC cells treated with or without MALT1 protease inhibitors. We found that several secreted factors, including GM-CSF, CXCL1, and Pentraxin 3, were reduced by MALT1 protease inhibition. To assess the functional impact of the MALT1 protease-dependent secretome, we performed media transfer experiments comparing the effects of conditioned media (CM) from TNBC cells treated with or without MALT1 protease inhibitors. To account for species differences, experiments were performed evaluating human TNBC cell CM added to human PBMCs and mouse TNBC cell CM added to murine bone marrow cells. We found that the MALT1 protease-dependent secretome reduces the expression of iNOS in both human and murine macrophages. For use in validating our findings in human TNBC clinical specimens, we developed a “MALT1 protease active gene signature” using RNA-seq analysis. Conclusions/Future Directions: Our studies indicate that MALT1 protease activity in TNBC cells promotes expression of numerous immune-suppressive factors. In both human and mouse systems, blockade of MALT1 protease alters the tumor cell secretome and promotes an increase in expression of the anti-tumor M1-like marker iNOS in macrophages. Future studies will evaluate how this change in iNOS expression impacts functional properties of TNBC-associated macrophages. We will also assess the influence of the TNBC MALT1-dependent secretome on the TIME in vivo by comparing the effects of intratumoral injection of CM from MALT1 wild-type or MALT1 protease-deficient TNBC cells. Finally, we will utilize our MALT1 activity gene signature to evaluate TNBC clinical specimens for an association between MALT1 activity and tumor immune contexture, with an emphasis on tumor-associated macrophages. Our studies could nominate MALT1 protease as a potential therapeutic target for improving anti-tumor immunity in TNBC. Since multiple MALT1 protease inhibitors are in clinical trial, our findings could be rapidly translated to improve outcomes for patients with TNBC. Citation Format: John W. Little IV, Jeffrey A. Meridew, Dong Hu, Juliana Hofstätter Azambuja, Linda M. McAllister-Lucas, Peter C. Lucas. The GPCR-MALT1 protease signaling axis drives an immunosuppressive secretome in triple-negative breast cancer [abstract]. In: Proceedings of the AACR Immuno-Oncology Conference (AACR IO): Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2026 Feb 18-21; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2026;14(2 Suppl):Abstract nr B046.

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point