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<\/a><\/a>\nThe accepted abstracts underscore the broad clinical utility of BostonGene\u2019s AI-powered models and advanced analytical platforms, including the Tumor Portrait\u00ae<\/sup> test and Kassandra\u2122 cell deconvolution. Spanning both tissue and peripheral blood analysis, the featured research showcases how integrating deep genomic, transcriptomic, and immunomic profiling can identify novel actionable biomarkers, predict immunotherapy response and toxicity, and map complex resistance mechanisms. Developed in collaboration with leading institutions such as the UT MD Anderson Cancer Center, Weill Cornell Medicine, and the Parker Institute for Cancer Immunotherapy, these studies highlight BostonGene's pivotal role in accelerating biomarker discovery and optimizing patient stratification for clinical trials.<\/p> \nDetails about the abstracts selected for presentation can be found below:<\/p> \nPoster presentations\n \nBostonGene and MD Anderson Cancer Center collaborated to perform comprehensive genomic profiling of localized and metastatic anal cancers, assessing TACSTD2<\/i> (TROP2) expression as a potential therapeutic biomarker. The analysis found high TACSTD2<\/i> expression that correlated with TROP2 immunohistochemistry, supporting its potential use in identifying patients who may benefit from anti-TROP2 antibody-drug conjugates. The study underscores the clinical utility of BostonGene\u2019s Tumor Portrait\u00ae<\/sup> test in biomarker discovery and validation.<\/p> \nResearch conducted in collaboration with UT MD Anderson<\/i><\/p> \nPoster number:<\/b> 257\n \nBostonGene developed an AI-driven harmonized tumor microenvironment (HTME) classification system to improve patient stratification for immunotherapy and targeted therapies across solid tumors. By applying machine learning approaches\u2014including agglomerative clustering, K-nearest neighbors classification, and transcriptomic pathway analysis\u2014to RNA-seq data from more than 40,000 cancer samples, the study identified nine reproducible TME subtypes capturing key biological features such as immune activity, fibrosis, vascularization, and hypoxia. The AI-based framework outperformed existing methods by integrating multiple biological dimensions simultaneously and accurately predicting treatment response and progression-free survival across different cancer types and therapies.<\/p> \nPoster number:<\/b> 323\n \nIn collaboration with Parker Institute for Cancer Immunotherapy, BostonGene applied AI-based models of the immune system to analyze blood samples from more than 1,000 cancer patients receiving immune checkpoint inhibitor (ICI) therapy in the RADIOHEAD pan-cancer cohort. By combining RNA sequencing, flow cytometry, and machine learning models trained on 45,000 patient profiles, the study identified immune signatures linked to both treatment response and severe immune-related toxicities. The AI-driven embeddings and multimodal immunoprofiling approach successfully stratified patients into responder, non-responder, and high-risk toxicity groups, highlighting the potential of AI-powered peripheral blood profiling to improve immunotherapy patient selection.<\/p> \nResearch conducted in collaboration with Parker Institute for Cancer Immunotherapy<\/i><\/p> \nPoster number:<\/b> 360\n \nBostonGene utilized its immune model leveraging multimodal TCR\/BCR repertoire data to address two critical clinical challenges: predicting severe immune-related adverse events (irAEs) in cancer patients receiving immune checkpoint inhibitors and detecting rheumatoid arthritis from peripheral blood. By integrating adaptive immune receptor repertoire embeddings with gene expression signatures, the model significantly outperformed traditional immune repertoire analysis approaches\u2014such as clonality, diversity, and V(D)J gene usage metrics\u2014in both predictive accuracy and cross-cohort robustness. These advances enabled earlier risk stratification and more precise immunotherapy management than conventional biomarker methods.<\/p> \nPoster number: <\/b>533\n \nBostonGene applied its multimodal molecular profiling platform to characterize the systemic immune remodeling driven by tazemetostat priming in patients with B-cell lymphomas receiving CART therapy. By integrating high-parameter flow cytometry, RNA sequencing, immune cell deconvolution, and functional gene signature analysis, BostonGene\u2019s analysis revealed systemic immune remodeling associated with enhanced antigen presentation, T-cell activation, and reduced immunosuppressive signaling\u2014supporting the use of EZH2 inhibition for improved CART efficacy and durability.<\/p> \nResearch conducted in collaboration with Weill Cornell Medicine<\/i><\/p> \nPoster number:<\/b> 268b\n \nIn a trial in progress with UT MD Anderson Cancer Center, BostonGene will perform multimodal longitudinal blood and tissue analysis for mILC patients receiving the bispecific antibody targeting PD-1 and VEGF, ivonescimab. Moving beyond traditional efficacy metrics, complex correlates linking clinical outcomes with molecular features are ongoing. By integrating real-time ctDNA dynamics with predictive responder scores, BostonGene aims to identify critical markers of response and resistance needed to optimize treatment durability and refine future trial strategies.<\/p> \nResearch conducted in collaboration with UT MD Anderson<\/i><\/p> \nOnline only\n \nStage III triple\u2011negative inflammatory breast cancer patients receiving KN522 therapy were analyzed with BostonGene\u2019s integrative multimodal platform. By combining intrinsic subtype classification with immune deconvolution, BostonGene successfully mapped the molecular landscape of treatment response. Non-responders were marked by angiogenic and endothelial remodeling and potential biomarkers of brain metastasis risk, demonstrating the platform\u2019s unique capability to predict both therapeutic outcomes and disease progression.<\/p> \nResearch conducted in collaboration with UT MD Anderson<\/i><\/p> \nAbstract number:<\/b> e13080\n \nResearch conducted in collaboration with UT MD Anderson<\/i><\/p> \nAbstract number:<\/b> e16004\n \nResearchers at MD Anderson Cancer Center leveraged BostonGene\u2019s transcriptomic classifiers and KassandraTM<\/sup> cell deconvolution to characterize the tumor microenvironment (TME) of metabolic disease-associated biliary tract cancers. The analysis uncovered a distinct TME in metabolic disease-associated cholangiocarcinoma that likely impairs response to immune checkpoint inhibitors, demonstrating the value of BostonGene\u2019s AI-powered solutions for therapeutic response prediction and trial design.<\/p> \nResearch conducted in collaboration with UT MD Anderson<\/i><\/p> \nFor more information, please visit the 2026 ASCO Annual Meeting website<\/a>. The abstracts will be published online in the Journal of Clinical Oncology<\/i> supplement for the ASCO Annual Meeting Proceedings.<\/p> \nAbout BostonGene Corporation<\/b><\/p> \nBostonGene powers an AI model of tumor and immune biology to deliver disease-level insights and enable precision decision-making across oncology and immune-mediated diseases, spanning drug development and clinical care. By integrating multimodal data, including genomic, transcriptomic, immune, and clinical signals, BostonGene generates biologically grounded intelligence to optimize patient selection, trial design, and therapeutic strategy. This creates a scalable AI decision layer that improves development outcomes and clinical management. BostonGene partners with leading biopharmaceutical organizations and academic institutions to accelerate the delivery of precision therapies while continuously expanding its capabilities across new diseases and complex biological systems. For more information, visit www.BostonGene.com<\/a>.<\/p> \nMedia Contact:<\/b><\/p>