<p>Hematologic malignancies remain among the most challenging cancers to treat due to genetic heterogeneity, clonal evolution, and therapy resistance. Extracellular vesicles (EVs), particularly small EV (sEV)-enriched populations, have emerged as active mediators of disease biology, contributing to tumor progression, immune evasion, and chemoresistance through intercellular transfer of bioactive cargo. Recent advances in EV engineering have repositioned these vesicles as programmable delivery platforms capable of transporting nucleic acids, proteins, and chemotherapeutic agents with improved targeting potential. Preclinical studies across multiple hematologic models demonstrate that engineered EVs can induce immune activation, modulate oncogenic signaling pathways, and partially overcome drug resistance. However, these findings remain largely confined to experimental settings, with limited standardization of loading efficiency, biodistribution, and functional potency. Clinically, EV-based applications in hematology are still at an early stage, with most studies focused on biomarker discovery and supportive therapies rather than direct antitumor interventions. In parallel, theranostic EV platforms and liquid biopsy approaches offer promising opportunities for minimally invasive disease monitoring, although their clinical validation remains incomplete. Artificial intelligence (AI) further enhances this field by enabling advanced biomarker analysis and guiding cargo design and targeting strategies, yet its therapeutic applications are still largely exploratory. Despite key challenges, including vesicle heterogeneity, donor variability, suboptimal cargo loading, and manufacturing constraints, these limitations are primarily technical and may be addressed through standardization and engineering optimization. Collectively, EV-based systems represent a promising but still maturing platform with the potential to contribute to next-generation precision oncology in hematologic malignancies.</p>

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Engineered small extracellular vesicles in hematologic malignancies: mechanisms, therapeutic strategies, and translational challenges

  • Chou-Yi Hsu,
  • Saidmurodkhon Murtazaev,
  • Sally Hassan Zubair,
  • Mohammad Abohassan,
  • Pareshkumar N. Patel,
  • Gunjan Singh,
  • Vimal Arora,
  • Priya Priyadarshini Nayak,
  • Muhammad Shahid Iqbal,
  • Zahraa Khudhair Abbas

摘要

Hematologic malignancies remain among the most challenging cancers to treat due to genetic heterogeneity, clonal evolution, and therapy resistance. Extracellular vesicles (EVs), particularly small EV (sEV)-enriched populations, have emerged as active mediators of disease biology, contributing to tumor progression, immune evasion, and chemoresistance through intercellular transfer of bioactive cargo. Recent advances in EV engineering have repositioned these vesicles as programmable delivery platforms capable of transporting nucleic acids, proteins, and chemotherapeutic agents with improved targeting potential. Preclinical studies across multiple hematologic models demonstrate that engineered EVs can induce immune activation, modulate oncogenic signaling pathways, and partially overcome drug resistance. However, these findings remain largely confined to experimental settings, with limited standardization of loading efficiency, biodistribution, and functional potency. Clinically, EV-based applications in hematology are still at an early stage, with most studies focused on biomarker discovery and supportive therapies rather than direct antitumor interventions. In parallel, theranostic EV platforms and liquid biopsy approaches offer promising opportunities for minimally invasive disease monitoring, although their clinical validation remains incomplete. Artificial intelligence (AI) further enhances this field by enabling advanced biomarker analysis and guiding cargo design and targeting strategies, yet its therapeutic applications are still largely exploratory. Despite key challenges, including vesicle heterogeneity, donor variability, suboptimal cargo loading, and manufacturing constraints, these limitations are primarily technical and may be addressed through standardization and engineering optimization. Collectively, EV-based systems represent a promising but still maturing platform with the potential to contribute to next-generation precision oncology in hematologic malignancies.