AI-integrated RNA therapeutics in precision oncology
4 affiliations
- 1Translational Molecular Pathology Department, MD Anderson Cancer Center, University of Texas, Houston, TX 77030, USA
- 2Department of Biostatistics, MD Anderson Cancer Center, University of Texas, Houston, TX 77030, USA
- 3Cancer Biology Department, MD Anderson Cancer Center, University of Texas, Houston, TX 77030, USA
- 4The RNA Interference and Non-coding RNA Center, MD Anderson Cancer Center, University of Texas, Houston, TX 77030, USA
Abstract
RNA therapeutics can silence oncogenic transcripts, modulate RNA processing, restore protein expression, or encode tumor antigens. Their development, however, is constrained by target selection, molecular design, delivery, immunogenicity, and inter-patient heterogeneity. Artificial intelligence (AI) is increasingly applied to these bottlenecks through multi-omics target prioritization, RNA structure and sequence modeling, delivery optimization, neoantigen selection, patient stratification, and manufacturing quality control. The maturity of these applications is uneven: within oncology, messenger RNA (mRNA) cancer vaccines are the most clinically advanced RNA-based strategy, whereas therapeutic approaches involving long non-coding RNAs and circular RNAs remain predominantly preclinical or early clinical, and most AI-enabled methods lack prospective clinical validation. In this review, we evaluate AI across the RNA therapeutic development pipeline, distinguish evidence-supported applications from preclinical and speculative uses, and discuss the validation, regulatory, and data-governance requirements for translation into precision oncology.
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BibTeX
@article{symmans2026aiintegrated,
title = {{AI-integrated RNA therapeutics in precision oncology}},
author = {William S.O. Symmans and Ziyi Li and George A. Calin and Akanksha Samuel},
journal = {Cell Reports Medicine},
year = {2026},
month = oct,
doi = {10.1016/j.xcrm.2026.103100},
url = {https://doi.org/10.1016/j.xcrm.2026.103100},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - AI-integrated RNA therapeutics in precision oncology
AU - William S.O. Symmans
AU - Ziyi Li
AU - George A. Calin
AU - Akanksha Samuel
PY - 2026
DA - 2026/10/09
JO - Cell Reports Medicine
DO - 10.1016/j.xcrm.2026.103100
UR - https://doi.org/10.1016/j.xcrm.2026.103100
AB - RNA therapeutics can silence oncogenic transcripts, modulate RNA processing, restore protein expression, or encode tumor antigens. Their development, however, is constrained by target selection, molecular design, delivery, immunogenicity, and inter-patient heterogeneity. Artificial intelligence (AI) is increasingly applied to these bottlenecks through multi-omics target prioritization, RNA structure and sequence modeling, delivery optimization, neoantigen selection, patient stratification, and manufacturing quality control. The maturity of these applications is uneven: within oncology, messenger RNA (mRNA) cancer vaccines are the most clinically advanced RNA-based strategy, whereas therapeutic approaches involving long non-coding RNAs and circular RNAs remain predominantly preclinical or early clinical, and most AI-enabled methods lack prospective clinical validation. In this review, we evaluate AI across the RNA therapeutic development pipeline, distinguish evidence-supported applications from preclinical and speculative uses, and discuss the validation, regulatory, and data-governance requirements for translation into precision oncology.
SN - 2666-3791
ER -