Intelligent Omics-Driven Patient Stratification for Cancer Therapeutic Re-profiling

• Dr. Latha Kiran Krishna Rajendran

  Author

The imperative for individualized cancer treatment necessitates innovative approaches to patient selection and drug repurposing. This paper outlines an advanced artificial intelligence-driven platform specifically engineered for the discovery of predictive molecular markers to facilitate the precise re-application of existing therapeutics in oncology. By integrating comprehensive multi-omics datasets, spanning genomic, transcriptomic, proteomic, and epigenomic profiles, with clinical response data, sophisticated machine learning models were developed. These models successfully identified novel biomarker signatures correlating with differential drug sensitivity across a spectrum of cancer types, exemplified by a detailed case study in non-small cell lung cancer. The findings underscore the significant potential of this omics-guided computational strategy to redefine patient stratification, accelerate the identification of optimal therapeutic pathways, and enhance personalized treatment outcomes in cancer care.

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