Transcriptome sequencing (RNA-seq) of cancers is widely employed in cancer research to investigate gene expression patterns and their roles in disease progression. Somatic copy-number aberrations (SCNAs)—critical genomic drivers of tumorigenesis—can also be inferred directly from RNA-seq, yielding a “two-for-one” return of quantitative expression measures plus structural-variation calls at a fraction of the cost of separate DNA assays.
Dr. Hongzhe Li and his team developed a new AI tool, RCANE, which is a deep-learning-based method that predicts genome-wide SCNAs across diverse cancer types using only RNA-seq data. The key feature of the approach is a novel AI architecture that effectively captures both local and cross-chromosomal dependencies of copy-number aberrations observed in cancer samples. Trained on The Cancer Genome Atlas (TCGA) and DepMap cell-line cohorts, RCANE consistently outperforms existing approaches, delivering a scalable, robust solution for improving somatic copy-number aberration profiling in cancer diagnostics and therapeutic decision-making.