Inferring translational efficiency from transcriptomes improves noncanonical neoantigen prioritization and cancer...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Accurate protein translation assessment is vital for understanding disease variant functions, but mRNA-protein discrepancies limit transcriptomics in clinical oncology.
• Ribosome profiling directly measures translation but is limited by cost and complexity for clinical use.
• Deep learning models infer translation efficiency from RNA sequencing (RNA-seq), but their clinical utility compared to standard RNA-seq is unproven.

Purpose of the Study:

• To evaluate the clinical utility of in silico translatomes derived from translational inference strategies.
• To assess if inferred translational profiles offer superior clinical insights compared to conventional RNA-seq.
• To integrate translational inference into a precision oncology framework for improved patient stratification and target identification.

Main Methods:

• Developed a multidimensional framework to evaluate translational inference across 15 independent datasets.
• Compared inferred translational profiles with conventional RNA-seq proxies for recapitulating ribosome occupancy.
• Integrated the translational inference strategy into a translation-aware neoantigen pipeline.
• Applied the framework to glioma patient stratification and survival analysis.

Main Results:

• Inferred translational profiles demonstrated superior performance in recapitulating ribosome occupancy compared to RNA-seq proxies.
• Identified the "dark proteome" by predicting translational potential from long non-coding RNAs (lncRNAs).
• Discovered high-confidence noncanonical neoantigens missed by expression-based filtering.
• Revealed distinct glioma subtypes and corrected misclassification of high-risk patients, validated by survival analysis.

Conclusions:

• Translational inference offers a cost-effective enhancement for precision oncology, improving upon standard RNA-seq.
• This approach refines patient stratification, leading to more accurate prognoses.
• Expanded the scope of potential immunotherapeutic targets by uncovering noncanonical neoantigens.