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Published on: November 17, 2018
Repurposing public sarcoma multi-omics for neoantigen discovery
Panagiotis Mantas1, Karen A Krogfelt2
1Department of Health Technology, Technical University of Denmark, 2800, Kongens Lyngby, Denmark. panman@dtu.dk.
Background:
Soft tissue sarcomas, particularly complex karyotype sarcomas (CKS), are characterized as "immunologically cold" malignancies driven by structural instability rather than a high tumor mutational burden (TMB). Public "legacy" cohorts are a useful resource to uncover immunotherapy biomarkers. This study used the whole-exome sequencing (WES) and RNA-sequencing of CKS patients, to overcome technical limitations and to identify and prioritize neoantigens.
Methods:
The systematic immunogenomic reanalysis was performed on a landmark cohort of CKS patients (Kim et al., 2018) with a custom bioinformatics workflow which was developed to uncover interpretable immunogenomic signals. This approach consisted of: (1) defining a quality-controlled "callable territory" and normalizing TMB metrics, respectively; (2) utilizing RNA-seq not only for expression filtering but as an orthogonal validation check for variant transcription and to distinguish functional amplifications from technical depth artifacts; and (3) applying a multi-modal epitope prediction pipeline to identify and prioritize high-affinity neoantigens derived from both somatic SNVs, indels and expressed gene fusions.
Results:
The reanalysis shows that standard genome-wide metrics frequently underestimated the immunogenic potential. Normalizing the TMB refined quantitative mutation burden estimates and improved interpretation of low-coverage samples without essentially changing the overall cohort classification. Furthermore, integration of transcriptomic data facilitated the recovery of actionable targets in "low-TMB" tumors. A subset of fusion-derived peptides demonstrated predicted binding affinities competitive with SNV-derived candidates.
Conclusion:
This study illustrates that technically constrained multi-omic datasets can be systematically re-analyzed to identify potential therapeutic targets. These data argue for looking beyond aggregate biomarkers; patient-specific, expressed neoepitopes may exist even in sarcomas typically described as immunologically "cold".
