Optimized neoantigen selection based on tumor exome data

Date

09 Oct 2016

Session

Poster display

Presenters

Christina Kyzirakos

Citation

Annals of Oncology (2016) 27 (6): 359-378. 10.1093/annonc/mdw378

Authors

C. Kyzirakos1, C. Mohr2, S. Armeanu-Ebinger1, M. Feldhahn1, D. Hadaschik1, M. Walzer3, D. Döcker1, M. Menzel1, S. Nahnsen4, O. Kohlbacher5, S. Biskup1

Author affiliations

  • 1 Tumor Diagnostics, CeGaT GmbH, 72076 - Tübingen/DE
  • 2 Center For Bioinformatics And Dept. Of Computer Science, Quantitative Biology Center (qbic), University of Tübingen, Tübingen/DE
  • 3 Center For Bioinformatics And Dept. Of Computer Science, University of Tübingen, Tübingen/DE
  • 4 Quantitative Biology Center (qbic), University of Tübingen, Tübingen/DE
  • 5 Center For Bioinformatics And Dept. Of Computer Science, Quantitative Biology Center (qbic), University of Tübingen; Max Planck Institute for Developmental Biology, Tübingen/DE
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Background

Virtually every tumor harbors somatic mutations. These mutations can lead to mutation-derived neoantigenic peptides presented on the MHC molecules of the patient's tumor. T cells are able to recognize those alterations and may in turn kill the transformed cells. The importance of such neoantigens in an effective T cell-derived tumor defense has recently been demonstrated in various immunotherapeutic contexts like immune checkpoint inhibition, TIL therapy and vaccination. The quality and quantity of those neoantigens was shown to be of high prognostic and therapeutic value. To provide a practicable method for the identification of these highly individual tumor-specific neoantigens, we have established an optimized workflow combining exome and transcriptome sequencing, database derived expression data, HLA genotyping and peptide epitope prediction. The workflow is applicable to FFPE as well as fresh frozen tumor samples.

Methods

FFPE samples are revised and macrodissected by pathologists to maximize the tumor content of the sample. Exome sequencing of a tumor and normal tissue sample is performed and tumor-specific somatic single nucleotide variants and the patient's HLA type are determined. Transcriptome analysis can be performed in parallel using fresh frozen or RNA-stabilized tumor samples. A complex bioinformatics pipeline integrates these results and predicts affected neo-epitopes for the patient's HLA type. Resulting peptide sequences are ranked using expression data, peptide motifs, potential relevance of the respective variant for tumor development and biochemical features.

Results

Datasets from FFPE and fresh frozen tumor samples were generated. For both sample types, a set of highly promising peptides could be compiled. The whole workflow can be accomplished within three weeks.

Conclusions

The established workflow provides an efficient and time saving method for the identification of tumor-specific mutations and putative patient-individual neoantigens for multiple purposes including the development of cancer-specific vaccines, adoptive T-cell transfer, prediction of clinical utility of immune checkpoint inhibitors and immune monitoring.

Clinical trial identification


Legal entity responsible for the study

CeGaT

Funding

CeGaT

Disclosure

All authors have declared no conflicts of interest.

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