Atlas Translational PhD Program

Whole-Genome Sequencing and individualised cancer treatment

Scientific background

Neoantigens are tumor-specific peptides that can be recognised by T cells on HLA molecules and mediate antitumor T cell response. Identifying and prioritizing neoantigens is a central aspect of individualized neoantigen-specific immunotherapy (iNest) and, at BioNTech is implemented in our individual cancer mutation detection pipeline (iCaM) pipeline (Sahin et al 2017). We constantly re-evaluate and improve this pipeline.

PhD project description

Currently,whole exome sequencing is used for this pipeline. As whole sequencing becomes cheaper, we would like to take advantage of this new source of information and to investigate the possibilities to use them here to further advance our pipelines and personalized cancer treatment in general.

Required profile of the candidate

  • Master degree in a relevant field, e.g., Bioinformatics, Computational Biology, or Genomics (with computational background).

  • Background in cancer biology, whole-genome-sequencing, RNAseq, and bioinformatics.

  • Experience in programming (ideally Python) and familiar with unix environments.

  • Excellent communication skills in German and/or English.

  • The ideal candidate will have bioinformatics/computational biology background and have come already in contact with sequencing analyses. Tumor biology and immunology background in addition are of advantage.

Publications relevant to the project

  • S. Cherry, K. W. Lynch, Alternative splicing and cancer: insights, opportunities, and challenges from an expanding view of the transcriptome. Genes Dev 34, 1005-1016 (2020). doi: 10.1101/gad.338962.120

  • A. J. Cornish et al., Whole genome sequencing of 2,023 colorectal cancers reveals mutational landscapes, new driver genes and immune interactions. bioRxiv, 2022.2011.2016.515599 (2022). doi:10.1101/2022.11.16.515599

  • A. T. Dilthey et al., HLA*LA-HLA typing from linearly projected graph alignments. Bioinformatics 35, 4394-4396 (2019). doi: 10.1093/bioinformatics/btz235

  • Y. Haga, Y. Sakamoto, M. Arai, Y. Suzuki, A. Suzuki, Long-Read Whole-Genome Sequencing Using a Nanopore Sequencer and Detection of Structural Variants in Cancer Genomes. Methods Mol Biol 2632, 177-189 (2023). doi: 10.1007/978-1-0716-2996-3_13

  • F. Martinez-Jimenez et al., Genetic immune escape landscape in primary and metastatic cancer. Nat Genet 55, 820-831 (2023). doi: 10.1038/s41588-023-01367-1

  • U. Sahin et al., Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222-226 (2017). doi: 10.1038/nature23003

  • T. I. Shaw et al., Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients. Front Oncol 12, 1051487 (2022). doi: 10.3389/fonc.2022.1051487

  • C. Xiao et al., Personalized genome assembly for accurate cancer somatic mutation discovery using tumor-normal paired reference samples. Genome Biology 23, 237 (2022). doi: 10.1186/s13059-022-02803-x

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