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Bioinformatics

Bioinformatics

Bioinformatics

In the Bioinformatics department, we drive the discovery of novel biomarkers and therapeutic targets through advanced computational tools, models, and analyses, and we closely collaborate with experimental teams to validate our predictions. We combine this approach with structural bioinformatics to identify and develop new therapeutic options. All these efforts are underpinned by seamless, scalable data management solutions that provide the reliability and accessibility essential for true data-driven decision-making across projects.

Link to our github profile

  • Dr. Barbara Schrörs

    Director of Bioinformatics

Hier die durchsuchbare Abbildung

Our vision

Driven by the goal of ensuring the optimal treatment decision for every patient, we are dedicated to harnessing the full potential of omics profiles, structural data, and contextual metadata.

Data Management

  • Dr. Ludmila Schemarow

    Head of Functional Unit Data Management

  • Annica Seidel

    Deputy Head of Functional Unit Data Management

Our activities and competences

Our methods and platforms

High-Performance Computing

We operate TRON’s HPC cluster, providing over 1,500 CPU cores to support large-scale computational analysis.

GPU Cluster

We provide dedicated GPU resources to enable the efficient execution of computationally demanding applications like deep learning and simulations.

High-Performance Storage

We provide a parallel file system (BeeGFS) to enable fast, concurrent access to shared data. This architecture is specifically optimized for HPC workloads, ensuring efficient scaling and avoiding performance bottlenecks during simultaneous access by multiple clients.

Systems Immunology & Medicine

  • Dr. Barbara Schrörs

    Head of Functional Unit Systems Immunology & Medicine

  • Dr. Franziska Lang

    Deputy Head of Functional Unit Systems Immunology & Medicine

Our research activities

Our methods and platforms

NeoFox

NeoFox annotates neoantigen candidates with neoantigen features such as MHC-I and MHC-II binding and self-similarity, improving target prioritization.

Neofox at github

OBLX

OBLX builds standardized NGS reference libraries, improving reproducibility and interoperability of NGS data analyses.

OBLX at github

Selected Publications

Somatic mutations in cancer cells cangenerate tumour-specific neoepitopes, which are recognized by autologous Tcells in the host. As neoepitopes are not subject to central immune toleranceand are not expressed in healthy tissues, they are attractive targets fortherapeutic cancer vaccines. Because the vast majority of cancer mutations areunique to the individual patient, harnessing the full potential of this richsource of targets requires individualized treatment approaches. Manycomputational algorithms and machine-learning tools have been developed toidentify mutations in sequence data, to prioritize those that are more likelyto be recognized by T cells and to design tailored vaccines for every patient.In this Review, we fill the gaps between the understanding of basic mechanismsof T cell recognition of neoantigens and the computational approaches fordiscovery of somatic mutations and neoantigen prediction for cancerimmunotherapy. We present a new classification of neoantigens, distinguishingbetween guarding, restrained and ignored neoantigens, based on how they conferproficient antitumour immunity in a given clinical context. Such context-baseddifferentiation will contribute to a framework that connects neoantigen biologyto the clinical setting and medical peculiarities of cancer, and will enablefuture neoantigen-based therapies to provide greater clinical benefit.

Head and neck squamous cell carcinomas (HNSCCs) are heterogeneous in terms of origin and aetiology. In addition, there is uncertainty about the genetic evolution from initial diagnosis to recurrence after primary treatments and further disease progression following systemic treatment. Changes in the genetic profile have implications on the selection of appropriate treatments for patients, especially in the era of targeted therapies and immunotherapies.

Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.

Engineering of an individualized cancer vaccine relies on the selection of neoantigen candidates that are capable of inducing a potent and clinically relevant T-cell response from all mutations identified for a cancer patient. Multiple aspects of a mutant peptide can be assessed to predict its likelihood to elicit T-cell responses. Here, we present “NeoFox: annotating neoantigen candidates with neoantigen features”, a tool that allows to annotate predicted neoantigen candidates with neoantigen features. NeoFox annotates given neoantigen candidates with features that cover aspects such as presentation of a neoantigen candidate on MHC (major histocompatibility complex), dissimilarity to the self-proteome and combinatorial ad hoc or machine learning approaches. We implemented sophisticated data models for biological entities such as neoantigens, which minimizes data heterogeneity and builds a bridge between biology and bioinformatics. This work was supported by an European Research Council (ERC) Advanced Grant to Ugur Sahin [ERC-AdG 789256].‍Read the original publication.

T cells directed against mutant neo-epitopes drive cancer immunity. However, spontaneous immune recognition of mutations is inefficient. We recently introduced the concept of individualized mutanome vaccines and implemented an RNA-based poly-neo-epitope approach to mobilize immunity against a spectrum of cancer mutations. Here we report the first-in-human application of this concept in melanoma. We set up a process comprising comprehensive identification of individual mutations, computational prediction of neo-epitopes, and design and manufacturing of a vaccine unique for each patient. All patients developed T cell responses against multiple vaccine neo-epitopes at up to high single-digit percentages. Vaccine-induced T cell infiltration and neo-epitope-specific killing of autologous tumour cells were shown in post-vaccination resected metastases from two patients. The cumulative rate of metastatic events was highly significantly reduced after the start of vaccination, resulting in a sustained progression-free survival. Two of the five patients with metastatic disease experienced vaccine-related objective responses. One of these patients had a late relapse owing to outgrowth of β2-microglobulin-deficient melanoma cells as an acquired resistance mechanism. A third patient developed a complete response to vaccination in combination with PD-1 blockade therapy. Our study demonstrates that individual mutations can be exploited, thereby opening a path to personalized immunotherapy for patients with cancer.

Computational Genomics

  • Dr. Jonas Ibn-Salem

    Head of Functional Unit Computational Genomics

Our research activities

Our methods and platforms

EasyFuse

EasyFuse is a pipeline for accurate fusion gene detection from RNA-seq data.

EasyFuse at github

k4neo

k4neo screens target sequences across large RNA-seq datasets to quantify tumor specificity.

k4neo at github

DEEPctMut

DEEPctMut is a tumor-naïve ctDNA mutation detection pipeline with deep learning-based background noise filtering (DeepES).

DEEPctMut at github

VariantMedium

VariantMedium provides sensitive and generalizable somatic point mutation calling with 3D DenseNets.

VariantMedium at github

Splice2neo

Splice2neo is an R package to analyze aberrant splicing junctions in tumor samples to identify neoepitopes.

Splice2neo at github

Selected Publications

Background

Circulating tumor DNA (ctDNA) detection offers minimally invasive monitoring of cancer from blood samples. While tumor-informed approaches are sensitive, their clinical application is limited by cost, tissue availability, and turnaround time. In contrast, tumor-naïve assays offer faster turnaround times and broader applicability, but often sacrifice sensitivity.

Methods

Here, we developed DEEPctMUT, a computational pipeline for tumor-naïve ctDNA detection that integrates unique molecular identifiers (UMIs) with three complementary error-polishing strategies: (1) machine learning-based sequencing error suppression, (2) deep learning-based background noise filtering (DeepES), and (3) removal of clonal hematopoietic and germline variants using patient matched PBMC. We designed an efficient sequencing panel and developed and validated DEEPctMUT using cell lines, spike-in samples, healthy, and CRC plasma samples.

Results

DEEPctMUT accurately detected mutations down to 0.03% variant allele frequency (VAF), outperforming other tumor-naïve methods. In a head-to-head comparison, our pipeline identified pre-surgical CRC cases with 100% sensitivity, whereas the Roche Avenio Surveillance Kit achieved only 50% sensitivity. Furthermore, a panel-independent version of DEEPctMUT could improve the performance of Roche Avenio data without additional assay-specific training. We provide an extensive ground-truth dataset and make DEEPctMUT, including all trained models, available as an easy-to-use Nextflow pipeline.

Conclusions

By using advanced computational error-polishing techniques, DEEPctMUT can substantially reduce technical artifacts, allowing our tumor-naïve method to achieve a sensitivity comparable to tumor-informed approaches.

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Cancer-associatedgene fusions are a potential source for highly immunogenic neoantigens, but thelack of computational tools for accurate, sensitive identification of personalgene fusions has limited their targeting in personalized cancer immunotherapy. Herewe present EasyFuse, a machine learning computational pipeline for detectingcancer-specific gene fusions in transcriptome data obtained from human cancersamples. EasyFuse predicts personal gene fusions with high precision andsensitivity, outperforming previously described tools. By testingimmunogenicity with autologous blood lymphocytes from patients with cancer, wedetected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%) and for 1 of30 (3%) identified gene fusions, respectively. The high frequency of T cellresponses detected in patients with cancer supports the relevance of individualgene fusions as neoantigens that might be targeted in personalizedimmunotherapies, especially for tumors with low mutation burden.

Computational Structural Immunology

  • Dr. Barbara Schrörs

    Head of Functional Unit Computational Structural Immunology

  • Dr. Floris van Eerden

    Deputy Head of Functional Unit Computational Structural Immunology

Our research activities