Philipp Lange

Associate Professor

Research Interests

Cell Signaling and Cancer
mass spectrometry
pediatric cancer
personalized medicine
post translational protein modification

Relevant Thesis-Based Degree Programs

Affiliations to Research Centres, Institutes & Clusters



My research team strives to develop new diagnostic and therapeutic approaches to detect and treat children suffering from cancer earlier, better and with reduced impact on their life.

The fundamental question is how cancer cells are different from healthy, normal cells? If we understand this we will be able to better detect and kill cancer while leaving the rest of the body untouched.

Our research focusses on proteins, the structural and functional building blocks of a cell. To do this we combine genomics and proteomics, a technology that enables us to monitor all of the proteins in our body and detect how they are changed in childhood cancer. We then use computational approaches to further analyze and integrate our findings and to make them accessible to clinicians and fellow scientists around the world.

Research Methodology

Mass Spectrometry
machine learning


Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round
  • translational portoemics in childhood cancer
  • advancing precision medicine in childhood cancer
  • proteolytic regulation of cell-cell communication
  • computational and experimental approaches to better understand and classify proteoforms
  • new algorithms in quantitative mass spectrometry data analysis  
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

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Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Exploring cell surface-associated proteolytic proteoforms in acute lymphoblastic leukemia (2023)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Development of high-efficiency undecanal-based N termini enrichment (HUNTER) for monitoring proteolytic processing in limited samples (2020)

Genes encode the information for the amino acid backbone of proteins. This information can be altered by genetic variation or alternative splicing and alternative initiation of translation. After translation the protein can further alter by post-translational modification. All these different versions of a protein encoded by one gene are termed proteoforms. Protein N termini can be used to identify truncated (proteolytically cleaved), alternatively translated, or N terminally modified proteoforms that often have distinct functions. Cleavage of proteins by proteases is frequently altered in disease, including cancers and following the occurrence and loss of protein N termini can pinpoint abnormal proteolytic activity in disease. Selective enrichment of N-terminal peptides is necessary for proteome-wide coverage for unbiased identification of site-specific proteolytic processing and protease substrates; however, for comprehensive study of N termini so-called N-terminome analysis, most N termini enrichment techniques require relatively large amounts of starting material in the range of several hundred micrograms to milligrams of crude protein lysate. Due to sample constraints, this type of analysis cannot be routinely applied to clinical biopsies, especially those from pediatric patients. We present High-efficiency Undecanal-based N Termini EnRichment (HUNTER), a robust, sensitive, and scalable method for the analysis of previously inaccessible microscale samples. With this approach, >1,000 N termini are identified from a minimum of 2 µg raw HeLa cell lysate and >5,000 termini from 200 µg of raw HeLa lysate with high-pH pre-fractionation. We demonstrate the broad applicability of HUNTER with the first N-terminome analysis of sorted human primary immune cells and enriched mitochondrial fractions from pediatric cancer patients. The workflow was implemented on a liquid handling system to demonstrate the feasibility of automated liquid biopsy processing from pediatric cancer patients. In general, HUNTER method benefits in handling rare and precious clinical samples.

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Detection of enriched patterns in protein sequence data (2019)

Proteolysis is a form of post-translational modification consisting of the cleavage of a protein at the site of a peptide bond. This process is primarily mediated by a class of enzymes known as proteases, which exhibit varying specificity for the protein sequences they cleave. Although advances in proteomics have enabled sequencing of complex mixtures of proteins from biological samples, direct detection of protease activity remains challenging due to low protease abundance and the fact that observation of a protease is not always indicative of its activity level. Detection of proteolysis is therefore typically accomplished indirectly by observation of protease substrates in protein sequencing data. However, many proteases’ cleavage-site specificities are not well-understood, restricting the utility of supervised classification methods. We present a tool to overcome this limitation through unsupervised detection of overrepresented patterns in protein sequence data, providing insight into the specificities of the proteases contributing to a sample’s composition, even if the proteases themselves are poorly characterized. These patterns can be compared to those detected in sets of established protease substrate sequences, and patterns identified in both sets can be interpreted as indicators of mutual protease activity. Here we apply this methodology to the proteolytic cleavage event data in the MEROPS database, identifying specificity patterns corresponding to over 100 distinct proteases. The statistical validity of the algorithm is assessed through a series of tests on in silico data sets, and the performance of the algorithm is compared to alternative existing motif detection and clustering tools. Multiple clinical data sets are then analyzed using the algorithm, yielding patterns consistent with markers of both cancer and cellular response to chemotherapy treatment. The utility of the algorithm is then discussed in light of these findings, several potential use cases are presented, and possible future enhancements are proposed.

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