Philipp Lange: Associate Professor at Department of Pathology & Laboratory Medicine, UBC Faculty of Medicine

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

Recruitment

Looking to recruit:

Master's students

Doctoral students

Postdoctoral Fellows

Desired start dates: Any time / year round

Potential research project areas:

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

Other options:

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.

Complete these steps before you reach out to a faculty member!

Check requirements

Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.

Focus your search

Identify specific faculty members who are conducting research in your specific area of interest.
Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.

Make a good impression

Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
Be enthusiastic, but don’t overdo it.

Attend an information session

G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.

ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS

These videos contain some general advice from faculty across UBC on finding and reaching out to a potential thesis supervisor.

Supervision Enquiry

If you have reviewed some of this faculty member's publications, understand their research interests and have reviewed the admission requirements, you may .

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.

View record

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.

View record

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.

View record