Philipp Lange

Chemotherapy is the primary treatment for pediatric T cell acute lymphoblastic leukemia (T-ALL). While 80% of patients achieve long-term survival, the treatment's harsh nature can lead to acute and long-term side effects, posing significant psychosocial and economic burden for patients and their families. Therefore, there is an urgent need for more specific therapeutic targets for pediatric T-ALL. Protein mutations exposed on the cell surface are ideal therapeutic targets. However, previous studies have demonstrated that pediatric cancers have a lower mutational burden compared to adult cancers, resulting, in principle, in fewer targetable neoantigens, and presenting a major challenge for developing therapeutics.Proteolytic proteoforms present a new type of neoantigen with the potential to inform the development of highly selective pediatric treatments. In cancer, the microenvironment moulds a unique protective niche promoting carcinogenesis. This “safe heaven” has been partially characterized by dysregulated protease activity and associated cancer-specific proteolytic cleavages of cell surface proteins. As such, I hypothesized that T-ALL's unique microenvironment will generate cancer-specific proteolytic proteoforms on the cell surface of T-ALL cells. Identifying these alternate proteoforms can expand the list of potential therapeutic targets for pediatric T-ALL. Current global mass spectrometric workflows lack in coverage of cell surface N-termini. To meet this need, we developed a 1) biotin-based enrichment strategy for cell surface N termini; 2) mass spectrometric workflow; and 3) data analysis pipeline that allowed for the comprehensive assessment of the cell surface N terminome. I then applied this workflow to interrogate the cell surface terminomic changes (1) in Jurkat cells grown in acidic microenvironment and (2) in patient derived xenograft (PDX) expanded T-ALL cells in different organs and throughout disease progression.The strategy we developed was effective with a limited starting material (5 million cells). In, the PDX dataset, I identified HLA-B, CD2, ITAL4, CD99, TACT and PECA1 as candidates for further validation by flow and/or western blots. Ultimately, the identification of these cell surface termini, unique to T-ALL cells, offers potential targets for further validation for highly selective pediatric leukaemia treatments, presenting a promising therapeutic avenue.

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Immunotherapies offer new possibilities for treating pediatric cancers, but achieving treatment specificity remains challenging. Pediatric cancers have lower mutation rates than adult cancers, making it harder to identify selective therapeutic targets. A promising strategy involves focusing on cancer-specific proteolysis—a posttranslational modification that generates unique protein termini on cancer cells. These termini can serve as highly selective targets for N-recognin proteins such as ClpS. Furthermore, protein fragments released during this process may circulate in the bloodstream, serving as biomarkers for cancer-specific protein termini, thereby aiding in the development of precise therapies.However, targeting protein termini is difficult due to differences in epitopes recognized by antibodies or CAR-T cells and the complexity of detecting shed protein fragments in blood plasma, where protein abundances span >12 orders of magnitude. The low abundance of protein fragments shed by cancer cells further complicates detection. My research aimed to establish proof-of-concept methods to target cell surface protein termini and detect these fragments in blood plasma as potential biomarkers.My specific aims were: 1A) Optimize the expression and purification of ClpS and characterize its binding specificity to peptide termini. 1B) Develop tools for investigating ClpS binding to truncated proteins with diverse N-terminal sequences on Hela cells. 2) Establish a gel fractionation method based on molecular weight to enrich protein fragments. I successfully established the expression and automated purification of ClpS, validated its functionality in a biochemical binding assay, and demonstrated that electro-elution SDS-PAGE coupled with mass spectrometry can separate proteins based on molecular size and detect small proteins in plasma.This thesis provides foundational work towards establishing a new class of therapeutic targets and diagnostic markers. It addresses the critical challenge of preventing damage to healthy cells while targeting and destroying cancer cells. The project leverages the N-recognin protein class to target cancer-specific cell surface proteins and lays the foundation for a novel method to target and potentially kill cancer cells more selectively. Additionally, it develops a new methodology for the size-selective enrichment of protein fragments, with potential applications in liquid biopsy and immunotherapy selection to improve patient outcomes.

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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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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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