Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems.

Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.

Targetable lesions and proteomes predict therapy sensitivity through disease evolution in pediatric acute lymphoblastic leukemia.

Childhood acute lymphoblastic leukemia (ALL) genomes show that relapses often arise from subclonal outgrowths. However, the impact of clonal evolution on the actionable proteome and response to targeted therapy is not known. Here, we present a comprehensive retrospective analysis of paired ALL diagnosis and relapsed specimen. Targeted next generation sequencing and proteome analysis indicate persistence of actionable genome variants and stable proteomes through disease progression. Paired viably-frozen biopsies show high correlation of drug response to variant-targeted therapies but in vitro selectivity is low. Proteome analysis prioritizes PARP1 as a pan-ALL target candidate needed for survival following cellular stress; diagnostic and relapsed ALL samples demonstrate robust sensitivity to treatment with two PARP1/2 inhibitors. Together, these findings support initiating prospective precision oncology approaches at ALL diagnosis and emphasize the need to incorporate proteome analysis to prospectively determine tumor sensitivities, which are likely to be retained at disease relapse.

SQuAPP-simple quantitative analysis of proteins and PTMs.

SUMMARY: The comprehensive analysis of the proteome and its modulation by post-translational modification (PTM) is increasingly used in biological and biomedical studies. As a result, proteomics data analysis is ever more carried out by scientists with limited expertise in this type of data. While excellent software solutions for comprehensive and rigorous analysis of quantitative proteomic data exist, most are complex and not well suited for non-proteomics scientists. Integrative analysis of multi-level proteomics data on protein and diverse PTMs, like phosphorylation or proteolytic processing, remains particularly challenging and inaccessible to most biologists. To fill this void, we developed SQuAPP, an R-Shiny web-based analysis pipeline for the quantitative analysis of proteomic data. SQuAPP uses a streamlined workflow model to guide expert and novice users through quality control, data pre-processing, statistical analysis and visualization steps. Processing the protein, peptide and PTM datasets in parallel and their quantitative integration enable rapid identification of protein-level-independent modulation of protein modifications and intuitive interpretation of dynamic dependencies between different protein modifications. AVAILABILITY AND IMPLEMENTATION: SQuAPP is available at http://squapp.langelab.org/. The source code and local setup instructions can be accessed from https://github.com/LangeLab/SQuAPP.

PDX models reflect the proteome landscape of pediatric acute lymphoblastic leukemia but divert in select pathways.

BACKGROUND: Murine xenografts of pediatric leukemia accurately recapitulate genomic aberrations. How this translates to the functional capacity of cells remains unclear. Here, we studied global protein abundance, phosphorylation, and protein maturation by proteolytic processing in 11 pediatric B- and T- cell ALL patients and 19 corresponding xenografts. METHODS: Xenograft models were generated for each pediatric patient leukemia. Mass spectrometry-based methods were used to investigate global protein abundance, protein phosphorylation, and limited proteolysis in paired patient and xenografted pediatric acute B- and T- cell lymphocytic leukemia, as well as in pediatric leukemia cell lines. Targeted next-generation sequencing was utilized to examine genetic abnormalities in patients and in corresponding xenografts. Bioinformatic and statistical analysis were performed to identify functional mechanisms associated with proteins and protein post-translational modifications. RESULTS: Overall, we found xenograft proteomes to be most equivalent with their patient of origin. Protein level differences that stratified disease subtypes at diagnostic and relapse stages were largely recapitulated in xenografts. As expected, PDXs lacked multiple human leukocyte antigens and complement proteins. We found increased expression of cell cycle proteins indicating a high proliferative capacity of xenografted cells. Structural genomic changes and mutations were reflected at the protein level in patients. In contrast, the post-translational modification landscape was shaped by leukemia type and host and only to a limited degree by the patient of origin. Of 201 known pediatric oncogenic drivers and drug-targetable proteins, the KMT2 protein family showed consistently high variability between patient and corresponding xenografts. Comprehensive N terminomics revealed deregulated proteolytic processing in leukemic cells, in particular from caspase-driven cleavages found in patient cells. CONCLUSION: Genomic and host factors shape protein and post-translational modification landscapes differently. This study highlights select areas of diverging biology while confirming murine patient-derived xenografts as a generally accurate model system.

Sensitive Determination of Proteolytic Proteoforms in Limited Microscale Proteome Samples.

Protein N termini unambiguously identify truncated, alternatively translated or modified proteoforms with distinct functions and reveal perturbations in disease. Selective enrichment of N-terminal peptides is necessary to achieve proteome-wide coverage for unbiased identification of site-specific regulatory proteolytic processing and protease substrates. However, many proteolytic processes are strictly confined in time and space and therefore can only be analyzed in minute samples that provide insufficient starting material for current enrichment protocols. Here we present High-efficiency Undecanal-based N Termini EnRichment (HUNTER), a robust, sensitive and scalable method for the analysis of previously inaccessible microscale samples. HUNTER achieved identification of >1000 N termini from as little as 2 µg raw HeLa cell lysate. Broad applicability is demonstrated by the first N-terminome analysis of sorted human primary immune cells and enriched mitochondrial fractions from pediatric cancer patients, as well as protease substrate identification from individual Arabidopsis thaliana wild type and Vacuolar Processing Enzyme-deficient mutant seedlings. We further implemented the workflow on a liquid handling system and demonstrate the feasibility of clinical degradomics by automated processing of liquid biopsies from pediatric cancer patients.