Chih-Chiang Tsou

T cell receptor (TCR)–based immunotherapy has emerged as a promising therapeutic approach for the treatment of patients with solid cancers. Identifying peptide–human leukocyte antigen (pHLA) complexes highly presented on tumors and rarely expressed on healthy tissue in combination with high-affinity TCRs that when introduced into T cells can redirect T cells to eliminate tumor but not healthy tissue is a key requirement for safe and efficacious TCR-based therapies. To discover promising shared tumor antigens that could be targeted via TCR-based adoptive T cell therapy, we employed population-scale immunopeptidomics using quantitative mass spectrometry across ~1500 tumor and normal tissue samples. We identified an HLA-A*02:01-restricted pan-cancer epitope within the collagen type VI α-3 (COL6A3) gene that is highly presented on tumor stroma across multiple solid cancers due to a tumor-specific alternative splicing event that rarely occurs outside the tumor microenvironment. T cells expressing natural COL6A3-specific TCRs demonstrated only modest activity against cells presenting high copy numbers of COL6A3 pHLAs. One of these TCRs was affinity-enhanced, enabling transduced T cells to specifically eliminate tumors in vivo that expressed similar copy numbers of pHLAs as primary tumor specimens. The enhanced TCR variants exhibited a favorable safety profile with no detectable off-target reactivity, paving the way to initiate clinical trials using COL6A3-specific TCRs to target an array of solid tumors.

Antileukemia immunity plays an important role in disease control and maintenance of tyrosine kinase inhibitor (TKI)-free remission in chronic myeloid leukemia (CML). Thus, antigen-specific immunotherapy holds promise for strengthening immune control in CML but requires the identification of CML-associated targets. In this study, we used a mass spectrometry–based approach to identify naturally presented HLA class I– and class II–restricted peptides in primary CML samples. Comparative HLA ligandome profiling using a comprehensive dataset of different hematological benign specimens and samples from CML patients in deep molecular remission delineated a panel of novel frequently presented CML-exclusive peptides. These nonmutated target antigens are of particular relevance because our extensive data-mining approach suggests the absence of naturally presented BCR-ABL– and ABL-BCR–derived HLA-restricted peptides and the lack of frequent tumor-exclusive presentation of known cancer/testis and leukemia-associated antigens. Functional characterization revealed spontaneous T-cell responses against the newly identified CML-associated peptides in CML patient samples and their ability to induce multifunctional and cytotoxic antigen-specific T cells de novo in samples from healthy volunteers and CML patients. Thus, these antigens are prime candidates for T-cell–based immunotherapeutic approaches that may prolong TKI-free survival and even mediate cure of CML patients.

Background: CD133-positive liver cancer stem cells, which are characterized by their resistance to conventional chemotherapy and their tumor initiation ability at limited dilutions, have been recognized as a critical target in liver cancer therapeutics. In the current work, we developed a label-free quantitative method to investigate the proteome of CD133-positive liver cancer stem cells for the purpose of identifying unique biomarkers that can be utilized for targeting liver cancer stem cells. Label-free quantitation was performed in combination with ID-based Elution time Alignment by Linear regression Quantitation (IDEAL-Q) and MaxQuant. Results: Initially, IDEAL-Q analysis revealed that 151 proteins were differentially expressed in the CD133-positive hepatoma cells when compared with CD133-negative cells. We then analyzed these 151 differentially expressed proteins by MaxQuant software and identified 10 significantly up-regulated proteins. The results were further validated by RT-PCR, western blot, flow cytometry or immunofluorescent staining which revealed that prominin-1, annexin A1, annexin A3, transgelin, creatine kinase B, vimentin, and EpCAM were indeed highly expressed in the CD133-positive hepatoma cells. Conclusions: These findings confirmed that mass spectrometry-based label-free quantitative proteomics can be used to gain insights into liver cancer stem cells.

Aberrant protein phosphorylation plays important roles in cancer-related cell signaling. With the goal of achieving multiplexed, comprehensive, and fully automated relative quantitation of site-specific phosphorylation, we present a simple label-free strategy combining an automated pH/acid-controlled IMAC procedure and informatics-assisted SEMI (sequence, elution time, mass-to-charge, and internal standard) algorithm. The SEMI strategy effectively increased the number of quantifiable peptides more than 4-fold in replicate experiments (from 262 to 1171, p < 0.05, false discovery rate = 0.46%) by using a fragmental regression algorithm for elution time alignment followed by peptide cross-assignment in all LC−MS/MS runs. In addition, the strategy demonstrated good quantitation accuracy (10−12%) for standard phosphoprotein and variation less than 1.9 fold (within 99% confidence range) in proteome scale and reliable linear quantitation correlation (R² = 0.99) with 4000-fold dynamic concentrations, which was attributed to our reproducible experimental procedure and informatics-assisted peptide alignment tool to minimize system variations. In an attempt to explore metastasis-associated phosphoproteomic alterations in lung cancer, this approach was used to delineate differential phosphoproteomic profiles of a lung cancer metastasis model. Without sample fractionation, the SEMI algorithm enabled quantification of 1796 unique phosphopeptides (false discovery rate = 0.56%) corresponding to 854 phosphoproteins from a series of non-small cell lung cancer lines with varying degrees of in vivo invasiveness. Nearly 40% of the phosphopeptides showed >2-fold change in highly invasive cells; validation of phosphoprotein subsets by Western blotting not only demonstrated the consistency of data obtained by our SEMI strategy but also revealed that such dramatic changes in the phosphoproteome result mostly from translational or post-translational regulation. Mapping of these differentially expressed phosphoproteins in multiple cellular pathways related to cancer invasion and metastasis suggests that the site and degree of phosphorylation might have distinct patterns or functions in the complex process of cancer progression.

We studied the seminal vesicle secretion (SVS) of transgenic mice by using one-dimensional gel electrophoresis combined with LTQ-FT ICR MS analysis to explore protein expression profiles. Using unique peptide numbers as a cut-off criterion, 79 proteins were identified with high confidence in the SVS proteome. Label-free quantitative analysis was performed by using the IDEAL_Q software program. Furthermore, western blot assays were performed to validate the expression of seminal vesicle proteins. Sulfhydryl oxidase 1, glia-derived nexin, SVS1, SVS3, and SVS6 showed overexpression in SVS during cancer development. With high sequence similarity to human semenogelin, SVS2 is the most abundance protein in SVS and is dramatically decreased during the tumorigenic process. Our results indicate that these protein candidates could serve as potential targets for monitoring seminal vesicle carcinoma. Moreover, this information can provide clues for investigating seminal vesicle secretion-containing seminal plasma for related human diseases.

Isotope labeling combined with liquid chromatography–mass spectrometry (LC–MS) provides a robust platform for analyzing differential protein expression in proteomics research. We present a web service, called MaXIC-Q Web (http://ms.iis.sinica.edu.tw/MaXIC-Q_Web/), for quantitation analysis of large-scale datasets generated from proteomics experiments using various stable isotope-labeling techniques, e.g. SILAC, ICAT and user-developed labeling methods. It accepts spectral files in the standard mzXML format and search results from SEQUEST, Mascot and ProteinProphet as input. Furthermore, MaXIC-Q Web uses statistical and computational methods to construct two kinds of elution profiles for each ion, namely, PIMS (projected ion mass spectrum) and XIC (extracted ion chromatogram) from MS data. Toward accurate quantitation, a stringent validation procedure is performed on PIMSs to filter out peptide ions interfered with co-eluting peptides or noise. The areas of XICs determine ion abundances, which are used to calculate peptide and protein ratios. Since MaXIC-Q Web adopts stringent validation on spectral data, it achieves high accuracy so that manual validation effort can be substantially reduced. Furthermore, it provides various visualization diagrams and comprehensive quantitation reports so that users can conveniently inspect quantitation results. In summary, MaXIC-Q Web is a user-friendly, interactive, robust, generic web service for quantitation based on ICAT and SILAC labeling techniques.