Clustering and Filtering Tandem Mass Spectra Acquired in Data-Independent Mode

Data-independent mass spectrometry activates all ion species isolated within a given mass-to-charge window (m/z) regardless of their abundance. This acquisition strategy overcomes the traditional data-dependent ion selection boosting data reproducibility and sensitivity. However, several tandem mass (MS/MS) spectra of the same precursor ion are acquired during chromatographic elution resulting in large data redundancy. Also, the significant number of chimeric spectra and the absence of accurate precursor ion masses hamper peptide identification. Here, we describe an algorithm to preprocess data-independent MS/MS spectra by filtering out noise peaks and clustering the spectra according to both the chromatographic elution profiles and the spectral similarity. In addition, we developed an approach to estimate the m/z value of precursor ions from clustered MS/MS spectra in order to improve database search performance. Data acquired using a small 3 m/z units precursor mass window and multiple injections to cover a m/z range of 400-1400 was processed with our algorithm. It showed an improvement in the number of both peptide and protein identifications by 8% while reducing the number of submitted spectra by 18% and the number of peaks by 55%. We conclude that our clustering method is a valid approach for data analysis of these data-independent fragmentation spectra. The software including the source code is available for the scientific community. Figure

A Phase Ib Study of the Combination of Personalized Autologous Dendritic Cell Vaccine, Aspirin, and Standard of Care Adjuvant Chemotherapy Followed by Nivolumab for Resected Pancreatic Adenocarcinoma—A Proof of Antigen Discovery Feasibility in Three Patients

Despite the promising therapeutic effects of immune checkpoint blockade (ICB), most patients with solid tumors treated with anti-PD-1/PD-L1 monotherapy do not achieve objective responses, with most tumor regressions being partial rather than complete. It is hypothesized that the absence of pre-existing antitumor immunity and/or the presence of additional tumor immune suppressive factors at the tumor microenvironment are responsible for such therapeutic failures. It is therefore clear that in order to fully exploit the potential of PD-1 blockade therapy, antitumor immune response should be amplified, while tumor immune suppression should be further attenuated. Cancer vaccines may prime patients for treatments with ICB by inducing effective anti-tumor immunity, especially in patients lacking tumor-infiltrating T-cells. These "non-inflamed" non-permissive tumors that are resistant to ICB could be rendered sensitive and transformed into "inflamed" tumor by vaccination. In this article we describe a clinical study where we use pancreatic cancer as a model, and we hypothesize that effective vaccination in pancreatic cancer patients, along with interventions that can reprogram important immunosuppressive factors in the tumor microenvironment, can enhance tumor immune recognition, thus enhancing response to PD-1/PD-L1 blockade. We incorporate into the schedule of standard of care (SOC) chemotherapy adjuvant setting a vaccine platform comprised of autologous dendritic cells loaded with personalized neoantigen peptides (PEP-DC) identified through our own proteo-genomics antigen discovery pipeline. Furthermore, we add nivolumab, an antibody against PD-1, to boost and maintain the vaccine's effect. We also demonstrate the feasibility of identifying personalized neoantigens in three pancreatic ductal adenocarcinoma (PDAC) patients, and we describe their optimal incorporation into long peptides for manufacturing into vaccine products. We finally discuss the advantages as well as the scientific and logistic challenges of such an exploratory vaccine clinical trial, and we highlight its novelty

Label-free protein quantification on tandem mass spectra acquired in a data-independent mode provides accurate measurements over five orders of concentration magnitude in complex matrices

Label free quantification using liquid chromatography and electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is widely used in quantitative proteomics. However, data-dependent bottom-up proteomics suffers from low reproducibility due to semi-random selection of precursor ions for tandem mass spectrometry. In addition, this acquisition mode is biased towards abundant peptides. To overcome these problems, alternative precursor-ion selection methods were developed, such as data-independent acquisition and pseudo-multiple selected reaction monitoring (p-mSRM). With these methods, several tandem mass spectra are acquired over the chromatographic elution time of precursor ions. In this report, we investigated if the acquired tandem mass spectra can be used for label-free quantification. For this, extracted fragment ion currents were correlated to relative protein concentration. A linear relationship between ion current and proteins concentration was observed over five orders of magnitude. Thus, we conclude that relative label-free peptide and protein quantification can be performed in an ion trap using the data-independent acquisition mode.

Deciphering HLA motifs across HLA peptidomes correctly predicts neo-antigens and identifies allostery in HLA specificity

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays

CIITA-transduced glioblastoma cells uncover a rich repertoire of clinically relevant tumor-associated HLA-II antigens

CD4+ T cell responses are crucial for inducing and maintaining effective anti-cancer immunity, and the identification of human leukocyte antigen class II (HLA-II) cancer-specific epitopes is key to the development of potent cancer immunotherapies. In many tumor types, and especially in glioblastoma (GBM), HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in GBM. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed and sensitive mass spectrometry based immunopeptidomics analysis, we here uncovered a remarkable breadth of the HLA-ligandome in HROG02, HROG17 and RA GBM cell lines. The effect of CIITA expression on the induction of the HLA-II presentation machinery was striking in each of the three cell lines, and it was significantly higher compared to interferon gamma (IFN&3) treatment. In total, we identified 16,123 unique HLA-I peptides and 32,690 unique HLA-II peptides. In order to genuinely define the identified peptides as true HLA ligands, we carefully characterized their association with the different HLA allotypes. In addition, we identified 138 and 279 HLA-I and HLA-II ligands, respectively, most of which are novel in GBM, derived from known GBM-associated tumor-antigens that have been used as source proteins for a variety of GBM vaccines. Our data further indicate that CIITA-expressing GBM cells acquired an antigen presenting cell-like phenotype as we found that they directly present external proteins as HLA-II ligands. Not only that CIITA-expressing GBM cells are attractive models for antigen discovery endeavors, but also such engineered cells have great therapeutic potential through massive presentation of a diverse antigenic repertoire

Deciphering the Mechanisms of Improved Immunogenicity of Hypochlorous Acid-Treated Antigens in Anti-Cancer Dendritic Cell-Based Vaccines.

Hypochlorous acid (HOCl)-treated whole tumor cell lysates (Ox-L) have been shown to be more immunogenic when used as an antigen source for therapeutic dendritic cell (DC)-based vaccines, improving downstream immune responses both in vitro and in vivo. However, the mechanisms behind the improved immunogenicity are still elusive. To address this question, we conducted a proteomic and immunopeptidomics analyses to map modifications and alterations introduced by HOCl treatment using a human melanoma cell line as a model system. First, we show that one-hour HOCl incubation readily induces extensive protein oxidation, mitochondrial biogenesis, and increased expression of chaperones and antioxidant proteins, all features indicative of an activation of oxidative stress-response pathways. Characterization of the DC proteome after loading with HOCl treated tumor lysate (Ox-L) showed no significant difference compared to loading with untreated whole tumor lysate (FT-L). On the other hand, detailed immunopeptidomic analyses on monocyte-derived DCs (mo-DCs) revealed a great increase in human leukocyte antigen class II (HLA-II) presentation in mo-DCs loaded with Ox-L compared to the FT-L control. Further, 2026 HLA-II ligands uniquely presented on Ox-L-loaded mo-DCs were identified. In comparison, identities and intensities of HLA class I (HLA-I) ligands were overall comparable. We found that HLA-II ligands uniquely presented by DCs loaded with Ox-L were more solvent exposed in the structures of their source proteins, contrary to what has been hypothesized so far. Analyses from a phase I clinical trial showed that vaccinating patients using autologous Ox-L as an antigen source efficiently induces polyfunctional vaccine-specific CD4+ T cell responses. Hence, these results suggest that the increased immunogenicity of Ox-L is, at least in part, due to qualitative and quantitative changes in the HLA-II ligandome, potentially leading to an increased HLA-II dependent stimulation of the T cell compartment (i.e., CD4+ T cell responses). These results further contribute to the development of more effective and immunogenic DC-based vaccines and to the molecular understanding of the mechanism behind HOCl adjuvant properties

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity

<div><p>The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any <i>a priori</i> knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for <i>in silico</i> predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and <i>in vitro</i> binding assays.</p></div

Correlation between amino acids frequencies at positions P4 to P7 in our HLA peptidomics data and in the human proteome.

<p>Correlation between amino acids frequencies at positions P4 to P7 in our HLA peptidomics data and in the human proteome.</p

General pipeline for HLA-I motif identification and annotation, and training of predictors.

<p>High accuracy HLA peptidomics data were first generated for 10 samples and collected from publicly available data for 40 other samples. In each sample motifs were identified using on our recent mixture model algorithm [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref024" target="_blank">24</a>]. Motifs were then annotated to their respective allele based on co-occurrence of alleles across samples (e.g., first HLA-A24:02, then HLA-A01:01 and HLA-C06:02, see also Fig B in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.s001" target="_blank">S1 Supporting Information</a> for another example). Finally all peptides assigned to each motif were pooled together to train our new HLA-I ligand predictor for each HLA-I allele (MixMHCpred v1.0).</p

Ranking of the neo-antigens identified in four melanoma samples [17,20].

<p>Column 2 shows the mutated neo-antigens (the mutated residue is highlighted in bold). Column 5 shows the ranking based on our predictions (i.e. number of peptide to be tested to find this neo-antigen). Columns 6 to 8 show the ranking based on NetMHC [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref008" target="_blank">8</a>], NetMHCpan [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref012" target="_blank">12</a>] and NetMHCstabpan [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref036" target="_blank">36</a>], respectively. The last column shows the total number of neo-antigen candidates (i.e., all possible 9- and 10-mers encompassing all missense mutations).</p