Dataset from a proteomics analysis of tumor antigens shared between an allogenic tumor cell lysate vaccine and pancreatic tumor tissue

C Stingl; S P Lau; S H van der Burg; J G Aerts; C H J van Eijck; T M Luider

doi:10.1016/j.dib.2022.108490

Dataset from a proteomics analysis of tumor antigens shared between an allogenic tumor cell lysate vaccine and pancreatic tumor tissue

Data Brief. 2022 Jul 25:44:108490. doi: 10.1016/j.dib.2022.108490. eCollection 2022 Oct.

Authors

C Stingl¹, S P Lau^{2

3}, S H van der Burg⁴, J G Aerts^{3

5}, C H J van Eijck², T M Luider¹

Affiliations

¹ Department of Neurology, Clinical and Cancer Proteomics, Erasmus University Medical Center, 's-Gravendijkwal 230, Rotterdam 3015CE, the Netherlands.
² Department of Surgery, Erasmus University Medical Center, 's-Gravendijkwal 230, Rotterdam 3015CE, the Netherlands.
³ Department of Pulmonary Medicine, Erasmus University Medical Center, 's-Gravendijkwal 230, Rotterdam 3015CE, the Netherlands.
⁴ Department of Medical Oncology, Oncode Institute, Leiden University Medical Center, P.O. Box 9600, Leiden 2300RC, the Netherlands.
⁵ Erasmus MC Cancer Institute, Erasmus University Medical Center, 's-Gravendijkwal 230, Rotterdam 3015CE, the Netherlands.

Abstract

The data described was acquired as part of a clinical study with the aim to investigate the potential of tumor-reactive T-cell response as response to vaccination of pancreatic cancer patients with an allogenic tumor cell lysate vaccine (Lau et al., 2022). Proteomics analysis was carried out to identify tumor antigens that are shared between the allogeneic tumor cell lysate used for the vaccine and pancreatic ductal adenocarcinoma (PDAC) tissue samples. To this objective, cell lysates of the vaccine and of nine tissue samples were enzymatically digested and isotopically labeled with tandem mass tags (TMT) in a so-called six-plex manner (Thermo Fisher Scientific). Three pools were prepared by mixing the samples according to their TMT-labels. Subsequently, the three sample pools were fractionated into 24 fractions with high-pH reversed phase chromatography. These fractions were first analyzed on a nano-liquid chromatography (LC) system online coupled to a high-resolution Eclipse Orbitrap mass spectrometer (MS) equipped with a high-field asymmetric-waveform ion-mobility spectrometry (FAIMS) source using a data-dependent MS2 shotgun method. Overall, 126,618 unique peptide sequences, on basis of 768,638 peptide spectra matches and corresponding to 7,597 protein groups, were identified in the total sample set including 61 tumor antigens (Supplement Table S2 in Lau et al. 2022) that were prioritized by Cheever and co-workers as vaccine target antigens on basis of a series of objective criteria (Cheever et al., 2009). In the second phase of the experiment, this set of tumor antigens was targeted using a serial precursor selection (SPS) MS3 method. From this data, ion trap MS2 and Orbitrap MS3 fragment spectra were extracted for peptide identification (protein sequence database-dependent search) and relative quantification using the TMT labels, respectively. The dataset ultimately allowed the identification and quantification of 51 proteins and 163 related peptide precursors with the TMT labels (see Fig. 2B and Supplemental Fig. 8, Lau et al. 2022).

Keywords: Deep proteome analysis; High-field asymmetric waveform ion mobility spectrometry (FAIMS); Liquid chromatography coupled to mass spectrometry (LC-MS); Multi-dimensional protein identification technology (MudPIT); Tandem mass tag (TMT) labelling; Tissue proteomics.