Abstract
The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1–3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.
Highlights
Gastric cancer (GC) is the third leading cause of cancer mortality in the world, in East Asia, which accounts for more than half of the cases worldwide1, 2
We examined 2451 GC cases deposited in the tumor tissue bank of Beijing Cancer Hospital and selected 84 diffuse-type tumors (T) together with their matching nearby tissues (N) that met our criteria for proteome profiling and targeted exome sequencing (Supplementary Fig. 1; Supplementary Data 1)
We presented a proteomic landscape of diffuse-type gastric cancer (DGC) with 84 pairs of tumors and matching nearby tissues
Summary
Gastric cancer (GC) is the third leading cause of cancer mortality in the world, in East Asia, which accounts for more than half of the cases worldwide . Published CPTAC studies analyzed tumor samples from many patients, but profiled few normal tissues from separate test subjects as normal controls While this is not an issue for genomic analysis, the heterogeneity introduced by variation from different test subjects can further complicate proteomics analyses and limit our ability to profile individually altered cancer proteome, and identify dysregulated-signaling pathways that can be tailored for individualized medicine. With trypsin and the resulting peptides were separated at high pH with small columns packed in pipette tips (sRP); fractionated peptides were pooled to 6 MS runs using a 75 min highperformance liquid chromatography gradient at low pH Such a workflow allowed the analysis of a proteome in half a day (Supplementary Fig. 1c). We selected the 2538 GPs that were differentially expressed over threefold between T and N in at least 10% of the cases and submitted them for clustering and subtyping classification
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