Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

Abstract

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

A Novel Splice Variant of BCAS1 Inhibits β-Arrestin 2 to Promote the Proliferation and Migration of Glioblastoma Cells, and This Effect Was Blocked by Maackiain

“…Therefore, it is imperative to understand the molecular mechanism by which GBM occurs and to establish a direct and clear treatment strategy [ 45 ]. Studies have shown that the BCAS1 gene is greatly amplified on the chromosomes of some cancer patients, but whether and how BCAS1 is involved in the development of cancer are unclear [ 32 ]. Dysregulation of the mRNA alternative splicing mechanism of eukaryotic cells, which results in abnormal gene expression or products, is known to be involved in the development of many diseases, especially cancer [ 46 ].…”

“…For example, Cheung et al analyzed exon expression arrays in patients with GBM and found that BCAS1 undergoes alternative splicing modification [ 26 ]. In addition, using the Clinical Proteomics Tumor Analysis Consortium (CPTAC) dataset, Prakash et al also found that the BCAS1 protein sequence significantly changed in GBM [ 32 ]. This aroused our interest in the relationship between alternative splicing of BCAS1 and the establishment of GBM.…”

“…BCAS1 (brain-enriched myelin-associated protein 1) is a candidate oncogene located in a region at 20q13 [ 27 , 28 , 29 ]. Although the function of BCAS1 has yet to be described in detail, its expression is amplified in various human cancers, including breast and prostate cancer, leading to more aggressive tumors [ 27 , 30 , 31 , 32 ]. Transcriptome research has shown that BCAS1 is also highly expressed in brain [ 33 ].…”

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches

“…Similarly, Clinical Proteomic Tumor Analysis Consortium (CPTAC) 97–99 initiated by NCI focuses on understanding the molecular basis of cancers including viral cancers with the help of proteomic techniques. Particularly, mass spectrometry is used by CPTAC to analyze the cancer samples for identification and quantification of protein constituents and perform phosphorylation.…”

Diagnostic Ion Data Analysis Reduction (DIDAR) allows rapid quality control analysis and filtering of multiplexed single cell proteomics data

“…Likewise, software designed for use on the Cloud have been demonstrated but have been slow to be adopted despite the promise of improved speed and increased biological relevance processing on the Cloud can allow. 10,11 One method to circumvent the size of the generated LCMS files is to apply pre-filters to remove tandem mass spectra (MS2) that are of interest. We have recently described Reporter Ion Data Analysis Reduction (RIDAR) which allows us to set minimum quality filters and relative quantification values in isobarically tagged proteomic datasets.…”