Session MOD. There are 4 abstracts in this session.



Session: Multi-omics, time: 3:00 - 3:25 pm

Utility of Proteogenomics in Immunotherapy


Bing Zhang
Baylor College of Medicine, Houston, TX

Using proteogenomics data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), I will present a few examples on how proeogenomic integration can expand our knowledge on cancer genes, prioritize cancer drivers, clarify puzzling genomic observations, and correct misinterpreted gene functions. I will also discuss our recent works on using proteogenomics to identify tumor antigens and understand immune evasion mechanisms. 

Tips and Tricks (if submitted):

Session: Multi-omics, time: 3:25 - 3:50 pm

Informing Cancer Biology using Computational Proteogenomics


Kelly Ruggles
NYU School of Medicine, New York, NY

Cancer has been well established as a disease of the genome, with a subset of somatic mutations frequently acting as drivers of tumor progression, and thereby influencing diagnosis, prognosis and treatment.  The integration of cancer genomics with mass spectrometry-based proteomics and phosphoproteomics can be used to supplement genomic information, determining the effect of genomic aberrations at the protein level, guiding biomarker development and predicting effective drug combinations for treatment. We have applied informatics methods focused on cancer proteogenomics to a number of diverse tumor types to identify novel peptides, aberrant kinase gene expression and phosphorylation status and clinically relevant druggability based on altered signaling pathways.

Tips and Tricks (if submitted):

Session: Multi-omics, time: 3:50 - 4:05 pm

Post-translationally modified proteins in plasma extracellular vesicles as candidate markers for breast cancer subtypes


Hillary Andaluz1; I-Hsuan Chen1; J. Sebastian Paez1; Anton B. Iliuk2; W. Andy Tao1
1Purdue University, West Lafayette, 0; 2Tymora Analytical Operations, West Lafayette, IN

Breast cancer is a complex disease that can be majorly classified into four molecular subtypes, Luminal A/B, Her 2 positive and triple negative. With a wide variety of pathological features and biological behaviors, the diagnosis or prognosis of specific subtypes is critical to assign treatment. Here, we present a novel strategy for developing serial PTM-omics in plasma-derived extracellular vesicles (EVs) as biomarkers to differentiate among breast cancer subtypes. Our study identified 11824, 192, 1259 and 805 of unique pS/T, pY phosphorylation, N-glycosylation and acetylation peptides respectively in EVs, isolated from plasma samples. Using label-free quantitative PTM-omics, several PTM sites showed significantly different increases across certain subtypes, and PCA further confirms that the expression profile of each PTM is also different. In addition, several targets were verified in each subtype by using parallel reaction monitoring (PRM) approach. Together, this study demonstrates the great potential of this strategy for developing the biomarkers for different subtypes in breast cancer.

Tips and Tricks (if submitted):

Session: Multi-omics, time: 4:05 - 4:20 pm

Discovery of lincRNA-encoded Peptides: An Integrated Transcriptomics, Proteomics and Bioinformatics Approach


Chin-Rang Yang; Cameron Flower; Lihe Chen; HyunJun Jung; Viswanathan Raghuram; Mark Knepper
NHLBI, National Institutes of Health, Bethesda,

Long non-coding RNA (lncRNA) refers to the family of RNA transcripts that cannot encode a protein and are more than two-hundred nucleotides in length. However, it has been shown that a subset of lncRNA transcripts do in fact contain open reading frames (ORFs), that have the potential to encode short peptides and show significant functional roles within the cell. Many of these peptides remain unannotated and uncharacterized due to relatively low molecular weight, low abundance, and tissue specificity. This study presents an integrated workflow combining proteomics, transcriptomics and bioinformatics to enable comprehensive profiling a subset of lncRNA transcripts called “lincRNA” (long intergenic noncoding RNAs that do not overlap with known genes) - encoded peptide. We test this workflow on the mouse kidney inner medulla (IM), a region that contains the collecting duct system responsible for regulated water transport. In brief, short peptides of molecular weights between 2 and 20 kDa were enriched by tricine protein gel, in-gel trypsinized into peptides, and then analyzed using high resolution mass spectrometry. However, identification of peptide sequences from the resulting fragmentation spectra requires a reference protein sequence database which must be generated de novo in the tissue of interest. To do this, an RNA-Seq based workflow was implemented to translate identified expressed lincRNA transcripts in mouse IM (using five biological replicates of strand-specific RNA-Seq experiments) into predicted ORFs. Candidates were further evaluated using several quality control criteria and bioinformatics tools. We will present three novel peptides that passed all criteria and conserved in rat and human may have potential roles in water transport in this conference. This workflow can be applied to other cell or tissue types to discover more novel lincRNA-encoded peptides.

Tips and Tricks (if submitted):