Oral Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Proteogenomic Analysis of Cancer Point Mutations - A Chromosomal Map (#176)

Iulia M. Lazar 1 , Arba Karcini 1 , Shreya Ahuja 1
  1. Virginia Tech, Blacksburg, UNITED STATES OF AMERICA, United States

The heterogeneous, unstable genome of cancerous cell states evolves over time as a result of an accumulation of mutations and chromosomal aberrations. Alterations in the structure of oncogenes and tumor suppressors lead to abnormal protein expression, gains or loss in protein function, perturbed protein-protein interactions, and ultimately to an abnormal cellular response that supports uncontrolled cell proliferation. High-throughput sequencing of thousands of tissues has revealed a critical need for deciphering the functional impact of the complex mutational landscape. The objective of this work was to address this need by exploring the protein-level profile of somatic mutations in cancer cells by using mass spectrometry (MS) detection.

The study was conducted using ER/HER2+ breast cancer and non-tumorigenic cells, cultured under proliferative and arrest-inducing conditions. The cell extracts were analyzed by nano-LC-MS/MS using LTQ-XL, QExactive and Orbitrap Lumos mass spectrometers. Protein/peptide identifications were performed with the Proteome Discoverer 2.3 and Mascot software packages (FDR=0.5-1 %) and an in-house built database containing ~2.5 million mutated peptide sequences.

The MS analysis of various cell states enabled the detection of >5,000 proteins and 150-250 point mutations in each cell line, reflecting the altered state of a number of oncogenes and tumor suppressors. One surprising finding was that the protein-level mutations did not mirror but rather complement the cDNA and RNA profiles. The sources of this discrepancy were explored, and the impact of this finding on identifying cancer driver genes was assessed. Criteria for selecting mutated peptide sequences are proposed, and chromosomal maps of missense mutations were constructed to evaluate diagnostic or prognostic potential. It was concluded that the combined use of genome- and proteome-level mutation data, complemented with protein abundance measurements, will help assess the cumulative effect of mutational hotspots, reveal susceptibility to cancer development, and guide the choice of effective therapeutic decisions.