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Genomics, transcriptomics, and proteomics in the clinical setting: Integrating whole genome and RNA sequencing with quantitative proteomics to better inform clinical treatment selection.
Background: Genomic panels, limited to a few hundred target mutations, are the current standard to provide therapeutic insights; however, lack of confirmation of expression of mutated genes is a limitation of these targeted genomics approaches. We report the first comprehensive pan-omic approach, overcoming these issues by integrating analysis of whole genome DNA sequencing with RNA data, including pathway analysis to provide predictive and, subsequently, quantitative proteomics to better identify clinically actionable targets in a timely manner. Methods: Over 50 unique tumors from primary and metastatic disease were selected for pan-omic tumor profiling. A cloud-based DNA and RNA supercomputing platform was developed to produce copy-number estimates, somatic variants, rearrangements, and RNA-abundance estimates from FFPE biopsies. Machine-read pathway analysis integrated whole genome DNA sequencing and RNA data to infer proteomics and predict drug targets. Quantitative, multiplexed proteomic analysis by mass-spectrometry validated therapeutic targets at attomoles per μg of tissue. Results: Approximately 80% of tumors had somatic events in previously published “actionable” genes. Multiple cases showed confirmation between predicted actionable genes and quantitatively increased protein expression; however, many mutations showed little or no expression at the transcriptomic level. These findings were confirmed by quantitative proteomic measurements. Also observed were genomic mutations and protein expression for which approved drugs are available, independent of anatomical tumor type. Conclusions: This is the first report of a fully integrated DNA, RNA, and proteomic diagnostic assay to establish a more accurate view of therapeutic interventions for patients, especially in this era of immuno-oncology. We conclude that the molecular signature of a cancer patient is independent of the anatomical tumor type and, given that many gene mutations were not expressed, that an informed clinical treatment decision requires insight into downstream protein expression and not just DNA alterations alone.
Abstracts by Shahrooz Rabizadeh:
Economic impact and outcome benefits of integrating quantitative proteomics analysis to guide treatment strategies for metastatic breast cancer patients.Meeting: 2016 ASCO Annual Meeting | Abstract No: e18242
Identifying patient-specific neoepitopes for cell-based and vaccine immunotherapy targets in breast cancer patients by HLA typing and predicting MHC presentation from whole genome and RNA sequencing data.Meeting: 2016 ASCO Annual Meeting | Abstract No: 11606Category: Tumor Biology - Tumor-Based Biomarkers