Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has widespread clinical use for detection of inborn errors of metabolism, therapeutic drug monitoring, and numerous other applications. This technique detects proteolytic peptides as surrogates for protein biomarker expression, mutation, and post-translational modification in individual clinical assays and in cancer research with highly multiplexed quantitation across biological pathways. LC-MRM for protein biomarkers must be translated from multiplexed research-grade panels to clinical use. LC-MRM panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. An application to visualize and communicate targeted proteomics data will empower translational researchers to move protein biomarker panels from discovery to clinical use. Therefore, we have developed a web-based tool for targeted proteomics that provides pathway-level evaluations of key biological drivers (e.g., EGFR signaling), signature scores (representing phenotypes) (e.g., EMT), and the ability to quantify specific drug targets across a sample cohort. This tool represents a framework for integrating summary information, decision algorithms, and risk scores to support Physician-Interpretable Phenotypic Evaluation in R (PIPER) that can be reused or repurposed by other labs to communicate and interpret their own biomarker panels.
Background: Multiple tests may consume limited biopsy tissue to elucidate the tumor signatures and guide treatment options, including clinical trials, creating a significant clinical challenge. Although genetic sequencing helps guide targeted therapy, recent proteogenomic studies emphasized the importance of protein expression in classifying tumor subtypes. Most drugs target proteins, but due to the gap in proteomics technology development, precision cancer treatment is largely based on genetic sequencing rather than proteomic testing. Therefore, to complement existing approaches for treatment and trial matching of lung cancer patients, we developed a multiplexed proteomic assay to quantify 97 biomarkers using small amounts of formalin fixed paraffin embedded (FFPE) tumor tissue. Methods: Candidate biomarkers were selected based on known biology and clinical relevance; unique proteotypic peptides were selected to have minimal biological and analytical complications. To build the assays, light and stable isotope-labeled standard peptides were analyzed with mass spectrometry to select fragment ions and optimize collision energies. Using a matrix prepared from 25 NSCLC cell lines, reverse calibration curves were used to evaluate assay sensitivity and linearity; repeatability experiments were also used to evaluate assay performance. When needed, samples were reviewed by a pathologist and processed with filter-aided sample preparation (Wiśniewski et al. Nat Methods. 2009, 6, 359). Results: The multiplexed LC-MRM assay was successfully applied to NSCLC cell lines (n = 25), FFPE specimens (n = 30) and frozen tissues (n = 108). The resulting data differentiated subtypes of 25 NSCLC cell lines and frozen lung squamous cell carcinomas (LSCC) based on the expression levels of proteins to assess tumor phenotype, cancer signaling, and immune status. The samples prepared from FFPE sections with limited sizes demonstrated compatibility with biopsies. The LC-MRM data from tumor specimens were consistent with pathology evaluations. Finally, the 108 tumors from LSCC patients could be grouped into subtypes, including immune hot and immune cold tumors based on B and T cell markers. Quantitation of cancer antigens can also assist with direction of immunotherapy. In the future, we will explore the MRM assay results of 108 LSCC tissues by comparing to previous proteogenomics results (Stewart et al. Nat. Commun. 2019, 10, 3578) to better understand the tumor biology. Conclusions: A highly multiplexed targeted proteomics assay was developed and applied to quantify biomarkers in lung cancer cell lines, FFPE sections, and frozen tumor tissues. Further, we will implement the MRM assay with tumor biopsies to enable therapy selection and trial matching. Citation Format: Sudhir Putty Reddy, Aileen Alontaga, Paul A. Stewart, Eric Welsh, Lamees Saeed, Lancia N.F. Darville, Bin Fang, Steven A. Eschrich, Eric B. Haura, Theresa A. Boyle, John M. Koomen. Multiplexed targeted proteomics of biomarkers for lung cancer treatment selection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB121.
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