MSACL 2017 US Abstract

Proteogenomics of Lung Squamous Cell Carcinoma

John Koomen (Presenter)
Moffitt Cancer Center

Bio: Dr. Koomen is currently an associate member of the Molecular Oncology department and the Chemical Biology and Molecular Medicine program as well as the Scientific Director of the Proteomics Core at the Moffitt Cancer Center. Using the perspective that biology defines biomarkers, the Koomen lab uses discovery proteomics and targeted quantification to provide improved measurements for cancer-related proteins, pathways, and processes to further understand tumor biology with the ultimate goal of improving patient care. This experience and expertise has been applied to lung cancer proteogenomics, which can contribute to elucidation of cancer biology and may ultimately impact molecular classification of patients for personalized cancer care.

Authorship: Bin Fang, Paul Stewart, Guolin Zhang, Robbert Slebos, Katherine Fellows, Sean Yoder, Eric Welsh, Theresa Boyle, Jamie Teer, Steven Eschrich, John Koomen, and Eric Haura
Moffitt Cancer Center

Short Abstract

An integrated proteogenomic study of lung squamous cell carcinomas has been undertaken to produce complete views of tumor architecture and contextual understanding of clinically relevant drug targets and immune-tumor interactions. The individual datasets include standard genotyping, DNA and RNA sequencing, and expression proteomics of 116 tumor tissues. This team science approach combines expertise in clinical medicine, mass spectrometry, genomics, and bioinformatics/biostatistics to enable extensive studies of the tumor proteogenomes, building on previous advances integrating complementary omics approaches to address hypotheses related to chemotherapy resistance, novel drug targets, and cancer signaling networks.

Long Abstract

Introduction: Patients with lung squamous cell carcinoma (SCC) have few treatment options and very poor clinical outcomes. To make progress, we must better understand the biology of these tumors, which can lead to new diagnostics and new drugs. Drug targets in cancer are embedded within a larger cellular context and modulated by the tumor microenvironment. Using cutting edge genomics tools, we know that tumors harbor multiple alterations in the genome that perturb cancer cell biology and produce the ‘hallmarks of cancer,’ but we are not able to predict patient outcomes or match treatments with that data alone. Therefore, the challenge is to annotate systems surrounding drug targets across a spectrum of tumor tissues (rather than preclinical models) using multiple “omics” approaches to understand molecules that influence drug response. Advances in these highly complex analyses allow better classification of human cancers, prioritization of cancer-associated proteins from genomic studies, and identification of neoantigens that drive response to novel immunotherapies. Prior studies from the NCI’s Clinical Proteomics Tumor Analysis Consortium, CPTAC, [1-3] and others [4] have given examples of these advances in cancer research. Improved classification could identify patients that do well on standardized therapy or form the basis of a more personalized treatment approach. Better understanding of specific pathway alterations in an individual patient’s tumor will allow the selection of novel drugs targeting specific molecular pathways, thus tailoring of therapy to each individual patient.

Methods: Frozen tissue samples were pulverized and resuspended in denaturing buffer prior to reduction, alkylation, and tryptic digestion. Digestion was assessed with data independent acquisition LC-MS/MS [5]. Samples were grouped into batches of 4 tumors. Expression proteomics was performed using sixplex tandem mass tag (TMT) chemical labeling. Each individual channel was analyzed separately with LC-MS/MS to insure labeling went to completion. Then, four individual tumor samples were mixed with two replicates of a pooled sample created by combining all 116 tumor digests using equal amounts of total protein. Peptides were fractionated with basic pH reversed phase liquid chromatography; bRPLC fractions were concatenated (final n = 12) and analyzed with LC-MS/MS (QExactive Plus, Thermo). Data analysis was performed with MaxQuant [6]. TMT ratios were calculated at the peptide level for each sixplex; then, each TMT ratio was multiplied by the maximum peptide ion signal observed for the pooled sample to generate normalized intensity values for comparison across all 29 TMT datasets. Proteomics data were evaluated for biological differences that could be used to classify the lung SCC tumors and compared against RNA sequencing and other genomic datasets. Pathway mapping (GeneGO, Metacore) was used to assist with integration of datasets.

Results: From the DIA-LC-MS/MS, only four samples provided significantly less total protein identifications than the average of the other samples, and the results were confirmed to be dependent on the biological sample rather than on the sample preparation. All samples were used for TMT expression proteomics; each individual labeling experiment went to completion (n = 696). Individual sixplex experiments with 12 bRPLC fractions provided quantitative data for 4,700 to 5,800 protein groups, as determined by MaxQuant. In order to facilitate comparison across all 29 TMT datasets, we compared the use of raw reporter ion intensities, TMT ratios to the pooled samples, and normalized intensity values calculated using the TMT ratios for each peptide and the maximum value for the peptide ion signal observed within the reporter ion channels containing the pooled samples. The latter metric led to an appropriate comparator across TMT batches that provided relative quantification as well as the scale of the measurement (which is used to identify the signal strength and thus the data quality). Hierarchical clustering based on the proteome consistently observed across samples was used for unsupervised categorization of tumor tissues; pathway maps were used to define relationships between the proteins that were significantly different between lung SCC categories. Additional comparisons based on mutation status and genomic classifiers were used to examine the expression proteomics datasets in further detail.

Conclusions: TMT expression proteomics provides a significant depth of proteome coverage, while providing the ability to analyze large cohorts of patients (n ~ 100) within a time frame of several months on a single instrument. Initial data have been produced for lung squamous cell carcinoma tumors from 116 patients, indicating utility for assessment of a large cohort similar to label-free, super-SILAC, and iTRAQ approaches previously published by other research groups. Data analysis and integration with genomics provides a molecular classification scheme for lung SCC tumors that can be explored for novel diagnostics and drug targets.


References & Acknowledgements:

References

1. Zhang B, et al. Nature. 2014, 513, 382-7.

2. Mertins P, et al. Nature. 2016, 534, 55-62.

3. Zhang H, et al. Cell. 2016, 166, 755-65.

4. Tyanova S, et al. Nat Commun. 2016, 7, 10259.

5. Prakash A, et al. J Proteome Res. 2014, 13, 5415-30.

6. Cox J, Mann M. Nat Biotechnol. 2008, 26, 1367-72

Support has been received from Moffitt’s Lung Cancer Center of Excellence. Proteomics, Molecular Genomics, Cancer Informatics, Biostatistics, Analytic Microscopy, and Tissue Core are supported by the National Cancer Institute (Cancer Center Support Grant P30-CA076292), and the Moffitt Foundation.


Financial Disclosure

DescriptionY/NSource
GrantsyesProteome Sciences/Electrophoretics
SalaryyesProteome Sciences/Electrophoretics
Board Memberno
Stockno
Expensesno

IP Royalty: no

Planning to mention or discuss specific products or technology of the company(ies) listed above:

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