A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma

We performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma. Data are available via ProteomeXchange with identifier PXD002622.

[1]  L. Cantley,et al.  Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation , 2009, Science.

[2]  W. Oyen,et al.  Glucose Metabolism in NSCLC Is Histology-Specific and Diverges the Prognostic Potential of 18FDG-PET for Adenocarcinoma and Squamous Cell Carcinoma , 2014, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[3]  Amy-Joan L Ham,et al.  Sample preparation and digestion for proteomic analyses using spin filters , 2005, Proteomics.

[4]  Steven J. M. Jones,et al.  Comprehensive molecular profiling of lung adenocarcinoma , 2014, Nature.

[5]  J. Miyoshi,et al.  Increased susceptibility to spontaneous lung cancer in mice lacking LIM‐domain only 7 , 2009, Cancer science.

[6]  Edward L. Huttlin,et al.  Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. , 2012, Analytical chemistry.

[7]  R. Weinshilboum,et al.  Role of the glutathione metabolic pathway in lung cancer treatment and prognosis: a review. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  Brandon M. Malone,et al.  The Proteogenomic Mapping Tool , 2011, BMC Bioinformatics.

[9]  Steven Eschrich,et al.  Libaffy: software for processing Affymetrix(R) GeneChip(R) data , 2007, Bioinform..

[10]  Z. Duan,et al.  Aberrant Signaling Pathways in Squamous Cell Lung Carcinoma , 2011, Cancer informatics.

[11]  Andrew R. Jones,et al.  ProteomeXchange provides globally co-ordinated proteomics data submission and dissemination , 2014, Nature Biotechnology.

[12]  P. Massion,et al.  In-depth Proteomic Analysis of Nonsmall Cell Lung Cancer to Discover Molecular Targets and Candidate Biomarkers* , 2012, Molecular & Cellular Proteomics.

[13]  M. Moran,et al.  Proteomic profiles of human lung adeno and squamous cell carcinoma using super‐SILAC and label‐free quantification approaches , 2014, Proteomics.

[14]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[15]  David A. Fenstermacher,et al.  Tissue-specific RMA models to incrementally normalize Affymetrix GeneChip data , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[17]  Andrey Tovchigrechko,et al.  PGP: parallel prokaryotic proteogenomics pipeline for MPI clusters, high-throughput batch clusters and multicore workstations , 2014, Bioinform..

[18]  M. Mann,et al.  Parts per Million Mass Accuracy on an Orbitrap Mass Spectrometer via Lock Mass Injection into a C-trap*S , 2005, Molecular & Cellular Proteomics.

[19]  Mark A. Hall,et al.  Blocking lactate export by inhibiting the Myc target MCT1 Disables glycolysis and glutathione synthesis. , 2014, Cancer research.

[20]  L. Paz-Ares,et al.  Proteomic biomarkers in lung cancer , 2013, Clinical and Translational Oncology.

[21]  Rafael Rosell,et al.  Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer , 2011, International journal of cancer.

[22]  Yixue Li,et al.  Identification of gene fusions from human lung cancer mass spectrometry data , 2013, BMC Genomics.

[23]  R. Branca,et al.  Quantitative accuracy in mass spectrometry based proteomics of complex samples: the impact of labeling and precursor interference. , 2014, Journal of proteomics.

[24]  Paul D. Smith,et al.  Activity of the Monocarboxylate Transporter 1 Inhibitor AZD3965 in Small Cell Lung Cancer , 2013, Clinical Cancer Research.

[25]  A. Nesvizhskii Proteogenomics: concepts, applications and computational strategies , 2014, Nature Methods.

[26]  Steven J. M. Jones,et al.  Comprehensive genomic characterization of squamous cell lung cancers , 2012, Nature.

[27]  Zlatko Trajanoski,et al.  Proteomic analysis of human cataract aqueous humour: Comparison of one-dimensional gel LCMS with two-dimensional LCMS of unlabelled and iTRAQ®-labelled specimens. , 2011, Journal of proteomics.

[28]  Philip Wenig,et al.  OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data , 2010, BMC Bioinformatics.

[29]  Alexey I Nesvizhskii,et al.  Interpretation of Shotgun Proteomic Data , 2005, Molecular & Cellular Proteomics.

[30]  Hyung-Ryong Kim,et al.  Lysyl oxidase-like-1 enhances lung metastasis when lactate accumulation and monocarboxylate transporter expression are involved. , 2011, Oncology letters.

[31]  E. Richardsen,et al.  Monocarboxylate Transporters 1–4 in NSCLC: MCT1 Is an Independent Prognostic Marker for Survival , 2014, PloS one.

[32]  P. Boutros,et al.  Onco-proteogenomics: cancer proteomics joins forces with genomics , 2014, Nature Methods.

[33]  John L Cleveland,et al.  Targeting lactate metabolism for cancer therapeutics. , 2013, Journal of Clinical Investigation.

[34]  C. Eyers Universal sample preparation method for proteome analysis , 2009 .

[35]  Frederick Mosteller,et al.  Understanding robust and exploratory data analysis , 1983 .

[36]  Anders Berglund,et al.  Iterative rank-order normalization of gene expression microarray data , 2013, BMC Bioinformatics.

[37]  Richard D. Smith,et al.  Normalization and missing value imputation for label-free LC-MS analysis , 2012, BMC Bioinformatics.

[38]  A. McCullough Comprehensive genomic characterization of squamous cell lung cancers , 2013 .

[39]  H. Rodriguez,et al.  Proteogenomic convergence for understanding cancer pathways and networks , 2014, Clinical Proteomics.

[40]  W. Pao,et al.  A Bioinformatics Workflow for Variant Peptide Detection in Shotgun Proteomics* , 2011, Molecular & Cellular Proteomics.

[41]  Gennifer E. Merrihew,et al.  Proteogenomic database construction driven from large scale RNA-seq data. , 2014, Journal of proteome research.

[42]  G. Balendiran,et al.  The role of glutathione in cancer , 2004, Cell biochemistry and function.

[43]  Jürgen Cox,et al.  1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data , 2012, BMC Bioinformatics.

[44]  A. Chiappori,et al.  Current clinical application of genomic and proteomic profiling in non-small-cell lung cancer. , 2014, Cancer control : journal of the Moffitt Cancer Center.

[45]  Holger Sültmann,et al.  Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes. , 2009, Lung cancer.

[46]  Paul Taylor,et al.  Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact , 2014, Nature Communications.

[47]  Pratik D Jagtap,et al.  Multi-omic data analysis using Galaxy , 2015, Nature Biotechnology.

[48]  E. Gratton,et al.  Wnt signaling directs a metabolic program of glycolysis and angiogenesis in colon cancer , 2014, The EMBO journal.

[49]  B. Kuster,et al.  Mass-spectrometry-based draft of the human proteome , 2014, Nature.

[50]  A. Sánchez-Palencia,et al.  Differential immunohistochemical localization of desmosomal plaque‐related proteins in non‐small‐cell lung cancer , 2013, Histopathology.

[51]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[52]  M. Mann,et al.  Andromeda: a peptide search engine integrated into the MaxQuant environment. , 2011, Journal of proteome research.

[53]  R. Zahedi,et al.  Proteomic insights into non-small cell lung cancer: New ideas for cancer diagnosis and therapy from a functional viewpoint , 2014 .

[54]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[55]  Samuel Leung,et al.  Optimal Immunohistochemical Markers For Distinguishing Lung Adenocarcinomas From Squamous Cell Carcinomas in Small Tumor Samples , 2010, The American journal of surgical pathology.

[56]  Jan Budczies,et al.  Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer , 2013, PloS one.

[57]  J. Lehtiö,et al.  Lung cancer proteomics, clinical and technological considerations. , 2010, Journal of proteomics.

[58]  Michael Stuart,et al.  Understanding Robust and Exploratory Data Analysis , 1984 .

[59]  Mathias Wilhelm,et al.  Global proteome analysis of the NCI-60 cell line panel. , 2013, Cell reports.

[60]  M. Moran,et al.  Primary tumor xenografts of human lung adeno and squamous cell carcinoma express distinct proteomic signatures. , 2011, Journal of proteome research.

[61]  James E. Johnson,et al.  Flexible and Accessible Workflows for Improved Proteogenomic Analysis Using the Galaxy Framework , 2014, Journal of proteome research.

[62]  Jeffrey R. Whiteaker,et al.  Proteogenomic characterization of human colon and rectal cancer , 2014, Nature.

[63]  C. Sima,et al.  Immunohistochemical algorithm for differentiation of lung adenocarcinoma and squamous cell carcinoma based on large series of whole-tissue sections with validation in small specimens , 2011, Modern Pathology.

[64]  Y. Istefanopulos,et al.  IEEE Engineering in Medicine and Biology Society , 2019, IEEE Transactions on Biomedical Engineering.

[65]  David E. Misek,et al.  Discordant Protein and mRNA Expression in Lung Adenocarcinomas * , 2002, Molecular & Cellular Proteomics.

[66]  M. Mann,et al.  The coming age of complete, accurate, and ubiquitous proteomes. , 2013, Molecular cell.

[67]  Gary D Bader,et al.  A draft map of the human proteome , 2014, Nature.