Evaluation of NCI-7 Cell Line Panel as a Reference Material for

May 2, 2018 - Parsing and Quantification of Raw Orbitrap Mass Spectrometer Data Using RawQuant. Journal of Proteome Research. Kovalchik, Moggridge ...
2 downloads 0 Views 1MB Size
Subscriber access provided by Kaohsiung Medical University

Article

Evaluation of NCI-7 Cell Line Panel as a Reference Material for Clinical Proteomics David J Clark, Yingwei Hu, William Bocik, Lijun Chen, Michael Schnaubelt, Rhonda Roberts, Punit Shah, Gordon Whiteley, and Hui Zhang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00165 • Publication Date (Web): 02 May 2018 Downloaded from http://pubs.acs.org on May 5, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 29 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

Evaluation of NCI-7 Cell Line Panel as a Reference Material for Clinical Proteomics

1 2 3

David J Clark1, Yingwei Hu1, William Bocik2, Lijun Chen1, Michael Schnaubelt1, Rhonda Roberts2, Punit Shah1,

4

Gordon Whiteley2, Hui Zhang1

5 6

1

Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; 2Antibody

7

Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer

8

Research, Frederick, MD, USA

9 *Corresponding author: Dr. David Clark at [email protected]

10 11 12

Keywords

13

Proteomics reference material, clinical proteomics, mass spectrometry

14 15 16 17 18 19 20 21 22 23 24 25 26

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

27

Page 2 of 29

ABSTRACT

28

Reference materials are vital to benchmarking the reproducibility of clinical tests and essential for

29

monitoring laboratory performance for clinical proteomics. The reference material utilized for mass spectrometric

30

analysis of the human proteome would ideally contain enough proteins to be suitably representative of the human

31

proteome, as well as exhibit a stable protein composition in different batches of sample regeneration. Previously,

32

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) utilized a PDX-derived comparative reference

33

(CompRef) materials for the longitudinal assessment of proteomic performance, however, inherent drawbacks of

34

PDX-derived material, including extended time needed to grow tumors and high level of expertise needed, has

35

resulted in efforts to identify a new source of CompRef material. In this study, we examined the utility of using a

36

panel of seven cancer cell lines, NCI-7 Cell Line Panel, as a reference material for mass spectrometric analysis of

37

human proteome. Our results showed that not only is the NCI-7 material suitable for benchmarking laboratory

38

sample preparation methods, but also NCI-7 sample generation is highly reproducible at both the global and

39

phosphoprotein levels. In addition, the predicted genomic and experimental coverage of the NCI-7 proteome

40

suggests the NCI-7 material may also have applications as a universal standard proteomic reference.

41 42 43 44 45 46 47 48 49 50 51 52

ACS Paragon Plus Environment

Page 3 of 29 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

53

Journal of Proteome Research

INTRODUCTION

54

The clinical proteomic analysis of tumor specimens seeks to improve our understanding of cancer via the

55

integration of genomic information with tumor proteome characterization. Building upon The Cancer Genome

56

Atlas (TCGA) initiative, which sought to identify genomic alterations in multiple tumor types, the Clinical

57

Proteomic Tumor Analysis Consortium (CPTAC) leverages highly innovative proteomic approaches and

58

advanced mass spectrometry instrumentation to comprehensively profile differences in protein expression and

59

protein post-translational modifications, among individual tumors and non-tumor tissue samples. Utilizing a

60

proteogenomic approach, linking protein and genomic information, the analysis of colorectal, ovarian, and breast

61

cancers has yielded novel observations in the biology of these respective tumors 1–3.

62

To evaluate the performance of mass spectrometry analysis at Proteome Characterization Centers (PCCs),

63

as well as longitudinally monitor workflow performance, a comparative reference material (“CompRef”) was

64

generated using patient-derived breast cancer tumor xenografts (PDXs). In a previous study, this CompRef

65

material was utilized to examine intra-lab and cross-center reproducibility of data generation across six different

66

platforms employing distinct mass spectrometry instrumentation and/or protein quantitation strategies

67

addition, in the CPTAC studies examining colorectal, ovarian, and breast cancer, respectively, the xenograft

68

CompRef material was interspersed during clinical tumor analysis as reference standard 6. Although PDXs

69

models are highly representative of the tumor’s heterogeneity and microenvironment 7, implantation requires a

70

high level of technical skill and tumor growth can be slow 8. In addition, the model animal tumor burden can limit

71

the total tumor size, which can impact overall tumor tissue yield. This latter aspect is especially concerning in the

72

context of generating a sufficient amount of material as a standard reference for multi-center analysis pipelines,

73

and may require the sacrifice of a large number of animals.

4,5

. In

74

The NCI-60 Cell Line Panel was initially established as an in vitro drug-discovery screening platform9,

75

and more recently the molecular characteristics of the individual cell lines in the panel, including, gene mutation

76

analysis

77

expression

78

the web-based database annotation tool, CellMiner was developed 16, and recently expanded to include web-based

10

, whole exome sequencing 14,15

11

, methylation patterns

12

, mRNA transcription profiles

13

, and protein

, have been examined. To facilitate access to these large datasets for the wider scientific audience

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 79 4 5 80 6 7 81 8 9 10 82 11 12 83 13 14 84 15 16 85 17 18 86 19 20 87 21 22 88 23 24 89 25 26 90 27 28 91 29 30 31 92 32 33 93 34 35 94 36 37 95 38 39 96 40 41 97 42 43 98 44 45 99 46 47 48 100 49 50 101 51 52 102 53 54 103 55 56 104 57 58 59 60

Page 4 of 29

tools that allow researchers to integrate these genomic, transcriptomic, protein, and pharmacological profiles 17. Over the past few decades, the NCI-60 Cell Line Panel has been an invaluable resource of the oncology research community, identifying novel anti-cancer compounds, in addition to its contributions to our understanding of cancer biology 18. Ironically, the longevity of the NCI-60 Cell Line Panel has impacted its future success in drug screening, as the cell lines have adapted to cell culturing and led some researchers to question the panel’s future role in drug discovery, with PDXs models suggested to be more reflective of in vivo tumor response 9. Still, researchers have continued to develop novel strategies to examine the NCI-60 Cell Line Panel, in addition to other cell lines, illustrating additional benefits of using these cell line models in cancer-related studies 19,20. Reference material derived from cell culture models, such as the NCI-60 Cell Line Panel, has the potential to address some of the previous cited drawbacks of PDX models, specifically growth time and tumor tissue yield, in addition to being more economical in terms of expense

21–23

. A single cancer cell line may not be

sufficient to reflect the protein profile of a tumor in it’s entirely, thus utilizing multiple cell lines from a variety of tissue types would allow for extensive coverage of protein expression. At the other extreme, the use of the entire NCI-60 panel as a reference material would be complex and impractical. To determine whether a subset of cell lines selected from the NCI-60 Tumor Cell Line can be used as a new reference material to evaluate proteomic performance, we employed quantitative proteomics to evaluate sample preparation and mass spectrometry analysis reproducibility, as well as examining the coverage of the human cancer proteome using the NCI-7 Cell Line Panel.

METHODS Chemicals and Materials. Chemicals were purchased from Sigma-Aldrich (St. Louis, MO) unless specified otherwise. C18 analytical LC column (NanoViper, 75 µm, 150 mm, 2 µm particle size) were from Fisher Scientific (Waltham, MA). C18 SepPak columns were purchased from Waters Corporation (Milford, MA), Trypsin and Trypsin/Lys-C Mix (mass spectrometry (MS) grade) was ordered from Promega Corporation (Madison, WI), and Lys-C was from Wako Laboratory Chemicals, USA (Richmond, VA). All cell lines (A549, COLO205, NCI H226, NCI H23, T-47D, CCRF-CEM and RPMI 8226) were purchased from American Type

ACS Paragon Plus Environment

Page 5 of 29 1 2 3 105 4 5 106 6 7 107 8 9 10 108 11 12 109 13 14 110 15 16 111 17 18 112 19 20 113 21 22 114 23 24 25 115 26 27 116 28 29 117 30 31 118 32 33 119 34 35 120 36 37 121 38 39 40 122 41 42 123 43 44 124 45 46 125 47 48 126 49 50 127 51 52 128 53 54 129 55 56 57 58 59 60

Journal of Proteome Research

Culture Collection (ATCC, Manassas, VA). RPMI 1640 media and heat inactivated FBS were from Life Technologies (Grand Island, NY); L-Glutamine was from Lonza (Walkersville, MD). Cell culturing. All cell lines were grown in RPMI 1640 medium supplemented with 10% HI FBS, and 1% LGlutamine in a humidified air incubator with 5% CO2 at 37°C. All cells lines were seeded on T75 flask and grown to 90% confluence before being transferred to a larger T150 flask and maintained. Generation of NCI-7 reference material. All cell lines grown to 90% confluence, washed with a volume of PBS twice, scrapped, collected and centrifuged at 3,000 x g. Individual cell pellets were resuspended in lysis buffer (8M urea, 75 mM NaCl, 50 mM Tris, pH 8.0, 1 mM EDTA, 2 µg/mL aprotinin, 10 µg. mL leupeptin, 1 mM PMSF, 10 mM NaF, Phosphatase Inhibitor Cocktail 2 and Phosphatase Inhibitor Cocktail 3 (1:100 dilution), and 20 µM PUGNAc), and homogenized using a Branson Sonifier 250 probe sonicator (Danbury, CT) with a Duty Cycle of 10%, Output Control 1 with a 10s/50s ON/OFF cycle (n = 5). Samples were centrifuged at 20,000 x g and the resulting supernatant retained, and protein concentration was measured via BCA Protein Assay. 1 mg of protein from each cell line was mixed to generate the NCI-7 reference material. Generation of PDX (P32/P33) CompRef material. Growth and preparation of PDX material has been described previously

1,2

. Cryopulverized tissue was resuspended in lysis buffer (8M urea, 75 mM NaCl, 50 mM Tris-HCl,

pH 8.0, 1 mM EDTA, 2 µg/mL aprotinin, 10 µg. mL leupeptin, 1 mM PMSF, 10 mM NaF, Phosphatase Inhibitor Cocktail 2 and Phosphatase Inhibitor Cocktail 3 (1:100 dilution), and 20 µM PUGNAc), and homogenized using a Branson Sonifier 250 probe sonicator (Danbury, CT) with a Duty Cycle of 10%, Output Control 1 with a 10s/50s ON/OFF cycle (n = 5). Samples were centrifuged at 20,000 x g and the resulting supernatant retained, and protein concentration was measured via BCA Protein Assay. Peptide digestion, Tandem-Mass-Tag labeling, and off-line fractionation. Both NCI-7 cells and PDX CompRef protein lysates were subjected to the same digestion, Tandem-Mass-Tag (TMT) labeling, and off-line fractionation conditions unless otherwise noted. Protein lysates were subjected to reduction with 5 mM 1,4Dithiothreitol for 30 mins at RT, followed by alkylation with 10 mM iodoacetamide for 45 mins at RT in the dark. Urea concentration was reduced < 2M using 50 mM Tris-HCl, pH 8.0. For NCI-7 samples, LysC/Trypsin was

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 130 4 5 131 6 7 132 8 9 10 133 11 12 134 13 14 135 15 16 136 17 18 137 19 20 138 21 22 139 23 24 140 25 26 27 141 28 29 142 30 31 143 32 33 144 34 35 145 36 37 146 38 39 147 40 41 148 42 43 149 44 45 46 150 47 48 151 49 50 152 51 52 153 53 54 154 55 56 57 58 59 60

Page 6 of 29

added in an enzyme to substrate ratio of 1:40 and samples incubated overnight at 37°C, while PDX CompRef samples were subjected to tandem digestion of Lys-C (1:50) for 2 hr at RT followed by trypsin (1:50) overnight at RT. The generated peptides were acidified to a final concentration of 1% formic acid, subjected to clean-up using C-18 SepPak columns (Waters), and then dried. Desalted peptides were labeled with 10-plex TMT reagents following manufacturer’s instructions. Peptides were resuspended in 50 mM HEPES, pH 8.5 and labeled with TMT reagent resuspended in anhydrous acetonitrile for 1 hr at RT. The reaction was quenched using 5% hydroxylamine at RT for 15 mins, and differentially labeled samples combined. The pooled peptides were then subjected to clean-up using C-18 SepPak columns and dried down. The desalted, TMT-labeled samples were reconstituted in a volume of 20 mM ammonium formate (pH 10) and 2% acetonitrile (ACN) and loaded onto a 4.6 mm x 250 mm RP Zorbax 300 A Extend-C18 column with 3.5 µm size beads (Agilent). Peptides were separated using an Agilent 1200 Series HPLC instrument using basic reversed-phase chromatography with Solvent A (2% ACN, 5 mM ammonium formate, pH 10) and a non-linear gradient of Solvent B (90% ACN, 5 mM ammonium formate, pH 10) at 1 mL/min as follows: 0% Solvent B (9 mins), 6% Solvent B (4 min), 6% to 28.5% Solvent B, (50 min), 28.% to 34% Solvent B (5.5 min), 34% to 60% Solvent B (13 min), and then held at 60% Solvent B for 8.5 min. Collected fractions were concatenated as reported previously 24; 5% of each the 24 fractions was aliquoted for global proteomic analysis, dried down, and resuspended in 3% ACN, 0.1% formic acid prior to ESI-LC-MS/MS analysis. The remaining sample was utilized for phosphopeptide enrichment. Phosphopeptide enrichment. The remaining 95% of the sample were further concatenated prior to phosphopeptide enrichment using immobilized metal affinity chromatography (IMAC) as previously described 24. In brief, N-NTA agarose beads were utilized to prepare Fe3+-NTA agarose beads, and then 300 µg of peptides reconstituted in 80% ACN/0.1% trifluoroacetic acid were incubated with 10 µL of the Fe3+-IMAC beads for 30 mins. Sample were then spun down and the supernatant containing unbound peptides was removed. The beads were washed twice and then loaded onto equilibrated C-18 Stage Tips with 80% ACN, 0.1% trifluoroacetic acid. Tips were rinsed twice with 1% formic acid, followed by sample elution off the Fe3+-IMAC beads and onto the C18 State Tips with 70 µL of 500 mM dibasic potassium phosphate, pH 7.0 three times. C-18 Stage Tips were

ACS Paragon Plus Environment

Page 7 of 29

Journal of Proteome Research

1 2 3 155 4 5 156 6 7 157 8 9 10 158 11 12 159 13 14 160 15 16 161 17 18 162 19 20 163 21 22 23 164 24 25 165 26 27 166 28 29 167 30 31 168 32 33 169 34 35 36 170 37 38 171 39 40 172 41 42 173 43 44 174 45 46 175 47 48 176 49 50 177 51 52 178 53 54 55 179 56 57 58 59 60

ACS Paragon Plus Environment

washed twice with 1% formic acid, followed by elution of the phosphopeptides from the C-18 Stage Tips with 50% ACN, 0.1% formic acid twice. Samples were dried down and resuspended in 3% ACN, 0.1% formic acid prior to ESI-LC-MS/MS analysis. Nano-ESI-LC-MS/MS analysis. As noted in the manuscript, two ESI-LC-MS/MS systems were used. For samples run on the Q Exactive HF system, 1 µg of peptide was separated utilizing a nanoACQUITY UPLC system (Waters) on an in-house packed 20 cm x 75 µm diameter C18 column (5 µm ProntoSil C-18-AQ beads (Bishoff Chromatography); Picofrit 10 µm opening (New Objective)). The column was heated to 50°C using a column heater (Phoenix-ST). The flow rate was 0.300 µl/min with 0.1% formic acid in water (A) and 0.1% formic acid, 90% acetonitrile (B). The peptides were separated with an 8–45% B gradient in 70 mins and analyzed using the Thermo Q Exactive HF mass spectrometer (Thermo Scientific). Parameters were as followed MS1: resolution – 120,000, mass range – 350 to 1800 m/z, AGC Target 3.0e6, Max IT – 50 ms, charge state include - 2-6, dynamic exclusion – 15 s top 15 ions selected for MS2; MS2: resolution – 60,000, high-energy collision dissociation activation energy (HCD) – 32, isolation width (m/z) – 1.0, AGC Target – 1.0e5, Max IT – 150 ms. For samples run on the Orbitrap Lumos Fusion system, 1 µg of peptide was separated using Easy nLC 1200 UHPLC system (Thermo Scientific) on an in-house packed 20 cm x 75 µm diameter C18 column (1.9 µm Reprosil-Pur C18-AQ beads (Dr. Maisch GmbH); Picofrit 10 µm opening (New Objective)). The column was heated to 50°C using a column heater (Phoenix-ST). The flow rate was 0.300 µl/min with 0.1% formic acid and 2% acetonitrile in water (A) and 0.1% formic acid, 90% acetonitrile (B). The peptides were separated with a 6–30% B gradient in 84 mins and analyzed using the Thermo Fusion Lumos mass spectrometer (Thermo Scientific). Parameters were as followed MS1: resolution – 60,000, mass range – 350 to 1800 m/z, RF Lens – 30%, AGC Target 4.0e5, Max IT – 50 ms, charge state include - 2-6, dynamic exclusion – 45 s, top 20 ions selected for MS2; MS2: resolution – 50,000, high-energy collision dissociation activation energy (HCD) – 37, isolation width (m/z) – 0.7, AGC Target – 2.0e5, Max IT – 105 ms. Determination of gene expression of NCI-60 and NCI-7 Cell Lines. The gene expression of NCI-60 Cell Lines was determined by the transcript datasets download from the publicly available CellMiner website

Journal of Proteome Research 1 2 3 180 4 5 181 6 7 182 8 9 10 183 11 12 184 13 14 185 15 16 186 17 18 187 19 20 188 21 22 189 23 24 190 25 26 191 27 28 192 29 30 31 193 32 33 194 34 35 195 36 37 196 38 39 197 40 41 198 42 43 199 44 45 200 46 47 201 48 49 50 202 51 52 203 53 54 204 55 56 205 57 58 59 60

Page 8 of 29

(https://discover.nci.nih.gov/cellminer/home.do). The RNA__5_Platform_Gene_Transcript_Average_intensities sheet was selected to be applied in the following analysis. For each cell line, the 25 percentile value of intensities of all the genes across all cell lines was used as a threshold to filter and reserving only the relatively strong signals of mRNA expression (Table S1 and S2). A minimal set of cell lines having most gene IDs represented from the whole genome was generated by a greedy algorithm of calculating the best gene coverage iteratively. After selection of the first ranked cell line, the algorithm added additional cell lines into the set based on the number of uncovered genes compared to all other remaining cells lines until gene coverage was 100% or beyond a predefined threshold (Table S3). Coverage for the selected cell lines for the NCI-7 Cell Line Panel was determined by identifying the gene expression of the individual cell lines using the same filtering criteria (Table S4), divided by the total number of reported genes in the human genome and expressed genes in the NCI-60 Cell Lines (Table S5). To facilitate identifying the coverage of the NCI-7 proteome, the Entrez Gene ID for the human, NCI-60, and NCI-7 genomes were converted to RefSeq IDs using the bioinformatic software tool DAVID (Database for Annotation and Integrated Discovery) (v 6.8)25,26. Data analysis for protein identification and quantification. All LC-MS/MS files were analyzed by a cloudbased proteomic pipeline developed in Johns Hopkins University to perform database search for spectrum assignments using MS-GF+ in this study against a combined human and mouse RefSeq database (version 20160914) 27,28. A decoy database was used to assess the false discovery rate (FDR) at PSM, peptide and protein levels 29. Peptides were searched with two tryptic ends, allowing up to two missed cleavages. Search parameters included 20 ppm precursor tolerance and 0.06 Da fragment ion tolerance, static modification of carbamidomethylation at cysteine (+57.02146), TMT-label modification of N-terminus and lysine (+229.16293) and variable modifications of oxidation at methionine (+15.99491) and phosphorylation at serine, threonine, and tyrosine (+79.96633). Filters used for global data analysis included one PSM per peptide and two peptides per protein, with a 1% FDR threshold at the protein level. Filters used for phosphoproteome data included one PSM per peptide and one peptide per protein, with a 1% FDR threshold at the peptide level. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository 30 with the dataset identifier PXD008952 and 10.6019/PXD008952.

ACS Paragon Plus Environment

Page 9 of 29 1 2 3 206 4 5 207 6 7 208 8 9 10 209 11 12 210 13 14 211 15 16 212 17 18 213 19 20 214 21 22 215 23 24 216 25 26 217 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 227 48 49 50 228 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

RESULTS AND DISCUSSION Determining coverage of the human genome by the NCI-7 Cell Line Panel The NCI-7 Cell Line Panel is comprised of the cell lines NCI-H23, RPMI-8226, T47D, A549, COLO205, NCI-H226, and CCRF CEM, which are representative of several tissues and various genetic mutations (Table 1). Extensive genomic profiling of the NCI-60 cell lines has been performed previously utilizing a variety of analysis platforms and pipelines

10,11,13

. In our

analysis, we examined the “5 Platform Gene Transcript” dataset from CellMiner to determine the 218

genes expressed by the individual cell lines in the

219

NCI-60 Cell Line Panel. Utilizing our filtering

220

criteria as described in the Methods section to

221

identify gene transcripts, we plotted the individual

222

cell lines against the number of their respective

223

unique genes expressed; ranking the NCI-60 Cell

224

Line Panel in order of their coverage of the human

Figure 1. NCI-60 Cell Line Panel ranked by coverage of the human 225 genome. NCI-7 cell lines are labeled and identified with red dots.

genome (Figure 1), and selected seven cell lines that

226

were readily available from ATCC. We found that 7

cell lines (NCI-7 Cell Line Panel) from the NCI-60 Cell Line Panel represented 92% of the NCI-60 Cell Line expressed genome, as well as representing 88% coverage of the entire human genome (Table S5).

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 229 4 5 230 6 7 231 8 9 10 232 11 12 233 13 14 234 15 16 235 17 18 236 19 20 237 21 22 238 23 24 239 25 26 240 27 28 241 29 30 31 242 32 33 243 34 35 244 36 37 245 38 39 246 40 41 247 42 43 248 44 45 249 46 47 250 48 49 50 251 51 52 252 53 54 253 55 56 57 58 59 60

Page 10 of 29

Assessing regeneration reproducibility of NCI-7 cell references One of the critical assessment for reference materials is whether the materials can be stably regenerated even after an extended period of time. Extended passaging of a single cell line has been shown to result in phenotypic and genotypic alterations that can influence protein expression

31

. To circumvent this drawback of cell lines,

recommendations include limiting cell culturing to time periods less than three months and obtaining new cell aliquots from a cell bank 32. Moreover, we hypothesized that by generating a reference material from several cell lines we might further reduce differences related to protein expression, as well as varied protein expression specific to the individual cell lines 33. We obtained three distinct vials of each individual cell line from ATCC to generate three preparations of the NCI-7 Cell Line Panel (Table S6), then employed quantitative proteomics to determine reproducibility of the NCI-7 cell reference generation from different seed cells as illustrated in Figure 2A. Following tryptic digestion, three channels from each NCI-7 Cell Line Panel preparation (NCI-7 Replicate #1, #2, and #3) were TMT labeled, and subjected

to

phosphopeptide analysis

on

basic

reverse-phase

enrichment, an

Orbitrap

and

fractionation,

nano-LC-MS/MS

Lumos

Fusion

mass

spectrometer. In total, 179,298 peptides from 10,926 proteins (Table S7) and 51,505 phosphopeptides (Table S8) were identified. We plotted the median protein ratio

Figure 2. Schematic of NCI-7 CompRef workflows. Equal amounts of protein from each individual cell line was pooled to generate protein reference standard. The pooled reference standard was then utilized to evaluate sample digestion and mass tag labeling (A) and NCI-7 sample generation reproducibility (B).

ACS Paragon Plus Environment

Page 11 of 29

Journal of Proteome Research

1 2 3 254 4 5 255 6 7 256 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 257 34 35 258 36 37 259 38 39 260 40 41 261 42 43 262 44 45 263 46 47 264 48 49 50 265 51 52 266 53 54 267 55 56 57 58 59 60

ACS Paragon Plus Environment

value for all of the individual channels, observing close correlation between our expected values and observed values (Figure S1). Using reported TMT ratios from the ten TMT-channels, we constructed correlation matrices for both global protein expression and phosphopeptide expression. As shown in Figure 3, Pearson’s correlation

Figure 3. Reproducibility of NCI-7 CompRef sample generation. Correlation matrix of three distinct NCI-7 CompRef pooled reference replicates using reported protein ratios for global proteomics (A) and peptide ratios for phosphoproteomics (B).

coefficients were high between the technical replicates for each of the NCI-7 preparations (>0.95) When evaluating the protein expression profile across the three NCI-7 replicates, a high correlation coefficient (>0.93) was also observed, demonstrating high reproducibility of the individual NCI-7 preparations. Examination of phosphopeptide expression revealed a similar pattern of high correlation between the technical (0.94-0.99) and biological replicates (0.88-0.95) of the NCI-7 material (Figure 3). The stable global and phosphoprotein profiles between the three distinct sample preparations of NCI-7 is extremely promising in the context of using this it as a universal protein reference material. Not only would using cell lines allow for large scale production of NCI-7, but as long as primary cell stocks are used, cell culturing methods remain consistent, and passage number monitored, the protein profile would remain relatively unchanged. This characteristic is not only important for studies that may span months, or even years, and require a longitudinal standard reference, but also identifying a potential universal reference standard for benchmarking various sample preparation pipelines, mass spectrometry

Journal of Proteome Research 1 2 3 268 4 5 269 6 7 270 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

instrumentation, and data analysis methodology

34,35

Page 12 of 29

. Overall, these results show that sample generation of the

NCI-7 Cell Line Panel material is highly reproducible at both global protein and phosphopeptide levels. Determining the coverage of the NCI-7 proteome

ACS Paragon Plus Environment

Page 13 of 29 1 2 3 271 4 5 272 6 7 273 8 9 10 274 11 12 275 13 14 276 15 16 277 17 18 278 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

Since previous CPTAC-related studies utilized a PDX CompRef material to assess instrument performance, we wanted to compare the NCI-7 to a PDX CompRef material comprised of the xenografts WHIM2-P32 (basal) and WHIM16-P33 (luminal-B), which have been well characterized previously

36,37

. The material from the two

xenografts followed a similar protocol of sample preparation and digestion, basic reverse-phase fractionation, phosphopeptide enrichment, and nano-LC-MS/MS analysis, with a distinct TMT-labeling schema (Figure S2). In this analysis, we identified a total of 170,120 peptides from 13,182 protein groups when searched against a combined mouse + human database (Table S9), and 147,981 peptides from 9,596 protein groups when searched against a human-only database (Table S10). When examining the phosphoproteome, we identified 44,168 and 279

39,353 phosphopeptides when searching against a

280

mouse + human (Table S11) and human-only (Table

281

S12), respectively. The total number of identified

282

peptides/proteins and phosphosites are summarized in

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 29

283

Table 2. For our comparative purposes, we only

284

evaluated proteins identified using the NCBI

285

human Ref-Seq database, observing 7,707

286

protein groups and 17,964 phosphopeptides

287

shared

288

CompRef datasets (Figure 4). As the results

289

indicate, there is a large percentage (~26%) of

290

mouse proteins present in the PDX CompRef

291

material, which is to be expected due to human

292

stromal cell component of the primary tumor

293

being replaced by murine cells as the tumor is

294

passaged 38. The occupancy of mouse proteins in

295

the MS spectrum did not appear to significantly

296

impact the overlap of global protein coverage

297

between the NCI-7 and P32/P33 material, and

298

commonly identified proteins between the two

299

datasets potentially provide further evidence of a

300

core human proteome

301

overlap of phosphosites between the two datasets

302

suggests either a distinct pattern of phosphosite

303

occupancy between the two reference materials,

304

or that stromal cells are a significant contributor

305

to the phosphoproteome in heterocellular models

306 Figure 4. Coverage of the NCI-7 Cell Line proteome. Overlap of human proteome between NCI-7 CompRef and PDX P32/P33 CompRef for global proteins (A) and phosphopeptides (B). Total coverage of the 307 human, NCI-60, and NCI-7 genome by the NCI-7 Cell Line material (C).

40

between

the

NCI-7

39

and

P32/P33

. However, the low

. In contrast, we found few bovine-derived

proteins in the NCI-7 material (sourced from the

ACS Paragon Plus Environment

Page 15 of 29 1 2 3 308 4 5 309 6 7 310 8 9 10 311 11 12 312 13 14 313 15 16 314 17 18 315 19 20 316 21 22 317 23 24 318 25 26 319 27 28 320 29 30 31 321 32 33 322 34 35 323 36 37 324 38 39 325 40 41 326 42 43 327 44 45 328 46 47 329 48 49 50 330 51 52 331 53 54 332 55 56 333 57 58 59 60

Journal of Proteome Research

fetal bovine serum utilized in the cell culturing), identifying less than 0.2% peptide spectral matches (PSMs) in our dataset (data not shown). Next, we wanted to define the coverage of the NCI-7 material to determine whether the cell line-derived material might be comparable to clinical tumor specimens. Previously, yeast S. cerevisiae has been examined as a proteomics reference standard, citing benefits including low cost, high protein yield, and protein complexity and dynamic range

41

. However, with improvements in mass spectrometry instrumentation and proteome dataset

exceeding 8,000 protein identifications

42

, the total proteome of yeast (comprising ~6,600 proteins)

43

, may be

insufficient as a reference material for comprehensive assessment of laboratory performance. The NCI-7 material shares many of the positive attributes of the yeast reference standard, while exceeding the number of identifiable gene products, as indicated by our mass spectrometry analysis of the NCI-7 material identifying over 10,000 protein groups, which is similar to the number of total number of proteins identified in the proteomic analysis of the entire NCI-60 Cell Line Panel 15. Utilizing the corresponding RefSeq IDs, further evaluation of our proteome dataset revealed coverage of the for the Human, NCI-60, and NCI-7 genomes to be 57.4%, 58.5% and 62.2%, respectively (Figure 4C). Interestingly, we identified 333 proteins that were not observed in the “Human Genome” (Table S13), however, additional investigation revealed that the CellMiner datasets we utilized were from studies performed prior to 201113,44,45, and subsequent NCBI Annotated Releases (including the RefSeq database used for protein identification) have been updated with newly annotated proteins. With CPTAC already having examined colorectal, ovarian, and breast cancer via mass spectrometry

1–3

, and

future plans to examine additional cancers, there will be a desire to link these various datasets to facilitate “pancancer” analyses. The Cancer Genome Atlas (TCGA) Research Network has already proposed and begun integrating the various levels of genomics information obtained in the multiple TCGA analyses scaled protein “pan-cancer” study has already been carried out

48

46,47

, and small

. With the ability to identify and quantify

thousands of proteins and integrate the genetic information from hundreds of clinical samples, the results of the CPTAC studies have the potential to significantly expand our understanding of underlying molecular basis of both individual and multiple tumor types. Similar to the previous CPTAC proteogenomic studies which included one isobaric channel in each quantitative set as an experimental reference, the inclusion of several NCI-7 channels

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 334 4 5 335 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 29

interspersed in the quantitative sets of future CPTAC analyses would allow for linking the data from distinct cancer types and enable pan-cancer analyses. Overall, our results show the NCI-7 material has a complex dynamic 336

range of protein expression, and sufficient coverage of

337

the human proteome to serve as a standard proteomic

338

reference material.

339

Applying NCI-7 Cell Line Panel to assess the

340

reproducibility of sample preparation and protein

341

digestion

342

Sample preparation is critical aspect in proteomic

343

analyses, and can be a major source of experimental

344

variation that can impact the accuracy of downstream

345

quantitation

346

proteomics using tandem-mass-tag (TMT) labeling is the

347

ability to multiplex biological samples and analyze them

348

in a single mass spectrometry analysis, which reduces

349

the analytical variation of LC-MS/MS, however, this

350

analytical strategy is especially sensitive to variations in

351

sample preparation, due to its incorporation protein

352

digestion

353

performance of protein sample preparation using NCI-7

354

Cell Line Panel, we devised the experimental procedure

355

described in Figure 2B. First, the cell lines comprising

356

the NCI-7 Cell Line Panel were cultured and cell pellets

357

generated. Following lysis, equal protein amounts from

358

each cell line were mixed to generate the NCI-7 cell

49–51

and

Figure 5. Accuracy of tandem-mass-tag (TMT) quantitation and reproducibility of sample digestion based by peptide aliquots and protein aliquots protein ratios, respectively. Box plots were generated for reported protein ratios using either a peptide aliquot channel (A) and protein aliquot channel (B) for Plus Environment ACS Paragon data normalization. (C) Influence of ion intensity on reporter tag accuracy. Data plotted is from Replicate #1, Fraction #1.

. One of the benefits of quantitative

purification

52

.

To

evaluate

lab

Page 17 of 29 1 2 3 359 4 5 360 6 7 361 8 9 10 362 11 12 363 13 14 364 15 16 365 17 18 366 19 20 367 21 22 368 23 24 369 25 26 370 27 28 371 29 30 31 372 32 33 373 34 35 374 36 37 375 38 39 376 40 41 377 42 43 378 44 45 379 46 47 380 48 49 50 381 51 52 382 53 54 383 55 56 384 57 58 59 60

Journal of Proteome Research

lysate mixture. To assess the efficiency of sample preparation and digestion, we generated several protein aliquots of various concentrations to be digested individually. In parallel, we generated a single digest, wherein the sample post-digest would be aliquoted at the peptide level. After the complete digest of all protein samples, the derived peptides were labeled with TMT reagents to facilitate protein quantitation, followed by basic reverse-phase fractionation, and nano-LC-MS/MS analysis. Global peptide fractions were analyzed on an Orbitrap QE HF mass spectrometer, identifying a total of 68,578 peptides derived from 6,428 protein groups (Table S14). To assess sample preparation reproducibility, we evaluated the TMT reporter channels corresponding to sample aliquoted at the protein level, where as to assess the accuracy of TMT-labeling, we utilized the TMT reporter channels corresponding to sample aliquoted at the peptide level. We plotted the median protein ratio value for all of the individual channels, observing close correlation between our expected values and observed values (Figure 5). Moreover, we utilized two distinct TMT channels to normalize our data, TMT-127C which corresponded to the 1fold aliquoted peptide sample (Figure 5A) and TMT128N, which corresponded to the 1-fold aliquoted protein sample (Figure 5B). Interestingly, we did not observe additional variation in the overall distribution of protein ratios comparing to peptide ratios when we normalized to our 1-fold protein aliquoted sample. With the accuracy of TMT reporter ratio being influenced by TMT reporter ion intensity in MS2, we evaluated the threshold in which accurate TMT reporter intensity was reduced or loss, finding an ion intensity below 10,000 could impact the accuracy of TMT ratios (Figure 5C, Figure S3A). When we included additional variables of cumulative PSM count

Figure 6. Starting sample amount influences identification rate. The number of identified peptides (top) and proteins (bottom) are reported for each TMT channel. Analysis revealed an ion threshold intensity of 10,000 produced accurate quantitation and was utilized as a filter for the number of identifications per channel.

and Coefficient of Variation (CV), we found a similar threshold cut-off of 10,000 ion intensity, recording a CV of approximately 14.1% that continued to drop with increasing ion intensity (Figure S3B). Finally, we wanted to

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 385 4 5 386 6 7 8 387 9 10 388 11 12 389 13 14 390 15 16 391 17 18 392 19 20 393 21 22 394 23 24 25 395 26 27 396 28 29 397 30 31 398 32 33 399 34 35 400 36 37 401 38 39 402 40 41 403 42 43 44 404 45 46 405 47 48 406 49 50 407 51 52 408 53 54 409 55 56 57 58 59 60

Page 18 of 29

determine the impact of sample dilution on the total number of peptides and proteins identified in each respective TMT channel. Our results indicated similar peptide and protein identification rates between our 1-fold (400 µg), 2-fold (200 µg), and 5-fold (80 µg) peptide and protein sample aliquots (Figure 6). In our 10-fold (40 µg) peptide sample aliquot we observed a reduction in the total number of peptides and protein identified, which was mirrored in our 10-fold protein sample aliquot. In total, these quantitative proteomic results indicate a high level of reproducibility for sample preparation and protein digestion, in addition to accurate peptide labeling using TMT isobaric reagents. Moreover, our experimental design evaluating sample preparation and TMT-labeling simultaneously is suitable for laboratory benchmarking to assess a lab’s technical and mass spectrometry performance when utilizing the NCI-7 Cell Line Panel as a reference standard.

CONCLUSION In this study, we evaluated the use of a new comparative reference material, NCI-7 Cell Line Panel, comprised of seven cancer cell lines from the NCI-60 cell line panel to be utilized as a longitudinal reference standard for CPTAC-related studies, as well as potentially be utilized for inter-laboratory instrumentation QC and intralaboratory benchmarking and comparison. In our analysis, we first we determined the reproducibility of generating the NCI-7 reference material, showing consistent global proteome and phosphoproteome expression profiles across three distinct sample preparations. Next, we estimated the NCI-7 cell line panel was representative of 88% of the human genome, and proteomic analysis was able to identify 63% of the predicted NCI-7 genome in a single dataset. Finally, devised a quantitative strategy that could assess sample preparation metrics, including sample digestion as well as labeling accuracy, that could be applied in future benchmarking studies as a measurement of laboratory performance. Together, this study has shown the NCI-7 cell line material is not only suitable as a reference standard for assessing the performance of CPTAC PCCs, but may also have future applications as a universal proteomic reference material.

ACKNOWLEDGEMENTS

ACS Paragon Plus Environment

Page 19 of 29 1 2 3 410 4 5 411 6 7 412 8 9 10 413 11 12 414 13 14 415 15 16 416 17 18 417 19 20 418 21 22 419 23 24 420 25 26 421 27 28 422 29 30 31 423 32 33 424 34 35 425 36 37 426 38 39 427 40 41 428 42 43 429 44 45 430 46 47 431 48 49 50 432 51 52 433 53 54 434 55 56 435 57 58 59 60

Journal of Proteome Research

This work was supported by the National Institutes of Health, National Cancer Institute, the Clinical Proteomic Tumor Analysis Consortium (CPTAC, U24CA160036). The authors declare no competing conflicts of interest.

SUPPORTING INFORMATION The Supporting Information is available free of charge on the ACS Publications website at DOI: Box plots of reported TMT ratios; schematic of TMT-labeling for PDX model; plot illustrating influence of ion intensity on reporter tag accuracy (PDF) NCI-60 Cell Lines Gene Transcript values; ranking of NCI-60 Cell Lines by gene expression; ranking of NCI-60 Cell Lines by genomic coverage; NCI-7 Gene Expression profiles; coverage of the human and NCI-60 genomes by NCI-7 Cell Line Panel; passage number and harvest dates of three NCI-7 Cell Line preparations; identified proteins from NCI-7 Cell Line sample regeneration reproducibility global proteome analysis; identified proteins from NCI-7 Cell Line sample regeneration reproducibility phosphoproteome analysis; identified proteins from PDX-model P32/P33 CompRef sample global proteome analysis (human + mouse database); identified proteins from PDX-model P32/P33 CompRef sample phosphoproteome analysis (human + mouse database); identified proteins from PDX-model P32/P33 CompRef sample global proteome analysis (human database); identified proteins from PDX-model P32/P33 CompRef sample

phosphoproteome analysis (human database); NCI-7

proteins not identified in CellMiner dataset; identified proteins from NCI-7 Cell Line sample preparation and digestion reproducibility global proteome analysis (XLSX)

REFERENCES (1)

Zhang, B.; Wang, J.; XiaojingWang; Zhu, J.; Liu, Q.; Shi, Z.; Chambers, M. C.; Zimmerman, L. J.; Shaddox, K. F.; Kim, S.; Davies, S. R.; Wang, S.; PeiWang; Kinsinger, C. R.; Rivers, R. C.; Rodriguez, H.; Townsend, R. R.; Ellis, M. J. C.; Carr, S. a; Tabb, D. L.; Coffey, R. J.; Slebos, R. J. C.; Liebler, D. C.;

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 436 4 5 437 6 7 438 8 9 10 439 11 12 440 13 14 441 15 16 442 17 18 443 19 20 444 21 22 445 23 24 446 25 26 447 27 28 448 29 30 31 449 32 33 450 34 35 451 36 37 452 38 39 453 40 41 454 42 43 455 44 45 456 46 47 457 48 49 50 458 51 52 459 53 54 460 55 56 461 57 58 59 60

Page 20 of 29

Cptac, the N. C. I. Proteogenomic Characterization of Human Colon and Rectal Cancer. Nature 2014, 1– 21. (2)

Mertins, P.; Mani, D. R.; Ruggles, K. V.; Gillette, M. A.; Clauser, K. R.; Wang, P.; Wang, X.; Qiao, J. W.; Cao, S.; Petralia, F.; Kawaler, E.; Mundt, F.; Krug, K.; Tu, Z.; Lei, J. T.; Gatza, M. L.; Wilkerson, M.; Perou, C. M.; Yellapantula, V.; Huang, K.; Lin, C.; McLellan, M. D.; Yan, P.; Davies, S. R.; Townsend, R. R.; Skates, S. J.; Wang, J.; Zhang, B.; Kinsinger, C. R.; Mesri, M.; Rodriguez, H.; Ding, L.; Paulovich, A. G.; Fenyö, D.; Ellis, M. J.; Carr, S. A.; Nci, C. Proteogenomics Connects Somatic Mutations to Signalling in Breast Cancer. Nature 2016, 534 (7605), 55–62.

(3)

Zhang, H.; Liu, T.; Zhang, Z.; Payne, S. H.; Zhang, B.; McDermott, J. E.; Zhou, J. Y.; Petyuk, V. A.; Chen, L.; Ray, D.; Sun, S.; Yang, F.; Chen, L.; Wang, J.; Shah, P.; Cha, S. W.; Aiyetan, P.; Woo, S.; Tian, Y.; Gritsenko, M. A.; Clauss, T. R.; Choi, C.; Monroe, M. E.; Thomas, S.; Nie, S.; Wu, C.; Moore, R. J.; Yu, K. H.; Tabb, D. L.; Feny??, D.; Vineet, V.; Wang, Y.; Rodriguez, H.; Boja, E. S.; Hiltke, T.; Rivers, R. C.; Sokoll, L.; Zhu, H.; Shih, I. M.; Cope, L.; Pandey, A.; Zhang, B.; Snyder, M. P.; Levine, D. A.; Smith, R. D.; Chan, D. W.; Rodland, K. D.; Carr, S. A.; Gillette, M. A.; Klauser, K. R.; Kuhn, E.; Mani, D. R.; Mertins, P.; Ketchum, K. A.; Thangudu, R.; Cai, S.; Oberti, M.; Paulovich, A. G.; Whiteaker, J. R.; Edwards, N. J.; McGarvey, P. B.; Madhavan, S.; Wang, P.; Chan, D. W.; Pandey, A.; Shih, I. M.; Zhang, H.; Zhang, Z.; Zhu, H.; Cope, L.; Whiteley, G. A.; Skates, S. J.; White, F. M.; Levine, D. A.; Boja, E. S.; Kinsinger, C. R.; Hiltke, T.; Mesri, M.; Rivers, R. C.; Rodriguez, H.; Shaw, K. M.; Stein, S. E.; Fenyo, D.; Liu, T.; McDermott, J. E.; Payne, S. H.; Rodland, K. D.; Smith, R. D.; Rudnick, P.; Snyder, M.; Zhao, Y.; Chen, X.; Ransohoff, D. F.; Hoofnagle, A. N.; Liebler, D. C.; Sanders, M. E.; Shi, Z.; Slebos, R. J. C.; Tabb, D. L.; Zhang, B.; Zimmerman, L. J.; Wang, Y.; Davies, S. R.; Ding, L.; Ellis, M. J. C.; Townsend, R. R. Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell 2016, 166 (3), 755–765.

(4)

Tabb, D. L.; Wang, X.; Carr, S. A.; Clauser, K. R.; Mertins, P.; Chambers, M. C.; Holman, J. D.; Wang, J.; Zhang, B.; Zimmerman, L. J.; Chen, X.; Gunawardena, H. P.; Davies, S. R.; Ellis, M. J. C.; Li, S.; Townsend, R. R.; Boja, E. S.; Ketchum, K. A.; Kinsinger, C. R.; Mesri, M.; Rodriguez, H.; Liu, T.; Kim,

ACS Paragon Plus Environment

Page 21 of 29 1 2 3 462 4 5 463 6 7 464 8 9 10 465 11 12 466 13 14 467 15 16 468 17 18 469 19 20 470 21 22 471 23 24 472 25 26 473 27 28 474 29 30 31 475 32 33 476 34 35 477 36 37 478 38 39 479 40 41 480 42 43 481 44 45 482 46 47 483 48 49 50 484 51 52 485 53 54 486 55 56 487 57 58 59 60

Journal of Proteome Research

S.; McDermott, J. E.; Payne, S. H.; Petyuk, V. A.; Rodland, K. D.; Smith, R. D.; Yang, F.; Chan, D. W.; Zhang, B.; Zhang, H.; Zhang, Z.; Zhou, J. Y.; Liebler, D. C. Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts. J. Proteome Res. 2016, 15 (3), 691–706. (5)

Zhou, J.; Chen, L.; Zhang, B.; Tian, Y.; Liu, T.; Thomas, S. N.; Chen, L.; Schnaubelt, M.; Boja, E.; Hiltke, T.; Kinsinger, C. R.; Rodriguez, H.; Davies, S. R.; Li, S.; Snider, J. E.; Erdmann-gilmore, P.; Tabb, D. L.; Townsend, R. R.; Ellis, M. J.; Rodland, K. D.; Smith, R. D.; Carr, S. A.; Zhang, Z.; Chan, D. W.; Zhang, H. Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues. J. Proteome Res. 2017.

(6)

Rudnick, P. A.; Markey, S. P.; Roth, J.; Mirokhin, Y.; Yan, X.; Tchekhovskoi, D. V.; Edwards, N. J.; Thangudu, R. R.; Ketchum, K. A.; Kinsinger, C. R.; Mesri, M.; Rodriguez, H.; Stein, S. E. A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline. J. Proteome Res. 2016, 15 (3), 1023–1032.

(7)

Whittle, J. R.; Lewis, M. T.; Lindeman, G. J.; Visvader, J. E. Patient-Derived Xenograft Models of Breast Cancer and Their Predictive Power. Breast Cancer Res. 2015, 17 (1), 17.

(8)

Workman, P.; Aboagye, E. O.; Balkwill, F.; Balmain, A.; Bruder, G.; Chaplin, D. J.; Double, J. A.; Everitt, J.; Farningham, D. A. H.; Glennie, M. J.; Kelland, L. R.; Robinson, V.; Stratford, I. J.; Tozer, G. M.; Watson, S.; Wedge, S. R.; Eccles, S. A. Guidelines for the Welfare and Use of Animals in Cancer Research. Br. J. Cancer 2010, 102 (11), 1555–1577.

(9)

Shoemaker, R. H. The NCI60 Human Tumour Cell Line Anticancer Drug Screen. Nat. Rev. Cancer 2006, 6 (10), 813–823.

(10)

Ikediobi, O. N.; Davies, H.; Bignell, G.; Edkins, S.; Stevens, C.; O’Meara, S.; Santarius, T.; Avis, T.; Barthorpe, S.; Brackenbury, L.; Buck, G.; Butler, A.; Clements, J.; Cole, J.; Dicks, E.; Forbes, S.; Gray, K.; Halliday, K.; Harrison, R.; Hills, K.; Hinton, J.; Hunter, C.; Jenkinson, A.; Jones, D.; Kosmidou, V.; Lugg, R.; Menzies, A.; Mironenko, T.; Parker, A.; Perry, J.; Raine, K.; Richardson, D.; Shepherd, R.; Small, A.; Smith, R.; Solomon, H.; Stephens, P.; Teague, J.; Tofts, C.; Varian, J.; Webb, T.; West, S.; Widaa, S.; Yates, A.; Reinhold, W.; Weinstein, J. N.; Stratton, M. R.; Futreal, P. A.; Wooster, R. Mutation

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 488 4 5 489 6 7 490 8 9 10 491 11 12 492 13 14 493 15 16 494 17 18 495 19 20 496 21 22 497 23 24 498 25 26 499 27 28 500 29 30 31 501 32 33 502 34 35 503 36 37 504 38 39 505 40 41 506 42 43 507 44 45 508 46 47 509 48 49 50 510 51 52 511 53 54 512 55 56 513 57 58 59 60

Page 22 of 29

Analysis of 24 Known Cancer Genes in the NCI-60 Cell Line Set. Mol. Cancer Ther. 2006, 5 (11), 2606– 2612. (11)

Abaan, O. D.; Polley, E. C.; Davis, S. R.; Zhu, Y. J.; Bilke, S.; Walker, R. L.; Pineda, M.; Gindin, Y.; Jiang, Y.; Reinhold, W. C.; Holbeck, S. L.; Simon, R. M.; Doroshow, J. H.; Pommier, Y.; Meltzer, P. S. The Exomes of the NCI-60 Panel: A Genomic Resource for Cancer Biology and Systems Pharmacology. Cancer Res. 2013, 73 (14), 4372–4382.

(12)

Reinhold, W. C.; Varma, S.; Sunshine, M.; Rajapakse, V.; Luna, A.; Kohn, K. W.; Stevenson, H.; Wang, Y.; Nogales, V.; Moran, S.; H, D. J. G. J. The NCI-60 Methylome and Its Integration into CellMiner. Cancer Res. 2017, 77 (3), 601–613.

(13)

Shankavaram, U. T.; Reinhold, W. C.; Nishizuka, S.; Major, S.; Morita, D.; Chary, K. K.; Reimers, M. a; Scherf, U.; Kahn, A.; Dolginow, D.; Cossman, J.; Kaldjian, E. P.; Scudiero, D. a; Petricoin, E.; Liotta, L.; Lee, J. K.; Weinstein, J. N. Transcript and Protein Expression Profiles of the NCI-60 Cancer Cell Panel: An Integromic Microarray Study. Mol. Cancer Ther. 2007, 6 (3), 820–832.

(14)

Nishizuka, S.; Charboneau, L.; Young, L.; Major, S.; Reinhold, W. C.; Waltham, M.; Kouros-Mehr, H.; Bussey, K. J.; Lee, J. K.; Espina, V.; Munson, P. J.; Petricoin, E.; Liotta, L. A.; Weinstein, J. N. Proteomic Profiling of the NCI-60 Cancer Cell Lines Using New High-Density Reverse-Phase Lysate Microarrays. Proc. Natl. Acad. Sci. U. S. A. 2003, 100 (24), 14229–14234.

(15)

Gholami, A. M.; Hahne, H.; Wu, Z.; Auer, F. J.; Meng, C.; Wilhelm, M.; Kuster, B. Global Proteome Analysis of the NCI-60 Cell Line Panel. Cell Rep. 2013, 4 (3), 609–620.

(16)

Shankavaram, U. T.; Varma, S.; Kane, D.; Sunshine, M.; Chary, K. K.; Reinhold, W. C.; Pommier, Y.; Weinstein, J. N. CellMiner: A Relational Database and Query Tool for the NCI-60 Cancer Cell Lines. BMC Genomics 2009, 10, 277.

(17)

Reinhold, W. C.; Sunshine, M.; Liu, H.; Varma, S.; Kohn, K. W.; Morris, J.; Doroshow, J.; Pommier, Y. CellMiner: A Web-Based Suite of Genomic and Pharmacologic Tools to Explore Transcript and Drug Patterns in the NCI-60 Cell Line Set. Cancer Res. 2012, 72 (14), 3499–3511.

(18)

Chabner, B. A. NCI-60 Cell Line Screening: A Radical Departure in Its Time. J. Natl. Cancer Inst. 2016,

ACS Paragon Plus Environment

Page 23 of 29 1 2 3 514 4 5 515 6 7 516 8 9 10 517 11 12 518 13 14 519 15 16 520 17 18 521 19 20 522 21 22 523 23 24 524 25 26 525 27 28 526 29 30 31 527 32 33 528 34 35 529 36 37 530 38 39 531 40 41 532 42 43 533 44 45 534 46 47 535 48 49 50 536 51 52 537 53 54 538 55 56 539 57 58 59 60

Journal of Proteome Research

108 (5), 1–7. (19)

Beroukhim, R.; Mermel, C. H.; Porter, D.; Wei, G.; Raychaudhuri, S.; Donovan, J.; Barretina, J.; Boehm, J. S.; Dobson, J.; Urashima, M.; Mc Henry, K. T.; Pinchback, R. M.; Ligon, A. H.; Cho, Y.-J.; Haery, L.; Greulich, H.; Reich, M.; Winckler, W.; Lawrence, M. S.; Weir, B. A.; Tanaka, K. E.; Chiang, D. Y.; Bass, A. J.; Loo, A.; Hoffman, C.; Prensner, J.; Liefeld, T.; Gao, Q.; Yecies, D.; Signoretti, S.; Maher, E.; Kaye, F. J.; Sasaki, H.; Tepper, J. E.; Fletcher, J. A.; Tabernero, J.; Baselga, J.; Tsao, M.-S.; Demichelis, F.; Rubin, M. A.; Janne, P. A.; Daly, M. J.; Nucera, C.; Levine, R. L.; Ebert, B. L.; Gabriel, S.; Rustgi, A. K.; Antonescu, C. R.; Ladanyi, M.; Letai, A.; Garraway, L. A.; Loda, M.; Beer, D. G.; True, L. D.; Okamoto, A.; Pomeroy, S. L.; Singer, S.; Golub, T. R.; Lander, E. S.; Getz, G.; Sellers, W. R.; Meyerson, M. The Landscape of Somatic Copy-Number Alteration across Human Cancers. Nature 2010, 463 (7283), 899– 905.

(20)

Bignell, G. R.; Greenman, C. D.; Davies, H.; Butler, A. P.; Edkins, S.; Andrews, J. M.; Buck, G.; Chen, L.; Beare, D.; Latimer, C.; Widaa, S.; Hinton, J.; Fahey, C.; Fu, B.; Swamy, S.; Dalgliesh, G. L.; Teh, B. T.; Deloukas, P.; Yang, F.; Campbell, P. J.; Futreal, P. A.; Stratton, M. R. Signatures of Mutation and Selection in the Cancer Genome. Nature 2010, 463 (7283), 893–898.

(21)

Adeegbe, D. O.; Liu, Y. Ex Vivo Engineering of the Tumor Microenvironment; Aref, A. R., Barbie, D., Eds.; Humana Press: New York City, NY, 2017.

(22)

Aparicio, S.; Hidalgo, M.; Kung, A. L. Examining the Utility of Patient-Derived Xenograft Mouse Models. Nat. Rev. Cancer 2015, 15 (5), 311–316.

(23)

Lee, J. Y.; Kim, S. Y.; Park, C.; Kim, N. K. D.; Jang, J.; Park, K.; Yi, J. H.; Hong, M.; Ahn, T.; Rath, O.; Schueler, J.; Kim, S. T.; Do, I.-G.; Lee, S.; Park, S. H.; Ji, Y. I.; Kim, D.; Park, J. O.; Park, Y. S.; Kang, W. K.; Kim, K.-M.; Park, W.-Y.; Lim, H. Y.; Lee, J.; Tae Kim, S.; Ji Yun Lee, S. Y. K. C. P. N. K. D. K. J. J. K. P. J. H. Y. M. H. T. A. O. R. J. S. S. T. K. I.-G. D. S. L. S. H. P. Y. I. J. D. K. J. O. P. Y. S. P. W. K. K. Patient-Derived Cell Models as Preclinical Tools for Genome-Directed Targeted Therapy. Oncotarget 2015, 6 (28), 25619–25630.

(24)

Mertins, P.; Qiao, J. W.; Patel, J.; Udeshi, N. D.; Clauser, K. R.; Mani, D. R.; Burgess, M. W.; Gillette, M.

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 540 4 5 541 6 7 542 8 9 10 543 11 12 544 13 14 545 15 16 546 17 18 547 19 20 548 21 22 549 23 24 550 25 26 551 27 28 552 29 30 31 553 32 33 554 34 35 555 36 37 556 38 39 557 40 41 558 42 43 559 44 45 560 46 47 561 48 49 50 562 51 52 563 53 54 564 55 56 565 57 58 59 60

Page 24 of 29

A.; Jaffe, J. D.; Carr, S. A. Integrated Proteomic Analysis of Post-Translational Modifications by Serial Enrichment. Nat. Methods 2013, 10 (7), 634–637. (25)

Huang , Sherman, B, T,, Lempicki, R, A, D. W.; Huang, D. W.; Sherman, B. T.; Lempicki, R. a. Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources. Nat. Protoc. 2008, 4 (1), 44–57.

(26)

Huang, D. W.; Sherman, B. T.; Lempicki, R. a. Bioinformatics Enrichment Tools: Paths toward the Comprehensive Functional Analysis of Large Gene Lists. Nucleic Acids Res. 2009, 37 (1), 1–13.

(27)

Kim, S.; Pevzner, P. A. MS-GF+ Makes Progress towards a Universal Database Search Tool for Proteomics. Nat. Commun. 2014, 5, 5277.

(28)

Kim, S.; Gupta, N.; Pevzner, P. A. Spectral Probabilities and Generating Functions of Tandem Mass Spectra a Strike Against the Decoy Database-Supplement-Supplementary_Info. J. Proteome Res. 2008, 7 (8), 1–6.

(29)

Elias, J. E.; Gygi, S. P. Target-Decoy Search Strategy for Increased Confidence in Large-Scale Protein Identifications by Mass Spectrometry. Nat. Methods 2007, 4 (3), 207–214.

(30)

Vizcaíno, J.; Deutsch, E.; Wang, R. ProteomeXchange Provides Globally Coordinated Proteomics Data Submission and Dissemination. Nat. Biotechnol. 2014, 32 (3), 223–226.

(31)

Jin, W.; Penington, C. J.; McCue, S. W.; Simpson, M. J. A Computational Modelling Framework to Quantify the Effects of Passaging Cell Lines. PLoS One 2017, 12 (7), 1–16.

(32)

Masters, J. R.; Stacey, G. N. Changing Medium and Passaging Cell Lines. Nat. Protoc. 2007, 2 (9), 2276– 2284.

(33)

Geiger, T.; Wehner, A.; Schaab, C.; Cox, J.; Mann, M. Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins. Mol. Cell. Proteomics 2012, 11 (3), M111.014050.

(34)

Bunk, D. M. Design Considerations for Proteomic Reference Materials. Proteomics 2010, 10 (23), 4220– 4225.

(35)

Klimek, J.; Eddes, J. S.; Hohmann, L.; Jackson, J.; Peterson, A.; Letarte, S.; Gafken, P. R.; Katz, J. E.;

ACS Paragon Plus Environment

Page 25 of 29 1 2 3 566 4 5 567 6 7 568 8 9 10 569 11 12 570 13 14 571 15 16 572 17 18 573 19 20 574 21 22 575 23 24 576 25 26 577 27 28 578 29 30 31 579 32 33 580 34 35 581 36 37 582 38 39 583 40 41 584 42 43 585 44 45 586 46 47 587 48 49 50 588 51 52 589 53 54 590 55 56 591 57 58 59 60

Journal of Proteome Research

Mallick, P.; Lee, H.; Schmidt, A.; Ossola, R.; Eng, J. K.; Aebersold, R.; Martin, D. B. The Standard Protein Mix Database: A Diverse Data Set to Assist in the Production of Improved Peptide and Protein Identification Software Tools. J. Proteome Res. 2008, 7 (1), 96–103. (36)

Ding, L.; Ellis, M. J.; Li, S.; Larson, D. E.; Chen, K.; Wallis, J. W. Genome Remodelling in a Basal-like Breast Cancer Metastasis and Xenograft. Nature. 2010, 464.

(37)

Li, S.; Shen, D.; Shao, J.; Crowder, R.; Liu, W.; Prat, A.; He, X.; Liu, S.; Hoog, J.; Lu, C.; Ding, L.; Griffith, O. L.; Miller, C.; Larson, D.; Fulton, R. S.; Harrison, M.; Mooney, T.; McMichael, J. F.; Luo, J.; Tao, Y.; Goncalves, R.; Schlosberg, C.; Hiken, J. F.; Saied, L.; Sanchez, C.; Giuntoli, T.; Bumb, C.; Cooper, C.; Kitchens, R. T.; Lin, A.; Phommaly, C.; Davies, S. R.; Zhang, J.; Kavuri, M. S.; McEachern, D.; Dong, Y. Y.; Ma, C.; Pluard, T.; Naughton, M.; Bose, R.; Suresh, R.; McDowell, R.; Michel, L.; Aft, R.; Gillanders, W.; DeSchryver, K.; Wilson, R. K.; Wang, S.; Mills, G. B.; Gonzalez-Angulo, A.; Edwards, J. R.; Maher, C.; Perou, C. M.; Mardis, E. R.; Ellis, M. J. Endocrine-Therapy-Resistant ESR1 Variants Revealed by Genomic Characterization of Breast-Cancer-Derived Xenografts. Cell Rep. 2013, 4 (6), 1116–1130.

(38)

DeRose, Y. S.; Wang, G.; Lin, Y. C.; Bernard, P. S.; Buys, S. S.; Ebbert, M. T. Tumor Grafts Derived from Women with Breast Cancer Authentically Reflect Tumor Pathology, Growth, Metastasis and Disease Outcomes. Nat Med. 2011, 17.

(39)

Schirle, M.; Heurtier, M.-A.; Kuster, B. Profiling Core Proteomes of Human Cell Lines by OneDimensional PAGE and Liquid Chromatography-Tandem Mass Spectrometry. Mol. Cell. Proteomics 2003, 2 (12), 1297–1305.

(40)

Tape, C. J.; Ling, S.; Dimitriadi, M.; McMahon, K. M.; Worboys, J. D.; Leong, H. S.; Norrie, I. C.; Miller, C. J.; Poulogiannis, G.; Lauffenburger, D. A.; Jørgensen, C. Oncogenic KRAS Regulates Tumor Cell Signaling via Stromal Reciprocation. Cell 2016, 165 (4), 910–920.

(41)

Paulovich, A. G.; Billheimer, D.; Ham, A.-J. L.; Vega-Montoto, L.; Rudnick, P. A.; Tabb, D. L.; Wang, P.; Blackman, R. K.; Bunk, D. M.; Cardasis, H. L.; Clauser, K. R.; Kinsinger, C. R.; Schilling, B.; Tegeler, T. J.; Variyath, A. M.; Wang, M.; Whiteaker, J. R.; Zimmerman, L. J.; Fenyo, D.; Carr, S. A.; Fisher, S. J.;

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 592 4 5 593 6 7 594 8 9 10 595 11 12 596 13 14 597 15 16 598 17 18 599 19 20 600 21 22 601 23 24 602 25 26 603 27 28 604 29 30 31 605 32 33 606 34 35 607 36 37 608 38 39 609 40 41 610 42 43 611 44 45 612 46 47 613 48 49 50 614 51 52 615 53 54 616 55 56 617 57 58 59 60

Page 26 of 29

Gibson, B. W.; Mesri, M.; Neubert, T. A.; Regnier, F. E.; Rodriguez, H.; Spiegelman, C.; Stein, S. E.; Tempst, P.; Liebler, D. C. Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance. Mol. Cell. Proteomics 2010, 9 (2), 242–254. (42)

Davis, S.; Charles, P. D.; He, L.; Mowlds, P.; Kessler, B. M.; Fischer, R. Expanding Proteome Coverage with CHarge Ordered Parallel Ion aNalysis (CHOPIN) Combined with Broad Specificity Proteolysis. J. Proteome Res. 2017, 16 (3), 1288–1299.

(43)

Picotti, P.; Clément-Ziza, M.; Lam, H.; Campbell, D. S.; Schmidt, A.; Deutsch, E. W.; Röst, H.; Sun, Z.; Rinner, O.; Reiter, L.; Shen, Q.; Michaelson, J. J.; Frei, A.; Alberti, S.; Kusebauch, U.; Wollscheid, B.; Moritz, R. L.; Beyer, A.; Aebersold, R. A Complete Mass-Spectrometric Map of the Yeast Proteome Applied to Quantitative Trait Analysis. Nature 2013, 494 (7436), 266–270.

(44)

Reinhold, W. C.; Mergny, J. L.; Liu, H.; Ryan, M.; Pfister, T. D.; Kinders, R.; Parchment, R.; Doroshow, J.; Weinstein, J. N.; Pommier, Y. Exon Array Analyses across the NCI-60 Reveal Potential Regulation of TOP1 by Transcription Pausing at Guanosine Quartets in the First Intron. Cancer Res. 2010, 70 (6), 2191– 2203.

(45)

Liu, H.; D’Andrade, P.; Fulmer-Smentek, S.; Lorenzi, P.; Kohn, K. W.; Weinstein, J. N.; Pommier, Y.; Reinhold, W. C. mRNA and microRNA Expression Profiles of the NCI-60 Integrated with Drug Activities. Mol. Cancer Ther. 2010, 9 (5), 1080–1091.

(46)

Chang, K.; Creighton, C. J.; Davis, C.; Donehower, L.; Drummond, J.; Wheeler, D.; Ally, A.; Balasundaram, M.; Birol, I.; Butterfield, Y. S. N.; Chu, A.; Chuah, E.; Chun, H.-J. E.; Dhalla, N.; Guin, R.; Hirst, M.; Hirst, C.; Holt, R. a; Jones, S. J. M.; Lee, D.; Li, H. I.; Marra, M. a; Mayo, M.; Moore, R. a; Mungall, A. J.; Robertson, a G.; Schein, J. E.; Sipahimalani, P.; Tam, A.; Thiessen, N.; Varhol, R. J.; Beroukhim, R.; Bhatt, A. S.; Brooks, A. N.; Cherniack, A. D.; Freeman, S. S.; Gabriel, S. B.; Helman, E.; Jung, J.; Meyerson, M.; Ojesina, A. I.; Pedamallu, C. S.; Saksena, G.; Schumacher, S. E.; Tabak, B.; Zack, T.; Lander, E. S.; Bristow, C. a; Hadjipanayis, A.; Haseley, P.; Kucherlapati, R.; Lee, S.; Lee, E.; Luquette, L. J.; Mahadeshwar, H. S.; Pantazi, A.; Parfenov, M.; Park, P. J.; Protopopov, A.; Ren, X.; Santoso, N.; Seidman, J.; Seth, S.; Song, X.; Tang, J.; Xi, R.; Xu, A. W.; Yang, L. L.; Zeng, D.; Auman, J.

ACS Paragon Plus Environment

Page 27 of 29 1 2 3 618 4 5 619 6 7 620 8 9 10 621 11 12 622 13 14 623 15 16 624 17 18 625 19 20 626 21 22 627 23 24 628 25 26 629 27 28 630 29 30 31 631 32 33 632 34 35 633 36 37 634 38 39 635 40 41 636 42 43 637 44 45 638 46 47 639 48 49 50 640 51 52 641 53 54 642 55 56 643 57 58 59 60

Journal of Proteome Research

T.; Balu, S.; Buda, E.; Fan, C.; Hoadley, K. a; Jones, C. D.; Meng, S.; Mieczkowski, P. a; Parker, J. S.; Perou, C. M.; Roach, J.; Shi, Y.; Silva, G. O.; Tan, D.; Veluvolu, U.; Waring, S.; Wilkerson, M. D.; Wu, J.; Zhao, W.; Bodenheimer, T.; Hayes, D. N.; Hoyle, A. P.; Jeffreys, S. R.; Mose, L. E.; Simons, J. V; Soloway, M. G.; Baylin, S. B.; Berman, B. P.; Bootwalla, M. S.; Danilova, L.; Herman, J. G.; Hinoue, T.; Laird, P. W.; Rhie, S. K.; Shen, H.; Triche, T.; Weisenberger, D. J.; Carter, S. L.; Cibulskis, K.; Chin, L.; Zhang, J. J.; Getz, G.; Sougnez, C.; Wang, M.; Dinh, H.; Doddapaneni, H. V.; Gibbs, R.; Gunaratne, P.; Han, Y.; Kalra, D.; Kovar, C.; Lewis, L.; Morgan, M.; Morton, D.; Muzny, D.; Reid, J.; Xi, L.; Cho, J.; Dicara, D.; Frazer, S.; Gehlenborg, N.; Heiman, D. I.; Kim, J.; Lawrence, M. S.; Lin, P.; Liu, Y. Y.; Noble, M. S.; Stojanov, P.; Voet, D.; Zhang, H.; Zou, L.; Stewart, C.; Bernard, B.; Bressler, R.; Eakin, A.; Iype, L.; Knijnenburg, T.; Kramer, R.; Kreisberg, R.; Leinonen, K.; Lin, J.; Liu, Y. Y.; Miller, M. M. L.; Reynolds, S. M.; Rovira, H.; Shmulevich, I.; Thorsson, V.; Yang, D.; Zhang, W.; Amin, S.; Wu, C.-C. C.J.; Wu, C.-C. C.-J.; Akbani, R.; Aldape, K.; Baggerly, K. a; Broom, B.; Casasent, T. D.; Cleland, J.; Creighton, C. J.; Dodda, D.; Edgerton, M.; Han, L.; Herbrich, S. M.; Ju, Z.; Kim, H.; Lerner, S.; Li, J.; Liang, H.; Liu, W.; Lorenzi, P. L.; Lu, Y.; Melott, J.; Mills, G. B.; Nguyen, L.; Su, X.; Verhaak, R.; Wang, W.; Weinstein, J. N.; Wong, A.; Yang, Y.; Yao, J.; Yao, R.; Yoshihara, K.; Yuan, Y.; Yung, A. K.; Zhang, N.; Zheng, S.; Ryan, M.; Kane, D. W.; Aksoy, B. A.; Ciriello, G.; Dresdner, G.; Gao, J.; Gross, B.; Jacobsen, A.; Kahles, A.; Ladanyi, M.; Lee, W.; Lehmann, K.-V.; Miller, M. M. L.; Ramirez, R.; Rätsch, G.; Reva, B.; Sander, C.; Schultz, N.; Senbabaoglu, Y.; Shen, R.; Sinha, R.; Sumer, S. O.; Sun, Y.; Taylor, B. S.; Weinhold, N.; Fei, S.; Spellman, P.; Benz, C.; Carlin, D.; Cline, M.; Craft, B.; Ellrott, K.; Goldman, M.; Haussler, D.; Ma, S.; Ng, S.; Paull, E.; Radenbaugh, A.; Salama, S.; Sokolov, A.; Stuart, J. M.; Swatloski, T.; Uzunangelov, V.; Waltman, P.; Yau, C.; Zhu, J.; Hamilton, S. R.; Abbott, S.; Abbott, R.; Dees, N. D.; Delehaunty, K.; Ding, L.; Dooling, D. J.; Eldred, J. M.; Fronick, C. C.; Fulton, R.; Fulton, L. L.; Kalicki-Veizer, J.; Kanchi, K.-L.; Kandoth, C.; Koboldt, D. C.; Larson, D. E.; Ley, T. J.; Lin, L.; Lu, C.; Magrini, V. J.; Mardis, E. R.; McLellan, M. D.; McMichael, J. F.; Miller, C. a; O’Laughlin, M.; Pohl, C.; Schmidt, H.; Smith, S. M.; Walker, J.; Wallis, J. W.; Wendl, M. C.; Wilson, R. K.; Wylie, T.; Zhang, Q.; Burton, R.; Jensen, M. a; Kahn, A.; Pihl, T.; Pot, D.; Wan, Y.; Levine, D. a; Black, A. D.; Bowen, J.;

ACS Paragon Plus Environment

Journal of Proteome Research 1 2 3 644 4 5 645 6 7 646 8 9 10 647 11 12 648 13 14 649 15 16 650 17 18 651 19 20 652 21 22 653 23 24 654 25 26 655 27 28 656 29 30 31 657 32 33 658 34 35 659 36 37 660 38 39 661 40 41 662 42 43 663 44 45 664 46 47 665 48 49 50 666 51 52 667 53 54 668 55 56 669 57 58 59 60

Page 28 of 29

Frick, J.; Gastier-Foster, J. M.; Harper, H. a; Helsel, C.; Leraas, K. M.; Lichtenberg, T. M.; McAllister, C.; Ramirez, N. C.; Sharpe, S.; Wise, L.; Zmuda, E.; Chanock, S. J.; Davidsen, T.; Demchok, J. a; Eley, G.; Felau, I.; Ozenberger, B. a; Sheth, M.; Sofia, H.; Staudt, L.; Tarnuzzer, R.; Wang, Z.; Yang, L. L.; Zhang, J. J.; Omberg, L.; Margolin, A.; Raphael, B. J.; Vandin, F.; Wu, H.-T.; Leiserson, M. D. M.; Benz, S. C.; Vaske, C. J.; Noushmehr, H.; Wolf, D.; Veer, L. V.; Collisson, E. a; Anastassiou, D.; Ou Yang, T.-H.; Lopez-Bigas, N.; Gonzalez-Perez, A.; Tamborero, D.; Xia, Z.; Li, W.; Cho, D.-Y.; Przytycka, T.; Hamilton, M.; McGuire, S.; Nelander, S.; Johansson, P.; Jörnsten, R.; Kling, T.; Sanchez, J.; Shaw, K. R. M. The Cancer Genome Atlas Pan-Cancer Analysis Project. Nat. Genet. 2013, 45 (10), 1113–1120. (47)

Leiserson, M. D. M.; Vandin, F.; Wu, H.-T.; Dobson, J. R.; Eldridge, J. V; Thomas, J. L.; Papoutsaki, A.; Kim, Y.; Niu, B.; McLellan, M.; Lawrence, M. S.; Gonzalez-Perez, A.; Tamborero, D.; Cheng, Y.; Ryslik, G. A.; Lopez-Bigas, N.; Getz, G.; Ding, L.; Raphael, B. J. Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes. Nat. Genet. 2014, 47 (2), 106–114.

(48)

Akbani, R.; Ng, P. K. S.; Werner, H. M. J.; Shahmoradgoli, M.; Zhang, F.; Ju, Z.; Liu, W.; Yang, J.-Y.; Yoshihara, K.; Li, J.; Ling, S.; Seviour, E. G.; Ram, P. T.; Minna, J. D.; Diao, L.; Tong, P.; Heymach, J. V; Hill, S. M.; Dondelinger, F.; Städler, N.; Byers, L. A.; Meric-Bernstam, F.; Weinstein, J. N.; Broom, B. M.; Verhaak, R. G. W.; Liang, H.; Mukherjee, S.; Lu, Y.; Mills, G. B. A Pan-Cancer Proteomic Perspective on The Cancer Genome Atlas. Nat. Commun. 2014, 5 (May), 3887.

(49)

Zhang, G.; Fenyö, D.; Neubert, T. a. Evaluation of the Variation in Sample Preparation for Comparative Proteomics Using Stable Isotope Labeling by Amino Acids in Cell Culture. J. Proteome Res. 2009, 8, 1285–1292.

(50)

Piehowski, P. D.; Petyuk, V. A.; Orton, D. J. Sources of Technical Variability in Quantitative {LC-MS} Proteomics: Human Brain Tissue Sample Analysis. J. Proteome Res. 2013, 12 (5), 2128–2137.

(51)

Scheerlinck, E.; Dhaenens, M.; Van Soom, A.; Peelman, L.; De Sutter, P.; Van Steendam, K.; Deforce, D. Minimizing Technical Variation during Sample Preparation prior to Label-Free Quantitative Mass Spectrometry. Anal. Biochem. 2015, 490, 14–19.

ACS Paragon Plus Environment

Page 29 of 29 1 2 3 670 4 5 671 6 7 672 8 9 10 673 11 12 674 13 14 675 15 16 17 18 19 20 21 22 23 24 676 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(52)

Journal of Proteome Research

Rauniyar, N.; Yates, J. R. Isobaric Labeling-Based Relative Quanti Fi Cation in Shotgun Proteomics. J. Proteome Res. 2014, 13, 5293–5309.

For TOC only

ACS Paragon Plus Environment