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© 2017 by The American Society for Biochemistry and Molecular Biology, Inc. This paper is available on line at http://www.mcponline.org Integrated Cellular and Plasma Proteomics of Contrasting B-cell Cancers Reveals Common, Unique and Systemic Signatures*□S

Harvey E. Johnston‡§, Matthew J. Carter‡, Kerry L. Cox‡, Melanie Dunscombe‡, Antigoni Manousopoulou§¶, Paul A. Townsendʈ, Spiros D. Garbis§¶, and Mark S. Cragg‡**

Approximately 800,000 leukemia and lymphoma cases are both subunits of the interleukin-5 (IL5) receptor. IL5 treat- diagnosed worldwide each year. Burkitt’s lymphoma (BL) ment promoted E␮-TCL1 tumor proliferation, suggesting and chronic lymphocytic leukemia (CLL) are examples of an amplification of IL5-induced AKT signaling by TCL1. contrasting B-cell cancers; BL is a highly aggressive Tumor plasma contained a substantial tumor lysis signa- Downloaded from lymphoid tumor, frequently affecting children, whereas ture, most prominent in E␮-myc plasma, whereas E␮- CLL typically presents as an indolent, slow-progressing TCL1 plasma contained signatures of immune-response, leukemia affecting the elderly. The B-cell-specific overex- inflammation and microenvironment interactions, with pression of the myc and TCL1 oncogenes in mice induce putative biomarkers in early-stage cancer. These findings

spontaneous malignancies modeling BL and CLL, respec- provide a detailed characterization of contrasting B-cell http://www.mcponline.org/ tively. Quantitative mass spectrometry proteomics and tumor models, identifying common and specific tumor isobaric labeling were employed to examine the biology mechanisms. Integrated plasma proteomics allowed the underpinning contrasting E␮-myc and E␮-TCL1 B-cell tu- dissection of a systemic response and a tumor lysis sig- mors. Additionally, the plasma proteome was evaluated nature present in early- and late-stage cancers, respec- using subproteome enrichment to interrogate biomarker tively. Overall, this study suggests common B-cell cancer emergence and the systemic effects of tumor burden. signatures exist and illustrates the potential of the further Over 10,000 proteins were identified (q<0.01) of which evaluation of B-cell cancer subtypes by integrative

8270 cellular and 2095 plasma proteins were quantita- proteomics. Molecular & Cellular Proteomics 16: by guest on September 10, 2018 tively profiled. A common B-cell tumor signature of 695 10.1074/mcp.M116.063511, 386–406, 2017. overexpressed proteins highlighted ribosome biogenesis, cell-cycle promotion and chromosome segregation. E␮- myc tumors overexpressed several methylating enzymes Burkitt’s lymphoma (BL)1 and chronic lymphocytic leukemia ␮ and underexpressed many cytoskeletal components. E - (CLL) represent the extremes of B cell cancers; BL is highly TCL1 tumors specifically overexpressed ER stress re- aggressive and frequently affects children, whereas CLL typ- sponse proteins and signaling components in addition to ically presents as an indolent, slow-progressing leukemia in the elderly (1, 2). From the ‡Antibody and Vaccine Group, Cancer Sciences Unit, BL is a hallmark myc-driven tumor; induced by chromo- Faculty of Medicine, General Hospital, University of Southampton, somal translocation of the transcription factor myc to immu- Southampton SO16 6YD, UK; §Centre for Proteomic Research, Insti- noglobulin (Ig) enhancers (3). Myc influences ϳ15% of the tute for Life Sciences, University of Southampton, Highfield Campus, genome, regulating processes such as cell proliferation, me- Southampton, SO17 1BJ, UK; ¶Clinical and Experimental Sciences tabolism, adhesion, angiogenesis and de-differentiation (4– Unit, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; ʈMolecular and Clinical Cancer Sciences, Paterson 6). Such neoplastic-like traits make myc an aggressive onco- Building, Manchester Cancer Research Centre, Manchester Aca- gene, dysregulated or overexpressed in most cancers (7). demic Health Science Centre, University of Manchester, Manchester, Formal proof of its oncogenic properties were demonstrated M20 4BX when human myc was placed into the ␮ Ig heavy chain Received August 27, 2016, and in revised form, December 19, 2016 enhancer (E␮) region of the mouse. These ‘E␮-myc’ mice Published, MCP Papers in Press, January 4, 2017, DOI 10.1074/ mcp.M116.063511 Author contributions: Conceptualization: H.E.J., P.A.T., S.D.G., and 1 The abbreviations used are: BL, Burkitt’s lymphoma; CLL, M.S.C. Methodology: H.E.J. Validation: H.E.J. and M.J.C. Formal Chronic lymphocytic leukaemia; DEP, Differentially expressed pro- analysis: H.E.J. Investigation: H.E.J. Resources: H.E.J., M.D., K.L.C., tein; E␮, ␮ Ig heavy chain enhancer; Ig, Immunoglobulin; iTRAQ, A.M., and S.D.G. Writing – Original draft: H.E.J. and M.S.C. Writing – Isobaric tags for relative and absolute quantitation; Rs, Regulation ϩ Review & Editing: H.E.J., M.S.C., and M.J.C. Visualization: H.E.J. score (mean of log2(ratios)/(SD of log2(ratios) 1)); SEC, Size exclu- Supervision: M.S.C., P.A.T., and S.D.G. Funding Acquisition: M.S.C., sion chromatography; SuPrE, Subproteome enrichment; TMT, Tan- P.A.T., and H.E.J. dem mass tags; WT, Wild type.

386 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors produced high-penetrance lymphomas within around 100 (WT) mice. The resulting proteomes have been interrogated to days, recapitulating several molecular and pathological as- identify common and tumor-specific signatures and to under- pects of BL (3, 8–10). stand the combined cellular and extracellular characteristics In contrast, CLL is a relatively indolent B-cell cancer, pre- of B-cell tumors. senting with a CD5ϩCD19ϩ leukemia (11, 12). Up to 90% of CLL cases express TCL1—a protein involved in lymphocyte MATERIALS AND METHODS development (13–15). TCL1 is suggested to promote cell sur- Materials—Tris(hydroxymethyl)aminomethane (TRIS), SDS, vival and proliferation by amplification of AKT phosphoryla- Na2EDTA, NH4Cl, NaHCO3, sodium deoxycholate (DOC), tion, induced by growth factor-, cytokine- and B-cell receptor- guanidine hydrochloride, glycine, HPLC and LC-MS grade induced PI3K signaling (16, 17). The E␮-TCL1 mouse, ACN and formic acid (FA) and 100 ␮m cell sieves were pur- overexpressing TCL1 in B-cells again through the ␮ enhancer, chased from Fisher Scientific, Loughborough, UK. Tween20 was developed as a potential model of CLL (18). E␮-TCL1 (tween), sodium heparin, propidium iodide (PI), carboxyfluo- mice recapitulate the expanded bone marrow, splenic and rescein succinimidyl ester (CFSE), asparagine, 2-mercapto- circulatory CD5ϩ B-cell populations of CLL, detectable from ethanol, octylphenoxypolyethoxyethanol (IgePalCA630), triton around 3–5 months of age. The disease progresses to lethal x-100, protease inhibitors, ponceau S, acetic acid, methyl splenomegaly and leukemia at ϳ12 months (18, 19). Currently methanethiosulfonate (MMTS), tris(2-carboxyethyl)phosphine it is regarded as one of the most useful preclinical models of (TCEP), triethylammonium bicarbonate (TEAB), DMSO, hy- Downloaded from

CLL (20, 21). droxylamine and ammonium hydroxide (NH4OH) were pur- Together, these models provide the opportunity to study chased from Sigma, St. Louis, MO. oncogenesis mediated by two contrasting oncogenes at op- Dulbecco’s modified Eagle’s medium (DMEM), glutamine, posing ends of the proliferative spectrum. E␮-myc tumors, pyruvate, penicillin and streptomycin were purchased from like BL, are highly aggressive, have rapid onset and form Life Technologies, Carlsbad, CA. Mouse B-cell isolation kits http://www.mcponline.org/ lymphoid tumors, whereas E␮-TCL1 mice, like CLL, present a and magnetic cell sorting columns were purchased from relatively indolent and slow-developing leukemia with sec- Miltenyi Biotech, Bergisch Gladbach, Germany. Twenty- ondary lymphoid organ involvement (3, 18). three-gauge needles and 1-ml syringes were purchased from In addition to cellular characterizations of tumors, plasma BD Biosciences, Franklin Lakes, NJ, Interleukin 5 (IL5) recom- analysis holds the potential to identify additional biological binant protein from Peprotech, Rocky Hill, NJ, Immobilon 0.45 signatures arising during oncogenesis. In particular, extracel- ␮m pore PVDF membrane from Millipore, Billerica, MA and

lular fluids can provide insight into the tumor-host dialogue nonfat milk from Marvel, Birmingham, UK. 30ϫ DTT and 3ϫ by guest on September 10, 2018 between the immune system and micro-environment and re- red loading buffer were purchased for Cell Signaling Technol- port on the metabolic and homeostatic aberrations that tu- ogies, Danvers, MA. Enhanced chemiluminescence reagents, mors display. Further, combining cellular and plasma charac- tandem mass tags (TMT) 10-plex isobaric labeling reagents terization can provide greater insight into the mechanisms and 2 kDa Mw cut-off Slide-A-Lyzer dialysis cassettes and by which any biomarkers of disease may be entering the proteomics grade lys-c were purchased from Thermo Scien- circulation. tific, Waltham, MA. Proteomics grade trypsin was purchased Liquid chromatography coupled with mass spectrometry from Roche, Basel, Switzerland. Isobaric tags for relative and (LC-MS) proteomics currently provides the best means of absolute quantitation (iTRAQ) 8-plex reagents were pur- establishing global differential protein expression profiles of chased from ABSciex, Framingham, MA. Antibodies are de- cellular and plasma samples. These approaches are evolving tailed in Supplementary Methods. rapidly and yielding ever-increasing proteome coverage (22, Animals—Mice were bred and maintained in-house with 23). Nonetheless plasma/serum proteomics still presents sig- procedures carried out in accordance with home office li- nificant challenges because of the vast dynamic range of censes PPL30/2450 and 30/2970 and PIL30/9925. Female protein concentrations (24). Although advances continue to E␮-myc [C57BL/6J-TgN(Ighmyc)22Bri/J] hemizygous and be made with immunodepletion strategies (25–27), an alter- E␮-TCL1 [C57BL/6J-TgN(IghTCL1)22Bri/J] hemizygous mice native method termed, Subproteome Enrichment by Size Ex- were used. The E␮-myc and E␮-TCL1 transgenes were de- clusion Chromatography (SuPrE-SEC), offers an effective tected with PCR. E␮-myc primers (annealing temperature; alternative. SuPrE-SEC uses size-dependent protein fraction- 55 °C): 5Ј- CAG CTG GCG TAA TAG CGA AGA G -3Ј and 5Ј- ation to deplete high-abundance proteins and enrich for low- CTG TGA CTG GTG AGT ACT CAA CC -3Јϳ900 bp product, abundance proteins. The resulting reduction of the protein E␮-TCL1 primers (annealing temperature; 58 °C): 5Ј- GCC concentration dynamic range facilitates a greater depth of GAG TGC CCG ACA CTC -3Ј and 5Ј- CAT CTG GCA GCT LC-MS proteomics coverage (28, 29). CGA -3Јϳ250 bp product. E␮-TCL1 mice were screened This investigation has applied multiplexed, LC-MS pro- monthly for count and percentage of B220ϩ CD5ϩ B-cells in teomics to the characterization of plasma subproteomes and the blood, being considered terminal either with Ͼ80% leu- isolated B-cell material from E␮-myc,E␮-TCL1 and wildtype kemic cells or palpated splenomegaly Ͼ3 cm. E␮-myc mice

Molecular & Cellular Proteomics 16.3 387 Cellular and Plasma Proteomics of B-cell Tumors

were checked daily and were considered terminal, typically Plasma Subproteome Enrichment—20 ␮l was taken from upon visible lymph node tumor presentation. each of the six replicate plasma samples described above to Experimental Design and Statistical Rationale—Sample give pools of 120 ␮l. For tumor samples, two 120 ␮l pools of pooling, detailed in supplemental Figs. S1, was used to ac- 3 ϫ 40 ␮l were formed to provide biological replicates (sum- commodate comparative experiments in single isobaric label marized in Figs. 1 and supplemental Fig. S1). sets. Tumorous spleens were collected from 4 E␮-myc and 4 SuPrE-SEC was adapted from that described previously E␮-TCL1 mice with terminal tumor presentation. For nontu- (28). Each 120 ␮l plasma pool was diluted with 380 ␮lof6M mor controls, 6 spleens, handled as two pools of 3 spleens, guanidine hydrochloride in 10% methanol and separated by 3 were collected from 6 week old and 200 day WT littermates KW804 SEC columns in series at 1.2 ml/min and 30 °C. The and 6 week-old E␮-myc and E␮-TCL1 mice with no signs of low molecular weight subproteome was isolated during elu- tumor or splenomegaly (summarized in supplemental Fig. S1). tion from 42–55 min (Supplemental Fig. S1G) and dialyzed (2 ⍀ Ϫ1 Nontumor pools were analyzed with six samples per isobaric kDa Mw cut-off) into ultrapure water (18.2 M cm ) with five label, whereas tumor samples were analyzed as biological exchanges into 5 liters. Protein was lyophilized and re-solu- ␮ replicate pools of two tumors. bilized in 0.5 M TEAB with 0.05% SDS with 30 g digested For plasma, six samples were individually collected for each and labeled for MS analysis as described below. of the above tumor and nontumor conditions, in parallel with Sample Preparation for MS—Snap frozen cell pellets were Downloaded from B-cell isolation. In addition, 6 samples were isolated from lysed on ice by trituration with a 23-gauge needle in 0.5 M E␮-TCL1 mice at a preterminal “30%” stage, when 30% TEAB with 0.05% SDS. Disrupted cells were further sonicated ϫ ␮ B220ϩ CD5ϩ cells were present in the blood. To minimize and lysates cleared at 16,000 g for 10 min at 4 °C. 100 g of cell lysate or 30 ␮g of plasma subproteome was reduced bias these were selected from a cohort of 14 E␮-TCL1 mice, with 50 mM TCEP and alkylated with 200 mM MMTS, before from every other mouse reaching the 30% threshold. Tumor digestion overnight at RT with a 30:1 ratio of proteomics http://www.mcponline.org/ and preterminal “30%” tumor plasma were also analyzed as grade trypsin. Plasma proteins were additionally digested for biological replicates, with three samples allocated to each a further2hat37°Cwith 100:1 proteomics grade Lys-c. label. Peptides were incubated with either iTRAQ 8-plex (B-cell Each pair of tumor pools was compared with two WT sam- peptides) or TMT 10-plex isobaric tags (plasma peptides) ple pools, generating four ratios which could be evaluated for according to the manufacturer’s instructions. Samples were consistency and significance to ensure the reproducibility of then lyophilized and labeled peptides serially reconstituted, quantitative results. Replicate ratios were analyzed for con-

for each proteome, in 100 ␮l of 2% v/v ACN, 0.1% v/v by guest on September 10, 2018 sistency with an FDR-corrected t test and considered signif- NH OH. icant with a p value of Ͻ0.05. p values were coupled with a 4 Peptide Prefractionation—Peptides were resolved using measure of magnitude detailed in Quantitative and Statistical high-pH (0.1% v/v NH OH) RP C8 chromatography (150 Analysis of MS Data, below. 4 mm ϫ 3mmIDϫ 3.5 ␮m particle, XBridge, Waters, Milford, B-cell Isolation—B-cells were isolated from single-cell sus- MA) at 300 ␮l/min with a LC-20AD HPLC system (Shimadzu) pensions using the mouse negative selection B-cell isolation maintained at 30 °C, using the mobile phases (MP); A - 99.9% kits and MACS columns according to the manufacturer’s H2O, 0.1% NH4OH, B - 99.9% ACN, 0.1% NH4OH. The 2 h instructions, washed once at room temperature (RT) in red gradient was as follows; 0 min; 2% B, 10 min; 2% B, 75 min; blood cell lysis buffer (155 mM NH4CL, 10 mM NaHCO3, 0.1 30% B, 105 min; 85% B, 120 min; 2% B. Fractions were mM Na2EDTA (pH 7.4) before three washes in PBS. B-cell collected in a peak-dependent manner and lyophilized. isolation efficiency was assessed by flow cytometry immuno- Peptide Fraction Resolution and Characterization by LC- Ͼ staining with CD19 and CD3, typically yielding 90% purity MS/MS—Lyophilized peptide fractions were individually re- ϩ with less than 2% T-cell (CD3 ) contamination (supplemental constituted in 2% ACN, 0.1% FA, and ϳ500 ng of peptides Fig. S1). B-cell pellets were snap frozen and stored in liquid were subjected to nano-ultra-performance liquid chromatog- nitrogen prior to lysate preparation. raphy by a Dionex Ultimate 3000 (Thermo Scientific). Condi- Plasma Isolation—To isolate plasma, maximize sample pu- tions used varied between the plasma and B-cell proteomes, rity, minimize red blood cell lysis and accommodate for organ detailed in Supplementary Methods. In summary, peptides displacement from tumors, an adaption of bleeding from the were trapped by C18 and eluted over a reverse phase gradi- inferior vena cava under terminal anesthesia (30) was utilized. ent, of which several lengths were used depending on the Using a 23-gauge needle, 700–1000 ␮l of blood was collected peptide fraction abundance and proteome. The total MS time for each animal into 50 ␮g/ml sodium heparin in PBS. Blood for the iTRAQ-labeled B-cell proteome was ϳ200 h, with the was immediately placed on ice and centrifuged at 2000 ϫ g TMT-labeled plasma proteome analyzed over ϳ250 h. for 15 min at 4 °C with plasma stored in liquid nitrogen. Peptide elution was directly coupled to electrospray ioniza- Samples were rejected if red blood cell lysis was visible by tion at 2.4 kV using a PicoTip nESI emitter (New Objective, eye. Woburn, Massachusetts), and were characterized with an

388 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

Orbitrap Elite Velos Pro mass spectrometer (Thermo Scien- est fold change for each biological state, an FDR-corrected tific). MS characterization of eluting peptides was conducted one sample t test was performed and average values deter- between 350 and 1900 m/z at 120,000 mass resolution. The mined. To derive a single, robust measure, representative of top 12 ϩ 2 and ϩ3 precursor ions per MS scan (minimum both the magnitude and consistency of differential expres- intensity 1000) were characterized by tandem MS with high- sion, a ratio was defined between that of the mean and the energy collisional dissociation (HCD) (30,000 mass resolution standard deviation of the 4 log2 ratios, termed the regulation (15,000 for the B-cell proteome), 1.2 Da isolation window, 40 score (Rs ϭ mean/(standard deviation ϩ1)). For proteins with keV normalized collision energy) and CID (ion trap MS, 2 Da no, or inconsistent differential expression, the Rs tends to- isolation window, 35 keV). Additionally, the DMSO ion at ward 0. The Rs also correlated more strongly with p values for 401.922718 was used as a MS lockmass (31). each set of ratios (Supplementary Methods). In all instances, MS Data Processing—Target-decoy searching of raw spec- differentially expressed proteins (DEPs) were defined as those tra data was performed with Proteome Discoverer software with an FDR-corrected p value of Ͻ0.05 and an Rs threshold version 1.4.1.14 (Thermo Scientific). Spectra were subject to a of Ͼ0.5 or Ͻ-0.5. Although arbitrary, evaluation of this thresh- two stage search, both using SequestHT (version 1.1.1.11), old demonstrated a minimum average fold change of 1.4, for with Percolator used to estimate FDR with a threshold of q Յ which a fold change variation of just 6% was observed. This 0.01. The first allowed only a single missed cleavage, mini- therefore fulfilled the power analysis principles described by mum peptide length of 7, precursor mass tolerance of 5 ppm, Levin, 2011 (33), but on a case-by-case basis, by incorporat- Downloaded from no variable modifications and searched against the mouse ing variation into each Rs via the S.D. as a denominator. The UniProt Swissprot database, supplemented with the human outliers for this threshold were additionally highlighted to sequences for (downloaded 01/15, 16,676 protein se- demonstrate that consistent differential expression was still quences). The second search used only spectra with q Ͼ 0.01 observed (Supplementary Methods). Other p values were de- from the first search, allowed two missed cleavages, minimum termined by a false discovery rate-corrected 1- or 2-sample, http://www.mcponline.org/ peptide length of 6, searched against the human/mouse Uni- two tailed, t test with no assumption of equal variance. Prot trembl database (with human myc and TCL1, down- Bioinformatics Analyses—Proteins reaching the thresholds loaded 01/15, 52,469 protein sequences), precursor mass outlined above were submitted to either Ingenuity Pathway tolerance of 10 ppm and a maximum of two variable (1 equal) Analysis (IPA) or Database for Annotation, Visualization and modifications of; TMT or iTRAQ (Tyr), oxidation (Met), deami- Integrated Discovery (DAVID). For DAVID analyses, the default dation (Asn, Gln) or phospho (Ser, Thr, Tyr). In both searches, settings were used for pathway and gene ontology (GO) term

fragment ion mass tolerances of 0.02 Da and 0.5 Da were enrichment, with Benjamini-corrected p values of Ͻ0.05 con- by guest on September 10, 2018 used for HCD and CID spectra, respectively. Fixed modifica- sidered significant. For B-cell/tumor proteome enrichments, tions of Methythio (Cys), TMT or iTRAQ (Lys and N terminus) all quantified proteins were used as a background. For IPA were used. PhosphoRS was used to predict the probability of analyses, default settings were used. Upstream regulator specific phosphorylated residues. Reporter ion intensities analysis was determined using all DEPs (RsϾ0.5/Ͻ-0.5, p Ͻ were extracted from nonredundant PSMs with a tolerance of 0.05). Annotations of biomarkers and drug targets were con- 20 ppm. To reduce ratio compression, peptide spectrum ducted by IPA. Plasma protein deconvolution was conducted match data for proteins (qϽ0.01) were exported from Pro- by removing any proteins annotated by IPA as predominantly teome Discoverer and submitted to Statistical Processing for cellular in addition to any proteins with more than 30 PSMs Isobaric Quantitation Evaluation (SPIQuE) at spiquetool- from the B-cell proteome. .com. This method weighted the contributions of each PSM SDS-PAGE and Western blotting—Cells were lysed in ra- quantitation to a protein’s quantitation on the basis of PSM dioimmunoprecipitation assay (RIPA) cell lysis buffer (0.15 M features (manuscript in preparation). For example, high-inten- NaCl, 1% v/v octylphenoxypolyethoxyethanol (IgePalCA630), sity peptides with low isolation interference were given a 0.5% w/v sodium deoxycholate (DOC), 0.1% w/v sodium greater weighting factor. Although the effects upon the ratios dodecyl sulfate (SDS), 0.05 M TRIS (pH 8) and 1% v/v prote- were minimal, the overall trend demonstrated efficient ratio ase inhibitor) and as described previously (34). Lysate super- decompression (Supplementary Methods). An example of the natants were isolated by centrifugation (16,000 ϫ g for 10 min effect on the ratio decompression to the human TCL1 protein, at 4 °C). Protein concentrations were determined by Bradford discretely expressed in E␮-TCL1 B-cells, is outlined in Sup- assay. Lysates were reduced in loading buffer with 2-mercap- plementary Methods. toethanol at 95 °C for 5 min. Lysates were resolved by SDS- The raw data and processed outputs have been deposited PAGE and transferred to PVDF. Membranes were blocked in to the ProteomeXchange Consortium (32) via the PRIDE part- 5% (w/v) nonfat milk for 1 h before probing with primary ner repository with the data set identifier PXD004608. antibodies, as detailed in Supplementary Methods. Expres-

Quantitative and Statistical Analysis of MS Data—Log2 (ra- sion was detected by incubation with a horseradish peroxi- tios) were generated describing each sample pool relative to dase-conjugated secondary antibody using enhanced chemi- the two WT controls. To define those proteins with the great- luminescence reagents and a ChemiDoc-It imaging system

Molecular & Cellular Proteomics 16.3 389 Cellular and Plasma Proteomics of B-cell Tumors

(UVP). GAPDH or tubulin were used as loading controls. Rel- of characteristics observed for the model. The median termi- ative quantification of protein band intensity was determined nal presentations, for example (supplemental Fig. S1A), of 92 using Image J, normalized against loading controls and ratios and 318 days for E␮-myc and E␮-TCL1 tumors, respectively, to WT B-cell lysates were derived. For E␮-myc tumor valida- agreed with earlier reports (3, 8, 18, 19). tions, lysate protein concentrations were determined by bi- Splenic B-cells from tumors and controls were lysed, tryp- cinchoninic acid assay (BCA), reduced with DDT and diluted sin digested, and assigned to the eight isobaric labels of in 3x loading buffer. Western blots were visualized with fluo- iTRAQ 8-plex (Fig. 1) to provide relative protein expression rescent antibodies and an Odyssey Imaging System (Li-cor) quantitation. The labeled B-cell peptides were pooled and and quantified using Image Studio 2.0 (Li-cor). characterized by two-dimensional (2D) LC-MS identifying Cell Culture—Cells derived from E␮-myc or E␮-TCL1 tu- 9260 proteins (qϽ0.01). 8270 proteins were relatively quanti- mors were cultured in DMEM supplemented with 2 mM glu- tated across all eight sample pools. The depth of proteome tamine, 1 mM pyruvate, 45 units/ml penicillin, 45 ␮l/ml strep- coverage and quantitation is summarized in supplemental Fig. tomycin, 200 ␮M asparagine, 50 ␮M 2-mercaptoethanol, and S1H. 10% FCS. E␮-myc tumors were cultured a density of 5 ϫ 107 Plasma samples were subjected to an adapted form of ␮ cells/ml at 37 °C in 10% CO2.E -TCL1 cells were cultured at SuPrE-SEC (28), reducing the dynamic range and facilitating ϫ 6 a density of 5 10 cells/ml at 37 °C in 5% CO2. Cells were deeper proteome coverage by the exclusion of the majority of cultured for 24 h prior to treatment. Serum starvation was high-abundant proteins (supplemental Table S1, supplemen- Downloaded from conducted for 4 h in the above media with 0.5% BSA replac- tal Fig. S1G). 2D LC-MS characterization of the plasma sub- ing FCS. proteome provided relative quantitation for 2095 of the 2688 Flow Cytometry—Cells were stained with either the man- identified proteins (qϽ0.01). Peptide spectrum matches ␮ ϭ ϭ ufacturer’s recommended concentration, or 10 g/ml, of an- (PSMs) to the higher-Mw protein albumin (n 3835, Mw

tibody (Supplementary Methods) for 30 min in the dark, 68.7kDa), for example, were of far lower abundance than http://www.mcponline.org/ ϭ ϭ washed and analyzed by flow cytometry with a FACScan or transthyretin (n 7769, Mw 15.8kDa) or apolipoprotein A-II ϭ ϭ FACScalibur (BD) (35, 36). Relative expression was deter- (n 7129, Mw 11.3kDa). Given approximate plasma con- mined using the geometric means. Cell cycle status was centrations of 40 mg/ml, 260 ␮g/ml and 300 ␮g/ml for these assessed by hypotonic PI (50 ␮g/ml PI, 0.1% w/v sodium proteins (37), respectively, this represented around a 275-fold

citrate, 0.1% w/v triton x-100) incubated at 4 °C for 15 min. PI enrichment of low Mw proteins. fluorescence was measured by FL2 on a linear scale at the Reproducibility and Validation of Quantitative Proteomics

lowest flow rate. Cell division was tracked by CFSE dilution of Results—The relative quantitative proteomes demonstrated by guest on September 10, 2018 cells stained with a concentration of 5 ␮M for 15 min at room tumor-dependent hierarchical clustering identifying common temperature. trends of differential tumor expression compared with both WT controls (Fig. 2A). Evaluation of the reproducibility be- RESULTS tween tumor sample pools by linear regression highlighted Quantitative Proteomics of E␮-myc and E␮-TCL1 Tumors the strong correlation between the 5 pairs of independent bio- and Plasma—To characterize the global proteome expression logical replicates (Fig. 2B). Additionally, an overlap of 1288 of E␮-myc and E␮-TCL1 tumors, an 8-plex iTRAQ experiment proteins were quantitated in both the B-cell and plasma was designed incorporating terminal tumor, premalignant and proteomes. age-matched WT B-cells (Fig. 1). Sample pooling in combi- Individual protein expression was considered for a number nation with biological replicates enabled the averaging of of candidates to confirm the successful characterization and biological variability of several samples within a single isobaric analysis of the quantitative proteome. The results presented tag experiment. Splenic tumors samples, because of a greater an anticipated model-specific overexpression of the myc and anticipated variability, were pooled in two pairs of two tumors TCL1 human transgenes (Fig. 2C, supplemental Fig. S2A, from each model; derived from a total of 8 terminal mice S2B), in addition to characteristic CD5 overexpression and (supplemental Fig. S1). Nontumor controls consisted of sam- B220 underexpression for E␮-TCL1 tumors. Several DEPs ples pooled from six animals. For plasma proteomics, con- were validated by antibody-based methods to further verify trols were again characterized as pools of six samples, with the relative quantitative accuracy of the proteomics; 10 for two pools of three plasma samples for each tumor condition. E␮-myc tumors and 7 for E␮-TCL1 tumors (supplemental Fig. ␮ Additionally, plasma from preterminal E -TCL1 tumors was S2G,S2H). The log2 ratios to WT of these validations were analyzed, derived from mice reaching a CD5ϩ B220ϩ leuke- plotted alongside the quantitative proteomics results (Fig. 2D). mia threshold of 30%, termed “30%” samples. To accommo- Additionally, previously reported DEPs in E␮-myc or E␮-TCL1 date these additional samples, TMT 10-plex isobaric labels tumors were compared with the proteomics results (supple- were used. mental Fig. S2D,S2F). A correlation was also observed with a The tumors selected for proteomics analysis, detailed in study describing relative mRNA expression in E␮-myc tumors supplemental Fig. S1A–S1F, were representative of the range (38) (supplemental Fig. S2J). Overall, the quantitative pro-

390 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

Wildtype Eμ-myc Eμ-TCL1

6 weeks Aged control 6 weeks Terminal 6 weeks Intermediate Terminal 200 days old No tumors No leukemia Early leukemia ‘30%’ n=6 n=6 n=6 n=4/6 n=6 n=6 n=4/6

B-cell isolation Splenocytes Plasma isolation

From splenocytes Pooling to 120 μl by negative isolation Magnetic isolation Controls: 6 x 20 μl Tumor plasma: 2 x (3 x 40 μl) (as biological replicates)

Cell lysis and pooling Sub-proteome enrichment by size exclusion chromatography (SuPrE-SEC)

B-cells/tumor cells

Tumor samples: 2 pools of 2 tumors Downloaded from (as biological replicates) Low molecular weight sub-proteome purification by dialysis

Protein reduction, alkylation, trypsinization and isobaric labelling of peptides Controls Terminal Eμ-TCL1 Eμ-myc myc TCL1

Wildtype Pre-terminal Eμ- Eμ- Wildtype Pre-terminalTerminal Terminal Pre http://www.mcponline.org/

iTRAQ TMT 8-plex 10-plex 113 114 115 116 117 118 119 121 126 127N 127C 128N 128C 129N 129C 130N 130C 131

Peptide prefractionation using high-pH C8 reverse-phase HPLC 1500 1500 69 peak- 70 peak- dependent dependent 1000 1000 fractions fractions by guest on September 10, 2018 500 500 OD (AU) 215nm OD 215nm (AU)

0 0 20 30 40 50 60 70 80 90 40 50 60 70 80 90 Retention time (min) Retention time (min)

Peptide characterization using low-pH C18 nanoflow HPLC coupled with ESI-MS/MS

Target-decoy peptide identification and protein grouping (q<0.01) 9260 protein 2688 protein identifications (q<0.01) identifications (q<0.01) Relative protein quantitations - comparing to WT controls

FIG.1. E␮-myc and E␮-TCL1 model proteomic characterization workflow. B-cells and plasma were isolated from splenocytes and blood, respectively, derived from terminal and preterminal E␮-myc and E␮-TCL1 mice and WT controls (supplemental Fig. S1C). Pooling was used to accommodate all samples within single isobaric-labelled experiments for B-cell (iTRAQ 8-plex) and plasma (TMT 10-plex) proteomics. Pools of 2, 3, or 6 samples were used for tumors, tumor plasma and pretumor controls, respectively. For all tumor samples, biological replicate pools were analysed. Plasma pools, totalling 120 ␮l per pool, were subjected to size exclusion chromatography (SEC) to isolate the low molecular weight subproteome (supplemental Fig. S1G). B-cells were isolated by negative selection and were lysed, quantified and 100 ␮gof protein pooled. Plasma and B-cell proteins were subjected to reducing conditions, cysteine alkylation, trypsin proteolysis and isobaric labelling and pooling of the generated peptides. The labeled peptides for each proteome were resolved by two-dimensional liquid chromatography and quantitatively characterized by mass spectrometry (MS). The MS data were then subject to target-decoy analysis and reporter ion quantitation to generate two quantitative proteomes. teomics correctly identified over or underexpression for the overall reliability of the DEP observations and the character- vast majority of the 38 DEPs observed by antibody-based ization and analysis by which they were generated. quantitations. This comparison also highlighted reporter ion Proteomic Characterization of B-cell Tumor Phenotypes— ratio compression, a commonly reported feature of isobaric Proteins were considered DEPs when both tumor pools dem- tag quantitation (39, 40). Anticipated plasma proteins, such as onstrated a clear, consistent over- or underexpression to both IgM overabundance in E␮-TCL1 tumors, was also observed 6-week and 200-day WT samples. Rather than an average of (supplemental Fig. S2I). These results therefore supported the the four derived ratios for each tumor, potentially misrepre-

Molecular & Cellular Proteomics 16.3 391 Cellular and Plasma Proteomics of B-cell Tumors

A. B-cell tumor proteome Plasma proteome Eμ-myc Eμ-TCL1 1.Tumor A / ‘30%’ Eμ-TCL1 Eμ-myc 5 12347 6 1234 2.Tumor B / 7 12346512341234 (6 week WT)

3.Tumor A / 4.Tumor B / (200 day WT)

5.6w Eμ-myc/ 6.6w Eμ-TCL1/ 7.200 day WT / (6 week WT)

Fold change from WT -8 0 8

B-cell tumor proteome Plasma (8270) proteome (2095) Downloaded from

6982 1288 807 http://www.mcponline.org/ B. 6 6 6 6 6 R²R² = 00.758.758 R² = 0.590 Eμ-TCL1CL1 R²R² = 00.570 Eμ-TCL1CL1 R²R² = 0.6030.603 Eμ-myc R²R² = 00.791.791 Eμ-Eμ-myc 4 Eμ-Eμ-TCLTCL11 4 ‘30%’%’ 4 terminalinal 4 terminalnal 4

2 2 2 2 2

0 0 0 0 0 -66 - -44 - -22 0 2 4 6 -66 -4-4 -2-2 02 4 6 -6 -4 -2-2 0 2 4 6 -6 -4 -2-2 0 2 4 6 -6- -4 -2-2 0 2 4 6

-2-2 -2-2 -2-2 -2-2 -2-2 (ratio to WT 6W) Tumor pool B 2 -4-4 -4-4 -4-4 -4 -4 by guest on September 10, 2018

Log -6-6 -66 -6 -6 -6

Tumor pool A Log2 (ratio to 6 week old WT B-cells)

D. Western blot and flow cytometry proteomics validation 5 C. Characteristic model expression 4 Proteomics Validation )

) 3 2 myc TCL1 1 A B A B 0 myc TCL1 -1 myc myc myc TCL1 TCL1 Symbol -2 Eμ- Eμ- Eμ- Eμ- Unique Peptides PSMs WT 200d 6w Eμ- 6w Eμ- Expected (Eμ- Expected (Eμ-

MYC 23 ↑↑ (ratio to WT-3 B cells) 2 TCL1A 5150 ↑ -4

CD5 618 ↑ Log -5 B220 49 666 ↓ -6 1 5 D M 90 P g

Fold change from WT P I IgM P C CD N MYC MOE S L

-4 4 SHP1 ENO1 MDH2 MYO9 CAPG CD200 CD79b H FOX3A COR1A Eµ-myc Eµ-TCL1 Validated proteins

FIG.2.Analysis and validation of the quality of quantitative proteomics data. A, Hierarchical clustering of all 8270 and 2095 fully profiled

protein log2 (ratios) relative to the 6 week WT control (in addition to the 200 day WT control for tumor samples) using Cluster 3.0 and Euclidian distance to represent the topological similarities and differences for each sample. A Venn diagram highlights proteins identified in both

proteomes. B, Linear regression highlighting the reproducibility of the log2 (ratios) relative to WT 6 week samples of the biological replicates analysed for each tumorous condition. C, Relative quantitative proteomics-derived fold changes of transgene-derived protein expression and characteristic E␮-TCL1 phenotypes, relative to WT B-cells. D, A summary of 17 Western blot and flow cytometry validations of E␮-myc and ␮ E -TCL1 tumor protein expressions relative to WT B-cell controls, plotted alongside the proteomics-derived expression changes (as log2 (ratios)). These values relate to the validations detailed in supplemental Fig. S2G,S2H.

392 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

Top 10 proteins Fold change from WT Eμ-myc tumors -4 4 A. in both tumors: 4 D. A B A B myc TCL1 3 myc myc TCL1 TCL1 Eμ- Eμ- Eμ- Eμ-

Symbol Peptides Unique PSMs WT 200d 6w Eμ- 6w Eμ- ASS1 7 374 PRC1 14 52 929 2056 DIAPH3 11 17 proteins proteins HMMR 17 57 TPX2 28 88 1.3 (p=0.05) 1 GMNN 615

(p-value) LMNA 38 511 10 KIF23 15 51

0 Overexpressed KIF11 33 167 -3 -2 -1-0.5 00.5 1 2 3 KIF20A 15 42 CD168 17 57 Eμ-TCL1 tumors SLC38A2 25 ProEnv 731 4 CD71 28 171

FDR-corrected -log CD98 (LC) 411 NOM O1 21 111 3 CD29 20 108 expression ABCD1 923

With cell surface TECR 316 Downloaded from CD98(HC) 18 109 2 354 1284 CDK1 13 184 proteins proteins POLE 22 68 AURKB 823 1 POLE2 523 CHEK1 923 PLK1 934 0 TOP2A 58 712 http://www.mcponline.org/ drug targets

Annotated as RRM 1 26 181 -3 -2 -1-0.5 0 1 2 3 0.5 TYMS 614 Protein expression relative to WT B-cells RRM 2 15 65

(Regulation score (Rs); mean/(SD+1) of log2 (ratios to WT)) DDX49 721 MRPL24 445 Commonly differentially expressed proteins METTL15 420 B. of Eμ-myc and Eμ-TCL1 tumors RPL18A 740 URB1 13 50 3 R² = 0.2578 C. Overexpressed MRPS10 413 tumor proteins: ESF1 28 117 2 myc TCL1 URB2 412 Eμ- Eμ- links to cancer With no specific (2056) (1284) PUSL1 419 1 HN1L 768 by guest on September 10, 2018 1361695 589 CD23 723 0 Ighd 632 -3 -2 -1 1 2 3 Underexpressed Ms4a4c 29 -1 tumor proteins: CR2 928 Eμ-myc Eμ-TCL1 KM O 10 16 SATB1 938 -2 (929) (354) CD200 25 TCL1 tumor expression (Rs) 737 192 162 CD40 411 -3 SLFN5 13 34 Eμ- Underexpressed Eμ-myc tumor expression (Rs) PTK2B 23 247

FIG.3.Differential protein expression in E␮-myc and E␮-TCL1 B-cell tumors. A, Volcano plots highlighting reproducible, significant differential protein expression in each B-cell tumor on the basis of the regulation score (Rs) and FDR-corrected p-values (one sample t-test).

The Rs was calculated from the mean and standard deviation (SD) of the 4 log2 (ratios) of tumor protein expression relative to both WT controls (Rs ϭ mean/(SDϩ1)). The number of proteins considered significantly (p Ͻ 0.05) overexpressed (Rs Ͼ 0.5) or underexpressed (Rs ϽϪ0.5) is detailed. B, A linear regression between the protein expression observed in E␮-myc and E␮-TCL1 B-cell tumors, both relative to WT B-cells. C, Venn diagrams highlighting proteins considered over- and underexpressed common to both E␮-myc and E␮-TCL1 B-cell tumors. D, Heat

maps of individual log2 (ratios to WT), for examples of proteins differentially expressed commonly in both tumors. The top 10 proteins falling into the following categories are presented; significantly overexpressed in both tumors with high confidence (3 unique peptides and unique quantitations), cell surface expression, proteins annotated by ingenuity pathway analysis as drug targets, proteins for which no specific links to any type of cancer have previously been made, based on PubMed searching and proteins significantly underexpressed in both tumors.

sentative of variable findings, a ratio of average to standard 3A, supplemental Fig. S3A). This analysis illustrated the broad deviation (mean/(S.D.ϩ1)) was calculated, termed the regula- extent of DEP signatures observed in each tumor, with ϳ3000 tion score (Rs). This provided a single value expressive of both and 1500 DEPs in E␮-myc and E␮-TCL1 tumors, respectively. magnitude and consistency for either, or both, tumors relative The 6-week E␮-myc and E␮-TCL1 B-cells exhibited signa- to WT B-cells. In combination with FDR-corrected p values, tures broadly indistinguishable from terminal E␮-myc tumors the Rs allowed careful selection of the most confidently and and WT B-cells, respectively (supplemental Fig. S3B). The consistently DEPs (detailed in Supplementary Methods). 6-week E␮-myc B-cell signature did however, present a lesser The range of significantly DEPs for each tumor were repre- extent of differential expression. When compared with one ␮ sented by volcano plot, comparing Rs to -log10 (p values) (Fig. another, a common signature became apparent between E -

Molecular & Cellular Proteomics 16.3 393 Cellular and Plasma Proteomics of B-cell Tumors myc and E␮-TCL1 tumors (Fig. 3B) demonstrating that many S4A). A network detailing all underexpressed proteins re- of the E␮-myc tumor-overexpressed proteins were also over- vealed a highly interrelated series of interactions highlight- expressed in E␮-TCL1 tumors, but to a lesser magnitude. ing major histocompatibility complex proteins, interferon Approximately 700 and 200 proteins were considered over- response proteins and B-cell-related signaling molecules and underexpressed in the tumors of both models, respec- (Fig. 4D). tively (Fig. 3C). Examples of the 10 most consistently DEPs To simultaneously analyze both proteome-wide over- and from this common signature were presented (Fig. 3D); and underexpression, upstream regulators were evaluated using annotated as predicted cell surface markers with potential as IPA. Upstream regulator activity was inferred by the compar- immunotherapy targets; drug targets, annotated by IPA; and ison of anticipated downstream expression profiles of pro- novel DEPs with no previous published links with cancer teins with those expression profiles derived by quantitative (detailed further in supplemental Fig. S3C–S3G). Among the proteomics for both tumors (supplemental Table S3, supple- DEPs common to both tumors were, for instance signatures mental Fig. S4B–S4E). Protein functionality was then inferred of cell cycle upregulation, such as the three kinesin proteins, from a combination of the resulting upstream regulator acti- KIF11, KIF20A, and KIF23 and an overall trend of cell sur- vation z-scores—a value proportional to overall predicted ac- face protein underexpression such as CD23, CR2, CD200, tivation (44)—and the proteomics-determined differential ex- CD40, IgG receptor FCGRT, CD38, CD22, and IL21R. The pression (Fig. 4E). Several regulators not quantitated by most consistently overexpressed surface proteins, HMMR/ proteomics, including miRNA were also inferred to be acti- Downloaded from CD168, has previously been associated with B-cell cancers vated or inactivated (supplemental Fig. S4C,S4E). (41, 42). Myc overexpression and inferred activation (Rs ϭ 1.11, z ϭ Bioinformatics Reveals Signatures Common to Contrasting 8.09, p ϭ 6.6 ϫ 10Ϫ26), alongside other proliferative drivers B-cell Tumors—Although individual DEPs can suggest func- and oncogenes (e.g. E2F3, MYCN, RABL6 (RsϾ0.5, zϾ4)) tional insight, approaches simultaneously considering all described several key, functional regulators influencing the http://www.mcponline.org/ DEPs, potentially offer a broader understanding of biologi- neoplastic B-cell phenotype (Fig. 4E, supplemental Table S3). cal mechanisms. Accordingly, topological, proteome-wide The combined overexpression and inferred inactivation of expression patterns were investigated by bioinformatics. TP53 (Rs ϭ 0.97, z ϭϪ3.76, p ϭ 1.6 ϫ 10Ϫ34) and retino- Gene ontology (GO) term enrichment was used to identify blastoma 1 (RB1) (Rs ϭ 0.59, z ϭϪ2.86, p ϭ 2.3 ϫ 10Ϫ21), processes overrepresented by the DEPs observed in both represented an anticipated evasion of tumor suppressor path- tumors (Fig. 4A, supplemental Table S3). Among the overex- ways. Interestingly, RB1-like 1 (RBL1) (Rs ϭ 0.92, z ϭϪ4.47, Ϫ16 pressed proteins GO term enrichment identified strong signa- p ϭ 2.2 ϫ 10 ), a lesser-established tumor suppressor, had by guest on September 10, 2018 tures of mechanisms relating to cell growth and proliferation, greater overexpression and inferred inhibition than its better- including; “ribosome biogenesis” (n ϭ 106, p ϭ 1.8 ϫ 10Ϫ27), studied family member. “translation” (n ϭ 135, p ϭ 1.2 ϫ 10Ϫ16), “chromosome seg- It was additionally possible to compare differential protein regation” (n ϭ 81, p ϭ 6.0 ϫ 10Ϫ14) and “cell cycle” (n ϭ 229, phosphorylation to infer activation or inhibition (supplemental p ϭ 2.5 ϫ 10Ϫ11). Among the underexpressed proteins were Fig. S4F–S4H). HDAC2 overexpression in E␮-myc tumors, for trends of several immune function-related terms, such as instance, was accompanied by a signature of downstream lymphocyte activation (n ϭ 41, p ϭ 8.8 ϫ 10Ϫ7), Ig-mediated inactivation (supplemental Fig. S4B) and a decreased phos- immune response (n ϭ 15, p ϭ 2.1 ϫ 10Ϫ5), and BCR signal- phorylation of Ser394 (supplemental Fig. S4H), a modification ing (n ϭ 11, p ϭ 2.9 ϫ 10Ϫ4). Additionally, several processes previously associated with the activation of HDAC2 (45). Sim- related to immune evasion, differentiation, and growth inhibi- ilarly, in E␮-TCL1 tumors, RBL1 overexpression was accom- tion were enriched. panied by apparent inactivation and decreased Thr385 phos- To visualize these processes and highlight the proteins phorylation. Differential phosphorylation of BCR signaling contributing to the enriched GO terms, networks were dis- pathway components demonstrated an increased phosphor- played descriptive of individual proteins and their interrela- ylation of downstream proteins such as NF-␬B, OCT2 and tionships, defined by STRING (43). Fig. 4B demonstrates a ETS1 (supplemental Fig. S4J). network generated from proteins annotated with “chromo- Characteristics Specific to E␮-myc B-cell Tumors—Al- some segregation” highlighting the scale and complexity of though the common tumor signature in both models was the dysregulation of this process seen in both tumors. “Ri- strong, several tumor-specific differences were also apparent, bosome biogenesis” was also illustrated in this way (Fig. particularly in the more aggressive E␮-myc tumors. To eval- 4C), highlighting those overexpressed proteins responsible uate tumor-specific expression, proteins were filtered to in- for the term’s enrichment. A cluster of interacting ribosome clude those which exhibited significant over- or underexpres- proteins was identified, alongside several assembly regula- sion in one tumor model (RsϾ0.5/Ͻ-0.5, p Ͻ 0.05) but not the tors. Several canonical pathways, including ribosome-, other (RsϾ0.25/Ͼ-0.25). This identified 572 and 537 proteins translation-, and cell cycle-related pathways demonstrated with specific over- or underexpression in E␮-myc tumors, significant enrichment in both tumors (supplemental Fig. respectively. The 10 most over- and underexpressed proteins

394 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

Gene ontology (GO) term enrichment for proteins differentially expressed in both tumors Upstream regulator enrichment A. E. Overexpressed Underexpressed Overexpressed proteins Activated regulators Underexpressed proteins No/inconsistent activity Cell cycle Immune processes; 3 Inhibited regulators regulation Lymphocyte BCR signalling, Ig CYP51A1 Chromosome production, MHC CDK1 segregation adhesion RB1 TYMS CKS1B processes antigen presentation TP53 CDK2 S100A6 CDK4 Organelle TCF3 2 SCAP Histone RBL1 MYBL2 separation and regulation PTEN TFDP1 biogenesis EIF4G1 DYRK1A 1 ABL1 MYC TGM2 E2F3 MYCN RICTOR RABL6 SYVN1 0 Translation, Cell activation, Ribosome -8 -6 -4 -2 0 2 4 6 8 10 tRNA -ve proliferation CCND1 biogenesis RBL2 metabolism regulation EIF4E KDM5B E2F2 and DNA KDM5A -1 EP400 replication PARP9 Differentiation Irgm1 FOXO1 HDAC1 -2 IRF1 FLI1 Common 10-30 GO term Tumor expression vs. WT (Rs) PAX5 BCL6 CD40 SATB1 Background similarity STAT1 NTRK2 Enrichment CDKN1B CD38 GO term (semantic -3 p-value Inferred activation status (z-score) Downloaded from frequency space) Rare 0.05 Ribosome biogenesis Ddx11 Rps25 Rps28 Curated Chromosome C. Rps8 Experimental Dscc1 Chtf18 Textmining segregation Top3b Rpl35a Rps2 Rps27l

Co-expression http://www.mcponline.org/ B. Ncapd3 Rpl10 Rb1 Recql4 Rpl38 Rpl7 Rplp0 Mrps9 Homology Recql5 Mrps11 Gene neighborhood Csnk2a1 Rpl26 Rpl23a Rpl6 Rpl3 Mrps7 Ncapd2 Cdc6 Top2a Utp18 Mrpl11 Rpl5 Nol6 Rrp7a Esco2 Smc2 Wdr46 Utp15 Nom1 Rad51c Smc4 Kif18b Ncl Npm1 Mrto4 Imp3 Dcaf13 Tbl3 Heatr1 Nol11 Spag5 Cdca2Cdca2 Utp6 Brca1 Ncaph Kif22 Krr1 Wdr3 D2Ertd750e Hdac8 Pwp2 Npm3 Pa2g4 Rpf2 Utp20 Fancd2 Ccnb1

Rsl24d1 Tsr1 Nop14 by guest on September 10, 2018 Mad2l1 Cdca5 Rrp1b Imp4 Mphosph10 Ncapg Nle1 Ube2c Birc5 Cdca8 Cirh1a Lsg1 Pes1 Ddx52 Cdc27 Bub1b Sgol1 Cenph Ppan Ftsj3 Kif11 Bub1 Rpf1 Nat10 Rcl1 Ddx3x Rrp1 Pdcd11 Nusap1 Sgol2 Nop2 Ttk Plk1 Ndc80 Ccdc99 Ddx27 Diexf Nde1 Nob1 Utp11l Xpo1 Nmd3 Gnl3 Nip7 Rrs1 Ect2 Espl1 Cenpf Spc25 Bop1 Utp14a Cit Casc5 Mis12 Nop16 Utp3 Cenpe Ddx21 Ddx56 Nuf2 Nvl Racgap1 Incenp Zw10 Kif2c Ska3 Ska1 Pmf1 Anti-viral response Tex14 Kif18a Dsn1 Zwint Samhd1 Psrc1 Il4ra Dpp4 Ifit2 Ddx3x Stat2 Ifit3 Usp9x Mef2c Gsn Pecam1 Trim34a Histocompatibility Oasl1 Pml Snca Prkcb Cd47 complex Tlr3 H2-DMa Ikzf3 H2-DMb2 Underexpression of Ptk2b Irf8 H2-Oa D. Cib1 Cd2 immune regulators Cd38 Cd74 H2-DMb1 Tespa1 Ptprc H2-Aa Btla Fcer2a in both tumors: Camk1d H2-Eb1 H2-Ab1 Sell Tyrobp Bcl11a Bcl6 H2-Ob Cd79b Cd40 Serpinb9 Ctsh Fcgrt Tnfsf13b Cd79a Cd84 Pag1 Cr2 Cd36 Itgax B-cell activation Cd300a and proliferation Lig4 Nhej1 Vav3 Relb Ighg2c Tnfaip3 regulation Cd200 Cd1d1 Satb1 Cd55 Icosl

FIG.4.Bioinformatic interrogation of commonly differentially expressed B-cell tumor proteins. A, Gene ontology (GO) term enrichment analysis for proteins over- and underexpressed in both tumors, visualized using Revigo (94) and summarized based on parent GO terms and Revigo-defined semantic space. B–D, StringDB networks highlighting interactions and relationships for proteins; B, overexpressed in both tumors annotated with the term ‘chromosome segregation’ (GO:0007059) (n ϭ 81), C, overexpressed in both tumors annotated as “ribosome biogenesis” (GO:0042254) (n ϭ 106), and D, those proteins underexpressed in both tumors relating to terms descriptive of immune regulation. E, Upstream regulator activation z-scores inferred by IPA for all consistently differentially expressed B-cell tumor proteins, plotted against tumor protein expression, relative to WT B-cells. A positive z-score indicates a signature of protein activation.

Molecular & Cellular Proteomics 16.3 395 Cellular and Plasma Proteomics of B-cell Tumors

myc tumor-specific GO term enrichment Fold change from WT B. -4 4 Overexpressed Underexpressed Cell cycle phase Regulation of Cell motility, Chromatin transition immune-related endocytosis regulation B A. A signal transduction and secretion B A myc TCL1 DNA replication, Kinase myc myc TCL1 TCL1 TCL1 TCL1 myc myc repair and epigenetic regulation regulation Eμ- Eμ- Eμ- Symbol Eμ- 6w Eμ- 6w Eμ- Unique Peptides Unique PSMs WT 200d BCAT1 945 CCDC85C 423 SLC39A6 13 ALPL 10 37 Translation DNA2 10 39 regulation SOX4 12 SLC20A1 17 NAF1 320 Histone and Cytoskeletal GSTO1 15 99 protein Leukocyte regulation Overexpressed PSMB5 615 modification differentiation TNFAIP3 413 ZBP1 11 48 Eμ-myc-specific overexpressed proteins BEND5 716 C. annotated with ‘methylation’ (GO:0032259)

GIMAP7 11 71 Nsun5 Gm17296 Downloaded from TAPBPL 614 Gm17296 Nsun5 D2Wsu81eD2Wsu81e SERPINB9 10 51 Trmt2aTrmt2a Nsun2Nsun2 Rnmtl1 RHOF 517 Rnmtl1 Trmt112 Tmsb15b1 24 Emg1 Smad4 Prmt7 KYNU 12 64 Ftsj1 Nop2 Prmt5

Underexpressed NFKBIE 10 34 Prmt1 Tet3 Trmt13 Ftsj3 Ftsj3 Tet3 Rrp8 SLC20A1 17 Tet2Tet2 http://www.mcponline.org/ Prmt6 PTPRS 46 Trmt10a Etf1 Suv39h1 Hdac2 SLC29A1 16 Dnmt3b CD147 737 Gspt1 Pih1d1 CD249 24 Carm1 Pih1d1 Ntmt1 SLC1A5 333 Dnmt1 Kdm3a Ehmt2 Ogt ABCC4 14 32 Tgs1 Ezh2 CD298 841 Ehmt1 Whsc1 Cell surface IL1-R3 410 INSR 28 Suz12 Nsd1 Setd3 by guest on September 10, 2018

Isolated splenic myc B-cells Eμ-myc lymphoma cells D. Eμ- -specific underexpressed proteins E. myc annotated with ‘actin cytoskeleton organisation’ GO:0030036 WT Eμ- Eμ 6 Eμ 8 Eμ 13 Eμ 14 Eμ 15 myc Stap1 Plek Sh3bgrl3 Capg GAPDH Cd2ap Lcp1 Coro1b Hck Arrb1 Hcls1 Coro1a COR1A Dbnl GAPDH Spna2 Tmsb4x Arpc1b Srf Coro2a LCP1 Kras Rhoa Flnb Wdr1 Actr2 GAPDH Fmnl3 Rac2 Arpc5l Cap1 Ssh1 CAPG Prkcd Cdc42 GAPDH Actr3 Arpc5 Arpc3 Fhl3 Add1 Grb2 MYH9 Pfn1 Prkar1a Rhof Arpc2 GAPDH Twf2 Arpc4 MOE Arhgap26 Ssh3 Vasp GAPDH ␮ FIG.5.E -myc-specific tumor characteristics. A, Heat maps of individual log2 (ratios to WT), for the top 10 proteins specifically over- and ␮ Ͼ Ͻ Ͻ Ͻ Ͼ underexpressed in E -myc tumors (Rs E␮-myc 0.5/ -0.5, p 0.05, Rs E␮-TCL1 0.25/ -0.25). Additionally, the top 10 proteins with specific cell surface expression are shown. B, GO term enrichment analysis for differential expression specific to E␮-myc tumors (as described for Fig. 4). C–D, StringDB networks derived from E␮-myc-specific differentially expressed proteins annotated with C, “methylation” (GO:0032259) (n ϭ 50), and D, “actin cytoskeleton organization” (GO:0030036) (n ϭ 49). E, Western blot evaluation of the three “actin cytoskeleton organization”- annotated proteins, coronin 1a (COR1A), lymphocyte cytosolic protein 1 (LCP1) and actin regulatory protein CAPG, expression in E␮-myc tumors, relative to nontumor B-cells. Two addition cytoskeletal proteins, myosin-9 (MYH9) and moesin (MOE) are also evaluated alongside myc.

in addition to cell surface proteins were illustrated to highlight The over- and underexpressed proteins specific to E␮-myc examples of this specific differential expression pattern in tumors were evaluated, as for Fig. 4A, for GO term enrichment E␮-myc tumors (Fig. 5A). (Fig. 5B, supplemental Fig. S3). The majority of GO terms

396 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

observed for both tumors were also observed for the E␮-myc- S5E). For both IL5R subunits a clear, specific overexpression specific proteins, further highlighting trends of increased pro- was apparent. Furthermore, the signature for IL5 activity was liferation and growth of cells. Exceptions included trends of apparent through various bioinformatics analyses, including epigenetic-related processes and underexpressed cytoskel- IPA regulator analyses (supplemental Fig. S4C,S5F). A sig- etal processes. nature additionally became apparent in E␮-TCL1 terminal and Most notably specific to overexpressed E␮-myc tumor pro- preterminal plasma indicative of an IL5-induced proliferation teins was the enrichment of the term “methylation” (n ϭ 50, signature, as well as plasma signatures of quantitated pro- p ϭ 0.03). A network was formed from these 50 proteins (Fig. teins and inferred regulators potentially upstream of IL5 in- 5C), highlighting overexpressed methylation enzymes relating duction (supplemental Fig. S5G–S5I). to gene expression; both epigenetically (histone methylation, Given the strength of this evidence, the surface expression n ϭ 18) and post-transcriptionally (RNA methylation, n ϭ 10). of IL5R␣, the subunit specific to IL5 recognition, was evalu- CpG methylation appeared widely dysregulated with 2/3 ated by flow cytometry for terminal and pretumor E␮-TCL1 methylation (DNMT1, DNMT3B) and 2/3 demethylation (TET2, B-cells, relative to WT B-cells (Fig. 6E, supplemental S5J– TET3) enzymes overexpressed. S5K). Although IL5R␣ expression was observed across a wide E␮-myc tumors demonstrated a broad downregulation of distribution for WT B-cells, for pretumor E␮-TCL1 B-cells, the processes relating to the cytoskeleton, for example, “actin emerging CD5ϩ population was observed with high IL5R␣

cytoskeleton organization” (n ϭ 49, p ϭ 1.2 ϫ 10Ϫ6), as expression. The E␮-TCL1 tumor cells exhibited the same Downloaded from illustrated in a network (Fig. 5D). This illustrated the underex- CD5ϩIL5R␣ϩ expression as the emerging pretumor E␮-TCL1 pression of six actin-related protein 2/3 complex subunits B-cell population. (ARPC) and three coronin proteins. Three interrelated proteins Interleukin 5 Drives Proliferation in E␮-TCL1 B-cell Tumors from this network, coronin 1a (COR1A), lymphocyte cytosolic via AKT—The observation of substantial overexpression of

protein 1 (LCP1) and actin regulatory protein CAPG, were both IL5R␣ and IL5R␤, strongly suggested a functional role http://www.mcponline.org/ evaluated for relative expression in E␮-myc tumors, relative to for the IL5R and IL5 in driving E␮-TCL1 tumors. To investigate nontumor B-cells by Western blot (Fig. 5E). In each instance, and functionally validate the IL5R overexpression, terminal underexpression was observed in line with that of the quan- tumors from E␮-TCL1 mice were treated with 0, 10 and 100 titative proteomics findings (compared in Fig. 2D). Addition- ng/ml of IL5 for 48 h in vitro. First, cell density indicated a ally, myc overexpression was evaluated alongside two further dose-dependent expansion induced by IL5 (Fig. 7A). CFSE cytoskeleton-related proteins; a broadly functioning regulator labeling confirmed that cells were proliferating in a dose-de-

of cytoskeletal activity, myosin-9 (MYH9) and a cytoskeletal- pendent manner with as many as 70% of cells being postmi- by guest on September 10, 2018 membrane junction protein, moesin (MOE) again demonstrat- totic after 48 h of 100 ng/ml IL5 treatment (Fig. 7B). Further- ing anticipated differential expression. more, evaluation of cell cycle phases by hypotonic PI staining Characteristics Specific to E␮-TCL1 B-cell Tumors—Pro- demonstrated that almost three times as many cells (14.5%) ␮ tein expression specific to E -TCL1 tumors was also eval- were in S/G2/M phase after 100 ng/ml IL5 treatment, com- uated, with the top 10 DEPs and membrane proteins pre- pared with no treatment (4.9%) (Fig. 7C). sented (Fig. 6A). As validation this illustrated the discrete Given the proposed role of TCL1 in amplifying AKT signal- expression of human TCL1 (supplemental Fig. S2A)inE␮- ing (16, 17), AKT activation by IL5 was investigated (Fig. 7D). TCL1-derived B-cell samples. GO term enrichment identified Serum starvation and the subsequent addition of IL5 induced upregulated processes relating to intracellular compartments, AKT phosphorylation in a dose-dependent manner at doses such as ER stress, vesicular transport and glycosylation. Ex- of greater than 5 ng/ml. Downstream signaling (summarized in tracellular interactions, including signaling, locomotion and Fig. 7E) was also apparent, with dose-dependent phosphor- adhesion appeared as the strongest underexpressed process ylation of the S6 ribosomal protein and S6 kinase, indicative of in E␮-TCL1 tumors (Fig. 6B). A network formed from E␮- mTOR activation. TCL1-specific overexpressed proteins (Fig. 6C) presented Plasma proteomics reveals signatures of tumor lysis and clusters of associated proteins contributing to enriched GO immune response—To investigate the overall plasma pro- terms, such as ER and golgi proteins. Several signaling pro- teome signature, those proteins quantified commonly or dis- teins were also specifically overexpressed, such as phospha- cretely within the B-cell and plasma proteomes (supplemental tidylinositol kinases and phosphatases. Associated with these Table S2) were dissected based on canonical protein local- signaling proteins were the alpha and beta subunits of the izations (Fig. 8A). The vast majority (82%) of the 1288 proteins interleukin 5 receptor (IL5R␣ and IL5R␤), also apparent observed as common to both proteomes, were annotated among the top 10 membrane proteins, highlighted in Fig. 6A. with a canonical cellular localization, compared with just 40% Interleukin 5 Receptor is Overexpressed by E␮-TCL1 B-cell of proteins identified discretely in the plasma proteome. Fur- Tumors—The overexpression of the IL5R was first evaluated thermore, evaluation of the relative abundances in terminal by plotting the individual iTRAQ ratios for each unique peptide tumor plasma revealed a striking signature of overabundant matching IL5R␣ and IL5R␤ (Fig. 6D, supplemental Fig. S5A– cell-derived proteins (Fig. 8B). The tumor origin of this plasma

Molecular & Cellular Proteomics 16.3 397 Cellular and Plasma Proteomics of B-cell Tumors

Eμ-TCL1 tumor-specific GO term enrichment Fold change from WT B. -4 4 Overexpressed Underexpressed

Cell signalling and communication A B A. A B myc TCL1 Coagulation myc myc TCL1 TCL1 Lipid, glycosylation and hemostasis and carbohydrate

Symbol Eμ- Eμ- Eμ- Eμ- Unique Peptides Unique PSMs WT 200d 6w Eμ- 6w Eμ- metabolism AHNAK2 30 270 TCL1A 5 150 C11orf53 16 GNG13 35 Cell locomotion PSTPIP2 16 83 Protein localisation; and adhesion FRAS1 39 vesicular ER/golgi ER stress and transport protein complex MICU2 817 biogenesis AF067061 450 AVIL 19 54 Overexpressed Gm21761 227 PRKD3 410 AKR1E2 320 DPP4 11 28 AKT3 221 UCHL1 10 124 Eμ-TCL1-specific overexpressed proteins AHSG 34 C. STXBP1 618 Lipid metabolism ITGA2 625

TRIP10 512 Echdc1 Downloaded from Etfb Cryl1 Pla2g16Pla2g16 Signaling regulation

Underexpressed ACP2 410 Cept1Cept1 Acads Cyp4f13CyCyp4f13p4 3 Mtss1Mtss11 PLSCR1 34 Acaa1a CttnCttn AAcAcaa1aca Spn Ptpn1Ptpn1 IL5RA 515 Suclg2 Aldh7a17aaa1 Pla2g6PPla2g66 CD108 510 Camk2d Csf2rb EhhadhEEhh h Acox3A EzrEEzr CSF2RB 17 DDlg1 (IL5rb) Mccc2ccc2cc22 Marcks Rock2Rock22 CD321 19 Aldh2 Marckskss Dlg1 Jak3JakJaJJak3ak Tyk2 Prkca CD268 29 Mccc1 PPrkca GnGGng13 MMccc1 F11rF11rr Gng13 SLC39A4 13 AuhAuhh

Il5ra http://www.mcponline.org/ CD5 618 Pik3r5PikPiik3rk3r

Cell surface Fgd2 Casp9 RELT 24 Glycosylation Sos1SSoos1s1 Fgd2 Rnf170 CdiptCdipt CD86 16 Man2a1 Arhgap24Arhgggap24p2 Cds2Cds2Agpat3t3 Man2a1 Erlin22 Pik3r6P 6 Erlin2 Arhgap24 Pik3r6 Agpat3 Mgat1 Inpp5bnnpInpp5b D. 4 IL5rα/CD125 Csf2rb/CD131 Mgat1 Atp2a32a3 Pik3c2b3cc Man1b1Man1b11 IItItpr1tptp (IL5rβ) BckdhbBckdhbb Itpr1 Pik3c2b Ccdc109b Man1aMan1aa Ergic1Ergic1 Inpp5k GanabGGanab Inpp4aIn Micu2 Ccdc109a Txnrd2TTxnrd2 Prkcshrkc h Efha1a11 ErErp44 2 P4hbP4hhbb Erp44 Mtmr14 Gpx7 Micu1MiMicM GGpx7px7 Ero1lbErEEroroo1lboo11lblb Micu1 Erp29 Fras1 Ero1lEroro1oo1l Eps15 Mitochondrial Ube2j1 0 Pdia3P Copz1Copz1opz1 Tsg101 calcium uptake

Syvn1SyS 1 Hsp90b1HHsHspssp90b1p90b190b1b11 by guest on September 10, 2018 Tap1 CopaCopCoop Cnpy3C 3 Tap1 Copb1 Lrsam1L Erlec1ErleErlrlec Surf4Surf4 Dnajb4 -2 Os9Oss99 Arfgap3AArfgArfgarfgagapgap3ap Gosr1GosGosr1os Pdia4PPdPdia IgtpIgg Arf4A 4 Derl2Derl2 Creld2 Cog3CoCog3

(ratios to WT B-cells) Cnpy22 Sacm1lS 2 Gbp7 Cog2 ManfMMa GbGbp7 Gbp3G Cog2Cogog2 -4 Abhd6A 6 Cog4CCog4

log Golgi s s s Relative peptide abundance L1 r rs r 0d ER stress FaahF vesicle mycTC mycTCL1 mo - mor transport μ- μ- tumo tu T 200d μ tu tumo response WT 20 E 1 W 6w E w myc CL 6w Eμ- myc 6 - T 6w E TCL1 μ- Eμ E Eμ- Eμ-

E. Isolated splenic B cells (2 month-old) Terminal Eμ-TCL1 Wildtype Eμ-TCL1 tumor splenocytes 4 4 10 10

3 3 10 10 7.38% 12.81% 2 2 10 10

IL5Rα 1 1 10 10

0 0 10 10 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 CD5 ␮ ␮ FIG.6.E -TCL1-specific tumor characteristics. A, Heat maps for proteins and surface proteins specific to E -TCL1 tumors (Rs E␮-TCL1 Ͼ Ͻ Ͻ Ͻ Ͼ ␮ 0.5/ -0.5, p 0.05, Rs E␮-myc 0.25/ -0.25). B, GO term enrichment analysis for differential expression specific to E -TCL1 tumors. C,A StringDB network derived from E␮-TCL1-specific differentially expressed proteins demonstrating interaction scores of Ͼ0.7 highlighting

processes in protein clusters. (ER; endoplasmic reticulum). D, Individual iTRAQ quantifications (as log2 (ratios to WT)) for the peptides uniquely matching to IL5RA and it receptor partner CSF2RB (IL5RB). E, Flow cytometry evaluation of IL5 receptor alpha subunit (IL5R␣) expression on an E␮-TCL1 tumor (representative of four evaluated tumors (supplemental Fig. S5K)), compared to splenic B cells pooled and isolated from three 2-month-old WT and E␮-TCL1 mice.

398 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

[IL5] ng/ml 48h: A. 200 Eμ-TCL1 C. 0 10 100 10 tumors (48h): 160

120

/ml) 8 4.9% 6.9% 14.5% 6 80 6 Initial

Cell count 40 culture density 4 0 Hypotonic PI 2

Cell density (x10 [IL5] (ng/ml) (30 minutes) : 0 0.1 0.5 1 5 10 50 100 500 1000 0 D. pAKT 0 10 100 (S473) IL5 concentration (ng/ml) Total AKT pS6K (T389) B. [IL5] ng/ml 48h: pS6 RP (S235/236) 200 pERK 160 0 (T202/204) Downloaded from 22.8% 120 α-Tubulin

80 E. IL5 receptor signaling in Eμ-TCL1 tumors 40

0 1 2 3 4 Transcriptional 20010 10 10 10 regulator http://www.mcponline.org/ 160 10 Kinase 56.3% Trans- 120 membrane receptor 80 Activates/ 40

Cell count up-regulates

0 Inactivates/ 1 2 3 4 20010 10 10 10 down-regulates 100 160 Catalyses 70.2% Isoforms 120 by guest on September 10, 2018

80 Inferred activated 40 protein

0 101 10 2 10 3 104 Rs of log (ratios to WT) CSFE 2 -1 0 1

FIG.7.IL5 drives E␮-TCL1 tumor proliferation. A, Tumor cells from three spleens of terminal E␮-TCL1 mice were cultured at an initial density of 5 ϫ 106 cells/ml and treated in with IL5. Cell density was measured after 48 h, and the mean and SD presented. B, Cell division analysis measured by CFSE staining of terminal E␮-TCL1 tumors treated in vitro with 0, 10, or 100 ng/ml IL5 for 48 hours C, IL5-treated E␮-TCL1 tumors (48 h) were stained with hypotonic propidium iodide and DNA content measured with flow cytometry to determine the proportion of cells entering mitosis. D, Serum starved (4 h) E␮-TCL1 tumor cells were evaluated for IL5 treatment dose response after 30 minutes by Western blotting for the phosphorylation of AKT, ERK, S6, and S6K. E, Summary of potential signal transduction pathway components, overlaid with E␮-TCL1 tumor proteomics quantitations, highlighting a mechanism of IL5-induced AKT phosphorylation, amplified by TCL1. signature was additionally indicated by GO term enrichment as biomarkers derived from functional tumor regulators (sup- (Fig. 8C), with highly similar trends to that of the B-cell tumors plemental Fig. S6S–S6T). (Fig. 4A), and an inter-proteome correlation between the ap- To explore differential protein abundance resulting from proximate protein abundances (supplemental Fig. S6A). Ad- the extrinsic response to the tumor, the plasma proteome ditionally proteins were illustrated with biomarker applications was subject to deconvolution, removing proteins with a (supplemental Fig. S6H), specific tumor plasma signatures clear cellular origin. The remaining proteins, termed the (supplemental Fig. S6J–S6P), differential abundance relative “lysis-free” plasma proteome (supplemental Table S2, sup- to approximate relative plasma concentrations (supplemental plemental Fig. S6F), provided a signature descriptive of an Fig. S6Q), over- and underabundance in both the tumor and immune response. Terms including “defense response” plasma proteomes (supplemental Fig. S6R) and with potential (n ϭ 16, p ϭ 4.0 ϫ 10Ϫ5) and “extrinsic apoptotic signaling

Molecular & Cellular Proteomics 16.3 399 Cellular and Plasma Proteomics of B-cell Tumors

Terminal tumor plasma protein abundance, relative to WT plasma A. 2 208 B-cell proteome 3% 845 B. 12% 2204 (8270) 585 32% 8% 3140 45% 1

6982 TCL1 plasma abundance (Rs) 112 117 311 9% 9% 1288 144 24% 11% 0 604 -2 -1 0 1 2 3 4 807 47% Plasma myc plasma abundance (Rs) proteome (2095) 149 55 145 Canonical protein 19% 7% 18% -1 localisation: 124 Cytoplasmic/nuclear proteins Nucleus 334 15% Cytoplasm 41% Extracellular proteins Plasma membrane Eμ-myc Rs = Eμ-TCL1 Rs Extracellular space Other/unknown -2 localisation D. Lysis-free terminal signature (both tumors) Cfp

Tgfbi Downloaded from Cfp Overabundant terminal plasma protein GO term enrichment Lyz2 Fibrinogens Both tumors C. Fga Fgg All plasma proteins ‘Lysis-free’ plasma Cpn2 Fgb Extrinsic apoptosis and immune-response- Inter-alpha- Napsa related terms inhibitors Itih4 http://www.mcponline.org/ mRNA metabolism Itih1 Itih2 and splicing Itih3 Serpine1 Translation Lgals1 Chromatin Timp1 organisation Cp Hp Pdgfb Ribosome Prg4 biogenesis Leukocyte Saa3 Ccl2 migration and Proteolysis chemotaxis regulation Chemotaxis-related Ccl21a chemokines and proteins Ccl9 by guest on September 10, 2018 E. Eμ-myc-specific signature Eμ-TCL1-specific signature Correlative markers of Eμ-TCL1 tumorigenesis RibosomeRibosome F. biogenesisbiogenesis 3.5 Haptoglobin Nucleolin 3.0 60S ribosomal protein L35 Leukocyte cell 2.5 Heat shock 70 kDa protein 4 ProteinProtein adhesion aandnd RNA Cytoskeletal/organelle 2.0 60S ribosomal protein L23a metametabolismbolism organisation Inter-alpha-trypsin inhibitor 1.5 heavy chain H1

Protein localisation 1.0 Immune response regulation Protein abundance 0.5 (average ratio to WT plasma)) (average ratio to WT 2 0.0

(log Wildtype 6w Eμ-TCL1 Eμ-TCL1 Terminal 30% Eμ-TCL1 -0.5 Stage

FIG.8. Model-dominant plasma signatures of E␮-myc and E␮-TCL1 tumors. A, Interproteome comparison detailing the canonical cellular localizations, annotated by Ingenuity Pathway Analysis (IPA), of proteins commonly or discretely quantified in plasma or B-cells. B, The relative plasma protein abundances relative to WT plasma for terminal E␮-myc and E␮-TCL1 tumors, highlighting those proteins annotated as intracellular (cytosolic/nuclear proteins) and extracellular. C, GO term enrichment for plasma proteins overabundant in both tumors, considering all plasma proteins and additional analyzing only those extracellular proteins not traceable to the B-cell tumor proteome, termed the “lysis-free” plasma. D. StringDB networks highlighting interactions and relationships for proteins overabundant in the “lysis-free” plasma for both tumors. Ͼ Ͻ Ͻ E, GO term enrichment for plasma proteins overabundant specific to each tumor (Rs 0.5 and p 0.05, Rsother tumor 0.5). F, Proteins demonstrating progressive increases in abundance correlating with tumorigenesis in E␮-TCL1 mice. pathway via death domain receptors” (n ϭ 6, p ϭ 1.4 ϫ lysis-free signature further, a network was generated from 10Ϫ4) were identified as enriched among these overabun- all overabundant lysis-free plasma proteins (Fig. 8D). Pro- dant “lysis-free” plasma proteins (Fig. 8C). To detail this tein clusters included chemotaxis regulators Ccl2, Ccl9 and

400 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

Ccl21a, wound and inflammation response proteins, fibrin- pacts and biology of biomarker emergence. Furthermore, ogens ␣, ␤, and ␥ and the hyaluronan-binding, interalpha- greater understanding of these models offers an opportunity trypsin inhibitor proteins, ITIH1-ITIH4. to appreciate their strengths and weaknesses in preclinical Comparison between the terminal plasma signatures for applications. each tumor model (Fig. 8B) demonstrated the dominance of Overall, isobaric-labeled LC-MS proteomics of B-cell and this tumor lysis signature in E␮-myc tumors, whereas E␮- plasma samples (Fig. 1) provided biologically reproducible TCL1 terminal plasma contained a dominant signature of (Fig. 2A,2B), high-depth (supplemental Fig. S1H) and repre- extracellular proteins. In both cases these signatures were sentative (supplemental Fig. S1) characterizations of the E␮- observed for the opposing tumor, but to a lesser extent. GO myc and E␮-TCL1 tumor models. The accuracy of the quan- term enrichment for overabundant plasma proteins specific to titative results was validated by anticipated transgene E␮-myc and E␮-TCL1 terminal tumors illustrated model-spe- expression (Fig. 2C), Western blotting and flow cytometry cific signatures of tumor lysis and extracellular proteins, re- (Figs. 2D, supplemental Fig. S2G,S2H) alongside compari- spectively (Fig. 8E). E␮-myc tumor plasma specific GO terms sons to previously published protein expressions (supplemen- had a strong resemblance to that of the B-cell tumors, high- tal Fig. S2D,S2F) and mRNA expression data (supplemental lighting cellular processes similar to both the tumors and the Fig. S2J). Interestingly, a brief analysis revealed that those common plasma signature (Fig. 4A and 8C). Overabundant proteins overexpressed without corresponding mRNA over- plasma proteins specific to terminal E␮-TCL1 tumors en- expression were enriched with proteins annotated with the Downloaded from riched for several GO terms related to immune processes, term “translation.” such as “immune response” (n ϭ 17, p ϭ 1.5 ϫ 10Ϫ4) and A Common B-cell Tumor Signature in Divergent Models— “lymphocyte activation” (n ϭ 12, p ϭ 4.8 ϫ 10Ϫ4). The expression correlation between the contrasting E␮-myc Finally, consideration was given to proteins emerging in the and E␮-TCL1 tumors (Fig. 3B) was indicative of a common preterminal “30%” E␮-TCL1 plasma (Fig. 8F). Plasma pro- B-cell tumor signature that was proportional to aggression. http://www.mcponline.org/ teins demonstrating a correlation between overabundance Indeed, this signature strongly highlighted canonical tumor and tumorigenesis were plotted, highlighting the extracellular characteristics such as upregulated cell proliferation and immune-response proteins haptoglobin (Hp) and ITIH1, both growth (Fig. 4). Furthermore, frequent observations of these also observed in the lysis free signature. Several additional overexpressed proteins in several human cancers (supple- proteins demonstrating overabundance in preterminal “30%” mental Fig. S3C) highlighted potential relevance to non-B-cell E␮-TCL1 plasma are detailed in supplemental Fig. S6I. cancers. This was particularly noteworthy, given the many

clinically relevant cell surface proteins and drug targets char- by guest on September 10, 2018 DISCUSSION acterized (Fig. 3D, supplemental S3F,S3G). HMMR/CD168, B-cell tumors have been intensively investigated by genom- for instance, has previously been considered as an immuno- ics and transcriptomics in recent years, advancing clinical and therapy target in B-cell cancers (41, 53). Similarly, amino acid biological understanding (46–49). Such information, is how- and zinc transporter overexpression could offer targets of ever sometimes limited, especially in a functional context, metabolic inhibition or immunotherapy. The observation of because of the weak correlations observed between mRNA proteins, and even putative proteins, in this common B-cell and protein expression (50, 51). A comparison between our tumor signature with no prior links to cancer offers several proteomics data and a former E␮-myc tumor mRNA expres- new hypotheses (supplemental Fig. S3E). DDX49 and MAK16, sion data set (supplemental Fig. S2J) highlighted an example for instance, are poorly characterized proteins overexpressed of this, demonstrating an anticipated but limited correlation in all four tumor pools, and could be inferred to have a role in (52). Furthermore, genomics and transcriptomics are limited in ribosome biogenesis, given their homology, orthologous func- capturing organism-wide tumor biology from acellular sam- tions and the prevalence of ribosome biogenesis upregulation ples such as plasma. in both tumors. This study aimed to combine and implement recent ad- Downregulation of immune functions were indicative of the vances in quantitative MS proteomics to comprehensively loss of B-cell characteristics, not essential and potentially characterize the tumors and plasma of contrasting mouse even inhibitory toward tumor development. Constitutive Ig B-cell cancer models; E␮-myc and E␮-TCL1. By comparing synthesis, for instance, would provide a negative selective tumors driven by contrasting oncogenes, with differing phe- pressure on resource-limited tumor cells. MHC component notypes and rates of progression, any apparent common underexpression suggested a clear mechanism of immune signatures could suggest the most conserved and essential evasion, described previously (54, 55). B-cell cancer mechanisms. Model-specific signatures could Model Specific Signatures of E␮-myc and E␮-TCL1 Tu- provide insight into the molecular basis of the discrete cancer mors—E␮-myc- and E␮-TCL1-specific DEPs (Figs. 5–6) high- phenotypes and oncogenes, potentially of relevance to BL lighted distinct expression patterns responsible for the con- and CLL. Simultaneous plasma characterization offered po- trast in tumor phenotypes; such as the vast protein tential insight into tumor-host dialogue, systemic cancer im- dysregulation and proliferation produced by the aggressive

Molecular & Cellular Proteomics 16.3 401 Cellular and Plasma Proteomics of B-cell Tumors

and pleiotropic nature of myc. The contrast between these indicates a role for antigen-receptor signaling in E␮-TCL1 models was also apparent prior to tumorigenesis (supplemen- tumors, observed previously (70) (supplemental Fig. S4J). This tal Fig. S3B), with little to no tumor signature in 6-week E␮- highlights potential examples of negative regulation or re- TCL1 B-cells, whereas E␮-myc 6-week old B-cells were al- dundant functions for tumor growth and survival e.g. BCL6, most indistinguishable from terminal tumors. Although none FOXO1, and CD45. Upregulated phosphorylations, such as of these 6-week E␮-myc mice presented with splenomegaly, PI3Ks and FOXO1, specifically in E␮-TCL1 tumors may also metastases or ill health, it remains possible that tumor devel- illustrate tumor mechanisms (supplemental Fig. S4J, supple- opment was present at an early stage in one or more of the mental Table S4). mice. E␮-myc-specific DNA and histone methylation dysregu- Interleukin 5 Receptor Overexpression and Signaling in E␮- lation (Figs. 5B,5C) suggests dynamic epigenomic instability TCL1 Tumors—Signaling dysregulation (Fig. 6C) and in- as a basis of myc-induced tumor characteristics, previously creased expression and phosphorylation of PI3Ks (supple- observed in B-cell lymphomas (56, 57). This potentially drives mental Fig. S4J) suggested the presence of strong receptor accelerated trait acquisition via nonmutational evolution (58). signaling underlying E␮-TCL1 tumorigenesis. IL5R overex- Broad underexpression of actin cytoskeletal organizing pression (Fig. 6, supplemental Fig. S5) strongly implied a role components was potentially indicative of defects to normal for the IL5:IL5R signaling pathway in E␮-TCL1 tumors. Al- B-cell migration and adhesion (Fig. 5D), identifying a possible though not previously described in E␮-TCL1 mice, other

mechanism promoting lymph node metastases; frequently mouse CLL-like B-cell tumors have demonstrated IL5 sensi- Downloaded from observed for E␮-myc tumors. These proteins may also have tivity (71–74), in addition to a CLL-like leukemia arising in mice been underexpressed as a result of redundant immune-re- with constitutive overexpression of IL5 (75). lated functions such as those described in Fig. 4D.Ofthe The dose-dependent proliferation of E␮-TCL1 tumor cells three inter-connected actin cytoskeletal organizing proteins upon IL5 treatment (Fig. 7A–7C) provided additional, func-

validated with underexpression (Fig. 5E), all three demon- tional validation of IL5R overexpression. IL5R signaling has http://www.mcponline.org/ strated both immune and cytoskeletal function. Cor1A pro- been suggested to induce activation of Lyn, JAK2, Syk, BTK, motes F-actin disassembly and cell motility (59) and muta- NF-␬B, and PI3K (76–78). Given the suggested role of AKT tions have been shown to impair lymphocyte function (60, 61). amplification by TCL1, PI3K signaling was a likely candidate for LCP1 regulates actin bundling and B-cell development (62, the effectuation of the IL5 response. Indeed, the PI3K catalytic 63), whereas LCP1 knock out impaired B-cell migration (64). subunit type 2 beta (PIK3C2B), alongside other PI3K isoforms, CAPG caps actin filaments with CAPGϪ/Ϫ mice exhibiting appeared as the most overexpressed of the IL5R downstream

immune defects (65). Given observations of the pro-meta- signaling molecules. Dose-dependent AKT phosphorylation by guest on September 10, 2018 static nature of these proteins in nonlymphocyte tumors (66, upon IL5 treatment confirmed a role for the PI3K pathway, 67), this indicated metastasis may be a more passive event for highlighting TCL1 as a potential factor in the amplification of E␮-myc tumors. Overexpression of adhesion components IL5R signaling (Fig. 7D–7E). such as integrin beta 1 may have facilitated this. Noncanoni- The observation of IL5RA expression by the expanded cal cytoskeletal component AHNAK, a titin-related and can- CD5ϩ peritoneal and splenic B-cell populations in young E␮- cer-associated cytoskeletal protein, shown to be essential for TCL1 mice (supplemental Fig. S5J) strongly suggests a pre- migration and invasion (68); was overexpressed in E␮-myc cursor cell population with characteristics of B-1 B-cells. tumors, potentially highlighting an alternative metastatic path- Given the induction of B-cell proliferation observed with IL5 way. Additionally, other cytoskeletal components such as (79, 80) and that IL5Ϫ/Ϫ mice are deficient for CD5ϩ B-1 those directing chromosome segregation were overex- B-cells (81), IL5R signaling emerges as a probable component pressed. Validation of two further underexpressed proteins driving E␮-TCL1 tumorigenesis from CD5ϩ IL5RAϩ B-1 B- extended the observation of cytoskeletal dysregulation, with cells. moesin and myosin-9 annotated with roles in leukocyte mi- The role for IL5 in B-cells, and B-cell tumors, appears to not gration. Together these observations suggest a trend for fur- be conserved between species (80, 82) highlighting a limita- ther investigation. tion in the recapitulation of human CLL with previous reports The E␮-TCL1-specific features, such as ER stress response suggesting IL5 induces spontaneous apoptosis in these cells and aberrant glycosylation upregulation (Fig. 6B–6C) have (83). However, the similarities of IL5R signaling in E␮-TCL1 previously been observed (69). Additional features, such as tumors with IL4 signaling in CLL, suggest continued potential integrin-␣2 underexpression, transmembrane protein proc- for E␮-TCL1 mice in modeling many aspects of the human essing, post-translational modification, transport and mem- disease. brane lipid composition, may suggest several potential mech- Plasma Proteomics and Systemic Tumor Signatures—The anisms by which circulatory, leukemic cell formation may be Ͼ250-fold depletion of high-abundance proteins by SuPrE- promoted in E␮-TCL1 tumors. SEC and the identification of a wide range of tumor and Generally unaltered BCR pathway component expression, systemic proteins (supplemental Fig. S6, Table S1) suggested ␮ relative to the underexpression observed for E -myc tumors, proteomic characterization of the low Mw subproteome pro-

402 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

vided an effective approach to plasma analysis. The capture Preterminal E␮-TCL1 plasma additionally offered several of the degradome and peptidome, portions of plasma con- interesting findings, describing a more clinically-relevant time taining fragmented proteins and potential biomarkers, likely point in tumorigenesis when a leukemia is present, but is contributed to the success of this method (84–86). otherwise asymptomatic. The observation of overabundant The overabundance of tumor-derived proteins in terminal preterminal “30%” E␮-TCL1 plasma proteins common to the plasma strongly suggested tumor lysis as a dominant mech- terminal signature (Fig. 8F, supplemental S6I,S6O,S6P) sug- anism (Figs. 8A–8E, supplemental S6), a process described in gested the characterization of progressive, early biomarkers aggressive therapies and lymphoma (87–89). Lysis product of B-cell tumors. Components of the tumor lysis-free signa- overabundance correlated with tumor aggression and higher ture describing a systemic immune response were more prev- rates of cell death, most clearly in E␮-myc tumors. A corre- alent, such as Hp and ITIH1. Proteins correlating with tumor lation between total protein abundance in the B-cell proteome progression, traceable to tumor lysis were, however, also and overabundance in the plasma was observed, regardless present to some extent, suggesting that both lysis and im- of tumor overexpression (supplemental Fig. S6G–S6I). Sev- mune signatures offer a source of biomarkers at earlier stages eral of these proteins were annotated biomarkers (supple- of tumor development. The more detailed evaluation of the mental Fig. S6H) highlighting tumor-lysis products in late- multiple stages of tumor progression, of these and other stage, aggressive tumors, as the dominant biomarker tumor models, could therefore offer considerable insight into Downloaded from signature. It is likely that exosomes and apoptotic blebs also the dynamics of biomarker emergence, better informing bio- contributed to this signature. marker discovery and application. The systemic immune response signature observed for ter- CONCLUDING REMARKS minal E␮-TCL1 plasma suggested that slower tumor devel- opment elicited a greater inflammatory response, perhaps In conclusion, this study has detailed the protein expression http://www.mcponline.org/ because of gradual accumulation, chronic inflammation and changes present across highly contrasting B-cell tumors, a loss of immune regulation (Figs. 8E). An anti-tumor im- highlighting dominant features of proliferation and growth. ␮ mune signature emerged for both models, when deconvo- E -myc tumors demonstrated a specific trend of epigenomic instability and cytoskeletal component underexpression, luted to give the “lysis-free” plasma proteome, which sug- whereas E␮-TCL1 tumors specifically exhibited ER stress, gested several potential systemic biomarkers (Fig. 8C,8D). dysregulated signaling, and IL5-driven proliferation. Many The contrasting signatures of tumor lysis and immune re- specific targets of kinase inhibitors, nucleoside analogues and sponse in E␮-myc and E␮-TCL1 terminal plasma respectively,

immunotherapy emerged for each tumor type. Plasma pro- by guest on September 10, 2018 was reflective of the vastly differing rates of tumor develop- teomics indicated lysis products as a major late-stage tumor ment. However, in both models the signatures were observed, biomarker signature, dominantly in the more aggressive E␮- to a lesser extent, in the opposing tumor. myc tumors, whereas E␮-TCL1 tumor plasma had a dominant Several systemic signatures were also observed upon inte- signature of an anti-tumor immune response. Integration of gration of the B-cell tumor and plasma proteomes. Overabun- plasma and B-cell proteomics data, alongside tumor lysis, dance of the carriers of the extracellular matrix component highlighted IL5, HMMR/CD168, ITIH proteins, FN1, S100A6, hyaluronan ITIH1–4 (Fig. 8E), substantial tumor overexpres- and EIF4G1 as candidate biomarkers for further investigation. sion of CD168 (HMMR, hyaluronan-mediated motility recep- These findings reinforce that aggressive, targeted, chemother- tor) (Fig. 3D) and links between hyaluronan, inflammation and apy and immunostimulatory antibodies promise effective tumor growth (90) suggest hyaluronan as a component of the means of treating E␮-myc and E␮-TCL1 tumors, respectively B-cell tumor microenvironment. Given their high concentra- with the results offering exciting possibilities when combined tions in plasma (supplemental Table S1, supplemental Fig. and integrated with further additional ‘omics and functional S6Q)(37), ITIH proteins hold potential as biomarkers of B-cell data. Finally, these findings suggest that global, integrative eval- tumors and aberrant hyaluronan metabolism and transport. uation of tumors can provide systemic insight into tumor biology Fibronectin (FN1), recognized in the promotion of cancers not captured by the evaluation of cells or plasma in isolation. (91), was observed as marginally overabundant and inferred to be functional (supplemental Fig. S6S). Given the overex- Acknowledgments—We thank Roger Allsopp and Derek Coates for pression of FN1-binding integrin ␤1 on tumors, this may rep- raising funds for Hope for Guernsey for PAT that allowed the pur- chasing of the FT-MS proteomics platform at the University of South- resent another microenvironment signature of pro-oncogenic ampton. We would like to acknowledge the Biomedical Research interactions with B-cell tumors. Facility and their staff, the use of the IRIDIS High Performance Com- Functional biomarker analysis for overabundant plasma puting Facility and associated support services at the University of proteins with inferred functionality in tumors highlighted Southampton, and the PRIDE team in the submission of our data to the PRIDE database. We would also like to thank Cory White for S100A6, a previously proposed biomarker (92, 93), and SPIQuE development, Dr. Xunli Zhang for HPLC access and Theo EIF4G1 (supplemental Fig. S6T) suggesting candidates for Roumeliotis for proteomics training. Also thanks to Graham Packham noninvasive testing directly related to tumor function. for critical feedback on this work.

Molecular & Cellular Proteomics 16.3 403 Cellular and Plasma Proteomics of B-cell Tumors

* This work was supported by an MRC iCASE studentship awarded chronic lymphocytic leukemia that correlates with molecular subtypes to P.A.T. and M.S.C. for H.E.J., a supplementary MRC fellowship and proliferative state. Leukemia 20, 280–285 awarded to H.E.J., and Grants from Bloodwise (10012 and 12050). 14. Kang, S. M., Narducci, M. G., Lazzeri, C., Mongiovi, A. M., Caprini, E., Bresin, A., Martelli, F., Rothstein, J., Croce, C. M., Cooper, M. D., and □S This article contains supplemental material. Russo, G. (2005) Impaired T- and B-cell development in Tcl1-deficient ** To whom correspondence should be addressed: University of mice. Blood 105, 1288–1294 Southampton, Southampton General Hospital, Southampton, SO16 15. Said, J. W., Hoyer, K. K., French, S. W., Rosenfelt, L., Garcia-Lloret, M., ϩ 6YD, UK. Tel.: 44–2381 208452; Fax: 44 (0) 23 80704061; E-mail: Koh, P. J., Cheng, T. C., Sulur, G. G., Pinkus, G. S., Kuehl, W. M., [email protected]. Rawlings, D. J., Wall, R., and Teitell, M. A. (2001) TCL1 oncogene Conflict of Interest Disclosure: MSC is a retained consultant for expression in B cell subsets from lymphoid hyperplasia and distinct Bioinvent and has performed educational and advisory roles for classes of B cell lymphoma. Laboratory Investigation 81, 555–564 Baxalta. He has received research funding from Roche, Gilead and 16. Pekarsky, Y., Koval, A., Hallas, C., Bichi, R., Tresini, M., Malstrom, S., GSK. PAT is cofounder of Karus Therapeutics, a NED for the Russo, G., Tsichlis, P., and Croce, C. M. (2000) Tcl1 enhances Akt kinase Aptamer Group and has performed educational and advisory roles activity and mediates its nuclear translocation. Proc. Natl. Acad. Sci. for Abcam, BioRelate, ReactaBiotech and Waters amongst others. U. S. A. 97, 3028–3033 He is also Adjunct Professor, Singapore Institute of Immunology, 17. Laine, J., Kunstle, G., Obata, T., Sha, M., and Noguchi, M. (2000) The protooncogene TCL1 is an Akt kinase coactivator. Mol. Cell 6, 395–407 A*Star, Singapore; and Honorary Professor, Department of Histol- 18. Bichi, R., Shinton, S. A., Martin, E. S., Koval, A., Calin, G. A., Cesari, R., ogy and Embryology, University of Athens, Greece. Russo, G., Hardy, R. R., and Croce, C. M. (2002) Human chronic lym- phocytic leukemia modeled in mouse by targeted TCL1 expression. REFERENCES Proc. Natl. Acad. Sci. U. S. A. 99, 6955–6960

19. Johnson, A. J., Lucas, D. M., Muthusamy, N., Smith, L. L., Edwards, R. B., De Downloaded from 1. Iversen, U., Iversen, O. H., Bluming, A. Z., Ziegler, J. L., and Kyalwasi, S. Lay, M. D., Croce, C. M., Grever, M. R., and Byrd, J. C. (2006) Character- (1972) Cell kinetics of African c.ases of Burkitt lymphoma. A preliminary ization of the TCL-1 transgenic mouse as a preclinical drug development report. Eur. J. Cancer 8, 305–308 tool for human chronic lymphocytic leukemia. Blood 108, 1334–1338 2. Molyneux, E. M., Rochford, R., Griffin, B., Newton, R., Jackson, G., Menon, 20. Pekarsky, Y., Drusco, A., Kumchala, P., Croce, C. M., and Zanesi, N. (2015) G., Harrison, C. J., Israels, T., and Bailey, S. (2012) Burkitt’s lymphoma. The long journey of TCL1 transgenic mice: lessons learned in the last 15 Lancet 379, 1234–1244 years. Gene Expr. 16, 129–135

3. Adams, J. M., Harris, A. W., Pinkert, C. A., Corcoran, L. M., Alexander, http://www.mcponline.org/ 21. Simonetti, G., Bertilaccio, M. T., Ghia, P., and Klein, U. (2014) Mouse W. S., Cory, S., Palmiter, R. D., and Brinster, R. L. (1985) The c-myc models in the study of chronic lymphocytic leukemia pathogenesis and oncogene driven by immunoglobulin enhancers induces lymphoid ma- therapy. Blood 124, 1010–1019 lignancy in transgenic mice. Nature 318, 533–538 22. Wilhelm, M., Schlegl, J., Hahne, H., Moghaddas Gholami, A., Lieberenz, M., 4. Dang, C. V., O’Donnell, K. A., Zeller, K. I., Nguyen, T., Osthus, R. C., and Li, Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., Ma- F. (2006) The c-Myc target gene network. Semin. Cancer Biol. 16, thieson, T., Lemeer, S., Schnatbaum, K., Reimer, U., Wenschuh, H., 253–264 Mollenhauer, M., Slotta-Huspenina, J., Boese, J. H., Bantscheff, M., 5. Zeller, K. I., Jegga, A. G., Aronow, B. J., O’Donnell, K. A., and Dang, C. V. Gerstmair, A., Faerber, F., and Kuster, B. (2014) Mass-spectrometry- (2003) An integrated database of genes responsive to the Myc oncogenic based draft of the human proteome. Nature 509, 582–587 transcription factor: identification of direct genomic targets. Genome

23. Kim, M. S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chae- by guest on September 10, 2018 Biol. 4, R69 rkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., Thomas, 6. Blackwell, T. K., Kretzner, L., Blackwood, E. M., Eisenman, R. N., and J. K., Muthusamy, B., Leal-Rojas, P., Kumar, P., Sahasrabuddhe, N. A., Weintraub, H. (1990) Sequence-specific DNA binding by the c-Myc protein. Science 250, 1149–1151 Balakrishnan, L., Advani, J., George, B., Renuse, S., Selvan, L. D., Patil, 7. Nesbit, C. E., Tersak, J. M., and Prochownik, E. V. (1999) MYC oncogenes A. H., Nanjappa, V., Radhakrishnan, A., Prasad, S., Subbannayya, T., and human neoplastic disease. Oncogene 18, 3004–3016 Raju, R., Kumar, M., Sreenivasamurthy, S. K., Marimuthu, A., Sathe, 8. Harris, A. W., Pinkert, C. A., Crawford, M., Langdon, W. Y., Brinster, R. L., G. J., Chavan, S., Datta, K. K., Subbannayya, Y., Sahu, A., Yelamanchi, and Adams, J. M. (1988) The E mu-myc transgenic mouse. A model for S. D., Jayaram, S., Rajagopalan, P., Sharma, J., Murthy, K. R., Syed, N., high-incidence spontaneous lymphoma and leukemia of early B cells. J. Goel, R., Khan, A. A., Ahmad, S., Dey, G., Mudgal, K., Chatterjee, A., Exp. Med. 167, 353–371 Huang, T. C., Zhong, J., Wu, X., Shaw, P. G., Freed, D., Zahari, M. S., 9. Mori, S., Rempel, R. E., Chang, J. T., Yao, G., Lagoo, A. S., Potti, A., Bild, Mukherjee, K. K., Shankar, S., Mahadevan, A., Lam, H., Mitchell, C. J., A., and Nevins, J. R. (2008) Utilization of pathway signatures to reveal Shankar, S. K., Satishchandra, P., Schroeder, J. T., Sirdeshmukh, R., distinct types of B lymphoma in the Emicro-myc model and human Maitra, A., Leach, S. D., Drake, C. G., Halushka, M. K., Prasad, T. S., diffuse large B-cell lymphoma. Cancer Res. 68, 8525–8534 Hruban, R. H., Kerr, C. L., Bader, G. D., Iacobuzio-Donahue, C. A., 10. Zhu, D., Qi, C. F., Morse, H. C., 3rd, Janz, S., and Stevenson, F. K. (2005) Gowda, H., and Pandey, A. (2014) A draft map of the human proteome. Deregulated expression of the Myc cellular oncogene drives develop- Nature 509, 575–581 ment of mouse “Burkitt-like” lymphomas from naive B cells. Blood 105, 24. Nanjappa, V., Thomas, J. K., Marimuthu, A., Muthusamy, B., Radhakrish- 2135–2137 nan, A., Sharma, R., Ahmad Khan, A., Balakrishnan, L., Sahasrabuddhe, 11. Hallek, M., Cheson, B. D., Catovsky, D., Caligaris-Cappio, F., Dighiero, G., N. A., Kumar, S., Jhaveri, B. N., Sheth, K. V., Kumar Khatana, R., Shaw, Dohner, H., Hillmen, P., Keating, M. J., Montserrat, E., Rai, K. R., Kipps, P. G., Srikanth, S. M., Mathur, P. P., Shankar, S., Nagaraja, D., Christo- T. J., and International Workshop on Chronic Lymphocytic, L. (2008) pher, R., Mathivanan, S., Raju, R., Sirdeshmukh, R., Chatterjee, A., Guidelines for the diagnosis and treatment of chronic lymphocytic leu- Simpson, R. J., Harsha, H. C., Pandey, A., and Prasad, T. S. (2014) kemia: a report from the International Workshop on Chronic Lymphocytic Plasma Proteome Database as a resource for proteomics research: 2014 Leukemia updating the National Cancer Institute-Working Group 1996 update. Nucleic Acids Res. 42, D959–965 guidelines. Blood 111, 5446–5456 25. Keshishian, H., Burgess, M. W., Gillette, M. A., Mertins, P., Clauser, K. R., 12. Hallek, M., Cheson, B. D., Catovsky, D., Caligaris-Cappio, F., Dighiero, Mani, D. R., Kuhn, E. W., Farrell, L. A., Gerszten, R. E., and Carr, S. A. G., Dohner, H., Hillmen, P., Keating, M. J., Montserrat, E., Rai, K. R., (2015) Multiplexed, quantitative workflow for sensitive biomarker discov- and Kipps, T. J. (2008) Guidelines for the diagnosis and treatment ery in plasma yields novel candidates for early myocardial injury. Mol. of chronic lymphocytic leukemia: a report from the International Cell. Proteomics 14, 2375–2393 Workshop on Chronic Lymphocytic Leukemia updating the 26. Jones, K. A., Kim, P. D., Patel, B. B., Kelsen, S. G., Braverman, A., Swinton, National Cancer Institute-Working Group 1996 guidelines. Blood 111, D. J., Gafken, P. R., Jones, L. A., Lane, W. S., Neveu, J. M., Leung, H. C., 5446–5456 Shaffer, S. A., Leszyk, J. D., Stanley, B. A., Fox, T. E., Stanley, A., Hall, M. J., 13. Herling, M., Patel, K. A., Khalili, J., Schlette, E., Kobayashi, R., Medeiros, Hampel, H., South, C. D., de la Chapelle, A., Burt, R. W., Jones, D. A., L. J., and Jones, D. (2006) TCL1 shows a regulated expression pattern in Kopelovich, L., and Yeung, A. T. (2013) Immunodepletion plasma proteom-

404 Molecular & Cellular Proteomics 16.3 Cellular and Plasma Proteomics of B-cell Tumors

ics by tripleTOF 5600 and Orbitrap elite/LTQ-Orbitrap Velos/Q exactive RHAMM/HMMR (CD168) is not an ideal target antigen for immunother- mass spectrometers. J. Proteome Res. 12, 4351–4365 apy of acute myeloid leukemia. Haematologica 97, 1539–1547 27. Patel, B. B., Barrero, C. A., Braverman, A., Kim, P. D., Jones, K. A., Chen, 42. Giannopoulos, K., Li, L., Bojarska-Junak, A., Rolinski, J., Dmoszynska, A., D. E., Bowler, R. P., Merali, S., Kelsen, S. G., and Yeung, A. T. (2012) Hus, I., Greiner, J., Renner, C., Dohner, H., and Schmitt, M. (2006) Expres- Assessment of two immunodepletion methods: off-target effects and sion of RHAMM/CD168 and other tumor-associated antigens in patients variations in immunodepletion efficiency may confound plasma pro- with B-cell chronic lymphocytic leukemia. Int. J. Oncol. 29, 95–103 teomics. J. Proteome Res. 11, 5947–5958 43. Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta- 28. Al-Daghri, N. M., Al-Attas, O. S., Johnston, H. E., Singhania, A., Alokail, Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., Kuhn, M., M. S., Alkharfy, K. M., Abd-Alrahman, S. H., Sabico, S. L., Roumeliotis, Bork, P., Jensen, L. J., and von Mering, C. (2015) STRING v10: protein- T. I., Manousopoulou-Garbis, A., Townsend, P. A., Woelk, C. H., Chrou- protein interaction networks, integrated over the tree of life. Nucleic sos, G. P., and Garbis, S. D. (2014) Whole serum 3D LC-nESI-FTMS Acids Res. 43, D447–452 quantitative proteomics reveals sexual dimorphism in the milieu interieur 44. Kramer, A., Green, J., Pollard, J., Jr., and Tugendreich, S. (2014) Causal of overweight and obese adults. J. Proteome Res. 13, 5094–5105 analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 29. Garbis, S. D., Roumeliotis, T. I., Tyritzis, S. I., Zorpas, K. M., Pavlakis, K., 523–530 and Constantinides, C. A. (2011) A novel multidimensional protein iden- 45. Eom, G. H., Nam, Y. S., Oh, J. G., Choe, N., Min, H. K., Yoo, E. K., Kang, G., tification technology approach combining protein size exclusion prefrac- Nguyen, V. H., Min, J. J., Kim, J. K., Lee, I. K., Bassel-Duby, R., Olson, E. N., tionation, peptide zwitterion-ion hydrophilic interaction chromatography, Park, W. J., and Kook, H. (2014) Regulation of acetylation of histone and nano-ultraperformance RP chromatography/nESI-MS2 for the in- deacetylase 2 by p300/CBP-associated factor/histone deacetylase 5 in the depth analysis of the serum proteome and phosphoproteome: applica- development of cardiac hypertrophy. Circ. Res. 114, 1133–1143 tion to clinical sera derived from humans with benign prostate hyperpla- 46. Ochs, R. C., and Bagg, A. (2012) Molecular genetic characterization of sia. Anal. Chem. 83, 708–718 lymphoma: application to cytology diagnosis. Diagnostic Cytopathol. 40,

30. Adeghe, A. J., and Cohen, J. (1986) A better method for terminal bleeding 542–555 Downloaded from of mice. Lab. Animals 20, 70–72 47. Love, C., Sun, Z., Jima, D., Li, G., Zhang, J., Miles, R., Richards, K. L., 31. Hahne, H., Pachl, F., Ruprecht, B., Maier, S. K., Klaeger, S., Helm, D., Dunphy, C. H., Choi, W. W., Srivastava, G., Lugar, P. L., Rizzieri, D. A., Medard, G., Wilm, M., Lemeer, S., and Kuster, B. (2013) DMSO enhances Lagoo, A. S., Bernal-Mizrachi, L., Mann, K. P., Flowers, C. R., Naresh, electrospray response, boosting sensitivity of proteomic experiments. K. N., Evens, A. M., Chadburn, A., Gordon, L. I., Czader, M. B., Gill, J. I., Nat. Methods 10, 989–991 Hsi, E. D., Greenough, A., Moffitt, A. B., McKinney, M., Banerjee, A., 32. Vizcaino, J. A., Deutsch, E. W., Wang, R., Csordas, A., Reisinger, F., Rios, Grubor, V., Levy, S., Dunson, D. B., and Dave, S. S. (2012) The genetic D., Dianes, J. A., Sun, Z., Farrah, T., Bandeira, N., Binz, P. A., Xenarios, landscape of mutations in Burkitt lymphoma. Nat. Gen. 44, 1321–1325 http://www.mcponline.org/ I., Eisenacher, M., Mayer, G., Gatto, L., Campos, A., Chalkley, R. J., 48. Yustein, J. T., Liu, Y. C., Gao, P., Jie, C., Le, A., Vuica-Ross, M., Chng, Kraus, H. J., Albar, J. P., Martinez-Bartolome, S., Apweiler, R., Omenn, W. J., Eberhart, C. G., Bergsagel, P. L., and Dang, C. V. (2010) Induction G. S., Martens, L., Jones, A. R., and Hermjakob, H. (2014) ProteomeX- of ectopic Myc target gene JAG2 augments hypoxic growth and tumor- change provides globally coordinated proteomics data submission and igenesis in a human B-cell model. Proc. Natl. Acad. Sci. U. S. A. 107, dissemination. Nat. Biotechnol. 32, 223–226 3534–3539 33. Levin, Y. (2011) The role of statistical power analysis in quantitative pro- 49. Xiao, W., Tran, B., Staudt, L. M., and Schmitz, R. (2013) High-throughput teomics. Proteomics 11, 2565–2567 RNA sequencing in B-cell lymphomas. Meth. Mol. Biol. 971, 295–312 34. Blunt, M. D., Carter, M. J., Larrayoz, M., Smith, L. D., Aguilar-Hernandez, 50. Gygi, S. P., Rochon, Y., Franza, B. R., and Aebersold, R. (1999) Correlation

M., Cox, K. L., Tipton, T., Reynolds, M., Murphy, S., Lemm, E., Dias, S., between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, by guest on September 10, 2018 Duncombe, A., Strefford, J. C., Johnson, P. W., Forconi, F., Stevenson, 1720–1730 F. K., Packham, G., Cragg, M. S., and Steele, A. J. (2015) The PI3K/ 51. Vogel, C., and Marcotte, E. M. (2012) Insights into the regulation of protein mTOR inhibitor PF-04691502 induces apoptosis and inhibits microenvi- abundance from proteomic and transcriptomic analyses. Nat. Rev. ronmental signaling in CLL and the Emicro-TCL1 mouse model. Blood Genet. 13, 227–232 125, 4032–4041 52. Schwanhausser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., 35. Hussain, K., Hargreaves, C. E., Roghanian, A., Oldham, R. J., Chan, H. T., Chen, W., and Selbach, M. (2011) Global quantification of mammalian Mockridge, C. I., Chowdhury, F., Frendeus, B., Harper, K. S., Strefford, gene expression control. Nature 473, 337–342 J. C., Cragg, M. S., Glennie, M. J., Williams, A. P., and French, R. R. 53. Tabarkiewicz, J., and Giannopoulos, K. (2010) Definition of a target for (2015) Upregulation of FcgammaRIIb on monocytes is necessary to immunotherapy and results of the first Peptide vaccination study in promote the superagonist activity of TGN1412. Blood 125, 102–110 chronic lymphocytic leukemia. Transplant Proc. 42, 3293–3296 36. Ivanov, A., Cragg, M. S., Erenpreisa, J., Emzinsh, D., Lukman, H., and 54. Solana, R., Romero, J., Alonso, C., and Pena, J. (1992) MHC class I antigen Illidge, T. M. (2003) Endopolyploid cells produced after severe genotoxic expression is inversely related with tumor malignancy and ras oncogene damage have the potential to repair DNA double strand breaks. J. Cell product (p21ras) levels in human breast tumors. Invasion Metastasis 12, Sci. 116, 4095–4106 210–217 37. Farrah, T., Deutsch, E. W., Omenn, G. S., Campbell, D. S., Sun, Z., Bletz, 55. Paulson, K. G., Tegeder, A., Willmes, C., Iyer, J. G., Afanasiev, O. K., J. A., Mallick, P., Katz, J. E., Malmstrom, J., Ossola, R., Watts, J. D., Lin, Schrama, D., Koba, S., Thibodeau, R., Nagase, K., Simonson, W. T., Seo, B., Zhang, H., Moritz, R. L., and Aebersold, R. (2011) A high-confidence A., Koelle, D. M., Madeleine, M., Bhatia, S., Nakajima, H., Sano, S., human plasma proteome reference set with estimated concentrations in Hardwick, J. S., Disis, M. L., Cleary, M. A., Becker, J. C., and Nghiem, P. PeptideAtlas. Mol. Cell. Proteomics 10, M110 006353 (2014) Downregulation of MHC-I expression is prevalent but reversible in 38. Rounbehler, R. J., Fallahi, M., Yang, C., Steeves, M. A., Li, W., Doherty, Merkel cell carcinoma. Cancer Immunol. Res. 2, 1071–1079 J. R., Schaub, F. X., Sanduja, S., Dixon, D. A., Blackshear, P. J., and 56. Gaudet, F., Hodgson, J. G., Eden, A., Jackson-Grusby, L., Dausman, J., Cleveland, J. L. (2012) Tristetraprolin impairs myc-induced lymphoma Gray, J. W., Leonhardt, H., and Jaenisch, R. (2003) Induction of tumors and abolishes the malignant state. Cell 150, 563–574 in mice by genomic hypomethylation. Science 300, 489–492 39. Ow, S. Y., Salim, M., Noirel, J., Evans, C., and Wright, P. C. (2011) Mini- 57. Hopp, L., Nersisyan, L., Loffler-Wirth, H., Arakelyan, A., and Binder, H. mising iTRAQ ratio compression through understanding LC-MS elution (2015) Epigenetic heterogeneity of B-Cell lymphoma: chromatin modifi- dependence and high-resolution HILIC fractionation. Proteomics 11, ers. Genes 6, 1076–1112 2341–2346 58. Li, S., Garrett-Bakelman, F. E., Chung, S. S., Sanders, M. A., Hricik, T., 40. Karp, N. A., Huber, W., Sadowski, P. G., Charles, P. D., Hester, S. V., and Rapaport, F., Patel, J., Dillon, R., Vijay, P., Brown, A. L., Perl, A. E., Lilley, K. S. (2010) Addressing accuracy and precision issues in iTRAQ Cannon, J., Bullinger, L., Luger, S., Becker, M., Lewis, I. D., To, L. B., quantitation. Mol. Cell. Proteomics 9, 1885–1897 Delwel, R., Lowenberg, B., Dohner, H., Dohner, K., Guzman, M. L., 41. Snauwaert, S., Vanhee, S., Goetgeluk, G., Verstichel, G., Van Caeneghem, Hassane, D. C., Roboz, G. J., Grimwade, D., Valk, P. J., D’Andrea, Y., Velghe, I., Philippe, J., Berneman, Z. N., Plum, J., Taghon, T., R. J., Carroll, M., Park, C. Y., Neuberg, D., Levine, R., Melnick, A. M., Leclercq, G., Thielemans, K., Kerre, T., and Vandekerckhove, B. (2012) and Mason, C. E. (2016) Distinct evolution and dynamics of epigenetic

Molecular & Cellular Proteomics 16.3 405 Cellular and Plasma Proteomics of B-cell Tumors

and genetic heterogeneity in acute myeloid leukemia. Nat. Med. 22, (1994) IL-5 receptor-mediated tyrosine phosphorylation of SH2/SH3- 792–799 containing proteins and activation of Bruton’s tyrosine and Janus 2 59. Pollard, T. D., and Borisy, G. G. (2003) Cellular motility driven by assembly kinases. J. Exp. Med. 180, 2101–2111 and disassembly of actin filaments. Cell 112, 453–465 77. Coffer, P. J., Schweizer, R. C., Dubois, G. R., Maikoe, T., Lammers, J. W., 60. Foger, N., Rangell, L., Danilenko, D. M., and Chan, A. C. (2006) Require- and Koenderman, L. (1998) Analysis of signal transduction pathways in ment for coronin 1 in T lymphocyte trafficking and cellular homeostasis. human eosinophils activated by chemoattractants and the T-helper Science 313, 839–842 2-derived cytokines interleukin-4 and interleukin-5. Blood 91, 61. Haraldsson, M. K., Louis-Dit-Sully, C. A., Lawson, B. R., Sternik, G., San- 2547–2557 tiago-Raber, M. L., Gascoigne, N. R., Theofilopoulos, A. N., and Kono, 78. Adachi, T., and Alam, R. (1998) The mechanism of IL-5 signal transduction. D. H. (2008) The lupus-related Lmb3 locus contains a disease-suppress- Am. J. Physiol. 275, C623–633 ing Coronin-1A gene mutation. Immunity 28, 40–51 79. Wetzel, G. D. (1989) Interleukin 5 regulation of peritoneal Ly-1 B lymphocyte 62. Al Tanoury, Z., Schaffner-Reckinger, E., Halavatyi, A., Hoffmann, C., Moes, proliferation, differentiation and autoantibody secretion. Eur. J. Immunol. M., Hadzic, E., Catillon, M., Yatskou, M., and Friederich, E. (2010) Quan- 19, 1701–1707 titative kinetic study of the actin-bundling protein L-plastin and of its 80. Sanderson, C. J. (1988) Interleukin-5: an eosinophil growth and activation impact on actin turn-over. PLoS One 5, e9210 factor. Dev. Biol. Stand. 69, 23–29 63. Todd, E. M., Deady, L. E., and Morley, S. C. (2011) The actin-bundling 81. Kopf, M., Brombacher, F., Hodgkin, P. D., Ramsay, A. J., Milbourne, E. A., protein L-plastin is essential for marginal zone B cell development. Dai, W. J., Ovington, K. S., Behm, C. A., Kohler, G., Young, I. G., and J. Immunol. 187, 3015–3025 Matthaei, K. I. (1996) IL-5-deficient mice have a developmental defect in 64. Todd, E. M., Deady, L. E., and Morley, S. C. (2013) Intrinsic T- and B-cell CD5ϩ B-1 cells and lack eosinophilia but have normal antibody and defects impair T-cell-dependent antibody responses in mice lacking the cytotoxic T cell responses. Immunity 4, 15–24 actin-bundling protein L-plastin. Eur. J. Immunol. 43, 1735–1744 82. Clutterbuck, E., Shields, J. G., Gordon, J., Smith, S. H., Boyd, A., Callard,

65. Parikh, S. S., Litherland, S. A., Clare-Salzler, M. J., Li, W., Gulig, P. A., and R. E., Campbell, H. D., Young, I. G., and Sanderson, C. J. (1987) Re- Downloaded from Southwick, F. S. (2003) CapG(-/-) mice have specific host defense de- combinant human interleukin 5 is an eosinophil differentiation factor but ϩ ϩ fects that render them more susceptible than CapG( / ) mice to Listeria has no activity in standard human B cell growth factor assays. Eur. monocytogenes infection but not to Salmonella enterica serovar Typhi- J. Immunol. 17, 1743–1750 71 murium infection. Infect. Immun. , 6582–6590 83. Mainou-Fowler, T., and Prentice, A. G. (1996) Modulation of apoptosis with 66. Foran, E., McWilliam, P., Kelleher, D., Croke, D. T., and Long, A. (2006) The cytokines in B-cell chronic lymphocytic leukaemia. Leukemia Lymphoma leukocyte protein L-plastin induces proliferation, invasion and loss of 21, 369–377 E-cadherin expression in colon cancer cells. Int. J. Cancer. 118, http://www.mcponline.org/ 84. van Winden, A. W., van den Broek, I., Gast, M. C., Engwegen, J. Y., 2098–2104 Sparidans, R. W., van Dulken, E. J., Depla, A. C., Cats, A., Schellens, 67. Glaser, J., Neumann, M. H., Mei, Q., Betz, B., Seier, N., Beyer, I., Fehm, J. H., Peeters, P. H., Beijnen, J. H., and van Gils, C. H. (2010) Serum T., Neubauer, H., Niederacher, D., and Fleisch, M. C. (2014) Macro- degradome markers for the detection of breast cancer. J. Proteome Res. phage capping protein CapG is a putative oncogene involved in mi- 9, 3781–3788 gration and invasiveness in ovarian carcinoma. Biomed. Res. Int. 2014, 85. Karczmarski, J., Rubel, T., Mikula, M., Wolski, J., Rutkowski, A., Zagorow- 379847 icz, E., Dadlez, M., and Ostrowski, J. (2013) Pre-analytical-related vari- 68. Shankar, J., Messenberg, A., Chan, J., Underhill, T. M., Foster, L. J., and ability influencing serum peptide profiles demonstrated in a mass spec- Nabi, I. R. (2010) Pseudopodial actin dynamics control epithelial-mes- trometry-based search for colorectal and prostate cancer biomarkers. enchymal transition in metastatic cancer cells. Cancer Res. 70,

Acta Biochim. Pol. 60, 417–425 by guest on September 10, 2018 3780–3790 86. Yang, J., Yang, J., Gao, Y., Zhao, L., Liu, L., Qin, Y., Wang, X., Song, T., and 69. Kriss, C. L., Pinilla-Ibarz, J. A., Mailloux, A. W., Powers, J. J., Tang, C. H., Huang, C. (2014) Identification of potential serum proteomic biomarkers Kang, C. W., Zanesi, N., Epling-Burnette, P. K., Sotomayor, E. M., for clear cell renal cell carcinoma. PLoS One 9, e111364 Croce, C. M., Del Valle, J. R., and Hu, C. C. (2012) Overexpression of TCL1 activates the endoplasmic reticulum stress response: a novel 87. Davidson, M. B., Thakkar, S., Hix, J. K., Bhandarkar, N. D., Wong, A., and mechanism of leukemic progression in mice. Blood 120, 1027–1038 Schreiber, M. J. (2004) Pathophysiology, clinical consequences, and 70. Iacovelli, S., Hug, E., Bennardo, S., Duehren-von Minden, M., Gobessi, S., treatment of tumor lysis syndrome. Am. J. Med. 116, 546–554 Rinaldi, A., Suljagic, M., Bilbao, D., Bolasco, G., Eckl-Dorna, J., Nieder- 88. Wossmann, W., Schrappe, M., Meyer, U., Zimmermann, M., and Reiter, A. berger, V., Autore, F., Sica, S., Laurenti, L., Wang, H., Cornall, R. J., (2003) Incidence of tumor lysis syndrome in children with advanced Clarke, S. H., Croce, C. M., Bertoni, F., Jumaa, H., and Efremov, D. G. stage Burkitt’s lymphoma/leukemia before and after introduction of pro- (2015) Two types of BCR interactions are positively selected during phylactic use of urate oxidase. Ann. Hematol. 82, 160–165 leukemia development in the Emu-TCL1 transgenic mouse model of 89. Otrock, Z. K., Hatoum, H. A., and Salem, Z. M. (2008) Acute tumor lysis CLL. Blood 125, 1578–1588 syndrome after rituximab administration in Burkitt’s lymphoma. Intern. 71. Slavin, S., and Strober, S. (1978) Spontaneous murine B-cell leukaemia. Emerg. Med. 3, 161–163 Nature 272, 624–626 90. Misra, S., Hascall, V. C., Markwald, R. R., and Ghatak, S. (2015) Interactions 72. O’Garra, A., Barbis, D., Wu, J., Hodgkin, P. D., Abrams, J., and Howard, M. between hyaluronan and its receptors (CD44, RHAMM) Regulate the (1989) The BCL1 B lymphoma responds to IL-4, IL-5, and GM-CSF. Cell activities of inflammation and cancer. Front. Immunol. 6, 201 Immunol. 123, 189–200 91. Sponziello, M., Rosignolo, F., Celano, M., Maggisano, V., Pecce, V., De 73. Lohoff, M., Sommer, F., and Rollinghoff, M. (1989) Suppressive effect of Rose, R. F., Lombardo, G. E., Durante, C., Filetti, S., Damante, G., Russo, interferon-gamma on the BCL1 cell-dependent interleukin 5 bioassay. D., and Bulotta, S. (2016) Fibronectin-1 expression is increased in ag- Eur. J. Immunol. 19, 1327–1329 gressive thyroid cancer and favors the migration and invasion of cancer 74. Lasky, J. L., and Thorbecke, G. J. (1989) Characterization and growth factor cells. Mol. Cell. Endocrinol. 431, 123–132 requirements of SJL lymphomas. II. Interleukin 5 dependence of the in 92. Zhang, J., Zhang, K., Jiang, X., and Zhang, J. (2014) S100A6 as a potential vitro cell line, cRCS-X, and influence of other cytokines. Eur. J. Immunol. serum prognostic biomarker and therapeutic target in gastric cancer. 19, 365–371 Dig. Dis. Sci. 59, 2136–2144 75. Wen, X., Zhang, D., Kikuchi, Y., Jiang, Y., Nakamura, K., Xiu, Y., Tsurui, H., 93. Nishi, M., Matsumoto, K., Kobayashi, M., Yanagita, K., Matsumoto, T., Takahashi, K., Abe, M., Ohtsuji, M., Nishimura, H., Takatsu, K., Shirai, T., Nagashio, R., Ishii, D., Fujita, T., Sato, Y., and Iwamura, M. (2014) and Hirose, S. (2004) Transgene-mediated hyper-expression of IL-5 Serum expression of S100A6 is a potential detection marker in patients inhibits autoimmune disease but increases the risk of B cell chronic with urothelial carcinoma in the urinary bladder. Biomed. Res. 35, lymphocytic leukemia in a model of murine lupus. Eur. J. Immunol. 34, 351–356 2740–2749 94. Supek, F., Bosnjak, M., Skunca, N., and Smuc, T. (2011) REVIGO summa- 76. Sato, S., Katagiri, T., Takaki, S., Kikuchi, Y., Hitoshi, Y., Yonehara, S., rizes and visualizes long lists of gene ontology terms. PLoS One 6, Tsukada, S., Kitamura, D., Watanabe, T., Witte, O., and Takatsu, K. e21800

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