Proteomics, genomics and transcriptomics: their emerging roles in the discovery and validation of colorectal cancer biomarkers.

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SPECIAL FOCUS y Proteomics in translational cancer research

Review

Proteomics, genomics and transcriptomics: their emerging roles in the discovery and validation of colorectal cancer biomarkers Expert Rev. Proteomics 11(2), 179–205 (2014)

Kui Wang1,2, Canhua Huang*1 and Edouard Collins Nice*2 1 The State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, P.R. China 2 Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia *Authors for correspondence: Tel.: +86 132 5837 0346; +61 042 134 6716 Fax: +86 288 516 4060; +61 399 029 500 [email protected]; [email protected]

Colorectal cancer (CRC) is the second most common cancer in females and the third in males. Since CRC is often diagnosed at an advanced stage when prognosis is poor, identification of biomarkers for early diagnosis is urgently required. Recent advances in proteomics, genomics and transcriptomics have facilitated high-throughput profiling of data generated from CRC-related genes and proteins, providing a window of information for biomarker discovery and validation. However, transfer of candidate biomarkers from bench to bedside remains a dilemma. In this review, we will discuss emerging proteomic technologies and highlight various sample types utilized for proteomics-based identification of CRC biomarkers. Moreover, recent breakthroughs in genomics and transcriptomics for the identification of CRC biomarkers, with particular emphasis on the merits of emerging methylomic and miRNAomic strategies, will be discussed. Integration of proteomics, genomics and transcriptomics will facilitate the discovery and validation of CRC biomarkers leading to the emergence of personalized medicine. KEYWORDS: colorectal cancer • genomics • methylomics • miRNAomics • proteomics • transcriptomics

Colorectal cancer (CRC) is the second leading cause of cancer-related death in the developed world and the fourth worldwide [1,2]. Over 1.2 million new cases occur annually with nearly 50% of the patients ultimately dying from their malignancy [1]. Approximately 45% of CRC cases are diagnosed at an advanced stage when the tumor has already metastasized and the 5-year survival rate is less than 10% [3]. By contrast >90% of the patients can be cured of their disease by surgical resection if diagnosis is made when the tumor is still localized [4]. A number of screening modalities including colonoscopy, the fecal occult blood test (FOBT), stool DNA (sDNA) tests and serum carcinoembryonic antigen (CEA) levels have been investigated for CRC diagnosis. Colonoscopy is regarded as the gold standard for the diagnosis of colorectal polyps, adenomas and carcinomas, but it is expensive and invasive with poor patient compliance [5]. FOBT is informahealthcare.com

10.1586/14789450.2014.894466

cost-effective and less invasive and is the most widely adopted primary screening method, but is severely limited by suboptimal specificity and selectivity, resulting in both false-negative and false-positive results [6]. For this reason, all positive FOBTs require follow-up colonoscopy. Any significant improvements in assay sensitivity and specificity would immediately result in a reduction in the number of unnecessary colonoscopies that are currently being performed due to false-positive FOBT results. sDNA tests to screen for CRC have evolved significantly over time; however, the jury is still out on their suitability for early detection of CRC [7]. Serum CEA levels are effective for the postoperative detection of recurrence, but show poor sensitivity for early detection [4]. Hence, alternative cost-effective, non-invasive, easily-performed screening modalities with sufficient sensitivity and specificity are urgently required for CRC screening.

2014 Informa UK Ltd

ISSN 1478-9450

179

Review

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The first genome-wide insight into the global changes of DNA methylation affecting CpG islands in CRC


1999

2000

The first study applied genome-wide oligonucleotide array to demonstrate the differentially expression patterns between CRC tissues and normal colon mucosa

The first human colorectal miRNAome was characterized

2006

The most comprehensive characterization of genomic and transcriptomic alterations of CRC, including profiling of exome sequence, DNA copy number, promoter methylation, mRNA and miRNA expression

2007

The first whole sequencing of exons from CRC patients

2011

2012

The first wholegenome sequencing of CRC

Figure 1. Timeline showing crucial genomic and transcriptomic studies for colorectal cancer biomarker discovery. CRC: Colorectal cancer.

The use of molecular biomarkers, or panels of biomarkers, for early detection, prognosis and surveillance of CRC, or to guide patient therapy, is looking promising [8]; such markers should provide better sensitivity and the assays themselves are easier to perform than many of the current screening modalities [9]. It should be noted here that while many biomarker candidates may not be robust as individual biomarkers for CRC, they can be effective when used in a panel of biomarkers where each marker represents a different aspect of the tumor biology. In particular, the advent of newer high-throughput proteomic, genomic and transcriptomic strategies has invigorated research into biomarker discovery, identification and validation [9]. By using these state-of-the-art technologies, a large pool of CRC candidate biomarkers has been identified in a range of sample types. Although the majority of these candidate biomarkers have not yet been evaluated in detailed proof-ofprinciple studies, they provide an excellent resource for future studies examining the molecular mechanisms underlying CRC progression and pave the way for validation of novel biomarkers and biomarker panels with clinical utility [10,11]. In this review, we will focus on the application of a number of high-throughput proteomic approaches interrogating various human CRC sample types, including tumor cell lines, tumor tissues, serum/plasma and feces, for the discovery and validation of potential CRC biomarkers. In addition, employing meta-analysis, we provide a comprehensive list of previously reported CRCassociated proteins (CCAPs) to facilitate further studies SUPPLEMENTARY TABLE 1 (supplementary material can be found online at www. informahealthcare.com/suppl/10.1586/14789450.2014.894466). Furthermore, we will summarize recent advances in the identification of CRC biomarkers by genomics and transcriptomics, with particular emphasis on the merits of both methylomics and miRNAomics (FIGURE 1). The integration of proteomics, genomics and transcriptomics in a systems biology approach will facilitate the development of improved non-invasive tests using novel panels of biomarkers with sufficient sensitivity and specificity for 180

effective CRC diagnosis and disease surveillance and help lead the development of personalized medicine in the near future. Proteomics in the discovery & validation of CRC biomarkers

The proteome is functionally translated from the genome and transcriptome to gain exquisite control of specific biological phenomena at the protein level [12]. The blossoming of proteomic strategies in recent years has resulted in a major boost for cancer biomarker discovery [13]. Although the evolution of mass spectrometry (MS)-based technologies has greatly advanced our understanding of the nature of proteome, the sample heterogeneity, complexity and size of the human proteome poses larger hurdles than those encountered in genomic and transcriptomic research [12]. We will introduce some of the important recent advances in proteomic technology, which will help overcome these hurdles and detail the potential CRC biomarkers identified from the analysis of a range of human biological samples. Proteomic strategies for the discovery & validation of CRC biomarkers Discovery proteomics

Bottom-up proteomics (also referred to as shotgun proteomics or discovery proteomics) is currently the most commonly used strategy in proteomic studies [14]. In bottom-up proteomics, protein mixtures are digested into peptides, these peptides are then typically fractionated by liquid chromatography (LC), ionized and analyzed by MS [15]. To uncover candidate disease biomarkers, the differential abundance of proteins from different biological processes or disease states is analyzed. To this end, sensitive and specific quantitative proteomic strategies, complimenting the basic bottom-up proteome profiling strategy, have been developed and are now widely used for biomarker discovery (FIGURE 2) [16]. 2D electrophoresis (2DE) coupled with MS/MS has been extensively used to identify differentially expressed proteins for the discovery of CRC biomarkers [17]. Proteins are separated on Expert Rev. Proteomics 11(2), (2014)


Discovery & validation of colorectal cancer biomarkers

Review

gels and then the relative abundance 2-DE obtained by comparing the staining densiGel-based ties. However, proteins with extreme isoapproach 2D-DIGE electric points or molecular weight are not Spectral compatible with the gels. In addition, counting Label-free Quantitative hydrophobic membrane proteins and low quantitation proteomics Ion abundant proteins are not easily separated intensity or detected by 2DE [18]. By labeling the proteins with fluorescent tags, the fluoresSILAC MS-based MALDI Biomarker discovery cence difference 2D gel electrophoresis imaging approach and validation (2D-DIGE) has been developed, in which ICAT each of the samples are tagged with massSRM/MRM and charge-matched, spectrally resolvable Stable isotope Targeted iTRAQ labeling fluorescent dyes, the samples are then proteomics Hyperplexmixed and run on the same 2D gel, which MRM TMT eliminates any variation between gels. The labeling gels are imaged at the appropriate excitaDimethyl tion wavelength of each dye and the labeling images then superimposed to show differential expression [19,20]. 2D-DIGE is more Figure 2. Common proteomic strategies used for colorectal cancer biomarker discovery and validation. sensitive, accurate and reproducible than 2DE: 2D electrophoresis; 2D-DIGE: 2-D fluorescence difference gel electrophoresis; 2DE, but is expensive and still limited by ICAT: Isotope-coded affinity tag; iTRAQ: Isobaric tags for relative and absolute the separation capabilities of the gels [13,21]. quantitation; MALDI: Matrix-assisted laser desorption/ionization; MRM: Multiple reaction Given the drawbacks of 2DE-based monitoring; MS: Mass spectrometry; SILAC: Stable isotope labeling by amino acids in cell approaches, several MS-based strategies, culture; SRM: Selected reaction monitoring; TMT: Tandem mass tags. including isotope-labeled proteomics and label-free proteomics, have been developed to quantitatively map the proteome. The introduction of an iso- (MALDI) imaging MS (IMS), a powerful tool that allows the topic tag can be achieved by metabolic or chemical labeling [18]. spatial distribution of hundreds of proteins, especially those Stable isotope labeling by amino acids in cell culture (SILAC) is with low molecular weight, through the direct analysis of thin a metabolic labeling method that allows proteins to be labeled on tissue sections [24,25]. selected amino acids (usually Lys and/or Arg) in vivo before cell lysis. SILAC is very effective for monitoring proteome dynamics Targeted proteomics in cell-based systems, but is not suitable for clinical samples [22]. Now that the use of shotgun proteomics has yielded numerous Unlike SILAC, chemical labeling (e.g., isobaric tags for relative candidate disease biomarkers, targeted proteomic strategies (e.g., and absolute quantitation [iTRAQ], tandem mass tags [TMT], multiple reaction monitoring [MRM] or selected reaction moniisotope-coded affinity tag and dimethyl labeling) can introduce toring [SRM] and sequential window acquisition of all theoretical either light or heavy isotopic tags after protein isolation and spectra [SWATH]) have the potential to validate these candidates digestion [16,22]. Thus, chemical labeling strategies are capable of with excellent sensitivity and specificity in a multiplex format with quantitating proteins from clinical samples. However, there are high-throughput capacity, thereby bridging the gap between discertain limitations with these label-based approaches. The intro- covery and validation of the biomarker development pipeline [22,26]. duction of labeling reagents complicates the sample preparation Proteotypic peptides are selected from a list of predetermined preworkflow and increases the cost of analysis and it is impossible to cursor ions and subsequently detected and quantified in multiple achieve complete labeling. Additionally, only iTRAQ and TMT biological samples in a triple quadrupole mass spectrometer [14]. quantitation approaches, which are based on MS2 scans, allow The use of MRM or SWATH potentially enables hundreds of the comparison of multiple (up to eight) samples in one study [23]. candidates to be verified in a single MS analysis without the use of The emergence of label-free proteomics circumvents many of the antibodies. To accomplish the high-throughput manipulation limitations of labeling methods. The quantitation of protein required for the validation of biomarkers, Yin et al. developed a expression by label-free approaches can be performed by spectral Hyperplex-MRM strategy using mTRAQ/iTRAQ labeling for counting or ion intensity [16]. However, label-free quantitation multiplexed absolute quantification. Using this approach, they requires high-end MS instruments and sophisticated software identified three CRC biomarker candidates, adenosylhomocysteinase, cathepsin D and lysozyme C, from tissues of different disease tools, which are beyond the reach of many laboratories [12]. In addition, a number of novel proteomic strategies have stages, with high accuracy, sensitivity and reproducibility [27]. Using these proteomic strategies, a number of CRC biomarkers recently been developed and applied to the discovery of CRC biomarkers including matrix-assisted laser desorption/ionization have been discovered and validated in a range of human biological informahealthcare.com

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samples, including tumor cells, tumor tissues, serum/plasma and feces. In addition, proteomic studies in preclinical in vivo models have also been performed for CRC biomarker discovery.


Proteomic identification & validation of CRC candidate biomarkers in tumor cell lines

In vitro cultured CRC cell lines have been widely used as a starting point for CRC biomarker discovery [3]. They are easy to manipulate in an experimental setting, allowing investigation of specific signaling pathways involved in tumor biology [28]. However, in spite of the advantages, there are also some caveats on the use of CRC cell lines for biomarker discovery. Commonly, CRC cell lines are obtained from malignant tumors, and the corresponding normal epithelial control cells are not readily available, thus restricting comparative studies of normal cells and cells from different stages of CRC [28]. Moreover, cultured cancer cells growing on plastic cannot mimic the in vivo tumor biology arising from the complex tumor microenvironment, which is critically involved in CRC progression [8]. Therefore, validation of potential biomarkers in clinical samples such as tumor tissue, serum or plasma and feces remains pivotal [8]. TABLE 1 lists candidate CRC biomarkers identified in human CRC cell lines by proteomic studies. A number of exemplars are given below, with details on validation protocols. Our group reported a 2DE-MS-based proteomic study on SW480 and SW620 cells, which are derived from Dukes’ stage B colon carcinoma and lymph node metastases (LNM) of the same patient. In total, 63 differentially expressed proteins were identified (35 were upregulated and 28 were downregulated). Among them, ITGB3 was demonstrated to be a key regulator in reactive oxygen species-induced CRC migration and invasion, and the altered expression of ITGB3 was closely correlated with CRC metastatic potential in tissue samples from 46 CRC patients [29]. CRC cell lines are also well suited for the study of the subproteome, such as plasma membrane fractions, the secretome or exosomes [28]. By performing biotinylation followed by affinity purification, membrane proteins of KM12C and KM12SM cells were isolated and analyzed by SILAC-MS. Sixty altered cell surface proteins were identified, 4 of them (cadherin-17, F11R, g-catenin and occludin) were demonstrated to be associated with tumor metastasis [30]. In another study, 44 proteins were identified as promising candidate biomarkers for adenoma-to-carcinoma progression by nano-LC-MS/MS using 5 CRC cell lines [31]. The secretome (the repertoire of proteins released by a cell, tissue or organism) plays an important role in cell growth and differentiation, invasion and metastasis. Proteins secreted by cancer cells can gain access to the body fluids (such as blood or urine), and thus can be a promising model for studying cancer biomarkers [32]. In a secretome study, Xue et al. identified 145 differential proteins from the conditioned medium of SW480 and SW620 cells by label-free quantitative proteomics. Among them, TFF3 and GDF15 were further verified as potential biomarkers for the prediction of CRC metastasis in a large cohort of clinical tissue and serum samples [33]. Another group recently compared 182

the secretome of metastatic KM12SM CRC cells with the parental poorly metastatic KM12C cells using SILAC. One hundred and fifty-five proteins were found to be differentially expressed with greater than 1.5-fold changes, among which GDF15, S100A8/A9 and SERPINI1 showed potential to discriminate cancer serum samples from healthy controls by ELISA. A panel of six proteins (SOSTDC1, CTSS, EFNA3, CD137L/TNFSF9, ZG16B and Midkine) were shown to associate with poor prognosis and overall survival [34]. This study showed the potential of in silico analysis of the dysregulated proteins, showing that a significant percentage of proteins in the metastatic cells were involved in cell adhesion, migration and invasion. Exosomes are small extracellular 40–100 nm diameter membrane vesicles of late endosomal origin that are released by a number of cell types into the extracellular space. They contribute to the formation of the premetastatic niche facilitating survival and growth of disseminated cancer cells, and appear to be a rich source for discovering potential blood-based biomarkers [35]. Recently, Simpson and collaborators purified and profiled the exosomes of the SW480 and SW620 CRC cell lines. They found that several metastatic factors (e.g., MET, S100A8, S100A9, TNC) and signal transduction components (e.g., EFNB2, EGFR, JAG1, SRC, TNIK) were selectively enriched in metastatic SW620 cells [36]. CRC cell line studies can also be directed at analysis of the effect of specific genes (such as SMAD4 [37], BAX [38] and SRC [39]) or response to specific drug treatment (such as butyrate [40,41] and 5-fluorouracil [42]). In addition, recent studies have described how cell lines can be used to probe the tumor microenvironment for biomarker discovery. Differentially expressed proteins in cancerassociated fibroblasts isolated from a mouse model of human sporadic CRC and normal fibroblasts were profiled using iTRAQMS/MS. Validation of human biopsy samples suggested that a panel composing FSTL1, CALU and CDH11 were candidate stromal biomarkers with prognostic significance for CRC [43]. Proteomic identification & validation of CRC candidate biomarkers in tumor tissues

By comparing tissues from different patients with various disease states or samples from histologically different tissue sections/laser dissected tissue from the same patient, it is possible to identify various types of CRC candidate biomarkers, including those for early detection and surveillance of the disease. In order to identify CRC candidate biomarkers for early diagnosis, Xie et al. applied dimethyl isotope labeling and GeLC-MS/MS and identified 501 differentially expressed proteins in CRC tissues from stage I and stage II patients. Pathway analysis highlighted ubiquitination-proteasome and glycolysis/gluconeogenesis pathways as the most regulated in early CRC tumorigenesis. Further studies using tissue array and sera suggested that a1 antitrypsin and cathepsin D could be potential biomarkers for CRC early diagnosis [44]. In another study, purified membrane proteins of 28 paired tumor and adjacent normal tissues from patients in Dukes’ A (n = 4), Dukes’ B (n = 7), Dukes’ C (n = 11) and Dukes’ D (n = 6) stages were analyzed by label-free MS quantitation. Stomatin-like 2 Expert Rev. Proteomics 11(2), (2014)

informahealthcare.com

[169]

2007 HSP27 IHC on 68 CRC 11 proteins 2DE, MALDI-TOF-MS SW480, SW620

2DE: 2D electrophoresis; CAFs: Cancer-associated fibroblasts; CRC: Colorectal cancer; IHC: Immunohistochemistry; MALDI: Matrix-assisted laser desorption/ionization; MS: Mass spectrometry; WB: Western blot.

2008 CRMP-2 53 proteins in CRC cell secretome 1D SDS-PAGE, MALDITOF-MS Colo205, SW480

IHC on 169 CRC, ELISA on 201 CRC patients and 201 healthy controls

[167]

2008 TATI 9 proteins 2DE, MALDI-TOF-MS HT-29 5M21, HT-29 STD

Tumor xenografts

[33]

2010 TFF3, GDF15 145 proteins Label-free MS quantitation, LC-MS/MS SW480, SW620

ELISA on 144 CRC and 156 healthy sera; IHC on 69 CRC

[30]

2010 Cadherin-17, F11R, g-catenin, occludin 60 proteins SILAC, nano-LC-ESI-LTQ KM12C, KM12SM

IHC on 46 CRC tissues

[29]

2011 ITGB3 35 upregulated and 28 downregulated 2DE, ESI-Q-TOF MS/MS SW480, SW620


[166]

2011 53 upregulated and 94 downregulated iTRAQ, SCX-LC-MS/MS, MALDI-TOF/TOF SW480, SW620

WB and quantitative immunofluorescence on other 3 CRC cell lines

CacyBP

[31]

2012 GLUT1, prion protein 44 proteins 1D SDS-PAGE, nano-LCMS/MS HT-29, Caco-2, Colo205, HCT116 and RKO

IHC on 82 CRC and 82 colon adenomas

[165]

2012 Stathmin-1 74 proteins 2D-DIGE, MALDI-TOF/ TOF-MS HCT116 and its metastatic derivative E1


[43]

2013 FSTL1, CALU, CDH11 132 proteins in whole-cell extracts and 125 in supernatants iTRAQ, LC-MS/MS CAFs from colon cancer mouse model and normal fibroblasts

IHC on tissues from colon cancer mouse model and 80 human tissues

Ref. Year Validated biomarker candidates Validation strategies Differentially expressed proteins (cancer vs control) Proteomic strategies

Serum/plasma is one of the most readily accessible clinical samples for the investigation of biomarkers [49]. However, proteomic analysis for the discovery of specific biomarkers in blood is frequently likened to ‘finding the

Cell lines

Proteomic identification & validation of CRC candidate biomarkers in serum/plasma

Table 1. The discovery and validation of colorectal cancer candidate biomarkers from proteomic studies of human colorectal cancer cell lines.


showed potential as an early diagnostic biomarkers with 71% sensitivity (87% when combined with CEA measurements) [45]. To identify CRC metastatic biomarkers, a number of studies have analyzed the protein expression of LNM and non-LNM CRC tissues. Using 2DE combined with MALDITOF-MS, four proteins (heat shock protein 27, Annexin A2, GSTP1, LFABP) were found to be differentially expressed between LNM and nonLNM CRC tissues and further verified to be associated with CRC LNM by tissue array [46]. In a recent study, Meding et al. performed MALDI imaging combined label-free MS quantitation on 21 LNM and non-LNM CRC tissues. Ten discriminating m/z species and 28 differential proteins were identified. FXYD3, S100A11 and GSTM3 were further examined by tissue array and showed significant correlation with LNM [47]. In addition to LNM, liver metastasis in CRC has also been investigated. Chang et al. applied 2DE followed by MALDI-TOF-MS to profile the proteome of normal colonic mucosa, colon cancer, normal liver and metastasis cancer in tissues from a single patient. They found ATP5A1 was differentially expressed and significantly associated with liver metastasis [48]. TABLE 2 lists CRC candidate biomarkers identified in clinical tissues by proteomic strategies. In spite of the rapid development of MS-based label-free proteomic technology, 29 out of 41 studies (70.1%) were performed using 2DE- or 2D-DIGE-based proteomic strategies. Label-free quantitation will almost certainly be more widely used in the future for the discovery and validation of novel CRC biomarkers and biomarker panels using clinical tissues.

[168]

Review


183

184 26 upregulated, 20 downregulated (DFS 24 months)

886 proteins 67 upregulated and 114 downregulated

1D SDS-PAGE, nanoLC-MS/MS

iTRAQ, SCX-LC-MS/MS

2DE, ESI-Q-TOF-MS/ MS Label-free MS quantitation 2D-DIGE-MS/MS

2D-DIGE-MS/MS MALDI imaging; labelfree MS quantitation 2DE, MALDI-TOF/MS 2DE-LC-MS/MS

iTRAQ, MALDI-TOF/ TOF MS 2DE, nano-ESI-Q-TOF MS/MS Dimethyl stable isotope labeling, 2D-LC-MS/MS

Resected stage IV CRC with liver metastasis, 5 DFS 24 months

Stromal cells from 8 CAC and 8 NNCM tissues

8 CRC and matched normal tissues

3 stage II CRC, 1 stage III CRC and matched normal mucosa

20 stage I–IV CRC (5 for each stage) and 5 stage I matched normal mucosa

59 CRC and paired normal epithelia tissues

21 CRC without metastasis and 33 CRC with LNM

4 adenocarcinoma and matched non-tumor tissues

28 paired Dukes’ B CRC and normal mucosa

28 stage I–IV CRC

9 CRC and paired normal mucosa

16 LNM CRC and 16 non-LNM CRC


28 upregulated and 15 downregulated

S100A4

NDK A

2011

2011

2011

2011

14-3-3b, ALDH1

OLFM4

2012

2012

2012

2012

Moesin, KRT17

FXYD3, S100A11, GSTM3

EB1

GRP78, ALDOA, CA1, PPIA

2013

[180]

[179]

[178]

[177]

[176]

[47]

[175]

[174]

[173]

2DE: 2D electrophoresis; CAC: Colon adenocarcinoma; CRC: Colorectal cancer; DFS: Disease-free survival; IHC: Immunohistochemistry; LNM: Lymph node metastasis; MALDI: Matrix-assisted laser desorption/ionization; MS: Mass spectrometry; NBS: 2-Nitrobenzenesulfenyl; NLN: Normal lymph nodes; NNCM: Non-neoplastic colon mucosa; RT-PCR: Reverse transcription-PCR; SLNMM: Sentinel lymph node micrometastasis; WB: Western blot.

WB on 3 CRC and matched normal mucosa

IHC on 126 CRC

IHC on 515 primary CRC; 224 LNM and 50 normal mucosa

WB on 4 paired CRC; IHC in 176 CRC

IHC on 168 primary CRC

WB on 7 paired tissues; IHC on 132 CRC

WB on 15 different stage CRC; IHC for 103 CRC

IHC on 367 CC tissue samples

RAI3

2013

CA2

WB and RT-PCR on 2 paired CRC and normal tissues; IHC on 25 CRC

[172]

[171]

2013

Decorin, fibronectin, M2-PK, HSP90B1, S100A9, myosin-9, 14-3-3 z/d, tubulin b

WB and IHC on stromal cells from 4 CAC and 4 NNCM tissues

[170]

2013

Maspin

Ref.

IHC on stage II (n = 243) and III (n = 176) tumors

Year

Validated biomarker candidates

Validation strategies


555 proteins

45 proteins

69 proteins

10 discriminating m/z species, 28 proteins


36 proteins


Differentially expressed proteins (cancer vs control)

Proteomic strategies

Tissues

Table 2. The discovery and validation of colorectal cancer candidate biomarkers from proteomic studies of human clinical tissues.


Review Wang, Huang & Nice

Expert Rev. Proteomics 11(2), (2014)


2010

Prohibitin

2DE, MALDI-TOF/ TOF-MS

12 CRC and matched normal mucosa

WB on 12 CRC and paired normal mucosa; IHC on 187 CRC, 150 matched normal mucosa and 63 adenomas

[185]

[184]

[44]

[183]

[182]

[181]

[45]

Ref.


40 proteins

WB on 16 CRC and paired normal mucosa; IHC on 126 CRC and 70 matched normal mucosa

2010

22 proteins

LASP-1

2DE, MALDI-TOF-MS


2010

a1-antitrypsin, cathepsin D

WB on 14 early-stage CRC and matched normal tissues, and 42 early-stage CRC and healthy sera; IHC on 93 CRC and matched mucosa; in sera were validated in 84 CRC patients and healthy individuals

501 proteins

Dimethyl stable isotope labeling, GeLC-MS/MS

13 stage I, 24 stage II CRC and matched normal colon tissues

2010

LASP-1, S100A9, RhoGDIa

WB on 3 paired tissues; IHC on 126 CRC

2DE, MALDI-TOF-MS

Normal mucosa, non-metastatic CRC and metastatic CRC

22 proteins (CRC vs normal), 11 proteins (metastatic CRC vs non-metastatic CRC)

40 proteins

2DE, MALDI-TOF-MS

62 NLN and 43 SLNMM from 43 moderately differentiated adenocarcinomas

2010

2009

TM-b

WB on 20 CRC, 10 adenoma and matched normal mucosa; RT-PCR and IHC on 20 CRC, 20 adenoma and matched normal mucosa

18 proteins (adenoma vs normal mucosa); 5 proteins (adenoma vs CRC)

2D-DIGE, MALDI-TOFMS

8 CRC, concurrent adenomas and matched non-tumor mucosa

hnRNP A1, ezrin, tubulin b-2C, Annexin A1

2011

STOML2

IHC on 60 CRC tissues; ELISA on 70 CRC plasma and 70 healthy controls

216 in Dukes’ A, 176 Dukes’ B, 96 in Dukes’ C, 94 in Dukes’ D upregulated; 103 in Dukes’ A, 111 Dukes’ B, 45 in Dukes’ C, 54 in Dukes’ D downregulated

Label-free MS quantitation, LC-MS/ MS

28 paired tumor (4 Dukes’ A, 7 Dukes’ B, 11 Dukes’ C, 6 Dukes’ D) and adjacent normal tissues

WB and IHC

Year





Tissues

Table 2. The discovery and validation of colorectal cancer candidate biomarkers from proteomic studies of human clinical tissues (cont.).



Review

185

186 7 proteins

22 proteins

2DE, MALDI-TOF-MS 2DE, MALDI-TOF/TOFMS 2DE, MALDI-TOF/TOFMS

2DE, MALDI-TOF/TOFMS

2D-DIGE, MALDI-TOF/ TOF-MS 2DE, MALDI-TOF-MS

6 LNM CRC, 6 non-LNM CRC and matched normal mucosa

10 CRC and adjacent mucosa

10 CRC and adjacent mucosa, 10 adenoma, 10 fetal colorectal specimens


12 non-metastatic CRC and 12 metastatic CRC Heparin affinity chromatography, 2DE-MS/MS, 2D-DIGE-MS/MS

WB on 10 adenocarcinomas and adjacent normal mucosa; IHC on 2 adenocarcinomas and adjacent normal mucosa

56 proteins

PSB7, PRDX1 and SRP9

2008

2008

RhoGDIa

2009

Desmin

2008

2009

hnRNP A1

Annexin A2, Annexin A4, VDAC1

2009

2010 PPIB, TCPZ

HSP27

[195]

[194]

[193]

[192]

[191]

[190]

[189]

[188]

[187]

[186]

Ref.


10 colon adenocarcinomas and adjacent normal mucosa

WB on 12 non-metastatic CRC and 12 metastatic CRC; IHC on 126 CRC

IHC on 52 CRC and matched normal tissues

IHC on 152 CRC, 30paired normal tissues and 36 adenomas; ELISA on 92 CRC, 45 healthy and 25 benign bowel disease control sera

IHC on 152 CRC and adjacent normal mucosa, and 36 adenoma; ELISA on 92 CRC and 58 healthy blood samples

WB on 6 LNM CRC and 6 nonLNM CRC; IHC on 83 CRC

WB and IHC on 6 CRC and matched normal mucosa

11 upregulated

23 proteins

12 proteins

42 proteins

2009


Transgelin

56 proteins (CRC vs normal)

2D-DIGE -LC-MS/MS


IHC on 48 non-LNM CRC and 46 LNM CRC

2010

Transgelin-2

WB on 20 CRC and adjacent mucosa; IHC on 120 CRC and matched normal mucosa, 20 adenoma and 8 CRC with liver metastasis

67 upregulated, 70 downregulated

1D SDS-PAGE, acetylation stable isotopic labeling, LTQ-FT MS

20 CRC and adjacent mucosa

2010

FTH1, UCH-L1, cathepsin D

WB on 3 LNM CRC, 3 non-LNM CRC and matched normal tissues; IHC on 26 LNM CRC, 65 non-LNM CRC and 27 matched normal mucosa

6 proteins (LNM vs nonLNM); 12 upregulated and 21 downregulated (CRC vs normal)

2DE, MALDI-TOF-MS

5 LNM CRC, 5 non-LNM CRC and matched normal tissues

Year




Tissues






27 proteins Not denoted

Nano-flow 2-D-LC-ESIMS 2DE, MALDI-TOF-MS 2DE, MALDI-TOF-MS

2DE, MALDI-TOF-MS

2DE, MALDI-TOF-MS

2DE-MS/MS

2DE, MALDI-TOF-MS

2DE-MS/MS

2DE-MS 2DE, MALDI-TOF-MS


10 CRC, matched adenoma and normal mucosa

Normal mucosa, colon cancer, normal liver and metastatic cancer in liver from 1 patient



10 Dukes’ C adenocarcinoma and matched normal mucosa





20 Duke’ C CRC and matched normal tissues

2006

2006

2006

PSME3

SELENBP1

Secretagogin

IHC on 250 CRC

ELISA on 109 CRC and 317 healthy sera

WB on 17 CRC and matched normal tissues; RT-PCR on 22 CRC and matched normal tissues; IHC on 45 CRC and matched normal tissues

WB on 8-paired adenomas and CRC; IHC on 240 CRC, and 8-paired adenomas and CRC

WB on 6 CRC and matched normal mucosa; ELISA on 109 CRC and 317 healthy sera; IHC on tissue microarray

L-FABP

2004

2005

2006

TCPB, TCPE, hnRNP K

NNMT

2007

HSP27, Annexin A2, GSTP1, L-FABP

WB on 3 LNM CRC, 3 non-LNM CRC and matched normal mucosa IHC on 139 non-LNM CRC and 257 LNM CRC IHC on 53 Dukes’ A, 104 Dukes’ B, 111 Dukes’ C and 52 normal tissues

2007

ATP5A1

IHC on normal mucosa, primary CRC, normal liver and liver metastatic tumors from 67 patients

2008

S100A12

2007

2008

Year

PSB7, zyxin, RAN, RCN1, AHCY, galectin-1, vimentin


TXNDC5, mimecan

WB on 19 CRC, 8 adenoma and matched normal mucosa

WB on 4 CRC and matched normal mucosa; ELISA on 261 CRC and 394 healthy sera

WB and IHC on 10 CRC and matched normal mucosa


[205]

[204]

[203]

[202]

[201]

[199,200]

[46]

[48]

[198]

[197]

[196]

Ref.


Not denoted

272 proteins


14 proteins

5 proteins

Not denoted

25 proteins (cancer vs normal); 4 proteins (LNM CRC vs non-LNM CRC)

5 upregulated


NBS labeling, RP-LCMALDI-TOF-MS




Tissues




Review

187


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needle in the haystack’ due to the large dynamic range in protein concentration (up to 12 orders of magnitude) in blood and the fact that 22 proteins make up 99% of the total protein composition [3,49]. Therefore, the primary step in proteomic profiling of the serum proteome usually involves depletion/ chromatographic removal of the high-abundance proteins or enrichment of low-abundance proteins. Following the depletion of albumin/IgG using a 4.6 100 mm Agilent Multiple Affinity Removal Column, which removes albumin, transferrin, IgG, IgA, haptoglobin and antitrypsin, which together comprise 85–90% of the total protein mass in human serum, Ma et al. separated serum samples from 10 CRC patients and 10 healthy individuals by 2D-DIGE combined with MALDI-TOF-MS; 8 proteins were found to be differentially expressed. Of these, TALDO1 and TRIP11 were analyzed by ELISA in the serum from 30 CRC patients and 30 healthy controls and appeared to be promising candidate biomarkers [50]. Serum biomarkers are commonly mined for early detection and diagnosis, but can also be used to monitor CRC prognosis, metastasis and disease progression. Using the combination of hollow fiber membranebased low-molecular weight protein enrichment and 2D image converted analysis of liquid chromatography and mass spectrometry, which was developed as a shotgun proteomics analysis system, the expression of adipophilin was found to be elevated in serum of CRC patients. Further studies in a larger distinct cohort suggested that adipophilin could serve as a plasma biomarker for the detection of early-stage CRC [51]. To develop novel serum prognostic biomarkers for CRC, sera from a 60 sample set (including 10 healthy control, 10 adenoma, 10 stage I CRC, 10 stage II CRC, 10 stage III CRC and 10 stage IV CRC with liver metastasis) were subjected to a 2DE-coupled MALDI-TOF/TOF proteomic approach. The prognostic values were validated in a large cohort by quantitative reverse transcription PCR and immunohistochemistry. These results suggested that AZGP1, PEDF and PRDX2 may serve as prognostic markers for CRC [52]. Tsai et al. compared the serum protein expression of patients with primary and metastatic CRC by Cy-dye labeling combined with multidimensional fractionation-MS. They confirmed that gelsolin was a candidate biomarker for distance organ metastasis of CRC [53]. Moreover, to identify novel biomarkers for CRC progression, Choi et al. performed 2DE-MS/MS to profile the differentially regulated serum proteins from 30 patients with adenomas and 30 healthy individuals. Proteomic studies identified 11 upregulated and 13 downregulated proteins. These proteins showed significantly different regulation patterns for predicting progression from adenoma to carcinoma [54]. TABLE 3 details the proteomic studies performed on serum/plasma for the discovery and validation of CRC candidate biomarkers. Proteomic identification & validation of CRC candidate biomarkers in feces

Feces are among the most accessible of biological samples that can be used for biomarker discovery. Collection is non-invasive and can be done at home, and, unlike colonoscopy, requires 188

no unpleasant bowel preparation or anesthesia. Feces contain proteins and peptides, which are present due to leakage, secretion or exfoliation and may be potential biomarkers [55]. Our group explored fecal CCAPs by 1D SDS-PAGE followed by MRM-MS quantification. Using a hypothesis-driven approach, 19 out of 60 previously reported CCAPs were found in feces from a Dukes’ stage D CRC patient. These studies were extended to feces from five CRC patients and five normal volunteers, revealing hemoglobin, myeloperoxidase, S100A9, filamin A and L-plastin to be present only in feces of CRC patients [56]. Furthermore, a discovery-based proteomics strategy using orthogonal multidimensional fractionation (1D SDSPAGE, RP-HPLC, size exclusion chromatography) was performed to mine deeper into the fecal proteome. A library containing 108 human fecal proteins was generated, 40 of which were verified in feces from 8 CRC patients and 7 normal volunteers by MRM-MS. Nine proteins, including a-1-antityrpsin, a-1-acid glycoprotein, complement C3, fibrinogen, haptoglobin, hemoglobin a, hemoglobin b, myeloblastin and transferrin, were only present in feces of CRC patients [57]. These studies underscore the potential clinical utility of fecal protein biomarkers using MS-based proteomic strategies. Further studies are now underway to verify the candidate biomarkers in larger clinical cohorts including patients with benign bowel disease to determine the optimal proteomic strategies for clinical use. Proteomic identification of CRC candidate biomarkers using mouse model systems

In addition to tumor cells, the laboratory mouse is another preclinical model for CRC biomarker discovery and validation. Common CRC mouse models can be generated by inoculation, genetic engineering (Apc mutant mice, b-catenin transgenic mice or Msh2-/-, Msh6-/- and Mlh1-/- mice) or by chemical induction (DSS-induced inflammation-related mouse models and carcinogen-induced sporadic mouse models) [58]. Several new technologies to establish CRC mouse models including surgical manipulation combined with adeno-cre infection [59] and organoid culture [60] have also been reported. As no individual model recapitulates all of the characteristics of human CRC, it is critical to use a specific mouse model to address a particular research question [58]. PTMomics for the identification & validation of CRC candidate biomarkers

Many proteins are post-translationally modified following ribosomal translation by glycosylation, phosphorylation, ubiquitination, acetylation, lipidation, cysteine redox modifications, etc. [61]. These post-translational modifications (PTMs) frequently modulate the function of proteins by creating a continuously dynamic fine-tuned regulatory network implicated in a wide variety of cellular processes [62]. Aberrant PTM is often a hallmark of cancer. Therefore, it is necessary to delineate the dynamic CCAP PTMome for CRC biomarker discovery and validation. The conventional bottom-up proteomic strategy in Expert Rev. Proteomics 11(2), (2014)


SISCAPA, MALDI-FTICRMS, MRM

HFM combined with 2DICAL

2DICAL, nano-ESI-Q-TOF MS/MS

2DE, MALDI-TOF-MS

SELDI-TOF MS

2D-DIGE, MALDI-TOF-MS

ConA-Sepharose Affinity Chromatography, 2DE-MS

5 CRC and 5 healthy volunteers

22 CRC and 21 healthy controls


32 non-LNM CRC and 40 LNM CRC

42 advanced CRC 40 and healthy controls



Slot blot on 10 CRC and 10 healthy controls

ELISA on 30 CRC and 30 healthy sera

Comparing the peak intense prior and after chemotherapy

ELISA on 32 non-LNM CRC and 40 LNM CRC sera

Reverse-phase protein microarray on 115 CRC and 230 healthy plasma

WB on 7 CRC and 6 healthy plasma; reverse-phase protein microarray on 127 CRC and 196 healthy plasma; IHC on 20 CRC tissues

LC-MRM on 5 CRC and healthy sera

Clusterin

TALDO1, TRIP11

Apo AI

TTR

C9

2006

2009

2010

2011

2011

2011

2012

Gelsolin

Adipophilin

2012

ORM2

2012

2013

AZGP1, PEDF, PRDX2

TIMP-1

[54]

2013

LRG, HBB, IgA2C, CFB, AACT, vitronectin, CXCL10, TNF-a, IL-8

[211]

[50]

[210]

[209]

[208]

[51]

[71,207]

[53]

[206]

[52]

Ref.

Year


2DE: 2D electrophoresis; 2DICAL: 2D image converted analysis of liquid chromatography and mass spectrometry; ConA: Concanavalin A; IBD: Inflammatory bowel disease; IHC: Immunohistochemistry; HFM: Hollow fiber membrane; RT-PCR: Reverse transcription-PCR; SISCAPA: Stable isotope standard and capture by the antipeptide antibody; WB: Western blot.

Targeted 16 clusterin isoforms


38 peaks

8 proteins

90 MS peaks

103 MS peaks

Not denoted

WB on 3 CRC and 3 healthy plasma; ELISA for 32 CRC and 32 healthy plasma; IHC for 148 CRC tissues

Cy-dye labeling combined with multi-dimensional fractionation and MS

3 primary CRC and 3 CRC with metastasis

8 proteins

iTRAQ, microQ-TOF MS

10 CRC and 10 healthy volunteers

ELISA on 405 CRC and 129 healthy control sera; RT-PCR and IHC on 334 CRC and control tissues

ELISA on 419 sera (65 control, 59 hyperplastic polyp, 62 IBD, 53 adenomas, 180 CRC); WB on 41 CRC and matched normal mucosa tissues


2DE-MS/MS

10 healthy control, 10 adenomas, 10 early-stage CRC, 10 developing stage CRC and 10 CRC with liver metastasis

ELISA on 20 adenomas and 20 CRC sera




2DE-MS/MS

30 adenomas and 30 CRC



Serum/plasma donors

Table 3. The discovery and validation of colorectal cancer candidate biomarkers from proteomic studies of human serum/plasma.



Review

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conjunction with affinity enrichment can be used to characterize PTM peptides or proteins [61]. In particular, N- or O-linked glycosylated proteins can be captured by selected lectins [63]; phosphorylated peptides can be enriched by phosphotyrosine (pY)-specific antibodies or by immobilized metal affinity chromatography (IMAC) by which means phosphorylated peptides can be adsorbed onto chelated metal irons (Fe3+ or Ga3+) through metal-phosphate ion-pair interactions [64,65]; Ubiquitinated peptides are usually enriched with antibodies directed against a glycine-glycine stub on lysine residues, K(GG) [66]; acetylated peptides are enriched with antibodies directed against the acetylated epsilon amino group of lysine residues, K (Ac) [67] and the redox modification of cysteines can be enriched by sulfenic acid-specific antibodies or S-glutathionylation-specific antibodies, or quantified by OxICAT technology [68–70]. Several of these proteomic strategies have been successfully applied to identify potential CRC PTM biomarkers. For example, Ahn et al. applied L-PHA lectin to capture GlcNAcylated TIMP1. Using stable isotope standard capture by antipeptide antibodies enrichment and MRM quantitation, they found that TIMP1 glycoforms were five-times more abundant in CRC serum than that in non-cancerous serum, with obvious potential as CRC biomarker candidate [71]. In another study, to profile the HGF-MET phosphor-proteome in CRC, using the MET-expressing CRC cell line, DLD1, Organ et al. performed immunoaffinity purification using pY-specific antibodies followed by LC-MS/MS analysis. Two hundred and sixty-six unambiguous pY sites spanning 168 proteins were identified, generating a promising outline of HGF-regulated tyrosine phosphorylations [72]. However, in spite of proven success in profiling of the PTMome, bottom-up proteomic strategies have some intrinsic limitations. Since the proteins must be digested into peptide fragments before MS analysis, it is time-consuming and the peptide sequences of proteins are often only partially covered [73]. Digestion can also result in loss of labile PTMs [73]. In addition, protein regulation is often achieved by a complex interplay of different PTMs, not by a single PTM [74]. Although an integrated analysis of phosphorylation, ubiquitination and acetylation has been performed successfully by serial enrichments of different post-translational modifications (SEPTM) from the same biological sample [75], there is still a long way to go before bottom-up proteomics can effectively identify different PTMs simultaneously. To this end, the advent of functional top-down strategies is raising new hope for the comprehensive analysis of PTMs [76]. Instead of using proteolytic peptides as a proxy for the proteins, top-down proteomic strategies characterize intact proteins without preliminary digestion, which ensures that labile PTMs are preserved for further MS identification [74,77]. To date, most top-down studies mapping PTMs have been focused on identifying various isoforms of a single protein family. Effective intact protein fractionation strategies need to be integrated with the top-down MS analysis [74]. Kelleher’s group applied a four-dimensional separation system, comprising solution isoelectric focusing and 190

gel-eluted liquid fraction entrapment electrophoresis, followed by nano-LC/MS analysis to successfully identify more than 3000 protein species in HeLa S3 cells with detailed PTM information [78]. Using this same approach, the same group recently performed the largest top-down study to date, in the transformed human non-small cell lung carcinoma cell line H1299. Over 5000 proteoforms were identified, most of which harbored PTMs such as phosphorylation and methylation modifications [79]. These studies indicate the potential of top-down strategies to interrogate the PTMs of proteins from whole cells on a proteome scale. It is believed that a top-down proteomic approach will shed new light on the discovery and validation of CRC biomarkers in the foreseeable future. Meta-analysis of CRC candidate protein biomarkers

We performed a meta-analysis of previous studies where CCAPs have been proposed as candidate CRC biomarkers in clinical samples, including tissues, serum/plasma or feces. In total, 438 CCAPs were identified (SUPPLEMENTARY TABLE 1). Entry names (UniProt), key references, the clinical sample used for the study and the roles these candidate biomarkers might play in CRC diagnosis, prognosis and therapy have been detailed. Gene ontology analysis was performed using Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) [80] to reveal the gene ontology classifications of these CCAPs (FIGURE 3A–3C) [80]. Pathway analysis shows that these CCAPs are involved in the regulation of apoptosis, cell cycle, DNA damage, p53 signaling pathway, Wnt signaling pathway, EGFR signaling, matrix metalloproteinases inhibition, heat shock proteinregulated stress induction and inflammation responses (FIGURE 3D). In addition, we performed network analysis on these CCAPs using protein–protein interaction information retrieved from the Search Tool for the Retrieval of Interacting Genes/Proteins database, and mapped using the interaction network Cytoscape (FIGURE 4) [81,82]. These data will hopefully assist future studies on the identification and validation of novel biomarkers/biomarker panels for CRC. Genomics & methylomics in the discovery & validation of CRC biomarkers

The progression of CRC is known to be driven in part by the loss of genomic stability, which facilitates the acquisition of multiple tumor-associated mutations [83]. Genomic instability in CRC arises in two ways: microsatellite instability (MSI) and chromosomal instability [84]. A myriad of studies have uncovered several key oncogenic mutations of genes and pathways in CRC including adenomatous polyposis coli (APC), TP53, KRAS, BRAF, CTNNB1, PIK3CA, WNT signaling, TGF-b signaling and DNA mismatch repair pathways to name but a few [85,86]. Despite these fruitful investigations on distinct genetic factors, further studies are required to reveal the complete map and comprehensively understand the effect of genetic alterations in CRC. Exome-wide sequencing of CRC was first performed on 11 affected individuals, and a number of additional new Expert Rev. Proteomics 11(2), (2014)


Review


A

Others Biological adhesion Reproduction Multi-organism process Locomotion Immune system process Localization Cellular component organization Developmental process Metabolic process Multicellular organismal process Response to stimulus Biological regulation Single-organism process Cellular process 0 B

50

100

150 C

Others

200

250

300

400

0

100

450

Others

Antioxidant activity

Extracellular matrix

Structural molecue activity

Macromolecular complex

Receptor activity

350

Extracellular region

Molecular transducer activity

Membrane-enclosed lumen

Enzyme regulator activity Transcription factor activity

Membrane

Binding

Organelle 0

100

200

300

400

D

4% 23%

16% 13%

6% 6%

10% 7%

10%

5%

200

300

400

Apoptosis Cell cycle regulation Inflammatory response DNA damage p53 signaling pathway Stress induction of HSP regulation Inhibition of MMPs WNT signaling pathway Others EGFR signaling

Figure 3. Gene ontology (GO) classification of previous reported colorectal cancer-associated proteins (CCAPs). (A) GO biological process terms of CCAPs. (B) GO molecular function terms of CCAPs. (C) GO cellular component terms of CCAPs. (D) Pathway analysis of CCAPs achieved from BioRag [164]. HSP: Heat shock protein; MMPs: Matrix metalloproteinases.

recurrent oncogenic mutations with a role in carcinogenesis were identified for further functional study [87,88]. Wholegenome sequencing of CRC was recently first conducted on nine patients, and showed previously unidentified levels of genomic rearrangements [89]. Further studies will undoubtedly dig deeper into these genomic landscapes of CRC and open new avenues for comprehensive and in-depth understanding of the nature and heterogeneity of CRC, and help identify innovative new diagnostic and therapeutic strategies. In addition to genetic alterations, CRC also arises as a consequence of epigenetic changes [90]. Aberrant DNA methylation informahealthcare.com

(hypomethylation and hypermethylation) of cytosine bases in CpG islands is the most extensively characterized epigenetic pattern [91]. It appears that aberrant DNA methylation is predominantly involved with early events in CRC onset, implying that it is a promising target for early detection [91,92]. Altered DNA methylation in CRC was discovered early in 1983 [93]. Recently, studies have focused on the methylation of specific genes and regions of interest, and a cornucopia of methylated genes have been proposed as potential CRC biomarkers [94]. However, the role that aberrant DNA methylation plays in the molecular and genetic mechanisms of CRC still remains poorly 191

Wang, Huang & Nice


Review

Figure 4. Pathway analysis of previously reported colorectal cancer-associated proteins. Network interaction analysis using protein interaction information from the Search Tool for the Retrieval of Interacting Genes/Proteins database and visualized using Cytoscape. CCAPs: Colorectal cancer-associated proteins; GO: Gene ontology.

understood [95]. A more global understanding of DNA methylation patterns might, at least in part, answer this question. The recent advent of global methylomic profiling has already contributed considerably to our view of global genomic DNA methylation, and also to the identification of an increasing 192

number of novel methylated genes in CRC [95], providing an overwhelming amount of valuable data for the discovery and validation of epigenetic biomarkers for CRC diagnosis (TABLE 4). Schuebel et al. have developed a whole transcriptome microarray screen to profile the promoter hypermethylome in the Expert Rev. Proteomics 11(2), (2014)


CRC cell line HCT116

CRC cell lines, DLD-1 and HT-29

Colon tumors and matched normal mucosa

91 consecutive tumors and 28 adjacent normal colon mucosa

213 tumors and 9 matched normal colon mucosa; CRC cell lines, HCT116 and SW480

30 MSI and MSS tumors, 6 adenomas and 6 normal mucosa

24 tumors and matched normal colon tissues

22 paired tumors and adjacent normal mucosa

6 adenomas, 6 tumors and matched normal colon mucosa; CRC cell line, HT-29

24 tumors and matched normal colon mucosa

Cells

Cells

Tissues

Tissues

Tissues and cells

Tissues

Tissues

Tissues

Tissues and cells

Tissues

[99]

[100]

[214]

[105]

2011

2011

2012

2012

A total of 627 differentially methylated loci covering 513 genes were found, of which 535 were novel differentially methylated loci covering 465 genes 621 CpG sites located in promoter regions and CpG islands were identified to be significantly hypermethylated in tumors compared with normal mucosa This is a novel methylomic approach. The authors compared data obtained from transcriptome profiling of primary human samples and CRC cell lines; 154 genes were identified to be hypermethylated

Illumina Infinium HumanMethylation27 BeadChip Arrays Oligonucleotide array

MethylCap-seq

Illumina Infinium HumanMethylation27 BeadChip Arrays

2687 frequently hypermethylated and 468 frequently hypomethylated regions were identified, providing genome-wide DNA methylation maps of CRCs

[101]

2011

Illumina Infinium HumanMethylation27 BeadChip Arrays; Exon arrays

68 genes with tumor-specific hypermethylation were identified. The methylation patterns of 15 selected genes were validated using MS-HRM, 4 of which (MLH1, AOX1, EYA4 and TWIST1) were previously reported

[212]

2010

[213]

[103]

2009

2010

[97]

2007

CRC was clustered into high-, intermediate- and low-methylation epigenotypes. Intermediate-methylation epigenotype with KRASmutation (+) correlated with worse prognosis

[96]

2007

MeDIP-Chip; MassARRAY

Ref.

Year

A total of 202 CpG sites were differentially methylated between tumor and normal tissues. Three CRC subgroups (CIMP-low, CIMP-mid, CIMP-high) with distinctive clinicopathological and molecular features were identified

Most functionally important DNA methylation alterations occurred not in CpG islands or promoter regions, but in CpG island shores. Hypermethylation enriched closer to the associated CpG islands, while hypomethylation enriched further

350 genes were identified to be hypermethylated. Methylation of the UCHL1 gene promoter increased during the development and progression of CRCs

This study compared the DNA hypermethylome with gene mutations, suggesting that in addition to genetic alterations, epigenetic alterations are also important in driving CRC tumorigenesis

Major findings

Illumina Goldengate Methylation Arrays

CHARM microarray

Oligonucleotide array

Oligo Microarray

Method

CHARM: Comprehensive high-throughput array-based relative methylation; CIMP: CpG island methylator phenotype; MeDIP-Chip: Methylated DNA immunoprecipitation-Chip; MSI: Microsatellite instability; MSS: Microsatellite stability.

Donor

Specimen

Table 4. Methylomic analyses of human colorectal cancers.



Review

193

[104]

[106]

2013

2013

10,342 sites were hypermethylated of which mostly located in CpG islands, and 5325 were hypomethylated of which mostly located in CpG shores

A study for whole-genome methylation changes in CRCs. The differential methylation of GRASP gene was the highest rated in tumors versus normal tissues and adenomas versus normal tissues; ATM was the highest-rated gene when comparing tumors versus adenomas. DNA methylation changes occur mainly in the Netrin–DCC and SLIT–ROBO pathways

Illumina Infinium HumanMethylation450 BeadChip Arrays 22 paired tumors and adjacent tissues, and 19 colon tissue from cancer-free donors

5 adenomas, 10 tumors and matched normal colon mucosa

Tissues

Tissues

CHARM: Comprehensive high-throughput array-based relative methylation; CIMP: CpG island methylator phenotype; MeDIP-Chip: Methylated DNA immunoprecipitation-Chip; MSI: Microsatellite instability; MSS: Microsatellite stability.

[215]

2012 4 DNA methylation-based subgroups (CIMP-high, CIMP-low and 2 non-CIMP subgroups) were identified, each showed characteristic genetic and clinical features Illumina Infinium HumanMethylation27 BeadChip Arrays 125 tumors and 29 adjacent normal mucosa Tissues

Illumina Infinium HumanMethylation27 BeadChip Arrays

[98]

Ref. Year

2012

Major findings Method

Cells

Oligonucleotide array

Donor

CRC cell lines, HCT116, RKO, Colo320, SW480 and HT-29

Specimen


Table 4. Methylomic analyses of human colorectal cancers (cont.). 194

Wang, Huang & Nice

139 genes were identified to be hypermethylated. Methylation of THSD1 gene may play a role in CRC tumorigenesis

Review

human CRC cell line HCT116. By directly comparing the hypermethylome with the altered genome, they found that gene hypermethylation occurs frequently in individual tumors. They proposed that, in addition to genomic alterations, epigenetic alterations should also be a focus for the identification of new CRC biomarkers [96]. Since then, two more studies performed on other CRC cell lines (encompassing DLD-1, HT-29, RKO, Colo320 and SW480) have been reported. Hundreds of genes were recognized to be hypermethylated, among which the hypermethylation of THSD1 and UCHL1 may play a role during the development and progression of CRC [97,98]. The methylomic profiling of CpG islands can also be performed in vivo in human primary tissues. Using Illumina Infinium HumanMethylation27 BeadChip Arrays to scan 27,578 CpG loci covering 14,475 genes, study groups have identified over 600 differentially hypermethylated CpG sites in CRC compared with matched normal mucosa [99,100]. Moreover, another study compared the differential methylation patterns not only in normal tissues and carcinomas, but also in adenomas. These data indicated that FLI1, ST6GALNAC5, TWIST1, ADHFE1, JAM2, IRF4, CNRIP1, NRG1 and EYA4 were hypermethylated in both adenomas and carcinomas, while ABHD9, AOX1 and RERG were hypermethylated only in carcinomas. Although the normal tissues and tumors were not from the same donors, these results suggested that hypermethylation patterns may be closely related to CRC tumorigenesis [101]. Most DNA methylation alterations in CRC are believed to occur in promoters or CpG islands [102]. However, by carrying out comprehensive high-throughput array-based relative methylation (CHARM) analysis, one group has suggested that most functionally important DNA methylation alterations in CRC occur in neither promoters nor CpG islands, but in ‘CpG island shores’ (regions within 2 kb flanking, but not inside, CpG islands). Hypermethylation is enriched closer to the associated CpG islands compared with hypomethylation sites [103]. Intriguingly, another group suggested that hypermethylated sites were mostly located in CpG islands, while hypomethylated sites were in CpG island shores [104]. These paradoxical findings may be due to different criteria defining for the CpG island shores or the different methylomic approaches used. However, these two reports clearly demonstrate that promoters or CpG islands are not the only functional sites that can be aberrantly methylated. Thus, whole-genome methylation profiling of all aberrant methylated sites is required. Some efforts to address this have been made recently. For instance, using MethylCapseq, comprehensive DNA methylation maps of CRC and matched normal colon tissues have been described, in which 2687 frequently hypermethylated and 468 frequently hypomethylated regions were identified [105]. A more recent study was performed using Illumina Infinium HumanMethylation27 BeadChip Arrays on 10 normal tissue-cancer sample pairs and 5 adenoma samples. Widespread changes in DNA methylation were observed in the transition from adenoma to carcinoma. Moreover, the differential methylation of the GRASP gene was found to be highest in carcinomas versus normal tissue and Expert Rev. Proteomics 11(2), (2014)


4 CRCs and 2 matched normal colon mucosa

46 CRCs and 8 matched normal tissues

69 paired tumors and normal tissues

84 colon adenocarcinomas and paired non-tumor tissues

6 paired rectal cancers and normal tissues

57 paired advanced rectal cancers and matched mucosa

8 paired CRCs and normal tissues

88 CRCs

49 stage II colon tumors and 10 normal mucosa

12 paired stage III CRCs and matched normal tissues

45 CRCs with different stages and 4 matched normal colon tissues, and 8 CRC cell lines

23 MSS and 16 MSI-H colon tumors

80 colon tumors and 28 normal colon tissues

54 CRCs (22 Lynch syndrome, 13 sporadic MSI, 19 MSS) and 20 normal colonic tissues

Tissues

Tissues

Tissues

Tissues

Tissues

Tissues

Tissues

Tissues

Tissues

Tissues

Tissues, cells

Tissues

Tissues

Tissues

TM

miRNA BeadArray

miRNA BeadArray

The unique miRNA expression profiles could discriminate between Lynch syndrome, sporadic MSI and sporadic MSS CRCs

39 miRNAs were differentially expressed, depending on mismatch repair status

The first report indicating the existence of expression differences of miRNA between MSS and MSI-H CRCs

11 miRNAs were differentially expressed in both tissue and cell samples. MiR-145 revealed oncogenic potential and correlated with CRC metastasis

MirVana miRNA Bioarrays

miRNA microarray

43 miRNAs were identified as differentially expressed. MiR-18a and miR-29a were verified in serum to be promising biomarkers for CRC screening and monitoring

MiR-145 was downregulated in cancer and might be a potential prognostic biomarker for CRC

The CRC miRNAome with 523 mature miRNAs was characterized

16 novel dysregulated miRNAs were identified

49 miRNAs were significantly differentially expressed, 13 of which were rectal cancer-specific

21 novel miRNA precursors are upregulated in rectal cancer while 24 novel ones were downregulated

37 miRNAs were differentially expressed. The high expression of miR-21 was associated with poor survival and poor therapeutic outcome

21 miRNAs were overexpressed in tumors, while miR-143 and miR-145 were downregulated. MiR-18a can be used as a CRC prognostic factor

The first large-scale study to characterize miRNA signatures in several different types of human solid tumors (including colon cancer) and matched normal tissues

The first genome-wide profiles of CRC miRNAome. 133 novel miRNAs and 112 uncharacterized miRNA* forms have been identified

Major findings

miRNA microarrays

miRCURY LNA microarray

Illumina deep sequencing

Illumina deep sequencing



TM

miRNA microarray

miRNA microarray

miRNA microarray

miRAGE

Method

CRC: Colorectal cancer; LNA: Locked nucleic acid; miRAGE: miRNA serial analysis of gene expression; MSI-H: High microsatellite instability; MSS: Microsatellite stability.

Donor

Specimen

Table 5. MiRNAomic analyses of human colorectal cancers.


[216]

[131]

[217]

2006

2009

2008

[138]

2009

2011

2009

[142]

[140]

[139]

[137]

2013

2009

[136]

[135]

[134]

[133]

2008

2013

2012

2012

[132]

[130]

2006

2012

Ref.

Year


Review

195

196

69 matched samples of MSS adenocarcinomas, adenomas with high- and low-grade dysplasia and normal mucosa

5 CRC plasma, corresponding cancer and normal colon tissues, 5 healthy plasma

10 CRCs and 10 healthy controls

30 stage IV colon cancers and 10 healthy controls

50 CRCs and 50 normal controls

20 CRCs, 9 adenomas, and 12 healthy controls

61 CRCs and healthy controls

15 CRC patients and 15 healthy controls

29 CRCs and 8 healthy controls

Tissues

Plasma, tissues

Plasma

Serum

Plasma

Plasma

Plasma

Feces

Feces

5 miRNAs were upregulated both in CRC plasma and tissue samples. MiR-92 might be a potential non-invasive biomarker for CRC screening

miRNA microarray

Affymetrix GeneChip miRNA 3.0 Array

Illumina miRNA microarray

RT-pre-amp-qPCR microarray

miRNA microarray

Microfluidic array technology

miRNA microarray


141 miRNAs were indentified as overexpressed in CRCs, while 41 were downregulated. The miRNA expression profiles in fecal samples were similar to those in colon mucosa

The expression profiles of miRNAs between fecal sample and normal colon mucosa are significantly similar. The reproducibility was as good as expected for array-based miRNA screening

This study demonstrated that the miRNAs are stable enough to be detected in the fecal microenvironment. MiR-144* was identified as a potential biomarkers for CRC diagnose

50 miRNAs were differentially expressed

8 miRNAs (miR-532-3p, miR-331, miR-195, miR-17, miR-142-3p, miR-15b, miR-532 and miR-652) were potential to distinguish polyps from controls

5 miRNAs (miR-29a, miR-106b, miR-133a, miR-342-3p, miR-532-3p) identified to be differentially expressed

20 miRNAs were differentially expressed

86 differentially expressed plasma miRNA with fold changes >2 between CRC and control. They found that plasma miR-601 and miR-760 were promising biomarkers for CRC early detection

230 miRNAs were significantly differentially expressed during CRC progression, including 19 not reported previously. The variation of miRNA expression plays important roles in the sequence of early molecular events

miRNA microarray


The expression profiles of miRNAs depend on different tumor locations and subtypes

Major findings

miRNA microarray

Method

CRC: Colorectal cancer; LNA: Locked nucleic acid; miRAGE: miRNA serial analysis of gene expression; MSI-H: High microsatellite instability; MSS: Microsatellite stability.

3 stage IV, 3 stage III, 3 stage II, 3 stage I CRCs and 3 healthy controls

40 colon tumors, 30 rectal tumors and 30 normal tissues

Tissues

Feces

Donor

Specimen

Table 5. MiRNAomic analyses of human colorectal cancers (cont.).


[147]

[218]

2009

2012

[150]

2010

[152]

[151]

2011

2013

[220]

[149]

2013

2013

[219]

2013

[148]

[143]

2011

2013

[141]

Ref.

2011

Year





adenomas versus normal tissue, suggesting that differential methylation of GRASP may be a potential biomarker for early detection of CRC [106]. Further studies need to be done using samples from a wider range of CRC individuals encompassing more detailed clinical and pathological features to unambiguously identify novel genes with differential methylation that could be candidate CRC biomarkers. With regard to the potential roles of aberrant DNA methylation in the early detection and screening of CRC [92], it would be of great value to uncover potential gene methylation markers in patient serum/plasma and feces, beside tumor cells and tissues. Indeed, by using methylation-specific PCR, the aberrant methylations of genes in serum/plasma, including SEPT9, p16, HPP1/TPEF, RUNX3, TMEFF2, NGFR, APC, MGMT, RASSF2A, Wif-1, THBD and C9ofr50, have been shown to have potential for either detection or monitoring of CRC [107–113]. In stools, genes such as SFRP2, VIM, TFPI2, RASSF2 and SFRP2 have been found to be aberrantly methylated and might act as potential markers for the screening of CRC [114–117]. However, since these studies investigated whether the methylated genes correlate with CRC by conducting a single methylation-specific PCR test for each gene, highthroughput screening strategies are required for the identification of more novel CRC-related methylated genes. Recently, using a microarray-mediated methylation assay, Cassinotti et al. profiled the methylation of 56 genes implicated in CRC carcinogenesis in plasma. They found that promoters of six genes (CYCD2, HIC1, PAX5, RASSF1A, RB1 and SRBC) were aberrantly methylated and might act as composite biomarkers [118]. Although this was only a very small-scale screen, it illustrates the potential of mapping the DNA methylome of CRC in serum/plasma or feces in the foreseeable future. To date, none of these methylation makers has been approved for clinical use. Thus on one hand, careful validation is required on suitable clinical cohorts while, on the other hand, additional discovery efforts on mapping the serum/ plasma and fecal DNA methylome could identify further potential DNA methylation markers. Transcriptomics & miRNAomics for the discovery and validation of CRC biomarkers

Genomic instability and epigenetic changes in CRC inevitably cause aberrant alterations of the transcriptome, which describes the complete set of RNA transcripts produced by the genome at a given time [119,120]. Although the comprehensive molecular characterization of CRC by combination of genomic and transcriptomic analysis has also been performed [121,122], these studies preselected CRC-related genes and their known transcripts in the arrays used and, the detection of novel transcripts was ignored [91]. Recently, as high-throughput next-generation sequencing (NGS) technologies for deep-sequencing have been developed, RNA sequencing (RNA-Seq) has been used to map the transcriptome more comprehensively and has uncovered novel transcripts, alternative splicing and gene fusion products [119,123]. Using RNA-Seq, high-throughput transcriptome informahealthcare.com

Review

sequencing has been performed for CRC, with adjacent nontumor and distant normal tissues from the same patient. These results have highlighted activated extracellular matrix and metabolic pathways, as well as inhibition of genes related to cell homeostasis, all of which are common features in tumor metastasis [124]. In another study, 183 cancer-related genes were sequenced in matched normal and tumor tissue pairs of 60 colorectal adenocarcinomas using the NGS approach. APC, TP53 and KRAS were identified as the most frequently altered genes [125]. Both studies have provided comprehensive data on genetic alteration leading to a better insight into the complex regulatory mechanisms in CRC progression [124,125]. RNA-Seq would appear to be the preferred method for profiling the CRC transcriptome in the future [119]. In addition to mRNAs, transcriptomics can also profile noncoding RNAs, such as miRNAs [119]. miRNAs comprise singlestranded RNAs of 18–25 nucleotides in length that downregulate gene expression by binding to target mRNAs [126]. miRNAs can act as oncogenes or tumor suppressors, and changes in expression of miRNAs have been shown to correlate with a number of human cancers, including CRC [127,128]. It has been suggested that miRNA profiles are more accurate and powerful than mRNA profiles for the classification of human cancers [129]. Following the characterization of the first human colorectal miRNAome by Cummins et al. in 2006 [130], high-throughput miRNA expression has been widely performed to reveal CRCrelated miRNA fingerprints, and to identify non-invasive miRNA candidate biomarkers for diagnostic, prognostic or therapeutic use (TABLE 5). For example, an miRNA microarray containing 455 human miRNA probes was used to profile miRNA expression. Twenty-one miRNAs were overexpressed in CRC tissues compared with paired normal tissues, while miR-143 and miR-145 were downregulated. MiR-18a was verified as a prognostic factor for predicting survival of CRC patients [131]. To identify new aberrantly expressed miRNA candidates, a largescale microarray (miRCURY LNA Array) containing 904 miRNAs was used to analyze 6 paired rectal cancers and associated normal tissues. Twenty-one novel miRNA precursors were found to be upregulated in rectal cancer, while an additional 24 were downregulated [132]. Using the same microarray chip, 49 miRNAs were found to be significantly differentially expressed in 57 advanced rectal cancers compared with matched mucosa. Among these, 13 miRNAs were identified only in rectal cancer, but not in colon cancer [133]. The rate-limiting step in the discovery of novel miRNAs is probably the actual size of the individual arrays used. Recently, the Illumina deep sequencing technology, which enables the screening of all miRNAs expressed in the tissues analyzed, was applied to miRNA profiling of eight paired CRC and adjacent normal tissues. In this study, 16 novel dysregulated miRNAs were identified [134]. Another deep sequencing study using 88 CRCs (but not matching normal tissues) identified 523 mature miRNAs [135]. It will be interesting to see the results of further studies using high-throughput deep sequencing of large cohorts of paired CRC and normal mucosa. TM

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Differentially expressed miRNA profiles have not only been generated for CRC and normal tissues, but have also been used to probe different stages of the disease. Schepeler et al. profiled the expression of miRNAs in 49 stage II colon cancers, differing with regard to microsatellite status and recurrence of disease, and 10 normal mucosas. They identified several differentially expressed miRNAs, which might act as prognostic biomarkers [136]. In another report, 43 differentially expressed miRNAs were identified between stage III CRC and matched nontumor tissues [137]. Moreover, Arndt and colleagues identified 11 common miRNAs, which were differentially expressed between CRC and normal controls in both clinical samples and cell lines. MiRNA-145 was further verified in different stages of CRC, and was shown to correlate with CRC metastasis [138]. Differential miRNA expression also appeared characteristic of the molecular subclassification of CRC. This was first proposed in an miRNA microarray study in which the expression of miRNAs between microsatellite stable and MSI-high CRCs exhibited significant differences [139]. Further studies showed variation of miRNA expression in tumors with different mismatch repair status [140], different tumor locations or different subtypes [141–143]. Recently, it has been reported that miRNAs are stable in serum/plasma or feces and can act as diagnostic or prognostic biomarkers for CRC patients [144–146]. To comprehensively analyze the miRNA signatures to identify potential biomarkers in serum/plasma or feces, high-throughput microarray was performed. Following analysis of a panel of 95 miRNAs, 5 of them were found to be upregulated in both CRC plasma and tissue samples. Further studies suggested that plasma miR-92 might be a potential non-invasive biomarker for CRC detection with 89% sensitivity and 70% specificity [147]. From a screening of serum samples from 30 patients with stage IV CRC and 10 healthy controls using a panel of 375 cancer-relevant miRNAs, 20 differentially expressed miRNAs were identified in the serum of stage IV colon cancers versus normal controls [148]. By screening a cohort containing 20 CRC patients, 9 adenomas patients and 12 healthy patients using microfluidic array technology, 8 out of 380 plasma miRNAs were identified with the potential to distinguish polyps from controls [149]. Link and colleagues compared the miRNA expression profiles of healthy stool samples with normal colon mucosa using Illumina miRNA microarray. As expected, the expression profiles of 284 miRNAs showed significant similarities, both in feces from different healthy individuals or feces collected serially from the same individuals. Furthermore, a number of fecal miRNAs were differentially expressed in CRC and adenomas compared with normal controls [150]. Importantly, a study using RT-pre-amplification-qPCR technology demonstrated that the miRNAs are sufficiently stable to be detected in the fecal microenvironment [151]. This group further identified fecal miR-144* as a potential biomarker for CRC diagnosis. A recent study was performed using Affymetrix GeneChip miRNA 3.0 Array analysis on fecal samples from 15 individuals, including those with different stages of CRC and healthy controls. One hundred and forty-one miRNAs were 198

identified as overexpressed in CRC, while 41 were downregulated [152]. In agreement with Link et al. [150], they also showed that the miRNA expression profiles in fecal samples were similar to those in colonic mucosa [152]. Further studies are now required to characterize fecal miRNA expression profiles in larger cohorts for the discovery and validation of potential biomarkers. Expert commentary

Recent advances in MS technologies have greatly facilitated high-throughput proteome profiling studies. However, due to the extremely complex nature of the proteome and the heterogeneity of clinical samples, it is not yet possible to profile the whole proteome in a single experiment. Thus, optimal combinations of protein separation approaches and improved MS technologies are essential. The development of scheduled MRM-MS now enables hundreds of candidate biomarkers to be rapidly quantified and validated in a single MS analysis without the use of antibodies. The advent of label-free quantitative proteomics has made it possible to identify differentially expressed proteins in large cohorts, facilitating biomarker discovery in heterogeneous clinical samples. In addition, the recent advances in top-down proteomics are raising new hopes for the comprehensive analysis of PTMs. The proteome is functionally translated from the genome and directly discerns molecular phenotype, but is of unprecedented complexity. Genetic mutations, epigenetic modifications, mRNA editing, miRNA regulation and protein PTMs will greatly influence the biological function of proteins encoded by each gene [153]. The flow of information from genome to transcriptome to proteome will enable characterization of the relationship of gene coding and expression patterns with the resulting protein pathways and interactomes [154]. There has recently been considerable discussion on the correlation between DNA and RNA levels and protein abundance. Recent advances in NGS and proteomics, such as those described in this article now enable such studies to be made [155]. Protein abundance reflects a dynamic balance between a series of linked processes, covering transcription, processing and degradation of mRNAs to the translation, localization, modification and programmed destruction of the expressed proteins. It is therefore perhaps not surprising that a recent article [156] comparing the expression of single RNAs with the corresponding proteins, showed only a mean correlation of 60% from all analyzed tissues and treatments. The involvement of epigenetic changes (such as gene methylation and histone acetylation), miRNAs and PTMs in the regulation of protein expression will be clarified in future studies. The development of in silico secretome analysis using transcriptomic data from NGS platform to predict and annotate secretory proteins will maximize the value of transcriptomic data for the discovery and further validation (such as targeted proteomics or ELISAs) of CRC biomarkers [157]. The Encyclopedia of DNA elements project, which aims at discovering and defining the functional elements encoded in the human genome, has provided new insight into the organization and Expert Rev. Proteomics 11(2), (2014)



regulation of genes and genome. The integration of the Encyclopedia of DNA elements and the chromosome-centric human proteome project will facilitate the interpretation of the transcriptome and proteome and undoubtedly improve our understanding of the complexity of the gene-encoded proteome [158,159]. Furthermore, browsers, such as The Protein Browser Web Portal enabling detailed and unbiased interrogation of genome, transcriptome and proteomics databases, are currently being developed [160]. There is absolutely no doubt that the integration of multi-omics data will enable more comprehensive understanding of the biological events in CRC and further enrich the biomarker landscape [161]. We believe that the use of these state-of-the-art systems biology approaches will lead to rapid advances in the discovery and validation of novel biomarkers, and panels of biomarkers, for the early detection and surveillance of CRC. This will have important positive ramifications not only for CRC patients, but also for global health expenditure. Five-year view

Although many CRC candidate biomarkers have been identified by genomic, transcriptomic and proteomic studies, to date few of them have been approved for clinical use, due at least in part to the requirement for the development of sensitive and specific assays capable of the rapid validation of large clinical cohorts. Label-free proteomics, MRM-MS and SWATH will play an important role in these studies, coupled with the development of specific immuno-MS assays for the detection of low abundance biomarkers (e.g., cytokines), while continued developments in top-down sequencing methods will facilitate the detection of post-translationally modified proteins. Another anticipated important technological advance in the next 5 years will be the comprehensive mapping of the methylome and miRNAome in serum/plasma and feces, which will help identify additional CRC-specific biomarkers. Aberrant DNA methylation, a characterized epigenetic pattern, is known to drive CRC

Review

progression, and the global profiling of the DNA methylome will contribute to the discovery of novel CRC biomarkers. miRNA expression patterns are differential not only between CRC and normal tissues, but also among different stages of CRC. The profiling of the miRNAome in blood and feces has the potential to reveal both diagnostic and prognostic biomarkers. Moreover, improved databases and browsers integrating genomic, transcriptomic and proteomic data will enable efficient and productive mining of very large, and ever-increasing, amounts of experimental data that will be generated globally. Importantly, the integration of genomic, transcriptomic and proteomic data, coupled with bioinformatics analysis, in a systems biology approach will clearly enable a more comprehensive understanding of the biological nature of cancer, and shed new light on the discovery and validation of CRC biomarkers. Advances in the understanding of the human proteome, underpinned by organizations such as the Human Proteome Organization, will advance the establishment of personalized medicine, which will lead to improved detection and surveillance of diseases such as CRC, where the patient himself/herself provides the ‘normal baseline’ essential for predicting the early onset of disease. Financial & competing interests disclosure

EC Nice was supported, in part, by NHMRC Project Grant 603130, 1017078 and Development Grant 1017078. C Huang was supported by grants from the National 973 Basic Research Program of China (2013CB911300, 2012CB518900 and 2011CB910703), the National Science and Technology Major Project (2011ZX09302-001-01, 2012ZX09501001-003) and Chinese NSFC (81172173, 81225015). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Key issues • While the genome is static, the proteome is dynamic and constantly altering due to changes in the environment. Thus, while the human genome is now known to encode for only 20,300 proteins, the proteome consists of hundreds of thousands of protein isoforms due to splice variants and post-translational modifications (PTMs) including phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation, lipidation and proteolysis. • PTMs help define biological function, which is frequently dysregulated in the disease process. Unraveling the specific roles of the PTMome in colorectal cancer (CRC) is essential for the understanding of CRC initiation and progression, and will ultimately reveal new biomarkers for the detection and surveillance of CRC. • The development of improved sensitive and specific top-down protocols for the high-throughput analysis of protein isoforms will be essential for identifying novel, disease-specific biomarkers. • Global deposition of raw data files in a common format to facilitate data mining is essential, and should be mandatory at publication. Key principals were developed around the Human Proteome Organization 2010 in Sydney, Australia, and policies to enforce this are currently being implemented by a number of the leading proteomic journals. • The generation of validated ‘gold standards’ for quantitative studies is essential. • Fully validated polyclonal and monoclonal antibodies are essential to complement mass spectrometry-based studies. This is being achieved through initiatives such as The Protein Atlas [162] and Antibodypedia [163].


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