Current state and future direction of screening tool for colorectal cancer

Table 1 Characteristics of colorectal cancer screening tests currently in use in the United States

Screening test		Interval	Evidence	Advantages	Disadvantages	Other considerations
Stool-based screening tests
FIT with high sensitivity123		Every year	Improved performance compared with high-sensitivity gFOBT Mortality reduction: indirect evidence from RCTs of guaiac-based stool tests	Can be performed at home Requires only a single specimen No diet or medication restrictions Does not require bowel preparation or anesthesia Inexpensive compared with structural examinations and mt-sDNA	High nonadherence to yearly testing (especially without reminder systems) Less effective for advanced adenoma detection Few accessible tests have published peer-reviewed performance data	Varies in test performance due to brand and version Follow-up colonoscopy for positive test may charge extra costs
gFOBT with high sensitivity12 (HSgFOBT)		Every year	Good RCT evidence for incidence and mortality reduction[112-116] Varies in test performance characteristics by version of the test	Inexpensive compared with structural examinations and mt-sDNA Can be done at home Does not require bowel preparation or anesthesia	High nonadherence to yearly testing (especially without reminder system) Less effective for advanced adenoma detection Difficulty in determining test performance among the many FDA-cleared tests Requires multiple samples Requires dietary and medication restriction Higher false-positive rate than FIT leads to more colonoscopies	Follow-up colonoscopy for positive test may charge extra costs
mt-sDNA1		Every 3 yr	Mortality reduction: indirect evidence from RCTs of guaiac-based stool tests Improved sensitivity for cancer and AA and poorer specificity compared with FIT	Can be done at home Does not require bowel preparation or anesthesia	More expensive than other stool-based tests Higher false-positive rate than FIT	Follow-up colonoscopy for positive test may charge extra costs A new test with limited data on screening outcomes. Uncertainty in management of positive results followed by a negative colonoscopy
FIT-DNA23		Every 1 or 3 yr	Test characteristic studies	Improved sensitivity compared with FIT per single screening test Does not require bowel preparation or anesthesia Can be done at home	Higher false-positive rate than FIT	Uncertainty in management of positive results followed by a negative colonoscopy
Direct visualization screening tests
Colonoscopy123	Every 10 yr		Non-RCT evidence of incidence and mortality reduction Prospective cohort study with mortality end point	Requires less frequent screening Screening, diagnosis, treatment and prevention through polypectomy can be done at the same-session. Gross visualization of the entire colon	Pain and discomfort Lower tolerability and compliance than FS[117] Possibility of bowel perforation / bleeding and cardiopulmonary complications from anesthesia Requires full bowel cleansing Performance varies upon adequacy of bowel preparation, the cecal intubation rate, withdrawal time, and adenoma detection rate Lower sensitivity for neoplasia in the proximal than the distal colon	Polypectomy and anesthesia may charge extra costs Most expensive test, but currently reimbursable with insurance Requires day-off (if sedation is used)
CTC123	Every 5 yr		Test characteristic studies Extrapolation from RCTs of sigmoidoscopy demonstrating mortality reduction	Rapid, non-invasive imaging method Well-tolerated by patients Does not require anesthesia Better tolerability and acceptance than colonoscopy and FS[118]	Exposure to low-dose radiation Requires full bowel cleansing A second bowel cleansing will be required before Follow-up colonoscopy for positive test	Follow-up colonoscopy for positive test may charge extra costs Insufficient evidence about the benefit-burden balance of additional tests on incidental extracolonic findings Relatively expensive and may not be covered by insurance
FS123	Every 5 yr		RCTs with mortality end points:	Does not require anesthesia Requires more limited bowel cleansing Better acceptance than colonoscopy[117]	Pain and discomfort Does not examine the proximal Colon Requires enema prior to procedure Abnormal findings require second colonoscopy	Follow-up colonoscopy for positive test may charge extra costs Concerns about lack of quality standards, limited availability, failure to achieve a complete examination
FS with FIT2	FS every 10 yr plus FIT every year		RCT with mortality end point (subgroup analysis)	More benefits than when combined with FIT or compared with other strategies It may be an potentially option for patients who want endoscopy screening but do not want colonoscopy		Test declined in the US

¹The American Cancer Society Guidelines recommend.

²US Preventive Services Task Force Guidelines recommend.

³The U.S. Multi-Society Task Force Guidelines recommend [Tier 1: Colonoscopy every 10 yr, annual fecal immunochemical test; Tier 2: CT colonography every 5 yr, FIT-fecal DNA every 3 yr, flexible sigmoidoscopy every 10 years (or every 5 yr); Tier 3: Capsule colonoscopy every 5 yr]. CRC: Colorectal cancer; CTC: Computed tomographic colonography; FDA: US Food and Drug Administration; FIT: Fecal immunochemical test; FS: Flexible sigmoidoscopy; gFOBT: Guaiac-based fecal occult blood test; mtsDNA: Multitarget stool DNA; RCT: Randomized controlled trial.

Table 2 Summary of the current and potential biomarkers for early diagnosis of colorectal cancer

Characteristics of the studies				Training set [test set] (if applicable)			Diagnostic performance (if applicable)
Ref.	Study type, country	Study group	Population (n)	Male (%)	Age (mean / SD)	Stage (0) / I/ II/ III/ IV/ (?)	Sample	Marker	Sn / Sp	AUC / P-value
Microsatellites loci
Piñol et al[119], 2005	Prospective, multicenter, nation-wide study/Spain	CRC	1222	59.8	70/11	161/510/337/214	Blood	Bethesda panel	81.8/98	N/A
Umar et al[71], 2004	Guidelines	N/A	N/A	N/A	N/A	N/A	Blood	Bethesda panel	81.8/98	N/A
Berg et al[120], 2009	Recommendations	N/A	N/A	N/A	N/A	N/A	Blood	Microsatellites instability (MSI)	55-90/90	N/A
Liang et al[67], 2013	Meta‐analysis/China	N/A	N/A	N/A	N/A	N/A	Blood	APC Polymorphisms	N/A	N/A
CRC-specific RNA markers
Wu et al[77], 2014	Case-control China	Normal	109	45.9	60.4/7.0	I + II/III + IV/(?) 24/76/4	Stool	MiRNA-135b	78 (CRC) 73(Advanced adenoma) 65(any adenoma) /68	0.79 (CRC) 0.71 (adenoma) / <0.0001
			Adenoma < 1cm	110	53.6
			Advanced adenoma	59	50.7
			CRC	104	57.7
			IBD	42	61.9
Kalimutho et al[78], 2011	Case-control, Italy	CRC	28	46	66	(5)/2/6/3/0/(NA:12)	Stool	miRNA-148	74/87	N/A
		HGD	12	67	62
		Cn	39	28	58
Koga et al[74], 2010	Case-control, Japan	CRC	206	67	63	23/46/133/4	Stool	PTGS2	74.1/74.1	N/A, <0.0001
		Cn	134	44	60
Methylation biomarkers
Luo et al[86], 2011	Meta‐Analysis/China	N/A	N/A	N/A	N/A	N/A	Stool	VIM	80/80	N/A
Guo et al[88], 2013	Case-control, China	CRC	75	61	58.5 (12.5)	12/30/30/3	Stool	FBNI	72/93.3	N/A, < 0.001
		Cn	30	67	58.4 (12.9)
Glockner et al[89], 2009	Case-control, United States	CRC	26 [47]	52 [45]	69.33 [71.1]	Stage I to III	Stool	TFP12	89/93	N/A
		Adenoma	[19]		[61.4]
		Cn	45 [30]	46 [54]	55 [52.3]
Oh et al[90], 2013	Case-control, South Korea	CRC	131	69	58.4	26/57/36/12	Blood	SDC2	87/95	0.927, < 0.0001
		Cn	125	64	51
Grützmann et al[121], 2008	Case-control, Germany	CRC	252[126]	57 [60]	61 [67]	63/83/59/29/(NA:19)	Blood	Septin 9	48/93	N/A
		Cn	102[183]	35 [41]	59 [56]	[22/37/54/11/(NA:3)]			[58/90]
Warren et al[91], 2011	Case-control, United States	CRC	50	54	62	I + II/III + IV	Blood/Stool	Septin 9	90/88	N/A
		Cn	94	45	58	38/12
Tóth et al[92], 2012	Case-control, Hungary	CRC	93	52	67.8 (9.8)	25/14/36/18	Stool	Septin9 (gFOBT)	100/100	N/A
		Cn	94	38	62.6 (9.9)

SD: Standard deviation; Sn: Sensitivity; Sp: Specificity; AUC: Area under the curve; CRC: Colorectal cancer; N/A: Not available; Cn: control; IBD: Inflammatory bowel disease; HGD: High grade dysplasia; VIM: Vimentin; TFP12: Tissue factor pathway inhibitor 2.

Table 3 Characteristics of colorectal cancer screening of bio fluidic sample types (blood, urine, stool)

Sample types	Evidence of efficacy	Advantage	Disadvantage
Blood-based biomarkers (serum, plasma, and dried blood spot)	A combination of 8 metabolites (99.3% sensitivity, 93.8% specificity, and AUC 0.996)[94] Gastrointestinal tract acid 446 (83.3% sensitivity, 84.8% specificity, 85.7%, and 52.1% , respectively)[96,97] Decanoic acid (87.87% sensitivity, 80.0% specificity, 71.0%, and 75.0%, respectively)[98,99]	Easily accessible Less affected by diet than urine Less diurnal variation and Less inter- and intra-subject variability than urine Stable over a 4-mo period frozen at -80 °C except at room temperature	Affected by smoking status More invasive than urine and stool Analysis can be more complex than urine
Urine	Cross-validated panel of seven metabolites (97.5% sensitivity, 100% specificity, and AUC 0.998)[104] 10 different metabolites (100% sensitivity, 80% specificity but small sample size)[103] N1, N12-Diacetylspermine[105,106]	Easily accessible Less invasive than blood	More affected by diet than serum samples More diurnal variation and More inter- and intra-subject variability than serum A full day storing at room temperature or on cool packs altered metabolite concentration More than 2 freeze and thaw cycles affected the metabolic profile significantly
Stool	A three metabolite panel (AUC 1.0 but very small sample size)[107] A metabolomics panel (AUC 0.94)[108]	Easily accessible Less invasive than blood	Inconvenient to collect of stool samples Low compliance

Table 4 High-throughput metabolomic studies of potential biomarkers in CRC screening

Sample type		Ref.	Analytical technique(s)	Major metabolites	Out-comes	Sn / Sp	Significant finding(s)
Dried blood		Jing et al[93], 2017	Direct infusion MS	AA (4) FA (4)	CRC	81.2/84	Establishing a reasonable diagnostic regression model with eight blood parameters
SERUM	BP	Zhang et al[122], 2018	UPLC-MS/MS	FA(2): Eicosanoids	CRC	N/A	Identification of eicosanoids as potential biomarkers for identifying among health, enteritis and CRC
		Guo et al[123], 2017	FTICR MS	FA(5): Male FA(2): Female	CRC	77.3/92.4 80.8/85.9	Presenting the relationship between the change trends of six phospholipids and cancer stages
		Farshidfar et al[124], 2016	GC-MS	AA (9) FA(7) CH (12) Others (13)	CRC	85.0/86.0	Discovery of a suite of CRC biomarkers that provide early detection, prognostication and preliminary staging information
		Zhang et al[125], 2016	FTICR MS	FA (6)	CRC	93.8/92.2	Identification of Free Fatty Acids as diagnostic indicators of early-stage CRC patients
		Gu et al[126], 2015	LC-MS/MS	AA (8)	CRC	65.0/95.0	Performing a combined analysis of amino acids in three different domains: FAAs, FSPAAs, and IPAAs
		Zhu et al[127], 2014	LC-MS	AA (7) FA (3) CH (3)	CRC	96.0/80.0	Establishing Partial least-squares-discriminant analysis (PLS-DA) models for distinguishing CRC patients
		Li et al[128], 2013	DI-ESI (±) -FTICR MS	FA (9)	CRC	86.5/96.2	Emphasize that the facile loss of methyl chloride from the [M  +  Cl] (-) form of LPC (16:0) in its tandem mass spectrum
		Tan et al[129], 2013	UPLC-QTOFMS	AA (6) FA (1) CH (3)	CRC	83.7/91.7	Identification of serum metabolite markers as diagnostic indicators for the detection of CRC
		Ma et al[130], 2012	GC-MS	AA (3) CH (3)	CRC	93.31/96.71	Emphasize integrated network connectivity analysis for the diagnosis
		Nishiumi et al[131], 2012	GC-MS	AA (3) CH (1)	CRC	83.1/81.0	Establishing potential predictive model for early detection of colorectal cancer
		Ritchie et al[132], 2010	FTICR MS	FA (3)	CRC	75.0/90.0	IdentifIcation of a systemic metabolic dysregulation comprising previously unknown hydroxylated polyunsaturated ultra-long chain fatty acid metabolites in CRC patients
		Ludwig et al[133], 2009	Hadamard-encoded TOCSY spectra	FA (1) CH (4)	CRC	70.0/95.0	Showing the potential of fast Hadamard-encoded TOCSY spectra for improved classification of serum samples from colorectal cancer patients using a metabolomics approach
	S	Hata et al[96], 2017	FIA–MS/MS	FA (1: GTA-446)	CRC	83.3/84.8	Identification of GTA-446 as promising tool for primary colorectal cancer screening
		Uchiyama et al[98], 2017	CE-TOFMS	FA (1): Benzoic FA (1): Octanoic FA (1): Decanoic AA (1): Histidine	CRC	89.0/82.0 76.0/71.0 71.0/75.0 63.0/82.0	The first report to determine the correlation between serum metabolites and CRC stage using CE-TOFMS Identification of benzoic acid as diagnostic indicators
		Ritchie et al[97], 2013	TQ-MS	FA (1)	CRC	85.7/~52.12	Identification of low-serum GTA-446 as significant risk factor for CRC and sensitive predictor of early-stage disease
		Ikeda et al[134], 2012	GC-MS	AA (1): Alanine CH (1): GluL AA(1): Glutamine	CRC	54.5/91.6 75.0/75.0 81.8/66.7	Showing the potential of metabolomics as an early diagnostic tool for cancer
		Leichtle et al[135], 2012	TIS-MS	AA (1)	CRC	N/A	Showing serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination
PLASMA	BP	Nishiumi et al[94], 2017	GC/QqQMS	AA (3) FA (3) CH (2)	Stage 0/I/II	99.3/93.8	Establishing potential predictive model of colorectal cancer that do not involve lymph node or distant metastasis
		Li et al[136], 2013	Lipid extraction MS	FA (3)	CRC	88.3/80.0	Identification of the plasma choline-containing phospholipid levels as potential biomarkers to distinguish between healthy controls, AP and CRC cases, implying their clinical usage in CRC and/or AP-CRC progression detection
		Miyagi et al[137], 2011	HLPC-ESI-MS	AA (10)	CRC	N/A	Showing the potential of plasma free amino acids profiling for improving cancer screening and diagnosis and understanding disease pathogenesis
		Okamoto et al[138], 2009	HLPC-ESI-MS	AA (6)	CRC	N/A	Presenting the possibility of plasma free amino acids profiling
		Zhao et al[139], 2007	LC- MS	FA (4)	CRC	82.0/93.0	Identification of percentage of 18:1-LPC or 18:2-LPC plasma levels compared with total saturated LPC levels, either individually or in combination as potential biomarkers for CRC
	S	Liu et al[140], 2018	N/A	AA(1) :Homocysteine	CRC/A	43.5/98.8	Presenting the possibility of using homocysteine with CEA in screening of early rectal cancer
		Shen et al[95], 2017	2D LC-QToF/MS	FA (1): PG FA (1): SM	CRC	1.00/1.00 1.00/1.00	Presenting the possibility of 2D LC-QToF/MS-based lipidomics profiling
		Crotti et al[99], 2016	GC-MS	FA (1)	CRC	87.8/80.0	Identification of the C10 fatty acid as valuable early diagnostic biomarker of CRC
		Cavia-Saiz et al[141], 2014	high pressure-LC	AA (1)	CRC	85.2/100	Identification of the plasma levels of l-kynurenine as a potential biomarkers of CRC
URINE	BP	Nakajima et al[105], 2018	LC- MS	AA (2)	CRC	N/A	Presenting the potential of polyamines and a machine-learning method as a screening tool of CRC
		Deng,Fang et al[142], 2017	1-dimensional NMR	AA (7) FA (2) CH (8)	A	82.6/42.4	Presenting novel urine-based metabolomic diagnostic test for the detection of adenomatous polyps
		Deng et al[101], 2017	LC- MS	FA (1) CH (2)	A	82.43/36.03	Presenting a clinically scalable MS-based urine metabolomic test for the detection of adenomatous polyps
		Wang et al[143],2017	H-NMR	AA (3) CH (1)	Stage I/II	87.5/91.3	Supporting the utility of NMR-based urinary metabolomics fingerprinting in early diagnosis of CRC
		Rozalski et al[144], 2015	GC-MS	CH (3)	CRC	78.6/75.0	Identification of Urinary 5-hydroxymethyluracil and 8-oxo-7,8-dihydroguanine as potential biomarkers
		Wang et al[102], 2014	1-dimensional NMR	AA (7) FA (2) CH (8)	A	82.7/51.2	Presenting a proof-of-concept spot urine-based metabolomic diagnostic test
		Hsu et al[145], 2013	HPLC-MS/MS	CH (6)	CRC	69.0/98.0	Identification of a set of six targeted nucleosides as marker
		Eisner et al[100], 2013	H-NMR	AA (2) CH (2)	Polyps	64.0/65.0	Presenting a machine-learned predictor of colonic polyps based on urinary metabolomics
		Yue et al[103], 2013	RRLC-QTOF/MS	FA (9) Others (1)	CRC	100/80.0	Identification of CRC urinary metabolites as marker
		Cheng et al[104], 2012	GC/TOF-MS UPLC-QTOFMS	AA (4) FA (1) CH (2)	CRC	97.5/100	Reporting a second urinary metabonomic study on a larger cohort of CRC (n = 101) and healthy subjects (n = 103)
		Chen[146], 2012	CE-MS	AA (8) CH (4)	CRC	N/A	Presenting the usefulness of the technique of CE-MS based on moving reaction boundary
		Wang et al[147], 2010	UPLC-MS SPE-HPLC	AA(4) FA(5) / CH (7)	CRC	N/A	Identification of urinary metabolic biomarker based on UPLC-MS and SPE-HPLC
		Feng[148], 2005	RP-HPLC	CH (2)	CRC	71.2/93.3	Identification of Pseu and m1G as novel biomarkers for colorectal cancer diagnosis and surgery monitoring
		Zheng et al[149], 2005	Column switching HPLC	CH (14)	CRC	71.0/96.0	Identification of urinary nucleosides determined by column switching high performance liquid chromatography method
	S	Johnson et al[150], 2006	LC- MS	FA (1)	ACN	90.0/45.0	Identification of urinary PGE-M as a potential biomarker of ACN
		Hiramatsu et al[106], 2005	ELISA	AA (1)	CRC	75.8/96.0	Indicating that urinary N(1), N(12)-Diacetylspermine is a more sensitive marker than CEA, CA19-9, and CA15-3
FECES	BP	Amiot et al[108], 2015	H-NMR	AA (2) FA (4) CH (1)	ACN	N/A	Identification of (1)H NMR Spectroscopy of Fecal Extracts as biomarker
		Phua et al[107], 2014	GC/TOF-MS	FA (1) CH (2)	CRC	N/A	Establishing proof-of-principle for GC/TOFMS-based fecal metabonomic detection of CRC
		Bezabeh et al[151], 2009	(1)H-MRS	AA (6) FA (1) CH (3)	CRC	85.2/86.9	Detecting colorectal cancer by 1H magnetic resonance spectroscopy of fecal extracts
	S	Lin et al[152], 2016	H-NMR	FA (1): Acetate FA (1): Succinate	Early stage	94.7/92.3 91.2/93.5	Identification of the potential utility of NMR-based fecal metabolomics fingerprinting as predictors

¹Sensitivity and specificity calculated from available data.

²Specificity was calculated for the intention to screening population (40–74 year-olds in the colonoscopy population).

³Additional results for different cut-off values can be read from the original article. BP: Biomarker panels; S: Single markers; MS: mass spectrometer; FTICR: Fourier transform ion cyclotron resonance; SM: sphingomyelins; PC: phosphatidylcholine; FIA-MS/MS: flow injection analysis–mass spectrometry; Arg: arginine; Val: valine; Phe: phenylalanine; Tyr: tyrosin; Ala: alanine; TQ-MS: triple-quadrupole tandem mass spectrometry; GluL: glucuronic lactone; TIS-MS: Turbo Ion Spray Source mass spectrometer; AP: adenomatous polyps; HLPC-ESI-MS: high-performance liquid chromatography-electrospray ionization-mass spectrometry; A: adenomas; GC/QqQMS: gas chromatography/triple-quadrupole mass spectrometry; 2D LC-QToF/MS: two dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry; PG: phosphatidylglycerol(34:0); SM: sphingomyelin (38:8); CE-TOFMS: capillary electrophoresis-time-of-flight mass spectrometry; GC-MS: gas chromatography-mass spectrometry; LC-MS/MS: liquid chromatography tandem MS; FAAs: free amino acids; FSPAAs: free and soluble-proteome amino acids; IPAAs: insoluble-proteome amino acids; DI-ESI(±): Direct-infusion positive and negative ion electrospray ionization; ACN: advanced colorectal neoplasms; NMR: nuclear magnetic resonance spectra; H-NMR: proton nuclear magnetic resonance spectroscopy; RRLC: rapid resolution liquid chromatography; UPLC-QTOFMS: Ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry; SPE, solid phase extraction; RP, reverse-phase; ELISA: Enzyme-linked immunosorbent assay; (1)H-MRS: (1)H magnetic resonance spectroscopy.