Associations between genetic variation in RUNX1, RUNX2, RUNX3, MAPK1 and eIF4E and risk of colon and rectal cancer: additional support for a TGF-β-signaling pathway

Abstract

The Runt-related transcription factors (RUNX), mitogen-activated protein kinase (MAPK) 1 and eukaryotic translation initiation factor 4E (eIF4E) are potentially involved in tumorigenesis. We evaluated genetic variation in RUNX1 (40 tagSNPs), RUNX2 (19 tagSNPs), RUNX3 (9 tagSNPs), MAPK1 (6 tagSNPs), eIF4E (3 tagSNPs), eIF4EBP2 (2 tagSNP) and eIF4EBP3 (2 tagSNPs) to determine associations with colorectal cancer (CRC). We used data from population-based studies (colon cancer n = 1555 cases, 1956 controls; rectal cancer n = 754 cases, 959 controls with complete genotype data). Four statistically significant tagSNPs were identified with colon cancer and three tagSNPs were identified with rectal cancer. Whereas the independent risk estimates for each of the tagSNPs ranged from 1.21 to 1.52, the combined risk was greater than additive for any of the three combined high-risk genotypes {combined risk range 1.98 [95% confidence interval (CI) 1.45, 2.70] for eIF4E, RUNX1 and RUNX3 to 3.32 [95% CI 1.34, 8.23] for eIF43, RUNX2 and RUNX3}. For rectal cancer, the strongest association was detected for the combined genotype of RUNX1 and RUNX3 (odds ratio 1.87 95% CI 1.22, 2.87). Associations with specific molecular tumor phenotypes showed consistent and strong associations for CIMP+/MSI+ tumors where the risk estimates were consistently >10-fold and lower confidence bounds were over 3.00 for high-risk genotypes defined by RUNX1, RUNX2 and RUNX3. For CIMP+/KRAS2-mutated colon tumors, the combined risk for high-risk genotypes of RUNX2, eIF4E and RUNX1 was 7.47 (95% CI 1.58, 35.3). Although the associations need confirmation, the findings and their internal consistency underline the importance of genetic variation in these genes for the etiology of CRC.

Introduction

The Runt-related transcription factors (RUNX) are thought to play an important role in carcinogenesis in addition to their role in normal development (1). The three RUNX genes, RUNX1, RUNX2 and RUNX3, although widely expressed, have tissue-specific properties (2). Of the three, RUNX3 has been shown to be specifically associated with gastrointestinal tract development (3). Studies in RUNX3 knockout mice have shown defects in apoptotic response to transforming growth factor (TGF)-β; RUNX2 transgenic mice have been shown to be hypersensitive to TGF-β (4). All three RUNX genes have potential for involvement in colorectal cancer (CRC) etiology given their role in signaling cascades mediated by TGF-β and bone morphogenetic protein (BMP) (4–7); all three RUNX genes have been shown to bind Smads that are also involved in the TGF-β signaling pathway (8–10).

Mitogen-activiated protein kinase (MAPK) 1, also known as extracellular signal-regulated kinase 2, is involved in eukaryotic signal transduction. MAPK1 has been shown to activate RUNX2 (11). Like RUNX, MAPK1 is involved in the TGF-β-signaling pathway including Smad signaling (12,13) Eukaryotic translation initiation factor 4E (eIF4E) is a translational regulator that acts downstream of Akt and mTOR, promoting Akt’s action in tumorigenesis (14). eIF4E has been shown to play a key role in cell growth and has been reported to be overexpressed in colon tumors (15). Expression of eIF4E in human colon cancer cells has been shown to promote the TGF-β stimulation of adhesion molecules (16).

Together, Runx, MAPK1 and eIF4E appear to be integral parts in the regulation of the TGF-β, Smad and BMP signaling pathways, each of which plays an important role in the etiology of colon and rectal cancer. However, little is known about how genetic variation in these genes relate to colon and rectal cancer. Furthermore, although other genes in the TGF-β-signaling pathway have been linked to CpG island methylator phenotype (CIMP+) and microsatellite instability (MSI+) tumors, it is unknown whether genetic variation in these genes contributes to specific molecularly defined phenotypes colorectal cancer (17). In this study, we evaluated the association between genetic variability in these genes and colon and rectal cancer and with specific tumor molecular phenotypes. We further report how these genes interact with other genes in the TGF-β-signaling pathway.

Methods

Two study populations are included in these analyses. The first study, a population-based case–control study of colon cancer, included cases (n = 1555 with complete genotype data) and controls (n = 1956 with complete genotype data) identified between 1 October 1991 and 30 September 1994 (18) living in the Twin Cities Metropolitan Area or a seven-county area of Utah or enrolled in the Kaiser Permanente Medical Care Program of Northern California (KPMCP). The second study, with identical data collection methods, included cases with cancer of the rectosigmoid junction or rectum (n = 754 cases and n = 959 controls with complete genotype data) who were identified between May 1997 and May 2001 in Utah and at the KPMCP (19). Eligible cases were between 30 and 79 years of age at the time of diagnosis, living in the study geographic area, English speaking, mentally competent to complete the interview and with no previous history of CRC and no previous diagnosis of familial adenomatous polyposis, ulcerative colitis or Crohn's disease. Cases who did not meet these criteria were ineligible as were individuals who were not black, white or Hispanic for the colon cancer study. A rapid reporting system was used to identify cases within months of diagnosis.

Controls were matched to cases by sex and by 5 years age groups. At KPMCP, controls were randomly selected from membership lists; in Utah, controls ≥65 years were randomly selected from the Health Care Financing Administration lists and controls <65 years were randomly selected from driver's license lists. In Minnesota, controls were selected from driver's license and state-identification lists. Study details have been previously reported (20,21).

Interview data collection

Data were collected by trained and certified interviewers using laptop computers. All interviews were audiotaped as described previously and reviewed for quality control purposes (22). The referent period for the study was 2 years prior to diagnosis for cases and selection for controls. Detailed information was collected on diet, physical activity, medical history, reproductive history, family history of cancer, regular use of aspirin and nonsteroidal anti-inflammatory drugs and body size.

Tumor marker data

We have previously evaluated tumors for CIMP, MSI, TP53 mutations and KRAS2 mutations (23–26) and were therefore able to evaluate variation in the specified genes in relation to molecularly defined subsets of CRC. Details for methods used to evaluate epigenetic and genetic changes have been described (23–26). Because of the rarity of MSI+ rectal tumors (27), we did not evaluate MSI in rectal tumors.

TagSNP selection and genotyping

TagSNPs were selected for genes RUNX1, RUNX2, RUNX3, MAPK1, eIF4E, eIF4EBP2 and eIF4EBP3 using the following parameters: linkage disequilibrium blocks using a Caucasian linkage disequilibrium map with r² = 0.8; minor allele frequency > 0.1; range = −1500 bp from the initiation codon to +1500 bp from the termination codon and one single nucleotide polymorphisms (SNP)/linkage disequilibrium bin. All markers were genotyped using a multiplexed bead array assay based on GoldenGate chemistry (Illumina, San Diego, CA). A genotyping call rate of 99.85% was attained. Blinded internal replicates represented 4.4% of the samples. The duplicate concordance rate was 100% genotyping of other genes along the candidate pathway, which were assessed for their interactive effects with RUNX, MAPK1 and eIF4E were genotyped on the same platform. Table I describes tagSNPs associated with colon or rectal cancer, whereas supplementary Table 1 (available at Carcinogenesis Online) has a listing of all tagSNPs included on the platform.

Table I.

Description of Runx, eIF4E and MAPK1 genes in the study population

Major/minor
Symbol	Alias	Location	SNP	Allele	MAF-NHW	MAF-Hispanic	MAF-AA	FDR (HWE)
EIF4E	CBP	4q21–q25	rs11727086	A/G	0.26	0.19	0.06	0.93
	EIF4E1		rs12498533	A/C	0.42	0.46	0.20a	0.93
	EIF4EL1
	MGC111573
EIF4EBP2	4EBP2	10q21–q22	rs7078987	A/G	0.46	0.43	0.13	0.93
EIF4EBP3	4E-BP3	5q31.3	rs250425	C/T	0.24	0.25	0.12	0.93
MAPK1	ERK	22q11.21	rs11913721	A/C	0.41	0.43	0.29	0.9
	ERK2		rs8136867	A/G	0.47	0.37	0.36	1
	ERT1		rs2298432	C/A	0.38	0.26	0.07	1
	MAPK2		rs9610375	G/T	0.46	0.49	0.4	0.97
	P42MAPK
	PRKM1
	PRKM2
	p38
	p40
	p41
	p41 mapk
RUNX1	AML1	21q22.3	rs1474479	G/A	0.35	0.27	0.32	0.96
	AML1-EVI-1		rs2248720	A/C	0.49	0.46a	0.24	0.96
	AMLCR1		rs8134380	A/T	0.44	0.36	0.17	0.14
	CBFA2		rs2071029	G/A	0.14	0.22	0.35	1
	EVI-1		rs2242878	C/T	0.19	0.18	0.05	0.98
	PEBP2aB		rs2834645	T/C	0.23	0.15	0.05	1
			rs2300395	C/T	0.3	0.25	0.19a	1
			rs1981392	T/C	0.4	0.49a	0.42	1
			rs11702779	G/A	0.35	0.46	0.38	1
			rs7279123	C/T	0.25	0.21	0.47	1
			rs11701453	G/C	0.21	0.16	0.21	1
			rs7280028	T/C	0.19	0.16	0.4	0.62
			rs2268281	A/G	0.16	0.29	0.27	1
			rs2834650	C/T	0.1	0.07	0.02	1
			rs1883067	A/G	0.08	0.07	0.02	1
RUNX2	RP1-166H4.1	6p21	rs7750470	T/C	0.19	0.22	0.36	0.91
	AML3		rs10948238	C/T	0.4	0.36	0.34a	0.95
	CBFA1		rs1321075	C/A	0.14	0.24	0.2	0.95
	CCD		rs2819863	G/C	0.1	0.07	0.03	1
	CCD1		rs12333172	C/T	0.2	0.18	0.05	1
	MGC120022		rs12208240	G/A	0.08	0.12	0.04	1
	MGC120023		rs2819854	C/T	0.48	0.49a	0.41a	0.98
	OSF2		rs1316330	G/T	0.25	0.17	0.05	1
	PEA2aA
	PEBP2A1
	PEB2A2
	PEBP2aA
	PEBP2aA1
RUNX3	RP3-398I9.1	1p36	rs2135756	A/G	0.5	0.45	0.41	0.96
	AML2		rs2236850	T/C	0.44	0.44	0.14a	0.62
	CBFA3		rs906296	C/G	0.23	0.2	0.28	1
	FLI34510		rs6672420	A/T	0.48	0.37a	0.48	0.83
	MGC16070		rs6688058	G/A	0.13	0.19	0.16	0.89
	PEBP2aC

^amajor/minor allele differs from NHW population. FDR (HWE), false discovery rate adjusted P value for Hardy–Weinberg Equilibrium test. Minor allele frequency (MAF) based on control population; HWE based on NHW control population (sample sizes range from 2519 to 2652).

NHW, non-Hispanic white; AA, African American

Statistical methods

All statistical analyses were performed using SAS® version 9.2 (SAS Institute, Cary, NC) unless otherwise stated. We report odds ratios (ORs) and 95% confidence intervals (CIs) derived from multiple logistic regression models for colon and rectal cancer separately based on minimal adjustments for age, sex, race and study center. Stepwise regression models were used to identify the tagSNPs and their inheritance models that contributed uniquely to the overall fit of the model for colon and rectal cancer; separate stepwise models were used to identify tagSNPs associated with specific molecular subtypes of tumors. Inclusion in the regression model was based on a score chi-square significance level of 0.05, whereas exclusion was determined based on a Wald chi-square 0.05 significance level. In addition to the minimal adjustments previously stated, the subset of SNPs returned from stepwise regression was also used as adjustment variables. Subsequent interaction analyses were based on tagSNPs identified as being statistically significant from stepwise regression. Adjusted multiple comparison P values were estimated taking into account all tagSNPs within the gene using the methods of Conneely and Boehnke (28) implemented in R version 2.11.0 (R Foundation for Statistical Computing, Vienna, Austria).

We evaluated interaction between RUNX1, RUNX2, RUNX3, MAPK1 and eIF4E and its binding proteins on the one hand and BMP-related genes, TGFβ1 and its receptors, Smad3, Smad4, Smad7 and nuclear factor-kappa B (NF-κB) 1 on the other hand given the biologic links to the TGF-β-signaling pathway. Possible interactions between SNPs and sex, age (30–64 or 65–79), recent aspirin or nonsteroidal anti-inflammatory drugs use, recent estrogen use and body mass index (<25, 25–30, >30) also were evaluated because of the mechanisms hypothesized for these genes. P values for interaction were determined by comparing a full model that included a categorical multiplicative interaction term to a reduced model without such an interaction term, using a likelihood ratio test. We evaluated risk estimates based on high-risk genotypes for each SNP as well as for the combined high-risk genotypes for those tagSNPs that were independently associated with colon or rectal cancer or with specific tumor subsets. High-risk genotypes were defined as those genotypes associated with statistically significant increased risk of colon or rectal cancer. The combined high-risk genotypes were determined based on the risk estimate for combinations of SNPs using either a dominant or a recessive model and compared with the referent genotype. Risk estimates were based on sets of combined high-risk genotypes compared with individuals without any of the high-risk genotypes designated (shown by asterisks in the table).

Tumors were defined by specific molecular alterations: any TP53 mutation, any KRAS2 mutation, MSI+, CIMP+ defined as at least two of five markers methylated and a combination of CIMP+/KRAS2-mutated or CIMP+/MSI+. As the proportion of MSI+ tumors in the rectal cases was <3% (27), we did not examine these tumor markers. Estimates of risk for molecular tumor phenotypes were made relative to controls.

Results

Of the genes assessed, four significant tagSNPs were identified that best represented the statistically significant associations with colon cancer, and similarly three tagSNPs were identified that captured the association with rectal cancer. One additional RUNX1 and two RUNX2 tagSNPs also were independently, statistically significantly associated with colon cancer. RUNX1 rs7279123 (OR 1.17 95% CI 1.02, 1.35 for the CT/TT genotype) and RUNX2 rs12208240 (OR 0.27 95% CI 0.08, 0.97 for the AA genotype) and rs2819854 (OR 1.21 95% CI 1.03, 1.42 for the TT genotype) were associated with colon cancer. Genes shown in Table II, best represented the independent and combined risk based on P values, magnitude of the association and frequency of the genotypes. The adjusted P values for multiple comparisons for RUNX1 were all above 0.2; the adjusted P value for RUNX2 rs12333172 was 0.12; the adjusted P value for RUNX3 rs667240 was 0.02; the adjusted P value for eIF4E was 0.02. Single SNP associations with colon cancer were generally modest and the combined risks from the high-risk genotypes were generally greater than that expected for an additive model. Nearly all of the two-way SNP combinations had an increased colon cancer risk greater than that would be expected on an additive scale. Combinations of three high-risk genotypes were generally associated with a 2- to 3-fold increased risk of colon cancer.

Table II.

Associations between Runx, MAPK1, eIF4E and colon and rectal cancer

Similar risk estimates were observed for rectal cancer, although only three tagSNPs in these genes captured the increased risk, RUNX1, MAPK1 and RUNX3. The following two RUNX1 tagSNPs were identified as being associated with rectal cancer risk, although they did not substantially alter the combined risk and were therefore omitted from the Table II: rs11701453 (OR 1.29 95% CI 1.05, 1.59 for the GC/CC genotype) and rs7280028 (OR 0.80 95% CI 0.65, 0.99 for the TC/CC genotype). Half of the combinations of high-risk genotypes presented had an increased rectal cancer risk that was greater than additive. The adjusted P value for RUNX1 rs11702799 was 0.43, for RUNX3 rs667240 was 0.17 and for MAPK1 was 0.10.

Various tagSNPs were associated with specific colon tumor molecular phenotypes (Table III). CIMP+ tumors were associated with variants in MAPK1, RUNX2 and RUNX1 with individual risks ranging from an OR of 1.32 for RUNX1 rs2071029 to 2.32 for RUNX2 rs12333172. Associations with mutations in KRAS2 and TP53 were more stable (the result of more cases with these tumor molecular subtypes). Four tagSNPs best-characterized associations with KRAS2: RUNX2 rs10948238, RUNX3 rs6672420, eIF4EBP2 rs7078987 and RUNX1 rs8134380. Associations ranged from a statistically significant OR of 1.35 for RUNX3 to greater than a 4-fold increased risk with various combinations of high-risk genotypes. Although imprecise, having all four high-risk genotypes were associated with more than an 8-fold increased risk of colon cancer (OR 8.66 95% CI 2.99, 25.09). Similar levels of risk were seen for TP53 for both independent and combined risk estimates. RUNX2 rs12333172, RUNX1 rs2248720, eIF4EBP3 rs250425 and RUNX3 rs6672420 best captured the risk-associated TP53-mutated tumors. Four tagSNPs illustrated the association with MSI+ colon tumors, RUNX1 rs2242878, eIF4EBP2 rs70798987, RUNX3 rs906296 and MAPK1 rs9610375. CIMP+/KRAS2-mutated tumors were associated with RUNX2 rs10948238, eIF4E rs11727086 and RUNX1 rs1474479. All combinations of two high-risk genotypes were associated with a >3-fold increased risk of CIMP+/KRAS2-mutated tumors, although the combination of eIF4E and RUNX1 did not reach statistical significance. The CIMP+/MSI+ tumors were associated with RUNX2 rs1321075, RUNX1 rs2242878, MAPK1 rs8136867 and RUNX3 rs906296. The risk of each independent tagSNP was associated with ∼2-fold increased likelihood of this combination of tumor types, having most combinations of three high-risk genotypes resulted in a statistically significant 10-fold increased risk.

Table III.

Associations between Runx, MAPK1, eIF4E and colon tumor mutations

HRG	MAPK1	RUNX2	RUNX1		CIMP+
	rs11913721 (A > C)	rs12333172 (C > T)	rs2071029 (G > A)		Controls	Cases	OR	(95% CI)
	CC	TT	GA/AA		Na	Nb
1	*				295	55	1.44	(1.04, 1.99)
1		*			65	20	2.32	(1.37, 3.92)
1			*		559	94	1.32	(1.00, 1.73)
2	*	*			13	1	0.55	(0.07, 4.28)
2	*		*		83	20	1.93	(1.14, 3.26)
2		*	*		17	5	2.51	(0.90, 7.00)
3	*	*	*		2	0	undefined
	RUNX2	RUNX3	EIF4EBP2	RUNX1	KRAS2
	rs10948238 (C > T)	rs6672420 (A > T)	rs7078987 (A > G)	rs8134380 (A > T)	Controls	Cases	OR	(95% CI)
	TT	AA/AT	GG	AA	N	Nc
1	*				316	81	1.55	(1.18, 2.05)
1		*			1504	284	1.35	(1.01, 1.81)
1			*		413	88	1.36	(1.04, 1.78)
1				*	605	144	1.53	(1.21, 1.94)
2	*	*			255	62	2.09	(1.37, 3.18)
2	*		*		69	18	1.85	(1.07, 3.19)
2	*			*	94	34	2.51	(1.61, 3.91)
2		*	*		321	71	1.81	(1.21, 2.73)
2		*		*	469	113	2.26	(1.49, 3.43)
2			*	*	123	39	2.24	(1.50, 3.35)
3	*	*	*		58	13	2.21	(1.08, 4.50)
3	*	*		*	77	28	4.59	(2.39, 8.82)
3	*		*	*	18	10	4.54	(2.01, 10.25)
3		*	*	*	96	30	3.48	(1.90, 6.36)
4	*	*	*	*	16	8	8.66	(2.99, 25.09)
	RUNX2	RUNX1	EIF4EBP3	RUNX3	TP53
	rs12333172 (C > T)	rs2248720 (A > C)	rs250425 (C > T)	rs6672420 (A > T)	Controls	Cases	OR	(95% CI)
	TT	AA	CC	AA	N	Nd
1	*				65	29	1.76	(1.12, 2.76)
1		*			503	162	1.29	(1.04, 1.60)
1			*		1120	328	1.30	(1.06, 1.60)
1				*	495	167	1.44	(1.17, 1.79)
2	*	*			11	7	2.65	(1.01, 6.92)
2	*		*		31	18	2.72	(1.48, 5.00)
2	*			*	17	14	3.56	(1.73, 7.33)
2		*	*		273	102	1.71	(1.26, 2.33)
2		*		*	136	52	1.74	(1.22, 2.48)
2			*	*	282	108	1.88	(1.40, 2.53)
3	*	*	*		3	6	11.35	(2.69, 47.88)
3	*	*		*	4	4	4.55	(1.12, 18.55)
3	*		*	*	8	9	5.89	(2.19, 15.81)
3		*	*	*	79	30	1.78	(1.09, 2.92)
4	*	*	*	*	1	3	17.80	(1.73, 183.42)
	RUNX1	EIF4EBP2	RUNX3	MAPK1	MSI+
	rs2242878 (C > T)	rs7078987 (A > G)	rs906296 (C > G)	rs9610375 (G > T)	Controls	Cases	OR	(95% CI)
	CT/TT	AA/AG	GG	GG	N	Ne
1	*				638	80	1.60	(1.17, 2.17)
1		*			1514	156	1.51	(1.00, 2.30)
1			*		115	17	1.58	(0.92, 2.69)
1				*	559	72	1.60	(1.17, 2.18)
2	*	*			485	64	2.66	(1.44, 4.94)
2	*		*		35	8	2.82	(1.26, 6.31)
2	*			*	194	36	2.68	(1.73, 4.14)
2		*	*		90	14	2.27	(1.13, 4.57)
2		*		*	429	57	2.91	(1.56, 5.45)
2			*	*	29	4	1.77	(0.61, 5.15)
3	*	*	*		25	7	7.07	(2.38, 21.01)
3	*	*		*	145	27	5.16	(2.16, 12.35)
3	*		*	*	9	1	1.39	(0.17, 11.29)
3		*	*	*	23	3	3.49	(0.88, 13.83)
4	*	*	*	*	5	1	6.88	(0.63, 75.06)
	RUNX2	EIF4E	RUNX1		CIMP+ and KRAS2
	rs10948238 (C > T)	rs11727086 (A > G)	rs1474479 (G > A)		Controls	Cases	OR	(95% CI)
	TT	AG/GG	GG/GA		N	Nf
1	*				316	19	1.79	(1.04, 3.07)
1		*			839	40	1.68	(1.04, 2.71)
1			*		1705	70	2.53	(0.91, 7.00)
2	*	*			125	13	3.63	(1.81, 7.30)
2	*		*		268	19	3.27	(1.08, 9.90)
2		*	*		721	38	3.38	(0.80, 14.31)
3	*	*	*		103	13	7.47	(1.58, 35.32)
	RUNX2	RUNX1	MAPK1	RUNX3	CIMP+ and MSI+
	rs1321075 (C > A)	rs2242878 (C > T)	rs8136867 (A > G)	rs906296 (C > G)	Controls	Cases	OR	(95% CI)
	CC	CT/TT	GG	GG	N	Ng
1	*				1437	90	1.75	(1.04, 2.94)
1		*			638	52	1.94	(1.31, 2.87)
1			*		411	39	2.20	(1.46, 3.33)
1				*	115	13	2.03	(1.10, 3.76)
2	*	*			463	44	3.30	(1.61, 6.77)
2	*		*		320	30	5.75	(2.55, 12.94)
2	*			*	86	12	3.75	(1.72, 8.21)
2		*	*		146	19	4.21	(2.30, 7.68)
2		*		*	35	6	4.07	(1.61, 10.33)
2			*	*	20	3	3.91	(1.09, 14.00)
3	*	*	*		105	14	9.75	(3.14, 30.31)
3	*	*		*	22	6	11.00	(3.37, 35.96)
3	*		*	*	14	2	10.30	(1.78, 59.71)
3		*	*	*	7	1	4.36	(0.50, 38.03)
4	*	*	*	*	3	1	76.32	(3.10, 1879.34)

HRG, high-risk genotypes. Rows are not mutually exclusive and include all individuals with the starred high-risk genotype versus those without any of the starred high-risk genotypes.

^a1956 controls total.

^b272 CIMP+ cases total.

^c348 KRAS2 cases total.

^d516 TP53 cases total.

^e185 MSI+ cases total.

^f74 CIMP+ and KRAS2 cases total.

^g108 CIMP and MSI+ cases total.

Associations with rectal tumors were generally less precise than those observed for colon cancer (Table IV). As with colon cancer, CIMP+, KRAS2-mutated, CIMP+/KRAS2-mutated and TP53-mutated tumors had unique associations with the genes under investigation. However, unlike colon cancer, the independent risk associated with the tagSNPs was generally higher, but the combined risk was usually additive or less. Risk estimates were highest for CIMP+, KRAS2-mutated and CIMP+/KRAS2-mutated tumors. The strongest associations were observed for CIMP+/KRAS2-mutated tumors, with combinations of RUNX1 rs1474479, eIF4EBP3 rs250425, RUNX2 rs2819863 and RUNX3 rs906296 associated with over a 10-fold statistically significant increased risk.

Table IV.

Associations between Runx, MAPK1, eIF4E and rectal tumor mutations

HRG	RUNX1	EIF4EBP3	RUNX2		CIMP+
	rs1981392 (T > C)	rs250425 (C > T)	rs2819863 (G > C)		Controls	Cases	OR	(95% CI)
	TT	TT	GC/CC		Na	Nb
1	*				344	29	1.77	(1.04, 3.01)
1		*			47	6	2.20	(0.90, 5.40)
1			*		156	17	2.07	(1.14, 3.76)
2	*	*			18	2	2.87	(0.62, 13.32)
2	*		*		60	10	3.70	(1.64, 8.35)
2		*	*		7	2	5.25	(1.03, 26.72)
3	*	*	*		2	1	9.75	(0.79, 121.07)
	EIF4E	RUNX1	RUNX2		KRAS2
	rs11727086 (A > G)	rs1474479 (G > A)	rs7750470 (T > C)		Controls	Cases	OR	(95% CI)
	AG/GG	AA	CC		N	Nc
1	*				431	92	1.46	(1.05, 2.03)
1		*			117	34	1.79	(1.17, 2.73)
1			*		32	12	2.18	(1.10, 4.33)
2	*	*			62	17	2.08	(1.14, 3.81)
2	*		*		17	7	2.85	(1.13, 7.18)
2		*	*		4	2	3.31	(0.59, 18.53)
3	*	*	*		4	1	2.02	(0.22, 18.80)
	EIF4E	RUNX3	RUNX1		TP53
	rs12498533 (A > C)	rs6672420 (A > T)	rs8134380 (A > T)		Controls	Cases
	AA	TT	TT		N	Nd	OR	(95% CI)
1	*				280	98	1.36	(1.02, 1.81)
1		*			218	83	1.40	(1.03, 1.89)
1			*		170	67	1.52	(1.10, 2.10)
2	*	*			57	26	1.85	(1.11, 3.08)
2	*		*		42	19	2.10	(1.17, 3.76)
2		*	*		37	20	2.14	(1.20, 3.80)
3	*	*	*		11	5	2.07	(0.69, 6.21)
	RUNX1	EIF4EBP3	RUNX2	RUNX3	CIMP+ and KRAS2
	rs1474479 (G > A)	rs250425 (C > T)	rs2819863 (G > C)	rs906296 (C > G)	Controls	Cases	OR	(95% CI)
	AA	TT	GC/CC	CC	N	Ne
1	*				117	6	2.95	(1.12, 7.77)
1		*			47	4	4.56	(1.47, 14.13)
1			*		156	9	4.04	(1.64, 9.93)
1				*	583	18	3.90	(1.14, 13.34)
2	*	*			5	0	undefined
2	*		*		21	2	8.96	(1.71, 46.96)
2	*			*	67	5	12.45	(2.34, 66.29)
2		*	*		7	2	24.65	(4.21, 144.43)
2		*		*	28	3	18.82	(2.94, 120.70)
2			*	*	96	8	11.95	(2.47, 57.74)
3	*	*	*		3	0	undefined
3	*	*		*	3	0	undefined
3	*		*	*	13	1	13.02	(0.89, 189.91)
3		*	*	*	3	2	undefined
4	*	*	*	*	1	0	undefined

HRG, high-risk genotypes. Rows are not mutually exclusive and include all individuals with the starred high-risk genotype versus those without any of the starred high-risk genotypes.

^a959 controls total.

^b59 CIMP+ cases total.

^c173 KRAS2 cases total.

^d277 TP53 cases total.

^e21 CIMP+ and KRAS2 cases total.

To establish how these candidate genes and tagSNPs associated with colon and rectal cancer, we assessed their interaction with other genes in the TGF-β-signaling pathway, including SNPS for TGFβ1, TGFβR1, BMP2, BMP4, BMPR1A, BMPR1B, Smad3, Samd4, Smad7 and NFκB1 (Table V). For colon cancer, the following statistically significant interactions were identified: RUNX1 with BMPR1B, BMPR1A, TGFβR1 and Smad7; RUNX2 with TGFβR1, Smad3 and Smad7; RUNX3 with NFκB1 and TGFβ1 and MAPK1 with BMP4, NFκβ1 and TGFβR1. Risk estimates for SNP combinations varied in magnitude of association. For some combinations such as RUNX1 and BMPR1B, the combined risk was 1.33 (95% CI 1.08, 1.64; P interaction 0.024), whereas for others such as MAPK1 and NF-κB1, the risk was considerably greater (OR 3.58 95% CI 1.56, 8.19; P interaction 0.005). For rectal cancer, there were also numerous interactions between candidate genes and other genes in the TGF-β-signaling pathway: RUNX1 interacted with BMPR1A, TFGβ1 and SMAD3; RUNX2 interacted with Smad4, Smad3 and NFκB1 and RUNX3 interacted with BMPR1B and NFκB1. Risk estimates were generally much stronger for rectal cancer than for colon cancer, with most interactions showing over a 2-fold increase in risk. Two interactions, RUNX1 and Smad3 and RUNX2 and Smad3, had over a 10-fold increase in risk (OR 15.17 95% CI 1.95, 117.96, P interaction 0.003 and OR 12.01 95% CI 1.51, 95.46, P interaction 0.0098, respectively).

Table V.

Interaction between Runx, MAPK1, eIF4E and other genes in candidate pathway

Gene	SNP	HRG	Pathway gene	SNP	HRG	Combined risk
						OR (95% CI)	Interaction P value
Colon cancer
RUNX1	rs2300395	CC	BMPR1B	rs17616243	CT/TT	1.33 (1.08, 1.64)	0.024
			BMPR1A	rs7088641	TT	1.25 (1.03, 1.52)	0.042
	rs7279123	CT/TT	TGFBR1	rs10733710	AA	1.77 (1.09, 2.89)	0.0359
	rs2248720	AA	Smad7	rs4464148	TC/CC	1.38 (1.12, 1.71)	0.019
RUNX2	rs10948238	TT	TGFBR1	rs1571590	GG	3.37 (1.48, 7.71)	0.0272
			Smad3	rs16950687	GG	1.82 (1.06, 3.13)	0.013
			Smad7	rs12953717	TT	2.18 (1.47, 3.24)	0.0056
RUNX3	rs6672420	AA	NFκB1	rs230510	TT	1.50 (1.12, 2.01)	0.0239
			TGFB1	rs4803455	AA	1.82 (1.38, 2.42)	0.0476
MAPK1	rs8136867	GG	BMP4	rs17563	TT	1.57 (1.13, 2.19)	0.023
	rs2298432	CC	NFkB1	rs13117745	CC/CT	3.58 (1.56, 8.19)	0.0061
			TGFBR1	rs6478974	TT	1.39 (1.11, 1.74)	0.0168
Rectal cancer
RUNX1	rs1981392	CC	BMPR1A	rs7088641	CC	2.94 (1.11, 7.73)	0.0171
	rs1474479	AA	TGFB1	rs1800469	AA	2.76 (1.11, 6.82)	0.0373
	rs11702779	GG/GA	Smad3	rs12708492	CT/TT	2.75 (1.49, 5.08)	0.0108
	rs8134380	TT		rs16950687	GG	5.05 (1.67, 15.26)	0.0084
				rs17293443	CC	15.17 (1.95, 117.96)	0.0086
RUNX2	rs2819863	GC/CC	Smad4	rs10502913	AA	3.30 (1.02, 10.60)	0.0496
	rs7750470	CC	Smad3	rs7163381	AA	12.01 (1.51, 95.46)	0.0119
			NFKB1	rs230510	AA	3.99 (1.67, 9.53)	0.0216
RUNX3	rs6672420	TT	BMPR1B	rs13134042	GA/AA	1.75 (1.26, 2.44)	0.046
	rs2135756	GG	NFKB1	rs4648110	AA	2.97 (1.04, 8.50)	0.0375

HRG, high-risk genotype group.

Discussion

Our data suggest the importance of RUNX, MAPK1 and eIF4E in the etiology of both colon and rectal cancer, although associations generally were stronger for colon than for rectal cancer. Multiple genetic variants appear to have an impact on risk of colon cancer, where associations are greater than that would be expected on an additive scale. Furthermore, our data support the involvement of these genes in the TGF-β-signaling pathway given the findings of interaction between genetic variants in the genes under investigation with other genes in that pathway. Finally, our data emphasize the importance of this signaling pathway in the development of CRC with a CIMP+ phenotype.

The TGF-β-signaling pathway plays an important role in numerous conditions including CRC (29). Studies have shown that loss of TGF-β growth control is a critical event in tumorigenesis (5). The TGF-β family is involved in cell proliferation, extracellular matrix synthesis, angiogenesis, apoptosis and cell differentiation. The TGF-β family of cytokines contains several related growth factors including TGF-β and its receptors, BMPs and growth differentiation factors. Smads are important to the pathway because they mediate TGF-β signaling. Smad 1, 5 and 6 are more responsive to BMP, whereas Smad 2 and 3 are more responsive to TGF-β. Smad 7 plays an inhibitory role in TGF-β signaling (30). MAPKs, including extracellular signal-regulated kinases, can induce or modulate the outcome of TGF-β signaling (13). RUNX genes have been shown to be involved in TGF-β and BMP signaling. RUNX1 and RUNX3 are involved in carcinogenesis; RUNX2 is a common target of TGF-β1 and BMP-2 and is induced indirectly by Smad (8). eIf4E and its binding proteins appear to be important in converging the TGF-β and AKT signaling pathways.

Of the RUNX genes analyzed, the role of RUNX3 is clearest biologically. The adjusted P value for rs667240 for colon cancer was 0.023, further indicating its potential importance. It is a key element in gastrointestinal tract development and is strongly expressed in that tissue (1). In addition to its involvement in the TGFβ-signaling pathway, RUNX3 has been shown to attenuate Wnt signaling in intestinal tumorigenesis. It is downregulated in serrated adenomas and hyperplastic polyps. RUNX3 hypermethylation has been identified as a key component in CIMP+ CRC (31,32). This is the first report of an association between genetic variation in this gene and colon and rectal cancer, particularly CIMP+ tumors to our knowledge. However, we also observed associations between RUNX genes and TP53, which may be indicative of involvement in other pathways such as Wnt signaling. This could also explain some of the differences observed between tagSNPs associated with rectal and colon cancer overall. At one end of the spectrum are the CIMP+/MSI+ phenotypes, which are almost unique to colon cancer, whereas rectal tumors have a higher proportion of TP53 mutations. Differences in associations with different tagSNPs could in part be the result of tumor molecular phenotype differences for colon and rectal cancer.

MAPKs are major signaling transduction molecules involved in the regulation of cell proliferation, differentiation and apoptosis (33). MAPK1 is an extracellular signal-regulated kinase, which, when activated through Raf signaling, modulates gene expression by activating other transcription factors. In human colon cancer, this pathway includes activated KRAS2 (34). It has been proposed that Ras signaling can inhibit TGF-β signaling via the mitogen-activated protein pathway (13). We observed an independent association between MAPK1 and rectal cancer but not colon cancer. However, for colon cancer, genetic variation in MAPK1 was associated with a greater likelihood of having a CIMP+ and/or MSI+ phenotype. We are not aware of other reports of the association between genetic variation in MAPK1 and risk of colon or rectal cancer, although there is strong biologic support for an association.

Expression of the translation initiation factor eIF4E has been shown to be important in colon tumorigenesis (15). eIF4E overexpression can cause neoplastic transformation of cells; overexpression of the inhibitory eIF4E bonding proteins can suppress the oncogenetic properties of cell lines; overexpression of eIF4E has been demonstrated for many solid tumors including colon cancer (15). We observed a statistically significant association between eIF4E and colon cancer overall that remained significant at the 0.02 level after adjusting for multiple comparison. Additionally, we observed that eIF4E bonding proteins were statistically significantly associated with specific tumor phenotypes, primarily those involving CIMP+ tumors. Again, we are not aware of reports of genetic variation in eIF4E and its binding proteins and colon or rectal cancer. Given the biological support for such an association, we encourage others to evaluate these associations.

The study has many strengths as well as some limitations. Our ability to examine colon and rectal cancers separately in a well-characterized dataset that includes tumor characteristics as well as lifestyle factors and genetic factors is a major strength. Although our sample size is large, it is limited in power to perform a test/retest analysis. Therefore, we provide adjusted P values for each gene in order to account for the number of tests performed. However, there are limitations with presentation of adjusted P values, in that we report risk estimates rather than P values to indicate associations with our candidate genes. Genes studied were selected based on their role in a biological pathway. Although we have specified these as candidate genes, there is little information on functional SNPs within these genes, hence the use of tagSNPs. We identified tagSNPs, which we believed were the best indicators of risk based on stepwise regression models. We evaluated those tagSNPs together to have a better idea of how genes in the pathway worked together. Our risk estimates for combined genotypes in many instances were imprecise; however, several risk estimates had a lower confidence bound over three. We interpret this as indicating the importance of these genes in the profile of risk associated with this pathway and regard the consistency of the patterns of association similarly. Our results also stress the need for follow-up studies to validate these findings, to determine which SNPs may be functionally involved and to test functionality.

It might be expected that once the TGF-β pathway is impaired, further less functional pathway members would not matter and that multiple suboptimal proteins would not result in even additivity in their impact, let alone something greater than additivity. The fact that we report here greater than additivity is important because it suggests that there may be limits to robustness. Robustness was initially defined by Waddington in the context of development; he said: ‘ … developmental reactions, as they occur in organisms submitted to natural selection are, in general, canalized. That is to say, they are adjusted so as to bring about one definite end result, regardless of minor variations in conditions during the course of the reaction’. Furthermore, he argued that the constancy of the wild-type is evidence of the buffering of the genotype against minor variations in genetics and environment (35). More generally, robustness is a property of systems that is characterized by relative insensitivity to the precise values of the component parameters (36).

It is not well established how much potentially deleterious variation can accumulate in a pathway before robustness begins to weaken; data are especially lacking for cancer. The fact that we find that a larger number of minor alleles in the same pathway are associated with a greater than additive risk suggests that, at some point, the integrity of the pathway becomes increasingly less robust, as a consequence of which cancer risk begins to rise. It is probably worth noting that the increasing impairment of a developmentally important pathway may indicate loss of morphostatic control over tissue architecture rather than a change involving epithelial mutation (37–39).

In summary, we interpret these findings as an indicator of the importance of these genes in the etiology of colon and rectal cancer. We infer from this only the biologic significance of the genes and the pathway, not the specific alleles. The somewhat stronger pattern of association with CIMP+ tumors suggests that they are particularly important in that molecular subset.

Supplementary material

Supplementary Table 1 can be found at http://carcin.oxfordjournals.org/

Funding

National Cancer Institute (CA48998, CA61757). This research also was supported by the Utah Cancer Registry, which is funded by Contract #N01-PC-67000 from the National Cancer Institute, with additional support from the State of Utah Department of Health, the Northern California Cancer Registry and the Sacramento Tumor Registry.

Glossary

Abbreviations

BMP

bone morphogenetic protein

CI

confidence interval

CIMP

CpG island methylator phenotype

CRC

colorectal cancer

eIF4E

eukaryotic translation initiation factor 4E

MAPK

mitogen-activated protein kinase

MSI

microsatellite instability

NF-κB

nuclear factor-kappa B

OR

odds ratio

RUNX

runt-related transcription

SNP

single nucleotide polymorphisms

TGF

transforming growth factor