Abstract
RPS6KA1, RPS6KA2, RPS6KB1, RPS6KB2, and PDK1 are involved in several pathways central to the carcinogenic process, including regulation of cell growth, insulin, and inflammation. We evaluated genetic variation in their candidate genes to obtain a better understanding of their association with colon and rectal cancer. We used data from two population-based case-control studies of colon (n=1574 cases, 1940 controls) and rectal (n=791 cases, 999 controls) cancer. We observed genetic variation in RPS6KA1, RPS6KA2, and PRS6KB2 were associated with risk of developing colon cancer while only genetic variation in RPS6KA2 was associated with altering risk of rectal cancer. These genes also interacted significantly with other genes operating in similar mechanisms, including Akt1, FRAP1, NFκB1, and PIK3CA. Assessment of tumor markers indicated that these genes and this pathway may importantly contributed to CIMP+ tumors and tumors with KRAS2 mutations. Our findings implicate these candidate genes in the etiology of colon and rectal cancer and provide information on how these genes operate with other genes in the pathway. Our data further suggest that this pathway may lead to CIMP+ and KRAS2-mutated tumors.
Keywords: Colon cancer, rectal cancer, S6K, RSK, PDK1, Akt, CIMP+, KRAS2
The ribosomal protein S6 kinase (RPS6K) family is involved in numerous pathways, many of which are central to the carcinogenic process. The RPS6K alpha genes, also known as RPS6KA1, RSK1, or p90-S6K1 and RPS6KA2, RSK2, or p90-S6K2, are mitogen-activating protein kinases. RSK1 has been identified as a key component in the regulation of IκBβ and NFκB[1], factors associated with gut inflammation and apoptosis[2,3], and it is weakly activated by insulin [4]. RPS6KB1 (i.e. S6K1) and B2 (i.e. S6K2) also known as p70-beta, or p54, share 80% homology and many of the same functions. Studies suggests that RPS6KB1 and RPS6KB2 are involved in cell growth and regulation given their activation by growth factors such as EGF, PDGF, and insulin and their regulation by mammalian Target of Rapamycin (mTOR). They are keenly involved in regulation of insulin. Both RPS6KS and RPS6KB proteins are members of the AGC protein kinase family and require3-phosphoinositide-dependent protein kinase-1 (PDK1) phosphorylation for activation [5,6]. PDK1 mediates the cellular influence of growth factors and insulin by activating both RSK and S6K and is essential for activation of PKB/Akt [5]
Studies have not examined genetic variation in the RPS6KA1, RPS6KA2, RPS6KB1, RPS6KB2, or the PDK1 genes and risk of colon or rectal cancer. Given the biological role of these genes in the regulation of cell growth and inflammation, it is reasonable to hypothesize that genetic variation of these genes may influence risk of colon and rectal cancer, either independently or in combination with other genes and lifestyle factors. In this study we evaluate whether genetic variation in these genes alters susceptibility to colon or rectal cancer. Data come from two large population-based studies, one of colon cancer and one of rectal cancer. We evaluate potential interaction between SNPs within and between genes as well as with recent use of aspirin/NSAIDs (an indicator of inflammation given their anti-inflammatory properties), BMI (an indicator of insulin), and estrogen status (an indicator of estrogen). Because of the role of these genes with other genes, such as FRAP1 (mTOR), PIK3CA, Akt1, and NFκB1, we also evaluate interaction with these genes. In an effort to more comprehensively understand how RPS6KA1, RPS6KA2, RPS6KB1, PRS6KB2, and PDK1 are associated with colon and rectal cancer we look at genetic variation in these genes with specific tumor markers to gain insight into disease pathways.
Methods
Data for the study come from two case-control studies conducted in Utah, the Northern California Kaiser Permanente Medical Care Program (KPMCP), and the Twin Cities Metropolitan area of Minnesota (colon cancer study only). Eligibility criteria included being between 30 and 79 years of age at time of diagnosis, English speaking, mentally competent to complete the interview, no previous history of colorectal cancer, and no known (as indicated on the pathology report or by self-report) familial adenomatous polyposis, ulcerative colitis, or Crohn's disease. Controls were frequency matched to cases by sex and five-year age groups. Controls were randomly selected from membership lists (KPMCP) or from driver’s license list (Minnesota). In Utah, controls 65 or older were randomly selected from lists provided by the Centers for Medicare and Medicaid Services and controls younger than 65 were randomly selected from driver’s license lists. Study eligibility and recruitment details of the study have been published previously [6,7]. Cooperation rates were 83% at KPMCP, 76% at Utah, and 67% Minnesota (colon cases); 73% at KPMCP, 69% at Utah, and 53% at Minnesota (colon controls); 75.4% at KPCMP and 69.7% at Utah (rectal cases); 69.9% at KPMCP and 67.2% at Utah (rectal controls).
The current analysis is restricted to subjects who provided a blood sample. The colon cancer study population consists of non-Hispanic white cases (n = 1444) and controls (n = 1841), Hispanic or American Indian cases (n = 60) and controls (n = 75), and African American cases (n = 70) and controls (n = 54). The rectal cancer study population consists of non-Hispanic white cases (n = 657) and controls (n = 856), Hispanic or American Indian cases (n = 63) and controls (n = 69), African American cases (n = 31) and controls (n = 44), and Asian cases (n = 40) and controls (n = 30). Race and ethnicity were self-reported at the time of interview.
Trained and certified interviewers collected diet and lifestyle data as previously described [8,9] using the same data questionnaire and study procedures for both the colon and rectal studies. The referent year for the study was the calendar year approximately two years prior to date of diagnosis (cases) or selection (controls). Information was collected on demographic factors such as age, sex, and study center, and on exposures including diet, physical activity, aspirin and non-steroidal drug use, body size, and other lifestyle factors including medical, family, and reproductive history.
DNA was extracted from blood drawn from study participants. Tagging SNPs were characterized as part of a customized Iluminia bead array platform. TagSNPs were identified using HapMap and the Illumina Platform tagSNP database. TagSNPs were selected for RPS6KA1, PDK1, RPS6KB1, and RPS6KB2 using the following parameters: an r2=0.9 defined LD blocks using a Caucasian LD map, minor allele frequency or MAF>0.1, and range= −1500 bps from the initiation codon to +1500 bps from the termination codon. The RPS6KA2 gene parameters were changed to an r2=0.5 given the gene size. We successfully tested 12 tagSNPS for RPS6KA1, 72 for RPS6KA2, 6 for RPS6KB1, 2 for RPS6KB2, and 3 for PDK1. Individual tagSNPs were assessed to determine if they were in Hardy-Weinberg equilibrium (HWE) in non-Hispanic white controls, constituting approximately 91% of the study controls, and were not carried forward if this criterion was not met after controlling the false discovery rate [10]. The sample success rate was 97.55%, the locus success rate was 89.91%; the genotype call rate was 99.85%. There was 100% reproducibility in quality control samples. Appendix 1 shows a list of all tagSNPs assessed. Table 1 summarizes those tagSNPs either associated independently or in combination with other candidate genes.
Appendix.
Description of RPS6KA1, RPS6KA2, RPS6KB1, RPS6KB2, and PDK1
| Symbol | Location | SNP | MAF | Major/Minor | FDR HWE | Colon | Rectal |
|---|---|---|---|---|---|---|---|
| Allele | Probability | Homozygote Rare OR | Homozygote Rare OR | ||||
| RPS6KA1 | 1p | rs12723936 | 0.27 | A/G | 1.00 | 0.81 (0.61, 1.08) | 0.75 (0.49, 1.15) |
| rs6666757 | 0.21 | A/T | 0.96 | 1.04 (0.74, 1.45) | 0.81 (0.45, 1.45) | ||
| rs737465 | 0.26 | C/T | 0.96 | 1.11 (0.85, 1.45) | 1.17 (0.78, 1.77) | ||
| rs2278978 | 0.26 | G/A | 0.98 | 1.17 (0.89, 1.53) | 1.15 (0.76, 1.74) | ||
| rs12025634 | 0.15 | C/T | 0.57 | 1.69 (1.05, 2.72) | 1.03 (0.49, 2.17) | ||
| rs17162190 | 0.21 | G/A | 1.00 | 1.01 (0.70, 1.45) | 0.61 (0.37, 1.03) | ||
| rs3816540 | 0.22 | A/C | 0.67 | 0.97 (0.70, 1.33) | 0.83 (0.51, 1.37) | ||
| rs11577405 | 0.30 | G/T | 0.67 | 0.94 (0.74, 1.20) | 0.91 (0.62, 1.34) | ||
| rs282175 | 0.28 | T/C | 1.00 | 0.85 (0.65, 1.12) | 0.69 (0.45, 1.05) | ||
| rs190737 | 0.47 | T/G | 0.81 | 0.97 (0.80, 1.17) | 0.96 (0.71, 1.29) | ||
| rs11113 | 0.45 | T/C | <.01 | 1.08 (0.90, 1.31) | 1.04 (0.77, 1.40) | ||
| RPS6KA2 | 6q27 | rs4709117 | <.01 | A/C | 1.00 | N/A | N/A |
| rs11961547 | 0.17 | A/T | 0.91 | 1.20 (0.81, 1.79) | 1.99 (1.00, 3.97) | ||
| rs1927430 | 0.33 | A/G | 0.96 | 0.93 (0.73, 1.17) | 0.86 (0.61, 1.21) | ||
| rs4710069 | 0.32 | A/G | 0.67 | 0.93 (0.73, 1.18) | 0.69 (0.48, 0.98) | ||
| rs9459678 | 0.33 | T/C | 0.96 | 1.25 (0.99, 1.59) | 0.81 (0.57, 1.15) | ||
| rs6456108 | 0.12 | T/G | 0.96 | 0.93 (0.53, 1.64) | 0.41 (0.15, 1.13) | ||
| rs3799620 | 0.36 | A/G | 0.99 | 0.92 (0.74, 1.15) | 0.96 (0.68, 1.35) | ||
| rs1202621 | 0.32 | A/C | 1.00 | 1.13 (0.89, 1.43) | 1.24 (0.88, 1.74) | ||
| rs3817782 | 0.41 | G/A | 1.00 | 1.09 (0.89, 1.35) | 0.73 (0.54, 1.00) | ||
| rs2187911 | 0.42 | G/A | 1.00 | 1.06 (0.87, 1.30) | 0.92 (0.67, 1.25) | ||
| rs909768 | 0.20 | T/C | 0.99 | 1.21 (0.86, 1.70) | 1.27 (0.73, 2.21) | ||
| rs12199759 | 0.19 | A/G | 0.96 | 1.35 (0.93, 1.96) | 0.61 (0.32, 1.14) | ||
| rs7760282 | 0.47 | C/A | 0.97 | 1.13 (0.93, 1.38) | 1.30 (0.97, 1.73) | ||
| rs2235296 | 0.32 | C/T | 0.97 | 1.07 (0.85, 1.36) | 1.27 (0.89, 1.82) | ||
| rs3799612 | 0.26 | G/C | 1.00 | 1.17 (0.89, 1.54) | 1.01 (0.67, 1.53) | ||
| rs6935542 | 0.13 | A/G | 1.00 | 1.25 (0.74, 2.09) | 0.59 (0.20, 1.70) | ||
| rs707768 | 0.45 | G/A | 1.00 | 1.10 (0.90, 1.34) | 1.17 (0.87, 1.58) | ||
| rs763187 | 0.17 | C/T | 0.95 | 1.06 (0.71, 1.60) | 0.95 (0.54, 1.67) | ||
| rs6918886 | 0.42 | G/A | 0.98 | 1.08 (0.88, 1.32) | 0.90 (0.66, 1.22) | ||
| rs3799647 | 0.11 | T/C | 0.74 | 0.90 (0.50, 1.61) | 0.83 (0.34, 2.03) | ||
| rs13198549 | 0.37 | A/G | 1.00 | 0.99 (0.80, 1.24) | 1.08 (0.78, 1.50) | ||
| rs3799631 | 0.35 | A/G | 0.99 | 1.03 (0.82, 1.30) | 1.21 (0.88, 1.68) | ||
| rs9295348 | 0.29 | A/C | 0.97 | 1.15 (0.89, 1.48) | 1.07 (0.74, 1.55) | ||
| rs16898963 | 0.12 | A/C | 0.39 | 2.57 (1.37, 4.84) | 0.95 (0.38, 2.38) | ||
| rs1894660 | 0.18 | G/A | 0.96 | 0.88 (0.60, 1.30) | 1.38 (0.77, 2.48) | ||
| rs3823200 | 0.17 | A/T | 1.00 | 0.91 (0.60, 1.38) | 0.70 (0.33, 1.46) | ||
| rs6456103 | 0.06 | G/A | 0.91 | 0.93 (0.75, 1.15)* | 0.98 (0.72, 1.34)* | ||
| rs4709127 | 0.30 | C/T | 1.00 | 0.94 (0.73, 1.22) | 0.82 (0.57, 1.19) | ||
| rs10946164 | 0.44 | C/T | 1.00 | 1.09 (0.89, 1.33) | 0.80 (0.59, 1.08) | ||
| rs9348131 | 0.14 | C/T | 1.00 | 1.46 (0.87, 2.47) | 1.03 (0.53, 2.00) | ||
| rs2049957 | 0.49 | T/C | 0.99 | 1.01 (0.83, 1.23) | 1.15 (0.85, 1.55) | ||
| rs2049956 | 0.15 | G/C | 1.00 | 0.91 (0.58, 1.42) | 1.41 (0.72, 2.78) | ||
| rs6921577 | 0.44 | A/G | 0.78 | 1.09 (0.89, 1.33) | 1.30 (0.96, 1.76) | ||
| rs12200581 | 0.21 | A/T | 1.00 | 0.96 (0.68, 1.36) | 0.90 (0.53, 1.53) | ||
| rs6926393 | 0.29 | A/G | 1.00 | 1.26 (0.97, 1.63) | 1.01 (0.70, 1.47) | ||
| rs9366015 | 0.34 | G/A | 1.00 | 1.08 (0.86, 1.36) | 0.81 (0.57, 1.15) | ||
| rs10946174 | 0.29 | G/A | 0.96 | 0.92 (0.70, 1.20) | 0.78 (0.50, 1.19) | ||
| rs2071941 | 0.13 | C/T | 0.74 | 1.39 (0.83, 2.36) | 1.09 (0.42, 2.86) | ||
| rs411236 | 0.28 | C/T | 1.00 | 1.15 (0.88, 1.51) | 0.97 (0.68, 1.40) | ||
| rs395475 | 0.44 | T/C | 1.00 | 0.93 (0.76, 1.13) | 0.95 (0.71, 1.28) | ||
| rs384502 | 0.40 | A/T | 1.00 | 1.04 (0.85, 1.27) | 1.17 (0.85, 1.60) | ||
| rs3778405 | 0.10 | A/G | 0.96 | 1.08 (0.91, 1.29)* | 1.13 (0.86, 1.48)* | ||
| rs3778401 | 0.48 | A/G | 1.00 | 0.93 (0.77, 1.13) | 1.01 (0.76, 1.35) | ||
| rs763193 | 0.49 | T/C | 0.96 | 1.01 (0.83, 1.23) | 0.71 (0.53, 0.95) | ||
| rs9295355 | 0.29 | T/A | 0.91 | 0.95 (0.74, 1.21) | 0.89 (0.59, 1.35) | ||
| rs3799577 | 0.09 | G/A | 1.00 | 1.04 (0.86, 1.25)* | 0.95 (0.73, 1.24)* | ||
| rs1040446 | 0.11 | T/G | 1.00 | 0.98 (0.55, 1.74) | 1.13 (0.38, 3.39) | ||
| rs6906202 | 0.26 | T/C | 0.96 | 1.03 (0.78, 1.38) | 1.10 (0.73, 1.66) | ||
| rs7766723 | 0.46 | G/A | 1.00 | 1.20 (0.98, 1.46) | 1.11 (0.83, 1.49) | ||
| rs7770200 | 0.13 | A/G | 1.00 | 1.12 (0.68, 1.83) | 1.51 (0.70, 3.26) | ||
| rs388033 | 0.20 | T/C | 0.85 | 1.19 (0.84, 1.68) | 0.92 (0.52, 1.61) | ||
| rs17518299 | 0.22 | G/A | 1.00 | 1.00 (0.72, 1.40) | 1.28 (0.79, 2.07) | ||
| rs12214750 | 0.17 | A/C | 1.00 | 0.87 (0.55, 1.37) | 0.59 (0.31, 1.14) | ||
| rs1309150 | 0.25 | T/C | 0.97 | 1.01 (0.75, 1.36) | 0.74 (0.48, 1.14) | ||
| rs6918384 | 0.20 | C/T | 0.39 | 0.76 (0.53, 1.09) | 0.62 (0.35, 1.09) | ||
| rs12208871 | 0.14 | C/T | 0.91 | 1.28 (0.75, 2.16) | 1.56 (0.77, 3.15) | ||
| rs9347128 | 0.39 | C/G | 1.00 | 1.13 (0.92, 1.39) | 1.24 (0.91, 1.69) | ||
| rs9295361 | 0.21 | C/A | 1.00 | 1.09 (0.79, 1.51) | 1.42 (0.85, 2.36) | ||
| rs932356 | 0.18 | C/G | 0.72 | 0.85 (0.59, 1.24) | 1.03 (0.59, 1.79) | ||
| rs9459715 | 0.11 | T/G | 0.67 | 0.51 (0.27, 0.96) | 1.10 (0.37, 3.30) | ||
| rs6911624 | 0.19 | G/A | 1.00 | 0.86 (0.58, 1.27) | 0.67 (0.35, 1.30) | ||
| rs1883361 | 0.42 | C/T | 1.00 | 1.05 (0.85, 1.28) | 1.06 (0.79, 1.44) | ||
| rs4710090 | 0.37 | C/T | 0.96 | 1.04 (0.83, 1.29) | 0.92 (0.66, 1.27) | ||
| rs388372 | 0.37 | C/T | 0.96 | 0.90 (0.72, 1.12) | 0.99 (0.72, 1.36) | ||
| rs409161 | 0.29 | G/A | 1.00 | 0.99 (0.76, 1.28) | 0.85 (0.58, 1.24) | ||
| rs661325 | 0.36 | G/C | 0.90 | 1.06 (0.84, 1.33) | 0.98 (0.70, 1.38) | ||
| rs2345067 | 0.43 | T/C | 1.00 | 1.08 (0.88, 1.32) | 0.68 (0.50, 0.92) | ||
| rs6456123 | 0.34 | G/A | 0.70 | 0.81 (0.63, 1.03) | 1.03 (0.73, 1.46) | ||
| rs2072638 | 0.30 | T/C | 0.25 | 1.18 (0.94, 1.48) | 2.16 (1.52, 3.08) | ||
| rs2072640 | 0.08 | G/T | 1.00 | 1.17 (0.96, 1.41)* | 1.11 (0.82, 1.50)* | ||
| rs7745781 | 0.12 | A/G | 1.00 | 1.12 (0.64, 1.95) | 1.67 (0.78, 3.55) | ||
| rs9348195 | 0.27 | T/C | 1.00 | 1.27 (0.97, 1.67) | 0.98 (0.66, 1.44) | ||
| RPS6KB1 | 17q23.1 | rs8071475 | 0.25 | T/C | 0.89 | 1.00 (0.75, 1.35) | 0.79 (0.50, 1.24) |
| rs1292033 | 0.20 | A/T | 0.79 | 0.89 (0.62, 1.29) | 1.12 (0.68, 1.85) | ||
| rs180535 | 0.15 | T/C | 0.61 | 1.03 (0.67, 1.58) | 0.64 (0.31, 1.34) | ||
| rs180531 | 0.25 | A/G | 0.61 | 1.23 (0.92, 1.64) | 0.70 (0.43, 1.13) | ||
| rs180519 | 0.45 | G/A | 0.61 | 0.86 (0.70, 1.05) | 0.92 (0.68, 1.25) | ||
| rs180515 | 0.36 | A/G | 0.89 | 0.89 (0.71, 1.11) | 1.08 (0.78, 1.50) | ||
| RPS6KB2 | 11q13.2 | rs917570 | 0.44 | C/G | 0.89 | 0.91 (0.74, 1.11) | 0.97 (0.72, 1.29) |
| rs1638588 | 0.43 | C/A | 0.06 | 0.98 (0.79, 1.21) | 0.89 (0.66, 1.19) | ||
| PDK1 | 2q31.1 | rs11904366 | 0.15 | G/T | 1.00 | 1.20 (0.75, 1.90) | 0.51 (0.22, 1.16) |
| rs4972842 | 0.19 | T/A | 1.00 | 0.97 (0.66, 1.41) | 1.31 (0.78, 2.19) | ||
| rs11686903 | 0.35 | C/T | 1.00 | 1.03 (0.82, 1.30) | 0.96 (0.68, 1.36) |
Indicates dominant model used due to MAF = 0.1.
Sample size ranges from 2723 to 2920
FDR = False Discovery Rate HWE= Hardy Weinberg Equilibrium test
Table 1.
Summary of key tagSNPs in candidate pathway
| Symbol | Alias | Chromosome | SNP | MAF | Major/Minor Allele | FDR HWE Probability |
|---|---|---|---|---|---|---|
| RPS6KA1 | RSK1 | 1p | rs12025634 | 0.14 | C/T | 0.39 |
| S6K-alpha1 | ||||||
| p-90-RSK1 | ||||||
| RPS6KA2 | RSK | 6q27 | rs16898963 | 0.12 | A/C | 0.21 |
| RSK3 | rs2071941 | 0.14 | C/T | 0.93 | ||
| S6K-alpha2 | rs3799620 | 0.37 | A/G | 0.53 | ||
| p90-RSK3 | rs2345067 | 0.44 | T/C | 0.64 | ||
| rs9295361 | 0.21 | C/A | 0.91 | |||
| rs9459678 | 0.34 | T/C | 0.8 | |||
| rs10946164 | 0.42 | C/T | 0.84 | |||
| rs9347128 | 0.41 | C/G | 0.89 | |||
| rs6911624 | 0.19 | G/A | 0.97 | |||
| rs7745781 | 0.13 | A/G | 0.9 | |||
| RPS6KB1 | S6K1 | 17q23.1 | rs180519 | 0.45 | G/A | 0.61 |
| p70-alpha | ||||||
| p70(S6K) Alpha | ||||||
| RPS6KB2 | S6K2 | 11q13.2 | rs917570 | 0.44 | C/G | 0.89 |
| p70-beta | ||||||
| p70(S6K) Beta |
Tumor Marker Data
We have previously evaluated tumors for CpG island methylator phenotype (CIMP), microsatellite instability (MSI), TP53 mutations, and KRAS2 mutations [11–14] and were therefore able to evaluate with genes assessed. Details for methods used to evaluate these epigenetic and genetic changes have been described in previous publications [11–14].
Statistical Analysis
Statistical analysis involved several steps and focused on testing the following hypotheses: SNPs within the candidate genes are associated with colon or rectal cancer; SNPs within candidate genes act jointly to alter risk of colon and rectal cancer; factors associated with insulin and inflammation that may influence individual susceptibility to genetic variation in these genes; and genetic variation in the candidate genes may influence specific disease pathways as indicated by epigenetic and genetic changes in tumors. Recent use (within two years of diagnosis) of aspirin/NSAIDs was used as an indicator of inflammation state and BMI was used as a crude indicator of insulin sensitivity. Initial weeding of tagSNPs was done by dropping one of the two SNPs if the r-squared measure of LD between the two was greater than or equal to 0.8, preferentially selecting the SNP with the lower proportion missing.
SAS 9.2 (Cary, NC) was used to perform all of the statistical analyses. SNPs from each gene were evaluated for their importance using step-wise logistic regression to determine which tagSNPs independently entered the step-wise logistic regression model and remained in the model as significantly except when use of hapConstructor is noted below. Multiplicative interaction models that included multiple tagSNPS within a gene as well as tagSNPs from other genes in the pathway were assessed both at an individual tagSNP and haplotype level.
Odds ratios for SNPs adjusted for age at diagnosis or selection, race/ethnicity, sex, and study site were calculated based on a specific inheritance model that predicted the best fit. The MAX test was used to assess whether there was any relationship (recessive, dominant, or unrestricted) between colon or rectal cancer and the individual tagSNPs. Additionally, haplotypes were constructed for those tagSNPs within genes that remained significant in the stepwise logistic regression models. Haplotypes were constructed based on tagSNPs that were important for each phenotype using the EM algorithm. Each haplotype was weighted within the logistic regression model based upon the haplotype probability estimate and compared to all other haplotypes for a given set of tagSNPs. Adjustment of BMI, cigarette smoking, dietary energy, fiber, and calcium, long-term vigorous physical activity, regular use of aspirin/NSAIDs within the past two years, and family history of colorectal cancer in first-degree relatives did not appreciably alter the risk estimates, therefore data are presented for the minimally adjusted model.
We used the data mining technique implemented in hapConstructor (a module of Genie 2.6.2 software; http://www-genepi.med.utah.edu/Genie/index.html) as a tool to evaluate combinations of SNPs across selected genes. A user-specified significance threshold of 0.01 was selected at two-locus step and p of 0.001 at the three locus step to take into account the number of comparisons being made for five genes. This level of stringency for significance in hapConstructor has been used previously in similar types of analyses [15]. The multi-locus genotype results from hapConstructor were then assessed using stepwise multiplicative interaction logistic regression models. We evaluated interaction between tagSNPs using a goodness of fit test that compared the maximum likelihood ratios of a model with and without an interaction term using Chi-squared test with one degree of freedom.
The phenotypic characteristics of the tumors were defined by the specific alterations detected and described as any TP53 mutation, any KRAS2 mutation, MSI+, CIMP+ defined as at least two of five markers methylated, or KRAS2 mutated plus CIMP+, or CIMP+ plus MSI+ to describe the phenotypic characteristics of the tumor. As the proportion of MSI+ tumors in the rectal cases was <3% [16], there was insufficient power to examine these tumor markers with genotype data. Population-based controls were used to assess associations for the population overall when examining multiple outcomes defined by tumor status. In order to compare specific types of mutations to controls while adjusting for the tumor mutations simultaneously in cases, a generalized estimating equation (GEE) with a multinomial outcome was used [17], because case subjects could contribute to multiple outcome observations depending on the number of tumor alterations or mutations (TP53, KRAS2, CIMP+, and additionally for colon cases, MSI+ and BRAF V600E) an individual had [18]. The GEE accounts for correlated outcomes introduced by including subjects multiple times and was implemented in SAS using the GENMOD procedure as described by Kuss and McLerran [19]. Since no significant associations were detected with survival after adjusting for stage and tumor markers, data are not presented for survival.
Results
An association was detected between the rs12025634 in the RPS6KA1 gene and colon cancer (Table 2). Additionally, three SNPs in the RPS6KA2 gene, rs16898963, rs2072638, and rs9459678, were independently associated with colon cancer. A recessive model was the most appropriate model for each of these SNP. A haplotype of these three tagSNPs (data not shown in table) showed a significant 20% increased risk (95% CI 1.06, 1.36) for one copy of the A-C-T (RPS6KA2 rs16898963, rs1072638, and rs9459678); this haplotype was present in 22% of the population.
Table 2.
Associations between colon cancer and RPS6KA1, RPS6KA2, and RPS6KB2
| Colon |
|||||
|---|---|---|---|---|---|
| Controls | Cases | ||||
| N | N | OR1 | (95% CI) | ||
| RPS6KA1 rs12025634(C>T) | |||||
| CC/CT | 1928 | 1518 | 1.00 | ||
| TT | 31 | 41 | 1.73 | (1.08, 2.78) | |
| P Max | 0.07 | ||||
| RPS6KA2 rs9459678(T>C) | |||||
| TT/TC | 1745 | 1345 | 1.00 | ||
| CC | 214 | 213 | 1.25 | (1.01, 1.53) | |
| P Max | 0.03 | ||||
| RPS6KA2 rs16898963(A>C) | |||||
| AA/AC | 1937 | 1522 | 1.00 | ||
| CC | 19 | 32 | 2.17 | (1.22, 3.85) | |
| P Max | 0.02 | ||||
| RPS6KA2 rs2072638(T>C) | |||||
| TT | 965 | 700 | 1.00 | ||
| TC/CC | 993 | 856 | 1.21 | (1.06, 1.38) | |
| P max | 0.03 | ||||
| RPS6KB2 (rs917570 C>G) | |||||
| CC | 653 | 604 | 1.00 | ||
| CG/GG | 1281 | 951 | 0.82 | (0.71, 0.94) | |
| P max | 0.003 | ||||
Adjusted for age, center, race/ethnicity, and sex.
Eight SNPs in RPS6KA2 were associated significantly with rectal cancer (Table 3). The association with rs9295361 was confounded by rs6911624 and the association with rs6456123 was confounded by rs2072638. The magnitude of the associations ranged from having a protective effect for rs6911624 (OR 0.58, 95% CI 0.32, 1.04) to a twofold increased risk for rs9295361 (OR 2.00, 95% CI 1.24, 3.23 for the recessive model). A haplotype analysis of the important tagSNPs showed three statistically significant haplotypes for the RPS6KA2 rs10946164, rs2072638 (T>C), rs2345067 (T>C), rs6911624, rs7745781, rs9295361, rs9347128. The T-C-C-G-C-T-A haplotype had a reduced risk of 0.83 (95% CI 0.69,1.00) per copy; the C-C-C-G-T-C-G haplotype was associated with an OR of 1.70 (95% CI 1.28,2.25); and the C-G-C-G-T-C-A haplotype was associated with an OR of 1.56 (95% CI 1.13,2.15). These haplotypes were present in 8%, 3%, and 2% of the population, respectively.
Table 3.
Associations between RPS6KA2 and rectal cancer
| Rectal |
|||||
|---|---|---|---|---|---|
| Controls | Cases | Cases | |||
| N | N | OR1 | (95% CI) | ||
| RPS6KA2 rs10946164(C>T) | |||||
| CC | 312 | 292 | 1.00 | ||
| CT/TT | 647 | 462 | 0.78 | (0.63, 0.95) | |
| P max | 0.02 | ||||
| RPS6KA2 rs9347128(C>G) | |||||
| CC/CG | 791 | 585 | 1.00 | ||
| GG | 168 | 169 | 1.34 | (1.05, 1.71) | |
| P max | 0.03 | ||||
| RPS6KA2 rs9295361(C>A) | |||||
| CC/CA | 920 | 710 | 1.00 | ||
| AA | 39 | 44 | 2.00 | (1.24, 3.23) | |
| p max | 0.21 | ||||
| RPS6KA2 rs6911624(G>A) | |||||
| GG | 614 | 530 | 1.00 | ||
| GA | 308 | 206 | 0.77 | (0.62, 0.95) | |
| AA | 37 | 18 | 0.58 | (0.32, 1.04) | |
| p max | 0.01 | ||||
| RPS6KA2 rs2345067(T>C) | |||||
| TT | 267 | 235 | 1.00 | ||
| TC | 463 | 358 | 0.85 | (0.68, 1.06) | |
| CC | 229 | 161 | 0.73 | (0.56, 0.97) | |
| p max | 0.22 | ||||
| RPS6KA2 rs2072638(T>C) | |||||
| TT/TC | 872 | 643 | 1.00 | ||
| CC | 79 | 102 | 1.79 | (1.31, 2.45) | |
| p max | <0.01 | ||||
| RPS6KA2 rs7745781(A>G) | |||||
| AA | 730 | 520 | 1.00 | ||
| AG/GG | 229 | 234 | 1.40 | (1.13, 1.74) | |
Adjusted for age, center, race/ethnicity, and sex.
Evaluation of combined genotypes showed several potentially important interactions between genotypes in the RPS6KA1 and RPS6KA2 SNPs (Table 4). For colon cancer, RPS6KA2 rs3799620 interacted with rs9459678 (interacting p value 0.01) and rs16898963 (interaction p<0.01); rs1894660 interacted with rs6926393 (interaction p<0.01); rs2049956 interacted with rs6926393 (interaction p 0.02); and rs2072638 interacted with rs9459678 (interaction p=0.04) (data not shown in table). RPS6KA2 rs2071941 interacted significantly with rs9347128 in rectal cancer (data not shown in table). RPS6KB1 rs180531 interacted with Akt1 rs2494738; RPS6KA2 rs3799620 interacted significantly with RPS6KA1 rs12025634; and RPS6KA2 rs2072638 interacted significantly with FRAP1 rs1057079 among colon cancer cases and controls (Table 4). For rectal cancer cases and controls, RPS6KB1 rs180519 and PIK3CA rs7640662 interacted significantly, as did RPS6KB1 rs180515 and RPKAG2 rs1104897, and the combination of RPS6KB2 rs917570 and PIK3CA rs76400662.
Table 4.
Interaction between genes in candidate pathway
| Controls | Cases | Controls | Cases | |||||
|---|---|---|---|---|---|---|---|---|
| Colon Cancer | N | N | OR | (95% CI) | N | N | OR | (95% CI) |
| Akt1 (rs2494738) | ||||||||
| RPS6KB1 (rs180531) | GG | GA/AA | ||||||
| AA | 913 | 708 | 1.00 | 150 | 147 | 1.24 | 0.97,1.59 | |
| AG | 643 | 499 | 1.01 | 0.86,1.17 | 104 | 72 | 0.88 | 0.64,1.21 |
| GG | 87 | 91 | 1.38 | 1.01,1.89 | 21 | 14 | 0.85 | 0.43,1.70 |
| P Interaction | 0.03 | |||||||
| RPS6KA2 (rs3799620) | ||||||||
| RPS6KA1 (rs12025634) | AA/AG | GG | ||||||
| CC/CT | 1638 | 1293 | 1.00 | 287 | 222 | 0.94 | 0.78,1.14 | |
| TT | 30 | 33 | 1.44 | 0.87,2.38 | 1 | 8 | 10.27 | 1.28,82.29 |
| P Interaction | 0.02 | |||||||
| FRAP1 rs1057079 | ||||||||
| RPSK6A2 (rs2072638) | AA | AG/GG | ||||||
| TT | 503 | 361 | 1.00 | 462 | 338 | 0.98 | 0.81,1.20 | |
| TC/CC | 569 | 411 | 1.01 | 0.84,1.22 | 421 | 443 | 1.46 | 1.21,1.77 |
| p interaction | 0.005 | |||||||
| NFκB1 (rs4648110) | ||||||||
| RPS6KA2 (rs395475)2 | TT | TA/AA | ||||||
| TT | 391 | 312 | 1.00 | 225 | 202 | 1.12 | 0.88,1.43 | |
| TC | 628 | 484 | 0.96 | 0.80,1.17 | 326 | 261 | 1.01 | 0.81,1.26 |
| CC | 227 | 204 | 1.12 | 0.88,1.43 | 162 | 96 | 0.73 | 0.54,0.98 |
| P Interaction | 0.01 | |||||||
| Rectal Cancer | PIK3CA (rs7640662) | |||||||
| RPS6KB1 (rs180519) | CC | CG/GG | ||||||
| GG | 173 | 175 | 1.00 | 84 | 40 | 0.48 | 0.31,0.74 | |
| GA | 382 | 308 | 0.8 | 0.62,1.03 | 103 | 72 | 0.71 | 0.49,1.02 |
| AA | 180 | 129 | 0.69 | 0.50,0.94 | 34 | 29 | 0.85 | 0.50,1.46 |
| P Interaction | 0.005 | |||||||
| PRKAG2 (rs1104897) | ||||||||
| RPS6KB1 (rs180515) | GG | GA/AA | ||||||
| AA | 305 | 197 | 1.00 | 92 | 118 | 1.95 | 1.41,2.71 | |
| AG | 301 | 237 | 1.23 | 0.96,1.58 | 137 | 108 | 1.20 | 0.88,1.64 |
| GG | 80 | 63 | 1.22 | 0.84,1.77 | 43 | 30 | 1.08 | 0.65,1.78 |
| P Interaction | 0.002 | |||||||
| PIK3CA (rs7640662) | ||||||||
| RPS6KB2 (rs917570) | CC | CG/GG | ||||||
| CC | 251 | 216 | 1.00 | 70 | 66 | 1.13 | 0.77,1.67 | |
| CG | 346 | 274 | 0.94 | 0.74,1.20 | 99 | 53 | 0.65 | 0.44,0.96 |
| GG | 129 | 110 | 1.03 | 0.74,1.41 | 49 | 20 | 0.49 | 0.28,0.85 |
| P Interaction | 0.009 | |||||||
| NFκB1 (rs4648110) | ||||||||
| RPS6KA2 (rs7745781)3 | TT | TA/AA | ||||||
| AA | 454 | 335 | 1.00 | 276 | 185 | 0.91 | 0.72,1.16 | |
| AG/GG | 159 | 137 | 1.15 | 0.88,1.50 | 70 | 97 | 1.90 | 1.35,2.67 |
| P Interaction | 0.009 | |||||||
Adjusted for age, center, race/ethnicity, and sex.
Same association seen with NFκB1 (rs13117745)
Same association seen with NFκB1 (rs13117745)
Statistically significant interactions were observed between RPS6KA1 rs12025634 and BMI and between RPS6KA1 rs12025634 and recent estrogen exposure for colon cancer (Table 5). Having the TT genotype of rs12025634 resulted in over a six-fold increase risk of colon cancer among those with a BMI of 30 or more. Those without recent estrogen exposure were also at greater risk of colon cancer if they had the TT genotype of RPS6KA1 rs12025634. For rectal cancer, recent aspirin/NSAID use was most protective among those who had the TT genotype of RPS6KA2 rs2072638.
Table 5.
Interaction between RPS6KA1, RPS6KA2, BMI, estrogen exposure and awspirin/NSAID use and colon and rectal cancer
| Controls | Cases | Controls | Cases | Controls | Cases | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N | N | OR | (95% CI) | N | N | OR | (95% CI) | N | N | OR | (95% CI) | ||
| Colon | BMI | ||||||||||||
| RPS6KA1 rs12025634 | <25 | 25 to <30 | 30 | ||||||||||
| CC/CT | 743 | 495 | 1.00 | 784 | 614 | 1.16 | (0.99, 1.36) | 397 | 404 | 1.49 | (1.25, 1.79) | ||
| TT | 16 | 11 | 1.06 | (0.49, 2.31) | 12 | 17 | 2.21 | (1.04, 4.67) | 3 | 13 | 6.26 | (1.77, 22.15) | |
| P Interaction | 0.04 | ||||||||||||
| Recent estrogen exposure | |||||||||||||
| RPS6KA1 rs12025634 | No | Yes | |||||||||||
| CC/CT | 514 | 438 | 1.00 | 356 | 216 | 0.59 | (0.45, 0.76) | ||||||
| TT | 9 | 12 | 1.60 | (0.67, 3.87) | 6 | 7 | 1.09 | (0.36, 3.35) | |||||
| P Interaction | 0.01 | ||||||||||||
| Rectal | Recent aspirin/NSAID use | ||||||||||||
| RPS6KA2 rs2072638 | No | Yes | |||||||||||
| TT | 249 | 245 | 1.00 | 250 | 120 | 0.50 | (0.37, 0.66) | ||||||
| TC | 214 | 162 | 0.80 | (0.61, 1.06) | 150 | 110 | 0.77 | (0.57, 1.05) | |||||
| CC | 53 | 65 | 1.30 | (0.87, 1.96) | 25 | 37 | 1.57 | (0.92, 2.70) | |||||
| P Interaction | 0.00 | ||||||||||||
Adjusted for age, center, race/ethnicity, and sex.
Assessment of those SNPs that were important from stepwise regression on various tumor phenotypes showed several interesting trends (Table 6). CIMP+ tumors were associated with RPS6KA2 variants for both colon and rectal tumors. Similarly RPS6KA2 was associated with KRAS2-mutated tumors for both colon and rectal cancer. Additionally, RPS6KA1 was associated with KRAS2-mutated colon tumors and with TP53 mutated colon tumors but not with rectal tumor mutations.
Table 6.
Combined genotypes and specific mutations in colon and rectal tumors
| Colon Cancer | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CIMP High | KRAS2 Mutation | TP53 Mutation | |||||||||||||
| Frequency | OR6 | (95% CI) | Frequency | OR6 | (95% CI) | Frequency | OR6 | (95% CI) | |||||||
| RPS6KA21 | RPS6KA1 and RPS6KA2 2 | RPS6KA1 and RPS6KA2 3 | |||||||||||||
| 0.32 | C-T | 0.87 | (0.70, 1.06) | 0.30 | C-A-A-G-A | 1.24 | (1.02, 1.50) | 0.50 | A-A-G | 0.95 | (0.83, 1.10) | ||||
| 0.25 | T-T | 1.03 | (0.82, 1.28) | 0.13 | C-G-A-G-A | 0.80 | (0.61, 1.05) | 0.18 | G-A-G | 0.81 | (0.67, 0.98) | ||||
| 0.24 | C-C | 0.91 | (0.73, 1.14) | 0.12 | C-A-A-A-A | 0.93 | (0.71, 1.22) | 0.15 | A-A-A | 1.31 | (1.09, 1.59) | ||||
| 0.18 | T-C | 1.31 | (1.04, 1.64) | 0.07 | T-A-A-G-A | 1.12 | (0.79, 1.58) | 0.07 | A-C-G | 1.27 | (0.97, 1.65) | ||||
| 0.07 | C-A-A-A-G | 1.46 | (1.07, 2.00) | 0.05 | G-A-A | 0.77 | (0.54, 1.12) | ||||||||
| 0.05 | C-A-C-G-G | 0.96 | (0.63, 1.48) | 0.03 | G-C-G | 0.96 | (0.64, 1.46) | ||||||||
| 0.05 | C-G-A-A-A | 0.54 | (0.31, 0.91) | 0.02 | A-C-A | 0.99 | (0.57, 1.73) | ||||||||
| 0.04 | T-A-A-A-A | 0.98 | (0.59, 1.62) | ||||||||||||
| 0.03 | C-G-A-G-G | 0.99 | (0.63, 1.55) | ||||||||||||
| 0.03 | C-G-C-G-G | 1.06 | (0.63, 1.78) | ||||||||||||
| 0.03 | C-A-A-A-G | 0.74 | (0.40, 1.40) | ||||||||||||
| 0.02 | T-A-A-G-G | 1.16 | (0.67, 2.01) | ||||||||||||
| 0.02 | C-A-C-A-G | 0.69 | (0.32, 1.46) | ||||||||||||
| 0.01 | C-G-A-A-G | 0.37 | (0.11, 1.19) | ||||||||||||
|
Rectal Cancer | |||||||||||||||
| 4RPS6KA2 | 5RPS6KA2 | ||||||||||||||
| 0.36 | T-A | 0.69 | (0.44, 1.08) | 0.24 | A-A-C | 0.95 | (0.71, 1.26) | ||||||||
| 0.35 | C-A | 0.88 | (0.58, 1.32) | 0.21 | A-C-C | 0.92 | (0.69, 1.23) | ||||||||
| 0.20 | T-G | 0.83 | (0.49, 1.42) | 0.18 | A-C-G | 0.82 | (0.61, 1.10) | ||||||||
| 0.10 | C-G | 2.66 | (1.65, 4.29) | 0.14 | A-A-G | 1.27 | (0.92, 1.75) | ||||||||
| 0.07 | C-A-C | 1.05 | (0.65, 1.71) | ||||||||||||
| 0.07 | C-C-C | 0.71 | (0.41, 1.23) | ||||||||||||
| 0.05 | C-C-G | 1.00 | (0.57, 1.75) | ||||||||||||
| 0.03 | C-A-G | 2.55 | (1.52, 4.26) | ||||||||||||
RPS6KA2 rs10946164(C>T), rs2345067(T>C); minor allele in bold
RPS6KA1 rs12025634(C>T), rs12723936(A>G) and RPS6KA2 rs16898963(G>A), rs409161(G>A), rs6926393(A>G); minor allele in bold
RPS6KA1 rs12723936(A>G) and RPS6KA2 rs16898963(A>C), rs17518299(G>A); minor allele in bold
RPS6KA2 rs395475(T>C), rs6926393(A>G); minor allele in bold
RPS6KA2 rs3816540(A>C), rs7760282(C>A), rs9347128(C>G); minor allele in bold
Adjusted for age, center, race/ethnicity, and sex; risk relative to all other haplotypes
Discussion
RSP6KA1, RPS6KA2, RPS6KB1, RPS6KB2, and PDK1 are part of a protein kinase family involved in the regulation of cellular growth, insulin, and cellular energy [20]. For both colon and rectal cancer, we observed several variants of these genes that influenced risk of developing the disease. For colon cancer, RPS6KA1, RPS6KA2, and RPS6KB2 variants were associated with increased risk of the disease; for rectal cancer the risk was mainly associated in variants of RPS6KA2. Variants appeared to be mainly associated with a CIMP+ and KRAS2-mutated tumors.
Our data suggest that these genes are involved in pathways that include insulin, estrogen, and inflammation as would be hypothesized given what we currently know about their function. RPS6KA1 is a component of the insulin signal transduction pathway, and may be a key element in regulating insulin resistance [21]; RPS6KB also has a role in growth factor regulation [22]. PDK1 is needed to activate both RPS6KB and S6K, RPS6KA and thereby mediating the effect of insulin and growth factors [5,20]. NFκB plays a critical role in the regulation of inflammation and data have shown that RPS6KB is involved in a signaling pathway that involves angiotensin II activation of NFκB [23]. Estrogen also is involved in inflammation-related processes [24]. We observed statistically significant interactions with RPS6KA1 and BMI and estrogen for colon cancer and PDK1 and aspirin for both colon and rectal cancer. RPS6KB2 interacted significantly with estrogen among those with rectal cancer. BMI is associated with inflammation, estrogen, and insulin and thus the interaction supports the hypothesized mechanisms. Recent use of aspirin/NSAID may influence the inflammatory state of the gut and our data suggest that genetic factors with an inflammation-related mechanism are associated with altered risk of both colon and rectal cancer.
The candidate genes examined in this paper are associated with the PI3K/Akt/FRAP1 (mTOR) pathway [25,26]. mTOR plays a role in cellular proliferation as a nutrient sensor and controls RPS6K1; RPS6K2 is regulated by mTor and PI3K [27]. RPS6KA is involved in several stages of translational control and is a mediator in the PI3K/Akt/mTor pathway [22]. PDK1 is an important regulator of Akt1 [20]. Our data support the interaction of these pathways in that genetic variants of RPS6KA1 interacted with variants of Akt1 and RPS6KB1 interacted with mTOR or FRAP1 among colon cancer cases. For rectal cancer both RPS6KB1 and RPS6KB2 interacted with variants in PIK3CA. These results suggest that genetic variation within the pathway influences other genes within the pathway.
Our data suggest that these genes are involved in both a CIMP+ and KRAS2 pathway. Data have shown that KRAS2 is influenced by both the RPS6KB and the RPS6KA [28,29]. To our knowledge, no one has reported an association between genetic variation in RPS6KA1 or RPS6KA2 and CIMP+ colon or rectal tumors. We have previously reported an association between CIMP and other variants in insulin-related genes [30]. Our current data reinforce the hypothesis that RPS6KA is associated with CIMP+ and KRAS2-mutated tumors. It also illustrates the need to evaluate subsets of the data by tumor phenotype. Patterns of association with tumor markers provide a better understanding of mechanisms involved in the carcinogenic process.
We believe that the data presented here provide new insights into the etiology of colon and rectal cancers; however there also are study limitations that need to be recognized. A tagSNP approach was used to characterize the genetic variation in our candidate genes. Although we hypothesized that these genes would be important and that they would have interactions with the specific factors described, we did not have the necessary information to hypothesize that any specific SNP would be associated. We view the data as an indication of variation in the gene and not that any one SNP is causal. Thus, to determine a causal variant and impact on disease pathways, further work, including work to determine functionality of individual SNPs is needed. Likewise, while we hypothesize that use of aspirin/NSAIDs may indicate a lower level of gut inflammation, use of anti-inflammatory drugs could also serve as an anti-platelet agent. We have taken full advantage of our unique dataset and have evaluated these candidate genes with specific tumor markers to obtain insight into disease pathways. Our approach has lead to many comparisons and therefore replication of these associations in other large studies is needed.
There is a biological basis for our hypothesis that genetic variation in RPS6KA1, RPS6KA2, RPS6KB1, and RPS6KB2, and PDK1 are associated with colon and rectal cancer. We have presented data that support the hypothesis that genetic variation in these genes contributes to risk of colon and rectal cancer. Our data further support that they are most important for a CIMP+ and KRAS2 pathway and may further interact with genetic and lifestyle factors. Replication of these findings is needed.
Acknowledgements
This study was funded by NCI grants CA48998 and 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. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute. We would like to acknowledge the contributions of Sandra Edwards, Roger Edwards, Leslie Palmer, Donna Schaffer, Dr. Kristin Anderson, Dr. Bette J. Caan, Dr. John D. Potter, and Judy Morse for data management and collection.
Footnotes
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