Literature DB >> 21949851

TGFBR2 and BAX mononucleotide tract mutations, microsatellite instability, and prognosis in 1072 colorectal cancers.

Kaori Shima1, Teppei Morikawa, Mai Yamauchi, Aya Kuchiba, Yu Imamura, Xiaoyun Liao, Jeffrey A Meyerhardt, Charles S Fuchs, Shuji Ogino.   

Abstract

BACKGROUND: Mononucleotide tracts in the coding regions of the TGFBR2 and BAX genes are commonly mutated in microsatellite instability-high (MSI-high) colon cancers. The receptor TGFBR2 plays an important role in the TGFB1 (transforming growth factor-β, TGF-β) signaling pathway, and BAX plays a key role in apoptosis. However, a role of TGFBR2 or BAX mononucleotide mutation in colorectal cancer as a prognostic biomarker remains uncertain. METHODOLOGY/PRINCIPAL
FINDINGS: We utilized a database of 1072 rectal and colon cancers in two prospective cohort studies (the Nurses' Health Study and the Health Professionals Follow-up Study). Cox proportional hazards model was used to compute mortality hazard ratio (HR), adjusted for clinical, pathological and molecular features including the CpG island methylator phenotype (CIMP), LINE-1 methylation, and KRAS, BRAF and PIK3CA mutations. MSI-high was observed in 15% (162/1072) of all colorectal cancers. TGFBR2 and BAX mononucleotide mutations were detected in 74% (117/159) and 30% (48/158) of MSI-high tumors, respectively. In Kaplan-Meier analysis as well as univariate and multivariate Cox regression analyses, compared to microsatellite stable (MSS)/MSI-low cases, MSI-high cases were associated with superior colorectal cancer-specific survival [adjusted HR, 0.34; 95% confidence interval (CI), 0.20-0.57] regardless of TGFBR2 or BAX mutation status. Among MSI-high tumors, TGFBR2 mononucleotide mutation was associated with CIMP-high independent of other variables [multivariate odds ratio, 3.57; 95% CI, 1.66-7.66; p = 0.0011].
CONCLUSIONS: TGFBR2 or BAX mononucleotide mutations are not associated with the patient survival outcome in MSI-high colorectal cancer. Our data do not support those mutations as prognostic biomarkers (beyond MSI) in colorectal carcinoma.

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Year:  2011        PMID: 21949851      PMCID: PMC3176811          DOI: 10.1371/journal.pone.0025062

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

Colorectal cancer represents a group of molecularly heterogeneous diseases with different sets of epigenetic and genetic abnormalities. High degree of microsatellite instability (MSI-high) is caused by deficiency of DNA mismatch repair system, and observed in approximately 15% of colorectal cancers. MSI testing is widely used as screening for patients with Lynch syndrome/hereditary nonpolyposis colorectal cancer (HNPCC) [1], [2], [3]. In addition, MSI is generally accepted as a prognostic marker [4], and likely a predictive marker for resistance to 5-fluorouracil [5]. Since Markowitz et al. [6] discovered mutations in the coding mononucleotide repeats of TGFBR2 in MSI-high colon cancer cells, similar mutations of coding mononucleotide repeats in many other genes (including BAX, MSH3, MSH6, IGF2R and PTEN) have been found in MSI-high colorectal cancers [3], [7], [8], [9]. Among those genes, mononucleotide coding repeats of TGFBR2 (A)10 and BAX (G)8 have frequent frameshift mutations resulting in the production of truncated, inactive form of the proteins [3], [10]. TGFB1 (transforming growth factor-β, TGF-β) and its receptor TGFBR2 constitute a signaling pathway that regulates the transcription of many genes, and functions as a tumor suppressor [11], [12], [13], [14] and an immune response regulator [15]. BAX generally promotes apoptosis and antagonizes the effect of BCL2 [16], [17], [18]. Thus, inactivation of TGFBR2 or BAX may contribute to tumor progression. Several previous studies have examined the prognostic role of TGFBR2 or BAX mononucleotide mutations in MSI-high colorectal cancers, yielding inconclusive results due to limited statistical power in most studies [19], [20], [21], [22], [23], [24], [25] ( ). All but one previous study [23] examined the prognostic role of TGFBR2 or BAX mononucleotide mutation in less than 100 MSI-high tumors (the number of MSI-high tumors ranging from 16 to 98) [19], [20], [21], [22], [23], [24], [25]. In addition, none of the previous studies [19], [20], [21], [22], [23], [24], [25] has comprehensively examined potential confounding effect of key molecular biomarkers in colorectal cancer, including the CpG island methylator phenotype (CIMP), and KRAS, BRAF and PIK3CA mutations. Thus, the prognostic role of TGFBR2 or BAX mononucleotide mutation in MSI-high tumors still remains uncertain.
Table 1

Studies on prognostic significance of TGFBR2 or BAX mononucleotide mutation in MSI-high colorectal cancer.

Ref.Authors (year)No. of hospitalsSample size for MSI determinationDisease stageChemotherapyMSI-high casesMononucleotide mutations in MSI-high colorectal cancerOther molecular covariates and notes
NOS eventsCS events TGFBR2 BAX
Cases number/MSI-high cases5-year survival P value by log-rank test (vs. wild type cases)Multivariate HR (95% CI) P value (vs. wild-type cases)Cases number/MSI-high cases5-year survival P value by log-rank test (vs. wild type cases)Multivariate HR (95% CI) P value (vs. wild -type cases)
[19] Iacopetta et al. (1998)1210Dukes' B and C-37--32/37OS at the end point* 72% (vs. 60%) p = 0.21----Proximal colon cancer only
[20] Ionov et al. (2000)?508--67--31/36OS: p = 0.55-19/36OS: inferior p<0.01-
[21] Watanabe et al. (2001)many298II–IIIAdjuvant chemo-therapy73--48/73OS: 74% (vs. 46%) p = 0.04-22/60--Multivariate model assessed effect of MSS vs. MSI-high and TGFBR2 mutation. 18q LOH was also assessed.
[22] Samowitz et al. (2002)many1427I–IV-174--134/170OS: 72% (vs. 67%) NSOS: 1.01 (0.54–1.88) NS63/160OS: 67% (vs. 71%) NSOS: 1.33 (0.80–2.21) NSCases from 8 county areas
[23] Fernández-Peralta et al. (2005)1155Dukes' A–D-16--13/16OS: superior p = 0.04-6/16OS: superior p<0.001-
[24] Jung et al. (2006)4172II-4811-35/45-OS: NS (univariate HR only)29/47--
[25] Kim et al. (2007)many542Dukes' B and CAdjuvant chemo-therapy 9826-54/98-OS (stratified by stage and treatment): 1.26 (0.57–2.80) NS---National Surgical Adjuvant Breast and Bowel Project (NSABP)
Shima et al. (current study)many1072I–IV-1625920117/159CS: 86% (vs. 90%), p = 0.55OS: 64% (vs. 60%), p = 0.66CS: 1.18 (0.29–4.89), p = 0.82OS: 0.61 (0.32–1.15), p = 0.1248/158CS: 92% (vs. 85%), p = 0.29OS: 56% (vs. 65%), p = 0.39CS: 0.73 (0.22–2.41), p = 0.60OS: 1.46 (0.80–2.65), p = 0.22Tumor molecular covariates include CIMP, LINE-1 methylation, and mutations in KRAS, BRAF and PIK3CA.

CI, confidence interval; CIMP, CpG island methylator phenotype; CS, colorectal cancer-specific survival; HR, hazard ratio; LOH, loss of heterozygosity; MSI, microsatellite instability; MSS, microsatellite stable; NS, not significant; OS, overall survival;

*Including MSS/MSI-low cases.

The authors (Kim et al. [25]) compared predictive effect of MSI for chemotherapy between the cases registered surgery alone and the cases registered chemotherapy.

CI, confidence interval; CIMP, CpG island methylator phenotype; CS, colorectal cancer-specific survival; HR, hazard ratio; LOH, loss of heterozygosity; MSI, microsatellite instability; MSS, microsatellite stable; NS, not significant; OS, overall survival; *Including MSS/MSI-low cases. The authors (Kim et al. [25]) compared predictive effect of MSI for chemotherapy between the cases registered surgery alone and the cases registered chemotherapy. We conducted this study to test the hypothesis that TGFBR2 or BAX mononucleotide mutations in colorectal cancer were associated with altered tumor behavior (beyond MSI), utilizing a database of 1072 stage I to IV colorectal cancers in two prospective cohort studies. Our current study represents the first study which utilized a database of prospective cohort studies to test the stated hypothesis. This fact increases generalizability of our study findings. Moreover, because we concurrently assessed clinical, pathologic and tumor molecular variables such as the CpG island methylator phenotype (CIMP), LINE-1 methylation, KRAS, BRAF and PIK3CA mutations, we could evaluate the effect of TGFBR2 or BAX mutation independent of these potential confounders.

Methods

Study group

We utilized the database of two prospective cohort studies, the Nurses' Health Study (N = 121,701 women followed since 1976) and the Health Professionals Follow-up Study (N = 51,529 men followed since 1986) [26], [27]. Participants have been sent biennial questionnaires to update information on potential risk factors and to identify newly diagnosed cancers in themselves and their first degree relatives. We collected paraffin-embedded tumor tissue blocks of incident colorectal cancers from hospitals throughout the U.S. where participants with colorectal cancer underwent tumor resection [26], [27]. Clinical characteristics of the cases are described in (on the left, under the column heading “All cases”). There was no significant difference in demographic features between cases with tissue available and those without available tissue among our cohort studies [26]. A majority of cases have previously been characterized for statuses of TGFBR2, MSI, CIMP, KRAS, BRAF, PIK3CA and LINE-1 methylation [28], [29], [30], [31]. However, none of our previous studies have analyzed the prognostic significance of mononucleotide mutation of TGFBR2 or BAX. BAX mutation has not been analyzed in any of our previous studies. Thus, this study represents a new study utilizing a resource of the existing materials and database, analogous to novel studies using well-described cell lines (e.g., SW480 cell line) or mouse models (e.g., Apc min mouse model).
Table 2

MSI status and TGFBR2 or BAX mononucleotide tract mutation in colorectal cancer.

Clinical, pathologic or molecular featureTotal NMSS/MSI-lowMSI-high TGFBR2 mononucleotide mutation in MSI-high tumors BAX mononucleotide mutation in MSI-high tumors
(−)(+)P value(−)(+)P value
All cases10729101624211711048
Sex0.450.51
Female (NHS)603 (56%)495 (54%)108 (67%)26 (62%)80 (68%)72 (65%)34 (71%)
Male (HPFS)469 (44%)415 (46%)54 (33%)16 (38%)37 (32%)38 (35%)14 (29%)
Mean age ± SD67.5±8.567.2±8.669.5±7.368.3±7.669.7±7.00.2668.7±7.470.6±6.40.13
Body mass index0.340.62
<30 kg/m2 871 (81%)740 (81%)131 (81%)32 (76%)97 (83%)88 (80%)40 (83%)
≥30 kg/m2 200 (19%)169 (19%)31 (19%)10 (24%)20 (17%)22 (20%)8 (17%)
Family history of colorectal cancer0.680.56
Absent866 (81%)743 (82%)123 (76%)31 (74%)90 (77%)85 (77%)35 (73%)
Present206 (19%)167 (18%)39 (24%)11 (26%)27 (23%)25 (23%)13 (27%)
Year of diagnosis0.280.68
Prior to 1995390 (36%)347 (38%)43 (26%)14 (33%)29 (25%)31 (28%)12 (25%)
1995 to 2004682 (64%)563 (62%)119 (73%)28 (67%)88 (75%)79 (72%)36 (75%)
Tumor location0.120.51
Proximal colon (cecum to transverse)492 (47%)351 (39%)141 (87%)35 (83%)105 (90%)95 (86%)44 (92%)
Distal colon337 (32%)321 (36%)16 (9.9%)4 (10%)11 (9.4%)11 (10%)4 (8.3%)
Rectum228 (22%)223 (25%)5 (3.1%)3 (7.1%)1 (0.9%)4 (3.6%)0
Disease stage0.0450.014
I256 (24%)224 (25%)32 (20%)15 (36%)17 (15%)26 (24%)6 (13%)
II308 (29%)221 (24%)87 (54%)18 (43%)66 (56%)48 (44%)35 (73%)
III282(26%)256 (28%)26 (16%)4 (9.5%)22 (19%)21 (19%)5 (10%)
IV146 (14%)136 (15%)10 (6.2%)3 (7.1%)7 (6.0%)8 (7.3%)2 (4.2%)
unknown80 (7.5%)73 (8.0%)7 (4.3%)2 (4.8%)5 (4.3%)7 (6.4%)0
Tumor grade0.860.19
Low962 (90%)851 (94%)111 (69%)29 (69%)79 (68%)78 (71%)29 (60%)
High104 (10%)53 (5.9%)51 (31%)13 (31%)38 (32%)32 (29%)19 (40%)
CIMP status0.00100.52
CIMP-0462 (44%)450 (51%)12 (7.5%)3 (7.3%)9 (7.8%)9 (8.3%)3 (6.3%)
CIMP-low411 (39%)382 (43%)29 (18%)15 (37%)12 (10%)21 (19%)6 (13%)
CIMP-high172 (16%)53 (6.0%)119 (74%)23 (56%)95 (82%)78 (72%)39 (81%)
KRAS mutation0.0160.81
(−)684 (64%)546 (60%)138 (86%)31 (76%)105 (91%)93 (86%)42 (88%)
(+)383 (36%)361 (40%)22 (14%)10 (24%)11 (9.5%)15 (14%)6 (13%)
BRAF mutation0.00490.59
(−)915 (86%)835 (92%)80 (50%)28 (68%)50 (43%)55 (50%)22 (46%)
(+)150 (14%)69 (7.6%)81 (50%)13 (32%)67 (57%)54 (49%)26 (54%)
PIK3CA mutation0.530.079
(−)789 (81%)679 (82%)110 (76%)26 (72%)82 (77%)71 (72%)37 (86%)
(+)189 (19%)154 (18%)35 (24%)10 (28%)24 (23%)27 (28%)6 (14%)
Mean LINE-1 methylation (%) ± SD62.0±9.461.2±9.366.1±8.566.2±9.865.9±8.00.8366.0±8.666.0±8.40.96

(%) indicates the proportion of cases with a specific clinical, pathologic or molecular feature among all cases, MSS/MSI-Low, TGFBR2 mutated or BAX mutated cases. CIMP, CpG island methylator phenotype; HPFS, Health Professionals Follow-up Study; MSI, microsatellite instability; MSS, microsatellite stable, NHS, Nurses' Health Study; SD, standard deviation.

(%) indicates the proportion of cases with a specific clinical, pathologic or molecular feature among all cases, MSS/MSI-Low, TGFBR2 mutated or BAX mutated cases. CIMP, CpG island methylator phenotype; HPFS, Health Professionals Follow-up Study; MSI, microsatellite instability; MSS, microsatellite stable, NHS, Nurses' Health Study; SD, standard deviation. Hematoxylin and eosin stained tissue sections from all colorectal cancer cases were reviewed by a pathologist (S.O.) unaware of other data. Tumor differentiation was categorized as well-moderate vs. poor (>50% vs. ≤50% glandular areas). We excluded cases which were preoperatively treated. Based on the availability of adequate follow-up and tumor tissue data, 1072 stage I–IV colorectal cancer cases diagnosed up to 2004 were included in the current study ( ). Patients were observed until death or June 30 2009, whichever came first. Death of a participant was confirmed by the National Death Index. Returning questionnaire indicated informed consent from all study subjects. Informed consent was obtained from all study subjects. Tissue collection and analyses were approved by the Human Subjects Committees at Harvard School of Public Health and Brigham and Women's Hospital.
Figure 1

Flow diagram of the current study.

Based on the availability of adequate follow-up and tumor molecular data among incident colorectal cancers identified in the Nurses' Health Study (NHS; N = 121,701) and the Health Professionals Follow-up Study (HPFS; N = 51,529), a total of 1072 stage I–IV colorectal cancer cases diagnosed up to 2004 were included. MSI, microsatellite instability; MSS, microsatellite stable.

Flow diagram of the current study.

Based on the availability of adequate follow-up and tumor molecular data among incident colorectal cancers identified in the Nurses' Health Study (NHS; N = 121,701) and the Health Professionals Follow-up Study (HPFS; N = 51,529), a total of 1072 stage I–IV colorectal cancer cases diagnosed up to 2004 were included. MSI, microsatellite instability; MSS, microsatellite stable.

Microsatellite instability (MSI) analysis and detection of TGFBR2 and BAX mononucleotide tract mutations

DNA was extracted from paraffin embedded tissue. MSI analysis was performed using 10 microsatellite markers (D2S123, D5S346, D17S250, BAT25, BAT26, BAT40, D18S55, D18S56, D18S67 and D18S487) [30]. MSI-high was defined as the presence of instability in ≥30% of the markers, and MSI-low/microsatellite stable (MSS) as instability in 0–29% of markers [30]. Mononucleotide tract mutations of TGFBR2 and BAX were examined in MSI-high tumors. Primers and PCR conditions for TGFBR2 were previously described [28]. Primer sequences for BAX were; 5′-(FAM) ATCCAGGATCGAGCAGGGCG-3′ and 5′-ACTCGCTCAGCTTCTTGGTG-3′. PCR condition was preheat at 95°C for 5 min, 45 cycles (at 94-55-72°C for 30-30-30 sec), and extension at 72°C for 2 min. PCR products were electrophoresed and analyzed by ABI 3730 DNA Analyzer (Applied Biosystems, Foster City, CA).

Pyrosequencing of KRAS, BRAF and PIK3CA

PCR and Pyrosequencing targeted for KRAS (codons 12 and 13) [32], BRAF (codon 600) [33]and PIK3CA (exons 9 and 20) were performed as previously described [29].

Methylation analyses for CpG islands and LINE-1

Sodium bisulfite treatment and subsequent real-time PCR (MethyLight [34]) were previously validated [35], and performed to quantify promoter methylation in eight CpG islands (CACNA1G, CDKN2A, CRABP1, IGF2, MLH1, NEUROG1, RUNX3 and SOCS1) [36], [37], [38]. CIMP-high was defined as the presence of ≥6/8 methylated markers, CIMP-low as the presence of 1/8 to 5/8 methylated markers, and CIMP-0 as the absence (0/8) of methylated markers [37], [39]. LINE-1 methylation levels were quantified by PCR-Pyrosequencing [40], [41].

Statistical analysis

We used SAS program (Version 9.1, SAS Institute, Cary, NC) for all statistical analyses. All p values were two-sided. When we perform multiple hypothesis testing (i.e., analyses of molecular correlates and interactions), a p value for statistical significance was adjusted to p = 0.0038 ( = 0.05/13) by Bonferroni correction. The chi-square test (or Fisher's exact test) was performed for categorical variables. The t test assuming unequal variances was done to compare mean age and mean LINE-1 methylation level. For survival analysis, the Kaplan-Meier method and log-rank test were used. For analyses of colorectal cancer-specific mortality, deaths as a result of causes other than colorectal cancer were censored. To control for confounding, we used multivariate stage-matched (stratified) Cox proportional hazards model to compute hazard ratio (HR) of death. To avoid residual confounding and overfitting, disease stage (I, II, III, IV, unknown) was used as a stratifying variable, utilizing the “strata” option in the SAS “proc phreg” command. The multivariate model initially included age at diagnosis (continuous), sex, year of diagnosis (continuous), body mass index (BMI; <30 vs. ≥30 kg/m2), family history of colorectal cancer in any first degree relative (present vs. absent), tumor location (proximal vs. distal), tumor differentiation (well-moderate vs. poor), CIMP (high vs. low/CIMP-0), LINE-1 methylation (continuous), KRAS, BRAF and PIK3CA. A backward elimination method with a threshold of p = 0.20 was used to limit the number of variables in the final model and avoid overfitting. For cases with missing information in any of the categorical variables [BMI (0.1%), tumor location (1.0%), tumor grade (0.6%), CIMP (2.5%), KRAS (0.5%), BRAF (0.7%) and PIK3CA (8.8%)], we included those cases in a majority category of a given covariate to avoid overfitting. We confirmed that excluding cases with missing information in any of the covariates did not substantially alter results (data not shown). A multivariate logistic regression analysis was performed to examine an independent relationship of each covariate with TGFBR2 mutation (as an outcome variable). The multivariate model initially included a similar, but not the same set of the covariates as the initial Cox model, considering possible cause-effect relationship with TGFBR2 mutation. Specifically, disease stage and tumor differentiation were likely consequences (rather than causes) of TGFBR2 mutation. Thus, those variables were not included in the logistic regression model. A backward elimination with a threshold of p = 0.10 was used to select variables in the final model and avoid overfitting.

Results

Mononucleotide mutations of TGFBR2 and BAX in MSI-high colorectal cancers

Among 1072 colorectal cancers in the two prospective cohort studies, MSI-high was observed in 162 (15%) tumors. TGFBR2 and BAX mononucleotide tract mutations were detected in 117 (of 159, 74%) and 48 (of 158, 30%) MSI-high tumors, respectively. Among MSI-high tumors, TGFBR2 mutation was significantly associated with CIMP-high (p = 0.0010) ( ).

Multivariate analysis to assess independent relations with TGFBR2 mutation

We performed multivariate logistic regression analysis to examine whether TGFBR2 mutation was independently associated with any clinical, pathologic and other molecular variables. In MSI-high tumors, TGFBR2 mutation was independently associated with CIMP-high [multivariate odds ratio (OR), 3.57; 95% confidence interval (CI), 1.66–7.66; p = 0.0011].

Mononucleotide mutations of TGFBR2 and BAX and colorectal cancer prognosis

During adequate follow-up (11.6 years of median follow-up of censored cases), there were 505 deaths including 302 colorectal cancer-specific deaths. Among all cases, MSI-high was significantly associated with longer colorectal cancer-specific survival compared to MSS/MSI-low cancers by log-rank test (p<0.0001), univariate and multivariate Cox regression analysis (adjusted HR, 0.34; 95% CI, 0.20–0.57; p<0.0001) ( ). When we separately examined TGFBR2-mutated MSI-high cases and TGFBR2-wildtype MSI-high cases, both groups showed significantly longer colorectal cancer-specific survival compared to MSS/MSI-low cases ( , ). When we separately examined BAX-mutated MSI-high cases and BAX-wildtype MSI-high cases, both groups showed significantly longer colorectal cancer-specific survival compared to MSS/MSI-low cases ( , ). In overall mortality analyses, although somewhat attenuated, results showed similar trends ( ). Among MSI-high cases, patient survival did not significantly differ by TGFBR2 or BAX mutation status.
Table 3

MSI status, TGFBR2 and BAX mononucleotide tract mutation and survival of colorectal cancer patients.

Colorectal cancer-specific mortalityOverall mortality
Total NDeaths/person-yearsUnivariate HR (95% CI)Multivariate stage-matched HR (95% CI)Deaths/person-yearsUnivariate HR (95% CI)Multivariate stage-matched HR (95% CI)
MSS/MSI-low tumors910282/80151 (referent)1 (referent)445/80151 (referent)1 (referent)
MSI-high tumors16220/14680.37 (0.24–0.58)0.34 (0.20–0.57)60/14680.72 (0.55–0.94)0.64 (0.47–0.89)
MSI-high tumors
TGFBR2 mutation (−)424/3900.30 (0.11–0.79)0.29 (0.10–0.83)17/3900.79 (0.48–1.28)0.79 (0.47–1.34)
TGFBR2 mutation (+)11716/10600.41 (0.25–0.67)0.35 (0.20–0.62)42/10600.69 (0.50–0.95)0.59 (0.41–0.86)
BAX mutation (−)11016/9990.44 (0.27–0.73)0.36 (0.20–0.62)38/9990.67 (0.48–0.93)0.57 (0.39–0.84)
BAX mutation (+)484/4430.25 (0.09–0.66)0.30 (0.11–0.83)21/4430.84 (0.54–1.30)0.82 (0.50–1.32)

The multivariate, stage-matched (stratified) Cox regression model initially included the TGFBR2 mutation or BAX mutation variable, sex, age at diagnosis, year of diagnosis, tumor location, body mass index, family history of colorectal cancer, tumor grade, CIMP, KRAS, BRAF, PIK3CA and LINE-1 methylation. A backward elimination with a threshold of p = 0.20 was used to select variables in the final models. Stage adjustment (I, II, III, IV, unknown) was done using the “strata” option in the SAS “proc phreg” command.

CI, confidence interval; HR, hazard ratio; MSI, microsatellite instability; MSS, microsatellite stable.

Figure 2

Kaplan-Meier curves according to MSI status and TGFBR2 or BAX mononucleotide mutation in colorectal cancer.

Kaplan-Meier curves for colorectal cancer-specific survival (A) and overall survival (B), according to TGFBR2 mononucleotide mutation status. Regardless of TGFBR2 status, MSI-high cases were associated with longer survival. Kaplan-Meier curves for colorectal cancer-specific survival (C) and overall survival (D), according to BAX mononucleotide mutation status. Regardless of BAX status, MSI-high cases were associated with longer survival. MSI, microsatellite instability; MSS, microsatellite stable.

Kaplan-Meier curves according to MSI status and TGFBR2 or BAX mononucleotide mutation in colorectal cancer.

Kaplan-Meier curves for colorectal cancer-specific survival (A) and overall survival (B), according to TGFBR2 mononucleotide mutation status. Regardless of TGFBR2 status, MSI-high cases were associated with longer survival. Kaplan-Meier curves for colorectal cancer-specific survival (C) and overall survival (D), according to BAX mononucleotide mutation status. Regardless of BAX status, MSI-high cases were associated with longer survival. MSI, microsatellite instability; MSS, microsatellite stable. The multivariate, stage-matched (stratified) Cox regression model initially included the TGFBR2 mutation or BAX mutation variable, sex, age at diagnosis, year of diagnosis, tumor location, body mass index, family history of colorectal cancer, tumor grade, CIMP, KRAS, BRAF, PIK3CA and LINE-1 methylation. A backward elimination with a threshold of p = 0.20 was used to select variables in the final models. Stage adjustment (I, II, III, IV, unknown) was done using the “strata” option in the SAS “proc phreg” command. CI, confidence interval; HR, hazard ratio; MSI, microsatellite instability; MSS, microsatellite stable. We compared colorectal cancer specific and overall survival between TGFBR2-mutated MSI-high cases and TGFBR2-wildtype MSI-high cases (or between BAX-mutated MSI-high cases and BAX-wild type MSI-high cases). There was no significant difference between the two groups ( ).

Discussion

We conducted this study to examine the prognostic significance of mononucleotide tract mutations in the coding regions of TGFBR2 or BAX in MSI-high colorectal cancers. We utilized two prospective cohort studies with a large number of clinically and molecularly well-annotated colorectal cancer cases with adequate follow-up. Our result showed that MSI-high tumors were associated with indolent tumor behavior regardless of TGFBR2 or BAX mononucleotide mutation status, independent of CIMP and other key tumor molecular biomarkers. Nonetheless, it may be of interest to examine interactions between these molecular alterations and dietary and lifestyle factors if there is a hypothesis in evolving science of molecular pathological epidemiology [42], [43]. It should be noted that small studies are more prone to “publication bias” than large studies [44]. This phenomenon of publication bias occurs because studies with null findings have a higher likelihood of being unwritten and unpublished compared to those with significant results. Compared to small studies (e.g., studies with a sample size of <200 cancers) with null data, large studies with null data are more likely published. As a result, large studies are less prone to publication bias than small studies. Furthermore, academic pressures might force investigators to design small studies which are easy to complete and get data for haste publications, which might contribute to bias [45], [46], [47]. Therefore, we should weigh more on large-scale studies when we evaluate the published literature on prognostic significance of any biomarker such as TGFBR2 or BAX mononucleotide mutation. Publishing null data in well-powered studies [44], [48], [49], [50], [51], [52] are important because publishing significant results in small underpowered studies leads to publication bias. Our data are generally consistent with some of previous studies [19], [22], [24], [25] ( ). Watanabe et al. [21] used stage II and III cases that underwent adjuvant chemotherapy, and reported that, TGFBR2 mutation was associated with improved 5-year overall survival among 73 MSI-high tumors. In another study [20], among 67 MSI-high tumors, BAX mutation was associated with poor prognosis. In an underpowered study by Fernández-Peralta et al. [23], among 16 MSI-high tumors, both TGFBR2 mutation and BAX mutation were associated with better prognosis. The largest study (total N = 1427; 170 MSI-high cancers) by Samowitz et al. [22] showed no prognostic role of TGFBR2 or BAX mutations among MSI-high colorectal cancer cases, in agreement with our current study - the second largest study to date and the only study which examined other key tumor molecular biomarkers such as CIMP, LINE-1 methylation and KRAS, BRAF and PIK3CA mutations. Studying somatic molecular changes and molecular correlates is important in cancer research towards personalized medicine [53], [54], [55], [56]. The CpG island methylator phenotype (CIMP) has been established as an epigenomic molecular classifier of colorectal cancer [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71]. In the past, Iacopetta et al. [20] showed no significant association between KRAS mutation and TGFBR2 mutation. We assessed the association between tumor molecular variables (CIMP, LINE-1, KRAS, BRAF and PIK3CA) and TGFBR2 mutation and did not find significant relation between TGFBR2 mutation and KRAS or BRAF mutation. Interestingly, we have found that, among MSI-high tumors, TGFBR2 mutation was associated with CIMP-high, independent of clinical and other molecular features. A recent study [72] has reported that genetic variants in the TGFB1 pathway related genes (MAPK1, RUNX1 and RUNX2) are associated with CIMP-high colon cancer. Further studies are needed to elucidate the exact mechanism of the relationship between CIMP and the TGFB1 pathway. There are limitations in this study. For example, data on cancer treatment were limited. Nonetheless, it is unlikely that chemotherapy use substantially differed according to TGFBR2 or BAX mutation status in tumor, since such data were typically unavailable for treatment decision making. As another limitation, beyond cause of mortality, data on cancer recurrences were unavailable in these cohort studies. Nonetheless, given median follow-up of over 11 years for censored cases, colorectal cancer-specific survival might be a reasonable surrogate of colorectal cancer-specific outcome. There are advantages in utilizing the database of the two prospective cohort studies, the Nurses' Health Study and the Health Professionals Follow-up Study, to examine prognostic significance of tumor biomarkers. Anthropometric measurements, family history, cancer staging, and other clinical, pathologic, and tumor molecular data were prospectively collected, blinded to patient outcome [26]. Cohort participants who developed cancer were treated at hospitals throughout the U.S., and thus more representative colorectal cancers in the U.S. population than patients in one to a few academic hospitals. There were no demographic difference between cases with tumor tissue analyzed and those without tumor tissue analyzed [26]. Finally, our rich tumor database enabled us to simultaneously assess pathologic and tumor molecular correlates and control for potential confounding by the tumor molecular features. In conclusion, our large tumor database has shown that, compared to MSS/MSI-low cases, MSI-high colorectal cancer is associated with longer cancer-specific survival, regardless of TGFBR2 or BAX mononucleotide tract mutation status. The importance of large-scale studies cannot be overemphasized because, compared to large studies, small studies are much more prone to publication bias, which can mislead clinical practice.
  72 in total

1.  CpG island methylator phenotype, microsatellite instability, BRAF mutation and clinical outcome in colon cancer.

Authors:  Shuji Ogino; Katsuhiko Nosho; Gregory J Kirkner; Takako Kawasaki; Jeffrey A Meyerhardt; Massimo Loda; Edward L Giovannucci; Charles S Fuchs
Journal:  Gut       Date:  2008-10-02       Impact factor: 23.059

2.  Microsatellite instability and loss of heterozygosity at chromosomal location 18q: prospective evaluation of biomarkers for stages II and III colon cancer--a study of CALGB 9581 and 89803.

Authors:  Monica M Bertagnolli; Mark Redston; Carolyn C Compton; Donna Niedzwiecki; Robert J Mayer; Richard M Goldberg; Thomas A Colacchio; Leonard B Saltz; Robert S Warren
Journal:  J Clin Oncol       Date:  2011-07-11       Impact factor: 44.544

3.  Precision and performance characteristics of bisulfite conversion and real-time PCR (MethyLight) for quantitative DNA methylation analysis.

Authors:  Shuji Ogino; Takako Kawasaki; Mohan Brahmandam; Mami Cantor; Gregory J Kirkner; Donna Spiegelman; G Mike Makrigiorgos; Daniel J Weisenberger; Peter W Laird; Massimo Loda; Charles S Fuchs
Journal:  J Mol Diagn       Date:  2006-05       Impact factor: 5.568

4.  CpG island methylator phenotype in colorectal cancer.

Authors:  M Toyota; N Ahuja; M Ohe-Toyota; J G Herman; S B Baylin; J P Issa
Journal:  Proc Natl Acad Sci U S A       Date:  1999-07-20       Impact factor: 11.205

5.  CpG island methylator phenotype (CIMP) of colorectal cancer is best characterised by quantitative DNA methylation analysis and prospective cohort studies.

Authors:  S Ogino; M Cantor; T Kawasaki; M Brahmandam; G J Kirkner; D J Weisenberger; M Campan; P W Laird; M Loda; C S Fuchs
Journal:  Gut       Date:  2006-01-11       Impact factor: 23.059

6.  Somatic frameshift mutations in the BAX gene in colon cancers of the microsatellite mutator phenotype.

Authors:  N Rampino; H Yamamoto; Y Ionov; Y Li; H Sawai; J C Reed; M Perucho
Journal:  Science       Date:  1997-02-14       Impact factor: 47.728

7.  Association of CTNNB1 (beta-catenin) alterations, body mass index, and physical activity with survival in patients with colorectal cancer.

Authors:  Teppei Morikawa; Aya Kuchiba; Mai Yamauchi; Jeffrey A Meyerhardt; Kaori Shima; Katsuhiko Nosho; Andrew T Chan; Edward Giovannucci; Charles S Fuchs; Shuji Ogino
Journal:  JAMA       Date:  2011-04-27       Impact factor: 56.272

8.  KRAS mutation in stage III colon cancer and clinical outcome following intergroup trial CALGB 89803.

Authors:  Shuji Ogino; Jeffrey A Meyerhardt; Natsumi Irahara; Donna Niedzwiecki; Donna Hollis; Leonard B Saltz; Robert J Mayer; Paul Schaefer; Renaud Whittom; Alexander Hantel; Al B Benson; Richard M Goldberg; Monica M Bertagnolli; Charles S Fuchs
Journal:  Clin Cancer Res       Date:  2009-11-24       Impact factor: 12.531

9.  Hypermethylator phenotype in sporadic colon cancer: study on a population-based series of 582 cases.

Authors:  Ludovic Barault; Céline Charon-Barra; Valérie Jooste; Mathilde Funes de la Vega; Laurent Martin; Patrick Roignot; Patrick Rat; Anne-Marie Bouvier; Pierre Laurent-Puig; Jean Faivre; Caroline Chapusot; Francoise Piard
Journal:  Cancer Res       Date:  2008-10-15       Impact factor: 12.701

10.  LINE-1 hypomethylation is inversely associated with microsatellite instability and CpG island methylator phenotype in colorectal cancer.

Authors:  Shuji Ogino; Takako Kawasaki; Katsuhiko Nosho; Mutsuko Ohnishi; Yuko Suemoto; Gregory J Kirkner; Charles S Fuchs
Journal:  Int J Cancer       Date:  2008-06-15       Impact factor: 7.396

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  22 in total

Review 1.  Prognostic role of tumor PIK3CA mutation in colorectal cancer: a systematic review and meta-analysis.

Authors:  Z B Mei; C Y Duan; C B Li; L Cui; S Ogino
Journal:  Ann Oncol       Date:  2016-07-19       Impact factor: 32.976

Review 2.  Role of BAX for outcome prediction in gastrointestinal malignancies.

Authors:  Filippo Pietrantonio; Pamela Biondani; Elisa Ciurlia; Giuseppe Fanetti; Anna Tessari; Gaia Bertarelli; Ilaria Bossi; Valeria Musella; Flavia Melotti; Maria Di Bartolomeo; Francesca Valvo; Alessandro Pellegrinelli; Massimo Milione; Federica Perrone; Filippo de Braud
Journal:  Med Oncol       Date:  2013-05-23       Impact factor: 3.064

Review 3.  Genetic status of KRAS influences Transforming Growth Factor-beta (TGF-β) signaling: An insight into Neuropilin-1 (NRP1) mediated tumorigenesis.

Authors:  Sneha Vivekanandhan; Debabrata Mukhopadhyay
Journal:  Semin Cancer Biol       Date:  2018-02-02       Impact factor: 15.707

Review 4.  Molecular and prognostic heterogeneity of microsatellite-unstable colorectal cancer.

Authors:  Jung Ho Kim; Gyeong Hoon Kang
Journal:  World J Gastroenterol       Date:  2014-04-21       Impact factor: 5.742

5.  Detection and differential diagnosis of colon cancer by a cumulative analysis of promoter methylation.

Authors:  Qiong Yang; Ying Dong; Wei Wu; Chunlei Zhu; Hui Chong; Jiangyang Lu; Dehai Yu; Libing Liu; Fengting Lv; Shu Wang
Journal:  Nat Commun       Date:  2012       Impact factor: 14.919

Review 6.  TGF-β Family Signaling in Tumor Suppression and Cancer Progression.

Authors:  Joan Seoane; Roger R Gomis
Journal:  Cold Spring Harb Perspect Biol       Date:  2017-12-01       Impact factor: 10.005

Review 7.  Rate of dissemination and prognosis in early and advanced stage colorectal cancer based on microsatellite instability status: systematic review and meta-analysis.

Authors:  James W T Toh; Kevin Phan; Faizur Reza; Pierre Chapuis; Kevin J Spring
Journal:  Int J Colorectal Dis       Date:  2021-02-18       Impact factor: 2.571

8.  Multispectral imaging reveals hyper active TGF-β signaling in colorectal cancer.

Authors:  Lei Yang; Zheng Liu; Jinjing Tan; Hongwei Dong; Xiaojing Zhang
Journal:  Cancer Biol Ther       Date:  2017-12-08       Impact factor: 4.742

Review 9.  CD133: a cancer stem cells marker, is used in colorectal cancers.

Authors:  Fei Ren; Wei-Qi Sheng; Xiang Du
Journal:  World J Gastroenterol       Date:  2013-05-07       Impact factor: 5.742

10.  BaxΔ2 is a novel bax isoform unique to microsatellite unstable tumors.

Authors:  Bonnie Haferkamp; Honghong Zhang; Yuting Lin; Xinyi Yeap; Alex Bunce; Juanita Sharpe; Jialing Xiang
Journal:  J Biol Chem       Date:  2012-08-21       Impact factor: 5.157

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