Original Article | Published:

The cyclin D1 (CCND1) rs9344 G>A polymorphism predicts clinical outcome in colon cancer patients treated with adjuvant 5-FU-based chemotherapy

The Pharmacogenomics Journal volume 14, pages 130134 (2014) | Download Citation

Abstract

Recent evidence indicates a potential prognostic and predictive value for germline polymorphisms in genes involved in cell cycle control. We investigated the effect of cyclin D1 (CCND1) rs9344 G>A in stage II/III colon cancer patients and validated the findings in an independent study cohort. For evaluation and validation set, a total of 264 and 234 patients were included. Patients treated with 5-fluorouracil-based chemotherapy, carrying the CCND1 rs9344 A/A genotype had significantly decreased time-to-tumor recurrence (TTR) in univariate analysis and multivariate analysis (hazard ratio (HR) 2.47; 95% confidence interval (CI) 1.16–5.29; P=0.019). There was no significant association between CCND1 rs9344 G>A and TTR in patients with curative surgery alone. In the validation set, the A allele of CCND1 rs9344 G>A remained significantly associated with decreased TTR in univariate and multivariate analyses (HR 1.94; 95% CI 1.05–3.58; P=0.035). CCND1 rs9344 G>A may be a predictive and/or prognostic biomarker in stage II/III colon cancer patients, however, prospective trials are warranted to confirm our findings.

Introduction

Colon cancer is the second leading cause of cancer death in Europe and the United States. Approximately 50% of patients with colon cancer develop synchronous or metachronous metastases. The 5-year survival rate of colon cancer patients with metastatic disease is <10%.1, 2

In current practice, the majority of colon cancer patients do not benefit from adjuvant treatment, either because they were cured by surgery alone or because they will relapse despite adjuvant treatment. It is therefore essential to identify those patients who will benefit from adjuvant therapy, sparing others needless toxicity and the financial burden of chemotherapy that will not work. There is significant clinical interest in the identification of prognostic and predictive biomarkers that will improve clinical outcome through patient selection.3, 4

Currently, the tumor-node-metastasis stage is the only proven prognostic marker to aid in the identification of patients with aggressive disease.5, 6, 7 The once prevailing ‘one size fits all’ approach of cancer therapy is now becoming a relict of the past. For example, microsatellite instability (MSI) is considered to be a strong and validated prognostic marker in stage II colon cancer, and it is currently the only such biomarker in this setting. In the appropriate clinical setting, it has been advocated that MSI data may be used in clinical decision making, particularly in stage II colon cancer, for which a favorable outcome of the patients with MSI-high tumors suggests that these patients should not receive adjuvant chemotherapy.8

As our knowledge of the molecular characteristics of patients and this disease has expanded, the significant heterogeneity that exists in both has become more apparent. The existence of prognostic and predictive biomarkers would provide a strategy for stratifying colon cancer patients for risk of tumor relapse and chemo resistance, which in turn would allow treatment options to be tailored to the individual.

Current evidence indicates potential prognostic and predictive value for germline polymorphisms in genes involved in cell cycle control.9 Essential regulators of cell cycle progression of the G1/S phase are cyclin-dependent kinases (CDKs).10 CDKs are heterodimeric complexes composed of a catalytic subunit and a regulatory subunit, called cyclin.11 D-type cyclins (D1, D2 and D3) and their catalytic partners CDK4 or CDK6 function as critical integrators of mitogenic signals for cells.11 The active CDK/cyclin complex phosphorylates and thereby inactivates the tumor-suppressor protein retinoblastoma leading to transcription of proteins necessary for progression through S phase.12 Cyclin D1 (CCND1) overexpression disrupts the normal cell cycle and leads to early passage through G1/S transition.13, 14 A common and functional single-nucleotide polymorphism (CCND1 rs9344 G>A [G870A]) at codon 242 affects splicing of CCND1 transcript and causes abnormal cell proliferation.15, 16

In this study, we investigated the prognostic and predictive effect of CCND1 rs9344 G>A in patients with stage II and III colon cancer. In a second step, the findings were validated in an independent study cohort.

Materials and methods

Eligible patients

For the evaluation set, a total of 264 patients with histologically confirmed stage II and III colon cancer treated at the Division of Clinical Oncology, Department of Medicine, Medical University of Graz (MUG) from 2000 to 2009 were included in this study. Stage III and high-risk stage II patients were treated with 5-fluorouracil (5-FU)-based chemotherapy (n=192). For the validation set, a total of 234 patients with histologically confirmed stage III and high-risk stage II colon cancer treated at the Norris Comprehensive Cancer Center/University of Southern California (NCCC/USC) or the Los Angeles County/USC-Medical Center (LAC/USCMC) from 1987 to 2007 were included. All patients in the validation set were treated with 5-FU-based chemotherapy.

High-risk stage II colon cancer patients were defined if they presented with at least one of the following features: lymph node sampling <12; poorly differentiated tumor; vascular, lymphatic or perineural invasion; tumor presentation with obstruction or perforation and pT4.

All patients were included in the colon cancer surveillance program of MUG, NCCC/USC or LAC/USCMC, providing history and physical examination and carcinoembryonic antigen determination every 3 months for 3 years and every 6 months at years 4 and 5 after surgery, colonoscopy at year 1 and thereafter every 3–5 years and computed tomography scans of chest and abdomen every 6 months for the first 3 years.

Patient data were collected retrospectively through chart review. Whole blood was collected at the time of diagnosis and stored at −80 °C. The study was approved by the Institutional Review Boards of MUG and USC and all study participants signed informed consent for the analysis of molecular correlates.

Genotyping

Genomic DNA was extracted from whole blood samples using the QIAmp-kit (Qiagen, Hilden, Germany). In the evaluation set, genotyping was performed using a 5 V-nuclease assay (TaqMan, Vienna, Austria) with primers and probes designed and manufactured using Applera’s ‘Assay by-Design’ custom service (Applied Biosystems, Vienna, Austria). PCR and evaluation of fluorescence data were performed as recently described.17 For each sample, one negative control containing water instead of DNA was added to check for contamination. In the validation set, genotyping was performed by direct DNA sequencing. For genotyping quality control purposes, a total of 10% of the samples were re-analyzed in both study sets. The investigators responsible for genotyping were blinded to the clinical data.

Statistical analysis

The end point of the study was time-to-tumor recurrence (TTR). TTR was calculated from the date of diagnosis of colon cancer to the date of the first observation of tumor recurrence. TTR was censored at the time of death or at the last follow-up if the patient remained tumor recurrence-free at that time. Allelic distribution of CCND1 rs9344 G>A was tested for deviation from Hardy–Weinberg equilibrium using χ2-test. The true mode of inheritance of CCND1 rs9344 G>A is not established yet and we assumed an additive, dominant or recessive genetic model where appropriate. The association of CCND1 rs9344 G>A with TTR was analyzed using Kaplan–Meier curves and log-rank test. In the multivariate Cox-regression analysis, the model was adjusted for stage and type of adjuvant therapy. Case-wise deletion for missing polymorphisms was used in univariate and multivariate analyses. All analyses were performed using SAS 9.2 (SAS Institute, Cary, NC, USA).

Results

The baseline characteristics of the evaluation and validation set are summarized in Table 1. In the evaluation set, the median age at time of diagnosis was 62.2 years (range 25–83), with a median follow-up time of 53.5 months (range 7–125). In the validation set, the median age at time of diagnosis was 59 years (range 22–87), with a median follow-up time of 52.8 months (range 4.8–201.6). Median overall survival has not been reached yet in both study cohorts.

Table 1: Baseline patient characteristics of MUG and USC patients

The genotyping quality control by TaqMan and direct DNA sequencing provided a genotype concordance of 99%. Genotyping was successful in 97% of MUG patients (wild type: 77, heterozygous mutant: 129 and homozygous mutant: 50) and 72% of USC patients (wild type: 51, heterozygous mutant: 73 and homozygous mutant: 44). In failed cases, genotyping was not successful because of limited quantity and/or quality of extracted genomic DNA. The allelic frequencies of CCND1 rs9344 G>A were within the probability limits of Hardy–Weinberg equilibrium (MUG cohort: P=0.96, USC cohort: P=0.09).

In the evaluation set, we found no significant association between CCND1 rs9344 G>A and TTR in both univariate and multivariate analysis (hazard ratio (HR) 0.87; 95% confidence interval (CI) 0.58–1.31; P=0.513; HR 0.84; 95% CI 0.55–1.28, P=0.414, respectively). However, in patients treated with 5-FU-based chemotherapy, the CCND1 rs9344 A/A genotype was significantly associated with decreased TTR in the univariate analysis. Patients with at least one CCND1 rs9344 G allele had a median TTR of 102.6 months. In contrast, patient carrying the CCND1 rs9344 A/A genotype showed a median TTR of 84.8 months (HR 2.14; 95% CI 1.02–4.47; P=0.044; Figure 1). There was no significant interaction between CCND1 rs9344 and clinical stage on TTR (P for interaction=0.12) In the multivariate analysis, the CCND1 rs9344 A/A genotype remained significantly associated with decreased TTR (HR 2.47; 95% CI 1.16–5.29; P=0.019). There was no significant association between CCND1 rs9344 G>A and TTR in patients with curative surgery alone in univariate or multivariate analysis (HR 0.70; 95% CI 0.2–2.52; P=0.587; HR 0.45; 95% CI 0.09–2.19; P=0.323, respectively; Figure 2).

Figure 1
Figure 1

Time-to-tumor recurrence (TTR) by cyclin D1 (CCND1) rs9344 G>A (recessive genetic model) in Medical University of Graz (MUG) patients treated with 5-fluorouracil (5-FU)-based chemotherapy.

Figure 2
Figure 2

Time-to-tumor recurrence (TTR) by cyclin D1 (CCND1) rs9344 G>A (recessive genetic model) in Medical University of Graz (MUG) patients treated with surgery alone.

In the validation set using a recessive genetic model (G/G and G/A vs A/A), we found no significant association between CCND1 rs9344 G>A and TTR in both univariate and multivariate analysis (HR 0.97; 95% CI 0.56–1.67; P=0.91; HR 0.93; 95% CI 0.53– 1.64, P=0.81, respectively). However, in a dominant model (G/G vs G/A and A/A) the A allele of CCND1 rs9344 G>A was significantly associated with decreased TTR in univariate analysis. Patients harboring the CCND1 rs9344 G/G genotype showed a median TTR of 128.4 months, compared with 60 months for patients carrying at least one A allele (HR 1.91; 95% CI 1.04–3.51; P=0.032; Figure 3). There was no significant interaction between CCND1 rs9344 and clinical stage on TTR (P for interaction=0.13) In the multivariate analysis, the CCND1 rs9344 A allele remained significantly associated with decreased TTR (HR 1.94; 95% CI 1.05–3.58; P=0.035).

Figure 3
Figure 3

Time-to-tumor recurrence (TTR) by cyclin D1 (CCND1) rs9344 G>A (dominant genetic model) in University of Southern California (USC)/USC-Medical Center (USCMC) patients treated with 5-fluorouracil (5-FU)-based chemotherapy.

To investigate if the differences of allele frequencies between ethnicities (Ensembl genome browser, population genetics, rs9344) within the validation cohort cause the significant associations for different genetic models in the evaluation and validation cohort, we restricted the validation cohort to Caucasians only (n=123). However, we found no significant associations between CCND1 rs9344 G>A and TTR in an additive (HR 1.16; 95% CI 0.48–2.76; P=0.24), recessive (HR 0.79; 95% CI 0.41–1.55; P=0.49) and dominant genetic model (HR 1.50; 95% CI 0.72–3.13; P=0.27) among Caucasians in the validation set.

Discussion

In this study, the prognostic and predictive effect of CCND1 rs9344 G>A in patients with stage II and stage III colon cancer was investigated. The results indicate a potential predictive effect of CCND1 rs9344 G>A in patients with colon cancer treated with adjuvant 5-FU-based chemotherapy.

Betticher et al.15 showed that the CCND1 rs9344 G>A polymorphism, located in the splicing region of exon 4, leads to alternate splicing of CCND1 mRNA into two transcripts. The altered transcript-b is primarily encoded by the variant allele A, whereas the wild-type mainly encodes transcript-a.15 The main difference affects the C-terminal region, which is responsible for rapid intracellular degradation.18 Variant transcript-b results in increased CCND1 because of a prolonged half-life.15 High CCND1 protein levels have been associated with decreased survival in various malignancies.19, 20, 21, 22, 23

There are limited data investigating CCND1 as a prognostic biomarker in colon cancer, however, overexpression of CCND1 protein was associated with decreased disease-free survival and overall survival in a small study including 123 patients with colorectal cancer.24 We found no association between CCND1 rs9344 G>A and TTR in patients treated with curative surgery alone, indicating no prognostic effect in our study cohort. Our results are in line with a study by McKay et al.25 showing no influence of CCND1 rs9344 G>A on clinical outcome in patients with colorectal cancer.

A growing number of studies evaluated the association between CCND1 rs9344 G>A and chemoresistance in various tumor entities. Li et al.26 investigated CCND1 isoforms in mediating response to DNA-damaging agents by treating human kidney cancer, colorectal cancer and breast cancer cells with doxorubicin or ionizing radiation and showed that transcript-a (encoded by the wild-type G-allele) was more likely to induce cell cycle arrest and double-stranded DNA breaks in contrast to transcript-b. Gautschi et al.27 investigated CCND1 rs9344 G>A in patients with non-small cell lung cancer and found that patients carrying the A-allele show a significant lack of response to platinum-based chemotherapy. In patients with metastatic colon cancer refractory to irinotecan, enrolled in a randomized controlled trial that compared bevacizumab plus cetuximab vs bevacizumab plus cetuximab plus irinotecan, the A/A genotype has been associated with a significant shorter TTR (6.8 vs 8.4 months; P=0.001) in the irinotecan group.28 In third-line therapy setting, patients with metastatic colon cancer treated with cetuximab alone harboring the A/A genotype showed a significant shorter median overall survival compared with those with any G-allele (2.3 vs 8.7 months; P=0.019).29 This is in line with our results showing a decreased TTR in patients treated with 5-FU-based chemotherapy carrying the CCND1 rs9344 A/A genotype.

Interestingly, patients treated at the MUG harboring a heterozygote genotype tended, in their clinical outcome, to be patients carrying the wild-type allele. In contrast, heterozygous patients treated at USC tended to be patients carrying the homozygous mutant genotype. When we validated our finding in an independent study cohort, we found no significant associations in the recessive genetic model, but in a dominant model. To clarify if this finding is dependent on differences of allele frequencies between ethnicities within the study cohort, we restricted the validation cohort to Caucasians only, but found no significant association between CCND1 rs9344 G>A and TTR in any genetic model. We therefore concluded that there was a selection bias in our retrospective cohort study and this may cause the significant associations in different genetic models (recessive model in the evaluation set and dominant model in USC cohort). This conclusion is further supported by the clinical outcome differences in both study cohorts (Figures 1 and 3).

This study has a number of limitations that need to be considered. MSI status was not available in our study cohorts, and therefore no conclusion can be drawn regarding the value of CCND1 rs9344 G>A above and beyond that offered by MSI status, which is now routinely performed in many centers for patients with colorectal cancer. Moreover, because of the retrospective design of our study, a selection bias cannot be fully excluded. Median OS has not been reached yet in either study cohort, therefore no statistical association between the polymorphism and OS could be performed.

In conclusion, CCND1 rs9344 G>A may be a predictive and/or prognostic biomarker in stage II/III colon cancer patients. Larger and prospective study cohorts are warranted to clarify these findings.

References

  1. 1.

    , , , , , et al. Cancer statistics, 2008. CA Cancer J Clin 2008; 58: 71–96.

  2. 2.

    , . Cancer incidence and mortality in Europe, 2004. Ann Oncol 2005; 16: 481–488.

  3. 3.

    , , , , , et al. Prognostic and predictive biomarkers in resected colon cancer: current status and future perspectives for integrating genomics into biomarker discovery. Oncologist 2010; 15: 390–404.

  4. 4.

    , , , , , et al. Colorectal cancer. Lancet 2010; 375: 1030–1047.

  5. 5.

    , , , , , et al. Pooled analysis of fluorouracil-based adjuvant therapy for stage II and III colon cancer: who benefits and by how much? J Clin Oncol 2004; 22: 1797–1806.

  6. 6.

    , . Molecular predictive and prognostic markers in colon cancer. Cancer Treat Rev 2010; 36: 550–556.

  7. 7.

    , , , , , et al. Colon cancer survival is associated with increasing number of lymph nodes analyzed: a secondary survey of intergroup trial INT-0089. J Clin Oncol 2003; 21: 2912–2919.

  8. 8.

    , . Clinical implications of microsatellite instability in sporadic colon cancers. Curr Opin Oncol 2009; 21: 369–373.

  9. 9.

    , , , , , . Germline genetic variation, cancer outcome, and pharmacogenetics. J Clin Oncol 2010; 28: 4029–4037.

  10. 10.

    . The role of p34 kinases in the G1 to S-phase transition. Annu Rev Cell Biol 1992; 8: 529–561.

  11. 11.

    . Cycling to cancer with cyclin D1. Cancer Biol Ther 2002; 1: 226–231.

  12. 12.

    , . Role of the retinoblastoma protein in the pathogenesis of human cancer. J Clin Oncol 1997; 15: 3301–3312.

  13. 13.

    , , , , . Cyclin d1 overexpression sensitizes breast cancer cells to fenretinide. Clin Cancer Res 2003; 9: 1877–1884.

  14. 14.

    , , , , , et al. Cyclin D1 overexpression in bronchial epithelia of patients with lung cancer is associated with smoking and predicts survival. J Clin Oncol 2003; 21: 2085–2093.

  15. 15.

    , , , , , . Alternate splicing produces a novel cyclin D1 transcript. Oncogene 1995; 11: 1005–1011.

  16. 16.

    , , , . Cyclin D1-mediated inhibition of repair and replicative DNA synthesis in human fibroblasts. Genes Dev 1994; 8: 1627–1639.

  17. 17.

    , , , , , et al. Association of interleukin-10 gene variation with breast cancer prognosis. Breast Cancer Res Treat 2010; 119: 701–705.

  18. 18.

    , , . Amino acid sequences common to rapidly degraded proteins: the PEST hypothesis. Science 1986; 234: 364–368.

  19. 19.

    , , , , , . Overexpression of cyclin D1 correlates with recurrence in a group of forty-seven operable squamous cell carcinomas of the head and neck. Cancer Res 1995; 55: 975–978.

  20. 20.

    , , , , , et al. CCND1 G870A polymorphism contributes to breast cancer susceptibility: a meta-analysis. Breast Cancer Res Treat 2009; 116: 571–575.

  21. 21.

    , , , , , et al. Cyclin D1 polymorphism and expression in patients with squamous cell carcinoma of the head and neck. Am J Pathol 2001; 159: 1917–1924.

  22. 22.

    , , , , , et al. Overexpression of cyclin D1 occurs in both squamous carcinomas and adenocarcinomas of the esophagus and in adenocarcinomas of the stomach. Hum Pathol 1999; 30: 1087–1092.

  23. 23.

    , , , , , . Cyclin D1 overexpression related to retinoblastoma protein expression as a prognostic marker in human oesophageal squamous cell carcinoma. Br J Cancer 1998; 77: 92–97.

  24. 24.

    , , , , , et al. Cyclin D1 overexpression and prognosis in colorectal adenocarcinoma. Oncology 1998; 55: 145–151.

  25. 25.

    , , , , , et al. Cyclin D1 protein expression and gene polymorphism in colorectal cancer. Aberdeen Colorectal Initiative. Int J Cancer 2000; 88: 77–81.

  26. 26.

    , , , , , et al. Alternative cyclin D1 splice forms differentially regulate the DNA damage response. Cancer Res 2010; 70: 8802–8811.

  27. 27.

    , , , , , et al. Cyclin D1 (CCND1) A870G gene polymorphism modulates smoking-induced lung cancer risk and response to platinum-based chemotherapy in non-small cell lung cancer (NSCLC) patients. Lung Cancer 2006; 51: 303–311.

  28. 28.

    , , , , , et al. Molecular predictors of combination targeted therapies (cetuximab, bevacizumab) in irinotecan-refractory colorectal cancer (BOND-2 study). Anticancer Res 2010; 30: 4209–4217.

  29. 29.

    , , , , , et al. Cyclin D1 and epidermal growth factor polymorphisms associated with survival in patients with advanced colorectal cancer treated with Cetuximab. Pharmacogenet Genomics 2006; 16: 475–483.

Download references

Acknowledgements

This work was supported by the START research grant of the Medical University of Graz and the Daniel Butler Memorial Fund.

Author information

Author notes

    • G Absenger
    •  & L Benhaim

    These authors contributed equally to this work.

Affiliations

  1. Division of Clinical Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria

    • G Absenger
    • , J Szkandera
    • , M Pichler
    • , M Stotz
    • , H Samonigg
    •  & A Gerger
  2. Research Unit Genetic Epidemiology and Pharmacogenetics, Medical University of Graz, Graz, Austria

    • G Absenger
    • , J Szkandera
    • , M Stotz
    • , W Renner
    •  & A Gerger
  3. Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    • L Benhaim
    • , W Zhang
    • , D Yang
    • , M J Labonte
    •  & H-J Lenz
  4. Clinical Institute of Medical and Laboratory Diagnostics, Medical University of Graz, Graz, Austria

    • W Renner
  5. USC Center for Molecular Pathways and Drug Discovery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    • H-J Lenz

Authors

  1. Search for G Absenger in:

  2. Search for L Benhaim in:

  3. Search for J Szkandera in:

  4. Search for W Zhang in:

  5. Search for D Yang in:

  6. Search for M J Labonte in:

  7. Search for M Pichler in:

  8. Search for M Stotz in:

  9. Search for H Samonigg in:

  10. Search for W Renner in:

  11. Search for A Gerger in:

  12. Search for H-J Lenz in:

Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to A Gerger.

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/tpj.2013.15

Further reading