Skilled use of DNA polymorphisms as a tool for polygenic cancers

Identification of novel SNPs associated with risk and prognosis in patients with castration-resistant prostate cancer

AIM

Metabolism and transport play major roles in life-long exposure to endogenous and exogenous carcinogens. We therefore explored associations between polymorphisms in absorption, distribution, metabolism and elimination genes and the risk and prognosis of castration-resistant prostate cancer (CRPC).

MATERIALS & METHODS

A total of 634 genotypes were tested in 74 patients using the Affymetrix DMETv1.0 platform.

RESULTS

No relation to risk was found. Three SNPs were associated with CRPC prognosis in Caucasians: ABCB11 rs7602171G>A (p = 0.003; n = 30; hazard ratio [HR]: 0.307), GSTP1 rs1799811C>T (p = 0.001; n = 38; HR: 0.254) and SLC5A6 rs1395 (p = 0.004; n = 35; HR: 3.15). Two other polymorphisms among Caucasians were associated with interesting trends: ABCB4 rs2302387C>T (p = 0.039) and ABCC5 rs939339A>G (p = 0.018).

CONCLUSION

This exploratory study is the first to show that polymorphisms in several absorption, distribution, metabolism and elimination genes may be associated with CRPC prognosis.

Association of The Common CYP1A1*2C Variant (Ile462Val Polymorphism) with Chronic Myeloid Leukemia (CML) in Patients Undergoing Imatinib Therapy

OBJECTIVE

Cytochrome P450 is one of the major drug metabolizing enzyme families and its role in metabolism of cancer drugs cannot be less emphasized. The association be- tween single nucleotide polymorphisms (SNPs) in CYP1A1 and pathogenesis of chronic myeloid leukemia (CML) has been investigated in several studies, but the results observed vary based on varied risk factors. The objective of this study was to investigate the risk factors associated with the CYP1A1*2C [rs1048943: A>G] polymorphism in CML patients and its role in therapeutic response to imatinib mesylate (IM) affecting clinico-pathological parameters, in the Indian population.

MATERIALS AND METHODS

In this case-control study, CYP1A1*2C was analysed in CML patients. After obtaining approval from the Ethics Committee of oncology hospital, we collected blood samples from 132 CML patients and 140 matched controls. Genom- ic DNA was extracted and all the samples were analysed for the presence of the CYP1A1*2C polymorphism using allele-specific polymerase chain reaction, and we examined the relationship of genotypes with risk factors such as gender, age, phase of the disease and other clinical parameters.

RESULTS

We observed a significant difference in the frequency distribution of CYP1A1*2C genotypes AA (38 vs. 16%, P=0.0001), AG (57 vs. 78%, P=0.0002) and GG (5 vs. 6%, P=0.6635) between patients and controls. In terms of response to IM therapy, significant variation was observed in the frequencies of AA vs AG in major (33 vs 67%) and poor (62 vs 31%) hematological responders, and AA vs AG in major (34 vs. 65%) and poor (78 vs. 22%) cytogenetic responders. However, the patients with the GG homozygous genotype did not show any significant therapeutic outcome.

CONCLUSION

The higher frequency of AG in controls indicates that AG may play a protec- tive role against developing CML. We also found that patients with the AG genotype showed favorable treatment response towards imatinib therapy, indicating that this polymorphism could serve as a good therapeutic marker in predicting response to such therapy.

Relationships between familial risks of cancer and the effects of heritable genes and their SNP variants

Familial risks for cancer can be used in many ways in guiding gene identification efforts and, more broadly, in understanding cancer etiology. Gene identification efforts may be properly designed and targeted if the familial risks are well characterized and the mode of inheritance is identified. Single nucleotide polymorphisms (SNPs) are extensively used in case-control studies of practically all cancer types. They are used for the identification of inherited cancer susceptibility genes and those that may interact with environmental factors. However, being genetic markers, they are applicable only on heritable conditions, which is often a neglected fact. Based on the data in the nationwide Swedish Family-Cancer Database, we review familial risks for all main cancers and discuss the evidence for a heritable component in cancer. The available evidence, including differences in cancer incidence between regions and temporal changes within regions, indicates that cancer is mainly an environmental disease, with a minor heritable etiology. The large environmental component will hamper the success of SNP-based genetic association studies. Empirical familial risks should be used to evaluate the feasibility of such studies. We develop figures for the assessment of genetic parameters based on familial risks. Such data are helpful in the estimation of the expected genetic effects in cancer. Overall, we consider the likelihood of a successful application of SNPs in gene-environment studies small, unless established environmental risk factors are tested on proven candidate genes.

Genotype, phenotype and cancer: role of low penetrance genes and environment in tumour susceptibility

Role of heredity and lifestyle in sporadic cancers is well documented. Here we focus on the influence of low penetrance genes and habits, with emphasis on tobacco habit in causing head and neck cancers. Role of such gene-environment interaction can be well studied in individuals with multiple primary cancers. Thus such a biological model may elucidate that cancer causation is not solely due to genetic determinism but also significantly relies on lifestyle of the individual.

Single nucleotide polymorphisms (SNPs) are inherited from parents and they measure heritable events

Single nucleotide polymorphisms (SNPs) are extensively used in case-control studies of practically all cancer types. They are used for the identification of inherited cancer susceptibility genes and those that may interact with environmental factors. However, being genetic markers, they are applicable only on heritable conditions, which is often a neglected fact. Based on the data in the nationwide Swedish Family-Cancer Database, we review familial risks for all main cancers and discuss the evidence for a heritable component in cancer. The available evidence is not conclusive but it is consistent in pointing to a minor heritable etiology in cancer, which will hamper the success of SNP-based association studies. Empirical familial risks should be used as guidance for the planning of SNP studies. We provide calculations for the assessment of familial risks for assumed allele frequencies and gene effects (odds ratios) for different modes of inheritance. Based on these data, we discuss the gene effects that could account for the unexplained proportion of familial breast and lung cancer. As a conclusion, we are concerned about the indiscriminate use of a genetic tool to cancers, which are mainly environmental in origin. We consider the likelihood of a successful application of SNPs in gene-environment studies small, unless established environmental risk factors are tested on proven candidate genes.

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

Association of NQO1 polymorphism with spontaneous breast cancer in two independent populations

Eight different single-nucleotide polymorphisms (SNPs) in six different genes were investigated for possible association with breast cancer. We used a case–control study design in two Caucasian populations, one from Tyrol, Austria, and the other from Prague, Czech Republic. Two SNPs showed an association with breast cancer: R72P inTP53 and P187S in NQO1. Six SNPs, Q356R and P871L in BRCA1, N372H in BRCA2, C112R (E4) and R158C (E2) in ApoE and C825T in GNB3, did not show any sign of association. The P187S polymorphism in NQO1 was associated with breast cancer in both populations from Tyrol and Prague with a higher risk for carriers of the 187S allele. Combining the results of the two populations, we observed a highly significant difference (P=0.0004) of genotype and allele frequencies (odds ratio (OR)=1.46; 95% confidence interval (CI) 1.16–1.85; P=0.001) and of the homozygote ratio (OR=3.8; 95% CI 1.73–8.34; P=0.0001). Combining the two ‘candidate’ SNPs (P187S and R72P) revealed an increased risk for breast cancer of double heterozygotes (P187S/R72P) of the NQO1 and TP53 genes (OR=1.88; 95% CI 1.13–3.15; P=0.011), suggesting a possible interaction of these two loci.

Genetic epidemiology of cancer: From families to heritable genes

A reliable determination of familial risks for cancer is important for clinical counseling, prevention and understanding cancer etiology. Family-based gene identification efforts may be targeted if the risks are well characterized and the mode of inheritance is identified. Medically verified data on familial risks have not been available for all types of cancer but they have become available through the use of the nationwide Swedish Family-Cancer Database, which includes all Swedes born in 1932 and later with their parents, totaling over 10 million individuals. Over 150 publications have emanated from this source. The familial risks of cancer have been characterized for all main cancers and the contribution of environmental and heritable effects to the familial aggregation has been assessed. Furthermore, the mode of inheritance has been deduced by comparing risks from parental and sibling probands. Examples are shown on familial clustering of cancers, for which heritable susceptibility genes are yet unknown, such as squamous cell carcinoma of the skin, intestinal carcinoids, thyroid papillary tumors, brain astrocytomas and pituitary adenomas. Some common cancers, such as lung and kidney cancers, appear to show an early-onset recessive component because familial risks among siblings are much higher than those in families where parents are probands. Many of the cancer sites showing high familial risks lack guidelines for clinical counseling or action level. In conclusion, we recommend that any future gene identification efforts, either using linkage or association designs, devise their strategies based on data from family studies. Clinical genetic counseling would benefit from reviewing established familial risks on all main types of cancer.

Genetic susceptibility, biomarker respones, and cancer

A large number of studies have reported associations between polymorphisms of xenobiotic-metabolizing enzymes (XMEs) and various cancers. However, the carcinogenic exposures behind such findings have usually been unclear. Information on susceptibility to specific carcinogens could better be obtained by examining situations where the exposure and the endpoint studied are nearer in time, i.e., by studying biomarkers of carcinogen exposure and early (genotoxic) effect in exposed humans. For example, analyses of DNA adducts and cytogenetic endpoints have indicated an increased susceptibility of glutathione S-transferase M1 (GSTM1) null genotype to genotoxicity of tobacco smoking, supporting the view that the associations of the GSTM1 null genotype with bladder and lung cancer are partly related to smoking. In vitro genotoxicity studies with human cells offer an experimental tool that can be used, within the limits of the cell systems, to predict individual sensitivity and genotype-carcinogen interactions. In vitro sensitivity to the genotoxicity of 1,2:3,4-diepoxybutane, an epoxide metabolite of 1,3-butadiene has clearly been shown to depend on GSTT1 genotype, which has also been implicated to modify, along with GSTM1 genotype, the in vitro genotoxicity of 1,2-epoxy-3-butene, another epoxide metabolite of 1,3-butadiene. These genotypes appear to modulate the excretion of 1,3-butadiene-specific mercapturic acids, and influence genotoxicity biomarker levels in 1,3-butadiene-exposed workers. The excretion of specific mercapturic acids (PHEMA) in workers exposed to styrene has clearly been shown to depend on GSTM1 genotype, and GSTT1 genotype seems to modulate the excretion of one PHEMA diastereoisomer. These genotypes have also been implicated to modulate the in vitro genotoxicity of styrene. In general, the genetic polymorphisms potentially important for biomarker response largely depend on the exposing agent, biological material examined, and ethnicity of the population under study. Individual exposure level may vary a lot, and a reliable estimate of the exposure is essential for correct interpretation of genotype-exposure interaction. Besides XME polymorphisms, any polymorphisms that affect cellular response to DNA damage could, in principle, modify individual sensitivity to genotoxins. For instance, those concerning DNA repair proteins are presently being studied by many laboratories.