LITHUANIAN UNIVERSITY OF HEALTH SCIENCES

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LITHUANIAN UNIVERSITY OF HEALTH

SCIENCES

Institute of Oncology, Oncology Research laboratory

Dor Monosevich

DNA Sequence Variations rs2228611 and rs2228612 in DNA Methyltransferases DNMT1 and their Effect on Breast Cancer Pathomorphological Characteristics and Patient

Prognosis.

Master’s Thesis

Thesis Supervisor: Prof. Rasa Ugenskiene

Faculty of Medicine

Kaunas, 2021

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1. SUMMARY

Author: Dor Monosevich.

Scientific Supervisor: Prof. Rasa Ugenskiene.

Research Title: DNA Sequence Variations rs2228611 and rs2228612 in DNA Methyltransferases

DNMT1 and their Effect on Breast Cancer Pathomorphological Characteristics and Patient

Prognosis.

Aim: The aim of the research is to identify DNA sequence variation in DNMT1 and their effect on

tumor phenotype and breast cancer patient prognosis.

Objectives of Study:

• To examine the distribution of alleles and genotypes in DMNT1 rs2228611 and rs2228612. • To analyze the association between DNMT1 variants and tumor pathomorphological

characteristics.

• To determine the relationship between DNA sequences variants in DNMT1 and breast cancer prognosis.

Methodology: Polymerase chain reaction (PCR) was performed for gene amplification, than the

amplicons were digested using restriction mix containing different endonucleases. All the PCR-RFLP products were separated using agarose gel electrophoresis. The statistical data analysis was performed with SPSS program.

Results: In DNMT1 (rs2228611) polymorphism patients with AG and GG genotype had lower

probability of tumor vascular infiltration. In DNMT1 (rs2228612) polymorphism the association between G allele and lymph node status was observed. The non-carriers of G allele were more likely to have positive lymph nodes than the carriers of G allele. None of the polymorphisms showed any significant association with overall survival (OS), progression-free survival (PFS) and metastasis-free survival (MFS).

Conclusions:

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The distribution of alleles and genotypes in rs2228612 and rs2228611 polymorphisms was

as follows:

• rs2228612 polymorphism A allele-94.6%, G allele-5.4%, AA-89.1% and AG-10.9%. • rs2228611 polymorphism A allele-49%, G allele-51%, AA-24.3%, AG-49.5%,

GG-26.2%.

2. The non-carriers of G allele in rs2228512 polymorphism had 5.3 times higher risk of

positive lymph nodes than the carrier. Patients with AG and GG genotypes had lower probability of tumor vascular infiltration than patients with AA genotype.

3. There were no significant associations between rs2228612, rs2228611 and patient OS,

PFS and MFS in genotype and allelic models determined.

Recommendations: More research on the subject is needed as it can provide us with additional

information on the disease progression and/or prognosis and help us in the future for more individualized patient approach.

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1. SANTRAUKA

Autorius: Dor Monosevich. Vadovas: Prof. dr. Rasa Ugenskienė.

Pavadinimas: DNR metiltransferazės DNMT1 sekos variantai rs2228611, rs2228612 ir jų

reikšmė krūties vėžio patomorfologinėms charakteristikoms ir pacientų prognozei.

Tyrimo tikslas: Tyrimo tikslas yra nustatyti DNMT1 sekos variantus ir jų poveikį naviko fenotipui

ir krūties vėžio paciento prognozei.

Uždaviniai:

• Ištirti DMNT1 rs2228611 ir rs2228612 alelių ir genotipų pasiskirstymą. • Išanalizuoti DNMT1 variantų ir naviko patomorfologinių parametrų sąsajas. • Nustatyti DNMT1 variantų ryšį su krūties vėžio prognoze.

Tyrimo metodika: Genų amplifikacija atlikta polimerazės grandininė reakcija (PGR), o

polimorfizmai tirti taikant restrikcinių fragmenų ilgio polimorfizmo (RFLP) analizės metodiką. Visi PCR-RFLP produktai buvo atskirti naudojant agarozės gelio elektroforezę. Statistinė duomenų analizė atlikta taikant SPSS paketą.

Rezultatai: DNMT1 (rs2228611) polimorfizmo AG ir GG genotipo pacientams buvo mažesnė

naviko kraujagyslių infiltracijos tikimybė. Nustatytas DNMT1 (rs2228612) G alelio ryšys su sritinių limfmazgių būkle. Pacientams be G alelio dažniau buvo nustatyti teigiami limfmazgiai palyginus su G alelį turinčiais pacientais. Nei vienas iš polimorfizmų neparodė statistiškai reikšmingo ryšio su bendru išgyvenamumu, išgyvenimu be ligos progresavimo ir išgyvenamumu be metastazių.

Išvados:

1. Nustatyta rs2228612 ir rs2228611 alelių ir genotipų pasiskirstyms:

• rs2228612 polimorfizmo A alelis – 94,6%, G alelis – 5,4%, AA – 89,1% ir AG – 10,9%.

• rs2228611 palomorfizmo A alelis - 49%, G alelis - 51%, AA – 24,3%, AG – 49,5%, GG – 26,2%.

2. Pacientams neturintiems G alelio rs2228512 polimorfizme nustatyta 5,3 karto didesnė teigiamų limfmazgių tikimybė negu pacientams su G aleliu. Pacientai su AG ir GG genotipu turėjo mažesnę naviko kraujagyslinės infiltracijos tikimybę negu pacientai su AA genotipu.

3. Sasajų tarp rs2228612, rs2228611 polimorfizmų ir pacientų bendro išgyvenamumo,

išgyvenimo be ligos progresavimo ir išgyvenamumo be metastazių nenustatyta.

Rekomendacijos: Svarbu atlikti daugiau tyrimų šioje srityje, nes tai gali suteikti mums

papildomos informacijos apie ligos progresavimą ir (arba) prognozę ir ateityje prisidėti prie individualizuoto požiūrio į pacientą.

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2. ACKNOWLEDGMENTS

I am grateful for the help and guidance of Rasa Ugenskiene from the Institute of Oncology, Lithuanian University of Health Sciences. I would also like to thank the patients for the participation in this research.

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3. CONFLICTS OF INTEREST

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4. PERMISSION OF ETHICS COMMITTEE

The study research protocol was approved by Kaunas Regional Biomedical Research Ethical Committee (protocol number BE-2-10 and BE-2-10/2014) and Lithuanian University of Health Sciences Bioethics Center (protocol number BEC-MF-02).

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5. ABBREVIATIONS

• DNMT- DNA methyltransferase.

• PCR- Polymerase chain reaction.

• PCR-RFLP- polymerase chain reaction- restriction fragment length polymorphism. • OS- overall survival.

• PFS- progression free survival. • MFS- metastasis free survival. • ER- estrogen receptor.

• PR- progesterone receptor.

• HER2- human epidermal growth factor 2 receptor. • BRCA1- breast cancer type 1 susceptibility gene. • BRCA2- breast cancer type 2 susceptibility gene. • IDC- infiltrating ductal carcinoma.

• SNP- Single nucleotide polymorphisms.

• TP53- tumor protein 53 gene.

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6. INTRODUCTION

Cancer is the leading cause of death worldwide. The number of new cases and mortality is expected to grow rapidly with population growth. Furthermore, populations adopt lifestyle behaviors that are known to increase cancer risk, such as smoking, physical inactivity, excess body weight and poor diet, different reproductive patterns- women choosing to have less children, have their first pregnancy later, and breastfeed for shorter period [1][2][3].

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer related death among women worldwide. With about 1.67 million cases and 521,900 deaths in 2012 (Fig.1), which is 25% of all cancer cases and 15% of all cancer related deaths among females [1]. The increase in breast cancer incidence during the years can be attributed to changes in reproductive patterns of women. Further, the increase in incidence in 1980 is largely due to increase use of mammography screening. The screening allowed to diagnose cancer which would otherwise be diagnosed 1-3 years later [4].

Breast cancer is not homogenous group of cancer but a heterogeneous group originating from epithelial cells lining the milk ducts [5]- mainly ductal, but can be also lobular, mixed, mucinous, cribriform and tubular carcinomas [6].

Molecular classification is used in breast cancer to choose the best individualized therapies, which leads to serious improvements in disease specific survival. Using gene expression profiling, breast cancers can be grouped into four major subtypes: luminal A, luminal B, human epidermal growth factor receptor 2+_(HER2+_{), and basal like [5][7].}

In clinical setting, breast cancer is classified according to estrogen receptor (ER) and HER2 status. HER2+ _{tumors treatment strategy is anti-HER2 therapy while in ER+ tumors we use}

hormone therapy, typically resulting in positive results; the problem is with ER-/HER- tumors to which we currently don’t have a target therapy which is commonly used. Another receptor which we look at is progesterone receptor (PR), which in PR- but ER+ tumors we can anticipate a lack of response to hormone therapy [8].

Epigenetic regulation plays a major role of supervising the cellular RNA expression patterns, this is important for the normal biological functions in multicellular organisms. Epigenetics is the formation of heritable changes in gene expression without modifications in primary DNA sequence [9]. Instead, those epigenetic alteration can lead to changes in the structure of the chromatin which result in a non-active chromatin state and as a result the silencing of the gene expression and

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11 transcription of DNA to RNA [10]. Epigenetic regulation has an important role in different biological processes including embryonic development, gene imprinting, adult cell renewal, and X chromosome inactivation. The regulation of this process depends on important epigenetic mechanisms including histone modifications, DNA methylation, and nucleosome remodeling. If we have dysregulation of these process it may lead to various diseases including cancer [9]. DNA methylation is an epigenetic modification, it has a role in genomic imprinting, X chromosome inactivation, regulation of gene expression and tumorigenesis [11]. DNA methyltransferases (DNMTs) have key role in establishing and maintaining DNA methylation patterns, they do so by converting in cytosine-guanine (CpG) dinucleotides the cytosine residues to 5-methylcytosine (5mC) [12]. In mammals the generally recognized types of DNMTs are DNMT1, DNMT3A and

DNMT3B and they perform the genomic methylation process; the three of them have different

function in the methylation process [11].

Altered patterns of DNA methylation are considered as hallmark of human cancers. The normally unmethylated promoters can become methylated, this change can result in the silencing of important genes, for example tumor suppressor genes. Other genes that are usually methylated can become hypermethylated, this can lead to abnormal gene activation that normally are inactive [13]. This abnormal DNA methylation patterns are present in the process of malignant transformation [14].

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7. AIM AND OBJECTIVES

7.1. Aim

The aim of the research is to identify DNA sequence variation in DNMT1 and their effect on tumor phenotype and breast cancer patient prognosis.

7.2. Objectives

• To examine the distribution of alleles and genotypes in DMNT1 rs2228611 and rs2228612. • To analyze the association between DNMT1 variants and tumor pathomorphological

characteristics.

• To determine the relationship between DNA sequences variants in DNMT1 and breast cancer prognosis.

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8. LITERATURE REVIEW

8.1. Cancer

Cancer is the leading cause of death worldwide, in both more and less developed countries. The number of new cases and mortality is expected to grow rapidly with population growth, this is especially important in less developed countries, in which 82% of the world’s population resides. Furthermore, populations adopt lifestyle behaviors that are known to increase cancer risk, such as smoking, physical inactivity, excess body weight and poor diet, different reproductive patterns- women choosing to have less children, have their first pregnancy later, and breastfeed for shorter period [1][2][3].

8.1.1. Estimated number of new cases and deaths

An estimated 14.1 million new cancer cases and 8.2 million deaths due to cancer took place in 2012 worldwide [1].

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Figure 1. Estimated new cancer cases and deaths worldwide by sex and level of economic development.

Source: GLOBOCAN 2012 [1]

As seen in figure 1 in developed countries lung cancer is the leading cause of cancer related death among men and women alike, breast cancer is more frequently diagnosed in women and prostate cancer in men. On the other hand, in developing countries the leading cause of cancer related death is different for women and it is breast cancer, while for men it is the same as in developed countries- lung cancer. Also in developing countries lung cancer is most frequently diagnosed in men and breast cancer is diagnosed in women.

Less developed countries account for only 57% of cancer cases and 65% of cancer death worldwide, although those countries percentile in the population is higher. This is mainly because of the competing causes of death, beginning of the tobacco massive use, and the fact that the population is younger in less developed countries. Nonetheless, new cases of cancer will continue to increase in less developed countries as the population grow and ages and the increasing prevalence of known cancer risk factors [1].

8.1.2. Incidence and mortality rates of cancer

Human development index (HDI) is useful in classification of globalization of cancer. HDI consider education and life expectancy and national income, categorizing countries into four levels according to development: low, medium, high and very high [3].

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15 The cancer rates are higher in more developed countries than in less developed countries. Even though the incidence rate is higher in developed countries, the mortality rates are only 8% to 15% higher than in less developed countries [1]. The variation between different HDI countries can be owned to two factors: lower survival rates in medium and low HDI countries, and higher rates of newly diagnosed cancers in the high and very high HDI countries [3]. This can be attributed to the difference in cancer type which is more prevalent, availability of treatment and the availability of methods for diagnosis of cancer (Fig.2) [1].

For example, usually in less developed countries cancer is diagnosed more often in later stages [1]. On the other hand in high HDI countries you have earlier detection of the disease, which include screening modalities for cancers. This screening, early detection and diagnosis of cancer helped to reduce the mortality from breast cancer and cervical cancer in low and middle income countries. Taking Africa as an example, Africa has a big gap from high HDI countries in terms of cancer treatment possibilities and care; many nations in Africa don’t have pathology or radiotherapy services (Fig.2) [3].

Figure 2. Estimated percentage of patients able to access radiotherapy, 2013.

Source: the cancer atlas, second edition, as obtained from the international atomic energy agency [1].

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8.2. Brest cancer

8.2.1. Breast cancer statistics

Brest cancer is the most frequently diagnosed cancer and the leading cause of cancer related death among women worldwide. With about 1.67 million cases and 521,900 deaths in 2012 (Fig.1), which is 25% of all cancer cases and 15% of all cancer related deaths among female. Developed countries account for about half of breast cancer cases and 38% of deaths. The difference in breast cancer incidence in different countries can tell us about the differences in the availability of early detection as well as risk factors [1].

Figure 3. Incidence and mortality rates of female breast cancer by age, United States, 1975 to 2010.

Sources: Incidence: Surveillance, Epidemiology, and End Results (SEER) Program, SEER 9 registries, 1975-2010. Bethesda, MD: National Cancer Institute, Division of Cancer Control and Population sciences; 2013; data were adjusted for reporting delay. Mortality: National Center for

Health and Statistics, Centers for Disease Control and Prevention, as provided by the SEER program [4].

The increase of breast cancer incidence during the years can be attributed to changes in reproductive patterns of women, which include increase age of women when bearing first child and having less children, which are known risk factors for breast cancer. Further, the increase in

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17 incidence in 1980 is largely due to the increased use of mammography screening. The screening allowed to diagnose cancer which would otherwise be diagnosed 1-3 years later.

Looking at Figure 3 we can see that after some years of increase in the mortality of breast cancer we have a decrease in the mortality at around 1990, the breast cancer mortality rate decreased from 1990 till 2010 by 34%. This decline is connected to the advancement in treatment and early detection of breast cancer [4].

8.2.2. Breast cancer types and subtypes

Breast cancer is not homogenous group of cancer but a heterogeneous group originating from epithelial cells lining the milk ducts [5]- mainly ductal, but can be also lobular, mixed, mucinous, cribriform and tubular carcinomas [6].

Molecular classification is used in breast cancer to choose the best individualized therapies, which leads to serious improvements in disease specific survival. Using gene expression profiling, breast cancers can be grouped into four major subtypes: luminal A, luminal B, human epidermal growth factor receptor 2+_(HER2+_{), and basal like [5][7].}

Majority of the basal-like breast cancer are triple-negative breast cancer (TNBC) and majority of the triple-negative breast cancers are basal-like, it has been decided that triple-negative and basal-like breast cancer will be referred as synonyms. Although some clinical and immunohistochemical data shows that it is not always the case and triple-negative breast cancer include other subtypes of breast cancers [15]. TNBC potential to invasiveness and metastasis is greater than other types of breast cancer, this fact associates TNBC to poor prognosis [16].

Table 1. Different subtypes of breast cancer and their receptors expression

Subtypes Estrogen receptors

(ER) Progesterone receptors (PR) HER2 Luminal A + +/- - Luminal B + +/- +/- HER2+ - - + TNBC - - -

In clinical setting, breast cancer is classified according to ER and HER2 status. HER2+ _tumors

treatment strategy is anti-HER2 therapy while in ER+ tumors we use hormone therapy, typically resulting in positive results; the problem is with ER-/HER- tumors to which we currently don’t have a target therapy which is commonly used. Another receptor which we look at is PR, which in PR- but ER+ tumors we can anticipate a lack of response to hormone therapy [8]. Around 70% of

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18 breast cancer are ER+. Estrogen is important in the induction and progression of breast cancer, thus therapy on ER+ patient is aimed towards reduction of estrogen levels or blocking the signaling pathway [17].

When we look at the difference between luminal A and B tumors, we see that luminal B tumor usually have lower expression of ER, lower or no PR, higher tumor grade, higher expression of proliferation related genes, and the activation of growth receptor signaling pathways. Moreover luminal B tumors are considered to be less sensitive to endocrine treatment but higher sensitivity to chemotherapy than luminal A tumors [6].

Although the treatment option of HER2+ tumors, HER2+ tumor display an aggressive course and poor outcome; those tumors are about 20% of all breast cancers [18].

8.3. Oncogenesis

Epigenetic regulation plays a major role of supervising the cellular RNA expression patterns, this is important for the normal biological functions in multicellular organisms. Epigenetics is the formation of heritable changes in gene expression without modifications in primary DNA sequence [9]. Instead, those epigenetic alteration can lead to changes in the structure of the chromatin which result in a non-active chromatin state and as a result the silencing of the gene expression and transcription of DNA to RNA [10]. Epigenetic regulation has an important role in different biological processes including embryonic development, gene imprinting, adult cell renewal, and X chromosome inactivation. The regulation of this process depends on important epigenetic mechanisms including histone modifications, DNA methylation, and nucleosome remodeling. If we have dysregulation of these process it may lead to various diseases including cancer [9].

Genomic instability is a component of almost all human cancers. Most cancers have chromosomal instability (CIN), although other forms exist too including microsatellite instability (MSI) and forms in which we have increased frequencies of base pair mutations [19].

In different stages of tumor evolution, cells escape the normal physiological regulation of proliferation, differentiation and cell death, which leads to uncontrolled cell growth. This process comprise abnormal activation of oncogenes, inactivation of tumor suppressor genes, and altered expression of non-coding RNAs. This abnormal state is caused by both genetic abnormalities, including mutations and genomic instability events, and epigenetic alterations [9].

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8.3.1. Somatic mutation in cancer cells

Despite the fact that majority of the somatic mutations that occur in our cells are innocent, sometimes a mutation would affect a gene or regulatory element of the cell which will bring about a phenotypic consequence. Some of those mutation will provide an advantage to the cell, promoting the growth or survival of a clone [20].

Two terms are used talking about mutations: “driver mutations” and “passenger mutations”. “Driver mutations” are those are mutations that lead to positive selection within a population of cells, in other words mutations that lead to phenotypic changes which will lead to the advantage of one cell on the other. “Passenger mutations” are mutations that do not give an advantage to one cell over the other- does not produce a phenotypic consequences or biological effect [20]. Mutations originate from DNA damage or replication errors that are repaired incorrectly or left unrepaired. DNA damage can be a result of endogenous factors, including reactive oxygen species and mitotic error; or by exogenous factors, such as chemicals and ionizing radiation. Also viruses can cause insertions of DNA sequences. This can manifest in different ways depending on which process is affected, and can manifest as microsatellite instability, very high rate of point mutations, or as chromosome instability [20].

8.3.2. Cancer stem cell hypothesis

For a long time we know that a cancer contains heterogeneous cell populations with distinct tumorigenic potential. From this understanding cancer stem cell hypothesis came to be, in which only some subpopulation of cells, cancer stem cells, can form and maintain a tumor [9].

In this theory it is suggested that a single mutated cells gain unlimited proliferative potential and create a tumor. The proliferative cells that initiate the tumor growth are rare and possess some stem cell qualities. The heterogeneity within a tumor is the outcome of the multipotent nature of these cells resulting in abnormal differentiation and epigenetic changes of the progeny. Majority of the progeny do not add to tumor growth, cannot form metastasis, and they have the same abnormalities as the cell they originate from. The population of stem-like cells are the one responsible for tumor growth, tumor recurrence and the resistance of the tumor to treatment [21].

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20 In the cancer stem cell model as represented in fig 4 only a small population of cells, stem cell, possess the ability for self-renewal and tumor initiation.

8.3.3. Mutator hypothesis

As mentioned before cancer is a disease of mutations resulting from exogenous and endogenous damage to the DNA. The damage is random in the DNA, if the damage is not repaired properly it can cause misplacement of nucleotides in daughter DNA strand in each cell division. These mutations can cause various consequences leading to advantages over other cells in the environment. By the time a tumor is clinically detectable there are already more than thousands of different mutations. Even though the same type of tumor can be in different individuals we can see that different genes are mutated. This difference in subclonal mutations explains the phenotypic heterogeneity of tumors and their resistance to treatment [22].

The source of multiple mutations has not been well established. Single nucleotide substitutions predominate according to tumor DNA sequencing databases. Single nucleotide substitutions may act as passenger mutations, those do not alter cellular phenotypes, and so could be endured. Mutations of deletion, rearrangement, and insertions are more prone to be detrimental or lethal. According to studies the most probable cause for this mutations are misincorporation by DNA polymerases or spontaneous deamination of cytosine to uracil; other sources can cause those mutations such as mutations in base excision and mismatch repair genes [22].

8.3.4. Genomic instability

In hereditary cancers, mutations in DNA repair genes has been linked to both CIN and non-CIN forms of genomic instability. This finding supports the mutator hypothesis, in which tumor development is initiated by genomic instability in precancerous lesions that leads to increased spontaneous mutation rate. This genomic instability is associated to precancerous lesions to mutations in caretaker genes, those genes are responsible to maintain genomic stability. We see that in inherited cancers germline mutations which target DNA repair genes are present in every cell of the patient’s body. And so a single even, the loss of the remaining wild type allele, would cause a genomic instability and lead to tumor development, fitting with the mutator hypothesis [19].

DNA repair genes and mitotic checkpoint genes are the classical caretaker genes. Also considered as caretaker genes are the tumor suppressor gene TP53, encoding p53, and the ataxia telangiectasia mutated (ATM) as they both have function in the DNA damage response. The presence of genomic instability in inherited cancers can be explained by germline mutations

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21 in caretaker genes but the same cannot be said about sporadic cancers. This is due to the belief that the genomic instability would occur only after both alleles of caretaker gene would be mutated. And so, in contrast to hereditary cancers, the molecular basis of genomic instability in case of sporadic cancers is still unclear [19].

8.3.4.1. Oncogene-induced DNA stress model

According to this model, the mechanism of genomic instability caused by activated oncogene is associated with DNA replication stress. There are specific sites, common fragile sites, those sites are especially sensitive to DNA replication stress. The high prevalence of TP53 mutations in human cancers could be a response to oncogene-induced DNA damage. As seen in precancerous lesions that frequently preserve wild type p53 function, the damage of DNA caused by oncogene action evoke p53-dependent apoptosis, which restrict the growth of the lesion. But when the function of p53 is lost, cells start to escape apoptosis and the precancerous lesion can transform to cancerous [19].

8.3.4.2. Cancer hallmarks

The hallmark of cancer were described by Hanahan and Weinberg around 20 years ago, they described six functional capabilities of cancers. These hallmarks are: self-sufficiency in growth signals, unlimited replication potential, insensitivity to anti-growth signals, evasion of apoptosis, continuous angiogenesis, and tissue invasion and metastasis. The mutations preceding and leading to the hallmarks did not have to be acquired in a specific order. They were also separated from the genomic instability, as genomic instability is not a functional capacity of the cancer but a feature that allows the acquisition of the hallmarks.

More hallmarks were added with the years. These include evading immune system and hallmarks related to the presence of stress in cancer- DNA damage and replication stress, oxidative stress, proteotoxic stress, mitotic stress and metabolic stress. These hallmark are different are quite different from the original hallmarks as they describe the state of the cancer cells, characterized by the presence of various stresses, and not by the functional capabilities of cancer. This expansion of the hallmark concept allowed the inclusion of genomic instability as one of the hallmarks (Fig 5.) [19].

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Figure 5. Hallmark of cancer [19].

In Fig 5 the hallmarks self-sufficiency in growth signaling and insensitivity to anti-growth signals are incorporated into one hallmark of activated growth signaling.

8.3.5. Inflammation and oncogenesis

Most of the solid tumors are infiltrated with inflammatory and immune cells. This fact can show an ongoing anti-tumor response of the immune system or a sign that the tumor uses the immune system to its own benefit. The immune system may play an anti-tumorigenic role, especially in the blood, by eradicating pre-malignant and also fully transformed cells. This process depends on various factors that allow the immune system to recognize the tumor antigen as “non self”, those include alteration of immunogenic epitopes expressed by cancer cells, necrosis, and other signals. Cancer cells have an ability to change rapidly and grow, allowing to cancer cells to deceive the immune system through the rise of low immunogenic or resistant clones or by directly using the immune system anti-tumor response to its own benefit. Tumors can also persist in a dormant state for a long time, this reflect an ‘equilibrium’ between immune system destruction of the tumor and tumor growth, which can be immune dependent or independent.

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23 And so even though the immune system has an important role in detection and destruction of tumors, immune system and inflammatory processes that foreshadow or are consequent to cancer development play a critical pro-tumorigenic role. It is thought that various immune cells, such as T and B lymphocytes, macrophages and neutrophils, may at first be recruited to the tumor as part of the anti-tumor response, but once those cells are within the tumor microenvironment they are redirected towards pro-tumorigenic responses. Generally, there is no decisive correlation between tumor prognosis and the presence of a T cell infiltrate, for example a breast cancer with infiltrate with high CD4+_{to CD8}+_{ratio correlated to worst prognosis while in colon cancer infiltrate}

of T cells represent better prognosis.

Figure 6.Inflammation in different stages of carcinogenesis [23].

The contribution of the inflammatory process to the tumor can be different. Plenty of inflammatory mediators (cytokines) are important survival and growth factors that stimulate the proliferation and survival of pre-malignant cells, inflammatory mediators may also activate oncogenic transcription factors and some oncogenes can facilitate an inflammatory response, the tumor associated

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24 inflammation can suppress the anti-tumor immune response and also change anti-tumorigenic specific immune cells to become pro-tumorigenic, inflammation may stimulate tumor angiogenesis, inflammation may stimulate the tumor invasiveness and dissemination of metastasis [23].

8.4. Cancer risk factors

8.4.1. Tobacco

Over the past 50 years, the use of tobacco has been established as a causal agent for multiple cancers. Moreover, the continued use of tobacco in cancer patients decreases the success of cancer treatment and increases the toxicity of the treatment [24]. Tobacco is carcinogenic especially when smoked. The tobacco smoke consist of more than 5,000 chemicals, including more than 60 carcinogens [25]. Tobacco smoking is a leading cause of morbidity and mortality in the world which is preventable.

While cigarettes continue to be the main killer, for youth addiction to tobacco is sustained by other means, in the past decades it has been waterpipe smoking (hookah). This increased use of waterpipe smoking by youth can be explained by the perception that it is less harmful than smoking a cigarette, sweetened flavors. One study showed that waterpipe smoking increase the risk for lung cancer by more than twice [26]. If we reduce the tobacco consumption using primary prevention we could avoid a large number of cancer death globally [3].

8.4.2. Occupational carcinogens

The International Agency for Research on Cancer classified 179 agents as probable or known human carcinogens, there are another 285 agents that are classified as possible human carcinogens; a huge portion of which can be found at work or present in the workplace. Exist an elimination and control protocols in order to protect workers from exposure to these harmful agents, all those exist to prevent occupational cancer, in theory this type of cancer can be completely preventable. But as of now, there are still cases of occupational cancer occurring. The current evaluation of occupationally related cancers are the result of exposure to hazardous agents decades ago, but we can still find those substances in workplace and they pose a risk for future disease [27].

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8.4.3. Diet, obesity and physical inactivity

Obesity is one of the most severe public health issue worldwide, it is a problem even in developing countries. Obesity prevalence has drastically increased in the last few years, meeting epidemic proportions [28]. Obesity increases the risk of several chronic illnesses including cancer development. Approximately 20% of all cancers are a product of excess weight. Many prospective epidemiological studies have established a direct association between cancer and overweight, but obesity does not increase the risk in all tissues in the same extent [29].

Physical inactivity has direct effect on cancer development risk and an indirect effect through the changes in body-mass index (BMI). Diet also has an effect on cancer development risk, for example eating red and processed meats and a diet low in fiber have been correlated with colorectal cancer [3].

8.4.4. Infectious agents

De Martel and colleagues have analyzed in 2008 the fraction of cancers that can be associated to infectious agents and found that 16% can be attributed to infection, this fraction was lower in more developed countries than in less developed countries. Helicobacter pylori, Hepatitis C and B viruses, and human papillomaviruses (HPV) are the cause of majority of the stomach, liver and cervical cancer respectively. The availability of vaccination against possible infectious agent is important in the prevention of cancer development; for example, the HPV vaccine to prevent cervical cancer [3].

8.4.5. Environmental carcinogens

It is unknown exactly what the extent of exposure to environmental carcinogens is, we can estimate that the burden is several hundred thousands, even if we limit the estimation to the main known carcinogenic exposure, which are arsenic, air pollution, aflatoxin, radon and asbestos. The effect of other carcinogens is hard to quantify because there is almost no information available for the number of exposed people. Exposure to diesel exhaust emissions is classified as human carcinogen. Excessive exposure to sunlight, which includes sunbeds, is an environmental and a preventable risk factor for skin cancer [3].

8.4.6. Genes

Genes and their mutation are important for cancer development. As discussed before genes play an important role in oncogenesis. They might facilitate the development of cancer and might aid its progression.

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8.5. Breast cancer risk factors

Menstrual and reproductive factors- hormonal factors, are considered to be the most important breast cancer risk factors. Menarche at younger age, nulliparity, first live birth at older age and no breastfeeding have been persistently found to increase breast cancer risk [30]. Long period of breastfeeding was consistently associated with decrease risk of breast cancer. Some studies have found a possible positive association between an older age at menopause and certain subtypes of breast cancer, luminal A cancer for example.

A strong risk factor is family history of breast cancer, it was persistently found that the hazard of breast cancer among those with family history is one and a half to two times the hazard of breast cancer among those without a family history.

The use of oral contraceptives was persistently associated with increased risk of triple negative breast cancer. Yet, in case of luminal A subtype, two out of three studies showed a decreased risk of breast cancer with the use of oral contraceptives. The use of menopausal hormone therapy was persistently associated with increase in the risk of luminal A breast cancer, but with other subtypes more research is needed [7].

8.5.1. BRCA1, BRCA2 and TP53

There is an association between breast cancer and different types of somatic genetic alteration, including mutations in oncogenes and tumor suppressor genes. TP53 gene is the most frequent mutated gene in breast tumors [31].

BRCA1 and BRCA2 are two genes which are associated with an increased risk for breast cancer

development [32]. BRCA1 and BRCA2 genes inherited mutations account for half of inherited breast carcinoma cases. In sporadic breast cancer the transcript and the protein are absent or reduced [33].

8.6. DNA methyltransferases

DNA methylation is an epigenetic modification, it has a role in genomic imprinting, X chromosome inactivation, regulation of gene expression and tumorigenesis [11]. DNA methyltransferases (DNMTs) have key role in establishing and maintaining DNA methylation patterns, they do so by converting in cytosine-guanine (CpG) dinucleotides the cytosine residues to 5-methylcytosine (5mC) [12]. In mammals the generally recognized types of DNMTs are

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DNMT1, DNMT3A and DNMT3B and they perform the genomic methylation process; the three of

them have different function in the methylation process [11].

The DNMTs are proteins that in their N-terminus they have a regulatory domain, which allows them to attach in the nucleus and recognize nucleoproteins or nucleic acids, and their C-terminus has a catalytic domain, which is in charge of the enzymatic activity [14]. In mammalian cells

DNMT1 is the most abundant and is viewed as key maintenance methyltransferase [11], DNMT1

has a role in maintaining all of the methylation in the genome; during the replication process,

DNMT1 reconstruct the specific methylation pattern of the parental DNA on the daughter strand

[14]. Regulation of DNMT1 expression is done by microRNAs, which breast cancer tissues are globally down regulated, and phosphorylation of DNMT1 leads to reduction of methyltransferase activity [32]. DNMT3A and DNMT3B are named de novo methyltransferases, they are important in establishing DNA patterns of methylation during embryogenesis and constructing genomic imprints during germ cell development. DNMT3A is more important in late stages of embryonic development and especially after birth, while DNMT3B is more important in early stages of embryogenesis [14].

8.6.1. DNMTs role in malignant transformation

Altered patterns of DNA methylation are considered as hallmark of human cancers. The normally unmethylated promoters can become methylated, this change can result in the silencing of important genes, for example tumor suppressor genes. Other genes that are usually methylated can become hypermethylated, this can lead to abnormal gene activation that normally are inactive [13]. This abnormal DNA methylation patterns are present in the process of malignant transformation. Lesions in DNMT genes can be classified into three categories: overexpression, mutation, and deletion [14].

8.6.1.1. Overexpression

DNMTs overexpression in many tumors result in hypermethylation and oncogenic activation. For example, the overexpression of DNMT1 correspond well with abnormal DNA methylation in solid tumors, resulting in lymph node metastasis and poor prognosis in patients. Furthermore, overexpression of DNMT3B as well as CTCF are crucial in the epigenetic inactivation of BRCA1 in sporadic breast tumors. Few studies were done providing explanations for the link between

DNMTs overexpression and tumorigenesis. Zhao et al. have demonstrated that DNMT1

knockdown has an inhibitory effect on the cell cycle in esophageal squamous cell carcinoma, revealing that the increase in methylation levels promote cell mitosis [14].

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8.6.1.2. Mutation

DMNTs somatic mutations are the leading features of many tumors and extensively contribute to

the malignant transformation. Several studies were done and DNMT3A mutations were observed in hematological cancer genome and DNMT1 mutations were found in colon tumor genome. Significant findings on DNMT3A variation have implied that DNMT3A is often mutated in acute myeloid leukemia, myelodysplastic syndrome and adult early T-cell precursor acute lymphoblastic leukemia and correlates with disease aggressiveness and the resistance to treatment. The studies that were done suggest that the mutation in DNMTs genes disturb genomic methylation and play significant roles in tumor formation [14].

Figure 7. Epigenetic alterations involving DMNTs in tumorigenesis [14]

8.6.1.3. Deletion

Deletion of de novo methyltransferases can lead to fatal phenotypes as demonstrate in an in vivo mouse model with embryonically inactive DNMT3A and DNMT3B. Studies have shown that

DNMT3A and DNMT3B act as tumor suppressors in various tumors, for example, DNMT3A

inactivation leads to the progression of lung cancer and peripheral T cell lymphoma. The lack of maintenance methyltransferase activity is associated with carcinogenesis. Studies showed that deletion of DNMT1 leads to DNA demethylation and that DNMT1 is significant for T cell lymphoma prevention and maintenance, assisting to abnormal methylation by de novo and maintenance methylation. Thus, deletion of genes the encoding DNMTs also play role in tumor development [14].

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8.6.2. DNMTs and epigenetic disorders

Epigenetic disorders are connected in part to DNMT dysfunction. DNMTs have an important role in the maintenance of chromosomal homeostasis due to its catalytic role and inhibition of target gene transcription. An abnormal DNMTs lead to imbalances in DNA and histone modification, therefore the result is chromatin remodeling, genomic instability and gene inactivation. The genome of a tumor cell present with global hypomethylation with localized hypermethylation in certain regions. Also, the interaction between DNMTs and other chromatin regulator, for example histone methyltransferases, has great importance in epigenetic disruption (Fig.7). In clinical application these characteristics are important for diagnosis and targeted treatment [14].

Global hypomethylation of tumor cells contribute to reduction of 5-methylcytosine (5-mc), especially in gene coding regions and satellite repeats. The above mentioned changes cause mitotic recombination, chromosomal rearrangements and copy number deletion, and loss of genomic imprinting. In non-cancerous somatic cells, DNA methylation takes place mainly in dinucleotides which contain less CpG, while CpG-enriched region is unmethylated. When the cell goes through a malignant transformation, the levels of global methylation changes, leading to Cpg island hypermethylation and non-CpG island hypomethylation. This result in increased genes that are hypermethylated in their promotor region. Notably, the hypermethylation process leads to the silencing of few key tumor-suppressor genes that play important roles in tumor progression. Butcher et al. demonstrated that in few sporadic breast tumors, hypermethylation of BRCA1 promoter is in part due to DMNT3B overexpression [14].

In active chromatin regions of the genome we can observe unmethylated DNA and acetylated histones, in contrast in inactive chromatin regions we see methylated histones and in suppressed regions of the genome methylated DNA. From this feature we can conclude that DNA methylation and histone modification are closely related [14].

8.7. DNA methyltransferases and breast cancer

When we look at breast cancer the global DNA hypomethylation is much more prevalent (up to 50%) when comparing to other cancers. Global DNA hypomethylation is associated with poor prognosis factor, for example tumor stage and grade and tumor size. More frequently, in breast cancer we will observe hypermethylation of breast cancer related genes leading to silencing comparing to non-cancerous tissue. In breast cancer methylated genes include genes

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30 that are important for evasion of apoptosis, growth, invasion and metastasis and cell differentiation. In invasive carcinomas specimens, virtually all contain at least one gene which is hypermethylated [10]. It has been shown that in breast cancer the methylation of promotor regions in tumor suppressor genes may provide growth advantage to malignant cells. For instance, hypermethylation in ER-promoter leads to loss of ER protein expression. As a result the tumor growth is no longer under estrogen control [32].

In one study all breast cancer patients’ samples showed amplicons of DNMT1, DNMT3A and

DNMT3B. The levels of DNMTs were compared to normal breast tissues, and an elevation in the

levels of DNMTs were observed in tissues of breast cancer compared to normal tissue taken form the breast (Fig.8). In the same study they wanted to clear up the influence of DNMTs expression on the hormonal receptor status in breast cancer, they observed a moderate level of correlation between ER or PR and DNMTs [11].

Figure 8. Expression of DNMTs in cancer tissues. The levels of DNMTs mRNA in breast cancer tissue were compared to normal Breast tissue levels [11].

The promoter of the ER gene contains CpG island, this finding lead to the examination of the prevalence of ER methylation. In normal cells and several ER+ breast cancer cells the ER gene is unmethylated at the CpG island. It was established that 36% of breast cancers that contain both ER and PR are methylated at the ER promoter, 72% of the breast cancers that are methylated at the ER promoter are ER+ and PR- , and 100% of the cancers that are both PR and ER negative are methylated at the ER promoter. These findings are important for further investigations, if the reversal of the methylation at the ER promoter can bring about the sensitization of the tumor cells to hormone treatment in ER and PR negative breast cancers [10].

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31 Another investigation was done using methylome analysis, the patterns of methylation was analyzed in breast cancer genes, a higher frequency of methylation was found in ER+ cancers. This is important in understanding and predicting the clinical outcomes of breast cancer in relation to the methylation of specific breast cancer genes [10]. The understanding ER status and its link to epigenetic changes, offers a new approach to treating ER+ tumors. Because of the ability to reverse epigenetic changes, this feature is ideal for target therapy [13].

The profiling of DNA methylation also allowed us to establish specific breast cancer subtypes that are different form the classification by gene-expression (luminal A and luminal B, HER2+). Methylome analysis helped us to identify six different methylation clusters [10]. This finding can help to clarify breast cancer taxonomy. Beside this, DNA methylation profiling reveled clinically relevant markers which can assist in cancer screening and prognosis. One notable finding is that the DNA methylation profile may reflect the cell-type composition in the microenvironment of the tumor, and especially a T lymphocyte infiltration [13], notably in basal like tumors and HER2 enriched tumors. What is interesting about the increased expression of certain immune related gene is that they are associated with improved relapse free survival [10].

8.8. Germline DNMT1 variants

The location of DNMT1 is on human chromosome 19p13.2 and the protein it encodes to contains 1632 amino acids. DNMT1 contains three important structural domains: the first one is N-terminus, which is a regulatory domain, it is needed for the localization of DNMT1; the second one is C-terminal, which is the catalytic domain, it participates in the binding of the substrates; and a third domain which is a central linker that contain repeated glycine-lysine dipeptides. Single nucleotide polymorphisms (SNPs) that cause genetic variation is the most frequent form of altered gene structure. There are three DNMT1 SNPs that are most often studied, those are rs16999593 (T/C), rs2228612 (A/G) and rs2228611 (G/A), they are located in the coding regions, thus can influence DNMT1 expression.

Recently, numerous studies have pointed out that DNMT1 polymorphism may play a critical role in carcinogenesis. SNP rs2228611 (G/A) was initially associated to gastric cancer, while SNPs rs2228612 (A/G) and rs16999593 (T/C) were linked to breast cancer risk.

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32 SNP rs2228612 (A/G) leads to substitution at amino acid 327 in the DNMT1 protein of isoleucine to phenylalanine, this may affect the function of DNMT1 and its effect in the carcinogenesis. In one meta-analysis SNP rs2228612 (A/G) was linked to risk of overall cancer.

SNP rs2228611 (G/A) may alter the regulation of DNMT1 splicing by inducing a synonymous mutation at amino acid 463. Due to the alteration in splicing of DNMT1 multiple transcript variants of DNMT1 gene were found. In a subgroup analysis, it was found that subjects with the GA phenotype of rs2228611 (G/A) were linked to higher risk of breast cancer, on the other hand they had decreased risk in one study for esophageal cancer. This difference can be due to the fact that various types of cancer have different mechanisms of carcinogenesis [12].

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9. RESEARCH METHODOLOGY AND METHODS

9.1. Research organization, study object and patient selection

The research was organized and conducted at the Oncology Research laboratory, Oncology institute, Lithuanian University of Health sciences (LUHS). This research is part of ongoing study at the Oncology institute. The protocol for this study research was approved by Kaunas Regional Biomedical Research Ethical Committee.

In this study there were 202 participators with breast cancer. Patient peripheral blood samples, which were obtained by clinicians in the years 2014-2016, were used for genomic DNA extraction. The patient’s clinical information was collected earlier from medical records.

The inclusion and exclusion criteria of the patients in this research are represented in table 2.

Table 2. Patient inclusion and exclusion criteria Patient inclusion criteria Patient exclusion criteria

Early breast cancer stage (I-II) Presence of other malignancies

Poor performance statues

Incomplete medical documentation

DNMT1.c.1389 A>G (rs2228611) and DNMT1.c.979 A>G (rs2228612) polymorphisms were

selected for the study based on the literature reports. The previous published studies propose that the inherited DNA variants could be important for the pathogenesis of oncological diseases. Even though the effect of DNA variation on the development of cancer is an area of comprehensive scientific research, not enough research was made on DNA methyltransferases polymorphism and their role in carcinogenesis in general and of breast cancer in particular. This lead for the study initiation.

9.2. Research method

9.2.1. DNA isolation kit and additional materials

The DNA was isolated utilizing “GeneJet Genomic DNA purification Kit” (Thermo Fisher scientific). The GeneJet kit components are: RNase A solution, Proteinase K solution, Lysis solution, Digestion solution, Elution buffer, Wash buffer 1, Wash buffer 2, Purification columns with collection tubes and additional collection tubes.

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34 Additional materials were used to complement the kit, they were as follows: pipets, vortex, 1.5 mL microcentrifuge tubes, pipets tips, ethanol, microcentrifuge, thermomixer and disposable gloves.

9.2.2. DNA isolation protocol

At the beginning we added 20 µLof proteinase K solution and 400 µL of Lysis solution to 200 µL

of blood sample and vortexed everything together to get a uniform suspension. After that the samples were incubated at 56°C for 10 minutes while vortexing. Then 200 µL of ethanol (96%-100%) was added. The solution was transferred to GeneJET DNA purification columns and the samples were centrifuged for 1 minute. After the centrifugation was finished 500 µL of Wash buffer 1 was added and the samples were centrifuged for another minute. After the centrifugation was completed 500 µL of Wash buffer 2 was added and the samples were centrifuged for 3 minutes. Afterwards the samples were transferred to the sterile 1.5 mL microcentrifuge tubes and 200 µL of Elution buffer was added. The tubes with the samples were incubated for 10 minutes in room temperature and later the samples were centrifuge for 1 minute. At the end the purified DNA was stored at -20°C for further use.

9.2.3. DNMT1 (rs2228611)

PCR mixture was prepared according to the protocol (Table 3).

Table 3. PCR mixture preparation protocol

Reagent 1 sample volume (µL)

H2O 7.7

2x Green PCR Master mix 10.0

F primer (20 pmol/uL) 0.4

R primer (20 pmol/uL) 0.4

The primer sequences were earlier reported by Yildiz, et al (2017):

Forward (F): 5’-GTACTGTAAGCACGGTCACCTG-3’

Reverse (R): 5’-TATGTTGTCCAGGCTCGTCTC-3’

18.5 µl of the PCR mix and 1.5 µl of DNA was added to each PCR tube. A specific program (Table 4) was used for DNA amplification.

After the PCR was completed, the amplification was checked by 2% agarose-gel electrophoresis. The samples were run at 90 V for 20 minutes. For the next step of the analysis, restriction fragment length polymorphism (RFLP) assay was implemented. The RFLP mix was prepared according to the protocol (Table 5).

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Table 4. PCR program for rs2228611

Temperature (°C) Time No. of cycles

94 5 min - 94 30 s 35 63 45 s 72 30 s 72 10 min - 4 ∞ -

Table 5. Restriction mix preparation protocol for rs2228611

Reagent Volume (µL)

for 1 sample

H2O 2.75

10x Buffer Tango 1.5

Alw26I (BsmAI) (10 U/µL) 0.75

In each tube 5 µl of restriction mix together with 10 µl of PCR product was added. Then the incubation at 37°C for 1 hour was performed. After the incubation RFLP products were separated on 2% agarose gel electrophoresis at 90 V for 25 minutes (Fig.9) In case of G allele restriction endonuclease Alw26I yielded 124, 111 and 26 bp fragment. In the samples with A allele, following digestion 235 and 26 bp were present.

Figure 9. Agarose gel electrophoresis of PCR-RFLP product for DMNT1 (rs2228611)

Lane M - DNA molecular marker GeneRuler Ultra low range DNA ladder (Thermo Fisher Scientific Baltics, Lithuania); Lanes 1,3,7,15,17 and 19 - AA genotype; Lanes 2,4,8,9,12,13,14,16 and 18 - AG genotype; Lanes 5,6,10 and 11 - GG genotype.

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9.2.4. DNMT1 (rs2228612)

PCR mix was prepared according to the protocol (Table 3).

The primer sequences were selected from the paper by Arakawa, et al (2012):

Forward (F): 5′-AGAACCTGAAAAAGTAAATCCACCG-3′

Reverse (R): 5′-CATGTGATTCACCCGCTTCAG-3′

18.5 µl of the PCR mix and 1.5 µl of DNA was added to each PCR tube. A specific program (Table 6) was used for DNA amplification.

Table 6. PCR program for rs2228612

Temperature (°C) Time No. Of cycles

96 5 min - 94 30 s 35 52 30 s 72 30 s 72 7 min - 4 ∞ -

After the PCR was completed, the amplification was verified by 3% agarose-gel electrophoresis- 90V for 20 minutes. After confirming the amplification RFLP mix was prepared, according to the protocol in Table 7.

Table 7. Restriction mix preparation protocol for rs2228612

Reagent Volume (µL) for 1 sample

H2O 2.75

10x Buffer Tango 1.5

MspI (HpaII) (10 U/µL) 0.75

10 µl of PCR product and 5 µl of the restriction mix was added into each tube. The samples were incubated for 1 hour at 37°C. After the incubation was done 3% agarose-gel was used for RFLP analysis. The samples were run at 90V for 40 minutes (Fig.10).

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Figure 10. Agarose gel electrophoresis of PCR-RFLP product for DMNT1 (rs2228612)

Lane M - DNA molecular marker GeneRuler Ultra low range DNA ladder (Thermo Fisher Scientific Baltics, Lithuania); Lanes 1,2,4,5,6, and 7 - AA genotype; Lane 3 - AG genotype.

9.3. Statistical analysis and data

Data were processed using MS Excel 2010 and analysed using IBM SPSS Statistics, version 27. The descriptive analysis of categorical variables included the calculation of the prevalence (number of participants) and percentages (%). The continuous variables (such as age, time to progression, time to diagnosis, time to death) were presented as mean±standard deviation (SD).

Comparisons were done using Chi-Squared test (χ2) or Fisher's exact test in genotype and allelic model. The statistically significant associations from Chi-Squared test (χ2) or Fisher's exact test were analysed further using logistic regression analysis both in univariate and multivariate models. Kaplan–Meier test was used for survival analysis and for drawing the survival curves. In all the analysis, the statistical significance level was set at p<0.05.

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10. RESULTS AND DATA ANALYSIS

10.1. Tumor characteristics and SNP frequencies

In our study, more than half of the patients were positive to estrogen (68.3%) and progesterone (59.9%) receptors, while HER2 overexpression was found in 18.8% of the tumors (Table 8). Less than half of the studied breast cancer patients (39.6%) had positive lymph nodes. Most of the tumors were well to moderate differentiated -G1 or G2 (77.7%) and most of them were classified as T1 (64.4%).

Table 8. The clinicopathological characteristics of the study group.

Characteristics Subgroups Frequencies

Age ≤50 years 140 (69.3%)

>50 years 62 (30.7%)

Pathological tumor size (T) 1-2 cm (T1) 130 (64.4%)

>2-5cm (T2) 72 (35.6%)

Pathological lymph node involvement (N)

No lymph nodes involvement (N0)

122 (60.4%)

Lymph nodes involvement (N1)

80 (39.6%)

Tumor grade (G) G1+G2 157 (77.7%)

G3+G4 45 (22.3%)

Lymphatic infiltration (L) Negative 96 (48%)

Positive 104 (52%)

Vascular infiltration (V) Negative 117 (58.8%)

Positive 82 (41.2%)

Estrogen receptors (ERs) ER negative 64 (31.7%)

ER positive 138 (68.3%)

Progesterone receptors (PRs) PR negative 81 (40.1%)

PR positive 121 (59.9%)

HER2 HER2 negative 164 (81.2%)

HER2 positive 38 (18.8%)

Two polymorphisms were analyzed in our study, DNMT1 rs2228612 and rs2228611. In the rs2228612 polymorphism analysis, the A allele (94.6%) was more frequent than the G allele (5.4%). The distribution of genotypes was as followed: AA - 89.1% and AG - 10.9% (Table 9). In rs2228611 polymorphism the A and G alleles had almost the same frequencies: A allele - 49%, G allele- 51%. The distribution of genotypes was as followed: AA - 24.3%, AG - 49.5%, GG - 26.2% (Table 10).

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Table 9. DNMT1 (rs2228612) frequencies

Genotype AA 180 (89.1%)

AG 22 (10.9%)

Allele A Non-carriers of A allele -

A allele carriers 202 (100%)

Allele G Non-carriers of G allele 180 (89.1%)

G allele carriers 22 (10.9%)

Table 10. DNMT1 (rs2228611) frequencies

Genotype AA 49 (24.3%)

AG 100 (49.5%)

GG 53 (26.2%)

Allele A Non-carriers of A allele 53 (26.2%)

A allele carriers 149 (73.8%)

Allele G Non-carriers of G allele 49 (24.3%)

G allele carriers 153 (75.7%)

10.2. Association analysis

In the association analysis, the possible link between the selected SNP (in genotype and allelic model) and tumor pathomorphological characteristics (ER, PR, HER2 status, G, T, L and V) was assessed. In case of DNMT1 (rs2228612) only allelic mode was used as there was no sample with GG genotype for this SNP in our study.

10.2.1. Chi-square test and logistic regression analysis for rs2228612

In Chi-square test there was a significant association between rs2228612 G allele and positive lymph nodes (p=0.037). This association was confirmed in logistic regression analysis. In logistic regression analysis it was determined that the non-carriers of G allele in rs2228512 polymorphism had 4.35 times higher risk of positive lymph nodes than the carrier of G allele (OR=4.35; 95% CI 1.641-11.523; p=0.003) (Tables 11, 12, and 13).

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Table 11. Univariate logistic regression analysis. The odds ratio for association between DMNT1 (rs2228612) and tumor receptor status.

Genotypes and alleles

ER PR HER2

Odds 95% CI p Odds 95% CI p Odds 95% CI p

The carriers of G allele versus the non-carriers 0.625 0.237-1.648 0.342 0.642 0.254-1.623 0.349 0.679 0.189-2.444 0.554

Table 12. Univariate logistic regression analysis. The odds ratio for association between DMNT1 (rs2228612) and tumor characteristic and lymph node status.

G T N

The carriers of G allele versus the non-carriers 1.213 0.415-3.544 0.724 0.846 0.326-2.193 0.731 4.348* 1.641-11.523* 0.003 *

*The data is taken from “the non-carrier versus the carriers of G allele” because this data showed a statistical significant.

Table 13. Univariate logistic regression analysis. The odds ratio for association between DMNT1 (rs2228612) and lymphatic infiltration and vascular infiltration.

Genotypes and alleles L V

Odds 95% CI p Odds 95% CI p

The carriers of G allele versus the non-carriers

1.111 0.455-2.714 0.818 1.350 0.547-3.334 0.515

10.2.2. Chi-square test and logistic regression analysis for rs2228611

In Chi- square test there was a significant association between rs2228611 genotype and tumor lymphatic infiltration (p=0.016). This association was confirmed in logistic regression analysis. Moreover, there was a significant association between rs2228611 G allele and tumor lymphatic infiltration (p=0.045) which was also confirmed in logistic regression analysis. In logistic regression analysis it was determined that the patients with AG genotype had lower probability of tumor lymphatic infiltration than patients with AA genotype (OR=0.457; 95% CI 0.241-0.867;

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41 p=0.017). In the allelic model the association of G allele with tumor lymphatic infiltration showed borderline significance (p=0.049).

In Chi-square test there was a significant association between rs2228611 genotype and tumor vascular infiltration (p=0.004). This association was confirmed in logistic regression analysis. Moreover, there was a significant association between rs2228611 G allele and tumor vascular infiltration (p=0.006). This association was also confirmed in logistic regression analysis. In logistic regression analysis it was determined that patients with AG genotype had lower probability of tumor vascular infiltration than patients with AA genotype (OR=0.277; 95% CI 0.143-0.537; p=0.000). In the allelic model it was determined that the carriers of G allele had lower probability of tumor vascular infiltration when compared to the non-carriers (OR=0.437; 95% CI 0.223-0.858; p=0.016) (Tables 14, 15, and 16).

Table 14. Univariate logistic regression analysis. The odds ratio for association between DMNT1 (rs2228611) and tumor receptor status.

ER PR HER2

AG versus AA 1.062 0.485-2.324 0.881 0.956 0.460-1.985 0.903 0.821 0.386-1.743 0.607 GG versus AA 1.029 0.425-2.492 0.949 1.122 0.488-2.581 0.786 0.482 0.179-1.299 0.149 The carriers of A allele versus the non-carriers 1.013 0.503-2.039 0.971 0.864 0.444-1.681 0.667 1.343 0.608-2.966 0.466 The carriers of G allele versus the non-carriers 1.050 0.501-2.202 0.896 1.010 0.506-2.015 0.978 0.701 0.344-1.429 0.328

LITHUANIAN UNIVERSITY OF HEALTH SCIENCES