Lupine Publishers | Subjection between Breast Cancer and Body Mass Index, the Role of L-Carnitine in Prediction and Outcomes of the Disease

Lupine Publishers | Open Access Journal of Oncology and Medicine

 

Abstract

Increasing the effectiveness of antitumor therapy in breast cancer patients who take L-carnitine during preoperative systemic antitumor therapy compared with patients receiving standard neoadjuvant systemic antitumor therapy served as a prerequisite for studying possible antitumor mechanisms of L-carnitine. The positive effect of L-carnitine is due to the transfer of palm-n-LC through the inner membrane into the mitochondrial matrix, which promotes the formation of a significant number of ATP molecules. It has also been shown that L-carnitine can have a double protective effect, enhancing the energy dynamics of the cell and inhibiting the hyperexcitability of the cell membrane, that making it an ideal nutrient for the prevention and treatment of cancer. This article summarizes the results of epidemiological and clinical studies of the use of L-carnitine in the treatment of breast cancer
Keywords: Body mass index (BMI); Breast cancer (BC); Obesity; Overall survival; L carnitine

Introduction

The incidence of breast cancer in the world in general and in Ukraine in particular is growing. In 2017, in Ukraine the incidence reached 16 percent of female population, for which, the breast cancer ranked first in structure of oncological incidence among women. In analyzing the data of the National Cancer Registry of Ukraine, it should be noted, that in comparison with 2014 year, the prevalence rate of breast cancer in 2016has increased by 5,1%, that indicates importance of improvement diagnostic procedures and methods of treatment it [1]. Studying the scientific literature on this subject, we noticed that there is a strong biological relationship between obesity and a poor outcome of breast cancer. And having analysed the date of Ministry of Health in Ukraine it can be concluded, that about 26% of women in 2017 year had overweight or obesity.
Obesity has a chronic metabolic character, which is the result of the interaction of the endogenous factors, environmental conditions and lifestyle. Endogenous factors could be considered a violation of the genetic and hormonal balance. The external conditions and type of lifestyle include irregular rhythm nutrition, use of substandard products and sedentary lifestyle. Obesity is the first risk factor for metabolic syndrome, diabetes type II, cardiovascular disease and some forms of cancer, including breast cancer. Since overweight is a risk factor for breast cancer, there is reason to believe that among patients with breast cancer the percentage of obese women is higher than in the population. The risk of breast cancer in postmenopausal women by 30%, it is more than in premenopausal, women with obesity-50%. Furthermore it was proven that obesity is associated with poor prognosis in patients with breast cancer, regardless of menopausal status, and effectiveness of systemic medication breast cancer in patients that have over weight is lower than in patients with normal BMI.
Although obesity is associated with a poor outcome in women with breast cancer, it is unclear how weight loss after diagnosis will change its course and results. Recently, complementary and alternative medicine (CAM) is widely accepted among patients with breast cancer, which may provide several beneficial effects including reduction of therapy-associated toxicity, improvement of cancer-related symptoms, fostering of the immune system, and even direct anticancer effects [2]. L-carnitine is a metabolite of C₄ oil LC, which is involved in the transfer of palm-n-LC through the inner membrane into the mitochondrial matrix and is a substrate for the formation of ATP molecules. Carnitine is a trim ethylated amino acid naturally synthesized in the liver, brain and kidneys from protein lysine and methionine. Several factors, such as sex hormones and glucagon, can influence the distribution and level of carnitine in tissues [3,4].
In the absence of L-carnitine, the inner membrane of the mitochondria becomes impermeable to fatty acids, which entails a chain of various metabolic disorders in the human body. Carnitine has a modulating effect on the function of acetylcholine excitatory neurotransmitter, glutamate excitatory amino acid, insulin growth factor-1 (IGF-1) and nitric oxide (NO)[3]. Also proved, that L-carnitine may have a dual protective effect by enhancing the energy dynamics of the cell and inhibiting cell membrane hyper excitability, which make it an ideal nutrient for cancer prevention and treatment [5]. In view of the foregoing, the study of the influence of the body mass index on the effectiveness of systemic treatment of breast cancer is an urgent scientific problem and a promising field of research. This article presents the information of epidemiological and clinical studies of the influence of the body mass index on the effectiveness of breast cancer treatment by individualizing therapeutic measures taking into account the characteristics of patient's metabolism.
Studies on the Effects of BMI on The Course and Outcome of Breast Cancer and the Role of L-Carnitine in the Treatment of Cancer: The effectiveness of the prescribing of L-carnitine for breast cancers' treatment, as well as the effect of BMI on the outcome of the disease is proven in epidemiological and clinical studies.

Epidemiological and Clinical Studies

DSM Chan and co-authors [6] reported that women who have BMI> 30 course and outcomes of breast cancer are significantly worse than women with BMI <30. They proved, that women with BMI> 30 have the overall relative risk of total mortality 1.41, women with BMI of 25> 30 - 1.07. At the same time, for every 5 kg / m² of the increase BMI, the risk of both total mortality and mortality from breast cancer increased, namely by 18% and 14%, respectively M. Protani and co-authors [7] have shown that women with breast cancer, who are suffering in obesity, have lower survival rate than women with breast cancer without obesity. Recently published data of randomized clinical researches by ML Neuhouser and coauthors [8] demonstrated, that for women> 50 years old, with 2 and 3 stages of obesity (BMI> 35) is typically the development of GR+ breast cancer.
Similarly, B. Pajares et al. [9] who found significantly worse results for patients with BMI >35 compared with patients with BMI <25, stated that the magnitude of the effect depended on the cancer subtype (estrogen receptor (ER) / progesterone (PR) positive and HER2 negative, HER2 positive, triple negative). An analysis of the pooled data of the three adjuvant studies of the Eastern Cooperative Cancer Group showed significantly worse results for patients with obesity (BMI > 30) than for patients with normal BMI with a hormonal receptor-positive disease. And it was noted absence of negative effect of obesity on survival in patients with other breast cancer subtypes. C Fontanella et al. [10] studied the effect of BMI on different molecular subtypes of breast cancer and concluded that in women with ER / PR-positive and HER2-negative breast cancer, as well as with TNBC, the risk of death is significantly higher than in other subtypes of cancer.
It is proved that even the highest BMI figures are not a risk factor for death for patients with luminal A-like subtype of breast cancer. The reason for this is that fatty tissue produces an excessive amount of estrogen, a high level of which is associated with an increased risk of developing breast, endometrial, ovarian and some other cancers. It has also been proven that the level of adipokine, that promotes cell proliferation, increases in the blood with increasing of level of fat in organism. And adiponectin, which people with obesity have less than people with normal BMI, can have anti proliferative effects. Such data can serve as evidence of the effect of BMI on the course and outcome of breast cancer. Yet another proof of influence developing metabolic syndrome on the course and outcome of breast cancer was proposed by R. Bhandari et al. [11]. They proved that that the presence of metabolic disorders (that is, the metabolic syndrome) is associated with an increased risk of breast cancer in adult women.
The above data led to the need to investigate medicines that contribute to fat burning, such as L-carnitine. Based on the data provided by Rania M. Khalil and co-authors [12], we can prove the positive effect of this medicine on the course and outcome of breast cancer. The study showed that patients who received Tamoxifen with L-carnitine had significant decrease of Her-2 / neu and IGF-1 level (P <0.05) in the serum compared with patients who received only Tamoxifen. Using of L-carnitine led to significant decrease Her- 2 / neu level in the serum (P <0.05) compared to each of the control patients, namely, 59.5%. The effect of tamoxifen on IGF-1 (P <0.05) -decrease its level by 5.4% [13].However, it has been proved that using of L-carnitine in the treatment of ER+ breast cancer does not significantly reduce the level of estradiol, but leads to decrease both tumor markers CEA and CA15.3 (P <0.05,% decrease by 80.9% and 67, 8%, respectively) [13].
Using of L-carnitine in patients with breast cancer and obesity improves the metabolism of fatty acids in mitochondria, restores normal mitochondrial function and, thus, improves the general condition and quality of patients’ life [14]. Carnitine may alsomimic some of the biological activities of glucocorticoids, particularly immunomodulation, via suppressing TNF-a and IL-12 release from monocytes (5). L-carnitine as adjuvant therapy in cisplatin-treated cancer patients proved a beneficial effect in reducing the cisplatin- induced organ toxicity [15]. It is possible that, the extremely lipophilic nature of carnitine may be responsible for the decrease in EGFbinding [16]. Carnitine may insert in the cell membrane and/or interact with one of the many cellular enzymes having lipid substrates or cofactors. In addition, carnitine may interact directly with the EGFR [17].
Experimental evidence is available showing that ROS may induce the light and independent phosphorylation of the EGFR activating Her-2/neu. Moreover, the expression of the receptor is induced in conditions of oxidative stress [18]. L-carnitine, via its free radical scavenging and antioxidant properties, may inhibit ROS-mediated EGFR phosphorylation. It has been found that palmitoyl-carnitine can inhibit the activity of heart and brain protein kinase C in a competitive manner and subsequent phosphorylation of the EGFR [19]. Although the tumor markers and IGF-1 showed no significant difference in TAM-treated patients before and after administration of L-CAR, there was a tendency to decline after L-CAR supplementation [13]. The results of the above studies became a prerequisite for conducting clinical studies aimed at establishing the role of L-carnitine in the treatment of breast cancer.
To date, the search in the online clinical research registration system ClinicalTrials.gov using key words L-carnitine + breast cancer has revealed several studies evaluating the efficacy and safety of L-carnitine in the treatment of breast cancer patients. Analyzing the obtained results, we can conclude that L-carnitine was the drug of choice for neuropathies, as a consequence of chemotherapy, in patients with breast cancer.

Conclusion

L-carnitine is widely used in clinical practice. However, recently this medicine causes growing interest among oncologists. In a number of studies, L-carnitine has proven itself as a medicine that capable, during the preoperative systemic antitumor therapy, to increase its effectiveness compared with standard neoadjuvant systemic antitumor therapy. And also, taking L-carnitine with neoadjuvant systemic antitumor therapy helps to increase the number of cases of complete morphological regression (V degree of therapeutic pathomorphosis). To date, there are several clinical studies that are researching using L-carnitine in various malignant tumors, the results of which are the basis for further in-depth study of the effect of the medicine in the treatment of malignant neoplasms.


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Lupine Publishers | Somatic Mutations in Cancer-Free Individuals: A Liquid Biopsy Connection

Lupine Publishers | Open Access Journal of Oncology and Medicine

 

Abstract

Somatic mutations have been perceived as the causal event in the origin of the vast majority of cancers. Advanced massively parallel, highthroughput DNA sequencing have enabled the comprehensive characterization of somatic mutations in a large number of tumor samples for precision and personalized therapy. Understanding how these observed genetic alterations give rise to specific cancer phenotypes represents an ultimate goal of cancer genomics. However, somatic mutations are also commonly found in healthy individuals, which interfere with the effectiveness for cancer diagnostics.
Keywords: Somatic mutation; Germline; Cell-free DNA; Liquid biopsy; Next-generation sequencing
Abbreviations: NGS: Next-Generation Sequencing ; cfDNA: Cell-free DNA; MAF: Mutant Allele Frequency

Introduction

Mutations in healthy individuals are not all germline
Over the course of our lifetime, there are many millions of cell divisions in the body. By chance alone, mutations will definitely occur. Indeed, spontaneous somatic mutations constantly occur in individual cells. These background mutations arise either from replication errors or from DNA damage that is repaired incorrectly or left unrepaired, and have been detected in healthy tissues, including blood, skin, liver, colon, and small intestine [1-3]. Deepsequencing studies in normal tissues also surprisingly identified cancer-driving mutations, e.g., in blood, driver mutations can be detected in ~10%of individuals older than 65 years of age and resemble patterns seen in leukemia patients. Individuals carrying these driver mutations have an elevated future risk of blood cancers [4-6], suggesting that these are genuine precancerous clones. Further, a detailed analysis of 31,717 cancer cases and 26,136 cancer-free controls from 13 genome-wide association studies revealed that the majority, if not all, of aberrations that were observed in the cancer-associated cohort were also seen in cancer-free subjects, albeit at lower frequency [7,8].
Somatic mutations in healthy individuals are very prevalent, with an average mutation number of around 2–6 mutations/1 M bases [9,10]. The baseline somatic mutation spectrum in healthy population not only can help fill the gaps for the establishing early cancer diagnosis strategies, but also argues against the idea of using normal cells as germline control to make somatic mutation calls in sequencing tests. Moreover, the same driver mutation could exist in both tumor and normal cells yet with distinct biological effects, we should not simply define the threshold of mutation detection by removing the background mutations found in a healthy population. Taken together, we need to incorporate and carefully calibrate the background somatic mutations in healthy individuals; the fact is they are not all germline mutations.
Somatic driver mutations found in healthy population by liquid biopsy
With the dramatically decreased cost of next-generation sequencing (NGS) in recent years, it is now practical to screen a large number of individuals at ultra-deep sequencing depths to identify cancer-related mutations. Cell-free DNA (cfDNA) in the blood circulation of cancer patients (as liquid biopsy) have emerged as key biomarkers for cancer monitoring and treatment decisionmaking [11]. Both academic research groups and industry players are chasing the pan-cancer screening by a simple blood draw. However, the reliable and accurate application of cfDNA detection requires better understanding of background somatic information in healthy individuals.
We performed ultra-deep target sequencing on 50 cancerassociated genes for plasma cfDNA from a cohort of 129 apparently healthy cancer-free subjects. To increase the confidence of the called mutations, we here defined the mutation as the variant allele frequency greater than 1% and the average depth more than 5,000 xs for demonstration. Our data revealed an age-independent mutation spectrum with average 3.12 somatic mutations per subject (Figure 1). The most frequently mutated genes are TP53 (42%), KIT (6%), KDR (5.5%), PIK3CA (5.5%), EGFR (5%) and PTEN (3.7%). These results highlighted the prevalence of some cancer-associated driver mutations in healthy individuals as background mutations. We also demonstrated the concordance between our results and a recent study for revealing the real somatic mutation in healthy population.
Figure 1: Distribution plots of somatic mutation detected in a cohort of 129 healthy subjects.
The study by Xia et al. [12] examined the background somatic mutations in white blood cells and cfDNA in healthy controls based on sequencing data from 821 non-cancer individuals with the aim of understanding the baseline profile of somatic mutations detected in cfDNA. The data comparison was summarized in Figure 2. Although there are differences in study cohort composition, sample volume, extraction methodology and analytical platform, the end results are remarkably similar, i.e., average 3 mutations per subject with an almost identical list of frequently mutated genes. Although varying mutation spectra in cancers have often been attributed to cancerspecific processes, our data suggest that at least a subset of these mutations actually reflect normal tissue-specific processes. This concept is consistent with the idea that a substantial fraction of the mutations found in cancers occur in normal stem cells [13,14].
Figure 2: Comparison of somatic mutation detection in healthy population from two studies.
Normal tissue as a germline control not justified
There is evidence for the presence of tumor-derived cfDNA in early cancers [15]. However, the real fraction of cfDNA that shed by tumor rather than the background somatic mutations is not well illustrated. For clinical application, the low level of tumor mutation as well as the heterogeneity of background mutation present in the circulation needs to be clearly addressed and differentiated to achieve accuracy. Unfortunately, this goal can’t be achieved by pushing detection limit of current advanced technology to below 0.01% mutant allele frequency (MAF). Contrarily, the higher sensitivity will guarantee higher chance to pick up background somatic mutations. Also, the clinical relevance of those lowpercentage tumor mutations is still debatable in terms of treatment decision or regimen change. Each human individual is unique. Every cancer patient is different. No two tumors are the same even resides within the same patient; to distinguish the definitive cancer-specific mutations from background signals observable in plasma is extremely daunting. Evaluation of specificity in plasma cfDNA profiles from large numbers of healthy individuals as representative controls for the cancer population seems farfetched with uncertainty, especially when standardized protocol and optimized technology are still lacking.
Unlike tissue genomic DNA, circulating cfDNA is so diluted and dynamic with a relatively short half-life, making single-point measurement not suitable for clinical application. We reason that cfDNA in circulation is truly under a continuous selection pressure to select for highly aggressive/proliferative clones, as disease progressing the low-abundant tumor clones will either evolve and dominate or vanish by the immune clean-up processes, therefore longitudinal clinical follow-up should be performed to identify the best time and target for precision therapy, meanwhile to filter out contaminating background mutations. To achieve high clinical specificity, a cfDNA-based test must be capable of distinguishing between the background signals originating from non-cancer or pre-cancerous processes and the invasive malignancy of clinical interest. It is still possible that mutational signatures in cfDNA could distinguish basic biological processes from malignant and pathological processes.
Figure 3: A representative mutational trending curve after filtering out background mutations.
Here we propose a combined approach based on the tumor evolutional principle of “survival and domination of the fittest” in circulation that is to perform multiple time-point monitoring, filter out potential background mutations (e.g., <1% MAF), reduce sample input volume and interrogate multiple databases. A representative mutational trending curve following our approaches was shown (Figure 3). Our findings underscore the importance of an assessment of the landscape of somatic mutations in cancerfree population, and associated mutation signatures. Somatic mutations and mosaicism in healthy individuals have implications not only for early detection, diagnosis and treatment of cancer using liquid biopsy but also emerging technologies in healthcare. We recommend caution while extending the mutation conclusions to cancer patients by employing matched normal tissue as germline control. To increase sample input and push liquid biopsy sensitivity toward <1% may not serve the interest of detecting low-frequency mutant allele, but only to increase the chance of background mutation contamination. Application of artificial intelligence, machine-learning on big database to create an algorithm for highrisk population screening of cancer is a good idea for preventive medicine, yet the outcome is uncertain given the uniqueness of every patient, each tumor - one size can’t fit all.

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