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Open Access Publications from the University of California

Scholarly Works (23 results)

  • Article
  • Peer Reviewed
UCLA Previously Published Works (2018)
Longer duration of endocrine therapy decreases breast cancer recurrence and mortality, but these benefits need to be weighed against potential risks to overall health. Notable side effects of endocrine therapy include cataracts, uterine cancer, thromboembolic events, osteoporosis and fracture risk, chronic musculoskeletal complaints, as well as vaginal dryness and discharge, and vasomotor symptoms. Estrogen deprivation in healthy women younger than 50 years undergoing bilateral oophorectomy has been shown to accelerate the development of diseases related to aging, including coronary artery disease, cardiac arrhythmias, stroke, dementia, and osteoporosis, raising concern that even less dramatic modulation of estrogen homeostasis may adversely affect health outcomes. Diminished available estrogen at the cellular and molecular level may facilitate mechanisms that underlie the aging process, often termed the hallmarks of aging. In this review, we describe estrogen's role in normal physiology across tissues, review the effects of estrogen deprivation on health outcomes in the setting of both surgical and natural menopause, and examine the hallmarks of aging with attention to the effects of estrogen and estrogen blockade on each molecular mechanism underlying the aging process.
    Cover page: Potential Mechanisms of Age Acceleration Caused by Estrogen Deprivation: Do Endocrine Therapies Carry the Same Risks?
    • Article
    • Peer Reviewed
    Department of Statistics Papers (2009)
      Cover page: Extinction Models for Cancer Stem Cell Therapy
      • Thesis
      • Peer Reviewed
      UCLA Electronic Theses and Dissertations (2021)

      The advent of large scale data, particularly from the biological sciences, has accelerated interest in developing computational methods for analysis and prediction.Implementing such methods often requires software to either automate computational tasks or to carry out calculations that elude analytic techniques. This work focuses on the latter while paying respect to useful and elegant abstractions from mathematical theory.

      The diverse set of topics in this dissertation span applied probability, mathematical optimization, and epidemiological modelling. First, we investigate simulation techniques for stochastic processes. Second, we elaborate on the proximal distance technique of constrained optimization as a computational framework with examples in orthogonal projection, clustering, regression, and imaging. Third, we use deterministic equation modelling to evaluate school reopening strategies under pandemic conditions. Finally, we conclude with preliminary work on an application of the proximal distance method to hierarchical linear models.

        Cover page: Contributions to Scientific Computing and Mathematical Modelling: Stochastic Simulation, Constrained Optimization, and Infectious Disease
        • Article
        • Peer Reviewed
        Department of Statistics Papers (2009)

        Stem cells are now viewed as initiating and sustaining many cancers. This suggests that cancer can be cured by driving cancer stem cells to extinction. The problem with this strategy is that ordinary stem cells are apt to be killed in the process. This paper sets bounds on the killing differential that must exist for the survival of an adequate number of normal stem cells. Our main tools are birth-death Markov chains in continuous time. In this framework, we investigate the extinction times of cancer stem cells and normal stem cells. Application of extreme value theory from mathematical statistics yields an accurate asymptotic distribution and corresponding moments for both extinction times. We compare these distributions for the two cell populations as a function of the killing rates. Perhaps a more telling comparison involves the number of normal stem cells NH at the extinction time of the cancer stem cells. Conditioning on the asymptotic time to extinction of the cancer stem cells allows us to calculate the asymptotic mean and variance of NH . The full distribution of NH can be retrieved by the finite Fourier transform and, in some parameter regimes, by an eigenfunction expansion. Finally, we discuss the impact of quiescence (the resting state) on stem cell dynamics. Quiescence can act as a sanctuary for cancer stem cells and imperils the proposed therapy. We approach the complication of quiescence via multitype branching process models and stochastic simulation. Improvements to the τ-leaping method of stochastic simulation make it a versatile tool in this context. We conclude that the proposed therapy must target quiescent cancer stem cells as well as actively dividing cancer stem cells. The current cancer models demonstrate the virtue of attacking the same quantitative questions from a variety of modeling, mathematical, and computational perspectives.

          Cover page: Extinction Models for Cancer Stem Cell Therapy
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2022)
          Estrogen promotes breast tissue proliferation and telomerase activation. We investigated the effects of reproductive history on cell cycling and telomere length using a DNA methylation-based estimate of telomere length (DNAmTL) in breast and blood from healthy women donors. We demonstrate that DNAmTL is shorter in breast than in blood, and that nulliparous women have longer age-adjusted DNAmTL in both breast and blood, potentially explaining their higher risk of breast cancer.
            Cover page: The impact of reproductive factors on DNA methylation-based telomere length in healthy breast tissue.
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2012)
            The stem cell microenvironment is involved in regulating the fate of the stem cell with respect to self-renewal, quiescence, and differentiation. Mathematical models are helpful in understanding how key pathways regulate the dynamics of stem cell maintenance and homeostasis. This tight regulation and maintenance of stem cell number is thought to break down during carcinogenesis. As a result, the stem cell niche has become a novel target of cancer therapeutics. Developing a quantitative understanding of the regulatory pathways that guide stem cell behavior will be vital to understanding how these systems change under conditions of stress, inflammation, and cancer initiation. Predictions from mathematical modeling can be used as a clinical tool to guide therapy design. We present a survey of mathematical models used to study stem cell population dynamics and stem cell niche regulation, both in the hematopoietic system and other tissues. Highlighting the quantitative aspects of stem cell biology, we describe compelling questions that can be addressed with modeling. Finally, we discuss experimental systems, most notably Drosophila, that can best be used to validate mathematical predictions.
              Cover page: Stem Cell Niche Dynamics: From Homeostasis to Carcinogenesis
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2012)

              Introduction

              Trastuzumab dramatically improves survival in breast cancer patients whose tumor overexpresses HER2. A subpopulation of cells in human breast tumors has been identified with characteristics of cancer stem cells. These breast cancer stem-like cells (BCSCs) rely on HER2 signaling for self-renewal, suggesting that HER2-targeted therapy targets BCSCs even when the bulk of the tumor does not overexpress HER2. In order to guide clinical trials examining HER2-targeted therapy in the adjuvant setting, we propose a mathematical model to examine BCSC population dynamics and predict optimal duration of therapy.

              Methods

              Varying the susceptibility of BCSCs to HER2-targeted therapy, we quantify the average time to extinction of BCSCs. We expand our model using stochastic simulation to include the partially differentiated tumor cells (TCs) that represent bulk tumor population and examine effects of plasticity on required duration of therapy.

              Results

              Lower susceptibility of BCSCs and increased rates of dedifferentiation entail longer extinction times, indicating a need for prolonged administration of HER2-targeted therapy. We predict that even when therapy does not appreciably reduce tumor size in the advanced cancer setting, it will eventually eradicate the tumor in the adjuvant setting as long as there is at least a modest effect on BCSCs.

              Conclusions

              We anticipate that our results will inform clinical trials of targeted therapies in planning the duration of therapy needed to eradicate BCSCs. Our predictions also address safety, as longer duration of therapy entails a greater potential impact on normal stem cells that may also be susceptible to stem cell-targeted therapies.
                Cover page: Determining Duration of HER2-Targeted Therapy Using Stem Cell Extinction Models
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2017)

                Background

                Limited evidence suggests that female breast tissue ages faster than other parts of the body according to an epigenetic biomarker of aging known as the "epigenetic clock." However, it is unknown whether breast tissue samples from healthy women show a similar accelerated aging effect relative to other tissues, and what could drive this acceleration. The goal of this study is to validate our initial finding of advanced DNA methylation (DNAm) age in breast tissue, by directly comparing it to that of peripheral blood tissue from the same individuals, and to do a preliminary assessment of hormonal factors that could explain the difference.

                Methods

                We utilized n = 80 breast and 80 matching blood tissue samples collected from 40 healthy female participants of the Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center who donated these samples at two time points spaced at least a year apart. DNA methylation levels (Illumina 450K platform) were used to estimate the DNAm age.

                Results

                DNAm age was highly correlated with chronological age in both peripheral blood (r = 0.94, p < 0.0001) and breast tissues (r = 0.86, p < 0.0001). A measure of epigenetic age acceleration (age-adjusted DNAm Age) was substantially increased in breast relative to peripheral blood tissue (p = 1.6 × 10-11). The difference between DNAm age of breast and blood decreased with advancing chronologic age (r = -0.53, p = 4.4 × 10-4).

                Conclusions

                Our data clearly demonstrate that female breast tissue has a higher epigenetic age than blood collected from the same subject. We also observe that the degree of elevation in breast diminishes with advancing age. Future larger studies will be needed to examine associations between epigenetic age acceleration and cumulative hormone exposure.
                  Cover page: DNA methylation age is elevated in breast tissue of healthy women.
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2021)

                  Background

                  Estrogens are thought to contribute to breast cancer risk through cell cycling and accelerated breast aging. We hypothesize that lifetime estrogen exposure drives early epigenetic breast aging observed in healthy women. In this study, we examined associations between hormonal factors and epigenetic aging measures in healthy breast tissues.

                  Methods

                  We extracted DNA from breast tissue specimens from 192 healthy female donors to the Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center. Methylation experiments were performed using the Illumina EPIC 850K array platform. Age-adjusted regression models were used to examine for associations between factors related to estrogen exposure and five DNA methylation-based estimates: Grim age, pan-tissue age, Hannum age, phenotypic age, and skin and blood clock age.

                  Results

                  Women were aged 19-90 years, with 95 premenopausal, and 97 nulliparous women. The age difference (Grim age - chronologic age) was higher at earlier ages close to menarche. We found significant associations between earlier age at menarche and age-adjusted accelerations according to the Grim clock, the skin and blood clock, and between higher body mass index (BMI) and age-adjusted accelerations in the Grim clock, Hannum clock, phenotypic clock, and skin and blood clock.

                  Conclusions

                  Earlier age at menarche and higher BMI are associated with elevations in DNA methylation-based age estimates in healthy breast tissues, suggesting that cumulative estrogen exposure drives breast epigenetic aging.

                  Impact

                  Epigenetic clock measures may help advance inquiry into the relationship between accelerated breast tissue aging and an elevated incidence of breast cancer in younger women.
                    Cover page: The Effects of Lifetime Estrogen Exposure on Breast Epigenetic Age
                    • Article
                    • Peer Reviewed
                    UCLA Previously Published Works (2011)
                    From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites--in the promoters of the EDARADD, TOM1L1, and NPTX2 genes--is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.
                      Cover page: Epigenetic Predictor of Age
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