Identification, Exploitation and Manipulation of BRCA1-Dependent DNA Double-Strand Break and Interstrand Crosslink Repair in Breast and Ovarian Cancer Therapy

Expression of functional breast cancer susceptibility gene 1 (BRCA1) in human cancers is associated with resistance to platinum-based chemotherapeutics and poly(ADP-ribose) polymerase (PARP) inhibitors. BRCA1 is a nuclear phosphoprotein with broad tumor suppressor activities that, among other functions, is critical for resolving double-strand DNA breaks (DSBs) and interstrand crosslinks (ICLs) by homologous recombination (HR). In vitro, animal and human clinical data have demonstrated that BRCA1-deficient cancers are highly sensitive to ICL-inducing alkylative chemotherapeutic agents, are amenable to synthetic lethal approaches which exploit defects in DSB/ICL repair (e.g., PARP inhibitors), and are generally associated with more favorable responses to anti-neoplastic therapy and improved survival. Conversely, high expression of wild-type BRCA1 in a number of cancers, as well as frame-restoring intragenic mutations in BRCA1 mutant ovarian cancers, is associated with therapeutic resistance and poor prognosis. Accordingly, there has been much interest in identifying, exploiting and manipulating DSB/ICL repair capacity to restore or enhance sensitivity to cancer therapeutics. In this study, we demonstrate that the heat shock protein 90 (HSP90) inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG (Tanespimycin)), which is currently in Phase II/III clinical evaluation, induces BRCA1 ubiquitination and proteasomal degradation in numerous in vitro models. Mechanistically, we show that loss of HSP90 function completely abolishes both homologous recombination and non-homologous end joining of DSBs, that BRCA1-deficient cells are hypersensitive to 17-AAG due to enhanced replication stress and aberrant entry into mitosis, and that 17-AAG can reverse BRCA1-dependent repair-mediated resistance. Additionally, we assessed the role of BRCA1 promoter methylation in sporadic triple-negative breast cancers (TNBCs) and identify a novel biomarker for poor response to anthracycline regimens in human patients. In summary, we document a novel upstream HSP90-dependent regulatory point in the Fanconi anemia/BRCA DSB/ICL repair pathway, illuminate the role of BRCA1 in regulating damage-associated checkpoint and replication responses to HSP90 inhibitors, specifically identify BRCA1 as a novel, clinically relevant target for enhancing radio- and chemosensitivity in refractory and/or resistant malignancies, and identify a useful biomarker for studies of therapeutic sensitivity in human TNBCs

The New Survivors: The Longer Term Cognitive, Scholastic and Motor Outcomes of a Total Scottish Population of Surviving Very Low Birthweight Infants

The prevalence of learning problems and impairments in cognitive ability and neuromotor functioning in a total geographically based very low birthweight population (N=324) was compared at eight years of age with that in a population comprising two classroom peers, matched for gender and age (N=590). The sociodemographic characteristics of the index and comparison groups were similar. The analyses reported in this thesis do not include those children being educated in the special school sector - as appropriate controls could not be identified. The mean IQ score for the index group was significantly lower than the mean IQ score for the comparison group. A significantly greater proportion of the index group had serious cognitive impairment, that is, they were performing more than 2 SD below the mean. The index children were found to be significantly underperforming in relation to the comparison children on tests of reading and number - although after controlling for IQ (ANCOVA) the difference between the two groups was no longer significant for reading. In terms of neuromotor competence, a significantly greater proportion of the index group than the comparison group were functioning below the 10th percentile. The 10th percentile (for the comparison group) was taken as the cut off to define motor impairment and 36% of the index group were categorised as motor impaired. Furthermore, a significantly greater proportion of the index group were classified as "suspicious" or "abnormal" in terms of their neurological status. The performance of the index children was also analysed by birthweight groupings (below 1000g and 1000 to 1499g) because of increasing clinical interest in the outcomes of children born on the limits of viability. The mean IQ scores for both index groups were significantly lower than those of their respective comparison groups. In all cognitive subscales apart from that testing short term auditory sequential memory, both index groups performed less well. Both index groups performed less well in tests of reading and number - although the differences were no longer significant after controlling for IQ. Fifteen per cent of the below 1000g index children and six per cent of those with birthweights 1000 to l499g attended special schools. Index children in both groupings who attended mainstream schools performed significantly less well in tests of neuromotor function than their peers. The differential effects of being small for gestational age (SGA) and of appropriate size for gestational age (AGA) on outcome measures of cognitive ability, scholastic attainment and neuromotor functioning were investigated. No differences were found between SGA and AGA index children, probably because the mean gestational age of the AGA children was lower than that of the SGA children. The SGA comparison children performed significantly less well on some measures of cognitive ability than the AGA comparison children. Gender differences on measures of cognitive ability, scholastic attainment and neuromotor functioning were investigated for both index and comparison groups. No gender differences were found in the index group with the exception of the ball skills element of the motor skills assessment where the performance of the females was poorer. The picture was the same for the comparison group except that, additionally, females were outperforming boys on tests of scholastic attainment. The extent to which under reporting of serious cognitive impairment can result from the use of published test norms was investigated. A larger proportion of index children were classified as seriously impaired in terms of cognitive ability when their performance was measured against norms derived from the comparison group. The same was true also for performance on measures of scholastic attainment. The possibility that motor impairment might affect performance on the visual items of the cognitive assessment battery used in this study was explored. While there was some evidence of such an effect particularly for the index children of satisfactory overall cognitive ability, the results of this investigation were inconclusive. The relationship between motor competence, neurological functioning and performance on measures of scholastic attainment was investigated. This strand of the investigation demonstrated that the test of neurological functioning used in this study is a useful screening tool for identifying children at high risk of learning difficulties

Molecular Portraits of Cancer Evolution and Ecology

Research on the molecular lesions that drive cancers holds the translational promise of unmasking distinct disease subtypes in otherwise pathologically identical patients. Yet clinical adoption is hindered by the reproducibility crisis for cancer biomarkers. In this thesis, a novel metric uncovered transcriptional diversity within individual non-small cell lung cancers, driven by chromosomal instability. Existing prognostic biomarkers were confounded by tumour sampling bias, arising from this diversity, in ~50% of patients assessed. An atlas of consistently expressed genes was derived to address this diagnostic challenge, yielding a clonal biomarker robust to sampling bias. This diagnostic based on cancer evolutionary principles maintained prognostic value in a metaanalysis of >900 patients, and over known risk factors in stage I disease, motivating further development as a clinical assay. Next, in situ RNA profiles of immune, fibroblast and endothelial cell subsets were generated from cancerous and adjacent non-malignant lung tissue. The phenotypic adaptation of stromal cells in the tumour microenvironment undermined the performance of existing molecular signatures for cell-type enumeration. Transcriptome-wide analysis delineated ~10% of genes displaying cell-type-specific expression, paving the way for high-fidelity signatures for the accurate digital dissection of tumour ecology. Lastly, the impact of branching, Darwinian evolution on the detection of epistatic interactions was evaluated in a pan-cancer analysis. The clonal status of driver genes was associated with the proportion of significant epistatic findings in 44-78% of the cancer-types assessed. Integrating the clonal architecture of tumours in future analyses could help decipher evolutionary dependencies. This work provides pragmatic solutions for refining molecular portraits of cancer in the light of their evolutionary and ecological features, moving the needle for precision cancer diagnostics

SPHINGOSINE KINASE 1 REGULATES FASCIN EXPRESSION TO PROMOTE METASTASIS IN TRIPLE NEGATIVE BREAST CANCER

Distant metastasis is the primary cause of breast cancer–related mortality. To date, effective therapeutic drugs that target metastasis are still lacking. Triple negative breast cancer (TNBC) occurs in high frequency in young women and are more likely to recur and metastasize than are other breast cancer subtypes. Also, TNBC patients cannot benefit from currently available hormonal or targeted therapies, as they lack estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2. Thus, understanding the signaling pathways that promote TNBC metastasis and developing novel therapeutic approaches to target them are critical, in order to prolong the survival and improve the quality of life for these patients. Aiming for fast clinical-translation, I performed bioinformatic analysis of patient-derived TCGA dataset to identify gene targets whose expressions are elevated in TNBC and which also have FDA-approved or in-pipeline pharmaceutical agents for repurposed intervention. Here, I found that sphingosine kinase 1 (SPHK1) was expressed at higher levels in TNBCs patients samples than in other breast cancer subtypes and high SPHK1 expression is associated with poor survival in TNBC patients. Also, TNBC cell lines have relatively higher expression of SPHK1 at both protein and mRNA levels compared to cell lines of other subtypes of breast cancer. In this dissertation, I studied a role of SPHK1 in promoting TNBC progression and metastasis and identified a molecular mechanism by which SPHK1 promotes TNBC metastasis. Overall, this study identifies a targetable molecular axis which plays an important role in TNBC metastasis and provide an opportunity for fast repositioning of available therapeutics for treatment of TNBC metastasis

The Bison: 1970

This digital object was funded in part through a grant from the Andrew W. Mellon Foundation. The digitalization of this object was part of a collaborative effort with the Washington Research Library Consortium and George Washington University.https://dh.howard.edu/bison_yearbooks/1138/thumbnail.jp

MITF阻害活性とBRAF阻害剤との併用によるアポトーシス誘導活性を持つ新規化合物の同定

筑波大学 (University of Tsukuba)201

Benefits of using marginal opportunistic wildlife behavior data: Constraints and applications across taxa – a dominance hierarchy example relevant for wildlife management

This study is a new approach on collecting, handling and examining wildlife behavior data across mammal species in order to provide new and unique conclusions from efficient data collection schemes. Sophisticated dominance hierarchy patterns and the ability of individual recognition are well described in many large mammals such as monkeys and cetaceans through the effort of detailed long-term studies. Their implications are well known as important topics regarding management strategies, especially for endangered species. However worldwide, for other large mammals, e.g. bears, detailed long-term wildlife behavior studies are virtually not available. This is due to the inaccessibility and inefficient observation abilities for many animal species in the wild, especially long-term studies. Up to now, it is believed that long-term studies are necessary to describe the existence of social structures like dominance hierarchies and individual perception abilities reliably and to present results in a sophisticated ‘significant’ manner. To accomplish more detailed behavior investigations on species where we lack such long-term data, here a new approach to this discipline ‘behavior modeling’ is presented, concentrating on the use of marginal opportunistic samples. This statistical approach has never been conducted to behavior analysis so far. Marginal behavior data for six species were investigated and cSUMMARY.................................................................................................................................. I ZUSAMMENFASSUNG ............................................................................................................ III 1 INTRODUCTION................................................................................................................... 1 1.1 Social Structure - Why it Matters........................................................................................ 1 1.2 Social Structure and Dominance Hierarchy in Higher Mammals ........................................ 4 1.2.1 Overview ................................................................................................................ 4 1.2.2 Howling Monkeys (Alouatta palliata)...................................................................... 5 1.2.3 Humpback Whales (Megaptera novaeangliae)....................................................... 7 1.2.4 Brown Bears (Ursus arctos) ................................................................................... 9 1.2.5 Polar Bears (Ursus maritimus) ............................................................................. 11 1.2.6 Spotted Seals (Phoca largha)............................................................................... 13 1.2.7 Muskoxen (Ovibos moschatus) ............................................................................ 15 1.3 Review of Using Opportunistic and Marginal Data ........................................................... 16 1.4 Data Mining in Behavior Sciences ................................................................................... 18 1.4.1 Overview .............................................................................................................. 18 1.4.2 Why TreeNet........................................................................................................ 20 1.5 Meta-analysis – Overview................................................................................................ 22 1.6 Overall Logic for Approach .............................................................................................. 23 1.7 Justification of Approach.................................................................................................. 24 2 METHODS .......................................................................................................................... 26 2.1 Field work ........................................................................................................................ 29 2.1.1 Howling Monkeys ................................................................................................. 29 2.1.2 Humpback Whales ............................................................................................... 30 2.1.3 Brown Bears ........................................................................................................ 31 2.1.4 Polar Bears .......................................................................................................... 31 2.1.5 Spotted Seals....................................................................................................... 33 2.1.6 Muskoxen............................................................................................................. 33 2.2 Statistic Programs............................................................................................................ 34 2.2.1 Modeling with TreeNet ......................................................................................... 35 2.2.2 Prediction Accuracy of the TreeNet Model ........................................................... 37 2.2.3 Distance Histograms ............................................................................................ 382.2.4 Interaction Diagrams ............................................................................................ 38 2.2.5 Meta-analysis....................................................................................................... 38 3 RESULTS ........................................................................................................................... 39 3.1 Preface ............................................................................................................................ 39 3.1.1 Preface to Modeling with TreeNet ........................................................................ 39 3.1.2 Preface to Prediction Accuracy of the TreeNet Model .......................................... 39 3.1.3 Preface to Distance Histograms ........................................................................... 40 3.1.4 Preface to Interaction Diagrams ........................................................................... 40 3.1.5 Preface to Meta-analysis...................................................................................... 40 3.2 Howling Monkeys............................................................................................................. 41 3.2.1 Modeling with TreeNet ......................................................................................... 41 3.2.2 Prediction Accuracy of the TreeNet Model ........................................................... 43 3.2.3 Distance Histograms ............................................................................................ 44 3.2.4 Interaction Diagrams ............................................................................................ 46 3.2.5 Meta-analysis....................................................................................................... 47 3.3 Humpback Whales........................................................................................................... 49 3.3.1 Modeling with TreeNet ......................................................................................... 49 3.3.2 Prediction Accuracy of the TreeNet Model ........................................................... 51 3.3.3 Distance Histograms ............................................................................................ 52 3.3.4 Interaction Diagrams ............................................................................................ 54 3.3.5 Meta-analysis....................................................................................................... 55 3.4 Brown Bears .................................................................................................................... 57 3.4.1 Modeling with TreeNet ......................................................................................... 57 3.4.2 Prediction Accuracy of the TreeNet Model ........................................................... 59 3.4.3 Distance Histograms ............................................................................................ 59 3.4.4 Interaction Diagrams ............................................................................................ 62 3.4.5 Meta-analysis....................................................................................................... 63 3.5 Polar Bears...................................................................................................................... 65 3.5.1 Modeling with TreeNet ......................................................................................... 65 3.5.2 Prediction Accuracy of the TreeNet Model ........................................................... 67 3.5.3 Distance Histograms ............................................................................................ 67 3.5.4 Interaction Diagrams ............................................................................................ 70 3.5.5 Meta-analysis....................................................................................................... 71 3.6 Spotted Seals .................................................................................................................. 73 3.6.1 Modeling with TreeNet ......................................................................................... 733.6.2 Prediction Accuracy of the TreeNet Model ........................................................... 76 3.6.3 Interaction Diagrams ............................................................................................ 76 3.6.4 Meta-analysis....................................................................................................... 77 3.7 Muskoxen ........................................................................................................................ 79 3.7.1 Modeling with TreeNet ......................................................................................... 79 3.7.2 Prediction Accuracy of the TreeNet Model ........................................................... 81 3.7.3 Distance Histograms ............................................................................................ 82 3.7.4 Interaction Diagrams ............................................................................................ 83 3.7.5 Meta-analysis....................................................................................................... 84 4 DISCUSSION...................................................................................................................... 86 4.1 Social Structures in studied Species................................................................................ 86 4.1.1 Howling Monkeys ................................................................................................. 86 4.1.2 Humpback Whales ............................................................................................... 87 4.1.3 Brown Bears ........................................................................................................ 88 4.1.4 Polar Bears .......................................................................................................... 90 4.1.5 Spotted Seals....................................................................................................... 91 4.1.6 Muskoxen............................................................................................................. 92 4.2 Use of Opportunistic and Marginal Datasets for Evidence and in Behavior Studies ......... 93 4.3 Modeling with TreeNet..................................................................................................... 93 4.4 Meta-analysis .................................................................................................................. 95 4.5 Meaning and Context of Key Findings ............................................................................. 96 4.6 Strength and Weaknesses of Approach........................................................................... 97 4.7 Individual Perception in Bears.......................................................................................... 98 5 OVERALL CONCLUSIONS AND STUDY SUGGESTIONS ............................................... 99 6 ACKNOWLEDGEMENTS ................................................................................................. 100 7 REFERENCES.................................................................................................................. 101 8 APPENDICES................................................................................................................... 112 8.1 Appendix: General Definitions ....................................................................................... 113 8.2 Appendix: Observed Activities ....................................................................................... 115 8.3 Appendix: Interaction Categories ................................................................................... 116 8.3.1 General Interaction Definitions ........................................................................... 116 8.3.2 Species Categorisation ...................................................................................... 1208.4 Appendix: Ethograms .................................................................................................... 124 8.5 Appendix: TreeNet Model Setup .................................................................................... 131 8.6 Appendix: Additonal Result Figures ............................................................................... 132 8.6.1 Howling Monkeys ............................................................................................... 132 8.6.2 Humpback Whales ............................................................................................. 139 8.6.3 Brown Bears ...................................................................................................... 144 8.6.4 Polar Bears ........................................................................................................ 148 8.6.5 Spotted Seals..................................................................................................... 153 8.6.6 Muskoxen – females .......................................................................................... 154 8.6.7 Muskoxen – males ............................................................................................. 157 8.7 Appendix: Example Distance Histograms as expected in non-social Species ................ 158 8.8 Appendix: CD ................................................................................................................ 15

Investigating disease and radiotherapy response associations with rectal tumour expression of the DNA damage response proteins, ATM, MRE11, NBS1 and RAD50

Rectal cancers are thought to contribute approximately one third of all colorectal cancers worldwide, and are associated with considerable morbidity and mortality. Worryingly, the incidence of rectal cancer is increasing in developed economies, as genetics and environment converge to cause pathology in increasingly older populations. Despite current detailed knowledge of various molecular mechanisms responsible for oncogenesis, in general, the precise sequence of events causing disease, or influencing prognosis, in a particular patient is not completely understood. This is unsurprising given the multifactorial nature of disease and treatment responses in human populations with highly variable clinical histories. To overcome this knowledge gap, this thesis sought to further refine the understanding of the molecular mechanisms at work during rectal cancer development, and their effect on treatment responses and patient outcomes. Furthermore, by defining the molecular mechanisms of disease, biomarkers (single or multiple molecules whose expression levels serve to identify disease processes common to many patients with the same disease) can be developed and applied to clinical situations – helping to diagnose, prognosticate and determine treatment modalities, depending on the application in question. To this end, and given the large heritage literature concerning DNA damage response proteins and cancer pathophysiology, the expression of four central DNA repair proteins (ATM, MRE11, NBS1 and RAD50) in rectal cancers has been quantified by immunohistochemistry. This will enable correlation of expression levels in different regions of the tumour with available clinicohistopathological variables, such as overall and disease-free survival. Furthermore, although radiotherapy represents a first-line treatment for rectal cancer, highly variable treatment responses have been documented amongst patients. As not all patients stand to benefit from such treatments, the expression of the candidate proteins – central to repairing damaged DNA generated by radiotherapy – and the association with radiotherapy responses are investigated in rectal tumours. Firstly, in the case of ATM, it was found that reduced expression in the growing edge of the tumour (tumour periphery) was associated with better responses to radiotherapy and improved disease-free survival. ATM expression in the tumour centre was also associated with disease-free survival by uni- and multi-variate analyses. Secondly, MRE11 expression was found to be predictive of patient outcomes, when patients were also scored positive for high-grade disease, metastasis positive, and showed perineural invasion. In contrast, NBS1 expression levels in rectal tumours were only found to have a marginal association with patient overall survival, necessitating additional studies of NBS1 in rectal cancer. Low RAD50 expression was associated with worse disease-free survival. RAD50 levels also proved to be useful to delineate low- and high-grade disease subgroups. Together, the analysis of these four markers individually, led to several novel associations with regards to rectal cancer – highlighting their ‘biomarker’ potential in this clinical context. Furthermore, by combining expression of these proteins into combinatorial panels – made up of either ATM and MRE11, or MRE11, NBS1 and RAD50 – a greater predictive power of their expression levels with respect to patient outcomes was demonstrated, and support the use of multiple markers to better understand disease in different patient groups. Therefore, the utility of examining DDR proteins in the context of rectal cancer is demonstrated in this thesis, and the results provide evidence to support future studies investigating the roles of these proteins in larger rectal cancer patient cohorts and other cancers. Further studies and validation of the results of this thesis will help determine whether such proteins can serve as clinically-useful biomarkers for disease intervention