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In cancer research the efficacy of a new treatment is often assessed by means of a clinical trial. In such trials the outcome measure of interest is usually time to death from entry into the study. The time to intermediate events may also be of interest, for example time to the spread of the disease to other organs (metastases). Thus, cancer clinical trials can be seen to generate multi-state data, in which patients may be in anyone of a finite number of states at a particular time. The classical analysis of data from cancer clinical trials uses a survival regression model. This type of model allows for the fact that patients in the trial will have been observed for different lengths of time and for some patients the time to the event of interest will not be observed (censored). The regression structure means that a measure of treatment effect can be obtained after allowing for other important factors. Clinical trials are not conducted in isolation, but are part of an on-going learning process. In order to assess the current weight of evidence for the use of a particular treatment a Bayesian approach is necessary. Such an approach allows for the formal inclusion of prior information, either in the form of clinical expertise or the results from previous studies, into the statistical analysis. An initial Bayesian analysis, for a single non-recurrent event, can be performed using non-temporal models that consider the occurrence of events up to a specific time from entry into the study. Although these models are conceptually simple, they do not explicitly allow for censoring or covariates. In order to address both of these deficiencies a Bayesian fully parametric multiplicative intensity regression model is developed. The extra complexity of this model means that approximate integration techniques are required. Asymptotic Laplace approximations and the more computer intensive Gauss-Hermite quadrature are shown to perform well and yield virtually identical results. By adopting counting process notation the multiplicative intensity model is extended to the multi-state scenario quite easily. These models are used in the analysis of a cancer clinical trial to assess the efficacy of neutron therapy compared to standard photon therapy for patients with cancer of the pelvic region. In this trial there is prior information both in the form of clinical prior beliefs and results from previous studies. The usefulness of multi-state models is also demonstrated in the analysis of a pilot quality of life study. Bayesian multi-state models are shown to provide a coherent framework for the analysis of clinical studies, both interventionist and observational, yielding clinically meaningful summaries about the current state of knowledge concerning the disease/treatment process.

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The management of patients undergoing colorectal surgery has changed in recent decades. Efforts have been made to show that perioperative physiological stress to the patient can be minimised with standardised care programmes and thus improve short term outcome after colorectal surgery. Mechanical bowel preparation (MBP), for instance, has been questioned as part of standard management. There are studies highlighting the effect of cancer treatment and its side effects in the elderly, showing that geriatric patients benefit from oncological therapy in much the same way as younger patients. The impact of this information on surgical and oncological practice in Sweden today is not known. To assess the effectiveness of colorectal surgery we need both randomised controlled trials and population-based cohort studies. We have performed a trial on colonic surgery with and without preoperative mechanical bowel preparation, as well as a nation-wide register study comparing treatment and outcome of rectal cancer in two age groups. In a randomised controlled trial 1505 patients from 21 hospitals were randomised to MBP or no-MBP prior to open elective colonic resection. There were no differences in overall complication rates between the groups: cardiovascular 5.1% with MBP vs. 4.6% without MBP; general infection 7.9% vs. 6.8%; and surgical site complications 15.1% vs. 16.1%. The proportion of patients reaching at least one primary endpoint was 24.5% vs. 23.7% respectively. The patients experience of and postoperative recovery after MBP or no-MBP was evaluated in 105 of the patients in the bowel preparation trial at three of the participating hospitals. Sixty-five patients received MBP and 40 patients did not. In the MBP group 52% needed assistance with bowel preparation. Day 4 postoperatively patients in the no-MBP group perceived more discomfort than patients in the MBP group, p<0.05. Bowel emptying occurred significantly earlier in the no-MBP group than in the MBP group, p<0.05. In an experimental study the effect of MBP on intramucosal bacterial count was evaluated. Macroscopically normal colon mucosa was collected from 37 patients (20 MBP and 17 No-MBP) undergoing elective colorectal surgery at three hospitals. MBP did not influence the median colony count of E. coli, Bacteroides, or total median colony count, information that was previously unknown. These three studies imply that MBP can be omitted before elective colonic resection. In a population-based register study, treatment for rectal cancer in patients ≥ 75 years and those < 75 years was evaluated using data from the Swedish Rectal Cancer Register 1995-2004 (N=15104). This study revealed that preoperative radiotherapy was used less in patients > 75 years. There was also a higher threshold for surgery in this group, and they more often received a permanent stoma compared to younger patients. Outcome in terms of 5-year local recurrence rate and 5-year cancer-specific survival differed very little between the older and younger patient groups who underwent abdominal tumour resection with curative intent. We suggest future studies focusing on ways of reducing surgical and perioperative stress and on quality of life when assessing suitable treatment modalities for rectal cancer.

Cancer cells require survival strategies to respond to microenviromental changes and out-compete their neighbours. They activate stress response mechanisms under extreme microenvironmental conditions, some of which are controlled by the amino acid-sensitive kinase complex, mechanistic Target of Rapamycin Complex 1 (mTORC1). Exosomes are secreted nanovesicles made inside intracellular endosomal compartments that mediate a specialised and complex form of intercellular signalling that can reprogramme target cells via the action of multiple active cargos. I investigated whether mTORC1 activity might modulate the type of exosome secreted in response to microenvironmental changes. Here I identify a new form of mTORC1-regulated exosome biogenesis and signalling involving recycling multivesicular endosomes (rMVEs), a previously unrecognised site for exosome biogenesis. Reduced activity of a specific form of glutamine-sensitive mTORC1 in HCT116 colorectal cancer cells results in an ‘exosome switch’ in which exosomes are preferentially released from these compartments instead of late endosomes. Importantly, RAB11a is found in association with at least a proportion of rMVEs that generate these alternative exosomes and is loaded on to some of their ILVs, providing a RAB signature of compartmental origin. I provide evidence that this exosome switch is conserved in other cancer cell types. My study also presents a proteomics analysis of extracellular vesicle (EV) preparations from normal and mTORC1-inhibited cells. I demonstrate that EV preparations isolated following exosome switching have enhanced pro-angiogenic properties and novel tumour growth-promoting activities. Activation of the receptor tyrosine kinase c-MET and its downstream mitogen-activated protein kinase (MAPK) ERK via phosphorylation is stimulated by these EVs, providing a potential explanation for their growth-promoting effects. Subsequent studies in the lab have demonstrated that several of these pro-tumorigenic activities are mediated by exosomes. I conclude that stress-induced mTORC1 inhibition allows tumour cells to initiate a novel exosome secretion pathway that potentially mediates a cancer cell survival plan that reverses microenvironmental change and supports tumour adaptation. In the future, blocking this response could improve patient outcome following treatment with mTORC1-inhibitory or anti-angiogenic drugs that have currently met with limited success in the clinic.

The current study illustrates the utilization of artificial neural network in statistical methodology. More specifically in survival analysis and time series analysis, where both holds an important and wide use in many applications in our real life. We start our discussion by utilizing artificial neural network in survival analysis. In literature there exist two important methodology of utilizing artificial neural network in survival analysis based on discrete survival time method. We illustrate the idea of discrete survival time method and show how one can estimate the discrete model using artificial neural network. We present a comparison between the two methodology and update one of them to estimate survival time of competing risks. To fit a model using artificial neural network, you need to take care of two parts; first one is the neural network architecture and second part is the learning algorithm. Usually neural networks are trained using a non-linear optimization algorithm such as quasi Newton Raphson algorithm. Other learning algorithms are base on Bayesian inference. In this study we present a new learning technique by using a mixture of the two available methodologies for using Bayesian inference in training of neural networks. We have performed our analysis using real world data. We have used patients diagnosed with skin cancer in the United states from SEER database, under the supervision of the National Cancer Institute. The second part of this dissertation presents the utilization of artificial neural to time series analysis. We present a new method of training recurrent artificial neural network with Hybrid Monte Carlo Sampling and compare our findings with the popular auto-regressive integrated moving average (ARIMA) model. We used the carbon dioxide monthly average emission to apply our comparison, data collected from NOAA.

Considerable disparities in cancer survival rates exist between African Americans (AAs) and white Americans (WAs). Various factors such as differences in socioeconomic status (SES), cancer stage at time of diagnosis, and treatment - which this analysis considers primary explanatory factors - have accounted for many of these differences. An additional factor not usually considered is vitamin D. Previous studies have inversely correlated higher solar ultraviolet-B (UVB) doses and serum 25-hydroxyvitamin D (25(OH)D) concentrations with incidence and/or mortality rates for about 20 types of cancer and improved survival rates for eight types of cancer. Because of darker skin pigmentation, AAs have 40% lower serum 25(OH)D concentrations than WAs. This study reviews the literature on disparities in cancer survival between AAs and WAs. The journal literature indicates that there are disparities for 13 types of cancer after consideration of SES, stage at diagnosis and treatment: bladder, breast, colon, endometrial, lung, ovarian, pancreatic, prostate, rectal, testicular, and vaginal cancer; Hodgkin lymphoma and melanoma. Solar UVB doses and/or serum 25(OH)D concentrations have been reported inversely correlated with incidence and/or mortality rates for all of these cancers. This finding suggests that future studies should consider serum 25(OH)D concentrations in addressing cancer survival disparities through both measurements of serum 25(OH)D concentrations and increasing serum 25(OH)D concentrations of those diagnosed with cancer, leading to improved survival rates and reduced disparities.

Radiotherapy cures many cancers, but the optimum total doses and fractionations used to treat different cancer types remain uncertain. While conventional fractionation (≈2 Gy per fraction) is common in many countries, UK practice has been highly variable. This thesis compared different curative-intent radiotherapy dose-fractionations used in non-small cell lung and breast cancer. These two cancers together make up over a quarter of UK cancer incidence and mortality, and radiotherapy can increase cure rates of both cancers. Two studies were conducted: (A) A meta-analysis of randomised radiotherapy trials in non-small cell lung cancer and (B) A cohort study of non-small cell lung and breast cancer radiotherapy in the Thames Valley. For the meta-analysis, a systematic search was conducted. Eligible studies were randomised comparisons of two or more radiotherapy regimens. Median survival ratios were calculated for each comparison and pooled. 3,795 patients in 25 randomised comparisons of radiotherapy dose were studied. When radiotherapy was given alone, the higher dose within-trial resulted in increased survival (median survival ratio 1.13, 95% confidence interval 1.04-1.22). When radiotherapy was given with concurrent chemotherapy, the higher dose within-trial resulted in decreased survival (median survival ratio 0.83, 95% confidence interval 0.71-0.97). For the cohort study, multiple Public Health England data sources were combined to obtain information on radiotherapy, patient characteristics, and outcomes. Multivariable Cox regressions were conducted separately by cancer site. 324 non-small cell lung, 8,879 invasive breast, and 477 ductal carcinoma in situ patients were studied. In analyses of both non-small cell lung and invasive breast cancer, increasing radiotherapy dose was associated with improved survival in some treatment centres, while in other centres the opposite was true. These opposite trends by treatment centre were unlikely to be explained by chance, and they suggest that differences in patient selection were driving results. There were insufficient events among ductal carcinoma in situ patients to assess associations. Findings from the meta-analysis support consideration of further radiotherapy dose escalation trials, making use of modern methods to reduce toxicity. Findings from the cohort study suggest that it is not possible to use observational studies to examine causal effects of radiotherapy dose-fractionation. This thesis therefore shows the continued importance of conducting sufficiently large randomised trials to ascertain optimal dose-fractionation in radiotherapy.

Jamaican males are a high-risk population for aggressive prostate cancer (PrCa) due to genetic influences, and identifying empirical data on treatments, which provide survival benefits is a prime challenge for clinicians who manage Jamaican PrCa patients. Thus, the purpose of this investigation was to elucidate treatment effects of brachytherapy and ERBT in the survival of a Jamaican PrCa cohort. Differences in survival outcomes of brachytherapy and ERBT treated Jamaican, and White U.S.-born PrCa patients with localized PrCa were compared. The mechanism of radiation programmed cell death in PrCa carcinogenesis explained in the oxidative stress theory, was the theoretical base for interpreting the research questions. A retrospective cohort design was used, and included survival analysis of secondary data from the Surveillance Epidemiology and End Results database. The sample size was 10,752 Jamaican and White U.S.-born prostate cancer patients diagnosed between 1992 and 2011. Kaplan-Meier and Cox proportional hazard regression models confirmed that brachytherapy resulted in enhanced survival benefits to the Jamaicans, HR 0.63, 95% CI [0.55, 0.73], p < .001, but ERBT did not, HR 1.6, 95% CI [1.38, 1.84] p < .001. Hence, brachytherapy may be an appropriate treatment option for Jamaican PrCa patients. Clinicians and health care planners can utilize the results for policy decisions aimed at increasing access to brachytherapy treatments to Jamaicans. Improving access to efficient PrCa treatments could reduce the morbidity and mortality rates of PrCa among Jamaicans, decrease years of potential life lost from PrCa, and enhance the life expectancy of the Jamaican male population.

Treatment outcomes of young patients with invasive breast cancer treated radically at Groote Schuur Hospital from 2013 to 2017: A single centre study Background: Breast cancer is the leading cause of cancer- related deaths globally, and the commonest cancer in women under 40 years. There is currently a lack of data relating to treatment outcomes of young women with breast cancer particularly in low-and middle-income countries. Aim: This study aims to evaluate the treatment outcomes of young patients (under 40 years) treated radically for invasive breast cancer in a low-and middle-income setting. Settings: Groote Schuur Hospital, Cape Town, South Africa Methods: A retrospective review of 101 women under 40 years, with invasive breast cancer treated radically, between 2013 and 2017 was conducted. Patient characteristics, tumour characteristics, disease stage, treatment, and follow-up were recorded. Primary objectives included evaluating overall and disease free survival, and analysing recurrence patterns and clinicopathological features. Results: The five-year overall and disease free survival for the entire cohort was 77% and 51%, respectively. Five-year overall survival by molecular subtype showed that Luminal A had the best survival, while triple negative breast cancer had the worst overall survival. Conclusion: Young women with breast cancer have poor survival outcomes despite early presentation. There is limited data regarding breast cancer treatment outcomes in patients under forty years.