Dataset Information

The Improving Cancer Aftercare Study.

ABSTRACT: This is a qualitative interview study that aims to understand treatment burden in individuals who have experienced prostate or colorectal cancer treatment within the past five years. We intend to use patient and caregiver experiences to co-design interventions to optimise cancer aftercare. Treatment burden is the workload of healthcare for patients and the consequences of this workload on patient function. Treatment burden has been associated with negative outcomes in stroke, heart failure, diabetes, and renal failure. Cancer is increasingly becoming a chronic condition, and involves a variety of self-management tasks for patients and their caregivers. In this study investigators will investigate treatment burden in people after prostate and colorectal cancer. Investigators will seek to understand patient and caregiver perceptions about cancer aftercare, and ways that services could be redesigned and improved to reduce treatment burden, and improve patient outcomes. We will undertake a qualitative interview study, recruiting patients from general practices and oncology outpatient clinics who have completed potentially curative treatment for prostate or colorectal cancer, or who are on active surveillance or hormonal therapies for localised or locally advanced prostate cancer. We will purposively sample, to ensure that participants with comorbidities, those from lower socioeconomic groups, and rural dwellers are adequately represented. We will conduct interviews according to a schedule, informed by conceptual models of burden of treatment, Schwarzer’s Health Action Process Approach, and Normalisation Process Theory. Interviews will be filmed and/or audio-recorded and transcribed. Framework and thematic analysis will be used to analyse and synthesise the data. Participants will be given the chance to comment on outputs and findings (triangulation). Investigators plan to use the results of this study, and excerpts from video interviews during co-design events, and to create new interventions to optimise aftercare for patients with prostate and colorectal cancer.

DISEASE(S): Colorectal Cancer,Colorectal Neoplasms,Prostate Cancer,Cancer

PROVIDER: 2321924 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models Natalie Kronik1¤, Yuri Kogan1, Moran Elishmereni1, Karin Halevi-Tobias1, Stanimir Vuk-Pavlovic ́2.,Zvia Agur1*.1Institute for Medical BioMathematics, Bene Ataroth, Israel,2College of Medicine, Mayo Clinic, Rochester, Minnesota, United States of America Abstract Background:Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients.We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simplemathematical models.Methodology/Principal Findings:We developed a general mathematical model encompassing the basic interactions of avaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cellvaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes inprostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were dividedinto each patient’s training set and his validation set. The training set, used for model personalization, contained thepatient’s initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized modelswere simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set.The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients(the coefficient of determination between the predicted and observed PSA values wasR2= 0.972). The model could notaccount for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validatedpersonalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccinationregimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Conclusions/Significance:Using a few initial measurements, we constructed robust patient-specific models of PCaimmunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value andfeasibility of individualized model-suggested immunotherapy protocols.

Dataset Information

The Improving Cancer Aftercare Study.

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