Principal Investigator: Professor Eric Chuang
Department: National Taiwan UniversityTags: 54423, clinical phenotypes, genetic predisposition, mental health, pan-cancer, prediction modelling, survival analysis
Mood disorders are potential risk factors that reduce survival for cancer patients. Amongst others, anxiety, neuro-cognitive-dysfunction, sexual-dysfunctions, sleep-disturbance, stress-related disorders/PTSD, suicidal-tendencies, and bipolar and obsessive-compulsive disorders are symptoms that has been observed in cancer patients/survivors. Also, people with severe mental illness, including depression, are less likely to receive routine cancer screening. Therefore, there has been less work on the effect of psychiatric illness on cancer prognosis and survival. It has been found that a subset of cancer patients continue to be vulnerable to this complication even after treatment has ended, and often have difficulties with multitasking, short-term memory, word-finding, or attention. The underlying mechanism is not fully elucidated but may include direct neurotoxic effects of therapy, oxidative damage, and genetic predisposition. Lifestyle and environment of the patients are also important parameters that need to be investigated as a potential risk factor. Therefore, we propose to utilize data from different populations in a period of 3 years to examine the associations between mental health problems and cancer risk factors. The first year will be utilized to examine the factors that are potentially associated with risk of developing cancer for patients with mental health issues and to check for stratification effect due to sex, age and race for specific cancer types. Also demographic characteristics, health status, lifestyle effects and cancer risk factors will be compared for different mental health subgroups. Furthermore, genomic data will be used to conduct association tests to detect significantly associated SNP/CNVs and for genetic correlations amongst different cancer and mood-related conditions. The second year will be used to conduct survival analysis for patients with respect to different demographic, clinical and genomic factors. Also significantly associated findings from first year and imaging data would be used to train deep-learning models to predict survival. Finally, in the third year, comparison studies will be done between UK biobank and Taiwanese samples to establish ethnicity specific findings. As psychiatric patients have less likely, been receiving, specialist procedures, than general population, not adequate data is available for such studies. Therefore, it’s difficult to predict mental health impact on cancer diagnosis and treatment. This study will contribute on establishing factors that has an effect on the emotional vulnerability and comorbidity of mental health and cancer, among patients, thereby providing necessary information that has been lacking in this field of study.