Scientific rationale:
Immunotherapy, particularly the development of immune checkpoint inhibitors (ICIs), has revolutionized cancer treatment by enhancing the immune system’s ability to destroy cancer cells; however, only a small fraction (ranging from 10% to 40%) of patients experience meaningful responses across different tumor types. Meanwhile, a broad spectrum of irAEs may occur in almost 40% of patients after ICIs initiation. Some of them can be severe or even potentially life-threatening, requiring treatment interruption and prompt interventions. Unfortunately, current decision-making procedures have limited accuracy for optimizing patients’ outcomes and reducing relevant toxicities. Aside from a few imperfect predictors related to tumor genomics and microenvironment, predictive biomarkers based on plasma proteomic and metabolomic signatures are still unknown, which are essential for evaluating the responses and safety of ICI usage. Moreover, a growing body of research suggested that host-related characteristics, such as physical exercise, might help boost the immune system’s response to immunotherapy, while distressing emotions might inhibit the immune response.
The combination of predictive biomarkers outperforms the use of the individual marker approach. Thus, based on a large dataset from the UK Biobank, this pilot project aims:
1.To estimate the long-term real-world treatment responses of IO in cancer therapy compared to conventional strategies.
2.To investigate the effects of physical activity and mental health on immunotherapy outcomes and the occurrence of irAEs.
3.To explore relevant risk factors and potential biomarkers for prognosis assessment based on the multi-omics datasets.
4.To construct a multivariable prediction model incorporating detailed variables that can help clinicians stratify patients who may benefit from both the safety and efficacy of IO therapy.
The expected value of the research:
1.This research can provide additional evidence on the effects of physical activity and mental health on the prognosis of immunotherapy.
2.Promising predictors can potentially serve as a new recommendation for refining patient selection who are likely to benefit from IO treatment.
3.Risk factors and multi-omics-based biomarkers can help healthcare providers identify individuals at high risk of experiencing toxicities.
4.A robust predictive model could provide valuable insights into clinical decision-making regarding proper IO usage and aid in educating patients.
Project duration:
We estimate that this project will last 3 years.