Study overall design. (A) The evaluation of tumor microenvironment (TME) based on the 83 immunotherapy responsiveness-related features. (B) The construction of immunotherapy resistance score (IRS) based on the integrated machine learning methods. (C) The effectiveness of IRS was validated based on the published immunotherapy cohorts and Harbin Medical University cohort. (D) IRS can be applied in the exploration of comprehensive treatment strategies across different cancer types.

Immunotherapy has revolutionized cancer treatment in recent years, yet non-responsiveness of immunotherapy remains a challenge for cancer treatment. Therefore, the prediction method for potential clinical benefits of patients from immunotherapy is urgently needed. This study aims to develop an effective clinical practice assistance tool to evaluate the potential clinical benefits and therapy responsiveness of patients undergoing immunotherapy. We developed an immunotherapy resistance score (IRS), which performed well compared with conventional immunotherapy response indicators across different immunotherapy cohorts. Tumor microenvironment (TME) analysis showed that both immune and nonimmune features collectively impact immunotherapy responsiveness. Thus, IRS was constructed based on the TME features using machine learning approaches. The clinical application potential of IRS has been demonstrated in our in-house Harbin Medical University (HMU) cohort and an external validation cohort. Furthermore, we analyzed the correlation between IRS and pathways related to cancer therapy targets to explore the application potential of IRS in comprehensive cancer therapy. In conclusion, IRS is a robust tool for predicting patient immunotherapy prognosis, which has great potential to promote precise clinical therapy.

Article Access: https://doi.org/10.1002/mef2.70009

More about MedComm-Future Medicine: https://onlinelibrary.wiley.com/journal/27696456

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