Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation

Abstract

Background: The role of the tumor microenvironment (TME) in predicting prognosis and therapeutic efficacy has been demonstrated. Nonetheless, no systematic studies have focused on TME patterns or their function in the effectiveness of immunotherapy in triple-negative breast cancer.Methods: We comprehensively estimated the TME infiltration patterns of 491 TNBC patients from four independent cohorts, and three cohorts that received immunotherapy were used for validation. The TME subtypes were comprehensively evaluated based on immune cell infiltration levels in TNBC, and the TRG score was identified and systematically correlated with representative tumor characteristics. We sequenced 80 TNBC samples as an external validation cohort to make our conclusions more convincing.Results: Two TME subtypes were identified and were highly correlated with immune cell infiltration levels and immune-related pathways. More representative TME-related gene (TRG) scores calculated by machine learning could reflect the fundamental characteristics of TME subtypes and predict the efficacy of immunotherapy and the prognosis of TNBC patients. A low TRG score, characterized by activation of immunity and ferroptosis, indicated an activated TME phenotype and better prognosis. A low TRG score showed a better response to immunotherapy in TNBC by TIDE (Tumor Immune Dysfunction and Exclusion) analysis and sensitivity to multiple drugs in GDSC (Genomics of Drug Sensitivity in Cancer) analysis and a significant therapeutic advantage in patients in the three immunotherapy cohorts.Conclusion: TME subtypes played an essential role in assessing the diversity and complexity of the TME in TNBC. The TRG score could be used to evaluate the TME of an individual tumor to enhance our understanding of the TME and guide more effective immunotherapy strategies.

Immune Cytolytic Activity and Strategies for Therapeutic Treatment

“…Furthermore, the composition and characteristics of the TME can serve as prognostic tools for predicting the efficacy of immunotherapy. A systematic evaluation and construction of machine learning models based on the tumor microenvironment have been used to predict prognosis and immunotherapy efficacy in triple-negative breast cancer, showcasing the potential of leveraging TME insights for clinical decision making [157].…”

Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment

“…In addition, it is a fully data-driven learning method without rules-based programming. Currently, ML has been widely applied in the diagnosis and prognosis of various diseases and has shown encouraging results ( Gou et al, 2022 ; Palaniyappan et al, 2019 ; Zhang et al, 2019 ). In the field of headache, these approaches have been used for neuroimaging analysis.…”