| アブストラクト | PURPOSE: Recent evidence has shown that higher tumor mutational burden strongly correlates with an increased risk of immune-related adverse events (irAEs). By using an integrated multiomics approach, we further studied the association between relevant tumor immune microenvironment (TIME) features and irAEs. METHODS: Leveraging the US Food and Drug Administration Adverse Event Reporting System, we extracted cases of suspected irAEs to calculate the reporting odds ratios (RORs) of irAEs for cancers treated with immune checkpoint inhibitors (ICIs). TIME features for 32 cancer types were calculated on the basis of the cancer genomic atlas cohorts and indirectly correlated with each cancer's ROR for irAEs. A separate ICI-treated cohort of non-small-cell lung cancer (NSCLC) was used to evaluate the correlation between tissue-based immune markers (CD8(+), PD-1/L1+, FOXP3+, tumor-infiltrating lymphocytes [TILs]) and irAE occurrence. RESULTS: The analysis of 32 cancers and 33 TIME features demonstrated a significant association between irAE RORs and the median number of base insertions and deletions (INDEL), neoantigens (r = 0.72), single-nucleotide variant neoantigens (r = 0.67), and CD8(+) T-cell fraction (r = 0.51). A bivariate model using the median number of INDEL neoantigens and CD8 T-cell fraction had the highest accuracy in predicting RORs (adjusted r(2) = 0.52, P = .002). Immunoprofile assessment of 156 patients with NSCLC revealed a strong trend for higher baseline median CD8(+) T cells within patients' tumors who experienced any grade irAEs. Using machine learning, an expanded ICI-treated NSCLC cohort (n = 378) further showed a treatment duration-independent association of an increased proportion of high TIL (>median) in patients with irAEs (59.7% v 44%, P = .005). This was confirmed by using the Fine-Gray competing risk approach, demonstrating higher baseline TIL density (>median) associated with a higher cumulative incidence of irAEs (P = .028). CONCLUSION: Our findings highlight a potential role for TIME features, specifically INDEL neoantigens and baseline-immune infiltration, in enabling optimal irAE risk stratification of patients. |
|---|
| 投稿者 | Kerepesi, Csaba; Abushukair, Hassan Mohammed; Ricciuti, Biagio; Nassar, Amin H; Adib, Elio; Alessi, Joao V; Pecci, Federica; Rakaee, Mehrdad; Fadlullah, Muhammad Zaki Hidayatullah; Tokes, Anna-Maria; Rodig, Scott J; Awad, Mark M; Tan, Aik Choon; Bakacs, Tibor; Naqash, Abdul Rafeh |
|---|
| 組織名 | Institute for Computer Science and Control (SZTAKI), Hungarian Research Network;(HUN-REN), Budapest, Hungary.;Jordan University of Science and Technology, Irbid, Jordan.;Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical;School, Boston, MA.;Yale School of Medicine, New Haven, CT.;Brigham and Women's Hospital, Boston, MA.;Department of Medicine, Brigham and Women's Hospital, Harvard Medical School,;Boston, MA.;Departments of Oncological Sciences and Biomedical Informatics, Huntsman Cancer;Institute, University of Utah, Salt Lake City, UT.;Department of Pathology, Forensic and Insurance Medicine, Semmelweis University,;Budapest, Hungary.;ImmunoProfile, Brigham and Women's Hospital and Dana-Farber Cancer Institute,;Department of Pathology, Brigham and Women's Hospital, Boston, MA.;Department of Probability, Alfred Renyi Institute of Mathematics, The Eotvos;Lorand Research Network, Budapest, Hungary.;Medical Oncology/TSET Phase 1 Program, Stephenson Cancer Center @The University;of Oklahoma, Oklahoma City, OK. |
|---|
