Integrative analysis of risk factors for immune-related adverse events of checkpoint blockade therapy in cancer.

Immune-related adverse events (irAEs) induced by checkpoint inhibitors involve a multitude of different risk factors. Here, to interrogate the multifaceted underlying mechanisms, we compiled germline exomes and blood transcriptomes with clinical data, before and after checkpoint inhibitor treatment, from 672 patients with cancer. Overall, irAE samples showed a substantially lower contribution of neutrophils in terms of baseline and on-therapy cell counts and gene expression markers related to neutrophil function. Allelic variation of HLA-B correlated with overall irAE risk. Analysis of germline coding variants identified a nonsense mutation in an immunoglobulin superfamily protein, TMEM162. In our cohort and the Cancer Genome Atlas (TCGA) data, TMEM162 alteration was associated with higher peripheral and tumor-infiltrating B cell counts and suppression of regulatory T cells in response to therapy. We developed machine learning models for irAE prediction, validated using additional data from 169 patients. Our results provide valuable insights into risk factors of irAE and their clinical utility.

Establishment of a platform of non-small-cell lung cancer patient-derived xenografts with clinical and genomic annotation.

BACKGROUND: Preclinical models that can better predict therapeutic activity in clinical trials are needed in this era of personalized cancer treatment. Herein, we established genomically and clinically annotated patient-derived xenografts (PDXs) from non-small-cell lung cancer (NSCLC) patients and investigated whether these PDXs would faithfully recapitulate patient responses to targeted therapy. METHODS: Patient-derived tumors were implanted in immunodeficient mice and subsequently expanded via re-implantation. Established PDXs were examined by light microscopy, genomic profiling, and in vivo drug testing, and the successful engraft rate was analyzed with the mutation profile, histology, or acquisition method. Finally, the drug responses of PDXs were compared with the clinical responses of the respective patients. RESULTS: Using samples from 122 patients, we established 41 NSCLC PDXs [30 adenocarcinoma (AD), 11 squamous cell carcinoma (SQ)], among which the following driver mutation were observed: 13 EGFR-mutant, 4 ALK-rearrangement, 1 ROS1-rearrangement, 1 PIK3CA-mutant, 1 FGFR1-amplification, and 2 KRAS-mutant. We rigorously characterized the relationship of clinical features to engraftment rate and latency rates. The engraft rates were comparable across histologic type. The AD engraft rate tended to be higher for surgically resected tissues relative to biopsies, whereas similar engraft rates was observed for SQ, irrespective of the acquisition method. Notably, EGFR-mutants demonstrated significantly longer latency time than EGFR-WT (86 vs. 37days, P = 0.007). The clinical responses were recapitulated by PDXs harboring driver gene alteration (EGFR, ALK, ROS1, or FGFR1) which regressed to their target inhibitors, suggesting that established PDXs comprise a clinically relevant platform. CONCLUSION: The establishment of genetically and clinically annotated NSCLC PDXs can yield a robust preclinical tool for biomarker, therapeutic target, and drug discovery.