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Immunotherapy-Mediated Thyroid Dysfunction: Genetic Risk and Impact on Outcomes with PD-1 Blockade in Non–Small Cell Lung Cancer
PurposeGenetic differences in immunity may contribute to toxicity and outcomes with immune checkpoint inhibitor (CPI) therapy, but these relationships are poorly understood. We examined the genetics of thyroid immune-related adverse events (irAE).Experimental designIn patients with non-small cell lung cancer (NSCLC) treated with CPIs at Memorial Sloan Kettering (MSK) and Vanderbilt University Medical Center (VUMC), we evaluated thyroid irAEs. We typed germline DNA using genome-wide single-nucleotide polymorphism (SNP) arrays and imputed genotypes. Germline SNP imputation was also performed in an independent Dana-Farber Cancer Institute (DFCI) cohort. We developed and validated polygenic risk scores (PRS) for hypothyroidism in noncancer patients using the UK and VUMC BioVU biobanks. These PRSs were applied to thyroid irAEs and CPI response in patients with NSCLC at MSK, VUMC, and DFCI.ResultsAmong 744 patients at MSK and VUMC, thyroid irAEs occurred in 13% and were associated with improved outcomes [progression-free survival adjusted HR (PFS aHR) = 0.68; 95% confidence interval (CI), 0.52-0.88]. The PRS for hypothyroidism developed from UK Biobank predicted hypothyroidism in the BioVU dataset in noncancer patients [OR per standard deviation (SD) = 1.33, 95% CI, 1.29-1.37; AUROC = 0.6]. The same PRS also predicted development of thyroid irAEs in both independent cohorts of patients treated with CPIs (HR per SD = 1.34; 95% CI, 1.08-1.66; AUROC = 0.6). The results were similar in the DFCI cohort. However, PRS for hypothyroidism did not predict CPI benefit.ConclusionsThyroid irAEs were associated with response to anti-PD-1 therapy. Genetic risk for hypothyroidism was associated with risk of developing thyroid irAEs. Additional studies are needed to determine whether other irAEs also have shared genetic risk with known autoimmune disorders and the association with treatment response
The Genomic Landscape of SMARCA4 Alterations and Associations with Outcomes in Patients with Lung Cancer
PURPOSE: SMARCA4 mutations are among the most common recurrent alterations in NSCLC, but the relationship to other genomic abnormalities and clinical impact has not been established.
EXPERIMENTAL DESIGN: To characterize SMARCA4 alterations in NSCLC, we analyzed the genomic, protein expression, and clinical outcome data of patients with SMARCA4 alterations treated at Memorial Sloan Kettering.
RESULTS: In 4813 tumors from patients with NSCLC, we identified 8% (n= 407) patients with SMARCA4-mutant lung cancer. We describe two categories of SMARCA4 mutations: Class 1 mutations (truncating mutations, fusions and homozygous deletion) and Class 2 mutations (missense mutations). Protein expression loss was associated with Class 1 mutation (81% vs 0%, (P \u3c 0.001)). Both classes of mutation co-occured more frequently with KRAS, STK11, and KEAP1 mutations compared to SMARCA4 wildtype tumors (P \u3c 0.001). In patients with metastatic NSCLC, SMARCA4 alterations were associated with shorter overall survival, with Class 1 alterations associated with shortest survival times (P \u3c 0.001). Conversely, we found that treatment with immune checkpoint inhibitors was associated with improved outcomes in patients with SMARCA4-mutant tumors (P = 0.01), with Class 1 mutations having the best response to ICIs (p = 0.027).
CONCLUSIONS: SMARCA4 alterations can be divided into two clinically relevant genomic classes associated with differential protein expression as well as distinct prognostic and treatment implications. Both classes co-occur with KEAP1, STK11, and KRAS mutations, but individually represent independent predictors of poor prognosis. Despite association with poor outcomes, SMARCA4-mutant lung cancers may be more sensitive to immunotherapy
The Genomic Landscape of SMARCA4 Alterations and Associations with Outcomes in Patients with Lung Cancer
Purpose: SMARCA4 mutations are among the most common recurrent alterations in non-small cell lung cancer (NSCLC), but the relationship to other genomic abnormalities and clinical impact has not been established. Experimental Design: To characterize SMARCA4 alterations in NSCLC, we analyzed the genomic, protein expression, and clinical outcome data of patients with SMARCA4 alterations treated at Memorial Sloan Kettering. Results: In 4,813 tumors from patients with NSCLC, we identified 8% (n ¼ 407) of patients with SMARCA4-mutant lung cancer. We describe two categories of SMARCA4 mutations: class 1 mutations (truncating mutations, fusions, and homozygous deletion) and class 2 mutations (missense mutations). Protein expression loss was associated with class 1 mutation (81% vs. 0%, P < 0.001). Both classes of mutation co-occurred more frequently with KRAS, STK11, and KEAP1 mutations compared with SMARCA4 wild-type tumors (P < 0.001). In patients with metastatic NSCLC, SMARCA4 alterations were associated with shorter overall survival, with class 1 alterations associated with shortest survival times (P < 0.001). Conversely, we found that treatment with immune checkpoint inhibitors (ICI) was associated with improved outcomes in patients with SMARCA4-mutant tumors (P ¼ 0.01), with class 1 mutations having the best response to ICIs (P ¼ 0.027). Conclusions: SMARCA4 alterations can be divided into two clinically relevant genomic classes associated with differential protein expression as well as distinct prognostic and treatment implications. Both classes co-occur with KEAP1, STK11, and KRAS mutations, but individually represent independent predictors of poor prognosis. Despite association with poor outcomes, SMARCA4-mutant lung cancers may be more sensitive to immunotherapy
Additional file 4 of Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library
Additional file 4: Figure S6. Overall survival of patients based on OncoCast-MPM risk grou
Additional file 8 of Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library
Additional file 8: Figure S9. Gene expression changes in TCGA mesothelioma tumors as a function of overall survival
Additional file 1 of Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library
Additional file 1: Table S1. Patient demographics at the time of PDX collection by histology
Additional file 3 of Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library
Additional file 3: Figure S2-S5. Detailed annotation of comparative histology of available patient samples and PDX models
Additional file 5 of Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library
Additional file 5: Table S2. Mapping statistics for RNA-Seq datase