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Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

ABSTRACT: Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

PROVIDER: PRJNA592578 | ENA |

REPOSITORIES: ENA

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Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

Project description:Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

| PRJNA592579 | ENA

Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer.

Project description: Not available

| S-EPMC7514475 | biostudies-literature

Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

Project description:Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in non-responding tumors in three immunotherapy-treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature, and HLA status provided an improved predictor of response to immunotherapy that was independently validated.

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Circular RNAs to predict clinical outcome after cardiac arrest

Project description:Background: Cardiac arrest (CA) represents the third leading cause of death worldwide. Among survivors, severe neurological sequelae are frequent but difficult to predict. Novel prognostic biomarkers would offer clinicians the possibility to deliver personalized healthcare. The potential of small circulating noncoding RNAs (microRNAs) to predict neurological outcome and survival after CA has been reported. Objective: This study aims to identify circulating circular RNAs (circRNAs) associated with clinical outcome after CA. Methods and Results: Methods and Results: Whole blood samples obtained 48h after return of spontaneous circulation from 23 sex-matched survivors and 23 deceased cardiac arrest (CA) patients were enrolled in this study. Whole transcriptome RNA sequencing identified candidate RNAs associated with neurological outcome and survival. Conclusion: We have identified candidate RNAs associated with clinical outcome after CA whose predictive value remains to be confirmed in large populations.

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