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Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment.

ABSTRACT: Non-small cell lung cancer (NSCLC) is characterized by relatively rapid response to systemic treatments yet inevitable resistance and predisposed to distant metastasis. We thus aimed at performing sequencing analysis to determine genomic events and underlying mechanisms concerning drug resistance in NSCLC. We performed targeted sequencing of 40 medication-relevant genes on plasma samples from 98 NSCLC patients and analyzed impact of genetic alterations on clinical presentation as well as response to systemic treatments. Profiling of multi-omics data from 1024 NSCLC tissues in public datasets was carried out for comparison and validation of identified molecular events implicated in resistance. A genetic association of CYP2D6 deletion with drug resistance was identified through circulating tumor DNA (ctDNA) profiling and response assessment. FCGR3A amplification was potentially involved in resistance to EGFR inhibitors. We further verified our findings in tissue samples and focused on potential resistance mechanisms, which uncovered that depleted CYP2D6 affected a set of genes involved in EMT, oncogenic signaling as well as inflammatory pathways. Tumor microenvironment analysis revealed that NSCLC with CYP2D6 loss manifested increased levels of immunomodulatory gene expressions, PD-L1 expression, relatively high mutational burden and lymphocyte infiltration. DNA methylation alterations were also found to be correlated with mRNA expressions and copy numbers of CYP2D6. Finally, MEK inhibitors were identified by CMap as the prospective therapeutic drugs for CYP2D6 deletion. These analyses identified novel resistance mechanisms to systemic NSCLC treatments and had significant implications for the development of new treatment strategies.

SUBMITTER: Luo R

PROVIDER: S-EPMC8574960 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeUsing standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.Experimental designData were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, n = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, n = 50, CheckMate017; gefitinib, n = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity.ResultsThe radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55-1.00); docetaxel, 0.67 (0.37-0.96); and gefitinib, 0.82 (0.53-0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival.ConclusionsRadiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.

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Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment.

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