The dynamic use of EGFR mutation analysis in cell-free DNA as a follow-up biomarker during different treatment lines in non-small-cell lung cancer patients

Epidermal growth factor receptor (EGFR) mutational testing in advanced non-small-cell lung cancer (NSCLC) is usually performed in tumor tissue, although cfDNA (cell-free DNA) could be an alternative. We evaluated EGFR mutations in cfDNA as a complementary tool in patients, who had already known EGFR mutations in tumor tissue and were treated with either EGFR-tyrosine kinase inhibitors (TKIs) or chemotherapy. We obtained plasma samples from 21 advanced NSCLC patients with known EGFR tumor mutations, before and during therapy with EGFR-TKIs and/or chemotherapy. cfDNA was isolated and EGFR mutations were analyzed with the multiple targeted cobas EGFR Mutation Test v2. EGFR mutations were detected at baseline in cfDNA from 57% of patients. The semiquantitative index (SQI) significantly decreased from the baseline (median = 11, IQR = 9 5-13) to the best response (median = 0, IQR = 0-0, p < 0 01), followed by a significant increase at progression (median = 11, IQR = 11-15, p < 0 01) in patients treated with either EGFR-TKIs or chemotherapy. The SQI obtained with the cobas EGFR Mutation Test v2 did not correlate with the concentration in copies/mL determined by droplet digital PCR. Resistance mutation p.T790M was observed at progression in patients with either type of treatment. In conclusion, cfDNA multiple targeted EGFR mutation analysis is useful for treatment monitoring in tissue of EGFR-positive NSCLC patients independently of the drug received

The dynamic use of EGFR mutation analysis in cell-free DNA as a follow-up biomarker during different treatment lines in non-small-cell lung cancer patients

Epidermal growth factor receptor (EGFR) mutational testing in advanced non-small-cell lung cancer (NSCLC) is usually performed in tumor tissue, although cfDNA (cell-free DNA) could be an alternative. We evaluated EGFR mutations in cfDNA as a complementary tool in patients, who had already known EGFR mutations in tumor tissue and were treated with either EGFR-tyrosine kinase inhibitors (TKIs) or chemotherapy. We obtained plasma samples from 21 advanced NSCLC patients with known EGFR tumor mutations, before and during therapy with EGFR-TKIs and/or chemotherapy. cfDNA was isolated and EGFR mutations were analyzed with the multiple targeted cobas EGFR Mutation Test v2. EGFR mutations were detected at baseline in cfDNA from 57% of patients. The semiquantitative index (SQI) significantly decreased from the baseline (median = 11, IQR = 9 5-13) to the best response (median = 0, IQR = 0-0, p < 0 01), followed by a significant increase at progression (median = 11, IQR = 11-15, p < 0 01) in patients treated with either EGFR-TKIs or chemotherapy. The SQI obtained with the cobas EGFR Mutation Test v2 did not correlate with the concentration in copies/mL determined by droplet digital PCR. Resistance mutation p.T790M was observed at progression in patients with either type of treatment. In conclusion, cfDNA multiple targeted EGFR mutation analysis is useful for treatment monitoring in tissue of EGFR-positive NSCLC patients independently of the drug received

Whole exome sequencing and machine learning germline analysis of individuals presenting with extreme phenotypes of high and low risk of developing tobacco-associated lung adenocarcinomaResearch in context

Summary: Background: Tobacco is the main risk factor for developing lung cancer. Yet, while some heavy smokers develop lung cancer at a young age, other heavy smokers never develop it, even at an advanced age, suggesting a remarkable variability in the individual susceptibility to the carcinogenic effects of tobacco. We characterized the germline profile of subjects presenting these extreme phenotypes with Whole Exome Sequencing (WES) and Machine Learning (ML). Methods: We sequenced germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age (extreme cases) or who did not develop lung cancer at an advanced age (extreme controls), selected from databases including over 6600 subjects. We selected individual coding genetic variants and variant-rich genes showing a significantly different distribution between extreme cases and controls. We validated the results from our discovery cohort, in which we analysed by WES extreme cases and controls presenting similar phenotypes. We developed ML models using both cohorts. Findings: Mean age for extreme cases and controls was 50.7 and 79.1 years respectively, and mean tobacco consumption was 34.6 and 62.3 pack-years. We validated 16 individual variants and 33 variant-rich genes. The gene harbouring the most validated variants was HLA-A in extreme controls (4 variants in the discovery cohort, p = 3.46E-07; and 4 in the validation cohort, p = 1.67E-06). We trained ML models using as input the 16 individual variants in the discovery cohort and tested them on the validation cohort, obtaining an accuracy of 76.5% and an AUC-ROC of 83.6%. Functions of validated genes included candidate oncogenes, tumour-suppressors, DNA repair, HLA-mediated antigen presentation and regulation of proliferation, apoptosis, inflammation and immune response. Interpretation: Individuals presenting extreme phenotypes of high and low risk of developing tobacco-associated lung adenocarcinoma show different germline profiles. Our strategy may allow the identification of high-risk subjects and the development of new therapeutic approaches. Funding: See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript