> Details
Menzel, Michael
[Author];
Kirchner, Martina
[Author];
Kluck, Klaus
[Author];
Ball, Markus
[Author];
Beck, Susanne
[Author];
Allgäuer, Michael
[Author];
Assmann, Christin
[Author];
Schnorbach, Johannes
[Author];
Volckmar, Anna-Lena
[Author];
Tay, Timothy Kwang Yong
[Author];
Goldschmid, Hannah
[Author];
Tan, Daniel SW
[Author];
Thomas, Michael
[Author];
Kazdal, Daniel
[Author];
Budczies, Jan
[Author];
Stenzinger, Albrecht
[Author];
Christopoulos, Petros
[Author]
Genomic heterogeneity at baseline is associated with T790M resistance mutations in EGFR-mutated lung cancer treated with the first-/second-generation tyrosine kinase inhibitors
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- Media type: E-Article
- Title: Genomic heterogeneity at baseline is associated with T790M resistance mutations in EGFR-mutated lung cancer treated with the first-/second-generation tyrosine kinase inhibitors
- Contributor: Menzel, Michael [VerfasserIn]; Kirchner, Martina [VerfasserIn]; Kluck, Klaus [VerfasserIn]; Ball, Markus [VerfasserIn]; Beck, Susanne [VerfasserIn]; Allgäuer, Michael [VerfasserIn]; Assmann, Christin [VerfasserIn]; Schnorbach, Johannes [VerfasserIn]; Volckmar, Anna-Lena [VerfasserIn]; Tay, Timothy Kwang Yong [VerfasserIn]; Goldschmid, Hannah [VerfasserIn]; Tan, Daniel SW [VerfasserIn]; Thomas, Michael [VerfasserIn]; Kazdal, Daniel [VerfasserIn]; Budczies, Jan [VerfasserIn]; Stenzinger, Albrecht [VerfasserIn]; Christopoulos, Petros [VerfasserIn]
-
imprint:
March 2024
- Published in: The journal of pathology: clinical research ; 10(2024), 2 vom: März, Artikel-ID e354, Seite 1-10
- Language: English
- DOI: 10.1002/cjp2.354
- ISSN: 2056-4538
- Identifier:
- Keywords: epidermal growth factor receptor (EGFR) ; non-small cell lung cancer (NSCLC) ; T790M mutation ; tumor heterogeneity ; tyrosine kinase inhibitor ; whole exome sequencing (WES)
- Origination:
-
Footnote:
Vorab veröffentlicht: 17. Januar 2024
- Description: This study analyzed whether extended molecular profiling can predict the development of epidermal growth factor receptor (EGFR) gene T790M mutation, which is the most frequent resistance alteration in non-small cell lung cancer (NSCLC) after treatment with the first-/second-generation (1G/2G) EGFR inhibitors (tyrosine kinase inhibitors [TKIs]), but only weakly associated with clinical characteristics. Whole exome sequencing (WES) was performed on pretreatment tumor tissue with matched normal samples from NSCLC patients with (n = 25, detected in tissue or blood rebiopsies) or without (n = 14, negative tissue rebiopsies only) subsequent EGFR p.T790M mutation after treatment with 1G/2G EGFR TKI. Several complex genetic biomarkers were assessed using bioinformatic methods. After treatment with first-line afatinib (44%) or erlotinib/gefitinib (56%), median progression-free survival and overall survival were 12.1 and 33.7 months, respectively. Clinical and tumor genetic characteristics, including age (median, 66 years), sex (74% female), smoking (69% never/light smokers), EGFR mutation type (72% exon 19 deletions), and TP53 mutations (41%) were not significantly associated with T790M mutation (p > 0.05). By contrast, complex biomarkers including tumor mutational burden, the clock-like mutation signature SBS1 + 5, tumor ploidy, and markers of subclonality including mutant-allele tumor heterogeneity, subclonal copy number changes, and median tumor-adjusted variant allele frequency were significantly higher at baseline in tumors with subsequent T790M mutation (all p < 0.05). Each marker alone could predict subsequent development of T790M with an area under the curve (AUC) of 0.72-0.77, but the small number of cases did not allow confirmation of better performance for biomarker combinations in leave-one-out cross-validated logistic regression (AUC 0.69, 95% confidence interval: 0.50-0.87). Extended molecular profiling with WES at initial diagnosis reveals several complex biomarkers associated with subsequent development of T790M resistance mutation in NSCLC patients receiving first-/second-generation TKIs as the first-line therapy. Larger prospective studies will be necessary to define a forecasting model.
- Access State: Open Access