In:
Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 26, No. 4 ( 2020-02-15), p. 892-901
Kurzfassung:
The optimal systemic treatment for pulmonary large-cell neuroendocrine carcinoma (LCNEC) is still under debate. Previous studies showed that LCNEC with different genomic characteristics might respond differently to different chemotherapy regimens. In this study, we sought to investigate genomic subtyping using cell-free DNA (cfDNA) analysis in advanced LCNEC and assess its potential prognostic and predictive value. Experimental Design: Tumor DNA and cfDNA from 63 patients with LCNEC were analyzed by target-captured sequencing. Survival and response analyses were applied to 54 patients with advanced stage incurable disease who received first-line chemotherapy. Results: The mutation landscape of frequently mutated cancer genes in LCNEC from cfDNA closely resembled that from tumor DNA, which led to a 90% concordance in genomic subtyping. The 63 patients with LCNEC were classified into small-cell lung cancer (SCLC)-like and non–small cell lung cancer (NSCLC)-like LCNEC based on corresponding genomic features derived from tumor DNA and/or cfDNA. Overall, patients with SCLC-like LCNEC had a shorter overall survival than those with NSCLC-like LCNEC despite higher response rate (RR) to chemotherapy. Furthermore, treatment with etoposide-platinum was associated with superior response and survival in SCLC-like LCNEC compared with pemetrexed-platinum and gemcitabine/taxane-platinum doublets, while treatment with gemcitabine/taxane-platinum led to a shorter survival compared with etoposide-platinum or pemetrexed-platinum in patients with NSCLC-like LCNEC. Conclusions: Genomic subtyping has potential in prognostication and therapeutic decision-making for patients with LCNEC and cfDNA analysis may be a reliable alternative for genomic profiling of LCNEC.
Materialart:
Online-Ressource
ISSN:
1078-0432
,
1557-3265
DOI:
10.1158/1078-0432.CCR-19-0556
Sprache:
Englisch
Verlag:
American Association for Cancer Research (AACR)
Publikationsdatum:
2020
ZDB Id:
1225457-5
ZDB Id:
2036787-9
