Plasma cell-free DNA and circulating tumor cells as prognostic biomarkers in small cell lung cancer patients

血浆游离 DNA 和循环肿瘤细胞作为小细胞肺癌患者预后的生物标志物

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作者:Patricia Mondelo-Macía, Jorge García-González, Alicia Abalo, Manuel Mosquera-Presedo, Santiago Aguín, María Mateos, Rafael López-López, Luis León-Mateos #, Laura Muinelo-Romay #, Roberto Díaz-Peña #

Background

Lack of biomarkers for treatment selection and monitoring in small cell lung cancer (SCLC) patients with the limited therapeutic options, result in poor outcomes. Therefore, new prognostic biomarkers are needed to improve their management. The prognostic value of cell-free DNA (cfDNA) and circulating tumor cells (CTCs) have been less explored in SCLC.

Conclusions

We confirmed the prognostic utility of cfDNA quantitative analysis in SCLC patients before and during therapy. Our novel risk prognostic model in clinical practice will allow to identify patients who could benefit with actual therapies.

Methods

We quantified cfDNA in 46 SCLC patients at different times during first-line of chemotherapy or chemo-immunotherapy. Moreover, CTCs were analyzed in 21 patients before therapy onset using CellSearch® system. The possible association between both biomarkers and patients' outcomes was investigated in order to develop a prognostic model.

Results

High cfDNA levels before therapy were associated with shorter progression-free survival (PFS) and overall survival (OS). Furthermore, cfDNA levels at 3 weeks and at progression disease were also associated with patients' outcomes. Multivariate analyses confirmed the independence of cfDNA levels as a prognostic biomarker. Finally, the three-risk category prognostic model developed included Eastern Cooperative Oncology Group Performance Status (ECOG PS), gender and baseline cfDNA levels was associated with a higher risk of progression and death. Conclusions: We confirmed the prognostic utility of cfDNA quantitative analysis in SCLC patients before and during therapy. Our novel risk prognostic model in clinical practice will allow to identify patients who could benefit with actual therapies.

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