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Quantification of circulating cell-free DNA to predict patient survival in non-small-cell lung cancer.

ABSTRACT: We used computed tomography (CT) to explore the prognostic value of cell-free (cf) DNA quantification and its predictive efficacy over time after chemotherapy in non-small-cell lung cancer (NSCLC) patients. In total, 177 NSCLC patients were enrolled in a prospective biomarker trial. Consecutive paired blood collection was performed to determine cfDNA concentrations at baseline CT and throughout serial follow-ups. The best cfDNA cut-off value to predict progression-free and overall survival was determined using X-tile analysis. Among 112 chemo-naive patients with stage IV adenocarcinoma, 43 were available for follow-up analysis. Cox regression multivariate analysis indicated that a high cfDNA concentration was an independent negative prognostic factor for progression-free survival (hazard ratio: 2.60; 95% confidence interval: 1.65-4.10; p = 0.008) and overall survival (hazard ratio: 2.63; 95% confidence interval: 1.66-4.17; p < 0.001). However, cfDNA concentration changes during treatment did not correlate with radiological CT responses at first follow-up or best response. No pattern was noted in the percent change in the cfDNA concentration from baseline or subsequently measured level to progression. The serum cfDNA concentration is thus associated with NSCLC patient prognosis, but does not appear to be a clinically valid marker for tumor responses.

SUBMITTER: Hyun MH

PROVIDER: S-EPMC5706884 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

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Quantification of circulating cell-free DNA to predict patient survival in non-small-cell lung cancer.

Hyun Myung Han MH Sung Jae Sook JS Kang Eun Joo EJ Choi Yoon Ji YJ Park Kyong Hwa KH Shin Sang Won SW Lee Sung Yong SY Kim Yeul Hong YH

Oncotarget 20171010 55

We used computed tomography (CT) to explore the prognostic value of cell-free (cf) DNA quantification and its predictive efficacy over time after chemotherapy in non-small-cell lung cancer (NSCLC) patients. In total, 177 NSCLC patients were enrolled in a prospective biomarker trial. Consecutive paired blood collection was performed to determine cfDNA concentrations at baseline CT and throughout serial follow-ups. The best cfDNA cut-off value to predict progression-free and overall survival was d ...[more]

PMID: 29212238

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Project description:Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

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Quantification of circulating cell-free DNA to predict patient survival in non-small-cell lung cancer.

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Quantification of circulating cell-free DNA to predict patient survival in non-small-cell lung cancer.

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