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Increased detection rates of EGFR and KRAS mutations in NSCLC specimens with low tumour cell content by 454 deep sequencing.

ABSTRACT: Detection of activating EGFR mutations in NSCLC is the prerequisite for individualised therapy with receptor tyrosine kinase inhibitors (TKI). In contrast, mutant downstream effector KRAS is associated with TKI resistance. Accordingly, EGFR mutation status is routinely examined in NSCLC specimens, but the employed methods may have a dramatic impact on the interpretation of results and, consequently, therapeutic decisions. Specimens with low tumour cell content are at particular risk for false-negative EGFR mutation reporting by sequencing with Sanger chemistry. To improve reliability of detecting clinically relevant mutant variants of EGFR and KRAS, we took full advantage of 454 deep sequencing and developed a two-step amplification protocol for the analysis of EGFR exons 18-21 and KRAS exons 2 and 3. We systematically addressed the sensitivity, reproducibility and specificity of the developed assay. Mutations could be reliably identified down to an allele frequency of 0.2-1.5 %, as opposed to 10-20 % detection limit of Sanger sequencing. High reproducibility (0-2.1 % variant frequency) and very low background level (0.4-0.8 % frequency) further complement the reliability of this assay. Notably, re-evaluation of 16 NSCLC samples with low tumour cell content ≤40 % and EGFR wild type status according to Sanger sequencing revealed clinically relevant EGFR mutations at allele frequencies of 0.9-10 % in seven cases. In summary, this novel two-step amplification protocol with 454 deep sequencing is superior to Sanger sequencing with significantly increased sensitivity, enabling reliable analysis of EGFR and KRAS in NSCLC samples independent of the tumour cell content.

SUBMITTER: Moskalev EA

PROVIDER: S-EPMC3624006 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients' clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted. Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86-0.96) was higher than that of RM (0.82, 95% CI: 0.75-0.89) and DLM (0.90, 95% CI: 0.85-0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77-0.93) was also higher than that of RM (0.68, 95% CI: 0.52-0.84) and DLM (0.79, 95% CI: 0.67-0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models. Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

Project description:BACKGROUND:3'-Deoxy-3'-[18F]fluorothymidine ([18F]FLT) was proposed as an imaging biomarker for the assessment of in vivo cellular proliferation with positron emission tomography (PET). The current study aimed to validate [18F]FLT as a perfusion-independent PET tracer, by gaining insight in the intra-tumoural relationship between [18F]FLT uptake and perfusion in non-small cell lung cancer (NSCLC) patients undergoing treatment with a tyrosine kinase inhibitor (TKI). Six patients with metastatic NSCLC, having an activating epidermal growth factor receptor (EGFR) mutation, were included in this study. Patients underwent [15O]H2O and [18F]FLT PET/CT scans at three time points: before treatment and 7 and 28 days after treatment with a TKI (erlotinib or gefitinib). Parametric analyses were performed to generate quantitative 3D images of both perfusion measured with [15O]H2O and proliferation measured with [18F]FLT volume of distribution (V T ). A multiparametric classification was performed by classifying voxels as low and high perfusion and/or low and high [18F]FLT V T using a single global threshold for all scans and subjects. By combining these initial classifications, voxels were allocated to four categories (low perfusion-low V T , low perfusion-high V T , high perfusion-low V T and high perfusion-high V T ). RESULTS:A total of 17 perfusion and 18 [18F]FLT PET/CT scans were evaluated. The average tumour values across all lesions were 0.53?±?0.26 mL cm-?3 min-?1 and 4.25?±?1.71 mL cm-?3 for perfusion and [18F]FLT V T , respectively. Multiparametric analysis suggested a shift in voxel distribution, particularly regarding the V T : from an average of ??77% voxels classified in the "high V T category" to ??85% voxels classified in the "low V T category". The shift was most prominent 7 days after treatment and remained relatively similar afterwards. Changes in perfusion and its spatial distribution were minimal. CONCLUSION:The present study suggests that [18F]FLT might be a perfusion-independent PET tracer for measuring tumour response as parametric changes in [18F]FLT uptake occurred independent from changes in perfusion. TRIAL REGISTRATION:Nederlands Trial Register (NTR), NTR3557 . Registered 2 August 2012.

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Increased detection rates of EGFR and KRAS mutations in NSCLC specimens with low tumour cell content by 454 deep sequencing.

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