A large-scale, multicentered trial evaluating the sensitivity and specificity of digital PCR versus ARMS-PCR for detecting ctDNA-based EGFR p.T790M in non-small-cell lung cancer patients

[A digital droplet PCR detection technique based on filter faster R-CNN]

OBJECTIVE

To propose a method for mitigate the impact of anomaly points (such as dust, bubbles, scratches on the chip surface, and minor indentations) in images on the results of digital droplet PCR (ddPCR) detection to achieve high-throughput, stable, and accurate detection.

METHODS

We propose a Filter Faster R-CNN ddPCR detection model, which employs Faster R-CNN to generate droplet prediction boxes followed by removing the anomalies within the positive droplet prediction boxes using an outlier filtering module (Filter). Using a plasmid carrying a norovirus fragment as the template, we established a ddPCR dataset for model training (2462 instances, 78.56%) and testing (672 instances, 21.44%). Ablation experiments were performed to test the effectiveness of 3 filtering branches of the Filter for anomaly removal on the validation dataset. Comparative experiments with other ddPCR droplet detection models and absolute quantification experiments of ddPCR were conducted to assess the performance of the Filter Faster R-CNN model.

RESULTS

In low-dust and dusty environments, the Filter Faster R-CNN model achieved detection accuracies of 98.23% and 88.35% for positive droplets, respectively, with composite F1 scores reaching 99.15% and 99.14%, obviously superior to the other models. The introduction of the filtering module significantly enhanced the positive accuracy of the model in dusty environments. In the absolute quantification experiments, a regression line was plotted using the results from commercial flow cytometry equipment as the standard concentration. The results show a regression line slope of 1.0005, an intercept of -0.025, and a determination coefficient of 0.9997, indicating high consistency between the two results.

CONCLUSION

The ddPCR detection technique using the Filter Faster R-CNN model provides a robust detection method for ddPCR under various environmental conditions.

[A Rare Case of Lung Adenocarcinoma with EGFR L833V/H835L Co-mutation and Literature Review]

Epidermal growth factor receptor (EGFR) mutations are the most common driver genes in the development of non-small cell lung cancer (NSCLC), of which mutations in exons 18-21 are frequent, especially the loss of exon 19 and exon 21 L858R mutation are the most frequent. Other rare gene mutations are rare. Simultaneous occurrence of two or more rare EGFR mutations are extremely rare in lung cancer, and the incidence of EGFR L833V/H835L rare gene compound mutations is very low, and there is little clinical data and evidence of relevant treatment methods. Some EGFR-tyrosine kinase inhibitors (EGFR-TKIs) are effective in treating lung cancer patients with rare gene mutations. In this article, we reported a case of NSCLC patient with a rare gene compound mutation EGFR L833V/H835L, who responded to Afatinib in combination with Anilotinib treatment well after 5 months of treatment, and computed tomography (CT) showed shrinkage of lung lesions. Meanwhile, we also compiled previously reported NSCLC patients with EGFR L833V/H835L rare gene compound mutation and summarized the characteristics of this group of patients and the effect of applying different kinds of EGFR-TKIs treatment.
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Giant Magnetoresistance Based Biosensors for Cancer Screening and Detection

Early-stage screening of cancer is critical in preventing its development and therefore can improve the prognosis of the disease. One accurate and effective method of cancer screening is using high sensitivity biosensors to detect optically, chemically, or magnetically labeled cancer biomarkers. Among a wide range of biosensors, giant magnetoresistance (GMR) based devices offer high sensitivity, low background noise, robustness, and low cost. With state-of-the-art micro- and nanofabrication techniques, tens to hundreds of independently working GMR biosensors can be integrated into fingernail-sized chips for the simultaneous detection of multiple cancer biomarkers (i.e., multiplexed assay). Meanwhile, the miniaturization of GMR chips makes them able to be integrated into point-of-care (POC) devices. In this review, we first introduce three types of GMR biosensors in terms of their structures and physics, followed by a discussion on fabrication techniques for those sensors. In order to achieve target cancer biomarker detection, the GMR biosensor surface needs to be subjected to biological decoration. Thus, commonly used methods for surface functionalization are also reviewed. The robustness of GMR-based biosensors in cancer detection has been demonstrated by multiple research groups worldwide and we review some representative examples. At the end of this review, the challenges and future development prospects of GMR biosensor platforms are commented on. With all their benefits and opportunities, it can be foreseen that GMR biosensor platforms will transition from a promising candidate to a robust product for cancer screening in the near future.

Comparative analysis of QS3D versus droplet digital PCR for quantitative measures of EGFR T790M mutation from identical plasma

Objectives

The capacity of QuantStudio™ 3D (QS3D) and droplet digital PCR (dPCR) for the detection of plasma Epidermal Growth Factor Receptor (EGFR) mutations have been widely reported. Few comparative studies on the quantitative test of the identical DNA material, however, are carried out. Here we compared the performance of the two methods in detecting EGFR T790M mutation in cell-free DNA (cfDNA) from the same lung cancer patients.

Methods

We recruited 72 non-small cell lung cancer (NSCLC) patients who initially respond to tyrosine kinase inhibitor treatment but subsequently developed resistance. Two tubes of 10mL anticoagulant blood were collected and cfDNA was isolated from plasma. Identical cfDNA samples were analyzed for T790M mutation using QS3D and droplet dPCR in parallel.

Results

T790M mutation was detected in 15 and 21 cfDNA samples by QS3D and droplet digital PCR, respectively. The 6 discordant samples showed low mutation abundance (∼0.1%) and the discrepancy is caused by the stricter threshold settings for QS3D dPCR. The overall agreement between the two methods was 91.7% (66/72). The median allele frequencies for QS3D dPCR and droplet dPCR to detect T790M mutation was 2.01% and 2.62%, respectively. There was no significance in mutation abundance detected by both methods. Both methods are highly correlated with allele frequencies and copy numbers in T790M wild type and mutant, with R² of 0.98, 0.92 and 0.95, respectively.

Conclusion

Our study demonstrated that QS3D dPCR are highly consistent with droplet PCR for quantitative determination of EGFR T790M mutation in plasma cfDNA.

A Novel Hypoxia-Related Gene Signature with Strong Predicting Ability in Non-Small-Cell Lung Cancer Identified by Comprehensive Profiling

Background

Non-small-cell lung cancer (NSCLC) is the most common malignant tumor among males and females worldwide. Hypoxia is a typical feature of the tumor microenvironment, and it affects cancer development. Circular RNAs (circRNAs) have been reported to sponge miRNAs to regulate target gene expression and play an essential role in tumorigenesis and progression. This study is aimed at identifying whether circRNAs could be used as the diagnostic biomarkers for NSCLC.

Methods

The heterogeneity of samples in this study was assessed by principal component analysis (PCA). Furthermore, the Gene Expression Omnibus (GEO) database was normalized by the affy R package. We further screened the differentially expressed genes (DEGs) and differentially expressed circular RNAs (DEcircRNAs) using the DEseq2 R package. Moreover, we analyzed the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of DEGs using the cluster profile R package. Besides, the Gene Set Enrichment Analysis (GSEA) was used to identify the biological function of DEGs. The interaction between DEGs and the competing endogenous RNAs (ceRNA) network was detected using STRING and visualized using Cytoscape. Starbase predicted the miRNAs of target hub genes, and miRanda predicted the target miRNAs of circRNAs. The RNA-seq profiler and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the variables were assessed by the univariate and multivariate Cox proportional hazard regression models. Significant variables in the univariate Cox proportional hazard regression model were included in the multivariate Cox proportional hazard regression model to analyze the association between the variables of clinical features. Furthermore, the overall survival of variables was determined by the Kaplan-Meier survival curve, and the time-dependent receiver operating characteristic (ROC) curve analysis was used to calculate and validate the risk score in NSCLC patients. Moreover, predictive nomograms were constructed and used to predict the prognostic features between the high-risk and low-risk score groups.

Results

We screened a total of 2039 DEGs, including 1293 upregulated DEGs and 746 downregulated DEGs in hypoxia-treated A549 cells. A549 cells treated with hypoxia had a total of 70 DEcircRNAs, including 21 upregulated and 49 downregulated DEcircRNAs, compared to A549 cells treated with normoxia. The upregulated genes were significantly enriched in 284 GO terms and 42 KEGG pathways, while the downregulated genes were significantly enriched in 184 GO terms and 25 KEGG pathways. Moreover, the function analysis by GSEA showed enrichment in the enzyme-linked receptor protein signaling pathway, hypoxia-inducible factor- (HIF-) 1 signaling pathway, and G protein-coupled receptor (GPCR) downstream signaling. Furthermore, six hub modules and 10 hub genes, CDC45, EXO1, PLK1, RFC4, CCNB1, CDC6, MCM10, DLGAP5, AURKA, and POLE2, were identified. The ceRNA network was constructed, and it consisted of 4 circRNAs, 14 miRNAs, and 38 mRNAs. The ROC curve was constructed and calculated. The area under the curve (AUC) value was 0.62, and the optimal threshold was 0.28. Based on the optimal threshold, the patients were divided into the high-risk score and low-risk score groups. The survival rate in the high-risk score group was lower than that in the low-risk score group. The expression of SERPINE1, STC2, and LPCAT1; clinical stage; and age of the patient were significantly correlated with the high-risk score. Moreover, nomograms were established based on the risk factors in multivariate analysis, and the median survival time, 3-year survival probability, and 5-year survival were possibly predicted according to nomograms.

Conclusion

The ceRNA network associated with NSCLC was identified, and the hub genes, circRNAs, might act as the potential biomarkers for NSCLC.

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Lung cancer ranks first in the incidence and mortality of cancer in the world, of which more than 80% are non-small cell lung cancer (NSCLC). The majority of NSCLC patients are in stage IIIB~IV when they are admitted to hospital and have no opportunity for surgery. Compared with traditional chemotherapy, specific targeted therapy has a higher selectivity and fewer adverse reactions, providing a new treatment direction for advanced NSCLC patients. Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are the widely used targeted therapy for NSCLC patients. Their efficacy and prognosis are closely related to the mutation status of the EGFR gene. Clinically, detecting EGFR gene mutation is often limited by difficulty obtaining tissue specimens, limited detecting technology, and economic conditions, so it is of great clinical significance to find indicators to predict EGFR gene mutation status. Clinicopathological characteristics, tumor markers, liquid biopsy, and other predictors are less invasive, economical, and easier to obtain. They can be monitored in real-time, which is supposed to predict EGFR mutation status and provide guidance for the accurate, individualized diagnosis and therapy of NSCLC patients. This article reviewed the correlation between the clinical indicators and EGFR gene mutation status in NSCLC patients.