Correlation between serum CEA levels and EGFR mutations in Chinese nonsmokers with lung adenocarcinoma

The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA

Objective

The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means.

Materials and methods

We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations.

Results

EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency.

Conclusion

CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.

Correlation between carcinoembryonic antigen (CEA) expression and EGFR mutations in non-small-cell lung cancer: a meta-analysis

OBJECTIVES

The purpose of this meta-analysis was to investigate the relationship between serum carcinoembryonic antigen (CEA) expression and epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC).

METHODS

Databases such as PubMed, Cochrane, EMBASE and Google Scholar were systematically searched to identify studies assessing the association of serum CEA expression with EGFR mutations. Across 19 studies, 4168 patients were included between CEA expression and EGFR mutations odds ratio (OR) conjoint analysis of correlations.

RESULTS

Compared with CEA-negative NSCLC, CEA-positive tumors had an increased EGFR mutation rate (OR = 1.85, 95% confidence interval: 1.48-2.32, P < 0.00001). This association was observed in both stage IIIB/IV patients (OR = 1.60, 95% CI: 1.18-2.15, P = 0.002) and stage I-IIIA (OR = 1.67, 95% CI: 1.01-2.77, P = 0.05) patients. In addition, CEA expression was associated with exon 19 (OR = 1.97, 95% CI: 1.25-3.11, P = 0.003) and exon 21 (OR = 1.51, 95% CI: 1.07-2.12, P = 0.02) EGFR mutations. In ADC pathological type had also showed the correlation (OR = 1.84, 95% CI: 1.31-2.57, P = 0.0004).

CONCLUSIONS

This meta-analysis indicated that serum CEA expression was associated with EGFR mutations in NSCLC patients. The results of this study suggest that CEA level may play a predictive role in the EGFR mutation status of NSCLC patients. Detecting serum CEA expression levels can give a good suggestion to those patients who are confused about whether to undergo EGFR mutation tests. Moreover, it may help better plan of the follow-up treatment.

Detection of EGFR gene polymorphisms in non-small cell lung cancer Egyptian patients: a case-control study

BACKGROUND

Non-Small Cell Lung Cancer displays several genetic mutations including epidermal growth factor receptor. This study's objective was to determine if the EGFR exon19 rs121913438 and exon21 rs121434568 variations play a role in NSCLC susceptibility.

METHODS

Case-control research was done at the Mansoura university oncology center including 124 NSCLC patients, and 124 healthy volunteers. blood was used to obtain genomic DNA. ARMS-PCR was used to genotype single-nucleotide polymorphisms.

RESULTS

Molecular study for EGFR exon 19 del. showed NSCLC cases were significantly associated with a higher proportion of heterozygous WD, WD + DD dominant genotypes, and mutant D allele, (p < 0.05 for each), with a risk to develop NSCLC. also, NSCLC cases were significantly associated with a higher proportion of heterozygous TG, TG + GG dominant genotype, G mutant allele, (p < 0.05 for each), with a risk to develop LC (OR > 1 for each). regarding the two EGFR mutations, TTF1 staining was significantly associated with WD + DD genotypes for EGFR exon 19 del But not EGFR exon 21. No substantial differences were found among all studied cases with CK7 or napsin A Tumor cytochemistry.

CONCLUSIONS

The WD heterozygous genotype and D allele in exon 19 del. mutation as well as the TG heterozygous and G allele in exon 21 substitution mutation in EGFR gene are strongly associated with the development of advanced-NSCLC in the Egyptians.

Differential clinicopathological features, treatments and outcomes in patients with Exon 19 deletion and Exon 21 L858R EGFR mutation-positive adenocarcinoma non-small-cell lung cancer

The most common oncogenic driver in non-small-cell lung cancer (NSCLC) is the epidermal growth factor receptor (EGFR) gene mutations that occur more frequently among Asians (30%-50%) as opposed to Caucasians (10%-15%). Lung cancer is one of the most prevalent cancers in India, with a reported adenocarcinoma positivity ranging between 26.1% and 86.9% in NSCLC patients. The prevalence of EGFR mutations in adenocarcinoma patients (36.9%) in India is higher than that of Caucasian patients and lower than that of East Asian patients. The exon 19 deletion (Ex19del) is more common than exon 21 L858R mutations in Indian patients with NSCLC. Studies have shown that the clinical behaviour of patients with advanced NSCLC differs between EGFR Ex19del and exon 21 L858R mutation status. In this study, we investigated the differences in clinicopathological features and survival outcomes after first line and second-line treatment with EGFR tyrosine kinase inhibitors (EGFR TKIs) in NSCLC patients with Ex19del and exon 21 L858R EGFR mutation status. This study also focuses on the role and potential benefits of dacomitinib, a second-generation irreversible EGFR TKI, in patients with Ex19del and exon 21 L858R EGFR mutation-positive advanced NSCLC in Indian settings.

Identification of EGFR mutation status in male patients with non-small-cell lung cancer: role of 18F-FDG PET/CT and serum tumor markers CYFRA21-1 and SCC-Ag

BACKGROUND

The high incidence of epidermal growth factor receptor (EGFR) mutations is usually found in female patients with lung adenocarcinoma who have never-smoked. However, reports concerning male patients are scarce. Thus, this study aimed to explore a novel approach based on ¹⁸F-fluoro-2-deoxy-2-deoxyglucose (¹⁸F-FDG) PET/CT and serum tumor markers (STMs) to determine EGFR mutation status in male patients with non-small-cell lung cancer (NSCLC).

METHODS

A total of 121 male patients with NSCLC were analyzed between October 2019 and March 2022. All patients underwent ¹⁸F-FDG PET/CT scan before treatment and monitored 8 STMs (cytokeratin 19 fragment [CYFRA21-1], squamous cell carcinoma-related antigen [SCC-Ag], carcinoembryonic antigen [CEA], neuron-specific enolase [NSE], carbohydrate antigen [CA] 50, CA125, CA72-4, and ferritin). A comparison was done between EGFR mutant and wild-type patients in terms of the maximum standardized uptake value of primary tumors (pSUV_max) and 8 STMs. We performed receiver operating characteristic (ROC) curve and multiple logistic regression analyses to determine predictors for EGFR mutation status.

RESULTS

EGFR mutations were detected in 39 patients (32.2%). Compared with patients with EGFR wild-type, EGFR-mutant patients had lower concentrations of serum CYRFA21-1 (2.65 vs. 4.01, P = 0.002) and SCC-Ag (0.67 vs. 1.05, P = 0.006). No significant differences of CEA, NSE, CA 50, CA125, CA72-4 and ferritin were found between the two groups. The presence of EGFR mutations was significantly associated with low pSUV_max (< 8.75), low serum SCC-Ag (< 0.79 ng/mL) and CYFRA21-1 (< 2.91 ng/mL) concentrations. The area under ROC curve values were 0.679, 0.655, 0.685 and 0.754, respectively, for low CYFRA21-1, SCC-Ag, pSUV_max and the combination of these three factors.

CONCLUSIONS

We demonstrated that low concentrations of CYFRA21-1 and SCC-Ag, as well as low pSUV_max, were associated with EGFR mutations, and that the combination of these factors resulted in a higher differentiation of EGFR mutation status in male patients with NSCLC.

The rational application of liquid biopsy based on next-generation sequencing in advanced non-small cell lung cancer

BACKGROUND

Plasma and tissue biopsy have both used for targeting actionable driver gene mutations in lung cancer, whose concordance is imperfect. A reliable method to predict the concordance is urgently needed to ease clinical application.

METHODS

A total of 1012 plasma samples, including 519 with paired-tissue biopsy samples, derived from lung adenocarcinoma patients were retrospectively enrolled. We assessed the associations of several clinicopathological characteristics and serum tumor markers with the concordance between plasma and tissue biopsies.

RESULTS

When carcinoembryonic antigen (CEA) levels were higher than thresholds of 15.01 ng/ml and 51.15 ng/ml, the positive predictive value of concordance reached 90% and 95%, respectively. When CEA levels were lower than thresholds of 5.19 ng/ml and 3.26 ng/mL, the negative predictive value of concordance reached 45% and 50%. The performance of CYFRA21-1 in predicting concordance was similar but inferior to CEA (AUC: 0.727 vs. 0.741, p = 0.633). The performance of CEA combined with CYFRA21-1 in predicting the concordance was similar to that of the combination of independent factors derived from the LASSO regression model (AUC: 0.796 vs. 0.818, p = 0.067). CEA (r = 0.47, p < 0.01) and CYFRA21-1 levels (r = 0.45, p < 0.05) were significantly correlated with the maximum variant allele frequency, respectively.

CONCLUSIONS

CEA combined with CYFRA21-1 could effectively predict the concordance between plasma and tissue biopsies, which could be used for evaluating the priority of plasma and tissue biopsies for gene testing to timely guide clinical applications in advanced lung adenocarcinoma patients.

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study

Background

Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs).

Methods

In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models.

Results

The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection.

Conclusions

In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02053-2.

Dynamic monitoring serum tumor markers to predict molecular features of EGFR-mutated lung cancer during targeted therapy

To reveal the correlation of dynamic serum tumor markers (STMs) and molecular features of epidermal growth factor receptor‐mutated (EGFR‐mutated) lung cancer during targeted therapy, we retrospectively reviewed 303 lung cancer patients who underwent dynamic STM tests [neuron‐specific enolase (NSE), carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), carbohydrate antigen 153 (CA153), the soluble fragment of cytokeratin 19 (CYFRA21‐1), and squamous cell carcinoma antigen (SCC)] and circulating tumor DNA (ctDNA) testing with a panel covering 168 genes. At baseline, patients with EGFR mutation trended to have abnormal CEA, abnormal CA153, and normal SCC levels. Additionally, patients with Thr790Met (T790M) mutation were more likely to have abnormal CEA levels than patients without T790M mutation. Among patients with secondary resistance to EGFR tyrosine kinase inhibitors (TKI), the dynamic STMs showed a descending trend in the responsive stage and a rising trend in the resistant stage. However, the changing slopes differed between T790M subgroup and the non‐T790M subgroup in individual STMs. Our study demonstrated that the combination of baseline levels and variations of STMs (including the responsive stage and resistant stage) can be suggestive of secondary EGFR‐T790M mutation [area under the curve (AUC) = 0.897] and that changing trends of STMs (within 8 weeks after initiating the TKI therapy) can be potential predictors for the clearance of EGFR ctDNA [AUC = 0.871]. In conclusion, dynamic monitoring STMs can help to predict the molecular features of EGFR‐mutated lung cancer during targeted therapy.

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.

Radiomic Feature-Based Nomogram: A Novel Technique to Predict EGFR-Activating Mutations for EGFR Tyrosin Kinase Inhibitor Therapy

Objectives

To develop and validate a radiomic feature-based nomogram for preoperative discriminating the epidermal growth factor receptor (EGFR) activating mutation from wild-type EGFR in non-small cell lung cancer (NSCLC) patients.

Material

A group of 301 NSCLC patients were retrospectively reviewed. The EGFR mutation status was determined by ARMS PCR analysis. All patients underwent nonenhanced CT before surgery. Radiomic features were extracted (GE healthcare). The maximum relevance minimum redundancy (mRMR) and LASSO, were used to select features. We incorporated the independent clinical features into the radiomic feature model and formed a joint model (i.e., the radiomic feature-based nomogram). The performance of the joint model was compared with that of the other two models.

Results

In total, 396 radiomic features were extracted. A radiomic signature model comprising 9 selected features was established for discriminating patients with EGFR-activating mutations from wild-type EGFR. The radiomic score (Radscore) in the two groups was significantly different between patients with wild-type EGFR and EGFR-activating mutations (training cohort: P<0.0001; validation cohort: P=0.0061). Five clinical features were retained and contributed as the clinical feature model. Compared to the radiomic feature model alone, the nomogram incorporating the clinical features and Radscore exhibited improved sensitivity and discrimination for predicting EGFR-activating mutations (sensitivity: training cohort: 0.84, validation cohort: 0.76; AUC: training cohort: 0.81, validation cohort: 0.75). Decision curve analysis demonstrated that the nomogram was clinically useful and surpassed traditional clinical and radiomic features.

Conclusions

The joint model showed favorable performance in the individualized, noninvasive prediction of EGFR-activating mutations in NSCLC patients.

Value of serum tumor markers for predicting EGFR mutations and positive ALK expression in 1089 Chinese non-small-cell lung cancer patients: A retrospective analysis

PURPOSE

The role of serum tumor markers (STMs) in the modern management of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutations in lung cancer remains poorly described. In this study, we investigated whether STMs could be a valuable noninvasive tool to predict EGFR mutations and ALK positivity in non-small-cell lung cancer (NSCLC) patients.

EXPERIMENTAL DESIGN

We retrospectively reviewed and included 1089 NSCLC patients who underwent EGFR or ALK mutation testing and STMs measurement prior to treatment. The differences in several clinical characteristics and STMs between the subgroups were analyzed. Multivariate logistic regression analysis was performed to identify predictors of EGFR mutations and ALK positivity.

RESULTS

EGFR mutations were found more frequently in females (63.11%), never-smokers (59.69%), and those with lung adenocarcinoma (ADC) (53.87%). Negative carbohydrate antigen (CA) 125, ferritin (FERR), squamous cell carcinoma antigen (SCC), and soluble fragment of cytokeratin 19 (CYFRA 21-1) levels were significantly associated with EGFR mutations (p < 0.05). Multivariate analysis demonstrated that ADC, never-smoker status, and negative CA 125 and SCC results were predictors of EGFR mutations (p < 0.05). The receiver operating characteristic (ROC) curve yielded an area under the curve (AUC) of 0.715 (95% confidence interval [CI]: 0.673-0.758) for the combination of the four factors. Positive ALK expression was found more frequently in younger patients (median age: 49 years), females (8.40%), never-smokers (8.82%), and those negative for carcinoembryonic antigen (CEA) (8.02%). Multivariate analysis demonstrated that younger age and never-smoker status were the only independent predictors of ALK positivity (p < 0.05). The ROC curve yielded an AUC of 0.760 (95% CI: 0.677-0.844) for the combination of these two factors.

CONCLUSION

STMs are associated with mutant EGFR status and could be integrated with other clinical factors to enhance the ability to distinguish EGFR mutation status among NSCLC patients. For ALK-positive patients, younger age and never-smoker status could predict the mutation status, whereas STMs could not.

Gene status and clinicopathologic characteristics of lung adenocarcinomas with mediastinal lymph node metastasis

Lung cancer with mediastinal lymph node metastasis is more likely to develop recurrence and metastasis after complete resection and targeted therapy is a promising treatment strategy. We performed amplification refractory mutation system (ARMS) fluorescence quantitative PCR to detect the gene status of EGFR, ALK, ROS1 and RET in resected samples from 280 patients who were confirmed to have primary lung adenocarcinomas with N1-N2 lymph node metastasis. Of the 280 patients enrolled, the frequency of EGFR mutations, ALK fusions, ROS1 fusions, RET fusions and no mutations was 42.9%, 10.7%, 1.8%, 3.6% and 42.9%, respectively. Five patients exhibited the coexistence of the EGFR and ALK alterations. ALK, ROS1 and RET fusions were mutually exclusive. The frequency of EGFR mutation was significantly lower among patients with poor differentiation, while the rates of ALK and ROS1 fusions were the opposite. RET fusions also tended to be more prevalent in poorly differentiated patients. EGFR and ALK double positive tumors were characterized by significantly smaller size compared with those had single gene alteration. Our study comprehensively analyzed the distinct and common clinicopathologic characteristics according to genotypes of the cohort, which should help in categorizing patients for efficient screening.

Predictive and prognostic value of preoperative serum tumor markers is EGFR mutation-specific in resectable non-small-cell lung cancer

Background

The predictive and prognostic value of carcinoembryonic antigen (CEA), cytokeratin-19 fragments (Cyfra21-1), squamous cell carcinoma antigen (SCCA) and neuron-specific enolase (NSE) has been investigated in non-small-cell lung cancer (NSCLC) patients. However, few studies have directly focused on the association between these markers and epidermal growth factor receptor (EGFR) mutation status or mutation subtypes.

Patients and methods

We retrospectively analyzed 1016 patients with stage I-IIIA NSCLC who underwent complete resection between 2008 and 2012. Correlations between serum tumor marker levels and EGFR mutations and survival parameters were analyzed and prognostic factors were identified.

Results

Cyfra21-1 levels (P = 0.032 for disease-free survival [DFS]; P < 0.001 for overall survival [OS]) and clinical stage were identified as independent predictive and prognostic factors in EGFR-mutated adenocarcinoma patients. CEA levels (P < 0.001 for DFS; P = 0.002 for OS) and clinical stage were independently predictive and prognostic in EGFR wild-type adenocarcinoma patients. Further stratification analysis revealed that in EGFR exon 19 deletion adenocarcinomas, elevated Cyfra21-1 was an independent prognostic factor (P = 0.002). Within the Leu858Arg substitution subgroup, increased CEA (P = 0.005) and clinical stage were predictive factors of DFS, while elevated CEA (P = 0.005) and Cyfra21-1 (P = 0.027) were independent prognostic factors.

Conclusion

Cyfra21-1 and CEA exhibit different predictive and prognostic values between EGFR-mutated and wild-type adenocarcinomas, as well as between EGFR mutation subtypes. The prognostic impact of preoperative serum tumor markers should be evaluated together with EGFR mutation status.

[Correlation between Serum Tumor Markers and Efficacy of First-line EGFR-TKIs in Patients with Advanced Lung Adenocarcinoma]

BACKGROUND

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) significantly improve the survival of advanced lung adenocarcinoma patients harboring EGFR mutation. Limited to the standards of tumor tissue samples and detection methods, still some people can't receive target therapy following genetic guidance. This study was to explore the relevance between serum tumor markers and treatment of EGFR-TKIs.

METHODS

We retrospectively collected the clinical information of advanced lung adenocarcinoma patients harboring EGFR mutation, who received EGFR-TKIs as first-line therapy from June 2009 to June 2014 in Peking University Cancer Hospital, analyzed the relationship between serum tumor markers and efficacy of EGFR-TKIs.

RESULTS

The objective response rate (ORR) was 52.8% and the disease control rate (DCR) was 89.3%. The results showed that, patients with high CEA level before treatment responded better to TKIs (ORR 61.3% vs 35.9%, DCR 95.2% vs 74.4%, P<0.001). Similar phenomena was found in patients with CEA decreased 1 month later (61.5% vs 25%, P=0.002). Progression-free survival (PFS) significantly prolonged in patients with elevated baseline CEA (mPFS 9.8 mo vs 5.9 mo, P=0.027). To the opposite, PFS was significantly shorter in patients with elevated baseline CYFRA21-1 and CA125 (mPFS 9.0 mo vs 11.4 mo, P=0.029; 9.0 mo vs 11.5 mo, P=0.023, respectively). Multivariate analysis showed that Eastern Cooperative Oncology Group (ECOG) score of 0-1, normal baseline CYFRA21-1 and CEA decline predicted longer PFS. The overall survival (OS) was highly associated with elevated CYFRA21-1 and CA125 (median OS 25.1 mo vs 52.5 mo, P=0.003; 22.7 mo vs 55.0 mo, P<0.001, respectively), while independent of CEA.

CONCLUSIONS

High level of baseline CEA and decline 1 month after treatment could predict the efficacy of EGFR-TKIs in patients with advanced lung adenocarcinoma. While high levels of baseline CYFRA21-1 and CA125 indicated shortened survival.