Assessment of a Combined Panel of Six Serum Tumor Markers for Lung Cancer

Circ_0006949 as a potential non-invasive diagnosis biomarker promotes the proliferation of NSCLC cells via miR-4673/GLUL axis

The 5-year survival for non-small cell lung cancer (NSCLC) remains <20 %, primarily due to the early symptoms of lung cancer are inconspicuous. Prompt identification and medical intervention could serve as effective strategies for mitigating the death rate. We therefore set out to identify biomarkers to help diagnose NSCLC. CircRNA microarray and qRT-PCR reveal that sputum circ_0006949 is a potential biomarker for the early diagnosis and therapy of NSCLC, which can enhance the proliferation and clone formation, regulate the cell cycle, and accelerate the migration and invasion of NSCLC cells. Circ_0006949 and miR-4673 are predominantly co-localized in the cytoplasm of NSCLC cell lines and tissues; it upregulates GLUL by adsorption of miR-4673 through competing endogenous RNAs mechanism. The circ_0006949/miR-4673/GLUL axis exerts pro-cancer effects in vitro and in vivo. Circ_0006949 can boost GLUL catalytic activity, and they are highly expressed in NSCLC tissues and correlate with poor prognosis. In summary, circ_0006949 is a potential biomarker for the early diagnosis and therapy of NSCLC. This novel sputum circRNA is statistically more predictive than conventional serum markers for NSCLC diagnosis. Non-invasive detection of patients with early-stage NSCLC using sputum has shown good potential for routine diagnosis and possible screening.

Diagnostic potential of protein serum biomarkers for distinguishing small and non-small cell lung cancer in patients with suspicious lung lesions

BACKGROUND

Biomarkers play a role in identifying, managing, and predicting cancer outcomes. In lung cancer, they are used at various time points. Doubts remain regarding their accuracy for differential diagnosis and histological subtyping. A diagnostic test study was conducted. It included malignant lesions and controls with benign lesions. Before lung biopsy, all patients had the following biomarkers measured in serum (Pro-GRP,NSE,CYFRA21-1,SCC-Ag,CEA).

METHODS

The predictive capacity of serum biomarkers was evaluated to discriminate between lung cancer and benign pathology. The accuracy was also assessed for distinguishing between SCLC and NSCLC and explored their ability to perform histological subtyping.

RESULTS

93 patients were included, 60 with lung cancer, 33 with benign pathology. Pro-GRP and NSE were elevated in SCLC compared with NSCLC or nonmalignant disease. The most accurate for differentiating between malignant and benign pathology were CEA and CYFRA21-1. Pro-GRP had a poor predictive capacity for distinguishing NSCLC from SCLC. However, combined with CEA and CYFRA21-1, performance improved. For SCLC, the diagnostic capacity of Pro-GRP increased by combining with biomarkers, such as NSE/CYFRA21-1.

CONCLUSIONS

Biomarkers lacked the sensitivity and specificity for independent differential diagnosis or histological subtyping. However, the observed patterns in biomarker levels associated with specific histological subtypes suggest potential utility in a multi-biomarker approach or in conjunction with other diagnostic tools. This insight could guide future research to improve diagnostic accuracy and personalized treatment strategies in lung cancer.

Multidimensional biomarker approach integrating tumor markers, inflammatory indicators, and disease activity indicators may improve prediction of rheumatoid arthritis-associated interstitial lung disease

INTRODUCTION

Rheumatoid arthritis (RA) often leads to interstitial lung disease (ILD), significantly affecting patient outcomes. This study explored the diagnostic accuracy of a multi-biomarker approach to offer a more efficient and accessible diagnostic strategy for RA-associated ILD (RA-ILD).

METHODS

Patients diagnosed with RA, with or without ILD, at Beijing Tiantan Hospital from October 2019 to October 2023 were analyzed. A total of 125 RA patients were included, with 76 diagnosed with RA-ILD. The study focused on three categories of indicators: tumor markers, inflammatory indicators, and disease activity measures. The heatmap correlation analysis was employed to analyze the correlation among these indicators. Logistic regression was used to determine odds ratios (OR) for indicators linked to RA-ILD risk. Receiver-operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic potential of these indicators for RA-ILD.

RESULTS

The results of logistic regression analysis showed that tumor markers (carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and cytokeratin 19 fragment (CYFRA21-1)), as well as inflammatory indicators (neutrophil, neutrophil-to-lymphocyte ratio (NLR), platelet, C-reactive protein (CRP)) and disease activity measures (disease activity score-28-CRP (DAS28-CRP), rheumatoid factor (RF), and anti-cyclic peptide containing citrulline (anti-CCP)), were significantly associated with RA-ILD. The correlation coefficients among these indicators were relatively low. Notably, the combination indicator 4, which integrated the aforementioned three categories of biomarkers, demonstrated improved diagnostic accuracy with an AUC of 0.857.

CONCLUSION

The study demonstrated that combining tumor markers, inflammatory indicators, and disease activity measures significantly enhanced the prediction of RA-ILD. Key Points • Multidimensional strategy: Integrated tumor markers, inflammatory indicators, and disease activity measures to enhance early detection of rheumatoid arthritis-associated interstitial lung disease (RA-ILD). • Diagnostic accuracy: Employed heatmap correlation and logistic regression, identifying significant associations and improving diagnostic accuracy with a multidimensional biomarker combination. • Superior performance: The combined multidimensional biomarker strategy demonstrated higher diagnostic precision compared to individual or dual-category indicators. • Clinical relevance: Offers a promising, accessible approach for early detection of RA-ILD in clinical settings, potentially improving patient outcomes.

Novel Tools for Single Comparative and Unified Evaluation of Qualitative and Quantitative Bioassays: SS/PV-ROC and SS-J/PV-PSI Index-ROC Curves with Integrated Concentration Distributions, and SS-J/PV-PSI Index Cut-Off Diagrams

Background: This investigation is both a study of potential non-invasive diagnostic approaches for the bladder cancer biomarker UBC® Rapid test and a study including novel comparative methods for bioassay evaluation and comparison that uses bladder cancer as a useful example. The objective of the paper is not to investigate specific data. It is used only for demonstration, partially to compare ROC methodologies and also to show how both sensitivity/specificity and predictive values can be used in clinical diagnostics and decision making. This study includes ROC curves with integrated cut-off distribution curves for a comparison of sensitivity/specificity (SS) and positive/negative predictive values (PPV/NPV or PV), as well as SS-J index/PV-PSI index-ROC curves and SS-J/PV-PSI index cut-off diagrams (J = Youden, PSI = Predictive Summary Index) for the unified direct comparison of SS-J/PV results achieved via quantitative and/or qualitative bioassays and an identification of optimal separate or unified index cut-off points. Patients and Methods: According to the routine diagnostics, there were 91 patients with confirmed bladder cancer and 1152 patients with no evidence of bladder cancer, leading to a prevalence value of 0.073. This study performed a quantitative investigation of used-up test cassettes from the visual UBC® Rapid qualitative point-of-care assay, which had already been applied in routine diagnostics. Using a photometric reader, quantitative data could also be obtained from the test line of the used cassettes. Interrelations between SS and PV values were evaluated using cumulative distribution analysis (CAD), SS/PV-ROC curves, SS-J/PV-PSI index-ROC curves, and the SS-J/PV-PSI index cut-off diagram. The maximum unified SS-J/PV-PSI index value and its corresponding cut-off value were determined and calculated with the SS-J/PV-PSI index cut-off diagram. Results: The use of SS/PV-ROC curves with integrated cut-off concentration distribution curves provides improved diagnostic information compared to "traditional" ROC curves. The threshold distributions integrated as curves into SS/PV-ROC curves and SS-J/PV-PSI index-ROC curves run in opposite directions. In contrast to the SS-ROC curves, the PV-ROC and the novel PV-PSI index-ROC curves had neither an area under the curve (AUC) nor a range from 0% to 100%. The cut-off level of the qualitative assay was 7.5 µg/L, with a sensitivity of 65.9% and a specificity of 63.3%, and the PPV was 12.4% and the NPV was 95.9%, at a threshold value of 12.5 µg/L. Based on these set concentrations, the reader-based evaluation revealed a graphically estimated 5% increase in sensitivity and a 13% increase in specificity, as compared to the visual qualitative POC test. In the case of predictive values, there was a gain of 8% for PPV and 10% for NPV. The index values and cut-offs were as follows: visual SS-J index, 0.328 and 35 µg/L; visual PV-PSI index, 0.083 and 5.4 µg/L; maximal reader Youden index, 0.0558 and 250 µg/L; and maximal PV-PSI index, 0.459 and 250 µg/L, respectively. The maximum unified SS-J/PV-PSI index value was 0.32, and the cut-off was 43 µg/L. The reciprocal SS-J index correctly detected one out of three patients, while the reciprocal PV-PSI index gave one out of twelve patients a correct diagnosis. Conclusions: ROC curves including cut-off distribution curves supplement the information lost in "traditionally plotted" ROC curves. The novel sets of ROC and index-ROC curves and the new SS/PV index cut-off diagrams enable the simultaneous comparison of sensitivity/specificity and predictive value profiles of diagnostic tools and the identification of optimal cut-off values at maximal index values, even in a unifying SS/PV approach. Because the curves within an SS-J/PV-PSI index cut-off diagram are distributed over the complete cut-off range of a quantitative assay, this field is open for special clinical considerations, with the need to vary the mentioned clinical diagnostic parameters. Complete or partial areas over the x-axis (AOX) can be calculated for summarized quantitative or qualitative effectivity evaluations with respect to single and/or unified SS-J and PV-PSI indices and with respect to single, several, or several unified assays. The SS-J/PV-PSI index-AOX approach is a new tool providing additional joint clinical information, and the reciprocal SS-J indices can predict the number of patients with a correct diagnosis and the number of persons who need to be examined in order to correctly predict a diagnosis of the disease. These methods could be used in applications like medical or plant epidemiology, machine learning algorithms, and neural networks.

Standardized uptake valuemax of the primary lesion combined with tumor markers for clinically predicting distant metastasis in de novo lung adenocarcinoma

BACKGROUND

To examine standardized uptake valuemax of the primary lesion (pSUVmax) and tumor markers (TMs) for clinically predicting distant metastasis in novo lung adenocarcinoma.

METHODS

The current retrospective observational study examined individuals diagnosed with de novo lung adenocarcinoma at Shanxi Cancer Hospital between February 2015 and December 2019.

RESULTS

Totally, 532 de novo lung adenocarcinoma cases were included. They were aged 60.8 ± 9.7 years and comprised 224 women and 268 patients with distant metastasis. The areas under the curves (AUCs) of pSUVmax, lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA21-1), carbohydrate antigen 125 (CA125), and Grade of TMs for predicting distant metastasis were 0.742, 0.601, 0.671, 0.700, 0.736, and 0.745, respectively. The combination of pSUVmax, LDH, CEA, CYFRA21-1, CA125, and grade of TMs in predicting distant metastasis had an AUC value of 0.816 (95%CI: 0.781-0.851), with sensitivity of 89.2%, specificity of 58.7%, positive predictive value of 73.7%, and negative predictive value of 79.7%, respectively.

CONCLUSIONS

pSUVmax combined with serum levels of LDH, CEA, CYFRA21-1, CA125, and the grade of TMs may have good performance in predicting distant metastasis of de novo lung adenocarcinoma.

Quantification of the lung cancer tumor marker CYFRA 21-1 using protein precipitation, immunoaffinity bottom-up LC-MS/MS

OBJECTIVES

Numerous studies have proven the potential of cytokeratin 19 fragment 21-1 (CYFRA 21-1) detection in the (early) diagnosis and treatment monitoring of non-small cell lung cancer (NSCLC). Conventional immunoassays for CYFRA 21-1 quantification are however prone to interferences and lack diagnostic sensitivity and standardization. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an emerging approach based on a different, often superior, detection principle, which may improve the clinical applicability of CYFRA 21-1 in cancer diagnostics. Therefore, we developed and validated a protein precipitation, immunoaffinity (IA) LC-MS/MS assay for quantitative analysis of serum CYFRA 21-1.

METHODS

Selective sample preparation was performed using ammonium sulfate (AS) precipitation, IA purification, tryptic digestion and LC-MS/MS quantification using a signature peptide and isotopically labeled internal standard. The workflow was optimized and validated according to EMA guidelines and results were compared to a conventional immunoassay.

RESULTS

Significant interference effects were seen during IA purification, which were sufficiently solved by performing AS precipitation prior to IA purification. A linear calibration curve was obtained in the range of 1.0-100 ng/mL (R2=0.98). Accuracy and precision were well within acceptance criteria. In sera of patients suspected of lung cancer, the method showed good correlation with the immunoassay.

CONCLUSIONS

A robust AS precipitation-IA LC-MS/MS assay for the quantification of serum CYFRA 21-1 was developed. With this assay, the clinically added value of LC-MS/MS-based detection over immunoassays can be further explored.

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Au/PANI@PtCu-based electrochemical immunosensor for ultrasensitive determination of pro-gastrin-releasing peptide

An ultrasensitive sandwich-type electrochemical immunosensor for pro-gastrin-releasing peptide (ProGRP) detection was constructed based on PtCu nanodendrites functionalized Au/polyaniline nanospheres (Au/PANI@PtCu). The prepared Au/PANI@PtCu nanocomposites not only possessed excellent electro-catalytic activity of H2O2 reduction due to the synergistic effect between the Au/PANI and PtCu NDs but also provided large specific surface area for detection of antibodies (Ab2) immobilization. In addition, Au nanoparticles encapsulated multi-wall carbon nanotubes (AuNPs@MWCNTs) were also applied to modify the glassy carbon electrode interface for loading numerous capture antibodies (Ab1). In the presence of target ProGRP, a sandwich-type electrochemical immunosensor showed a strong current response from the electro-catalysis of Au/PANI@PtCu toward H2O2 reduction. Benefiting from the exceptional electro-catalytic performance of Au/PANI@PtCu and the high conductivity of AuNPs@MWCNTs, the sandwich-type immunoassay exhibited remarkable sensitivity in detection. The linear range extended from 100 fg/mL to 10 ng/mL, while achieving an impressively low limit of detection of 77.62 fg/mL.

Combined use of CYFRA 21-1 and CA 125 predicts survival of patients with metastatic NSCLC and stable disease in IMpower150

BACKGROUND

Patients with non-small cell lung cancer (NSCLC) and stable disease (SD) have an unmet clinical need to help guide early treatment adjustments.

OBJECTIVE

To evaluate the potential of tumor biomarkers to inform on survival outcomes in NSCLC SD patients.

METHODS

This post hoc analysis included 480 patients from the IMpower150 study with metastatic NSCLC, treated with chemotherapy, atezolizumab and bevacizumab combinations, who had SD at first CT scan (post-treatment initiation). Patients were stratified into high- and low-risk groups (overall survival [OS] and progression-free survival [PFS] outcomes) based on serum tumor biomarker levels.

RESULTS

The CYFRA 21-1 and CA 125 biomarker combination predicted OS and PFS in patients with SD. Risk of death was ~4-fold higher for the biomarker-stratified high-risk versus low-risk SD patients (hazard ratio [HR] 3.80; 95% confidence interval [CI] 3.02-4.78; p < 0.0001). OS in patients with the low- and high-risk SD was comparable to that in patients with the CT-defined partial response (PR; HR 1.10; 95% CI 0.898-1.34) and progressive disease (PD) (HR 1.05; 95% CI 0.621-1.77), respectively. The findings were similar with PFS, and consistent across treatment arms.

CONCLUSIONS

Biomarker testing shows potential for providing prognostic information to help direct treatment in NSCLC patients with SD. Prospective clinical studies are warranted.ClinicalTrials.gov: NCT02366143.

Screening approaches for lung cancer by blood-based biomarkers: Challenges and opportunities

Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening.

Improvement of differential diagnosis of lung cancer by use of multiple protein tumor marker combinations

BACKGROUND

Differential diagnosis of non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in hospitalized patients is crucial for appropriate treatment choice.

OBJECTIVE

To investigate the relevance of serum tumor markers (STMs) and their combinations for the differentiation of NSCLC and SCLC subtypes.

METHODS

Between 2000 and 2003, 10 established STMs were assessed retrospectively in 311 patients with NSCLC, 128 with SCLC prior systemic first-line therapy and 51 controls with benign lung diseases (BLD), by automatized electrochemiluminescence immunoassay technology. Receiver operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the diagnostic efficacy of both individual and multiple STMs with corresponding sensitivities at 90% specificity. Standards for Reporting of Diagnostic Accuracy (STARD guidelines) were followed.

RESULTS

CYFRA 21-1 (cytokeratin-19 fragment), CEA (carcinoembryonic antigen) and NSE (neuron specific enolase) were significantly higher in all lung cancers vs BLD, reaching AUCs of 0.81 (95% CI 0.76-0.87), 0.78 (0.73-0.84), and 0.88 (0.84-0.93), respectively. By the three marker combination, the discrimination between benign and all malignant cases was improved resulting in an AUC of 0.93 (95% CI 0.90-0.96). In NSCLC vs. BLD, CYFRA 21-1, CEA and NSE were best discriminative STMs, with AUCs of 0.86 (95% CI 0.81-0.91), 0.80 (0.74-0.85), and 0.85 (0.79-0.91). The three marker combination also improved the AUC: 0.92; 95% CI 0.89-0.96). In SCLC vs. BLD, ProGRP (pro-gastrin-releasing peptide) and NSE were best discriminative STMs, with AUCs of 0.89 (95% CI 0.84-0.94) and 0.96 (0.93-0.98), respectively, and slightly improved AUC of 0.97 (95% CI 0.95-0.99) when in combination. Finally, discrimination between SCLC and NSCLC was possible by ProGRP (AUC 0.86; 95% CI 0.81-0.91), NSE (AUC 0.83; 0.78-0.88) and CYFRA 21-1 (AUC 0.69; 0.64-0.75) and by the combination of the 3 STMs (AUC 0.93; 0.91-0.96), with a sensitivity of 88% at 90% specificity.

CONCLUSIONS

The results confirm the power of STM combinations for the differential diagnosis of lung cancer from benign lesions and between histological lung cancer subtypes.

Relevance of tumor markers for prognosis and predicting therapy response in non-small cell lung cancer patients: A CEPAC-TDM biomarker substudy

BACKGROUND

Protein tumor markers are released in high amounts into the blood in advanced non-small cell lung cancer (NSCLC).

OBJECTIVE

To investigate the relevance of serum tumor markers (STM) for prognosis, prediction and monitoring of therapy response in NSCLC patients receiving chemotherapy.

METHODS

In a biomarker substudy of a prospective, multicentric clinical trial (CEPAC-TDM) on 261 advanced NSCLC patients, CYFRA 21-1, CEA, SCC, NSE, ProGRP, CA125, CA15-3 and HE4 were assessed in serial serum samples and correlated with radiological response after two cycles of chemotherapy and overall (OS) and progression-free survival (PFS).

RESULTS

While pretherapeutic STM levels at staging did not discriminate between progressive and non-progressive patients, CYFRA 21-1, CA125, NSE and SCC at time of staging did, and yielded AUCs of 0.75, 0.70, 0.69 and 0.67 in ROC curves, respectively. High pretherapeutic CA15-3 and CA125 as well as high CYFRA 21-1, SCC, CA125 and CA15-3 levels at staging were prognostic for shorter PFS and OS -also when clinical variables were added to the models.

CONCLUSIONS

STM at the time of first radiological staging and pretherapeutic CA15-3, CA125 are predictive for first-line treatment response and highly prognostic in patients with advanced NSCLC.

Prognostic value of tumor markers ProGRP, NSE and CYFRA 21-1 in patients with small cell lung cancer and chemotherapy-induced remission

BACKGROUND

Despite successful response to first line therapy, patients with small-cell lung cancer (SCLC) often suffer from early relapses and disease progression.

OBJECTIVE

To investigate the relevance of serum tumor markers for estimation of prognosis at several time points during the course of disease.

METHODS

In a prospective, single-center study, serial assessments of progastrin-releasing peptide (ProGRP), neuron-specific enolase (NSE), cytokeratin-19 fragments (CYFRA 21-1) and carcino-embryogenic antigen (CEA) were performed during and after chemotherapy in 232 SCLC patients, and correlated with therapy response and overall survival (OS).

RESULTS

ProGRP, NSE and CYFRA 21-1 levels decreased quickly after the first chemotherapy cycle and correlated well with the radiological response. Either as single markers or in combination they provided valuable prognostic information regarding OS at all timepoints investigated: prior to first-line therapy, after two treatment cycles in patients with successful response to first-line therapy, and prior to the start of second-line therapy. Furthermore, they were useful for continuous monitoring during and after therapy and often indicated progressive disease several months ahead of radiological changes.

CONCLUSIONS

The results indicate the great potential of ProGRP, NSE and CYFRA 21-1 for estimating prognosis and monitoring of SCLC patients throughout the course of the disease.

Lung cancer biomarkers: Raising the clinical value of the classical and the new ones

Blood-based diagnostics for lung cancer support the diagnosis, estimation of prognosis, prediction, and monitoring of therapy response in lung cancer patients. The clinical utility of serum tumor markers has considerably increased due to developments in serum protein tumor markers analytics and clinical biomarker studies, the exploration of preanalytical and influencing conditions, the interpretation of biomarker combinations and individual biomarker kinetics, as well as the implementation of biostatistical models. In addition, circulating tumor DNA (ctDNA) and other liquid biopsy markers are playing an increasingly prominent role in the molecular tumor characterization and the monitoring of tumor evolution over time. Thus, modern lung cancer biomarkers may considerably contribute to an individualized companion diagnostics and provide a sensitive guidance for patients throughout the course of their disease. In this special edition on Tumor Markers in Lung Cancer, experts summarize recent developments in clinical laboratory diagnostics of lung cancer and give an outlook on future challenges and opportunities.

Missing prognostic value of soluble PD-1, PD-L1 and PD-L2 in lung cancer patients undergoing chemotherapy - A CEPAC-TDM biomarker substudy

BACKGROUND

Programmed cell death receptors and ligands in cancer tissue samples are established companion diagnostics for immune checkpoint inhibitor (ICI) therapies.

OBJECTIVE

To investigate the relevance of soluble PD-1, PD-L1 and PD-L2 for estimating therapy response and prognosis in non-small cell lung cancer patients (NSCLC) undergoing platin-based combination chemotherapies.

METHODS

In a biomarker substudy of a prospective, multicentric clinical trial (CEPAC-TDM) on advanced NSCLC patients, soluble PD-1, PD-L1 and PD-L2 were assessed in serial serum samples by highly sensitive enzyme-linked immunosorbent assays and correlated with radiological response after two cycles of chemotherapy and with overall survival (OS).

RESULTS

Among 243 NSCLC patients, 185 achieved response (partial remission and stable disease) and 58 non-response (progression). The distribution of PD-1, PD-L1 and PD-L2 at baseline (C1), prior to staging (C3) and the relative changes (C3/C1) greatly overlapped between the patient groups with response and non-response, thus hindering the discrimination between the two groups. None of the PD markers had prognostic value regarding OS.

CONCLUSIONS

Neither soluble PD-1, PD-L1 nor PD-L2 did provide clinical utility for predicting response to chemotherapy and prognosis. Studies on the relevance of PD markers in ICI therapies are warranted.

Circulating lung cancer biomarkers: From translational research to clinical practice

Fundamental studies on biomarkers as well as developed assays for their detection can provide valuable information facilitating clinical decisions. For patients with lung cancer, there are established circulating biomarkers such as serum progastrin-releasing peptide (ProGRP), neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC-Ag), carcinoembryonic antigen (CEA), and cytokeratin-19 fragment (CYFRA21-1). There are also molecular biomarkers for targeted therapy such as epidermal growth factor receptor (EGFR) gene, anaplastic lymphoma kinase (ALK) gene, KRAS gene, and BRAF gene. However, there is still an unmet need for biomarkers that can be used for early detection and predict treatment response and survival. In this review, we describe the lung cancer biomarkers that are currently being used in clinical practice. We also discuss emerging preclinical and clinical studies on new biomarkers such as omics-based biomarkers for their potential clinical use to detect, predict, or monitor subtypes of lung cancer. Additionally, between-method differences in tumor markers warrant further development and improvement of the standardization and harmonization for each assay.

Circulating tumor DNA as liquid biopsy in lung cancer: Biological characteristics and clinical integration

Lung cancer maintains high morbidity and mortality rate globally despite significant advancements in diagnosis and treatment in the era of precision medicine. Pathological analysis of tumor tissue, the current gold standard for lung cancer diagnosis, is intrusive and intrinsically confined to evaluating the limited amount of tissues that could be physically extracted. However, tissue biopsy has several limitations, including the invasiveness of the procedure and difficulty in obtaining samples for patients at advanced stages., there Additionally,has been no major breakthrough in tumor biomarkers with high specificity and sensitivity, particularly for early-stage lung cancer. Liquid biopsy has been considered a feasible auxiliary tool for tearly dianosis, evaluating treatment responses and monitoring prognosis of lung cancer. Circulating tumor DNA (ctDNA), an ideal biomarker of liquid biopsy, has emerged as one of the most reliable tools for monitoring tumor processes at molecular levels. Herein, this review focuses on tumor heterogeneity to elucidate the superiority of liquid biopsy and retrospectively discussdeciphersolution. We systematically elaborate ctDNA biological characteristics, introduce methods for ctDNA detection, and discuss the current role of plasma ctDNA in lung cancer management. Finally, we summarize the drawbacks of ctDNA analysis and highlight its potential clinical application in lung cancer.

Clinical and Paraclinical Characteristics of Endobronchial Pulmonary Squamous Cell Carcinoma-A Brief Review

BACKGROUND

Endobronchial squamous cell carcinoma is one of the most common types of tumors located inside the tracheobronchial tree. Patients often present in advanced stages of the disease, which most often leads to a targeted therapeutic attitude of pneumonectomy. Practicing lung parenchyma-preserving surgery led us to undertake this review.

MATERIALS AND METHODS

We used three search platforms-SCIENCE, MEDLINE, and PubMed-in order to identify studies presenting case reports, investigations, and reviews on endobronchial squamous cell carcinoma. We identified the clinical and paraclinical features of endobronchial squamous cell carcinoma. All the selected articles were in English and addressed the clinical criteria of endobronchial squamous cell carcinoma, autofluorescence bronchoscopy in endobronchial squamous cell carcinoma, imaging features of endobronchial squamous cell carcinoma, blood tumor markers specific to lung squamous cell carcinoma, and histopathological features of endobronchial squamous cell carcinoma.

RESULTS

In total, 73 articles were analyzed, from which 48 articles were selected as bibliographic references. We present the criteria used for the identification of endobronchial squamous cell carcinoma in order to highlight its main characteristics and the most reliable technologies that can be used for the detection of this type of cancer.

CONCLUSIONS

The current literature review highlights the clinical and paraclinical characteristics of endobronchial squamous cell carcinoma. It aims to open new paths for research and early detection with respect to the frequent practice of lung parenchymal preservation surgery.

Association of immunotoxicological indices with lung cancer biomarkers in poultry, grape, and rose farming workers

Exposure to occupational hazards like dust, pesticides, diesel emission particles, or physical hazards in the agricultural sector is known to cause adverse health effects on farm workers. Our study aimed at addressing the association of immunomodulatory status with plasma levels of lung cancer biomarkers in farming population, attempting to recognition of vulnerable farming group. Blood samples from apparently healthy 51 chicken husbandry, 19 grape orchard, and 21 rose greenhouse workers were subjected to evaluate plasma levels of two representative lung cancer biomarkers, pro-gastrin releasing peptide (Pro-GRP) and cytokeratin fragment 19 (CYFRA 21-1). Peripheral blood mononuclear cells obtained from farmers were used for natural killer (NK) cell phenotyping and cytokines (interferon-gamma, IFN-γ and interleukin-13, IL-13) profiling in the culture supernatant. Compared to the rose greenhouse farmers, the grape orchard and chicken husbandry workers revealed a significantly upregulated plasma Pro-GRP and CYFRA 21-1 level. A low proportion of NK cells was observed among the female grape orchard workers and a lowered IFN- γ:IL-13 ratio was seen in the grape and chicken husbandry workers than the rose workers. Our findings imply that grape orchard and chicken husbandry workers have more disturbed immune homeostasis implicated with augmentation in the levels of lung cancer biomarkers than the rose greenhouse workers.

Variability of serum CYFRA 21 - 1 and its susceptibility to clinical characteristics in individuals without cancer: a 4-year retrospective analysis

BACKGROUND

CYFRA 21 - 1 is a useful marker for diagnosing and monitoring lung cancer. However, its stability remains unclear. Moreover, while its applicability to screening is now being investigated, CYFRA 21 - 1 levels in individuals without cancer, who are targets for cancer screening, have not yet been the focus of research. Therefore, the present study investigated variability in and the factors increasing serum CYFRA 21 - 1 levels.

METHODS

This retrospective study recruited 951 individuals undergoing annual medical examinations for six years. We used data obtained in the first four years. Variability in serum CYFRA 21 - 1 levels over a period of four years were investigated. CYFRA 21 - 1 was categorized as normal (≤ 3.5 ng/ml) or elevated (> 3.5 ng/ml). The rate of an elevated level in one visit and the transition from an elevated to normal level between visits were visualized. A multiple logistic regression model was used to study the relationships between the frequency of elevated CYFRA 21 - 1 levels and clinical characteristics, such as age, sex, body mass index, weight changes, and the smoking status.

RESULTS

Approximately 5% of subjects had elevated CYFRA 21 - 1 levels once in five tests over four years, while 15% had elevated CYFRA 21 - 1 levels once or more. Among subjects with elevated CYFRA 21 - 1 levels in one blood test, between 63 and 72% had normal levels in the next test. The median CYFRA 21 - 1 level in subjects with elevations in one blood test significantly decreased in the next test at all four time points. The frequency of elevated CYFRA 21 - 1 levels was associated with an older age [odds ratio (OR) = 6.99, 95% confidence interval (CI) = 3.01-16.2], current heavy smoking (OR = 3.46, 95% CI = 1.52-7.9), and weight loss (OR = 1.86, 95% CI = 1.07-3.24).

CONCLUSIONS

Variability in and the factors increasing serum CYFRA 21 - 1 levels beyond the cut-off value need to be considered when interpretating CYFRA 21 - 1 test results. The future application of CYFRA 21 - 1 to lung cancer screening may require more than a single measurement.

SFPQ and Its Isoform as Potential Biomarker for Non-Small-Cell Lung Cancer

Cancer markers are measurable molecules in the blood or tissue that are produced by tumor cells or immune cells in response to cancer progression. They play an important role in clinical diagnosis, prognosis, and anti-drug monitoring. Although DNA, RNA, and even physical images have been used, proteins continue to be the most common marker. There are currently no specific markers for lung cancer. Metastatic lung cancer, particularly non-small-cell lung cancer (NSCLC), is one of the most common causes of death. SFPQ, YY1, RTN4, RICTOR, LARP6, and HELLS are expressed at higher levels in cells from NSCLC than in control or cells from inflammatory diseases. SFPQ shows the most difference between the three cell types. Furthermore, the cytoplasmic isoform of SFPQ is only found in advanced cancers. We have developed ELISAs to detect SFPQ and the long and short isoforms. Evidence has shown that the short isoform exists primarily in cancers. Furthermore, immunocytometry studies and IHC analysis have revealed that SFPQ levels are consistent with ELISA results. In addition, enhanced DNA methylation in the SFPQ gene may facilitate the SFPQ expression differences between control and cancer cells. Considering this, elevated SFPQ level and the isoform location could serve as a cancer diagnostic and prognostic marker.

Clinical utility and diagnostic value of tumor-educated platelets in lung cancer: a systematic review and meta-analysis

Background

The review addresses the knowledge gap concerning the diagnostic value and clinical utility of tumor-educated platelets (TEPs) in adult patients with lung cancer.

Methods

We searched twelve databases: PubMed, CENTRAL, EMBASE, CINAHL, MEDLINE, Scopus, ProQuest, MedRxiv, BioRxiv, SSRN, Clinicaltrials.gov, and CNKI up to 24 March 2023, to include any diagnostic study regarding TEPs and LC. TEPs diagnostic value was evaluated from pooled sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC). QUADAS 2 was used to assess the risk of bias. Heterogeneity analysis was assessed using the receiver operating characteristic (ROC) plane, Galbraith plot, bivariate boxplot, sensitivity analysis, and meta-regression. TEPs clinical utility was evaluated from Fagan's nomogram.

Results

44 reports from 10 studies, including 7,858 events and 6,632 controls, were analyzed. The pooled sensitivity, specificity, PLR, NLR, and DOR were 0.80 (95% CI 0.79-0.80), 0.69 (95% CI 0.69-0.70), 2.92 (95% CI 2.50-3.41), 0.26 (95% CI 0.21-0.32), and 12.1 (95% CI 8.61-16.76), respectively. In addition, the AUC of the Summary ROC curve was 0.85 (95% CI: 0.81-0.88). The overall risk of bias was low. Heterogeneity may result from cancer stage, cancer control, measuring equipment, and RNA types across studies. There was no apparent publication bias (p=0.29) with significant positive (79%) and negative (22%) post-test probability, according to Deeks funnel plot asymmetry test and Fagan's nomogram.

Conclusion

TEPs could be a moderately effective candidate biomarker for LC diagnosis.

A nomogram combining thoracic CT and tumor markers to predict the malignant grade of pulmonary nodules ≤3 cm in diameter

Background

With the popularity of computed tomography (CT) of the thorax, the rate of diagnosis for patients with early-stage lung cancer has increased. However, distinguishing high-risk pulmonary nodules (HRPNs) from low-risk pulmonary nodules (LRPNs) before surgery remains challenging.

Methods

A retrospective analysis was performed on 1064 patients with pulmonary nodules (PNs) admitted to the Qilu Hospital of Shandong University from April to December 2021. Randomization of all eligible patients to either the training or validation cohort was performed in a 3:1 ratio. Eighty-three PNs patients who visited Qianfoshan Hospital in the Shandong Province from January through April of 2022 were included as an external validation. Univariable and multivariable logistic regression (forward stepwise regression) were used to identify independent risk factors, and a predictive model and dynamic web nomogram were constructed by integrating these risk factors.

Results

A total of 895 patients were included, with an incidence of HRPNs of 47.3% (423/895). Logistic regression analysis identified four independent risk factors: the size, consolidation tumor ratio, CT value of PNs, and carcinoembryonic antigen levels in blood. The area under the ROC curves was 0.895, 0.936, and 0.812 for the training, internal validation, and external validation cohorts, respectively. The Hosmer-Lemeshow test demonstrated excellent calibration capability, and the fit of the calibration curve was good. DCA has shown the nomogram to be clinically useful.

Conclusion

The nomogram performed well in predicting the likelihood of HRPNs. In addition, it identified HRPNs in patients with PNs, achieved accurate treatment with HRPNs, and is expected to promote their rapid recovery.

Correlation analysis of lung mucosa-colonizing bacteria with clinical features reveals metastasis-associated bacterial community structure in non-small cell lung cancer patients

BACKGROUND

Microbes colonizing lower airways can regulate the host immune profile and consequently participate in lung disease. Increasing evidence indicate that individual microbes promote lung cancer progression and are involved in metastasis incidence. To date, however, no study has revealed the community structure of lung bacteria in metastatic non-small cell lung cancer (NSCLC) patients.

METHODS

We prospectively enrolled 50 healthy subjects and 57 NSCLC patients. All healthy subjects and NSCLC patients underwent bronchoscope procedures for brush specimen collection. The 16 S ribosomal RNA gene was sequenced to characterize the community structure of lung mucosa-colonizing bacteria. The peripheral blood of NSCLC patients was also measured for leukocytes and cancer markers.

RESULTS

The lung bacteria of healthy subjects and NSCLC patients were divided into four communities. All community 2 members showed increased abundance in NSCLC patients compared with healthy subjects, and most community 2 members showed increased abundance in the metastatic NSCLC patients compared with the non-metastatic group. These bacteria were significantly and positively correlated with eosinophils, neutrophils and monocytes in the metastatic NSCLC group. In addition, the correlation between lung bacteria and cancer markers differed between the metastatic and non-metastatic NSCLC patients. Furthermore, bronchoalveolar lavage fluid from lung adenocarcinoma patients directly promoted NSCLC cell migration.

CONCLUSIONS

The community structure of lung mucosa-colonizing bacteria was relatively stable, but changed from the healthy population to NSCLC patients, especially the metastatic group. This distinct community structure and specific correlation with immune cells and cancer markers could help to distinguish NSCLC patients with or without metastasis.

Analysis of Neuron-Specific enolase isozymes in human serum using immunoaffinity purification and liquid chromatography-tandem mass spectrometry quantification

Neuron-specific enolase (NSE) is a promising small-cell lung cancer (SCLC) biomarker composed of αγ and γγ isozyme dimers. As the conventional immunoassays are prone to interferences and cannot differentiate between the isozymes, we developed a multiplex immunoaffinity (IA) liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for the quantification of NSEα and NSEγ in human serum. A calibrator was prepared by performing cold denaturation of recombinantly expressed αα and γγ enolase dimers to induce a new dimer equilibrium that was determined to be approximately 1αγ:1γγ:1αα. Selective sample purification was achieved by performing IA extraction using an antibody specific towards NSEγ. The isolated αγ and γγ dimers were denatured and trypsin digested to allow quantification of the selected signature peptides and their corresponding isotopically labelled peptide internal standard. The obtained linear dynamic ranges were determined to be 1.5-56 ng/mL and 0.64-167 ng/mL for NSEα and NSEγ (R² = 0.88 and 0.97 respectively). Validation of the assay showed acceptable accuracy and precision for NSEα and NSEγ. The method was successfully applied to patient serum in which both isozymes were detected. Compared to the conventional immunoassay, substantially lower total NSE concentrations were measured in IA LC-MS/MS. With this multiplex IA LC-MS/MS assay, the clinical value of quantifying the individual isozymes can be explored. In addition, together with the calibrator described here, it may be applied to standardize NSE immunoassays across different platforms.

Prognostic value of CYFRA 21 - 1 and Ki67 in advanced NSCLC patients with wild-type EGFR

BACKGROUND

The prognostic value of cytokeratin 19 fragment (CYFRA 21 - 1) and Ki67 in advanced non-small cell lung cancer (NSCLC) patients with wild-type epidermal growth factor receptor (EGFR) remains to be explored.

METHODS

In this study, 983 primary NSCLC patients from January 2016 to December 2019 were retrospectively reviewed. Finally, 117 advanced NSCLC patients with wild-type EGFR and 37 patients with EGFR mutation were included and prognostic value of CYFRA 21 - 1 and Ki67 were also identified.

RESULTS

The patients age, smoking history and the Eastern Corporative Oncology Group (ECOG) performance scores were significantly different between CYFRA21-1 positive and negative groups (p < 0.05), while no significant differences were found in Ki67 high and low groups. The results of over survival (OS) demonstrated that patients with CYFRA21-1 positive had markedly shorter survival time than CYFRA21-1 negative (p < 0.001, For whole cohorts; p = 0.002, For wild-type EGFR). Besides, patients with wild-type EGFR also had shorter survival times than Ki67 high group. Moreover, In CYFRA 21 - 1 positive group, patients with Ki67 high had obviously shorter survival time compared to patients with Ki67 low (median: 24vs23.5 months; p = 0.048). However, Ki67 could not be used as an adverse risk factor for patients with EGFR mutation. Multivariate cox analysis showed that age (HR, 1.031; 95%CI, 1.003 ~ 1.006; p = 0.028), Histopathology (HR, 1.760; 95%CI,1.152 ~ 2.690; p = 0.009), CYFRA 21 - 1 (HR, 2.304; 95%CI,1.224 ~ 4.335; p = 0.01) and Ki67 (HR, 2.130; 95%CI,1.242 ~ 3.652; p = 0.006) served as independent prognostic risk factor for advanced NSCLC patients.

CONCLUSIONS

Our finding indicated that CYFRA 21 - 1 was an independent prognostic factor for advanced NSCLC patients and Ki67 status could be a risk stratification marker for CYFRA 21 - 1 positive NSCLC patients with wild-type EGFR.

Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer

OBJECTIVES

Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors. The diagnosis of LC could be supported in a minimally invasive manner using protein tumor markers (TMs) and circulating tumor DNA (ctDNA) measured in liquid biopsies (LBx). This study evaluates the performance of LBx-based decision-support algorithms for the diagnosis of LC and subtyping into small- and non-small-cell lung cancer (SCLC and NSCLC) aiming to directly impact clinical practice.

MATERIALS AND METHODS

In this multicenter prospective study (NL9146), eight protein TMs (CA125, CA15.3, CEA, CYFRA 21-1, HE4, NSE, proGRP and SCCA) and ctDNA mutations in EGFR, KRAS and BRAF were analyzed in blood of 1096 patients suspected of LC. The performance of individual and combined TMs to identify LC, NSCLC or SCLC was established by evaluating logistic regression models at pre-specified positive predictive values (PPV) of ≥95% or ≥98%. The most informative protein TMs included in the multi-parametric models were selected by recursive feature elimination.

RESULTS

Single TMs could identify LC, NSCLC and SCLC patients with 46%, 25% and 40% sensitivity, respectively, at pre-specified PPVs. Multi-parametric models combining TMs and ctDNA significantly improved sensitivities to 65%, 67% and 50%, respectively.

CONCLUSION

In patients suspected of LC, the LBx-based decision-support algorithms allowed identification of about two-thirds of all LC and NSCLC patients and half of SCLC patients. These models therefore show clinical value and may support LC diagnostics, especially in patients for whom pathologic subtyping is impossible or incomplete.

Prognostic evaluation of preoperative serum tumor marker-negative cases in non-small cell lung cancer: A retrospective study

BACKGROUND

The role of various serum tumor markers (TMs) has been reported in non-small cell lung cancer (NSCLC). However, the prognosis of patients with multiple TM-negative NSCLC remain unclear.

AIMS

This study aimed to describe the characteristics and outcomes of patients with NSCLC undergoing surgery and to investigate their prognostic association with preoperative serum TM-negative cases.

METHODS AND RESULTS

We retrospectively evaluated 442 patients who underwent complete resection of stage I NSCLC between January 2004 and December 2019. These 442 patients were classified into a group whose preoperative serum levels of carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA21-1), carbohydrate antigen 19-9 (CA19-9), and squamous cell carcinoma antigen (SCC Ag) were all negative (TM-negative group; n = 249, 56%) and a group with at least one positive marker (TM-positive group; n = 193, 44%). Among all patients, the TM-negative group showed higher 5-year recurrence-free survival (RFS) (92.6% vs. 79.1%; p < .01), and overall survival (OS) rates (86.3% vs. 68.6%; p < .01). After propensity score matching, patients in the TM-negative group still exhibited good 5-year RFS (92.1% vs. 81.4%; p = .01) and OS rates (87.6% vs. 72.6%; p < .01).

CONCLUSION

Our study suggests that NSCLC patients who are preoperatively negative for all serum TMs, such as CEA, CYFRA21-1, CA19-9, and SCC Ag, represent a subgroup with a particularly good prognosis.

Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells

Objective

Preoperative noninvasive diagnosis of the benign or malignant solitary pulmonary nodule (SPN) is still important and difficult for clinical decisions and treatment. This study aimed to assist in the preoperative diagnosis of benign or malignant SPN using blood biomarkers.

Methods

A total of 286 patients were recruited for this study. The serum FR⁺CTC, TK1, TP, TPS, ALB, Pre-ALB, ProGRP, CYFRA21-1, NSE, CA50, CA199, and CA242 were detected and analyzed.

Results

In the univariate analysis, age, FR⁺CTC, TK1, CA50, CA19.9, CA242, ProGRP, NSE, CYFRA21-1, and TPS showed the statistical significance of a correlation with malignant SPNs (P <0.05). The highest performing biomarker is FR⁺CTC (odd ratio [OR], 4.47; 95% CI: 2.57-7.89; P <0.001). The multivariate analysis identified that age (OR, 2.69; 95% CI: 1.34-5.59, P = 0.006), FR⁺CTC (OR, 6.26; 95% CI: 3.09-13.37, P <0.001), TK1 (OR, 4.82; 95% CI: 2.4-10.27, P <0.001), and NSE (OR, 2.06; 95% CI: 1.07-4.06, P = 0.033) are independent predictors. A prediction model based on age, FR⁺CTC, TK1, CA50, CA242, ProGRP, NSE, and TPS was developed and presented as a nomogram, with a sensitivity of 71.1% and a specificity of 81.3%, and the AUC was 0.826 (95% CI: 0.768-0.884).

Conclusions

The novel prediction model based on FR⁺CTC showed much stronger performance than any single biomarker, and it can assist in predicting benign or malignant SPNs.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

The Potential Role of MUC16 (CA125) Biomarker in Lung Cancer: A Magic Biomarker but with Adversity

Lung cancer is the second most commonly diagnosed cancer in the world. In terms of the diagnosis of lung cancer, combination carcinoembryonic antigen (CEA) and cancer antigen 125 (CA125) detection had higher sensitivity, specificity, and diagnostic odds ratios than CEA detection alone. Most individuals with elevated serum CA125 levels had lung cancer that was either in stage 3 or stage 4. Serum CA125 levels were similarly elevated in lung cancer patients who also had pleural effusions or ascites. Furthermore, there is strong evidence that human lung cancer produces CA125 in vitro, which suggests that other clinical illnesses outside of ovarian cancer could also be responsible for the rise of CA125. MUC16 (CA125) is a natural killer cell inhibitor. As a screening test for lung and ovarian cancer diagnosis and prognosis in the early stages, CA125 has been widely used as a marker in three different clinical settings. MUC16 mRNA levels in lung cancer are increased regardless of gender. As well, increased expression of mutated MUC16 enhances lung cancer cells proliferation and growth. Additionally, the CA125 serum level is thought to be a key indicator for lung cancer metastasis to the liver. Further, CA125 could be a useful biomarker in other cancer types diagnoses like ovarian, breast, and pancreatic cancers. One of the important limitations of CA125 as a first step in such a screening technique is that up to 20% of ovarian tumors lack antigen expression. Each of the 10 possible serum markers was expressed in 29-100% of ovarian tumors with minimal or no CA125 expression. Therefore, there is a controversy regarding CA125 in the diagnosis and prognosis of lung cancer and other cancer types. In this state, preclinical and clinical studies are warranted to elucidate the clinical benefit of CA125 in the diagnosis and prognosis of lung cancer.

Clinical usefulness of circulating tumor markers

Tumor markers are a heterogeneous group of substances released by cancer cells into bloodstream, but also expressed by healthy tissues. Thus, very small concentrations can be present in plasma and serum from healthy subjects. Cancer patients tend to show increased levels correlating with tumor bulk, but false positive results could be present in patients with benign conditions. The correct interpretation of TM results could be challenging and many factors should be considered, from pre-analytical conditions to patient concomitant diseases. In this line, the Clinical Chemistry and Laboratory Medicine journal has made important contributions though several publications promoting the adequate use of TM and therefore improving patient safety. TM measurement offers valuable information for cancer patient management in different clinical contexts, such as helping diagnosis, estimating prognosis, facilitating early detection of relapse and monitoring therapy response. Our review analyzes the clinical usefulness of tumor markers applied in most frequent epithelial tumors, based on recent evidence and guidelines.

One-Step and Real-Time Detection of microRNA-21 in Human Samples for Lung Cancer Biosensing Diagnosis

The rapid diagnosis of cancer, especially in its early stages, is crucial for on-time medical treatment and for increasing the patient survival rate. Lung cancer shows the highest mortality rate and the lowest 5-year survival rate due to the late diagnosis in advanced cancer stages. Providing rapid and reliable diagnostic tools is a top priority to address the problem of a delayed cancer diagnosis. We introduce a nanophotonic biosensor for the direct and real-time detection in human plasma of the microRNA-21-5p biomarker related to lung cancer. The biosensor employs a silicon photonic bimodal interferometric waveguide that provides a highly sensitive detection in a label-free format. We demonstrate a very competitive detectability for direct microRNA-21-5p biomarker assays in human plasma samples (estimated LOD: 25 pM). The diagnostic capability of our biosensor was validated by analyzing 40 clinical samples from healthy individuals and lung cancer patients, previously analyzed by reverse-transcription quantitative polymerase chain reaction (qRT-PCR). We could successfully identify and quantify the levels of microRNA in a one-step assay, without the need for DNA extraction or amplification steps. The study confirmed the significance of implementing this biosensor technique compared to the benchmarking molecular analysis and showed excellent agreement with previous results employing the traditional qRT-PCR. This work opens new possibilities for the true implementation of point-of-care biosensors that enable fast, simple, and efficient early diagnosis of cancer diseases.

Biomarkers and the microbiome in the detection and treatment of early-stage non-small cell lung cancer

Lung cancer is one of the most common and deadly cancers in the world. However, over the last several years, research into lung cancer screening and novel therapeutic approaches have provided promise that earlier detection combined with new treatment strategies may result in significantly improved outcomes. Biomarkers will most certainly play a major role in identifying those who may benefit from, and how to apply, these new treatment strategies. Here we discuss potential biomarkers, including the microbiome, in both detection and treatment strategies for early stage lung cancer.

Defective Fe Metal-Organic Frameworks Enhance Metabolic Profiling for High-Accuracy Diagnosis of Human Cancers

Cancers heavily threaten human life; therefore, a high-accuracy diagnosis is vital to protect human beings from the suffering of cancers. While biopsies and imaging methods are widely used as current technologies for cancer diagnosis, a new detection platform by metabolic analysis is expected due to the significant advantages of fast, simple, and cost-effectiveness with high body tolerance. However, the signal of molecule biomarkers is too weak to acquire high-accuracy diagnosis. Herein, precisely engineered metal-organic frameworks for laser desorption/ionization mass spectrometry, allowing favorable charge transfer within the molecule-substrate interface and mitigated thermal dissipation by adjusting the phonon scattering with metal nodes, are developed. Consequently, a surprising signal enhancement of ≈10 000-fold is achieved, resulting in diagnosis of three major cancers (liver/lung/kidney cancer) with area-under-the-curve of 0.908-0.964 and accuracy of 83.2%-90.6%, which promises a universal detection tool for large-scale clinical diagnosis of human cancers.

Development and validation of reassigned CEA, CYFRA21-1 and NSE-based models for lung cancer diagnosis and prognosis prediction

BACKGROUND

The majority of lung cancer(LC) patients are diagnosed at advanced stage with a poor prognosis. However, there is still no ideal diagnostic and prognostic prediction model for lung cancer.

METHODS

Data of CEA, CYFRA21-1 and NSE test of patients with LC and benign lung diseases (BLDs) or healthy people from Physical Examination Center was collected. Samples were divided into three data sets as needed. Reassign three kinds of tumor markers (TMs) according to their distribution characteristics in different populations. Diagnostic and prognostic models were thus established, and independent validation was conducted with other data sets.

RESULTS

The diagnostic prediction model showed good discrimination ability: the area under the receiver operating characteristic curve (AUC) differentiated LC from healthy people and BLDs (diagnosed within 2 months), being 0.88 and 0.84 respectively. Meanwhile, the prognostic prediction model did great in prediction: AUC in training data set and test data set were 0.85 and 0.8 respectively.

CONCLUSION

Reassigned CEA, CYFRA21-1 and NSE can effectively predict the diagnosis and prognosis of LC. Compared with the same TMs that were considered individually, this diagnostic prediction model can identify high-risk population for LC screening more accurately. The prognostic prediction model could be helpful in making more scientific treatment and follow-up plans for patients.

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.

Serum tumor markers level and their predictive values for solid and micropapillary components in lung adenocarcinoma

Background

This study aims to reveal the serum tumor marker (STM) levels in lung adenocarcinoma (LUAD) histological subtypes and evaluate their values in predicting the solid and micropapillary components (SMC).

Methods

We retrospectively analyzed 3100 invasive LUAD patients between January 2017 and December 2020. Associations between preoperative STMs (CEA, CYFRA21‐1, CA199, CA724, NSE, AFP) and LUAD subtypes were evaluated. Multivariate regression analyses were used to determine the independent predictors. Predictive models for SMC were constructed and AUC (area under the curve) was calculated.

Results

CEA and CYFRA21‐1 levels differed across the LUAD histological subtypes, with the SPA (solid‐predominant adenocarcinoma) having the highest level and the LPA (lepidic‐predominant adenocarcinoma) harboring the lowest level (p <0.001). Tumors with SMC also had higher CEA and CYFRA21‐1 levels than those absence of SMC. Gender, tumor size, CEA, Ki‐67, EGFR mutation (solid components only), and tumor differentiation were significantly independently associated with the containing of SMC. Patients were split into two data sets (training set: 2017–2019 and validation set: 2020). The model with gender and tumor size yielded an AUC of 0.723 (training set) and 0.704 (validation set) for the solid component. Combination of CEA, gender, and tumor size led to a significant increase in the predictive accuracy (training set: 0.771, p = 0.009; validation set: 0.747, p = 0.034). The AUC of the model for micropapillary component with only gender and tumor size was 0.699 and 0.711 in the training set and validation set, respectively. Integration of CEA with gender and tumor size significantly improved the predictive performance with an AUC of 0.746 (training set, p = 0.045) and 0.753 (validation set, p <0.001).

Conclusion

Serum CEA and CYFRA21‐1 varied considerably according to LUAD histological subtypes. The combination of serum CEA and other factors showed prominent values in predicting the SMC.

Long short-term memory model - A deep learning approach for medical data with irregularity in cancer predication with tumor markers

BACKGROUND

Machine learning (ML) has emerged as a superior method for the analysis of large datasets. Application of ML is often hindered by incompleteness of the data which is particularly evident when approaching disease screening data due to varied testing regimens across medical institutions. Here we explored the utility of multiple ML algorithms to predict cancer risk when trained using a large but incomplete real-world dataset of tumor marker (TM) values.

METHODS

TM screening data were collected from a large asymptomatic cohort (n = 163,174) at two independent medical centers. The cohort included 785 individuals who were subsequently diagnosed with cancer. Data included levels of up to eight TMs, but for most subjects, only a subset of the biomarkers were tested. In some instances, TM values were available at multiple time points, but intervals between tests varied widely. The data were used to train and test various machine learning models to evaluate their robustness for predicting cancer risk. Multiple methods for data imputation were explored and models were developed for both single time-point as well as time-series data.

RESULTS

The ML algorithm, long short-term memory (LSTM), demonstrated superiority over other models for dealing with irregular medical data. A cancer risk prediction tool was trained and validated for a single time-point test of a TM panel including up to four biomarkers (AUROC = 0.831, 95% CI: 0.827-0.835) which outperformed a single threshold method using the same biomarkers. A second model relying on time series data of up to four time-points for 5 TMs had an AUROC of 0.931.

CONCLUSIONS

A cancer risk prediction tool was developed by training a LSTM model using a large but incomplete real-world dataset of TM values. The LSTM model was best able to handle irregular data compared to other ML models. The use of time-series TM data can further improve the predictive performance of LSTM models even when the intervals between tests vary widely. These risk prediction tools are useful to direct subjects to further screening sooner, resulting in earlier detection of occult tumors.

Integrative Comparison of Burrows-Wheeler Transform-Based Mapping Algorithm with de Bruijn Graph for Identification of Lung/Liver Cancer-Specific Gene

Cancers of the lung and liver are the top 10 leading causes of cancer death worldwide. Thus, it is essential to identify the genes specifically expressed in these two cancer types to develop new therapeutics. Although many messenger RNA (mRNA) sequencing data related to these cancer cells are available due to the advancement of next-generation sequencing (NGS) technologies, optimized data processing methods need to be developed to identify the novel cancer-specific genes. Here, we conducted an analytical comparison between Bowtie2, a Burrows-Wheeler transform-based alignment tool, and Kallisto, which adopts pseudo alignment based on a transcriptome de Bruijn graph using mRNA sequencing data on normal cells and lung/liver cancer tissues. Before using cancer data, simulated mRNA sequencing reads were generated, and the high Transcripts Per Million (TPM) values were compared. mRNA sequencing reads data on lung/liver cancer cells were also extracted and quantified. While Kallisto could directly give the output in TPM values, Bowtie2 provided the counts. Thus, TPM values were calculated by processing the Sequence Alignment Map (SAM) file in R using package Rsubread and subsequently in python. The analysis of the simulated sequencing data revealed that Kallisto could detect more transcripts and had a higher overlap over Bowtie2. The evaluation of these two data processing methods using the known lung cancer biomarkers concludes that in standard settings without any dedicated quality control, Kallisto is more effective at producing faster and more accurate results than Bowtie2. Such conclusions were also drawn and confirmed with the known biomarkers specific to liver cancer.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Bimodal Multiplexed Detection of Tumor Markers in Non-Small Cell Lung Cancer with Polymer Dot-Based Immunoassay

Semiconducting polymer nanoparticles (Pdots) have been demonstrated to be a promising class of probes for use in fluorometric immunochromatographic test strips (ICTS). The advantages of Pdots in ICTSs include ultrahigh brightness, minimal nonspecific adsorption, and multicolor availability, which together contribute to the high sensitivity, good specificity, and multiplexing ability. These unique properties can therefore circumvent several significant challenges of commercial ICTSs, including insufficient specificity/sensitivity and difficulty in quantitative and multiplexed detection. Here, we developed a colorimetric and fluorescent bimodal readout ICTS based on gold-Pdot nanohybrids for the determination of carcinoembryonic antigen (CEA) and cytokeratin 19 fragment (CYFRA 21-1) expressed abnormally in human blood of non-small-cell lung cancer (NSCLS). The vivid color from Au nanomaterials can be used for rapid qualitative screening (colorimetry) in 15 min, while the bright fluorescence of Pdots is ideal for the advanced quantitative measurements of CEA and CYFRA21-1 concentrations in whole blood samples. This bimodal ICTS platform possesses phenomenal detection sensitivity of 0.07 and 0.12 ng/mL for CYFRA21-1 and CEA, respectively. The accuracy and reliability of this ICTS platform were further evaluated with clinical serum samples from NSCLS patients at different stages, showing good consistency with the results from electrochemiluminescence immunoassay.

A Cost-Effectiveness Analysis of Lung Cancer Screening With Low-Dose Computed Tomography and a Diagnostic Biomarker

Background

The Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) reduces the false-positive rate of lung cancer screening but introduces prolonged periods of uncertainty for indeterminate findings. We assess the cost-effectiveness of a screening program that assesses indeterminate findings earlier via a hypothetical diagnostic biomarker introduced in place of Lung-RADS 3 and 4A guidelines.

Methods

We evaluated the performance of the US Preventive Services Task Force (USPSTF) recommendations on lung cancer screening with and without a hypothetical noninvasive diagnostic biomarker using a validated microsimulation model. The diagnostic biomarker assesses the malignancy of indeterminate nodules, replacing Lung-RADS 3 and 4A guidelines, and is characterized by a varying sensitivity profile that depends on nodules' size, specificity, and cost. We tested the robustness of our findings through univariate sensitivity analyses.

Results

A lung cancer screening program per the USPSTF guidelines that incorporates a diagnostic biomarker with at least medium sensitivity profile and 90% specificity, that costs $250 or less, is cost-effective with an incremental cost-effectiveness ratio lower than $100 000 per quality-adjusted life year, and improves lung cancer-specific mortality reduction while requiring fewer screening exams than the USPSTF guidelines with Lung-RADS. A screening program with a biomarker costing $750 or more is not cost-effective. The health benefits accrued and costs associated with the screening program are sensitive to the disutility of indeterminate findings and specificity of the biomarker, respectively.

Conclusions

Lung cancer screening that incorporates a diagnostic biomarker, in place of Lung-RADS 3 and 4A guidelines, could improve the cost-effectiveness of the screening program and warrants further investigation.

The BUILT study: a single-center 5-year experience of Lung Cancer screening in Taiwan

Background: There are no uniform guidelines on low-dose computed tomography (LDCT) follow-up in lung cancer screening. Few studies have analyzed the incidental abnormalities and role of tumor markers in lung cancer screening. The purpose of this study was to investigate the diagnostic performance of LDCT, optimal follow-up duration, incidental findings, and role of tumor markers in diagnosing lung cancer. Methods: We retrospectively analyzed subjects who underwent their first LDCT in Taipei Tzu Chi Hospital between September 1, 2015, and August 31, 2016. All chest CT scans until August 31, 2020, were recorded. A non-calcified nodule with a diameter ≥2 mm on LDCT was defined as a positive result. We extracted the data, including possible risk factors of lung cancer and follow-up outcomes. Results: A total of 1502 subjects were recruited. Of the 38 subjects who underwent biopsy, 31 had confirmed lung cancer. Lung cancer in all patients was diagnosed within 4 years. Univariate logistic regression analysis revealed that a family history of lung cancer in first-degree relatives and abnormal serum carcinoembryonic antigen (CEA) levels were the significant risk factors for lung cancer. A cumulative lung cancer incidence of 54.7 patients per 1000 person-years was determined solely via radiological follow-up. In total, 271 (18%) subjects exhibited incidental findings on baseline LDCT. Conclusion: The overall lung cancer detection rate in this study was 2.1% in the 5-year study period. A family history of lung cancer and abnormal serum CEA levels are important risk factors for lung cancer. A minimum of 4-year follow-up is required to track suspicious nodules. A purely radiological follow-up detects a high incidence of lung cancer.

Carbohydrate antigen 125, carbohydrate antigen 15-3 and low-density lipoprotein as risk factors for intraocular metastases in postmenopausal breast cancer

The prognosis of patients with postmenopausal breast cancer (PBC) could be improved by the early detection of intraocular metastases (IOMs). However, serum biomarkers for IOMs in PBC remain elusive. In the current study, we investigated patients with PBC, and compared serum parameters in an IOM and a non-IOM group, and then differentiated the risk factors related to IOMs. A comparison between an IOM and a non-IOM (NIOM) group was performed using Student t-test and a Chi-Squared test. After constructing a Poisson regression model to identify risk factors, we plotted receiver operating characteristic curves to evaluate the predictive value of significant risk factors in detecting IOMs. The incidence of IOMs in PBC was 1.16%. The histopathology results were not significantly different between the 2 groups. The levels of serum carbohydrate antigen 125 (CA-125), carbohydrate antigen 15-3 (CA15-3) and alkaline phosphatase were significantly elevated in IOMs compared with NIOMs (P = .082, P < .001, and P < .001, respectively). Compared with NIOMs, age, carbohydrate antigen 19 to 9, hemoglobin, calcium, total cholesterol, low-density lipoprotein (LDL) and apolipoprotein A1 were remarkably lower in IOMs (P = .038, P < .001, P < .001, P = .032, P = .041, P < .001, and P = .001, respectively). Poisson regression suggested that CA-125, CA15-3 and LDL were contributing to IOMs in PBC as risk factors (OR = 1.003, 95% CI: 1.001-1.005; OR = 1.025, 95% CI: 1.019-1.033; OR = 0.238, 95% CI: 0.112-0.505, respectively). A receiver operating characteristic curve revealed that the cut-off values for CA-125, CA15-3 and LDL were 16.78 0 U/mL, 63.175 U/mL, and 2.415 mmol/L, respectively. The combination of CA-125 and CA15-3 showed significant diagnostic value (area under the curve [AUC] = 0.982, P < .001). Our investigation suggests that CA-125, CA15-3 and LDL remarkably predict IOMs in PBC as risk factors, and the combination of CA-125 and CA15-3 shows considerable diagnostic value.

The diagnostic value of the combination of carcinoembryonic antigen, squamous cell carcinoma-related antigen, CYFRA 21-1, neuron-specific enolase, tissue polypeptide antigen, and progastrin-releasing peptide in small cell lung cancer discrimination

BACKGROUND

The diagnostic value of six tumor markers was investigated and the appropriate combinations of those tumor markers to discriminate small cell lung cancer was explored.

METHODS

Patients suspected with lung cancer (1938) were retrospectively analyzed. Candidate tumor markers from carcinoembryonic antigen (CEA), squamous cell carcinoma-related antigen (SCC), cytokeratin 19 fragment 21-1 (CYFRA 21-1), neuron-specific enolase (NSE), tissue polypeptide antigen (TPA), and progastrin releasing peptide (ProGRP) were selected to construct a logistic regression model. The receiver operating characteristic curve was used for evaluating the diagnostic value of the tumor markers and the predictive model.

RESULTS

ProGRP had the highest positive rate (72.3%) in diagnosed small cell lung cancer, followed by neuron-specific enolase (68.3%), CYFRA21-1 (50.5%), carcinoembryonic antigen (45.5%), tissue polypeptide antigen (30.7%), and squamous cell carcinoma-related antigen (5.9%). The predictive model for small cell lung cancer discrimination was established, which yielded the highest area under the curve (0.888; 95% confidence interval: 0.846-0.929), with a sensitivity of 71.3%, a specificity of 95.0%, a positive predictive value of 49.0%, and a negative predictive value of 98.0%.

CONCLUSIONS

Combining tumor markers can improve the efficacy for small cell lung cancer discrimination. A predictive model has been established in small cell lung cancer differential diagnosis with preferable efficacy.

CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy

OBJECTIVES

The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both.

MATERIALS AND METHODS

This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients.

RESULTS

The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846).

CONCLUSIONS

Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future.

KEY POINTS

The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.

Clinical Utility of Circulating Tumor Cells in the Early Detection of Lung Cancer in Patients with a Solitary Pulmonary Nodule

Objective: Lung cancer is the most common cancer and can appear as a solitary pulmonary nodule. Early detection of lung cancer in this patient population would be beneficial for the disease management. In this study, the potential application of circulating tumor cells (CTCs) on early detection of lung cancer in this population was investigated. Methods: The number of CTCs in bronchoalveolar lavage fluid and serum levels of tumor-related markers, cancer antigen 125 (CA125), carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) were measured in patients with a solitary pulmonary nodule. The association between CTCs and lung cancer was examined. The diagnosis performances of CTCs and selected tumor-related markers were compared. Results: The CTC positivity was significantly associated with lung cancer (P = .009). The sensitivity of CTCs and CA125, CEA, NSE, and CA125/CEA/NSE was 75%, 5.6%, 0%, 25%, and 33%, respectively. The sensitivity of CTCs was improved from 75% to 83% by the combination with CA125 or NSE. Conclusion: CTCs may be helpful for the early detection of lung cancer in patients with a solitary pulmonary nodule.

Serum tumor markers for the prediction of concordance between genomic profiles from liquid and tissue biopsy in patients with advanced lung adenocarcinoma

Background

The concordance between mutations detected from plasma and tissue is critical for treatment choices of patients with advanced lung adenocarcinoma.

Methods

We prospectively analyzed the association of the serum tumor markers with the concordance between blood and tissue genomic profiles from 185 patients with advanced lung adenocarcinoma. The concordance was defined according to 3 criteria. Class 1 included all targetable driver mutations in 8 genes; class 2 included class 1 mutations plus mutations in KRAS, STK11, and TP53; class 3 included class 2 mutations plus tumor mutation burden (TMB) status.

Results

Collectively, 150 out of 185 patients had mutations in both tissue and plasma samples, while one patient was mutation-negative for both, resulting a concordance of 81.6%. The concordance rate for class 1 mutations was 80%, and 65% and 69% for class 2 and class 3, respectively. Carbohydrate antigen 19-9 (CA19-9) or cytokeratin 19 (CYFRA21-1) levels higher than the normal upper limit predicted the concordance of tissue and blood results in class 1 (P=0.005, P=0.011), class 2 (P=0.011, P<0.001), and class 3 (P=0.001, P=0.014). In class 1, the cutoff values of CA19-9 were 30, 36, and 284 U/mL to reach the concordance thresholds of 90%, 95%, and 100%, respectively (P=0.032, P=0.003, P=0.043). For CYFRA21-1, the cutoff values were 6, 18, and 52 µg/L (P=0.005, P=0.051, P=0.354). In class 2, the cutoff values for CYFRA21-1 were 18, 22, and 52 µg/L (P=0.001, P=0.001, P=0.052). In class 3, the cutoff values for CA19-9 were 36, 39, and 85 U/mL (P=0.003, P=0.001, P=0.008). For CYFRA21-1, the cutoff values were 22, 52, and 52 µg/L (P=0.900, P>0.99, P>0.99). When the sum score for 4 serum tumor markers was greater than 35, both class 1, class 2, and class 3 reached a predictive threshold of 90%.

Conclusions

Serum tumor markers can be used as easy and practical clinical predictors of concordance in mutation profiles between blood and tissue samples from patients with advanced lung adenocarcinoma.

The Past, Present, and Future (Liquid Biopsy) of Serum Tumor Markers in Lung Cancer: A Primer for the Radiologist

ABSTRACT

Lung cancer continues to be a major cause of death throughout the world. The ability to both accurately diagnose lung cancer in its early stages and monitor response to treatment is essential to reducing the morbidity and mortality associated with the disease. Serum tumor markers have been identified as potential biomarkers that may aid in lung cancer diagnosis and surveillance. These markers, when combined with cross-sectional imaging, may result in more robust screening and surveillance protocols. The future role of serum tumor markers in lung cancer includes the advancement of "liquid biopsies," in which peripheral blood samples are analyzed for tumor components without the need for a tissue biopsy.