Profiling Tumor-Associated Antibodies for Early Detection of Non-small Cell Lung Cancer:

A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment

BACKGROUND

 Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrates artificial intelligence (AI) analysis with biomarkers to enhance early detection and stratification of lung nodule malignancy.

METHODS

 The study retrospectively analyzed the patients with pathologically confirmed pulmonary nodules. AI technology was employed to assess CT features, such as nodule size, solidity, and malignancy probability. Additionally, lung cancer blood biomarkers were measured. Statistical analysis involved univariate analysis to identify significant differences among factors, followed by multivariate logistic regression to establish independent risk factors. The model performance was validated using receiver operating characteristic curves and decision curve analysis (DCA) for internal validation. Furthermore, an external dataset comprising 51 cases of lung nodules was utilized for independent validation to assess robustness and generalizability.

RESULTS

 A total of 176 patients were included, divided into benign/preinvasive (n = 76) and invasive cancer groups (n = 100). Multivariate analysis identified eight independent predictors of malignancy: lobulation sign, bronchial inflation sign, AI-predicted malignancy probability, nodule nature, diameter, solidity proportion, vascular endothelial growth factor, and lung cancer autoantibodies. The combined predictive model demonstrated high accuracy (area under the curve [AUC] = 0.946). DCA showed that the combined model significantly outperformed the traditional model, and also proved superior to models using AI-predicted malignancy probability or the seven lung cancer autoantibodies plus traditional model. External validation confirmed its robustness (AUC = 0.856), achieving a sensitivity of 0.80 and specificity of 0.86, effectively distinguishing between invasive and noninvasive nodules.

CONCLUSION

 This combined approach of AI-based CT features analysis with lung cancer biomarkers provides a more accurate and clinically useful tool for guiding treatment decisions in pulmonary nodule patients. Further studies with larger cohorts are warranted to validate these findings across diverse patient populations.

The diagnostic efficacy of seven autoantibodies in early detection of ground-glass nodular lung adenocarcinoma

Background

Persistent ground-glass nodules (GGNs) carry a potential risk of malignancy, however, early diagnosis remained challenging. This study aimed to investigate the cut-off values of seven autoantibodies in patients with ground-glass nodules smaller than 3cm, and to construct machine learning models to assess the diagnostic value of these autoantibodies.

Methods

In this multi-center retrospective study, we collected peripheral blood specimens from a total of 698 patients. A total of 466 patients with ground-glass nodular lung adenocarcinoma no more than 3cm were identified as a case group based on pathological reports and imaging data, and control group (n=232) of patients consisted of 90 patients with benign nodules and 142 patients with health check-ups. Seven antibodies were quantified in the serum of all participants using enzyme-linked immunosorbent assay (ELISA), and the working characteristic curves of the subjects were plotted to determine the cut-off values of the seven autoantibodies related ground-glass nodular lung adenocarcinoma early. Subsequently, the patients were randomly divided into a training and test set at a 7:3 ratio. Eight machine-learning models were constructed to compare the diagnostic performances of multiple models. The model performances were evaluated using sensitivity, specificity, and the area under the curve (AUC).

Results

The serum levels of the seven autoantibodies in case group were significantly higher than those in the control group (P < 0.05). The combination of the seven autoantibodies demonstrated a significantly enhanced diagnostic efficacy in identifying ground-glass nodular lung adenocarcinoma early when compared to the diagnostic efficacy of the autoantibodies when used respectively. The combined diagnostic approach of the seven autoantibodies exhibited a sensitivity of 84.05%, specificity of 91.85%, and AUC of 0.8870, surpassing the performance of each autoantibody used individually. Furthermore, we determined that Sparrow Search Algorithm-XGBoost (SSA-XGBOOST) had the best diagnostic performance among the models (AUC=0.9265), with MAGEA1, P53, and PGP9.5 having significant feature weight proportions.

Conclusions

Our research assessed the diagnostic performance of seven autoantibodies in patients with ground-glass nodules for benign-malignant distinction, and the nodules are all no more than 3cm especially. Our study set cut-off values for seven autoantibodies in identifying GGNs no more than 3cm and constructed a machine learning model for effective diagnosis. This provides a non-invasive and highly discriminative method for the evaluation of ground-glass nodules in high-risk patients.

The clinical value of combined detection of seven lung cancer-related autoantibodies in assisting the diagnosis of non-small-cell lung cancer

Aim: Evaluate the clinical value of lung-cancer-related autoantibodies (CAGE, GAGE7, GBU4-5, MAGEA1, P53, PGP9.5, SOX2) in auxiliary diagnosis of non-small-cell lung cancer (NSCLC).Methods: We detected the concentrations of above 7 antibodies and lung cancer markers (CEA, NSE, CYFRE21-1) and drew area under the receiver characteristic curve of 316 patients.Results: The concentrations of CAGE, GBU4-5, MAGEA1, P53, PGP9.5 and SOX2 of significantly higher than other groups (p < 0.01). The sensitivity of different stages and pathological types of NSCLC consistent. Among them, the sensitivity of combined-detection in diagnosing adenocarcinoma and squamous cell carcinoma significantly better than CEA, NSE and CYFRA21-1.Conclusion: The combined detection has better efficacy in assisting the diagnosis of NSCLC and has certain clinical promotion and application value.

Non-endoscopic Screening for Esophageal Squamous Cell Carcinoma: Recent Advances

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most common tumors in the gastrointestinal tract, and China has a high incidence area with a high burden on the disease. As early symptoms of ESCC are not obvious, the mortality rate is high, and it is often diagnosed in the intermediate and advanced stages. However, early screening and treatment may reduce morbidity and mortality.

METHODS

Screening methods are divided into endoscopic and non-endoscopic screening.

RESULTS

Endoscopic screening cannot be widely used because of its invasive nature and high cost. Currently, non-endoscopic screening consists primarily of tumor biomarkers and cytology, and tumor biomarkers including autoantibodies, circulating tumor cells, circulating tumor DNA, exosomes and serum metabolomics are more likely to be effective. But the efficiency of early diagnosis of esophageal cancer is low and the accuracy of screening needs to be improved. The aim of this study is to summarize advances in non-endoscopic esophageal cancer screening and strategies to provide a scientific basis and research idea for esophageal cancer prevention and control.

CONCLUSIONS

Non-endoscopic screening is better than endoscopic screening. And the application of tumor biomarkers is much better than other non-endoscopic screening methods.

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

The Usefulness of the Ratio of Antigen-Autoantibody Immune Complexes to Their Free Antigens in the Diagnosis of Non-Small Cell Lung Cancer

Autoantibodies against specific lung cancer-associated antigens have been suggested for the performance of lung cancer diagnosis. This study aimed to evaluate the diagnostic performance of the antigen-autoantibody immune complex (AIC) against its free antigens for CYFRA21-1, ProGRP, neutrophil gelatinase-associated lipocalin (NGAL), and neuron-specific enolase (NSE) in non-small cell lung cancer (NSCLC). In total, 85 patients with NSCLC and 120 healthy controls (HCs) were examined using a 9-guanine DNA chip method. The ratios of AICs to their antigens and the combinations of ratios consisting of two to four markers were calculated. The levels of AICs for CYFRA21-1, ProGRP, NGAL, and NSE were higher than those for their free antigens in all participants. The levels of each free antigens distinguished patients with NSCLC from the HCs. The ratios of the AIC to its antigen and seven combinations of two to four ratios were significantly higher in patients with NSCLC than in the HCs. Excellent diagnostic performance was observed for all combination ratios (C4-1), with 85.9% sensitivity and 86.7% specificity at a 3.51 cut-off. Higher sensitivity was observed in the early stages (0-I) and adenocarcinoma than in stages II-IV and other pathological types. Combining all ratios of AICs and their antigens for all four markers was useful when diagnosing NSCLC.

Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies

OBJECTIVE

To construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure.

METHODS

This study included 424 patients with radioactive pulmonary nodules who underwent preoperative 7-autoantibody (7-AAB) panel testing, CT-based AI diagnosis, and pathological diagnosis by surgical resection. The patients were randomly divided into a training set (n = 212) and a validation set (n = 212). The nomogram was developed through forward stepwise logistic regression based on the predictive factors identified by univariate and multivariate analyses in the training set and was verified internally in the verification set.

RESULTS

A diagnostic nomogram was constructed based on the statistically significant variables of age as well as CT-based AI diagnostic, 7-AAB panel, and CEA test results. In the validation set, the sensitivity, specificity, positive predictive value, and AUC were 82.29%, 90.48%, 97.24%, and 0.899 (95%[CI], 0.851-0.936), respectively. The nomogram showed significantly higher sensitivity than the 7-AAB panel test result (82.29% vs. 35.88%, p < 0.001) and CEA (82.29% vs. 18.82%, p < 0.001); it also had a significantly higher specificity than AI diagnosis (90.48% vs. 69.04%, p = 0.022). For lesions with a diameter of ≤ 2 cm, the specificity of the Nomogram was higher than that of the AI diagnostic system (90.00% vs. 67.50%, p = 0.022).

CONCLUSIONS

Based on the combination of a 7-AAB panel, an AI diagnostic system, and other clinical features, our Nomogram demonstrated good diagnostic performance in distinguishing lung nodules, especially those with ≤ 2 cm diameters.

KEY POINTS

• A novel diagnostic model of lung nodules was constructed by combining high-specific tumor markers with a high-sensitivity artificial intelligence diagnostic system. • The diagnostic model has good diagnostic performance in distinguishing malignant and benign pulmonary nodules, especially for nodules smaller than 2 cm. • The diagnostic model can assist the clinical decision-making of pulmonary nodules, with the advantages of high diagnostic efficiency, noninvasive, and simple measurement.

Antibodies as biomarkers for cancer risk: a systematic review

Increasing evidence has linked the humoral immune response with the development of various cancers. Therefore, there is growing interest in investigating the predictive value of antibodies to assess overall and tissue site-specific cancer risk. Given the large amount of antibody types and the broad scope of the search (i.e. cancer risk), the primary aim of this systematic review was to present an overview of the most researched antibodies (i.e. immunoglobulin (Ig) isotypes (IgG, IgM, IgA, and IgE), tumour and self-antigen-reactive antibodies, infection-related antibodies) in relation to overall and site-specific cancer risk. We identified various antibody types that have been associated with the risk of cancer. While no significant associations were found for IgM serum levels, studies found an inconsistent association among IgE, IgA, and IgG serum levels in relation to cancer risk. When evaluating antibodies against infectious agents, most studies reported a positive link with specific cancers known to be associated with the specific agent recognized by serum antibodies (i.e. helicobacter pylori and gastric cancer, hepatitis B virus and hepatocellular carcinoma, and human papillomavirus and cervical cancer). Several reports identified autoantibodies, as single biomarkers (e.g. anti-p53, anti-MUC1, and anti-CA125) but especially in panels of multiple autoantibodies, to have potential as diagnostic biomarkers for specific cancer types. Overall, there is emerging evidence associating certain antibodies to cancer risk, especially immunoglobulin isotypes, tumour-associated antigen-specific, and self-reactive antibodies. Further experimental studies are necessary to assess the efficacy of specific antibodies as markers for the early diagnosis of cancer.

Role of biomarkers in lung nodule evaluation

PURPOSE OF REVIEW

Worldwide, lung cancer is the leading cause of cancer mortality. Much of this mortality is thought to be secondary to detection in later stages, where treatment options and survivability are limited. The goals of lung nodule evaluation are to expedite the diagnosis and treatment of patients with malignant nodules and to minimize unnecessary diagnostic procedures in those with benign nodules. However, the differentiation between benign and malignant has been challenging and is further complicated by the benefits of early diagnosis competing with potential morbidity of invasive diagnostic procedures.

RECENT FINDINGS

Biomarkers have the potential to improve estimates of pretest probability of malignancy in pulmonary nodules, especially in the intermediate-risk subgroup. Four biomarkers have undergone extensive validation and are available for clinical use, and we will discuss each in this review.

SUMMARY

The application of biomarkers to lung cancer risk assessment has the potential to improve cancer probability assessments, which in turn can reduce unnecessary invasive testing and/or reduce delays in diagnosis and treatment.

A B-cell or a key player? The different roles of B-cells and antibodies in melanoma

The B‐cell system plays an important role in the melanoma immune response; however, consensus has yet to be reached in many facets. Here, we comprehensively review human studies only, due to fundamental differences in the humoral response with animal models. Tumour‐infiltrating B‐cells are associated with contradictory prognostic values, reflecting a lack of agreement between studies on cell subset classification and differences in the markers used, particularly the common use of a single marker not differentiating multiple subsets. Tertiary lymphoid structures (TLS) organise T‐cells and B‐cells within tumours to generate a local anti‐tumour response and TLS presence associates with improved survival in response to immune checkpoint blockade, in late‐stage disease. Autoantibody production is increased in melanoma patients and has been proposed as biomarkers for diagnosis, prognosis and treatment/toxicity response; however, no consistent targets are yet identified. The function of antibodies in an anti‐tumour response is determined by its isotype and subclass; IgG₄ is immune‐suppressive and robustly correlate with poor patient survival in melanoma. We conclude that the current B‐cell literature needs careful interpretation based on the methods used and that we need a consensus of markers to define B‐cells and associated lymphoid organs. Furthermore, future studies need to not only examine antibody targets, but also isotypes when considering functional roles.

Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome

The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.

New non-invasive approaches to the diagnosis of lymph node metastases from breast cancer by mass spectrometry

Early diagnosis of metastasis makes it possible to select the optimal treatment protocol and improve patient survival. Noninvasive and minimally invasive diagnostic techniques help to make a diagnosis with minimal damage to the body. The study was aimed to find biomarkers, being the hallmarks of the metastatic process initiation, and to develop a diagnostic model based on the plasma lipid profile using liquid chromatography-mass spectrometry. We studied blood plasma of 55 patients, 28 of them were diagnosed with the regional lymph node metastasis; the control group comprised 27 patients. The levels of lipids, belonging to the groups, such as oxidized lipids and sphingomyelins, in patients with metastases were significantly higher and significantly lower, respectively. The lipid panels were created by multivariate analysis, and the models based on these panels showed sensitivity and specificity of 79 and 74% (positive ion mode), and of 50 and 85% (negative ion mode) in leave-one-out cross-validation.

Overexpression of p62 Induces Autophagy and Promotes Proliferation, Migration and Invasion of Nasopharyngeal Carcinoma Cells through Promoting ERK Signaling Pathway

BACKGROUND

Increasing evidence has shown that p62 plays an important role in tumorigenesis. However, relatively little is known about the association between p62 and tumor invasion and metastasis; in addition, its role in NPC (nasopharyngeal carcinoma, NPC) has been rarely investigated.

OBJECTIVE

To investigate the effect of p62 on tumorigenesis and metastasis in nasopharyngeal carcinoma.

METHODS

Western blotting, immunofluorescent staining and immunohistochemistry were used to evaluate p62 protein expression. Subsequently, cell viability, colony formation, migration, invasion and autophagy assays were performed. anti-p62 autoantibodies in sera were detected by ELISA. These data were correlated with clinicopathological parameters.

RESULTS

We confirmed that p62 was significantly up-regulated in NPC tissues. Furthermore, high expression of p62 was observed in NPC cell lines, and especially in the highly metastatic 5-8F cells. In vitro, down-regulation of p62 inhibited proliferation, clone forming ability, autophagy, migration, and invasion in 5-8F cells, whereas p62 overexpression resulted in the opposite effects in 6-10B cells. Moreover, we confirmed that p62 promotes NPC cell proliferation, migration, and invasion by activating ERK (extracellular signal-regulated kinase, ERK). Clinical analysis indicated that high p62 expression correlates with lymph node and distant metastasis (P<0.05). Serum anti-p62 autoantibodies were increased in NPC patients and levels were associated with metastasis.

CONCLUSION

Our data establish p62 targeting ERK as potential determinant in the NPC, which supplies a new pathway to treat NPC. Furthermore, p62 is a potential biomarker which might be closely related to the tumorigenesis and metastasis in NPC.

The diagnostic efficiency of seven autoantibodies in lung cancer

This study aimed to evaluate the diagnostic efficacy of seven autoantibodies in all lung cancer, lung adenocarcinoma, lung squamous cell carcinoma and early-stage lung cancer patients. ELISA testing of a seven autoantibody panel was performed on 386 lung cancer patients and 238 normal controls. The sensitivity and specificity of each autoantibody were analyzed using the receiver operating characteristic curve analysis. The diagnostic efficacy of a combination of these seven autoantibodies was evaluated by binary logistic regression. The results indicated that six of the seven autoantibodies (p53, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE) had high specificity and low sensitivity, while PGP9.5 had high sensitivity and low specificity. Further analysis showed that all seven autoantibodies had better diagnostic value in lung squamous cell carcinoma patients when compared to lung adenocarcinoma or all lung cancer patients. Logistic regression showed that a combination of the seven autoantibodies resulted in more reliable detection of lung cancer than any individual autoantibody in early-stage lung cancer (sensitivity/specificity: 47.8%/81.4%, areas under the curve: 0.764, 95% confidence interval: 0.718-0.811). Additionally, this panel had a better sensitivity of 56.5% for detection of lung squamous cell carcinoma than for all lung cancer (50.1%) or adenocarcinoma (51.7%) (P < 0.05). Our results indicated that the seven autoantibody panel could be used for early lung cancer detection, and it had better sensitivity in diagnosis of lung squamous cell carcinoma.

Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence

Esophageal cancer (EC) is the seventh most common cancer and the sixth cause of cancer death worldwide. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) account for up to 90% and 20% of all ECs, respectively. Clinical symptoms such as dysphagia, odynophagia, and bolus impaction occur late in the natural history of the disease, and the diagnosis is often delayed. The prognosis of ESCC and EAC is poor in advanced stages, being survival rates less than 20% at five years. However, when the diagnosis is achieved early, curative treatment is possible, and survival exceeds 80%. For these reasons, mass screening strategies for EC are highly desirable, and several options are currently under investigation. Blood biomarkers offer an inexpensive, non-invasive screening strategy for cancers, and novel technologies have allowed the identification of candidate markers for EC. The esophagus is easily accessible via endoscopy, and endoscopic imaging represents the gold standard for cancer surveillance. However, lesion recognition during endoscopic procedures is hampered by interobserver variability. To fill this gap, artificial intelligence (AI) has recently been explored and provided encouraging results. In this review, we provide a summary of currently available options to achieve early diagnosis of EC, focusing on blood biomarkers, advanced endoscopy, and AI.

The Value of a Seven-Autoantibody Panel Combined with the Mayo Model in the Differential Diagnosis of Pulmonary Nodules

Background

Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs).

Methods

The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs was calculated using the Mayo predictive model. The performances of the 7-AAB panel and the Mayo model were analyzed by receiver operating characteristic (ROC) analyses, and the difference between groups was evaluated by chi-square tests (χ²).

Results

The combined area under the ROC curve (AUC) for all 7 AABs was higher than that of a single one. The sensitivities of the 7-AAB panel were 67.5% in the stage I-II LC patients and 60.3% in the stage III-IV patients, with a specificity of 89.6% for the healthy controls and 83.1% for benign lung disease patients. The detection rate of the 7-AAB panel in the early-stage LC patients was higher than that of traditional tumor markers. The AUC of the 7-AAB panel in combination with the Mayo model was higher than that of the 7-AAB panel alone or the Mayo model alone in distinguishing MPN from benign nodules. For early-stage MPN, the sensitivity and specificity of the combination were 93.5% and 58.0%, respectively. For advanced-stage MPN, the sensitivity and specificity of the combination were 91.4% and 72.8%, respectively. The combination of the 7-AAB panel with the Mayo model significantly improved the detection rate of MPN, but the positive predictive value (PPV) and the specificity were not improved when compared with either the 7-AAB panel alone or the Mayo model alone.

Conclusion

Our study confirmed the clinical value of the 7-AAB panel for the early detection of lung cancer and in combination with the Mayo model could be used to distinguish benign from malignant pulmonary nodules.

Peptidyl arginine deiminase 4 and its potential role in Alzheimer's disease

Alzheimer's disease (AD) is the main cause of dementia, and its pathogenesis is still not clear. Peptidyl arginine deiminases 4(PAD4) as one of the important members of PAD family, is the only protein with nuclear transfer function, it can regulate the expression of many proteins through citrullinating histone. PAD4 can also interact with many transcription factors, involved in regulating gene expression. PAD4 expression is closely related to the inflammatory factors secreted, cell autophagy, tumorigenesis and other neurodegenerative diseases. More importantly, PAD4 and its citrullinated protein were found in cortical and hippocampal neurons of AD patients. To study the expression and regulatory pathway of PAD4 in vivo and in vitro experiments on AD may be of helpful to elucidate the pathogenesis of AD. Meanwhile, detection of anti-citrullinated antibody will have potential value as novel biomarkers of AD.

Biomarkers for Lung Cancer Screening and Detection

Lung cancer is the leading worldwide cause of cancer mortality, as it is often detected at an advanced stage. Since 2011, low-dose CT scan-based screening has promised a 20% reduction in lung cancer mortality. However, effectiveness of screening has been limited by eligibility only for a high-risk population of heavy smokers and a large number of false positives generated by CT. Biomarkers have tremendous potential to improve early detection of lung cancer by refining lung cancer risk, stratifying positive CT scans, and categorizing intermediate-risk pulmonary nodules. Three biomarker tests (Early CDT-Lung, Nodify XL2, Percepta) have undergone extensive validation and are available to the clinician. The authors discuss these tests, with their clinical applicability and limitations, current ongoing evaluation, and future directions for biomarkers in lung cancer screening and detection.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Autoantibodies to tumor-associated antigens in lung cancer diagnosis

Lung cancer (LC) accounts for the majority of cancer-related deaths worldwide. Although screening the high-risk population by low-dose CT (LDCT) has reduced mortality, the cost and high false positivity rate has prevented its general diagnostic use. As such, better and more specific minimally invasive biomarkers are needed in general and for early LC detection, specifically. Autoantibodies produced by humoral immune response to tumor-associated antigens (TAA) are emerging as a promising noninvasive biomarker for LC. Given the low sensitivity of any one single autoantibody, a panel approach could provide a more robust and promising strategy to detect early stage LC. In this review, we summarize the background of TAA autoantibodies (TAAb) and the techniques currently used for identifying TAA, as well as recent findings of LC specific antigens and TAAb. This review provides guidance toward the development of accurate and reliable TAAb as immunodiagnostic biomarkers in the early detection of LC.

Diagnostic and Therapeutic Applications of Exosomes in Cancer with a Special Focus on Head and Neck Squamous Cell Carcinoma (HNSCC)

Exosomes are nanovesicles part of a recently described intercellular communication system. Their properties seem promising as a biomarker in cancer research, where more sensitive monitoring and therapeutic applications are desperately needed. In the case of head and neck squamous cell carcinoma (HNSCC), overall survival often remains poor, although huge technological advancements in the treatment of this disease have been made. In the following review, diagnostic and therapeutic properties are highlighted and summarised. Impressive first results have been obtained but more research is needed to implement these innovative techniques into daily clinical routines.

Tumor-associated autoantibodies in combination with alpha-fetoprotein for detection of early stage hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) continues to be a leading challenge in modern oncology. Early detection via blood-based screening tests has the potential to cause a stage-shift at diagnosis and improve clinical outcomes. Tumor associated autoantibodies (TA-AAbs) have previously shown the ability to distinguish HCC from patients with high-risk liver disease. This research aimed to further show the utility of TA-AAbs as biomarkers of HCC and assess their use in combination with Alpha-fetoprotein (AFP) for detection of HCC across multiple tumor stages.

METHODS

Levels of circulating G class antibodies to 44 recombinant tumor associated antigens and circulating AFP were measured in the serum of patients with HCC, non-cancerous chronic liver disease (NCCLD) and healthy controls via enzyme-linked immunosorbent assay (ELISA). TA-AAb cut-offs were set at the highest Youden's J statistic at a specificity ≥95.00%. Panels of TA-AAbs were formed using net reclassification improvement. AFP was assessed at a cut-off of 200 ng/ml.

RESULTS

Sensitivities ranged from 1.01% to 12.24% at specificities of 95.96% to 100.00% for single TA-AAbs. An ELISA test measuring a panel of 10 of these TA-AAbs achieved a combined sensitivity of 36.73% at a specificity of 89.89% when distinguishing HCC from NCCLD controls. At a cut-off of 200 ng/ml, AFP achieved a sensitivity of 31.63% at a specificity of 100.00% in the same cohort. Combination of the TA-AAb panel with AFP significantly increased the sensitivity for stage one (40.00%) and two (55.00%) HCC over the TA-AAb panel or AFP alone.

CONCLUSIONS

A panel of TA-AAbs in combination with AFP could be clinically relevant as a replacement for measuring levels of AFP alone in surveillance and diagnosis strategies. The increased early stage sensitivity could lead to a stage shift with positive prognostic outcomes.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

Screening of tumor-associated antigens based on Oncomine database and evaluation of diagnostic value of autoantibodies in lung cancer

OBJECTIVES

The purpose of this study is to discover novel tumor-associated antigens (TAAs) to improve the diagnosis of lung cancer (LC).

MATERIALS AND METHODS

Oncomine database was used to discover potential TAAs from LC tissues, enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of autoantibodies against TAAs in two independent sets (identification set, n = 368; validation set, n = 1011).

RESULTS

Analyses of sera from identification set showed that the sensitivity of autoantibodies against five TAAs (HMGB3, ZWINT, GREM1, NUSAP1 and MMP12) reached 57.1%, 42.4%, 38.0%, 36.4% and 20.7%, with area under ROC curve (AUC) of 0.85, 0.75, 0.71, 0.73 and 0.70, respectively. It also validated the diagnostic performances of these autoantibodies with AUC of 0.72, 0.65, 0.61, 0.64 and 0.64, respectively. Autoantibody against HMGB3 exhibited significantly increased frequency in early LC (53.3%) compared to advanced LC (29.3%) (P < .05). The positive rates of autoantibody against HMGB3 and NUSAP1 in serum of LC patients without distant metastasis were significantly higher than that of distant metastatic LC (P < .05). When each of the three protein biomarkers (CEA, CA125 and CYFRA21-1) was combined with anti-HMGB3 autoantibody, the sensitivity of early LC increased to 72.7%, 63.3% and 75.9% from 36.4%, 13.3% and 27.6%, respectively.

CONCLUSION

Autoantibodies against 5 TAAs (HMGB3, ZWINT, GREM1, NUSAP1 and MMP12) might have favorable diagnostic values in LC detection, and autoantibody against HMGB3 has the potential to serve as a serological biomarker in early-stage LC. The combination of protein biomarkers and anti-HMGB3 might contribute to detection of early-stage LC.

Autoantibodies as diagnostic biomarkers for lung cancer: A systematic review

Lung cancer (LC) accounts for the largest number of tumor-related deaths worldwide. As the overall 5-year survival rate of LC is associated with its stages at detection, development of a cost-effective and noninvasive cancer screening method is necessary. We conducted a systematic review to evaluate the diagnostic values of single and panel tumor-associated autoantibodies (TAAbs) in patients with LC. This review included 52 articles with 64 single TAAbs and 19 with 20 panels of TAAbs. Enzyme-linked immunosorbent assays (ELISA) were the most common detection method. The sensitivities of single TAAbs for all stages of LC ranged from 3.1% to 92.9% (mean: 45.2%, median: 37.1%), specificities from 60.6% to 100% (mean: 88.1%, median: 94.9%), and AUCs from 0.416 to 0.990 (mean: 0.764, median: 0.785). The single TAAb with the most significant diagnostic value was the autoantibody against human epididymis secretory protein (HE4) with the maximum sensitivity 91% for NSCLC. The sensitivities of the panel of TAAbs ranged from 30% to 94.8% (mean: 76.7%, median: 82%), specificities from 73% to 100% (mean: 86.8%, median: 89.0%), and AUCs from 0.630 to 0.982 (mean: 0.821, median: 0.820), and the most significant AUC value in a panel (M13 Phage 908, 3148, 1011, 3052, 1000) was 0.982. The single TAAb with the most significant diagnostic calue for early stage LC, was the autoantibody against Wilms tumor protein 1 (WT1) with the maximum sensitivity of 90.3% for NSCLC and its sensitivity and specificity in a panel (T7 Phage 72, 91, 96, 252, 286, 290) were both above 90.0%. Single or TAAbs panels may be useful biomarkers for detecting LC patients at all stages or an early-stage in high-risk populations or health people, but the TAAbs panels showed higher detection performance than single TAAbs. The diagnostic value of the panel of six TAAbs, which is higher than the panel of seven TAAbs, may be used as potential biomarkers for the early detection of LC and can probably be used in combination with low-dose CT in the clinic.

Enhancement of diagnostic performance in lung cancers by combining CEA and CA125 with autoantibodies detection

Objectives: Although low-dose computed tomography has been confirmed to have meaningful diagnostic utility, lung cancer is still the leading cause of cancer-related deaths for both genders worldwide. Thus, a novel panel with a stronger diagnostic performance for lung cancer is needed. This study aimed to investigate the efficacy of a new panel in lung cancer diagnosis.

Materials and Methods: The serum levels of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125) and seven autoantibodies were measured and statistically analyzed in samples from healthy controls and patients with lung cancer. The 316 candidates enrolled in this study were randomly assigned into two groups for the training and validation of a diagnostic panel.

Results: An optimal panel with four biomarkers (CEA, CA125, Annexin A1-Ab, and Alpha enolase-Ab) was established, with an area under the receiver operator characteristic (ROC) curve (AUC) of 0.897, a sensitivity of 86.5%, a specificity of 82.3%, a positive predictive value (PPV) of 88.3%, a negative predictive value (NPV) of 79.7%, and a diagnostic accuracy of 84.8% for the training group. The panel was validated, with an AUC of 0.856 and a sensitivity of 87.5% for the validation group. Furthermore, the new panel performed significantly better in lung cancer screening than did CEA and CA125 in all of the cohorts (p< .05).

Conclusion: The diagnostic performance of CEA and CA125 was significantly enhanced through their combination with two autoantibodies (Annexin A1-Ab, and Alpha enolase-Ab). Optimization of the measured autoantibodies is critical for generating a panel to detect lung cancer in patients.

Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer?

Lung cancer is one of the most lethal malignancies worldwide, mainly due to its late diagnoses. The detection of molecular markers on samples provided from routine bronchoscopy including several liquid-based cytology tests (e.g., bronchoaspirate, bronchoalveolar lavage) and/or on easily obtained specimens such as sputum could represent a new approach to improve the sensitivity in lung cancer diagnoses. Recently growing interest has been reported for "noninvasive" liquid biopsy as a valuable source for molecular profiling. Unfortunately, a biomarker and/or composition of biomarkers capable of detecting early-stage lung cancer has yet to be discovered even if in the last few years there has been, through the use of revolutionary new technologies, an explosion of lung cancer biomarkers. Assay sensitivity and specificity need to be improved particularly when new approaches and/or tools are used. We have focused on the most important markers detected in tissue, and on several cytological specimens and liquid biopsies overall.

Preferential Localization of MUC1 Glycoprotein in Exosomes Secreted by Non-Small Cell Lung Carcinoma Cells

Lung cancer remains to be the leading cause of cancer-related mortality worldwide. Finding new noninvasive biomarkers for lung cancer is still a significant clinical challenge. Exosomes are membrane-bound, nano-sized vesicles that are released by various living cells. Studies on exosomal proteomics may provide clues for developing clinical assays. In this study, we performed semi-quantitative proteomic analysis of proteins that were purified from exosomes of NCI-H838 non-small cell lung cancer cell line, with total cellular membrane proteins as control. In the exosomes, LC-MS/MS by data-independent analysis mode identified 3235 proteins. THBS1, ANXA6, HIST1H4A, COL18A1, MDK, SRGN, ENO1, TUBA4A, SLC3A2, GPI, MIF, MUC1, TALDO1, SLC7A5, ICAM1, HSP90AA1, G6PD, and LRP1 were found to be expressed in exosomes at more than 5-fold higher level as compared to total cellular membrane proteins. A well-known cancer biomarker, MUC1, is expressed at 8.98-fold higher in exosomes than total cellular membrane proteins. Subsequent analysis of plasma exosomes from non-small cell lung cancer (NSCLC) patients by a commercial electrochemiluminescence immunoassay showed that exosomal MUC1 level is 1.5-fold higher than healthy individuals (mean value 1.55 ± 0.16 versus mean value 1.05 ± 0.06, p = 0.0213). In contrast, no significant difference of MUC1 level was found between NSCLC patients and healthy individuals' plasma (mean value 5.48 ± 0.65 versus mean value 4.16 ± 0.49). These results suggest that certain proteins, such as MUC1, are selectively enriched in the exosome compartment. The mechanisms for their preferential localization and their biological roles remain to be studied.

Circulating anti-p16a IgG autoantibodies as a potential prognostic biomarker for non-small cell lung cancer

It has been reported that p16 protein is overexpressed in many types of solid cancer and its aberrant expression may trigger the immune response, leading to the secretion of anti‐p16 antibodies. Here, we developed an in‐house ELISA with three p16‐derived linear peptide antigens to examine plasma anti‐p16 antibody levels in patients with non‐small cell lung cancer (NSCLC). Blood samples were taken from 200 control subjects and 211 patients with NSCLC prior to anticancer therapy. A Mann–Whitney U test demonstrated that plasma anti‐p16a IgG levels were significantly higher in NSCLC patients than in control subjects (Z = −11.14, P < 0.001). However, neither plasma anti‐p16b nor plasma anti‐p16c IgG levels showed significant differences in patients with NSCLC as compared to control subjects. Moreover, further analysis indicated that anti‐p16a IgG levels increased with tumor stages, and patients with late stage NSCLC, namely group IV, had the highest IgG levels among four subgroups. Receiver operating characteristic analysis revealed that the anti‐p16a IgG assay had a sensitivity of 32.7% against a specificity of 95.0% in group IV, while Kaplan–Meier survival analysis revealed no significant difference in overall survival between patients with high anti‐p16a IgG levels and those with low anti‐p16a IgG levels (χ² = 0.24, P = 0.63). In conclusion, anti‐p16a IgG may be suitable for use as a prognostic biomarker for NSCLC.

Plasma anti-BIRC5 IgG may be a useful marker for evaluating the prognosis of nonsmall cell lung cancer

A recent study demonstrated that circulating levels of IgG antibodies against linear peptide antigens derived from baculoviral IAP repeat-containing protein 5 isoform 2 (BIRC5) and myc proto-oncogene protein (MYC) were significantly increased in nonsmall cell lung cancer (NSCLC). This study was undertaken to replicate this initial work in an independent sample. An enzyme-linked immunosorbent assay (ELISA) was developed in-house to examine plasma IgG antibodies for three linear peptide antigens derived from BIRC5a, BIRC5b, and MYC in 211 patients with NSCLC and 200 control subjects. A Mann-Whitney U-test demonstrated that plasma anti-BIRC5a IgG levels, but not anti-BIRC5b or anti-MYC IgG levels, were significantly higher in NSCLC patients than control subjects, especially in male patients. Both squamous cell cancer and adenocarcinoma showed increased anti-BIRC5a IgG levels, but the IgG levels were not found to be changed significantly in the early stage of NSCLC. Kaplan-Meier survival analysis showed that NSCLC patients with high anti-BIRC5b IgG levels had better prognosis and longer overall survival (OS) than patients with low anti-BIRC5b IgG levels, although this significant difference failed to survive the adjustment for age, gender, NSCLC stages, and types. Plasma anti-BIRC5a and MYC IgG levels did not show significant associations with OS. In conclusion, Plasma anti-BIRC5 IgG may be a useful marker for assessment of prognosis of NSCLC but not for early diagnosis of this malignancy.

The Prevalence of Cancer-Associated Autoantibodies in Patients with Gastric Cancer and Progressive Grades of Premalignant Lesions

Background: Serum autoantibodies against tumor-associated antigens (TAAs) are detectable in early-stage gastric cancer patients; however, the time point during cancerogenesis when they appear in circulation is still obscure.Methods: In this study, we developed a recombinant antigen microarray and analyzed the prevalence of autoantibodies against 102 TAAs in 829 gastric cancer patients and 929 healthy controls from Caucasian and Asian populations, as well as 100 patients with chronic atrophic gastritis and 775 individuals staged according to different grades of intestinal metaplasia.Results: Six antigens, including CTAG1B/CTAG2, DDX53, IGF2BP2, TP53, and MAGEA3, were predominantly reacting with sera from gastric cancer patients when compared with healthy controls, and the seroreactivity was associated with intestinal-type gastric cancer, but not with patients' Helicobacter pylori status, grade, age, gender, or stage of gastric cancer. We detected gastric cancer-associated seroreactivity in 13% of patients with advanced/severe intestinal metaplasia, which was increased in comparison with mild/moderate intestinal metaplasia (5.3%) and was comparable with that seen in early-stage gastric cancer patients (12%). Moreover, by testing serum samples taken 1 to 9 years before the clinical diagnosis of 18 incident gastric cancer cases, we detected autoantibody responses against several TAAs-SOX2, MYC, BIRC5, IGF2BP1, and MUC1.Conclusions: Our results suggest that humoral immune response against TAAs is generated already during premalignant stages.Impact: Based on the obtained results, cancer-associated autoantibodies might make a valuable contribution to the stratification of high-risk patients with premalignant lesions in the stomach through enhancing the positive predictive power of existing risk models. Cancer Epidemiol Biomarkers Prev; 26(10); 1564-74. ©2017 AACR.

Screening epitopes on systemic lupus erythematosus autoantigens with a peptide array

Systemic lupus erythematosus (SLE) is a common autoimmune disease. Many autoantibodies are closely associated with SLE. However, the specific epitopes recognized and bound by these autoantibodies are still unclear. This study screened the binding epitopes of SLE-related autoantibodies using a high-throughput screening method. Epitope prediction on 12 SLE-related autoantigens was performed using the Immune Epitope Database and Analysis Resource (IEDB) software. The predicted epitopes were synthesized into peptides and developed into a peptide array. Serum IgG from 50 SLE patients and 25 healthy controls was detected using the peptide array. The results were then validated using an enzyme-linked immunosorbent assay (ELISA). The diagnostic efficiency of each epitope was analyzed using a ROC curve. Seventy-three potential epitopes were screened for using the IEDB software after the epitopes on the 12 SLE-related autoantigens were analyzed. Peptide array screening revealed that the levels of the autoantibodies recognized and bound by 4 peptide antigens were significantly upregulated in the serum of SLE patients (P < 0.05). The ELISA results showed that the 4 antigens with significantly increased serum autoantibodies levels in SLE patients were acidic ribosomal phosphoprotein (P0)-4, acidic ribosomal phosphoprotein (P0)-11, DNA topoisomerase 1 (full length)-1, and U1-SnRNP 68/70 KDa-1 (P < 0.05), and the areas under the ROC curve for diagnosing SLE on the basis of these peptides were 0.91, 0.90, 0.93, and 0.91, respectively. Many autoantibodies specifically expressed in the serum of patients with SLE can be detected by specific peptide fragments and may be used as markers in clinical auxiliary diagnoses.

Serological proteome analysis approach-based identification of ENO1 as a tumor-associated antigen and its autoantibody could enhance the sensitivity of CEA and CYFRA 21-1 in the detection of non-small cell lung cancer

PURPOSE

Lung cancer (LC) is the leading cause of cancer-related deaths for both male and female worldwide. Early detection of LC could improve five-year survival rate up to 48.8% compared to 3.3% of late/distant stage. Autoantibodies to tumor-associated antigens (TAAs) have been described as being present before clinical symptoms in lung and other cancers. We aimed to identify more TAAs to improve the performance for discovering non-small cell lung cancer (NSCLC) patients from healthy individuals.

METHODS

Two independent sets were included in this study. Serological proteome analysis (SERPA) was used to identify TAAs from NSCLC cell line H1299 in a discovery set. In validation study, anti-ENO1 autoantibody was examined by immunoassay in sera from 242 patients with NSCLC and 270 normal individuals.

RESULTS

A 47 KDa protein was identified to be alpha-enolase (ENO1) by using SERPA. Analysis of sera from 512 participants by ELISA showed significantly higher frequency of anti-ENO1 autoantibodies in NSCLC sera compared with the sera from normal individuals, with AUC (95%CI) of 0.589 (0.539-0.638, P=0.001). There was no significant difference in frequency of anti-ENO1 in different stages, histological or metastasis status of NSCLC. When anti-ENO1 detection was combined with other two tumor protein biomarkers (CEA and CYFRA 21-1), the sensitivity of NSCLC increased to 84%.

CONCLUSIONS

ENO1 can elicit humoral immune response in NSCLC and its autoantibody has association with the tumorigenesis of NSCLC. Furthermore, these intriguing results suggest the possibility of autoantibody against ENO1 serving as a potential diagnostic biomarker in NSCLC and have implications for defining novel histological determinants of NSCLC.

Survivin Autoantibodies Are Not Elevated in Lung Cancer When Assayed Controlling for Specificity and Smoking Status

The high mortality rate in lung cancer is largely attributable to late diagnosis. Case-control studies suggest that autoantibodies to the survivin protein are potential biomarkers for early diagnosis. We tested the hypothesis that sandwich ELISA can detect autoantibodies to survivin before radiologic diagnosis in patients with early-stage non-small cell lung cancer (NSCLC). Because previous studies assayed survivin autoantibodies with the direct antigen-coating ELISA (DAC-ELISA), we first compared that assay with the sandwich ELISA. Based on the more robust results from the sandwich ELISA, we used it to measure survivin autoantibodies in the serum of 100 individuals from a well-controlled population study [the Dutch-Belgian Lung Cancer Screening Trial (NELSON) trial] composed of current and former smokers (50 patients with NSCLC, both before and after diagnosis, and 50 matched, smoking-habit control subjects), and another 50 healthy nonsmoking control subjects. We found no difference in specific autoantibodies to survivin in NSCLC patients, although nonspecific median optical densities were 24% higher (P < 0.001) in both NSCLC patients and smokers, than in healthy nonsmokers. Finally, we confirmed the ELISA results with Western blot analysis of recombinant and endogenous survivin (HEK-293), which showed no anti-survivin reactivity in patient sera. We conclude that specific anti-survivin autoantibody reactivity is most likely not present in sera before or after diagnosis. Autoantibody studies benefit from a comparison to a well-controlled population, stratified for smoking habit.

Cancer serum biomarkers based on aberrant post-translational modifications of glycoproteins: Clinical value and discovery strategies

Due to the increase in life expectancy in the last decades, as well as changes in lifestyle, cancer has become one of the most common diseases both in developed and developing countries. Early detection remains the most promising approach to improve long-term survival of cancer patients and this may be achieved by efficient screening of biomarkers in biological fluids. Great efforts have been made to identify specific alterations during oncogenesis. Changes at the cellular glycosylation profiles are among such alterations. The "glycosylation machinery" of cells is affected by malignant transformation due to the altered expression of glycogens, leading to changes in glycan biosynthesis and diversity. Alterations in the post-translational modifications of proteins that occur in cancer result in the expression of antigenically distinct glycoproteins. Therefore, these aberrant and cancer-specific glycoproteins and the autoantibodies that are produced in response to their presence constitute targets for cancer biomarkers' search. Different strategies have been implemented for the discovery of cancer glycobiomarkers and are herein reviewed, along with their potentialities and limitations. Practical issues related with serum analysis are also addressed, as well as the challenges that this area faces in the near future.

Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer

OBJECTIVES

"PAULA's" test (Protein Assays Utilizing Lung cancer Analytes) is a novel multiplex immunoassay blood test that incorporates both tumor antigens and autoantibodies to determine the risk that lung cancer (LC) is present in individuals from a high-risk population. The test's performance characteristics were evaluated in a study using 380 retrospective clinical serum samples.

METHODS

PAULA's test is performed on the Luminex xMAP technology platform, and detects a panel of 3 tumor antigens (CEA, CA-125, and CYFRA 21-1) and 1 autoantibody marker (NY-ESO-1). A training set (n = 230) consisting of 115 confirmed diagnoses of non-small cell lung carcinoma (NSCLC) cases and 115 age- and smoking history-matched controls was used to develop the LC predictive model. Data from an independent matched validation set (n = 150) was then used to evaluate the model developed, and determine the ability of the test to distinguish NSCLC cases from controls.

RESULTS

The 4-biomarker panel was able to discriminate NSCLC cases from controls with 74% sensitivity, 80% specificity, and 0.81 AUC in the training set and with 77% sensitivity, 80% specificity, and 0.85 AUC in the independent validation set. The use of NY-ESO-1 autoantibodies substantially increased the overall sensitivity of NSCLC detection as compared to the 3 tumor markers alone. Overall, the multiplexed 4-biomarker panel assay demonstrated comparable performance to a previously employed 8-biomarker non-multiplexed assay.

CONCLUSIONS

These studies confirm the value of using a mixed panel of tumor antigens and autoantibodies in the early detection of NSCLC in high-risk individuals. The results demonstrate that the performance of PAULA's test makes it suitable for use as an aid to determine which high-risk patients need to be directed to appropriate noninvasive diagnostic follow-up testing, especially low-dose CT (LDCT).

Acquisition of useful sero-diagnostic autoantibodies using the same patients'sera and tumor tissues

Cancer tissues are comprised of various components including tumor cells and the surrounding tumor stroma, which consists of the extracellular matrix and inflammatory cells. Since the tumor stroma plays critical roles in tumor development, investigation of the tumor stroma in addition to tumor cells is important to identify useful tumor-associated markers. To discover novel and useful sero-diagnostic markers, a comparative study of tumor-associated autoantibodies (AAbs) in sera from lung adenocarcinoma (AC) patients was investigated by two-dimensional immunoblotting with AC cell lines or each autologous AC tissues. Autoantigens identified from tissue and cell line samples comprised 58 (45 antigens) and 53 spots (41 antigens), respectively. Thirty-six proteins including Transforming growth factor-beta-induced protein ig-h3 (BIGH3) and Hyaluronan and proteoglycan link protein 1 (HAPLN1) were detected only from tissues, 32 proteins only from cell lines, and 9 proteins from both. BIGH3 and HAPLN1 expressions were confirmed in the tumor stroma, but not in AC cell lines by immunostaining and immunoblotting. These data suggest that autologous tumor tissue and serum are important to coincidently detect AAbs derived from the tumor stroma in addition to tumor cells.

Current status of circulating protein biomarkers to aid the early detection of lung cancer

ABSTRACT: 

Considerable efforts have been undertaken to produce an effective screening method to reduce lung cancer mortality. Imaging tools such as low-dose computed tomography has shown an increase in the detection of early disease and a reduction in the rate of death. This screening modality has, however, several limitations, such as overdiagnosis and a high rate of false positives. Therefore, new screening methods, such as the use of circulating protein biomarkers, have emerged as an option that could complement imaging studies. In this review, current imaging techniques applied to lung cancer screening protocols are presented, as well as up-to-date status of circulating protein biomarker panels that may improve lung cancer diagnosis. Additionally, diverse statistical and artificial intelligence tools applied to the design and optimization of these panels are discussed along with the presentation of two commercially available blood tests recently developed to help detect lung cancer early.

A novel autoantibody test for the detection of pre-neoplastic lung lesions

Background

Atypical adenomatous hyperplasia (AAH) and squamous cell dysplasia (SCD) are associated with the development of malignant lesions in the lung. Accurate diagnosis of AAH and SCD could facilitate earlier clinical intervention and provide useful information for assessing lung cancer risk in human populations. Detection of AAH and SCD has been achieved by imaging and bronchoscopy clinically, but sensitivity and specificity remain less than satisfactory. We utilized the ability of the immune system to identify lesion specific proteins for detection of AAH and SCD.

Methods

AAH and SCD tissue was surgically removed from six patients of Chinese descent (3 AAH and 3 SCD) with corresponding serum samples. Total RNA was extracted from the tissues and a cDNA library was generated and incorporated into a T7 bacteriophage vector. Following enrichment to remove "normal" reactive phages, a total of 200 AAH related and 200 SCD related phage clones were chosen for statistical classifier development and incorporation into a microarray. Microarray slides were tested with an independent double-blinded population consisting of 100 AAH subjects, 100 SCD subjects and 200 healthy control subjects.

Results

Sensitivity of 82% and specificity of 70% were achieved in the detection of AAH using a combination of 9 autoantibody biomarkers. Likewise, 86% sensitivity and 78% specificity were achieved in the detection of SCD using a combination of 13 SCD-associated markers. Sequencing analysis identified that most of these 22 autoantibody biomarkers had known malignant associations.

Conclusions

Both diagnostic values showed promising sensitivity and specificity in detection of pre-neoplastic lung lesions. Hence, this technology could be a useful non-invasive tool to assess lung cancer risk in human populations.

Detection of circulating antibodies to linear peptide antigens derived from ANXA1 and DDX53 in lung cancer

The EarlyCDT®-Lung test was the first autoantibody-based diagnostic tool for lung cancer, which was developed with a panel of recombinant protein antigens. To confirm whether the antibody test developed with linear peptide antigens has a similar power to that developed with the whole protein molecules, the present work was then undertaken to develop an in-house enzyme-linked immunosorbent assay with linear peptide antigens derived from annexin A1 (ANXA1) and DEAD box protein 53 (DDX53), which have been used to develop the EarlyCDT®-Lung test. A total of 272 patients with non-small cell lung cancer (NSCLC) and 227 control subjects matched in age and smoking history were recruited. Student's t test showed that the levels of circulating IgG to ANXA1-derived peptide antigens were significantly higher in patients with NSCLC than control subjects (t = 5.66, P < 0.0001), in which the increased anti-ANXA1 IgG levels were observed only in patients at stages I, II, or III, but not in those at stage IV. However, the levels of circulating IgG to DDX53-derived peptide antigens were not significantly altered in NSCLC (t = 1.78, P = 0.076). Receiver operating characteristic analysis showed that the sensitivity against specificity of >90% was 23.7% for ANXA1 IgG assay and 13.8% for DDX53 IgG assay. This work suggests that the linear peptide antigen derived from ANXA1 may be suitable for the development of diagnostic tool for lung cancer although further screening is needed to identify more such peptide antigens derived from tumor-associated antigens.

Autoantibodies as potential biomarkers for the early detection of esophageal squamous cell carcinoma

OBJECTIVES

Esophageal squamous cell carcinoma (ESCC) is one of the most frequent causes of cancer death worldwide and effective diagnosis is needed. We assessed the diagnostic potential of an autoantibody panel that may benefit early diagnosis.

METHODS

We analyzed data for patients with ESCC and normal controls in a test cohort and a validation cohort. Autoantibody levels were measured against a panel of six tumor-associated antigens (p53, NY-ESO-1, matrix metalloproteinase-7 (MMP-7), heat shock protein 70 (Hsp70), peroxiredoxin VI (Prx VI), and BMI1 polycomb ring finger oncogene (Bmi-1)) by enzyme-linked immunosorbent assay.

RESULTS

We assessed serum autoantibodies in 513 participants: 388 with ESCC and 125 normal controls. The validation cohort comprised 371 participants: 237 with ESCC, and 134 normal controls. Autoantibodies to at least 1 of 6 antigens demonstrated a sensitivity/specificity of 57% (95% confidence interval (CI): 52-62%)/95% (95% CI: 89-98%) and 51% (95% CI: 45-57%)/96% (95% CI: 91-99%) in the test and validation cohorts, respectively. Measurement of the autoantibody panel could differentiate early-stage ESCC patients from normal controls (sensitivity 45% (95% CI: 32-59%) and specificity 95% (95% CI: 89-98%) in the test cohort; 46% (95% CI: 35-58%) and 96% (95% CI: 91-99%) in the validation cohort). In either cohort, no significant differences were seen when patients were subdivided by age, gender, smoking status, size of tumor, site of tumor, depth of tumor invasion, histological grade, lymph node status, TNM stage, or early-stage and late-stage groups.

CONCLUSIONS

Measurement of an autoantibody response to multiple tumor-associated antigens in an optimized panel assay, to help discriminate early-stage ESCC patients from normal controls, may aid in early detection of ESCC.

Study of circulating antibodies against CD25 and FOXP3 in breast cancer

Our recent work suggests that circulating levels of anti-CD25 and anti-FOXP3 antibodies were significantly increased in patients with either lung cancer or esophageal cancer. To confirm if these two autoantibodies are specific for certain types of malignant tumors, the present work was thus undertaken to examine an alteration of anti-CD25 and anti-FOXP3 IgG levels in breast cancer. A total of 152 patients with breast cancer and 112 control subjects were recruited in this study. The levels of circulating anti-CD25 and anti-FOXP3 IgG antibodies were tested using an in-house enzyme-linked immunosorbent assay (ELISA). Student's t test showed no significant differences in the levels of either anti-CD25 IgG or anti-FOXP3 IgG between patients with breast cancer and control subjects, although patients at stage I had increased levels of anti-CD25 IgG compared with control subjects (t = 2.11, P = 0.037); there was no significant association of the anti-FOXP3 IgG levels with stages of breast cancer. In conclusion, circulating IgG autoantibody to CD25 instead of FOXP3 may be a potential biomarker for early diagnosis of breast cancer but further investigation remains needed to replicate this initial finding.

Mucins in pancreatic cancer and its microenvironment

Pancreatic cancer remains a lethal malignancy with poor prognosis owing to therapeutic resistance, frequent recurrence and the absence of treatment strategies that specifically target the tumour and its supporting stroma. Deregulated cell-surface proteins drive neoplastic transformations and are envisioned to mediate crosstalk between the tumour and its microenvironment. Emerging studies have elaborated on the role of mucins in diverse biological functions, including enhanced tumorigenicity, invasiveness, metastasis and drug resistance through their characteristic O-linked and N-linked oligosaccharides (glycans), extended structures and unique domains. Multiple mucin domains differentially interact and regulate different components of the tumour microenvironment. This Review discusses: the expression pattern of various mucins in the pancreas under healthy, inflammatory, and cancerous conditions; the context-dependent attributes of mucins that differ under healthy and pathological conditions; the contribution of the tumour microenvironment in pancreatic cancer development and/or progression; diagnostic and/or prognostic efficacy of mucins; and mucin-based therapeutic strategies. Overall, this information should help to delineate the intricacies of pancreatic cancer by exploring the family of mucins, which, through various mechanisms in both tumour cells and the microenvironment, worsen disease outcome.

Serum anti-CCNY autoantibody is an independent prognosis indicator for postoperative patients with early-stage nonsmall-cell lung carcinoma

Cyclin Y (CCNY) is a novel cyclin and almost nothing is known about its role in human cancers. To investigate the clinical significance of serum anti-CCNY autoantibodies in nonsmall-cell lung carcinoma (NSCLC), the serum levels of CCNY protein in 264 patients with NSCLC, 103 patients with tuberculosis, and 89 healthy controls were analyzed by immunohistochemistry. The result shows that, compared with normal lung tissues, the NSCLC tissues contained higher levels of CCNY protein. The levels of anti-CCNY autoantibodies were higher in the sera of the patients with NSCLC than in the sera of the healthy controls (P < 0.001) or the patients with tuberculosis (P = 0.027). Moreover, in a Cox regression analysis, anti-CCNY autoantibody was an independent factor that predicted poor prognosis for postoperative patients with early-stage NSCLC (P = 0.026) as well as for those with distant metastasis (P = 0.012). Our data indicated that Anti-CCNY autoantibody may be useful as a latent tumor marker to facilitate diagnosis and may represent a novel prognostic indicator for patients with early stage NSCLC.

Molecular biomarkers for future screening of lung cancer

The Landmark National Lung Screening Trial established the potential for low dose CT screening (LDCT) to reduce lung cancer-specific mortality in high-risk patients as defined by smoking history and age. However, the prevalence of lung cancer in asymptomatic smokers selected based on the NLST criteria is low. Recent advances have facilitated biomarker discovery for early diagnosis of lung cancer through the analysis of surrogate tissues, including airway epithelium, sputum, exhaled breath, and blood. Although a number of candidate diagnostic biomarkers have been described, none have been validated for use in the clinical setting. The NLST ACRIN biomarker repository is a valuable resource of annotated biological specimens that were collected during the NLST trial, which has the potential to facilitate validation of candidate biomarkers for early diagnosis identified in discovery trials. It will be important to perform retrospective and prospective analysis of biomarkers to screen for lung cancer. The review below summarizes some of our understanding of biomarkers in screening.