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

Molina, Rafael; Marrades, Ramón M.; Augé, Josep M.; Escudero, José M.; Viñolas, Núria; Reguart, Noemı́; Ramírez, José; Filella, Xavier; Molins, Laureano; Agustí, Àlvar

doi:10.1164/rccm.201404-0603oc

Cited by 149 publications

(144 citation statements)

References 38 publications

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“…Here we provide an improved classification algorithm achieving a superior sensitivity for LC in the context of RDU. In this refined algorithm, besides the smoking status, the routinely used CEA was incorporated, corroborating its diagnostic capacity especially for late-stage tumours4242526. Briefly, our approach involves the measurement of EGF, sCD26, CAL and CEA to generate a classification score for each individual to predict LC.…”

Section: Discussionmentioning

confidence: 95%

Highly Sensitive Marker Panel for Guidance in Lung Cancer Rapid Diagnostic Units

Blanco-Prieto

Chiara

Rodríguez-Girondo

et al. 2017

Sci Rep

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While evidence for lung cancer screening implementation in Europe is awaited, Rapid Diagnostic Units have been established in many hospitals to accelerate the early diagnosis of lung cancer. We seek to develop an algorithm to detect lung cancer in a symptomatic population attending such unit, based on a sensitive serum marker panel. Serum concentrations of Epidermal Growth Factor, sCD26, Calprotectin, Matrix Metalloproteinases −1, −7, −9, CEA and CYFRA 21.1 were determined in 140 patients with respiratory symptoms (lung cancer and controls with/without benign pathology). Logistic Lasso regression was performed to derive a lung cancer prediction model, and the resulting algorithm was tested in a validation set. A classification rule based on EGF, sCD26, Calprotectin and CEA was established, able to reasonably discriminate lung cancer with 97% sensitivity and 43% specificity in the training set, and 91.7% sensitivity and 45.4% specificity in the validation set. Overall, the panel identified with high sensitivity stage I non-small cell lung cancer (94.7%) and 100% small-cell lung cancers. Our study provides a sensitive 4-marker classification algorithm for lung cancer detection to aid in the management of suspicious lung cancer patients in the context of Rapid Diagnostic Units.

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Section: Discussionmentioning

confidence: 95%

Highly Sensitive Marker Panel for Guidance in Lung Cancer Rapid Diagnostic Units

Blanco-Prieto

Chiara

Rodríguez-Girondo

et al. 2017

Sci Rep

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“…These patients would currently be followed up with short-term imaging, which is costly and exposes them to additional radiation [32]. A panel of six TMs (CEA, cancer Antigen 15-3 [CA15-3], squamous cell carcinoma antigen [SCCA], cytokeratin 19 fragment [CYFRA 21-1], neuron-specific enolase [NSE], and ProGRP) was recently shown to be more accurate in predicting the presence of lung cancer than either a single TM alone or clinical factors such as tumor size and smoking status [33]. Thus, these TMs could be used for appropriate triage of indeterminate lung nodules.…”

Section: Role Of Tms In Current Clinical Practicementioning

confidence: 99%

“…All the more, the pattern of CEA, SCCA, CYFRA 21-1, NSE, and ProGRP has been shown to be very helpful to distinguish between small-cell and non-small-cell lung cancer subtypes (SCLC and NSCLC, resp.) [33]. This is quite relevant considering a significant portion of lung biopsies might be nondiagnostic [34].…”

Section: Role Of Tms In Current Clinical Practicementioning

confidence: 99%

“…Finally, research is ongoing to find novel TM, which alone or in combination will improve the diagnostic performance in certain indications, for example, the combination of ProGRP, NSE, CYFRA 21-1, and CEA in lung cancer subtyping [33]. In addition, recent data shows that treatment monitoring in patients with SCLC might be optimized using ProGRP and NSE as TMs [40].…”

Section: Future Directionsmentioning

confidence: 99%

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Clinically Meaningful Use of Blood Tumor Markers in Oncology

Holdenrieder

Pagliaro

Morgenstern

et al. 2016

BioMed Research International

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Before the introduction of modern imaging techniques and the recent developments in molecular diagnosis, tumor markers (TMs) were among the few available diagnostic tools for the management of cancer patients. Easily obtained from serum or plasma samples, TMs are minimally invasive and convenient, and the associated costs are low. Single TMs were traditionally used but these have come under scrutiny due to their low sensitivity and specificity when used, for example, in a screening setting. However, recent research has shown superior performance using a combination of multiple TMs as a panel for assessment, or as part of validated algorithms that also incorporate other clinical factors. In addition, newer TMs have been discovered that have an increased sensitivity and specificity profile for defined malignancies. The aim of this review is to provide a concise overview of the appropriate uses of both traditional and newer TMs and their roles in diagnosis, prognosis, and the monitoring of patients in current clinical practice. We also look at the future direction of TMs and their integration with other diagnostic modalities and other emerging serum based biomarkers, such as circulating nucleic acids, to ultimately advance diagnostic performance and improve patient management.

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“…So far, no single satisfactory circulating (i.e. liquid biopsy) tumour marker has been properly validated, but recently a panel of six tumour markers showed a very high specificity and sensitivity in patients referred to a tertiary hospital because of the clinical suspicion of lung cancer [104,105]. Given that inherited genetic variants play a significant role in lung cancer development [106], but contribute little to risk estimates of classical predictive statistical models [107][108][109], it is hoped that systems biology approaches will allow the comparison multilevel high-throughput omics data between tumour and normal tissue, and facilitate the identification of early diagnostic lung cancer biomarkers.…”

Section: Lung Cancermentioning

confidence: 99%

From systems biology to P4 medicine: applications in respiratory medicine

2018

Self Cite

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Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, "omics" data and bioinformatic computing power. It paves the way forward towards "P4 medicine" ( predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

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

Abstract: The combined assessment of a panel of six serum TMs is a more accurate marker for LC presence than these same TMs considered individually. The potential of these TMs in the diagnostic and screening settings deserves further research.

Cited by 149 publications

References 38 publications

Highly Sensitive Marker Panel for Guidance in Lung Cancer Rapid Diagnostic Units

Highly Sensitive Marker Panel for Guidance in Lung Cancer Rapid Diagnostic Units

Clinically Meaningful Use of Blood Tumor Markers in Oncology

From systems biology to P4 medicine: applications in respiratory medicine

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