A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules.

Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.

A Prediction Model Based on Biomarkers and Clinical Characteristics for Detection of Lung Cancer in Pulmonary Nodules.

Lung cancer early detection by low-dose computed tomography (LDCT) can reduce the mortality. However, LDCT increases the number of indeterminate pulmonary nodules (PNs), whereas 95% of the PNs are ultimately false positives. Modalities for specifically distinguishing between malignant and benign PNs are urgently needed. We previously identified a panel of peripheral blood mononucleated cell (PBMC)-miRNA (miRs-19b-3p and -29b-3p) biomarkers for lung cancer. This study aimed to evaluate efficacy of integrating biomarkers and clinical and radiological characteristics of smokers for differentiating malignant from benign PNs. We analyzed expression of 2 miRNAs (miRs-19b-3p and -29b-3p) in PBMCs of a training set of 137 individuals with PNs. We used multivariate logistic regression analysis to develop a prediction model based on the biomarkers, radiographic features of PNs, and clinical characteristics of smokers for identifying malignant PNs. The performance of the prediction model was validated in a testing set of 111 subjects with PNs. A prediction model comprising the two biomarkers, spiculation of PNs and smoking pack-year, was developed that had 0.91 area under the curve of the receiver operating characteristic for distinguishing malignant from benign PNs. The prediction model yielded higher sensitivity (80.3% vs 72.6%) and specificity (89.4% vs 81.9%) compared with the biomarkers used alone (all P<.05). The performance of the prediction model for malignant PNs was confirmed in the validation set. We have for the first time demonstrated that the integration of biomarkers and clinical and radiological characteristics could efficiently identify lung cancer among indeterminate PNs.

Small non-coding RNA biomarkers in sputum for lung cancer diagnosis.

The early detection of lung cancer can reduce the mortality. However, there is no effective means in clinical settings for noninvasively detecting lung cancer. We previously developed 3 sputum miRNA biomarkers and 2 sputum small nucleolar RNA (snoRNA) biomarkers that can potentially be used for noninvasively diagnosing lung cancer. Here we evaluate the individual and combined applications of the two types of biomarkers in different sets of lung cancer patients and controls. Combined analysis of the miRNAs and snoRNAs has a synergistic effect with 89 % sensitivity and 89 % specificity, and may provide a useful tool for lung cancer early detection.

Analysis of small nucleolar RNAs in sputum for lung cancer diagnosis.

Molecular analysis of sputum presents a noninvasive approach for diagnosis of lung cancer. We have shown that dysregulation of small nucleolar RNAs (snoRNAs) plays a vital role in lung tumorigenesis. We have also identified six snoRNAs whose changes are associated with lung cancer. Here we investigated if analysis of the snoRNAs in sputum could provide a potential tool for diagnosis of lung cancer. Using qRT-PCR, we determined expressions of the six snoRNAs in sputum of a training set of 59 lung cancer patients and 61 cancer-free smokers to develop a biomarker panel, which was validated in a testing set of 67 lung cancer patients and 69 cancer-free smokers for the diagnostic performance. The snoRNAs were robustly measurable in sputum. In the training set, a panel of two snoRNA biomarkers (snoRD66 and snoRD78) was developed, producing 74.58% sensitivity and 83.61% specificity for identifying lung cancer. The snoRNA biomarkers had a significantly higher sensitivity (74.58%) compared with sputum cytology (45.76%) (P < 0.05). The changes of the snoRNAs were not associated with stage and histology of lung cancer (All P >0.05). The performance of the biomarker panel was confirmed in the testing cohort. We report for the first time that sputum snoRNA biomarkers might be useful to improve diagnosis of lung cancer.

Analysis of Lung Flute-collected Sputum for Lung Cancer Diagnosis.

Molecular analysis of sputum can help diagnose lung cancer. We have demonstrated that Lung Flute can be used to collect sputum from individuals who cannot spontaneously expectorate sputum. The objective of this study is to further evaluate the performance of the Lung Flute by comparing the characteristics of parallel samples collected with and without the Lung Flute and the usefulness for diagnosis of lung cancer. Fifty-six early-stage lung cancer patients (40 current smokers and 16 former smokers) and 73 cancer-free individuals (52 current smokers and 21 former smokers) were instructed to spontaneously cough and use Lung Flute for sputum sampling. Sputum cytology and polymerase chain reaction analysis of three miRNAs (miRs-21, 31, and 210) were performed in the specimens. All 92 current smokers and 11 (28.7%) of 37 former smokers spontaneously expectorated sputum and also produced sputum when using the Lung Flute. Twenty-seven former smokers (70.3%) who could not spontaneously expectorate sputum, however, were able to produce sputum when using the Lung Flute. The specimens were of low respiratory origin without contamination from other sources, eg, saliva. There was no difference of sputum volume and cell populations, diagnostic efficiency of cytology, and analysis of the miRNAs in the specimens collected by the two approaches. Analysis of the sputum miRNAs produced 83.93% sensitivity and 87.67% specificity for identifying lung cancer. Therefore, sputum collected by the Lung Flute has comparable features as spontaneously expectorated sputum. Using the Lung Flute enables former smokers who cannot spontaneously expectorate to provide adequate sputum to improve sputum collection for lung cancer diagnosis.

Sputum microRNA biomarkers for identifying lung cancer in indeterminate solitary pulmonary nodules.

PURPOSE: The early detection of lung cancer in heavy smokers by low-dose CT (LDCT) can reduce the mortality. However, LDCT screening increases the number of indeterminate solitary pulmonary nodules (SPN) in asymptomatic individuals, leading to overdiagnosis. Making a definitive preoperative diagnosis of malignant SPNs has been a clinical challenge. We have demonstrated that sputum miRNAs could provide potential biomarkers for lung cancer. Here, we aimed to develop sputum miRNA biomarkers for diagnosis of malignant SPNs. EXPERIMENTAL DESIGN: Using quantitative RT-PCR, we evaluated expressions of 13 sputum miRNAs, previously identified sputum miRNA signatures of lung cancer, in a training set of 122 patients with either malignant (n = 60) or benign SPNs (n = 62) to define a panel of biomarkers. We then validated the biomarker panel in an internal testing set of 136 patients with either malignant (n = 67) or benign SPNs (n = 69), and an external testing cohort of 155 patients with either malignant (n = 76) or benign SPNs (n = 79). RESULTS: In the training set, a panel of three miRNA biomarkers (miRs21, 31, and 210) was developed, producing 82.93% sensitivity and 87.84% specificity for identifying malignant SPNs. The sensitivity and specificity of the biomarkers in the two independent testing cohorts were 82.09% and 88.41%, 80.52% and 86.08%, respectively, confirming the diagnostic value. CONCLUSIONS: Sputum miRNA biomarkers may improve LDCT screening for lung cancer in heavy smokers by preoperatively diagnosing malignant SPNs. Nevertheless, a prospective study in a large population to validate the biomarkers is needed.

Genome-wide small nucleolar RNA expression analysis of lung cancer by next-generation deep sequencing.

Emerging evidence indicates that small nucleolar RNAs (snoRNAs), a class of small noncoding RNAs, may play important function in tumorigenesis. Nonsmall-cell lung cancer (NSCLC) is the number one cancer killer for men and women. Systematically characterizing snoRNAs in NSCLC will develop biomarkers for its early detection and prognostication. We used next-generation deep sequencing to comprehensively characterize snoRNA profiles in 12 NSCLC tissues. We used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the findings in 40 surgical Stage I NSCLC specimens and 126 frozen NSCLC tissues of different stages. The 126 NSCLC tissues were divided into a training set and a testing set. Deep sequencing identified 458 snoRNAs, of which, 29 had a ≥3.0-fold expression level change in Stage I NSCLC tissues versus normal tissues. qRT-PCR analysis showed that 16 of 29 snoRNAs exhibited consistent changes with deep sequencing data. The 16 snoRNAs exhibited 0.75-0.94 area under receiver-operator characteristic curve values in distinguishing lung tumor from normal lung tissues (all ≤0.0001) with 70.0-95.0% sensitivity and 70.0-95.0% specificity. Six genes (snoRA47, snoRA68, snoRA78, snoRA21, snoRD28 and snoRD66) were identified whose expressions were associated with overall survival of the NSCLC patients. A prediction model consisting of three genes (snoRA47, snoRA68 and snoRA78) was developed in the training set of 77 cases, which could significantly predict overall survival of the NSCLC patients (p < 0.0001). The prognostic performance of the prediction model was confirmed in the testing set of 49 NSCLC patients. The identified snoRNA signatures may provide potential biomarkers for the early detection and prognostication of NSCLC.

Characterization of microRNA transcriptome in lung cancer by next-generation deep sequencing.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer death. Systematically characterizing miRNAs in NSCLC will help develop biomarkers for its diagnosis and subclassification, and identify therapeutic targets for the treatment. We used next-generation deep sequencing to comprehensively characterize miRNA profiles in eight lung tumor tissues consisting of two major types of NSCLC, squamous cell carcinoma (SCC) and adenocarcinoma (AC). We used quantitative PCR (qPCR) to verify the findings in 40 pairs of stage I NSCLC tissues and the paired normal tissues, and 60 NSCLC tissues of different types and stages. We also investigated the function of identified miRNAs in lung tumorigenesis. Deep sequencing identified 896 known miRNAs and 14 novel miRNAs, of which, 24 miRNAs displayed dysregulation with fold change ≥4.5 in either stage I ACs or SCCs or both relative to normal tissues. qPCR validation showed that 14 of 24 miRNAs exhibited consistent changes with deep sequencing data. Seven miRNAs displayed distinctive expressions between SCC and AC, from which, a panel of four miRNAs (miRs-944, 205-3p, 135a-5p, and 577) was identified that cold differentiate SCC from AC with 93.3% sensitivity and 86.7% specificity. Manipulation of miR-944 expression in NSCLC cells affected cell growth, proliferation, and invasion by targeting a tumor suppressor, SOCS4. Evaluating miR-944 in 52 formalin-fixed paraffin-embedded SCC tissues revealed that miR-944 expression was associated with lymph node metastasis. This study presents the earliest use of deep sequencing for profiling miRNAs in lung tumor specimens. The identified miRNA signatures may provide biomarkers for early detection, subclassification, and predicting metastasis, and potential therapeutic targets of NSCLC.

Analysis of MicroRNAs in sputum to improve computed tomography for lung cancer diagnosis.

INTRODUCTION: Computed tomography (CT) plays a central role in lung cancer diagnosis. However, CT has relatively low specificity, presenting a challenge in clinical settings. We previously identified 12 microRNAs (miRNAs) whose expressions in tumor tissues were associated with lung cancer. METHODS: Using quantitative reverse transcriptase polymerase chain reaction, we aimed to identify miRNA biomarkers in sputum that could complement CT for diagnosis of lung cancer. RESULTS: In a training set consisting of 66 lung cancer patients and 68 cancer-free smokers, 10 of the 12 miRNAs were differentially expressed between the cases and controls (p ≤ 0.01). From the miRNAs, a logistic regression model was built on the basis of miR-31 and miR-210, both of which had the best prediction for lung cancer, producing an area under receiver operating characteristic curve of 0.83. Combined use of the two miRNAs yielded 65.2% sensitivity and 89.7% specificity, CT had 93.9% sensitivity and 83.8% specificity for lung cancer diagnosis. Notably, combined analysis of the miRNA biomarkers and CT produced a higher specificity than does CT used alone (91.2% versus 83.8%; p < 0.05). The diagnostic performance of the biomarkers was confirmed in a testing set comprising 64 lung cancer patients and 73 cancer-free smokers. CONCLUSION: The sputum miRNA biomarkers might be useful in improving CT for diagnosis of lung cancer, but further independent validation on an external and prospective cohort of patients is required.

Digital PCR quantification of miRNAs in sputum for diagnosis of lung cancer.

PURPOSE: MicroRNAs (miRNAs) play important roles in the initiation and progression of lung cancer. Measuring miRNA expression levels in sputum could provide a potential approach for the diagnosis of lung cancer. The emerging digital PCR is a straightforward technique for precise, direct, and absolute quantification of nucleic acids. The objective of the study was to investigate whether digital PCR could be used to quantify miRNAs in sputum for lung cancer diagnosis. METHODS: We first determined and compared dynamic ranges of digital PCR and conventional quantitative reverse transcriptase PCR (qRT-PCR) for miRNA quantification using RNA isolated from sputum of five healthy individuals. We then used digital PCR to quantify copy number of two lung cancer-associated miRNAs (miR-31 and miR-210) in 35 lung cancer patients and 40 cancer-free controls. RESULTS: Copy number of the miRNAs measured by digital PCR displayed a linear response to input cDNA amount in a twofold dilution series over seven orders of magnitude. miRNA quantification determined by digital PCR assay was in good agreement with that obtained from qRT-PCR analysis in sputum. Furthermore, combined quantification of miR-31 and miR-210 copy number by using digital PCR in sputum of the cases and controls provided 65.71 % sensitivity and 85.00 % specificity for lung cancer diagnosis. CONCLUSION: As digital PCR becomes more established, it would be a robust tool for quantitative assessment of miRNA copy number in sputum for lung cancer diagnosis.

Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers.

BACKGROUND: Making a definitive preoperative diagnosis of solitary pulmonary nodules (SPNs) found by CT has been a clinical challenge. We previously demonstrated that microRNAs (miRNAs) could be used as biomarkers for lung cancer diagnosis. Here we investigate whether plasma microRNAs are useful in identifying lung cancer among individuals with CT-detected SPNs. METHODS: By using quantitative reverse transcriptase PCR analysis, we first determine plasma expressions of five miRNAs in a training set of 32 patients with malignant SPNs, 33 subjects with benign SPNs, and 29 healthy smokers to define a panel of miRNAs that has high diagnostic efficiency for lung cancer. We then validate the miRNA panel in a testing set of 76 patients with malignant SPNs and 80 patients with benign SPNs. RESULTS: In the training set, miR-21 and miR-210 display higher plasma expression levels, whereas miR-486-5p has lower expression level in patients with malignant SPNs, as compared to subjects with benign SPNs and healthy controls (all P ≤ 0.001). A logistic regression model with the best prediction was built on the basis of miR-21, miR-210, and miR-486-5p. The three miRNAs used in combination produced the area under receiver operating characteristic curve at 0.86 in distinguishing lung tumors from benign SPNs with 75.00% sensitivity and 84.95% specificity. Validation of the miRNA panel in the testing set confirms their diagnostic value that yields significant improvement over any single one. CONCLUSIONS: The plasma miRNAs provide potential circulating biomarkers for noninvasively diagnosing lung cancer among individuals with SPNs, and could be further evaluated in clinical trials.