Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients.

Importance: The overall low survival rate of patients with lung cancer calls for improved detection tools to enable better treatment options and improved patient outcomes. Multivariable molecular signatures, such as blood-borne microRNA (miRNA) signatures, may have high rates of sensitivity and specificity but require additional studies with large cohorts and standardized measurements to confirm the generalizability of miRNA signatures. Objective: To investigate the use of blood-borne miRNAs as potential circulating markers for detecting lung cancer in an extended cohort of symptomatic patients and control participants. Design, Setting, and Participants: This multicenter, cohort study included patients from case-control and cohort studies (TREND and COSYCONET) with 3102 patients being enrolled by convenience sampling between March 3, 2009, and March 19, 2018. For the cohort study TREND, population sampling was performed. Clinical diagnoses were obtained for 3046 patients (606 patients with non-small cell and small cell lung cancer, 593 patients with nontumor lung diseases, 883 patients with diseases not affecting the lung, and 964 unaffected control participants). No samples were removed because of experimental issues. The collected data were analyzed between April 2018 and November 2019. Main Outcomes and Measures: Sensitivity and specificity of liquid biopsy using miRNA signatures for detection of lung cancer. Results: A total of 3102 patients with a mean (SD) age of 61.1 (16.2) years were enrolled. Data on the sex of the participants were available for 2856 participants; 1727 (60.5%) were men. Genome-wide miRNA profiles of blood samples from 3046 individuals were evaluated by machine-learning methods. Three classification scenarios were investigated by splitting the samples equally into training and validation sets. First, a 15-miRNA signature from the training set was used to distinguish patients diagnosed with lung cancer from all other individuals in the validation set with an accuracy of 91.4% (95% CI, 91.0%-91.9%), a sensitivity of 82.8% (95% CI, 81.5%-84.1%), and a specificity of 93.5% (95% CI, 93.2%-93.8%). Second, a 14-miRNA signature from the training set was used to distinguish patients with lung cancer from patients with nontumor lung diseases in the validation set with an accuracy of 92.5% (95% CI, 92.1%-92.9%), sensitivity of 96.4% (95% CI, 95.9%-96.9%), and specificity of 88.6% (95% CI, 88.1%-89.2%). Third, a 14-miRNA signature from the training set was used to distinguish patients with early-stage lung cancer from all individuals without lung cancer in the validation set with an accuracy of 95.9% (95% CI, 95.7%-96.2%), sensitivity of 76.3% (95% CI, 74.5%-78.0%), and specificity of 97.5% (95% CI, 97.2%-97.7%). Conclusions and Relevance: The findings of the study suggest that the identified patterns of miRNAs may be used as a component of a minimally invasive lung cancer test, complementing imaging, sputum cytology, and biopsy tests.

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches. Previously, we published high-throughput sequencing based microRNA (miRNA) signatures in Alzheimer's disease (AD) patients in the United States (US) and Germany. Here, we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR. We computed models to assess the relation between miRNA expression and phenotypes, gender, age, or disease severity (Mini-Mental State Examination; MMSE). Of the 21 miRNAs, expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts. 18 miRNAs were significantly correlated with neurodegeneration (Benjamini-Hochberg adjusted P < 0.05) with highest significance for miR-532-5p (Benjamini-Hochberg adjusted P = 4.8 × 10-30). Machine learning models reached an area under the curve (AUC) value of 87.6% in differentiating AD patients from controls. Further, ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted P = 0.0002). Interestingly, the miRNAs with lower abundance in AD were enriched in monocytes and T-helper cells, while those up-regulated in AD were enriched in serum, exosomes, cytotoxic t-cells, and B-cells. Our study represents the next important step in translational research for a miRNA-based AD test.

A blood based 12-miRNA signature of Alzheimer disease patients.

BACKGROUND: Alzheimer disease (AD) is the most common form of dementia but the identification of reliable, early and non-invasive biomarkers remains a major challenge. We present a novel miRNA-based signature for detecting AD from blood samples. RESULTS: We apply next-generation sequencing to miRNAs from blood samples of 48 AD patients and 22 unaffected controls, yielding a total of 140 unique mature miRNAs with significantly changed expression levels. Of these, 82 have higher and 58 have lower abundance in AD patient samples. We selected a panel of 12 miRNAs for an RT-qPCR analysis on a larger cohort of 202 samples, comprising not only AD patients and healthy controls but also patients with other CNS illnesses. These included mild cognitive impairment, which is assumed to represent a transitional period before the development of AD, as well as multiple sclerosis, Parkinson disease, major depression, bipolar disorder and schizophrenia. miRNA target enrichment analysis of the selected 12 miRNAs indicates an involvement of miRNAs in nervous system development, neuron projection, neuron projection development and neuron projection morphogenesis. Using this 12-miRNA signature, we differentiate between AD and controls with an accuracy of 93%, a specificity of 95% and a sensitivity of 92%. The differentiation of AD from other neurological diseases is possible with accuracies between 74% and 78%. The differentiation of the other CNS disorders from controls yields even higher accuracies. CONCLUSIONS: The data indicate that deregulated miRNAs in blood might be used as biomarkers in the diagnosis of AD or other neurological diseases.

Treatment-independent miRNA signature in blood of Wilms tumor patients.

BACKGROUND: Blood-born miRNA signatures have recently been reported for various tumor diseases. Here, we compared the miRNA signature in Wilms tumor patients prior and after preoperative chemotherapy according to SIOP protocol 2001. RESULTS: We did not find a significant difference between miRNA signature of both groups. However both, Wilms tumor patients prior and after chemotherapy showed a miRNA signature different from healthy controls. The signature of Wilms tumor patients prior to chemotherapy showed an accuracy of 97.5% and of patients after chemotherapy an accuracy of 97.0%, each as compared to healthy controls. CONCLUSION: Our results provide evidence for a blood-born Wilms tumor miRNA signature largely independent of four weeks preoperative chemotherapy treatment.

A different view on DNA amplifications indicates frequent, highly complex, and stable amplicons on 12q13-21 in glioma.

To further understand the biological significance of amplifications for glioma development and recurrencies, we characterized amplicon frequency and size in low-grade glioma and amplicon stability in vivo in recurring glioblastoma. We developed a 12q13-21 amplicon-specific genomic microarray and a bioinformatics amplification prediction tool to analyze amplicon frequency, size, and maintenance in 40 glioma samples including 16 glioblastoma, 10 anaplastic astrocytoma, 7 astrocytoma WHO grade 2, and 7 pilocytic astrocytoma. Whereas previous studies reported two amplified subregions, we found a more complex situation with many amplified subregions. Analyzing 40 glioma, we found that all analyzed glioblastoma and the majority of pilocytic astrocytoma, grade 2 astrocytoma, and anaplastic astrocytoma showed at least one amplified subregion, indicating a much higher amplification frequency than previously suggested. Amplifications in low-grade glioma were smaller in size and displayed clearly different distribution patterns than amplifications in glioblastoma. One glioblastoma and its recurrencies revealed an amplified subregion of 5 Mb that was stable for 6 years. Expression analysis of the amplified region revealed 10 overexpressed genes (i.e., KUB3, CTDSP2, CDK4, OS-9, DCTN2, RAB3IP, FRS2, GAS41, MDM2, and RAP1B) that were consistently overexpressed in all cases that carried this amplification. Our data indicate that amplifications on 12q13-21 (a) are more frequent than previously thought and present in low-grade tumors and (b) are maintained as extended regions over long periods of time.