MicroRNA-Based Discovery of Biomarkers, Therapeutic Targets, and Repositioning Drugs for Breast Cancer.

Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues (n = 52) with an overall accuracy of 90.4%. Six miRNAs had concordant expression in both tumors and breast cancer patient blood samples compared with the normal control samples. Twelve miRNAs showed concordant expression in tumors vs. normal breast tissues and patient survival (n = 1093), with seven as potential tumor suppressors and five as potential oncomiRs. From experimentally validated target genes of these 86 miRNAs, pan-sensitive and pan-resistant genes with concordant mRNA and protein expression associated with in-vitro drug response to 19 NCCN-recommended breast cancer drugs were selected. Combined with in-vitro proliferation assays using CRISPR-Cas9/RNAi and patient survival analysis, MEK inhibitors PD19830 and BRD-K12244279, pilocarpine, and tremorine were discovered as potential new drug options for treating breast cancer. Multi-omics biomarkers of response to the discovered drugs were identified using human breast cancer cell lines. This study presented an artificial intelligence pipeline of miRNA-based discovery of biomarkers, therapeutic targets, and repositioning drugs that can be applied to many cancer types.

Expression-Based Diagnosis, Treatment Selection, and Drug Development for Breast Cancer.

There is currently no gene expression assay that can assess if premalignant lesions will develop into invasive breast cancer. This study sought to identify biomarkers for selecting patients with a high potential for developing invasive carcinoma in the breast with normal histology, benign lesions, or premalignant lesions. A set of 26-gene mRNA expression profiles were used to identify invasive ductal carcinomas from histologically normal tissue and benign lesions and to select those with a higher potential for future cancer development (ADHC) in the breast associated with atypical ductal hyperplasia (ADH). The expression-defined model achieved an overall accuracy of 94.05% (AUC = 0.96) in classifying invasive ductal carcinomas from histologically normal tissue and benign lesions (n = 185). This gene signature classified cancer development in ADH tissues with an overall accuracy of 100% (n = 8). The mRNA expression patterns of these 26 genes were validated using RT-PCR analyses of independent tissue samples (n = 77) and blood samples (n = 48). The protein expression of PBX2 and RAD52 assessed with immunohistochemistry were prognostic of breast cancer survival outcomes. This signature provided significant prognostic stratification in The Cancer Genome Atlas breast cancer patients (n = 1100), as well as basal-like and luminal A subtypes, and was associated with distinct immune infiltration and activities. The mRNA and protein expression of the 26 genes was associated with sensitivity or resistance to 18 NCCN-recommended drugs for treating breast cancer. Eleven genes had significant proliferative potential in CRISPR-Cas9/RNAi screening. Based on this gene expression signature, the VEGFR inhibitor ZM-306416 was discovered as a new drug for treating breast cancer.

MicroRNA, mRNA, and Proteomics Biomarkers and Therapeutic Targets for Improving Lung Cancer Treatment Outcomes.

The majority of lung cancer patients are diagnosed with metastatic disease. This study identified a set of 73 microRNAs (miRNAs) that classified lung cancer tumors from normal lung tissues with an overall accuracy of 96.3% in the training patient cohort (n = 109) and 91.7% in unsupervised classification and 92.3% in supervised classification in the validation set (n = 375). Based on association with patient survival (n = 1016), 10 miRNAs were identified as potential tumor suppressors (hsa-miR-144, hsa-miR-195, hsa-miR-223, hsa-miR-30a, hsa-miR-30b, hsa-miR-30d, hsa-miR-335, hsa-miR-363, hsa-miR-451, and hsa-miR-99a), and 4 were identified as potential oncogenes (hsa-miR-21, hsa-miR-31, hsa-miR-411, and hsa-miR-494) in lung cancer. Experimentally confirmed target genes were identified for the 73 diagnostic miRNAs, from which proliferation genes were selected from CRISPR-Cas9/RNA interference (RNAi) screening assays. Pansensitive and panresistant genes to 21 NCCN-recommended drugs with concordant mRNA and protein expression were identified. DGKE and WDR47 were found with significant associations with responses to both systemic therapies and radiotherapy in lung cancer. Based on our identified miRNA-regulated molecular machinery, an inhibitor of PDK1/Akt BX-912, an anthracycline antibiotic daunorubicin, and a multi-targeted protein kinase inhibitor midostaurin were discovered as potential repositioning drugs for treating lung cancer. These findings have implications for improving lung cancer diagnosis, optimizing treatment selection, and discovering new drug options for better patient outcomes.

Identification of Prognostic and Chemopredictive microRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data.

This study developed a novel methodology to correlate genome-scale microRNA (miRNA) expression profiles in a lung squamous cell carcinoma (LUSC) cohort (n = 57) with Surveillance, Epidemiology, and End Results (SEER)-Medicare LUSC patients (n = 33,897) as a function of composite tumor progression indicators of T, N, and M cancer stage and tumor grade. The selected prognostic and chemopredictive miRNAs were extensively validated with miRNA expression profiles of non-small-cell lung cancer (NSCLC) patient samples collected from US hospitals (n = 156) and public consortia including NCI-60, The Cancer Genome Atlas (TCGA; n = 1016), and Cancer Cell Line Encyclopedia (CCLE; n = 117). Hsa-miR-142-3p was associated with good prognosis and chemosensitivity in all the studied datasets. Hsa-miRNA-142-3p target genes (NUP205, RAN, CSE1L, SNRPD1, RPS11, SF3B1, COPA, ARCN1, and SNRNP200) had a significant impact on proliferation in 100% of the tested NSCLC cell lines in CRISPR-Cas9 (n = 78) and RNA interference (RNAi) screening (n = 92). Hsa-miR-142-3p-mediated pathways and functional networks in NSCLC short-term survivors were elucidated. Overall, the approach integrating SEER-Medicare data with comprehensive external validation can identify miRNAs with consistent expression patterns in tumor progression, with potential implications for prognosis and prediction of chemoresponse in large NSCLC patient populations.

Multi-Walled Carbon Nanotube-Induced Gene Expression Biomarkers for Medical and Occupational Surveillance.

As the demand for multi-walled carbon nanotube (MWCNT) incorporation into industrial and biomedical applications increases, so does the potential for unintentional pulmonary MWCNT exposure, particularly among workers during manufacturing. Pulmonary exposure to MWCNTs raises the potential for development of lung inflammation, fibrosis, and cancer among those exposed; however, there are currently no effective biomarkers for detecting lung fibrosis or predicting the risk of lung cancer resulting from MWCNT exposure. To uncover potential mRNAs and miRNAs that could be used as markers of exposure, this study compared in vivo mRNA and miRNA expression in lung tissue and blood of mice exposed to MWCNTs with in vitro mRNA and miRNA expression from a co-culture model of human lung epithelial and microvascular cells, a system previously shown to have a higher overall genome-scale correlation with mRNA expression in mouse lungs than either cell type grown separately. Concordant mRNAs and miRNAs identified by this study could be used to drive future studies confirming human biomarkers of MWCNT exposure. These potential biomarkers could be used to assess overall worker health and predict the occurrence of MWCNT-induced diseases.

A Predictive 7-Gene Assay and Prognostic Protein Biomarkers for Non-small Cell Lung Cancer.

PURPOSE: This study aims to develop a multi-gene assay predictive of the clinical benefits of chemotherapy in non-small cell lung cancer (NSCLC) patients, and substantiate their protein expression as potential therapeutic targets. PATIENTS AND METHODS: The mRNA expression of 160 genes identified from microarray was analyzed in qRT-PCR assays of independent 337 snap-frozen NSCLC tumors to develop a predictive signature. A clinical trial JBR.10 was included in the validation. Hazard ratio was used to select genes, and decision-trees were used to construct the predictive model. Protein expression was quantified with AQUA in 500 FFPE NSCLC samples. RESULTS: A 7-gene signature was identified from training cohort (n = 83) with accurate patient stratification (P = 0.0043) and was validated in independent patient cohorts (n = 248, P < 0.0001) in Kaplan-Meier analyses. In the predicted benefit group, there was a significantly better disease-specific survival in patients receiving adjuvant chemotherapy in both training (P = 0.035) and validation (P = 0.0049) sets. In the predicted non-benefit group, there was no survival benefit in patients receiving chemotherapy in either set. The protein expression of ZNF71 quantified with AQUA scores produced robust patient stratification in separate training (P = 0.021) and validation (P = 0.047) NSCLC cohorts. The protein expression of CD27 quantified with ELISA had a strong correlation with its mRNA expression in NSCLC tumors (Spearman coefficient = 0.494, P < 0.0088). Multiple signature genes had concordant DNA copy number variation, mRNA and protein expression in NSCLC progression. CONCLUSIONS: This study presents a predictive multi-gene assay and prognostic protein biomarkers clinically applicable for improving NSCLC treatment, with important implications in lung cancer chemotherapy and immunotherapy.

Similar and Differential Canonical Pathways and Biological Processes Associated With Multiwalled Carbon Nanotube and Asbestos-Induced Pulmonary Fibrosis: A 1-Year Postexposure Study.

Respiratory exposure to multiwalled carbon nanotubes (MWCNT) or asbestos results in fibrosis; however, the mechanisms to reach this end point may be different. A previous study by our group identified pulmonary effects and significantly altered messenger RNA (mRNA) signaling pathways following exposure to 1, 10, 40, and 80 µg MWCNT and 120 µg crocidolite asbestos on mouse lungs over time at 1-month, 6-month, and 1-year postexposure following pulmonary aspiration. As a continuation to the above study, this current study took an in-depth look at the signaling pathways involved in fibrosis development at a single time point, 1 year, and exposure, 40 µg MWCNT, the lowest exposure at which fibrosis was pathologically evident. The 120 µg asbestos exposure was included to compare MWCNT-induced fibrosis with asbestos-induced fibrosis. A previously validated computational model was used to identify mRNAs with expression profiles matching the fibrosis pathology patterns from exposed mouse lungs. mRNAs that matched the pathology patterns were then input into ingenuity pathway analysis to determine potential signaling pathways and physiological disease functions inherent to MWCNT and asbestos exposure. Both MWCNT and asbestos exposure induced changes in mouse lungs regarding gene expression, cell proliferation, and survival, while MWCNT uniquely induced alterations in pathways involved in oxidative phosphorylation, mitochondrial dysfunction, and transcription. Asbestos exposure produced unique alterations in pathways involved in sustained inflammation. Although typically considered similar due to scale and fiber-like appearance, the different compositional properties inherent to either MWCNT or asbestos may play a role in their ability to induce fibrosis after pulmonary exposure.

Multiwalled carbon nanotube-induced pulmonary inflammatory and fibrotic responses and genomic changes following aspiration exposure in mice: A 1-year postexposure study.

Pulmonary exposure to multiwalled carbon nanotubes (MWCNT) induces an inflammatory and rapid fibrotic response, although the long-term signaling mechanisms are unknown. The aim of this study was to examine the effects of 1, 10, 40, or 80 µg MWCNT administered by pharyngeal aspiration on bronchoalveolar lavage (BAL) fluid for polymorphonuclear cell (PMN) infiltration, lactate dehydrogenase (LDH) activity, and lung histopathology for inflammatory and fibrotic responses in mouse lungs 1 mo, 6 mo, and 1 yr postexposure. Further, a 120-µg crocidolite asbestos group was incorporated as a positive control for comparative purposes. Results showed that MWCNT increased BAL fluid LDH activity and PMN infiltration in a dose-dependent manner at all three postexposure times. Asbestos exposure elevated LDH activity at all 3 postexposure times and PMN infiltration at 1 mo and 6 mo postexposure. Pathological changes in the lung, the presence of MWCNT or asbestos, and fibrosis were noted at 40 and 80 µg MWCNT and in asbestos-exposed mice at 1 yr postexposure. To determine potential signaling pathways involved with MWCNT-associated pathological changes in comparison to asbestos, up- and down-regulated gene expression was determined in lung tissue at 1 yr postexposure. Exposure to MWCNT tended to favor those pathways involved in immune responses, specifically T-cell responses, whereas exposure to asbestos tended to favor pathways involved in oxygen species production, electron transport, and cancer. Data indicate that MWCNT are biopersistent in the lung and induce inflammatory and fibrotic pathological alterations similar to those of crocidolite asbestos, but may reach these endpoints by different mechanisms.

mRNAs and miRNAs in whole blood associated with lung hyperplasia, fibrosis, and bronchiolo-alveolar adenoma and adenocarcinoma after multi-walled carbon nanotube inhalation exposure in mice.

Inhalation exposure to multi-walled carbon nanotubes (MWCNT) in mice results in inflammation, fibrosis and the promotion of lung adenocarcinoma; however, the molecular basis behind these pathologies is unknown. This study determined global mRNA and miRNA profiles in whole blood from mice exposed by inhalation to MWCNT that correlated with the presence of lung hyperplasia, fibrosis, and bronchiolo-alveolar adenoma and adenocarcinoma. Six-week-old, male, B6C3F1 mice received a single intraperitoneal injection of either the DNA-damaging agent methylcholanthrene (MCA, 10 µg g(-1) body weight) or vehicle (corn oil). One week after injections, mice were exposed by inhalation to MWCNT (5 mg m(-3), 5 hours per day, 5 days per week) or filtered air (control) for a total of 15 days. At 17 months post-exposure, mice were euthanized and examined for the development of pathological changes in the lung, and whole blood was collected and analyzed using microarray analysis for global mRNA and miRNA expression. Numerous mRNAs and miRNAs in the blood were significantly up- or down-regulated in animals developing pathological changes in the lung after MCA/corn oil administration followed by MWCNT/air inhalation, including fcrl5 and miR-122-5p in the presence of hyperplasia, mthfd2 and miR-206-3p in the presence of fibrosis, fam178a and miR-130a-3p in the presence of bronchiolo-alveolar adenoma, and il7r and miR-210-3p in the presence of bronchiolo-alveolar adenocarcinoma, among others. The changes in miRNA and mRNA expression, and their respective regulatory networks, identified in this study may potentially serve as blood biomarkers for MWCNT-induced lung pathological changes.

A smoking-associated 7-gene signature for lung cancer diagnosis and prognosis.

Smoking is responsible for 90% of lung cancer cases. There is currently no clinically available gene test for early detection of lung cancer in smokers, or an effective patient selection strategy for adjuvant chemotherapy in lung cancer treatment. In this study, concurrent coexpression with multiple signaling pathways was modeled among a set of genes associated with smoking and lung cancer survival. This approach identified and validated a 7-gene signature for lung cancer diagnosis and prognosis in smokers using patient transcriptional profiles (n=847). The smoking-associated gene coexpression networks in lung adenocarcinoma tumors (n=442) were highly significant in terms of biological relevance (network precision = 0.91, FDR<0.01) when evaluated with numerous databases containing multi-level molecular associations. The gene coexpression network in smoking lung adenocarcinoma patients was confirmed in qRT-PCR assays of the identified biomarkers and involved signaling pathway genes in human lung adenocarcinoma cells (H23) treated with 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Furthermore, the western blotting results of p53, phospho­p53, Rb and EGFR in NNK-treated H23 and transformed normal human lung epithelial cells (BEAS-2B) support their functional involvement in smoking-induced lung cancer carcinogenesis and progression.

Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.

BACKGROUND: Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment. METHODOLOGY/PRINCIPAL FINDINGS: From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples. CONCLUSIONS/SIGNIFICANCE: The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.