Idiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues. We isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average ~62 million reads (53.4% of ~116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR < 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR < 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter® we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four. Our results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF.

Personalized medicine is focused on research disciplines which contribute to the individualization of therapy, like pharmacogenomics and pharmacotranscriptomics. Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood. It is one of the pediatric malignancies with the highest cure rate, but still a lethal outcome due to therapy accounts for 1–3% of deaths. Further improvement of treatment protocols is needed through the implementation of pharmacogenomics and pharmacotranscriptomics. Emerging high-throughput technologies, including microarrays and next-generation sequencing, have provided an enormous amount of molecular data with the potential to be implemented in childhood ALL treatment protocols. In the current review, we summarized the contribution of these novel technologies to the pharmacogenomics and pharmacotranscriptomics of childhood ALL. We have presented data on molecular markers responsible for the efficacy, side effects, and toxicity of the drugs commonly used for childhood ALL treatment, i.e., glucocorticoids, vincristine, asparaginase, anthracyclines, thiopurines, and methotrexate. Big data was generated using high-throughput technologies, but their implementation in clinical practice is poor. Research efforts should be focused on data analysis and designing prediction models using machine learning algorithms. Bioinformatics tools and the implementation of artificial i Lack of association of the CEP72 rs924607 TT genotype with intelligence are expected to open the door wide for personalized medicine in the clinical practice of childhood ALL.

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Methotrexate (MTX) is one of the staples of pediatric acute lymphoblastic leukemia (ALL) treatment. MTX targets the folate metabolic pathway (FMP). Abnormal function of the enzymes in FMP, due to genetic aberrations, leads to adverse drug reactions. The aim of this study was to investigate variants in pharmacogenes involved in FMP and their association with MTX pharmacokinetics (MTX elimination profile) and toxicity in the consolidation therapy phase of pediatric ALL patients. Eleven variants in the thymidylate synthetase (TYMS), methylenetetrahydrofolate reductase (MTHFR), dihydrofolate reductase (DHFR), SLC19A1 and SLCO1B genes were analyzed in 148 patients, using PCR- and sequencing-based methodology. For the Serbian and European control groups, data on allele frequency distribution were extracted from in-house and public databases. Our results show that the A allele of SLC19A1 c.80 variant contributes to slow MTX elimination. Additionally, the AA genotype of the same variant is a predictor of MTX-related hepatotoxicity. Patients homozygous for TYMS 6bp deletion were more likely to experience gastrointestinal toxicity. No allele frequency dissimilarity was found for the analyzed variants between Serbian and European populations. Statistical modelling did not show a joint effect of analyzed variants. Our results indicate that SLC19A1 c.80 variant and TYMS 6bp deletion are the most promising pharmacogenomic markers of MTX response in pediatric ALL patients.

The existence of a potential primary central nervous system lymphoma-specific genomic signature that differs from the systemic form of diffuse large B cell lymphoma (DLBCL) has been suggested, but is still controversial. We investigated 19 patients with primary DLBCL of central nervous system (DLBCL CNS) using the TruSeq Amplicon Cancer Panel (TSACP) for 48 cancer-related genes. Next generation sequencing (NGS) analyses have revealed that over 80% of potentially protein-changing mutations were located in eight genes (CTNNB1, PIK3CA, PTEN, ATM, KRAS, PTPN11, TP53 and JAK3), pointing to the potential role of these genes in lymphomagenesis. TP53 was the only gene harboring mutations in all 19 patients. In addition, the presence of mutated TP53 and ATM genes correlated with a higher total number of mutations in other analyzed genes. Furthermore, the presence of mutated ATM correlated with poorer event-free survival (EFS) (p = 0.036). The presence of the mutated SMO gene correlated with earlier disease relapse (p = 0.023), inferior event-free survival (p = 0.011) and overall survival (OS) (p = 0.017), while mutations in the PTEN gene were associated with inferior OS (p = 0.048). Our findings suggest that the TP53 and ATM genes could be involved in the molecular pathophysiology of primary DLBCL CNS, whereas mutations in the PTEN and SMO genes could affect survival regardless of the initial treatment approach.

Primary ciliary dyskinesia (PCD) is a rare inherited autosomal recessive or X-linked disorder that mainly affects lungs. Dysfunction of respiratory cilia causes symptoms such as chronic rhinosinusitis, coughing, rhinitis, conductive hearing loss and recurrent lung infections with bronchiectasis. It is now well known that pathogenic genetic changes lead to ciliary dysfunction. Here we report usage of clinical-exome based NGS approach in order to reveal underlying genetic causes in cohort of 21 patient with diagnosis of PCD. By detecting 18 (12 novel) potentially pathogenic genetic variants, we established the genetic cause of 11 (9 unrelated) patients. Genetic variants were detected in six PCD disease-causing genes, as well as in SPAG16 and SPAG17 genes, that were not detected in PCD patients so far, but were related to some symptoms of PCD. The most frequently mutated gene in our cohort was DNAH5 (27.77%). Identified variants were in homozygous, compound heterozygous and trans-heterozygous state. For detailed characterization of one novel homozygous genetic variant in DNAI1 gene (c. 947_948insG, p. Thr318TyrfsTer11), RT-qPCR and Western Blot analysis were performed. Molecular diagnostic approach applied in this study enables analysis of 29 PCD disease-causing and related genes. It resulted in mutation detection rate of 50% and enabled discovery of twelve novel mutations and pointed two possible novel PCD candidate genes.