Complex Heterogeneous Disease Research Articles

MLPA is a practical and complementary alternative to CMA for diagnostic testing in patients with autism spectrum disorders and identifying new candidate CNVs associated with autism.

BackgroundAutism spectrum disorder (ASD) is a complex heterogeneous developmental disease with a significant genetic background that is frequently caused by rare copy number variants (CNVs). Microarray-based whole-genome approaches for CNV detection are widely accepted. However, the clinical significance of most CNV is poorly understood, so results obtained using such methods are sometimes ambiguous. We therefore evaluated a targeted approach based on multiplex ligation-dependent probe amplification (MLPA) using selected probemixes to detect clinically relevant variants for diagnostic testing of ASD patients. We compare the reliability and efficiency of this test to those of chromosomal microarray analysis (CMA) and other tests available to our laboratory. In addition, we identify new candidate genes for ASD identified in a cohort of ASD-diagnosed patients.MethodWe describe the use of MLPA, CMA, and karyotyping to detect CNV in 92 ASD patients and evaluate their clinical significance.ResultPathogenic and likely pathogenic mutations were identified by CMA in eight (8.07% of the studied cohort) and 12 (13.04%) ASD patients, respectively, and in eight (8.07%) and four (4.35%) patients, respectively, by MLPA. The detected mutations include the 22q13.3 deletion, which was attributed to ring chromosome 22 formation based on karyotyping. CMA revealed a total of 91 rare CNV in 55 patients: eight pathogenic, 15 designated variants of unknown significance (VOUS)—likely pathogenic, 10 VOUS—uncertain, and 58 VOUS—likely benign or benign. MLPA revealed 18 CNV in 18 individuals: eight pathogenic, four designated as VOUS—likely pathogenic, and six designated as VOUS—likely benign/benign. Rare CNVs were detected in 17 (58.62%) out of 29 females and 38 (60.32%) out of 63 males in the cohort. Two genes, DOCK8 and PARK2, were found to be overlapped by CNV designated pathogenic, VOUS—likely pathogenic, or VOUS—uncertain in multiple patients. Moreover, the studied ASD cohort exhibited significant (p < 0.05) enrichment of duplications encompassing DOCK8.ConclusionMultiplex ligation-dependent probe amplification and CMA yielded concordant results for 12 patients bearing CNV designated pathogenic or VOUS—likely pathogenic. Unambiguous diagnoses were achieved for eight patients (corresponding to 8.7% of the total studied population) by both MLPA and CMA, for one (1.09%) patient by karyotyping, and for one (1.09%) patient by FRAXA testing. MLPA and CMA thus achieved identical reliability with respect to clinically relevant findings. As such, MLPA could be useful as a fast and inexpensive test in patients with syndromic autism. The detection rate of potentially pathogenic variants (VOUS—likely pathogenic) achieved by CMA was higher than that for MLPA (13.04% vs. 4.35%). However, there was no corresponding difference in the rate of unambiguous diagnoses of ASD patients. In addition, the results obtained suggest that DOCK8 may play a role in the etiology of ASD.

Imaging markers of disability in aquaporin-4 immunoglobulin G seropositive neuromyelitis optica: a graph theory study.

Neuromyelitis optica spectrum disorders lack imaging biomarkers associated with disease course and supporting prognosis. This complex and heterogeneous set of disorders affects many regions of the central nervous system, including the spinal cord and visual pathway. Here, we use graph theory-based multimodal network analysis to investigate hypothesis-free mixed networks and associations between clinical disease with neuroimaging markers in 40 aquaporin-4-immunoglobulin G antibody seropositive patients (age = 48.16 ± 14.3 years, female:male = 36:4) and 31 healthy controls (age = 45.92 ± 13.3 years, female:male = 24:7). Magnetic resonance imaging measures included total brain and deep grey matter volumes, cortical thickness and spinal cord atrophy. Optical coherence tomography measures of the retina and clinical measures comprised of clinical attack types and expanded disability status scale were also utilized. For multimodal network analysis, all measures were introduced as nodes and tested for directed connectivity from clinical attack types and disease duration to systematic imaging and clinical disability measures. Analysis of variance, with group interactions, gave weights and significance for each nodal association (hyperedges). Connectivity matrices from 80% and 95% F-distribution networks were analyzed and revealed the number of combined attack types and disease duration as the most connected nodes, directly affecting changes in several regions of the central nervous system. Subsequent multivariable regression models, including interaction effects with clinical parameters, identified associations between decreased nucleus accumbens (β = −0.85, P = 0.021) and caudate nucleus (β = −0.61, P = 0.011) volumes with higher combined attack type count and longer disease duration, respectively. We also confirmed previously reported associations between spinal cord atrophy with increased number of clinical myelitis attacks. Age was the most important factor associated with normalized brain volume, pallidum volume, cortical thickness and the expanded disability status scale score. The identified imaging biomarker candidates warrant further investigation in larger-scale studies. Graph theory-based multimodal networks allow for connectivity and interaction analysis, where this method may be applied in other complex heterogeneous disease investigations with different outcome measures.

Archival May-Grünwald-Giemsa-Stained Bone Marrow Smears Are an Eligible Source for Molecular DNA Research.

Biobanking is increasingly important in studying complex heterogeneous diseases. Therefore, it is essential to ensure the sample quality after long-term storage for reliable downstream analyses. The Clinical Biobank of the Jessa Hospital and the University Biobank Limburg (UBiLim) hold a continuously growing collection of hematological samples, including May-Grünwald-Giemsa (MGG)- and Perls' Prussian Blue (PPB)-stained bone marrow (BM) smears, stored at room temperature (RT) for up to 20 years. In this study, we investigated the effect of short- and long-term storage on the quality of DNA and RNA extracted from these BM smears to assess their fitness-for-purpose in downstream molecular applications, including agarose gel electrophoresis, bio-analyzer analysis, quantitative polymerase chain reaction (qPCR), and targeted next-generation sequencing (NGS). The RNA quality was very low for all samples, independent of storage time or staining method. The DNA from PPB-stained BM smears was already degraded after 1 year of storage and correspondingly could not be used for reliable downstream molecular analysis. In contrast, DNA extracted from MGG-stained BM smears stored for up to 10 years was able to generate high-quality data in qPCR and targeted NGS analyses. Longer storage periods (>15 years) of these samples revealed a high degree of degradation and a significant amount of DNA transitions and transversions. In conclusion, the DNA extracted from archival MGG-stained BM smears with a storage time up to at least 10 years was qualitatively good and fit for downstream analysis, including targeted NGS. This indicates that these samples are an eligible source for molecular DNA research and for studying complex diseases.

Primary Prevention of Asthma: Will It Be Possible in the Future?

Asthma is a major public health problem in both the developed and developing world. Asthma is a complex heterogeneous disease with a multifactorial etiology involving genetic, epigenetic, viral, microbial and environmental exposures. New studies have provided significant novel information on the interplay between inherited and modifiable risk factors in the development of asthma, suggesting possible primary preventive measures. Multi-faceted interventional strategies targeted to the personalized risk profile will likely be most effective in primary prevention of asthma.

Systems genetics of nonsyndromic orofacial clefting provides insights into its complex aetiology

Nonsyndromic oral clefting (NSOC) is although one of the most common congenital disorders worldwide, its underlying molecular basis remains elusive. This process has been hindered by the overwhelmingly high level of heterogeneity observed. Given that hitherto multiple loci and genes have been associated with NSOC, and that complex diseases are usually polygenic and show a considerable level of missing heritability, we used a systems genetics approach to reconstruct the NSOC network by integrating human-based physical and regulatory interactome with whole-transcriptome microarray data. We show that the network component contains 53% (23/43) of the curated NSOC-implicated gene set and displays a highly significant propinquity (P < 0.0001) between genes implicated at the genomic level and those differentially expressed at the transcriptome level. In addition, we identified bona fide candidate genes based on topological features and dysregulation (e.g. ANGPTL4), and similarly prioritised genes at GWA loci (e.g. MYC and CREBBP), thus providing further insight into the underlying heterogeneity of NSOC. Gene ontology analysis results were consistent with the NSOC network being associated with embryonic organ morphogenesis and also hinted at an aetiological overlap between NSOC and cancer. We therefore recommend this approach to be applied to other heterogeneous complex diseases to not only provide a molecular framework to unify genes which may seem as disparate entities linked to the same disease, but to also predict and prioritise candidate genes for further validation, thus addressing the missing heritability.

Type 2 Diabetes Mellitus in Rats on a High-Fat Diet with Streptozotocin Induction: Evaluation of the Model

Introduction: Type 2 diabetes mellitus (T2DM) is a complex heterogeneous disease linked with genetic conditions and lifestyle, especially dietary component. Development of the accessible experimental animal model of T2DM is indispensable for elucidation of its pathogenesis and identification of novel antidiabetic agents. Methods: Modeling of experimental T2DM were performed on male Wistar rats aged 9-12 weeks with 2 weeks of dietary manipulation (58% of calories from fats) followed with 35 mg·kg-1 i.p. streptozotocin injection. To evaluate the model, we used intraperitoneal glucose and insulin tolerance test and determined fasting blood concentration of glucose, triglycerides, total cholesterol, and insulin. Additional evaluation of the model was performed by administration of pioglitazone (10 mg/kg-1 p.o.) for 7 days. Student’s t-test was used for single variable comparison between two groups. One-way ANOVA followed by Dunnett post hoc test was used for multiple comparisons versus the control group. Results: T2DM model obtained reproduced key metabolic disorders of the disease, including hyperglycemia (p&lt;0.05), hyperinsulinemia (p&lt;0.05), dyslipidemia (p&lt;0.05) and impaired glucose and insulin tolerance (p&lt;0.05). Administration of pioglitazone significantly improved these metabolic disorders. Thus, we obtained a suitable rat model of T2DM for screening of antidiabetic agents. Conclusion: In comparison of our results with other authors’ data, we have found decreased severity of plasma glucose and basal total cholesterol levels impairment, which highlights the need for careful monitoring of biochemical parameters in high-fat diet and streptozotocin-treated rats.

Obesity phenotype in relation to gene polymorphism among samples of Egyptian children and their mothers

Obesity is complex heterogeneous disease controlled by genes, environmental factors, and their interaction. Genetic factors account for 40–90% of the body mass index variations. Body mass index (BMI) of children correlates more closely with maternal than paternal BMI. So, this studu was aimed to investigate the role of leptin receptor LEPR Gln223Arg, the uncoupling protein 2 (UCP2 G 866 A) and insulin receptor gene (INSR exon 17) polymorphisms in the pathogenesis of obesity. A cross-sectional study executed on 130 children and their obese mothers; classified into 2 groups according to their BMI. The 2 groups were evaluated regarding the anthropometry. Restriction fragment length analysis for LEPR Gln223Arg, UCP2 -866 G/A and INSR exon 17 polymorphisms were applied. It was reported that increased risk of obesity was found in LEPR AG + AA genotype and the A allele. Significant statistical difference was detected only in female children. Concerning UCP2, the AG followed by the GG genotype was the most frequent in all groups and the G allele was the mostly present in obese mothers and obese male children but with no statistical significance. There was difference in the INSR genotype and alleles between groups, but this difference was not statistically significant. This study concluded that the LEPR Gln223Arg, UCP2 G 866 A and INSR exon 17 polymorphisms are related to obesity in Egyptian population. Further researches on larger population are recommended to ascertain the implications of LEPR, UCP2 and INSR polymorphisms in obesity.

P105 - Back Pain and Leg Pain as Drivers of ODI Improvement in Adult Spinal Deformity

BACKGROUND CONTEXT: Adult spinal deformity (ASD) is a complex heterogeneous disease incorporating back pain, leg pain, deformity and disability. The relative contributions of leg and back pain on improvements in clinical HRQoL scores have not previously been described.

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses.

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

MYC-Driven Pathways in Breast Cancer Subtypes.

The transcription factor MYC (MYC proto-oncogene, bHLH transcription factor) is an essential signaling hub in multiple cellular processes that sustain growth of many types of cancers. MYC regulates expression of RNA, both protein and non-coding, that control central metabolic pathways, cell death, proliferation, differentiation, stress pathways, and mechanisms of drug resistance. Activation of MYC has been widely reported in breast cancer progression. Breast cancer is a complex heterogeneous disease and treatment options are primarily guided by histological and biochemical evaluations of the tumors. Based on biochemical markers, three main breast cancer categories are ER+ (estrogen receptor alpha positive), HER2+ (human epidermal growth factor receptor 2 positive), and TNBC (triple-negative breast cancer; estrogen receptor negative, progesterone receptor negative, HER2 negative). MYC is elevated in TNBC compared with other cancer subtypes. Interestingly, MYC-driven pathways are further elevated in aggressive breast cancer cells and tumors that display drug resistant phenotype. Identification of MYC target genes is essential in isolating signaling pathways that drive tumor development. In this review, we address the role of MYC in the three major breast cancer subtypes and highlight the most promising leads to target MYC functions.

Critical Review of Alcohol, Alcoholism and the Withdrawal Symptoms-III. An Introduction to Nanoparticles and their Applications in Alcoholism Treatment

Alcoholism is a complex heterogeneous disease with many contributing factors that may vary from person to person, and are known to have a major impact on treatment outcome. Thus, a single treatment strategy may not work for everyone, stressing an urgent need to develop personalized treatments based on the person's genetic and environmental factors.

Abstract 5568A: A network based analysis for the treatment of triple negative breast cancer

Abstract Background: Triple-negative breast cancer (TNBC) is a complex heterogeneous disease characterized by the absence of three hallmark receptors: human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR). Compared to other breast cancer subtypes, TNBC is more aggressive, has a higher prevalence in African Americans, and more frequently affects younger patients. Currently, TNBC lacks clinically-accepted targets for tailored therapy, warranting the need for candidate biomarkers. Methods: BiomarkerBase, a biomarker knowledgebase developed by Amplion uses a comprehensive list of synonyms to identify biomarkers registered in the records of clinical trials via the government website clinicaltrials.gov. Under each of the registered breast cancer biomarkers (with the exceptions of HER2, ER, and PR), sub-searches were conducted for clinical trials that explicitly used TNBC (or the full name, triple negative breast cancer) in the title of the biomarkers’ studies. Of note, most trials in this review were completed phase II or III. The selected candidate biomarkers were classified into four FDA biomarker categories (surrogate, prognostic, predictive, or pharmacodynamic (PD)) as well as by location (blood/plasma, cell surface, cytoplasm, or nucleus). Current literature about the biomarkers was further examined using PubMed. There, we sought to find papers that studied how the biomarkers may interact. These markers were further organized into a “cellular protein network” that demonstrates potential connectivity. Results: Blood biomarkers include VEGF/VEGFR and IL-8; cell surface biomarkers include EGFR, IGFBP, c-Kit, c-Met, and PD-L1; cytoplasm biomarkers include PIK3CA/AKT/mTOR-related metabolites (GPC, PCh, Lactate/ glucose), pPKM2, F1,6BP, SPHK1, STAT1/IRF2BP2, S6, p4E-BP1, SUV39H1, PTEN, and ALDH1; and nucleus biomarkers include BRCA1, the glucocorticoid receptor (GR), TP53, and Ki67. Table 1 provides an overview of the examined surrogate, prognostic, predictive, and PD biomarkers in TNBC in addition to current literature found in PubMed that provides support for their potential use. Conclusion: To date, there are no clinically-validated biomarkers for TNBC, which has hindered the development of tailored therapy for both chemosensitive and refractory TNBC. Before validation and use of these biomarkers occurs, stringent-clinical criteria must be met first. The list and cellular protein network provided is an inventory and reference point for promising biomarkers for breakthrough, targeted therapies in TNBC. Citation Format: Brett A. Fleisher, Sihem Ait-Oudhia. A network based analysis for the treatment of triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5568A. doi:10.1158/1538-7445.AM2017-5568A

Abstract 4885: Identification of actionable targets for refractory/relapsed childhood cancer leading to personalized targeted therapy (TRICEPS Study)

Abstract Childhood cancer is a group of heterogeneous complex diseases. Although 80% of these children are cured with conventional therapies, it remains the first cause of death among children in Western countries. A significant number of refractory/relapse patients will eventually succumb to their disease and the lack of therapeutic advances for these patients is even more worrisome. Indeed, no significant progress has been noted over the last decade for these patients, urging the need for new and more effective therapeutic approaches. Precision medicine and more effective personalized targeted therapies (PTT) are a major breakthrough leading to increased cure rates and decreased treatment-related morbidity and mortality for the patients with refractory or relapsed tumors. To address this challenge, the TRICEPS study was initiated on April 2014 at the Sainte-Justine UHC (Montreal, Canada) with an overreaching goal to explore the feasibility of performing genomic-driven targeted therapy in pediatric and adolescent (aged 0-21 years) patients with relapsed or refractory childhood cancer. This study offers in-depth genomic and transcriptomic investigation of patient’s tumoral material to identify patient-specific alterations and actionable driver mutation(s) that can be targeted with approved targeted drug and within a reasonable clinically relevant timeframe to assess the feasibility of going from biopsy to a detailed tumor analysis report. Over a period of 30 months, 44 relapsed/refractory cancer patients were recruited. Twenty-two of them underwent extensive genomic investigation (exomic and transcriptomic sequencing) within a median timeframe of 9.7 weeks from patient enrolment to return of results. Patient screen failures occurred due to benign/necrotic tumor biopsies or low tumor purity resulting in suboptimal DNA/RNA quantity or quality for genomic analysis. In all 22 patients, we have identified clinically relevant genomic alterations (SNVs, indels, fusions, CNAs) and relapse-specific mutations influencing patient management and providing options for personalized interventions. We assessed the functional impact of some of these cancer-specific alterations. This was the case of a novel relapse-specific rearrangement, identified on relapsed childhood ETP-ALL, and leading to asparagine synthetase (ASNS) up-regulation through a promoter exchange. The expression of this fusion was associated with reduced apoptosis following l-asparaginase treatment. This study shows that PPT based on next generation sequencing technology is a powerful approach that could be implemented in the clinic within a foreseeable future to guide treatment of hard-to-treat childhood cancers and to further improve patient care and outcomes. Citation Format: Fida Khater, Stephanie Vairy, Sylvie Langlois, Jasmine Healy, Sophie Dumoucel, Mathieu Lajoie, Thomas Sontag, Pascal St-Onge, Henrique Bittencourt, Dorothée Dal Soglio, Anne-Sophie Carret, Sonia Cellot, Josette Champagne, Michel Duval, Maja Krajinovic, Jean-Marie Leclerc, Valerie Larouche, Natalie Patey, Sébastien Perreault, Nelson Piché, Yvan Samson, Pierre Teira, Monia Marzouki, Daniel Sinnett. Identification of actionable targets for refractory/relapsed childhood cancer leading to personalized targeted therapy (TRICEPS Study) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4885. doi:10.1158/1538-7445.AM2017-4885

Are ILC2s Jekyll and Hyde in airway inflammation?

Asthma is a complex heterogeneous disease of the airways characterized by lung inflammation, airway hyperreactivity (AHR), mucus overproduction, and remodeling of the airways. Group 2 innate lymphoid cells (ILC2s) play a crucial role in the initiation and propagation of type 2 inflammatory programs in allergic asthma models, independent of adaptive immunity. In response to allergen, helminths or viral infection, damaged airway epithelial cells secrete IL-33, IL-25, and thymic stromal lymphopoietin (TSLP), which activate ILC2s to produce type 2 cytokines such as IL-5, IL-13, and IL-9. Furthermore, ILC2s coordinate a network of cellular responses and interact with numerous cell types to propagate the inflammatory response and repair lung damage. ILC2s display functional plasticity in distinct asthma phenotypes, enabling them to respond to very different immune microenvironments. Thus, in the context of non-allergic asthma, triggered by exposure to environmental factors, ILC2s transdifferentiate to ILC1-like cells and activate type 1 inflammatory programs in the lung. In this review, we summarize accumulating evidence on the heterogeneity, plasticity, regulatory mechanisms, and pleiotropic roles of ILC2s in allergic inflammation as well as mechanisms for their suppression in the airways.

Novel panel of protein biomarkers to predict response to bortezomib-containing induction regimens in multiple myeloma patients

BackgroundMultiple myeloma (MM) is a complex heterogeneous disease. Various risk stratification models have been recommended including cytogenetic and FISH analysis to identify high-risk patients who may benefit from novel treatments, but such facilities are not widely available. The International Scoring System (ISS) using beta-2-microglobulin and albumin remains a widely used prognostic scoring system in many clinical practices; however it is not useful in predicting response to treatment in MM. The aim of this study is to identify clinically useful biomarkers to predict response to treatment containing bortezomib. Methods17 MM patient serum samples (9 responders/8 non-responders) were used for the discovery phase (label-free mass spectrometry) and an additional 20 MM patient serum samples were used for the ELISA-based validation phase (14 responders/6 non-responders). ResultsCLU and ANG mean levels were higher in the responders group, while Complement C1q had lower concentrations. The combination of all standard biomarkers (albumin, beta-2-microglobulin (ß2M), paraprotein and kappa/lambda (K/L) ratio) had an AUC value of 0.71 with 65% correct classification, while an overall combination of new candidate protein biomarkers with standard biomarkers had an AUC value of 0.89 with 85.3% correct classification. ConclusionsA combination of new and standard biomarkers consisting of CLU, ANG, C1Q, albumin, ß2M, paraprotein and K/L ratio may have potential as a novel panel of biomarkers to predict MM response to treatment containing bortezomib. General significanceUse of this biomarker panel could facilitate a more personalized therapy approach and to minimize unnecessary side effects from ineffective drugs.

Management of severe asthma: targeting the airways, comorbidities and risk factors.

Severe asthma is a complex heterogeneous disease that is refractory to standard treatment and is complicated by multiple comorbidities and risk factors. In mild to moderate asthma, the burden of disease can be minimised by inhaled corticosteroids, bronchodilators and self-management education. In severe asthma, however, management is more complex. When patients with asthma continue to experience symptoms and exacerbations despite optimal management, severe refractory asthma (SRA) should be suspected and confirmed, and other aetiologies ruled out. Once a diagnosis of SRA is established, patients should undergo a systematic and multidimensional assessment to identify inflammatory endotypes, risk factors and comorbidities, with targeted and individualised management initiated. We describe a practical approach to assessment and management of patients with SRA.

Predicting outcome: role of gene signatures

Pathological assessments, such as Gleason grading, which is a strong clinical predictor of prostate cancer progression 1, have a role to play in predicting the outcome of a patient's response to therapy. The addition of PSA and TNM staging are further used to inform appropriate treatment strategies in patients who are at low, intermediate and high risk of disease progression. Unfortunately, approximately 30% of men with intermediate-risk prostate cancer will fail to be cured by surgery or radiation therapy approaches. This is not surprising as primary prostate cancer represents a complex heterogeneous disease that is clearly not fully explained by the current clinical prognostic factors, and further molecular characterization is required. There is now significant emerging evidence that the molecular characterization of tumours is important to enable us to stratify prostate cancer patients by their response to primary therapy and to identify the next appropriate steps in their treatment pathway. Long et al. 2 have identified gene signatures that can define different genomic subtypes of prostate cancer and are predictive of biochemical recurrence. Erho et al. 3 discovered and validated a 22-gene signature, which they termed a genomic classifier for the prediction of early metastasis after radical prostatectomy, while Penny et al. 4 have identified an mRNA expression signature of Gleason grade which is predictive of lethal prostate cancer. Long et al. 2 identified a 24-gene signature, which they discovered through RNA sequencing analysis of 100 formalin-fixed paraffin-embedded prostatectomy samples. They went on to validate their findings in a publically available independent gene expression microarray dataset of 140 patients. This 24-gene signature forms the basis of the present study by Pellegrini et al. 5, who refer to this gene signature as Sig24. In their study, they firstly undertook to determine if Sig24 was associated with pathological Gleason score as a marker of tumour aggressiveness, and then if it had prognostic value for the identification of patients at risk of metastasis or prostate cancer-specific mortality after radical prostatectomy. The Gleason score association study was carried out using the data from three independent case–control sets, including 182 patients from the Cleveland Clinic and two cohorts (cohort I, n = 545; cohort II, n = 235) from the Mayo Clinic, for which gene expression analysis had previously been conducted by Genome Dx using the Affymetrix Human 1.0 ST Genechip platform. The Sig24 score was calculated for each patient and higher Gleason score was associated with significantly higher Sig24 scores. The association studies for metastatic disease and prostate cancer-specific mortality, however, were only carried out in the Mayo Clinic cohort II. For both clinical endpoints the Sig24 score combined with the clinical model outperformed the clinical model alone of PSA, Gleason score and tumour stage. For metastatic disease the area under the curve for the clinical model alone was 0.69 (0.62–0.77) compared with 0.73 (0.66–0.78) for the clinical model combined with Sig24. For the prostate cancer-specific mortality endpoint, the area under the curve for the clinical model alone was 0.69 (0.67–0.87) compared with 0.74 (0.63–0.85) for the clinical model combined with Sig24. These gene signatures have significant potential for predicting the progression of disease rather than waiting for PSA relapse, which is currently used to identify disease recurrence, with interval to biochemical failure being the best univariate factor predicting prostate cancer mortality and overall survival 6. This would allow the initiation of additional therapies before recurrence and a better outcome for the patient. This concept has been demonstrated in a study in which the use of the Decipher gene signature was shown to improve the identification of patients who could benefit from adjuvant radiotherapy and thus only these patients were targeted for therapy 7. Incorporating these gene signatures in robust clinical assays and integrating them into clinical decision-making is the next essential step in order for these strategies to have an impact on patient outcomes. None declared.

RNA-Seq based genome-wide analysis reveals loss of inter-chromosomal regulation in breast cancer

Breast cancer is a complex heterogeneous disease. Common hallmark features of cancer can be found. Their origin may be traced back to their intricate relationships governing regulatory programs during the development of this disease. To unveil distinctive features of the transcriptional regulation program in breast cancer, a pipeline for RNA-seq analysis in 780 breast cancer and 101 healthy breast samples, at gene expression and network level, was implemented. Inter-chromosomal relationships between genes resulted strikingly scarce in a cancer network, in comparison to its healthy counterpart. We suggest that inter-chromosomal regulation loss may be a novel feature in breast cancer. Additional evidence was obtained by independent validation in microarray and Hi-C data as well as supplementary computational analyses. Functional analysis showed upregulation in processes related to cell cycle and division; while migration, adhesion and cell-to-cell communication, were downregulated. Both the BRCA1 DNA repairing signalling and the Estrogen-mediated G1/S phase entry pathways were found upregulated. In addition, a synergistic underexpression of the γ-protocadherin complex, located at Chr5q31 is also shown. This region has previously been reported to be hypermethylated in breast cancer. These findings altogether provide further evidence for the central role of transcriptional regulatory programs in shaping malignant phenotypes.

Translational inhibitors as potential therapeutic tool of human neuroblastoma through mitochondrial gene expression

Neuroblastoma is a solid neuroendocrine tumour and most common type of cancer of infancy. It is a complex heterogeneous disease and many factors such as molecular, cellular and genetic features are involved in its development. Mitochondria play a pivotal role in neuronal cell survival or death. Neurons are highly reliant on aerobic oxidative phosphorylation (OXPHOS) for their energy needs. Defective activities of mitochondrial complexes I, II, III and IV have been identified in many neurological and neurodegenerative diseases. Human mitochondria with its own genetic material meet the needs required for the assembly of subunits of the oxidative phosphorylation (OXPHOS) complexes. A number of translational inhibitors are known that could potentially effect translation of mitochondrial protein synthesis. Among these puromycin, homoharringtonine and cyclohexamide were selected for the present study. The effect of these translational inhibitors on mitochondrial gene expression for the treatment of neuroblastoma are not well established. Therefore, in this study, we have investigated the effects of these translational inhibitors on the expression of human mitochondrial gene expression in SH-SY5Y neuroblastoma cells.We observed a significant effect on the level of mitochondrial transcripts upon exposure to these translation inhibitors in SH-SY5Y cells, however, the effects on expression of mitochondrial proteins were minimal. This suggests that translational inhibitors might not directly affect the abundance of mitochondrial proteins. Translational inhibitors induce significant effect on mitochondrial gene expression that can be lead to the new-targeted therapy for treating neuroblastoma.

Systematic approach to understanding the pathogenesis of systemic sclerosis.

Systemic sclerosis (SSc) is a complex heterogeneous autoimmune disease. Progressive organ fibrosis is a major contributor to SSc mortality. Despite extensive efforts, the underlying mechanism of SSc remains unclear. Efforts to understand the pathogenesis of SSc have included genomics, epigenetics, transcriptomic, proteomic and metabolomic studies in the last decade. This review focuses on recent studies in SSc research based on multi-omics. The combination of these technologies can help us understand the pathogenesis of SSc. This review aims to provide important information for disease identification, therapeutic targets and potential biomarkers.