Subtype prediction in pediatric acute myeloid leukemia: Classification using differential network rank conservation revisited

Background: One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family's simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. Results: In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. Conclusions: Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types

Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis.

OBJECTIVES: To determine how gene expression profiles in osteoarthritis joint tissues relate to patient phenotypes and whether molecular subtypes can be reproducibly captured by a molecular classification algorithm. METHODS: We analysed RNA sequencing data from cartilage and synovium in 113 osteoarthritis patients, applying unsupervised clustering and Multi-Omics Factor Analysis to characterise transcriptional profiles. We tested the association of the molecularly defined patient subgroups with clinical characteristics from electronic health records. RESULTS: We detected two patient subgroups in low-grade cartilage (showing no/minimal degeneration, cartilage normal/softening only), with differences associated with inflammation, extracellular matrix-related and cell adhesion pathways. The high-inflammation subgroup was associated with female sex (OR 4.12, p=0.0024) and prescription of proton pump inhibitors (OR 4.21, p=0.0040). We identified two independent patient subgroupings in osteoarthritis synovium: one related to inflammation and the other to extracellular matrix and cell adhesion processes. A seven-gene classifier including MMP13, APOD, MMP2, MMP1, CYTL1, IL6 and C15orf48 recapitulated the main axis of molecular heterogeneity in low-grade knee osteoarthritis cartilage (correlation ρ=-0.88, p<10-10) and was reproducible in an independent patient cohort (ρ=-0.85, p<10-10). CONCLUSIONS: These data support the reproducible stratification of osteoarthritis patients by molecular subtype and the exploration of new avenues for tailored treatments

DNA Methylation Signatures of Chronic Low-Grade Inflammation Are Associated with Complex Diseases

Background: Chronic low-grade inflammation reflects a subclinical immune response implicated in the pathogenesis of complex diseases. Identifying genetic loci where DNA methylation is associated with chronic low-grade inflammation may reveal novel pathways or therapeutic targets for inflammation. Results: We performed a meta-analysis of epigenome-wide association studies (EWAS) of serum C-reactive protein (CRP), which is a sensitive marker of low-grade inflammation, in a large European population (n = 8863) and trans-ethnic replication in African Americans (n = 4111). We found differential methylation at 218 CpG sites to be associated with CRP (P \u3c 1.15 × 10–7) in the discovery panel of European ancestry and replicated (P \u3c 2.29 × 10–4) 58 CpG sites (45 unique loci) among African Americans. To further characterize the molecular and clinical relevance of the findings, we examined the association with gene expression, genetic sequence variants, and clinical outcomes. DNA methylation at nine (16%) CpG sites was associated with whole blood gene expression in cis (P \u3c 8.47 × 10–5), ten (17%) CpG sites were associated with a nearby genetic variant (P \u3c 2.50 × 10–3), and 51 (88%) were also associated with at least one related cardiometabolic entity (P \u3c 9.58 × 10–5). An additive weighted score of replicated CpG sites accounted for up to 6% inter-individual variation (R2) of age-adjusted and sex-adjusted CRP, independent of known CRP-related genetic variants. Conclusion: We have completed an EWAS of chronic low-grade inflammation and identified many novel genetic loci underlying inflammation that may serve as targets for the development of novel therapeutic interventions for inflammation

Molecular characterisation and clinical outcome of B-cell precursor acute lymphoblastic leukaemia with IG-MYC rearrangement

Rarely, immunophenotypically immature B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) carries an immunoglobulin-MYC rearrangement (IG-MYC-r). This can result in diagnostic confusion with Burkitt lymphoma/leukaemia and use of unproven individualised treatment schedules. Here we contrast the molecular characteristics of these conditions and investigate historic clinical outcome data. We identified 90 cases registered on a national BCP-ALL clinical trial/registry. Where present, diagnostic material underwent cytogenetic, exome, methylome and transcriptome analysis. Outcome was analysed to define 3-year event free survival (EFS) and overall survival (OS). IG-MYC-r was identified in diverse cytogenetic backgrounds, co-existing with either: established BCP-ALL specific abnormalities (high hyperdiploidy n=3, KMT2A-rearrangement n=6, iAMP21 n=1, BCR-ABL n=1); BCL2/BCL6-rearrangements (n=15); or, most commonly, as the only defining feature (n=64). Within this final group, precursor-like V(D)J breakpoints predominated (8/9) and KRAS mutations were common (5/11). DNA methylation identified a cluster of V(D)J rearranged cases, clearly distinct from Burkitt leukaemia/lymphoma. Children with IG-MYC-r within that subgroup had 3-year EFS of 47% and OS of 60%, representing a high-risk BCP-ALL. To develop effective management strategies this patient group must be allowed access to contemporary, minimal residual disease adapted, prospective clinical trial protocols

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets

Genomic and Transcriptomic Investigation of Endemic Burkitt Lymphoma and Epstein Barr Virus

Endemic Burkitt lymphoma (eBL) is the most common pediatric cancer in malaria-endemic equatorial Africa and nearly always contains Epstein-Barr virus (EBV), unlike sporadic Burkitt Lymphoma (sBL) that occurs with a lower incidence in developed countries. Despite this increased burden the study of eBL has lagged. Additionally, while EBV was isolated from an African Burkitt lymphoma tumor 50 years ago, however, the impact of viral variation in oncogenesis is just beginning to be fully explored. In my thesis research, I focused on investigating molecular genetics of the endemic form of this lymphoma with a particular emphasis on the role of the virus and its variation in pathogenesis using novel sequencing and bioinformatic strategies. First, we sought to understand pathogenesis by investigating transcriptomes using RNA sequencing (RNAseq) from 30 primary eBL tumors and compared to sBL tumors. BL tumor samples were prospectively obtained from 2009 until 2012 in Kenya. Within eBL tumors, minimal expression differences were found based on anatomical presentation site, in-hospital survival rates, and EBV genome type; suggesting that eBL tumors are homogeneous without marked subtypes. The outstanding difference detected using surrogate variable analysis was the significantly decreased expression of key genes in the immunoproteasome complex in eBL tumors carrying type 2 EBV compared to type 1 EBV. Secondly, in comparison to previously published pediatric sBL specimens, the majority of the expression and pathway differences were related to the PTEN/PI3K/mTOR signaling pathway and was correlated most strongly with EBV status rather than the geographic designation. Moreover, the common mutations were observed significantly less frequently in eBL tumors harboring EBV type 1, with mutation frequencies similar between tumors with EBV type 2 and without EBV. In addition to the previously reported genes, we identified a set of new genes mutated in BL. Overall, these suggested that EBV, particularly EBV type 1, supports BL oncogenesis alleviating the need for particular driver mutations in the human genome. Second, we sought to comprehensively define sequence variations of EBV across the viral genome in eBL tumor cells and normal infections, and correlate variations with clinical phenotypes and disease risk. We investigated the whole genome sequence of EBV from primary tumors (N=41) and plasma from eBL patients (N=21) as well as EBV in the blood of healthy children (N=29) within the same malaria endemic region. We conducted a genome wide association analysis study with viral genomes of healthy kids and BL kids. Furthermore, we found that the frequencies of EBV types among healthy kids were at equal levels while they were skewed in favor of type 1 (70%) among eBL kids. To pinpoint the fundamental divergence between viral genome subtypes, type 1 and type 2, we constructed phylogenetic trees comparing to all public EBV genomes. The pattern of variation defined the substructures correlated with the subtypes. This investigation not only deciphers the puzzling pathogenic differences between subtypes but also helps to understand how these two EBV types persist in the population at the same time. Overall, this research provides insight into the molecular underpinning of eBL and the role of EBV. It further provides the groundwork and means to unravel the complexity of EBV population structure and provide insight into the viral variation that may influence oncogenesis and outcomes in eBL and other EBV-associated diseases. In addition, genomic and mutational analyses of Burkitt lymphoma tumors identify key differences based on viral content and clinical outcomes suggesting new avenues for the development of prognostic molecular biomarkers and therapeutic interventions

Identification and Characterization of Targets of Metastasis in High-Grade Serous Ovarian Cancer

High-Grade Serous Ovarian Cancer (HGSC) is a highly metastatic cancer with the majority of patients presenting in advanced stages. Currently there are limited targeted treatment options for patients, especially for women with high metastatic tumor burdens. In order to improve patient outcomes, we have aimed to identify and characterize novel targets of metastasis utilizing sequencing from patient tumors and a functional genomic screen. First, we identified genetic alterations of metastasis and short survival by characterizing the genomic and transcriptomic alterations of primary and metastatic tumors in HGSC patients from 23 short-term survivors (overall survival (OS) \u3c3.5 years) and 16 long-term survivors (OS \u3e5 years). We compared somatic mutations, copy number alternations, mutational burdens, differential expression, immune cell fractions, and gene fusion predictions between the primary and metastatic tumors of the ST and LT survival groups. From this project we identified a TP53 R273H mutation, which may have a gain-of-function in ovarian cancer, suggested from evidence in triple negative breast cancer. We aimed to determine if this mutation can be targetable with PARP inhibitor combination treatments and if this mutation is gain-of-function in ovarian cancer cell lines. Third, we performed a siRNA functional genomic screen on 719 kinases in an attachment assay utilizing a collagen fibronectin matrix plated with primary ovarian fibroblasts and GFP-labeled ovarian cancer cell lines. From this initial screen, we have identified 4 candidate kinases to validate. Candidate kinases were validated by siRNA knock-down in ovarian cancer cell lines and assessed on their ability to migrate in a wound-healing assay

Identification and Characterization of Targets of Metastasis in High-Grade Serous Ovarian Cancer

High-Grade Serous Ovarian Cancer (HGSC) is a highly metastatic cancer with the majority of patients presenting in advanced stages. Currently there are limited targeted treatment options for patients, especially for women with high metastatic tumor burdens. In order to improve patient outcomes, we have aimed to identify and characterize novel targets of metastasis utilizing sequencing from patient tumors and a functional genomic screen. First, we identified genetic alterations of metastasis and short survival by characterizing the genomic and transcriptomic alterations of primary and metastatic tumors in HGSC patients from 23 short-term survivors (overall survival (OS)5 years). We compared somatic mutations, copy number alternations, mutational burdens, differential expression, immune cell fractions, and gene fusion predictions between the primary and metastatic tumors of the ST and LT survival groups. From this project we identified a TP53 R273H mutation, which may have a gain-of-function in ovarian cancer, suggested from evidence in triple negative breast cancer. We aimed to determine if this mutation can be targetable with PARP inhibitor combination treatments and if this mutation is gain-of-function in ovarian cancer cell lines. Third, we performed a siRNA functional genomic screen on 719 kinases in an attachment assay utilizing a collagen fibronectin matrix plated with primary ovarian fibroblasts and GFP-labeled ovarian cancer cell lines. From this initial screen, we have identified 4 candidate kinases to validate. Candidate kinases were validated by siRNA knock-down in ovarian cancer cell lines and assessed on their ability to migrate in a wound-healing assay