Identification of Immuno-Oncology Crosstalk Pathways in Lung Adenocarcinoma

poster abstractIdentifying dysregulated pathways from the high throughput data for biomarker detection is the rate limiting step in the complex diseases cure. Pathways don’t perform alone; they interact with each other through the overlapping genes. This phenomenon is known as crosstalk of pathways. The aim of the study is develop a methodology to find the highly interacting (cross-talk) immuneoncological pathways and their drug-gene-pathway modules which can be further validated invivo using Lung Adenocarcinoma (LUAD) as a case study. The reference pathway cross-talk matrix is built using the KEGG Knowledgebase, which consists of the 302 KEGG pathways associated with 6996 genes. The LUAD gene expression data available in The Cancer Genome Atlas (TCGA) is used for the study. The data of 32 patients was used in the study and of these, 9 patients were treated with immunotherapy drugs. A set of 3018 significant genes associated with 296 pathways [C.I. =95%, p-value <=0.05] are identified in this dataset, and a disease crosstalk matrix is constructed. Each cell in the matrix gives the cross-talk score of the pathways computed using the formula: ∩ ∪ . The interaction among the significant genes (3018 genes) in the crosstalk pathways were identified using the BioGrid physical gene-gene interaction map and a gene interaction network (10102 interaction) is generated. The significant genes in the network are annotated to their drugs as given in the clinical data of TCGA. The drug-genepathway modules of LUAD are identified using Seed-Based-Network Propagation Algorithm. These modules give the profile of the highest cross-talk pathways of LUAD that can be studied further for alternative drug targets. The study identified T-cell receptor signaling pathway and B cell receptor signaling pathway of LUAD have high crosstalk scores with Erbb Signaling pathway (18.67, 15.15) Vegf signaling pathway (17.77, 22.45); Osteoclast differentiation (16.35, 14.89)

MGP Panel is a comprehensive targeted genomics panel for molecular profiling of multiple myeloma patients

PURPOSE: We designed a comprehensive multiple myeloma (MM) targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. EXPERIMENTAL DESIGN: The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNAs). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical fluorescence in situ hybridization (FISH) (translocations), multiplex ligation probe analysis (MLPA) (CNAs), whole genome sequencing (WGS) (CNAs, mutations, translocations) or droplet digital PCR (ddPCR) of known standards (mutations). RESULTS: Canonical IgH translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for one patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2=0.969. VAFs for 74 mutations were compared between sequencing and ddPCR with concordance of R2=0.9849. CONCLUSIONS: In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost effective, comprehensive, clinically actionable and can be routinely deployed to assist risk stratification at diagnosis or post-treatment to guide sequencing of therapies

Alternative splicing in multiple myeloma is associated with the non-homologous end joining pathway

Abstract Alternative splicing plays a pivotal role in tumorigenesis and proliferation. However, its pattern and pathogenic role has not been systematically analyzed in multiple myeloma or its subtypes. Alternative splicing profiles for 598 newly diagnosed myeloma patients with comprehensive genomic annotation identified primary translocations, 1q amplification, and DIS3 events to have more differentially spliced events than those without. Splicing levels were correlated with expression of splicing factors. Moreover, the non-homologous end joining pathway was an independent factor that was highly associated with splicing frequency as well as an increased number of structural variants. We therefore identify an axis of high-risk disease encompassing expression of the non-homologous end joining pathway, increase structural variants, and increased alternative splicing that are linked together. This indicates a joint pathogenic role for DNA damage response and alternative RNA processing in myeloma

Identifying novel mechanisms of biallelic TP53 loss refines poor outcome for patients with multiple myeloma

Abstract Biallelic TP53 inactivation is the most important high-risk factor associated with poor survival in multiple myeloma. Classical biallelic TP53 inactivation has been defined as simultaneous mutation and copy number loss in most studies; however, numerous studies have demonstrated that other factors could lead to the inactivation of TP53. Here, we hypothesized that novel biallelic TP53 inactivated samples existed in the multiple myeloma population. A random forest regression model that exploited an expression signature of 16 differentially expressed genes between classical biallelic TP53 and TP53 wild-type samples was subsequently established and used to identify novel biallelic TP53 samples from monoallelic TP53 groups. The model reflected high accuracy and robust performance in newly diagnosed relapsed and refractory populations. Patient survival of classical and novel biallelic TP53 samples was consistently much worse than those with mono-allelic or wild-type TP53 status. We also demonstrated that some predicted biallelic TP53 samples simultaneously had copy number loss and aberrant splicing, resulting in overexpression of high-risk transcript variants, leading to biallelic inactivation. We discovered that splice site mutation and overexpression of the splicing factor MED18 were reasons for aberrant splicing. Taken together, our study unveiled the complex transcriptome of TP53, some of which might benefit future studies targeting abnormal TP53