Browsing by Author "Sudha, Parvathi"

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    Identification of Immuno-Oncology Crosstalk Pathways in Lung Adenocarcinoma
    (Office of the Vice Chancellor for Research, 2016-04-08) Sudha, Parvathi; Pradhan, Meeta; Palakal, Mathew
    Identifying 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).
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    Identifying 1q amplification and PHF19 expressing high-risk cells associated with relapsed/refractory multiple myeloma
    (Research Square, 2023-08-16) Johnson, Travis S.; Sudha, Parvathi; Liu, Enze; Blaney, Patrick; Morgan, Gareth; Chopra, Vivek S.; Dos Santos, Cedric; Nixon, Michael; Huang, Kun; Suvannasankha, Attaya; Abu Zaid, Mohammad; Abonour, Rafat; Walker, Brian A.; Biostatistics and Health Data Science, School of Medicine
    Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers have been identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and has already been studied using RNA-seq. In this study a massive (325,025 cells and 49 patients) single cell multiomic dataset was generated with jointly quantified ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identified an association between one plasma cell subtype with myeloma progression that we have called relapsed/refractory plasma cells (RRPCs). These cells are associated with 1q alterations, TP53 mutations, and higher expression of PHF19. We also identified downstream regulation of cell cycle inhibitors in these cells, possible regulation of the transcription factor (TF) PBX1 on 1q, and determined that PHF19 may be acting primarily through this subset of cells.
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    Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma
    (American Association for Cancer Research, 2022) Sudha, Parvathi; Ahsan, Aarif; Ashby, Cody; Kausar, Tasneem; Khera, Akhil; Kazeroun, Mohammad H.; Hsu, Chih-Chao; Wang, Lin; Fitzsimons, Evelyn; Salminen, Outi; Blaney, Patrick; Czader, Magdalena; Williams, Jonathan; Zaid, Mohammad I. Abu; Ansari-Pour, Naser; Yong, Kwee L.; van Rhee, Frits; Pierceall, William E.; Morgan, Gareth J.; Flynt, Erin; Gooding, Sarah; Abonour, Rafat; Ramasamy, Karthik; Thakurta, Anjan; Walker, Brian A.; Medicine, School of Medicine
    Purpose: We designed a comprehensive multiple myeloma 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 (CNA). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical 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 immunoglobulin heavy chain translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for 1 patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2 = 0.969. Variant allele frequency (VAF) 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 posttreatment to guide sequencing of therapies.