Showing papers by "Rehan Akbani published in 2019"

Next-generation characterization of the Cancer Cell Line Encyclopedia

Abstract: Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data

Abstract: We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)-mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations-comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the 'legacy' GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as 'harmonized' by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve.

Interactive Clustered Heat Map Builder: An easy web-based tool for creating sophisticated clustered heat maps.

Abstract: Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology. Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map. Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps. Preparation of the data and construction of a heat map is rarely a simple linear process. Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches. Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.

Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

Abstract: Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer In this artic

Analytical Platforms 3: Processing Samples via the RPPA Pipeline to Generate Large-Scale Data for Clinical Studies.

Abstract: Reverse phase protein array (RPPA) is a functional proteomics technology amenable to moderately high throughputs of samples and antibodies. The University of Texas MD Anderson Cancer Center RPPA Core Facility has implemented various processes and techniques to maximize RPPA throughput; key among them are maximizing array configuration and relying on database management and automation. One major tool used by the RPPA Core is a semi-automated RPPA process management system referred to as the RPPA Pipeline. The RPPA Pipeline, developed with the aid of MD Avnderson's Department of Bioinformatics and Computational Biology and InSilico Solutions, has streamlined sample and antibody tracking as well as advanced quality control measures of various RPPA processes. This chapter covers RPPA Core processes associated with the RPPA Pipeline workflow from sample receipt to sample printing to slide staining and RPPA report generation that enables the RPPA Core to process at least 13,000 samples per year with approximately 450 individual RPPA-quality antibodies. Additionally, this chapter will cover results of large-scale clinical sample processing, including The Cancer Genome Atlas Project and The Cancer Proteome Atlas.

Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome

Abstract: Purpose Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer. Datasets and methods We developed TransPRECISE, a multi-scale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intra-pathway activities, globally assess cell lines as representative models for patients and develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (>7700) from The Cancer Proteome Atlas across ≤30 tumor types and a set of 640 cancer cell lines from the M.D. Anderson Cell Lines Project spanning16 lineages, and ≤250 cell lines’ response to >400 drugs. Results TransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: head and neck patient tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines, and had different prognostic patterns (456 vs. 654 days of median overall survival; P=0.02). The TransPRECISE-based sample-specific pathway scores achieved high predictive accuracy for drug sensitivities in cell lines across multiple drugs (median AUC >0.8). Conclusion Our study provides a generalizable analytical framework to assess the translational potential of preclinical model systems and guide pathway-based personalized medical decision-making, integrating genomic and molecular data across model systems.

Generation of Raw RPPA Data and Their Conversion to Analysis-Ready Data

Abstract: Reverse phase protein array (RPPA) provides investigators with a powerful high-throughput, quantitative, cost-effective technology for functional proteomics studies. It is an antibody-based technique with procedures similar to that of Western blots. RPPA has a wide variety of applications that range from pharmacodynamics and drug sensitivity assessment to biomarker discovery, subtype classification, and prediction of patient prognosis and response to targeted therapy. In this paper, we describe the technology, its limitations, and some solutions to overcome them. We discuss the steps necessary to obtain raw RPPA data and convert them into robust, high-quality, analysis-ready data. We then illustrate the utility of the platform by highlighting some biomarkers and drug responses of cancer cell lines that confirm previous findings, as a means to validate the platform and the methods presented here.

Abstract 3382: A pan-cancer analysis reveals high frequency genetic alterations in mediators of signaling by the TGF-β superfamily

Abstract: Background: TGF-β/SMAD signaling is a crucial, often contradictory regulator in multiple stages of liver disease that include inflammation, cirrhosis and development of HCC as well as other cancers. The context-specific role of this pathway in treatment strategies has yet to be clarified. Therefore, understanding the multiple context-specific roles of the pathway across broad cancer types is critical towards deciphering the complexities of the pathway. Methods: We followed our previous analysis of HCCs, by extending and examining TGF-β pathway across 33 TCGA tumor types and 9125 samples to address this question. We focused on 43 core genes that encode components that regulate signaling by the TGF-β superfamily with 50 target genes collectively identified through a consensus among TCGA network members. In addition, we extended our analyses to functional studies in mouse mutants and human cell lines with alterations of TGF-β signaling. Results: Focusing on 43 core TGF-β pathway genes, we found at least one of them was genomically altered in 39% of samples (mutations: 24%, homozygous deletions: 10%, or amplifications: 14%). We observed the highest alteration frequencies with hotspot mutations, 65% of which were in liver and GI cancers. We identified hotspots in 6 genes, with new discoveries in TGFBR2 and BMP5 . Interestingly, with all 6 hotspot mutations we observed increased expression of TERT, HMGA2, IL6, MMP9, COL1A1/1A2/3A1, MYC, and FOXP3 . Surprisingly, CDH2 , and ALDH1A1 expression levels were markedly reduced in liver and GI cancers. Alterations in the core genes correlated positively with expression of metastasis-associated genes, and poor patient survival. Epigenetic silencing and miRNA expression were associated with limited activity of the pathway in a cancer dependent manner. Using proteomics data, elevated TGF-β pathway activity showed positive correlation activity of DNA damage repair and EMT pathways (R=0.24, p Conclusions: Our data suggest that TGF-β superfamily indices when combined with specific genes, such as HMGA2 and TERT , may represent strong prognostic markers, and targets in some cancer types such as HCC. This study provides a rich resource and broad molecular perspective that could guide future functional and therapeutic studies of the diverse set of cancer pathways mediated by TGF-β superfamily. In addition, when the pathway is disrupted, epithelial cells are more susceptible to transformation and invasion, potentially identifying specific populations that are more sensitive to chemotherapy such as cisplatin and 5FU, as well as radiation therapy. Citation Format: Kazufumi Ohshiro, Sobia Zaidi, Anil Korkut, Jian Chen, Shuyun Rao, Shoujun Gu, Wilma Jogunoori, Bibhuti Mishra, Rehan Akbani, Lopa Mishra. A pan-cancer analysis reveals high frequency genetic alterations in mediators of signaling by the TGF-β superfamily [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3382.