Global Transcriptional Dysregulation in Breast Cancer 4/12/2016 Global Transcriptional Dysregulation in Breast Cancer Hiren Karathia and Sridhar Hannenhalli Computational Biology Bioinformatics and Genomics University of Maryland, College Park, MD 20742 USA. hiren@umiacs.umd.edu

Motivation Cancer as heterogeneous and phenotypically diverse disease. 4/12/2016 Motivation Cancer as heterogeneous and phenotypically diverse disease. Each subtype have distinct behavior and response to therapy. At molecular level, the phenotypic variabilities are consequences of stochastic noise of cellular processes and molecular interactions. Cancer is heterogeneous in terms of variability of cellular phenotypes in between individuals tissue or at cellular levels. These phenotypic diversity is reflected by heterogeneous nature of genotype, microenvironment or molecular interactions.

4/12/2016 Motivation Cancer as a disease of pathways !! Context specific dysregulation of genes expression is one of the key factors for cancer transformation. Looking the molecular interactions at pathways levels reflects cellular nature of behavior. At granular level such interactions could be seen as gene-gene interaction where each gene is regulated at expression levels by various factors, i.e., methylation, transcription factors availability and binding to accessible sites of the gene. In normal circumstances, expression of a gene coordinate with expression of the TFs regulator that cooperatively regulate the genes expression It opens a question that whether can we predict expression of a gene explained by expression of the TFs?? However, Extent to which the genes dysregulation reflects a breakdown in the transcription regulatory network in context specific manner is poorly understood. AND It is not clear, whether, the variability in genes expression in cancer reflects heterogeneity OR a general deterioration of transcriptional control.

Motivation TCGA RNA-Seq RegNetwork miR-TarBase 4/12/2016 Zhi-Ping Liu et al., 2015 RegNetwork miR-TarBase Chou CH et al., 2016 TCGA RNA-Seq Chang, K. et al., 2013 To address this problem we need to have few type of data. One we need to have gene regulatory network, where each gene’s connection to various TFs are mandatory to know. Second is we need to have expression data from various samples, so that we can predict the genes expression control explained by TFs regulators. REGNET is very valuable database, which provide annotated connection of a gene to validated regulatory TFs Consortium i.e., TCGA, provides many valuable insights into the underlying genetic and genomic basis of normal and cancer samples.

Background Various approaches have been proposed to infer gene regulatory networks in cancers. However, This looks at overall changes in inferred regulatory networks and do not quantify gene-wise transcriptional dysregulation.

Objectives Compare breast cancer samples with their healthy counterparts to specifically quantify the transcriptional dysregulation in the cancer. Characterize the most dysregulated cancer driver genes. Characterize single-cell heterogeneity of breast cancer [Newly added].

Data & approach TCGA RNA-Seq Normal Breast Cancer RegNetwork TF  Gene 4/12/2016 Data & approach TCGA RNA-Seq Linear Regression for each TFs/miRNA  Gene Normal (n = 89) Chang, K. et al., 2013 Best fitted R2 in Normal samples Best fitted R2 in Cancer samples Breast Cancer (n = 629) Compare the R2 RegNetwork TF  Gene ( > 3= TFs) Zhi-Ping Liu et al., 2015 8898 Genes Higher the R2, better the genes expression explained by TFs/miRNA expression. Lower the R2, worsen the gene expression explained by TFs/miRNA expression. 920 Genes miR-TarBase miRNA  Gene Chou CH et al., 2016

Genes ~ TFs/miRNAs between Normal ~ Cancer Results Genes ~ TFs/miRNAs between Normal ~ Cancer (920) (8898) (920) Overall predictive strength of genes is substantially deteriorated in cancer. As expected predictive strength of the genes is explained better by combining miRNA and TFs regulators.

Cancer drivers (TSG/ONG) ~ TFs For Normal Vs. Cancer Results Cancer drivers (TSG/ONG) ~ TFs For Normal Vs. Cancer (624) (300) (7974) Dysregulation of the annotated driver genes in cancer is higher compared to non driver genes.

Potential Confounding Results Potential Confounding Mean expression # of Regulators Decrease in expression explainability of genes in cancer is not confounding to Number of regulators Mean expression Expression noise Does transcriptional dysregulation in cancer reflection of heterogeneity of cells??

Single-Metastases circulatory cell Results RNA-Seq data of Single-Cell Malignant Cells TFs  Gene ( > 3= TFs) 1135 Genes Single-Metastases circulatory cell (n = 15) Best fitted R2

Transcriptional dysregulation is likely a reflection of heterogeneity. Results Genes ~ TFs For Normal Vs. Cancer Vs. Single Malignant Cells (1135) Malignant Sc ~ Normal Tissue p value : 1e-03 Normal Tissue ~ Cancer Tissue p value : 8.9e-138 Malignant Sc ~ Cancer Tissue p value : 2.7e-67 Transcriptional dysregulation is likely a reflection of heterogeneity.

TSG/ONG ~ TFs For Normal Vs. Cancer Vs. Single Malignant Cells Results TSG/ONG ~ TFs For Normal Vs. Cancer Vs. Single Malignant Cells (101) (81) Transcriptional dysregulation of cancer drivers also is likely a reflection of heterogeneity.

Results Enrichment of Most Deregulated Genes in Normal compared to Single Cells Drug Genes adjP Imatinib CRKL, BCR, JAK2, IKZF1 , LYN <=0.1 Top 10% genes with most decreased R2 between normal tissues and Single-cells Drug Genes adjP cysteamine SOD1, DNAJB2 <=0.1 plicamycin ZNF148, SETDB1 Top 10% genes with most increased R2 between normal tissues and Single-cells ACTUALLY, YOU MIGHT AS WELL DO THIS FOR TOP 10% GENES WITH MOST INCREASE IN R2. SO THIS IS ALL THAT COMES UP? DOESN’T SEEM LIKE A LOT. Oxidative stress responsive genes i.e., SOD1 are highly dysregulated in single malignant cells.

Top 10% high correlated genes Results Capture Heterogeneity from Single-Cells through PCA PC-1 a1 … an Scalar Product g1 gn s1 e1,1 …. en1 P1 s15 e15,1 en15 P15 g1 e1,1 …. e15,1 Product a1 … an Top 10% high correlated genes (~ >= 0.5) ~

Pathways Enrichment for the top 10% genes from the PCA analysis Results Pathways Enrichment for the top 10% genes from the PCA analysis

Conclusions We proposed a direct approach to quantify gene-wise transcriptional dysregulation. Based on the Single-cell analyses, previously observed dysregulation at tissue level is likely a reflection of heterogeneity. Dysregulated genes may present an alternative approach to identify cancer driver genes. Importance of genes underlying the heterogeneity.

Acknowledgement Sridhar Hannenhalli Avinash Das, Mahfuza Sermin

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