BAYESIAN STATISTICAL INFERENCE FOR TUMOR MICROENVIRONMENT COMPOSITIONS

181 pagesThe complex interaction between tumor and its microenvironment is essential for oncogenesis, survival and growth of tumor. These interactions allow tumor to uptake nutrient from environment and evade from immune surveillances. Understanding these interactions is fundamental to the design of immunotherapies and other targeted therapies. Advances in sequencing technologies have enabled measurement of gene transcription and regulation across large cohorts of cancer patients and also down to the single cell resolution. In this work, using glioblastoma (GBM) as a model system, I present the bioinformatic characterization of tumors and their microenvironment, and the statistical models towards an unsupervised and automated way of understanding the compositions. The first part describes the new sequencing method, Chromatin Run-on Sequencing (ChRO-seq), and its use in characterizing the transcription regulatory landscape in primary glioblastoma. Taking advantage of the ability for ChRO-seq to quantify nascent RNAs directly from solid tissues, I developed bioinformatic tools called dREG-HD to map the genome-wide positions of transcription regulatory elements (TREs) based on their nascent RNA patterns, which formed the basis for quantifying the enhancer activity. As ChRO-seq also enables simultaneous quantification of transcription activity of genes, I developed the tool tfTarget to map the network formed between transcription factor, TREs and target genes. Using tfTarget I identified tumor-associated transcription modules and regulatory networks associated with known GBM subtypes. More importantly, I identified three transcription factors from the immune module that negatively correlated with patient survival. This work showed that ChRO-seq is a powerful tool for understanding transcription regulation in complex diseases, highlighting the clinical importance of tumor microenvironment in GBM. The second part develops a Bayesian statistical model for understanding the tumor compositions using bulk sample RNA-seq and/or ChRO-seq collected from large patient cohorts in conjunction with prior knowledge learned from the single cell RNA-seq and/or ATAC-seq data collected from normal and tumor tissues. This model is expected to address the following questions of central importance in cancer biology. First, what transcription pathways are ectopically regulated in tumor patients, and to what extent in each patient? Secondly, what are the cell type compositions in the tumor microenvironment of each patient? Lastly, do any of pathways or the cells present in the microenvironment interact among each other? Answers to these questions shall provide insights into new druggable targets through modulating tumor microenvironment

A bi-stable feedback loop between GDNF, EGR1, and ERα contribute to endocrine resistant breast cancer.

Discovering regulatory interactions between genes that specify the behavioral properties of cells remains an important challenge. We used the dynamics of transcriptional changes resolved by PRO-seq to identify a regulatory network responsible for endocrine resistance in breast cancer. We show that GDNF leads to endocrine resistance by switching the active state in a bi-stable feedback loop between GDNF, EGR1, and the master transcription factor ERα. GDNF stimulates MAP kinase, activating the transcription factors SRF and AP-1. SRF initiates an immediate transcriptional response, activating EGR1 and suppressing ERα. Newly translated EGR1 protein activates endogenous GDNF, leading to constitutive GDNF and EGR1 up-regulation, and the sustained down-regulation of ERα. Endocrine resistant MCF-7 cells are constitutively in the GDNF-high/ ERα-low state, suggesting that the state in the bi-stable feedback loop may provide a 'memory' of endocrine resistance. Thus, we identified a regulatory network switch that contributes to drug resistance in breast cancer

Validation of bi-stable feedback loop in MCF-7 cells and primary breast tumors.

<p>(A) Transcription at the <i>EGR1</i> locus in B7^TamS and G11^TamR cells before and after treatment with GDNF. PRO-seq densities on sense strand and anti-sense strand are shown in red and blue, respectively. dREG scores are shown in green. The number of reads mapping in EGR1 and SRF ChIP-seq data is shown in black. Arrow indicates the direction of annotated genes. (B) <i>EGR1</i> mRNA expression level in B7^TamS cell after treatment with 10 ng/mL GDNF for 4 or 24 hrs. Data are represented as mean ± SEM (n = 3). ** p < 0.01, *** p ≤ 0.001. (C) <i>EGR1</i> mRNA expression level in G11^TamR cells after treatment without (water) or with 10 ng/mL GDNF for 4 or 24 hrs. Data are represented as mean ± SEM (n = 3). * p < 0.05. (D) <i>GDNF</i> mRNA expression levels in G11^TamR cells after treatment without (water) or with 10 ng/mL GDNF for 4 or 24 hrs. Data are represented as mean ± SEM (n = 3). ** p < 0.005. (E) Boxplots show <i>EGR1</i> expression level before or following 90 days of treatment with letrozole (<i>p</i> = 1.8e-6, Wilcoxon Rank Sum Test). (F) Density scatterplots show the expression of <i>EGR1</i> versus <i>ESR1</i> based on mRNA-seq data from 1,177 primary breast cancers. ER+ breast cancers (n = 925), defined based on ESR1 expression (>1e-5), are highlighted in color. The trendline was calculated using Deming regression in the ER+ breast cancers (Pearson’s R = -0.21; <i>p</i> = 2.7e-10).</p

GDNF stimulates the rate at which paused Pol II transitions into productive elongation.

<p>(A) Heatmap depicting changes in RNA polymerase density following 1 hour of GDNF treatment in B7^TamS MCF-7 cells. (B) Changes in pausing index between treated (1 hour) and untreated TamS MCF-7 cells at the indicated class of genes. The Y-axis represents log base e of changes in read density in the promoter compared to the gene body.</p

Bi-stable feedback loop between <i>ESR1</i>, <i>EGR1</i>, and <i>GDNF</i>.

<p>(A) Transcriptional regulatory network of GDNF-dependent endocrine resistance highlighting the bi-stable feedback loop inferred between <i>ESR1</i>, <i>EGR1</i>, and <i>GDNF</i>. Each point represents a gene regulated following 1 or 24 hours of GDNF signaling. Only transcription factors or signaling molecules are shown. Blue and ref edges represent activation or repression relationships, respectively. (B) Transcription near the <i>GDNF</i> locus in B7^TamS cells. PRO-seq densities on sense strand and anti-sense strand are shown in red and blue, respectively. dREG scores are shown in green. The promoter is shown in light green shading. Arrows indicate the direction encoding annotated genes. (C) Dot plots of transcription levels of <i>ESR1</i> following GDNF treatment. (D) Transcription in the <i>ESR1</i> gene in B7^TamS cells. PRO-seq densities on sense strand and anti-sense strand are shown in red and blue, respectively. dREG scores are shown in green. Enhancers and promoters are shown in grey and light green shading, respectively. Arrow indicates the direction encoding annotated genes. (E) Difference in read counts in 3kb windows along <i>ESR1</i> between 1 hours of GDNF and untreated TamS MCF-7 cells. The location of the wave of RNA polymerase along <i>ESR1</i> was identified using a hidden Markov model and is represented by the yellow box. (F) <i>ESR1</i> mRNA expression levels in B7^TamS cells following 10 ng/mL GDNF treatment. Data are represented as mean ± SEM (n = 3). **** p <0.0001. (G) Immunoblot analysis of ERα and p-ERα in B7^TamS cells treatment with 10 ng/mL for 0, 1, 2, and 4 hours. (H) Dot plots representing transcription levels of <i>ERα</i> target genes (<i>PGR</i>, <i>GREB1</i>, and <i>ELOVL2</i>) following a time course of GDNF treatment. (I) Bar plot showing the fraction of genes whose transcription is up-regulated by 40 min. of E2 in all RefSeq annotated genes (left) or those which are downregulated by 1 (center) or 24 hours (right) of GDNF treatment. E2 target genes were enriched in those down-regulated following 24 hrs of GDNF treatment. The Y axis denotes the fraction of genes that are direct up-regulated E2 targets (defined based on Hah et. al. (2011) and also up-regulated in B7^TamS). # p = 1.098e-10, ## p = 6.556999e-19. Fisher’s exact test was used for statistical analysis.</p

Reagents and resources.

<p>Reagents and resources.</p

Primer sets.

<p>Primer sets.</p

Prediction of histone post-translational modification patterns based on nascent transcription data.

The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3, consistent with a role for RNA in recruiting PRC2. A subset of DNase-I-hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than an essential regulatory, role in transcription