Hatice U. Osmanbeyoglu

A mechanism for expansion of regulatory T-cell repertoire and its role in self-tolerance

T-cell receptor (TCR) signalling has a key role in determining T-cell fate. Precursor cells expressing TCRs within a certain low-affinity range for complexes of self-peptide and major histocompatibility complex (MHC) undergo positive selection and differentiate into naive T cells expressing a highly diverse self-MHC-restricted TCR repertoire. In contrast, precursors displaying TCRs with a high affinity for ‘self’ are either eliminated through TCR-agonist-induced apoptosis (negative selection) or restrained by regulatory T (Treg) cells, whose differentiation and function are controlled by the X-chromosome-encoded transcription factor Foxp3 (reviewed in ref. 2). Foxp3 is expressed in a fraction of self-reactive T cells that escape negative selection in response to agonist-driven TCR signals combined with interleukin 2 (IL-2) receptor signalling. In addition to Treg cells, TCR-agonist-driven selection results in the generation of several other specialized T-cell lineages such as natural killer T cells and innate mucosal-associated invariant T cells. Although the latter exhibit a restricted TCR repertoire, Treg cells display a highly diverse collection of TCRs. Here we explore in mice whether a specialized mechanism enables agonist-driven selection of Treg cells with a diverse TCR repertoire, and the importance this holds for self-tolerance. We show that the intronic Foxp3 enhancer conserved noncoding sequence 3 (CNS3) acts as an epigenetic switch that confers a poised state to the Foxp3 promoter in precursor cells to make Treg cell lineage commitment responsive to a broad range of TCR stimuli, particularly to suboptimal ones. CNS3-dependent expansion of the TCR repertoire enables Treg cells to control self-reactive T cells effectively, especially when thymic negative selection is genetically impaired. Our findings highlight the complementary roles of these two main mechanisms of self-tolerance.

PI3K pathway regulates ER-dependent transcription in breast cancer through the epigenetic regulator KMT2D

Tumor cells develop resistance to a drug used to treat breast cancer through a chromatin remodeling mechanism. Chromatin state dictates drug response Drugs inhibiting the phosphoinositide-(3)-kinase (PI3K) signaling pathway are effective in a subset of breast cancer patients. Tumors become resistant to these drugs, however, and this transition is often accompanied by increased transcription of genes regulated by the estrogen receptor. A better understanding of the mechanism linking PI3K signaling and estrogen receptor activity could potentially suggest strategies to prevent drug resistance. Toska et al. found that PI3K inhibition activates a specific epigenetic regulator, the histone methyltransferase KMT2D. The protein modifications catalyzed by KMT2D create a more open chromatin state, which unleashes estrogen receptor–dependent transcription. Thus, combination therapies consisting of PI3K inhibitors and KMT2D inhibitors may be more effective than PI3K inhibitors alone. Science, this issue p. 1324 Activating mutations in PIK3CA, the gene encoding phosphoinositide-(3)-kinase α (PI3Kα), are frequently found in estrogen receptor (ER)–positive breast cancer. PI3Kα inhibitors, now in late-stage clinical development, elicit a robust compensatory increase in ER-dependent transcription that limits therapeutic efficacy. We investigated the chromatin-based mechanisms leading to the activation of ER upon PI3Kα inhibition. We found that PI3Kα inhibition mediates an open chromatin state at the ER target loci in breast cancer models and clinical samples. KMT2D, a histone H3 lysine 4 methyltransferase, is required for FOXA1, PBX1, and ER recruitment and activation. AKT binds and phosphorylates KMT2D, attenuating methyltransferase activity and ER function, whereas PI3Kα inhibition enhances KMT2D activity. These findings uncover a mechanism that controls the activation of ER by the posttranslational modification of epigenetic regulators, providing a rationale for epigenetic therapy in ER-positive breast cancer.

Linking signaling pathways to transcriptional programs in breast cancer

Cancer cells acquire genetic and epigenetic alterations that often lead to dysregulation of oncogenic signal transduction pathways, which in turn alters downstream transcriptional programs. Numerous methods attempt to deduce aberrant signaling pathways in tumors from mRNA data alone, but these pathway analysis approaches remain qualitative and imprecise. In this study, we present a statistical method to link upstream signaling to downstream transcriptional response by exploiting reverse phase protein array (RPPA) and mRNA expression data in The Cancer Genome Atlas (TCGA) breast cancer project. Formally, we use an algorithm called affinity regression to learn an interaction matrix between upstream signal transduction proteins and downstream transcription factors (TFs) that explains target gene expression. The trained model can then predict the TF activity, given a tumor samples protein expression profile, or infer the signaling protein activity, given a tumor samples gene expression profile. Breast cancers are comprised of molecularly distinct subtypes that respond differently to pathway-targeted therapies. We trained our model on the TCGA breast cancer data set and identified subtype-specific and common TF regulators of gene expression. We then used the trained tumor model to predict signaling protein activity in a panel of breast cancer cell lines for which gene expression and drug response data was available. Correlations between inferred protein activities and drug responses in breast cancer cell lines grouped several drugs that are clinically used in combination. Finally, inferred protein activity predicted the clinical outcome within the METABRIC Luminal A cohort, identifying high- and low-risk patient groups within this heterogeneous subtype.

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Epigenomic data on transcription factor occupancy and chromatin accessibility can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. However, in many cancers, epigenomic analyses have been limited, and computational methods to infer regulatory networks in tumors typically use expression data alone, or rely on transcription factor (TF) motifs in annotated promoter regions. Here, we develop a novel machine learning strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine cell line chromatin accessibility data with large tumor expression data sets and model the effect of enhancers on transcriptional programs in multiple cancers. We generated a new ATAC-seq data set profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and applied PSIONIC to 723 RNA-seq experiments from ovarian, uterine, and basal breast tumors as well as 96 cell line RNA-seq profiles. Our computational framework enables us to share information across tumors to learn patient-specific inferred TF activities, revealing regulatory differences between and within tumor types. Many of the identified TF regulators were significantly associated with survival outcome in basal breast, uterine serous and endometrioid carcinomas. Moreover, PSIONIC-predicted activity for MTF1 in cell line models correlated with sensitivity to MTF1 inhibition. Therefore computationally dissecting the role of TFs in gynecologic cancers may ultimately advance personalized therapy.

Abstract LB-A04: Transcriptional regulatory programs in gynecological cancers

Cancer cells acquire genetic and epigenetic alterations that often lead to dysregulation of oncogenic signal transduction pathways, which in turn alter downstream transcriptional programs. The Cancer Genome Atlas (TCGA) has studied several of the most common and aggressive gynecologic tumors including high-grade serous ovarian carcinomas (HGSOC), uterine carcinosarcoma (UCS), and the serous-like subset of endometrial cancer (UCEC), together with basal breast cancer, which shares many genomic features with serous ovarian tumors. These tumors all lack accurate predictors of response and resistance and share an unmet need for adequate treatment of recurrent disease. We developed a multitask learning framework for integrating regulatory sequence from ATAC-mapped promoters and enhancers with RNA-seq data from patient tumors in order to infer transcription factor (TF) regulatory activities and explore similarities and differences between endometrial, ovarian, and basal breast tumors. We showed that our multitask learning framework enables us to selectively share the information across tumors and strongly improves the accuracy of gene expression prediction models for gynecological and basal breast tumors. Our analysis identified histologic type specific and common TF regulators of gene expression as well as predicted distinct dysregulated transcriptional regulators downstream of somatic alterations in these different cancers. Moreover, many of the identified TF regulators were significantly associated with survival outcome within the histological subtype. Computationally dissecting the role of TFs in these cancers may ultimately lead to new therapeutics tailored to subtype or individual. Citation Format: Hatice U. Osmanbeyoglu, Petar Jelinic, Douglas Levine, Christina S. Leslie. Transcriptional regulatory programs in gynecological cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr LB-A04.

Ets transcription factor GABP controls T cell homeostasis and immunity

Peripheral T cells are maintained in the absence of vigorous stimuli, and respond to antigenic stimulation by initiating cell cycle progression and functional differentiation. Here we show that depletion of the Ets family transcription factor GA-binding protein (GABP) in T cells impairs T-cell homeostasis. In addition, GABP is critically required for antigen-stimulated T-cell responses in vitro and in vivo. Transcriptome and genome-wide GABP-binding site analyses identify GABP direct targets encoding proteins involved in cellular redox balance and DNA replication, including the Mcm replicative helicases. These findings show that GABP has a nonredundant role in the control of T-cell homeostasis and immunity.T cells need to undergo rapid proliferation in response to antigenic stimulation. Here the authors show that the Ets family transcription factor GABP is required for T-cell homeostasis and response to infection by inducing Mcm3 and Mcm5 expression and enabling S-phase entry.

Abstract 885: Epigenetic regulation of estrogen receptor transcription by the PI3K pathway in breast cancer

Mutations in the PIK3CA gene are the most frequent genomic alterations in estrogen receptor (ER)-positive breast cancers. Direct pharmacological inhibition of PI3K signaling is therefore an attractive clinical strategy and a number of PI3K pathway inhibitors are currently under clinical development. Unfortunately, although the majority of ER-positive PIK3CA-mutant patients respond, mechanisms of resistance to these inhibitors inevitably emerge. By studying both mouse models and human samples, our laboratory has previously uncovered that inhibition of PI3K pathway increases ER transcriptional activity, which in turn renders cells more susceptible to endocrine therapy. The mechanisms by which ER and PI3K signaling pathway regulate each other in breast cancer cells, however, remain elusive. To better understand the cross-talk between the PI3K pathway and the ER transcriptional program, we developed an unbiased transposon activation mutagenesis screen with the goal of identifying modulators of resistance to PI3K inhibitors in ER-positive tumors. Among the genes identified, we found a number of key regulators of ER function including the pioneer transcription factors FOXA1 and PBX1. We further confirmed that FOXA1 and PBX1 expression and transcriptional activity was enhanced upon PI3K inhibition and validated these observations in both xenograft models and samples from patients undergoing treatment with the PI3Ka inhibitor BYL719. Moreover, chromatin imunoprecipitation (ChIP)-sequencing against ER and FOXA1 demonstrated that these factors occupy the same genomic regions, and their binding is increased upon PI3K inhibition. Silencing FOXA1 or PBX1 impaired the activation of the ER-dependent transcriptional program following PI3K blockade and sensitized cells to PI3K inhibition. To better understand the role of FOXA1 and PBX1 in the ER-PI3K crosstalk, we have then studied in detail the chromatin changes upon BYL719 treatment using transposase-accessible chromatin using high-throughput sequencing (ATAC-seq) in breast cancer cells. Epigenomic profiling using ATAC-seq is also being done on patient samples collected before BYL719 administration (pre-treatment) and during therapy (on-treatment). ATAC-seq and RNA-seq data from the same patients will be integrated using novel computational approaches. These analyses will help to dissect the ER-dependent epigenetic changes occurring upon PI3K inhibition and how the cells use these chromatin modifications to adapt to the pharmacological stress. Elucidating the interconnection between the PI3K pathway and ER activity may uncover novel mechanisms of resistance to either PI3K inhibitors or endocrine therapy in ER-positive breast cancer patients. Citation Format: Eneda Toska, Hatice Osmanbeyoglu, Moshe Elkabets, Carmen Chan, Pau Castel, Maura Dickler, Scott Armstrong, Christina Leslie, Maurizio Scaltriti, Baselga Jose. Epigenetic regulation of estrogen receptor transcription by the PI3K pathway in breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 885.

Abstract 781: Modeling the impact of somatic alterations across human cancers

Large-scale cancer genomics projects like The Cancer Genome Atlas have generated a comprehensive catalog of somatic mutations and copy number aberrations across many tumor types, but the role of most frequently altered genes remains obscure. To better model the impact of these alterations, we developed a novel computational strategy for exploiting parallel phosphoproteomics and mRNA sequencing data for large tumor sets by linking dysregulation of upstream signaling pathways with altered transcriptional response through the transcriptional circuitry. Our modeling allows us to interpret the impact of somatic alterations in terms of functional outcomes such as altered signaling and transcription factor (TF) activity. We used this novel machine learning strategy to train phosphoprotein-TF interaction models across 12 human cancers for which large reverse-phase protein array and RNA-seq data sets are available through TCGA. First, we used this approach to identify shared and cancer-specific roles of TF/signaling regulators across cancer types. Then we performed a statistical analysis to associate frequent somatic aberrations with alterations in inferred TF and signaling protein activities. From our analysis, we gained many novel insights into cancer biology. We identified both known (e.g FOXO1 for breast cancer) and novel TF regulators of cancer (e.g. ELK1 for head and neck cancer). Many of these identified TF regulators were significantly associated with survival outcome in bladder urothelial, renal cell clear and endometrial carcinoma. Next we performed a comprehensive cross-cancer analysis and identified relationships between somatic alterations and downstream transcriptional effects and signaling pathway activation. We observed that specific molecular aberrations have different functional consequences in different cancer types. For example, PIK3CA mutations are associated with altered activities of a diverse set of TFs across cancers that are involved in cell cycle, apoptosis, metabolism and MAPK/ERK signaling. Notably, in cell line models, we validated some of the altered TFs predicted by our model. We showed that PIK3CA mutation leads to ELK1 activation in breast and head and neck cancer models. We further found that different set of TFs are associated with a specific mutation in different cancers due to the different background of genomic aberrations in each cancer. For example, KRAS mutations are associated with a distinct set of TFs depending of cancer-specific co-mutation profiles (e.g. co-mutations of KRAS and TP53 in lung adenocarcinoma and co-mutations of KRAS and APC in colorectal cancer). Our analysis revealed both known and novel interactions of frequently altered genes with signaling pathways and transcriptional programs in a pan-cancer context. Patterns of co-alterations across cancers may provide new insights relevant to targeted therapy and may be crucial to optimizing combination therapies. Citation Format: Hatice U. Osmanbeyoglu, Eneda Toska, Jose Baselga, Christina Leslie. Modeling the impact of somatic alterations across human cancers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 781.

Abstract LB-C04: Modeling the impact of somatic alterations across human cancers

Modeling the impact of somatic alterations across human cancers Hatice U. Osmanbeyoglu1, Christina S. Leslie1,* 1Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY Abstract Large-scale cancer genomics projects like The Cancer Genome Atlas have generated a comprehensive catalog of somatic mutations and copy number aberrations across many tumor types, but the role of some frequently altered genes remains obscure. To better model the impact of these alterations, we developed a computational strategy for exploiting parallel phosphoproteomics and mRNA sequencing data for large tumor sets to link dysregulation of upstream signaling pathways with altered transcriptional response through the transcriptional circuitry. Our modeling allows us to interpret the impact of mutations and copy number events in terms of altered signaling and transcription factor (TF) activity. We used a novel machine learning strategy to train phosphoprotein-TF interaction models across 10 human cancers for which large reverse-phase protein array and RNA-seq data sets are available through TCGA. We then applied a novel algorithmic approach to extract networks of signaling proteins and TFs whose inferred activities are correlated across tumors and whose dysregulation is associated with specific somatically altered genes. Our analysis revealed both known and novel interactions of frequently altered genes with signaling pathways and transcriptional programs in a pan-cancer context. Moreover, our algorithmic approach provides a general strategy for modeling the impact of recurrent mutations and copy number alterations on signaling pathways and transcriptional programs through pan-cancer analysis. Citation Format: Hatice U. Osmanbeyoglu, Christina Leslie. Modeling the impact of somatic alterations across human cancers. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr LB-C04.