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Identifying network control elements in breast cancer oncogenic transformation via whole transcriptome analysis

Final Report Summary - CONTROLNETONCTRANS (Identifying network control elements in breast cancer oncogenic transformation via whole transcriptome analysis.)

Summary description of the project objectives: the core thinking that motivated the project is that new tools in transcriptome quantification, together with network biology, may shed a light on the process of oncogenic transformation. Such findings may improve our understanding of the transformation and produce novel targets for therapeutic intervention. At the center of this effort is a hypothesis that the molecular pathway, a subnetwork of well-defined interactions, is a quantifiable metric, which can be measured and utilized for classifying phenotypes.
This reasoning led us to follow three aims:
1. Establishing the role of the pathway metric as informative in the classification of phenotypes
2. Following an in vitro oncogenic transformation for its RNA and miRNA transcriptome modifications.
3. Identifying specific nodes and interaction within pathway as the basis for further study.
4. Characterizing isolated specific nodes and interactions for their role in phenotype
Description of the work we have performed since we started the project:
We have been working vigorously on these four aims. To establish the role of the pathway as a metric, we have been downloading extensive portion of The Cancer Genome Atlas (TCGA) and analyzing a multitude of phenotypes according to gene expression, copy number variations, genomic variation and methylation. This effort has produced 11 papers. Seven papers were reported previously, with the interim report [Ben-Hamo R, Efroni S. (2013) Micro RNA Targeting of a Signaling Pathway as a Mechanism in GBM Molecular Regulatory Cascade. Systems Biomedicine. In Print (Winner: Critical Assessment of Massive Data Analyses 2011; Ben-Hamo R, Efroni S. (2013) A transcriptome signature to Lung Cancer. Systems Biomedicine. In Print Winner: Systems Biology Verification Improver 2012 – an I.B.M. and Phillip Morris competition (Oct. 2012); Ben-Hamo R, Efroni S. (2013) The Network as Biomarker. Systems Biomedicine 1(1). In Print; Ben-Hamo R, Efroni S. (2013) MicroRNA Regulation of Genes as a Prognostic Biomarker. Systems Biomedicine 1(1). In Print; Feldstein O, Ben-Hamo R, Efroni S, Ginsberg, D (2012) RBM38 is a direct transcriptional target of E2F1 that limits E2F1-induced proliferation. Molecular Cancer Research. 10(9):1169-77; Ben-Hamo R, Efroni S. (2012) Biomarker robustness reveals the PDGF network as driving disease outcome in Ovarian cancer patients in multiple studies. BMC Systems Biology. 11;6:3; Ben-Hamo R, Efroni S. (2011) Gene-expression and network-based analysis reveals a novel role for hsa-mir-9 and drug control over the p38 network in Glioblastoma Multiforme progression. Genome Medicine. 3(11):77] and additional four manuscripts have been submitted and are in different stages of their submission cycle [Cohen, H, Ben-Hamo R, Gidoni M, Yitzhaki I, Efroni S. (2013) Shift in GATA3 Function and GATA3 Mutations Control Progression and Clinical Presentation in Breast Cancer; Gidoni M, Efroni S, Mir-190b differentially regulates estrogen receptor positive and estrogen receptor negative breast cancer subtypes via an estrogen receptor mediated network; Ben-Hamo R, Efroni S, MicroRNA-gene Association as a Prognostic Biomarker in Cancer Exposes Disease Mechanisms; Ben-Hamo R, Gidoni M, Efroni S. Phenonet: identification of key networks associated with disease phenotype]
The second and third aim, which have required extensive experimentation, have produced the above mentioned Cohen et al manuscript, which has been submitted for publication.

Description of the main results achieved so far. Experimentation has followed the logic detailed in the original proposal, of RNA-seq and micro RNA seq of the transcriptome of the oncogenic transformation. For that, we needed to establish the protocol in the lab, produce needed biological material, and send for sequencing. As previously reported, analyses from TCGA data, from other resources and from our own experimentation with in vitro oncogenic transformations, have led us to believe that the transcription factor GATA-3 is of special importance in breast cancer. We have experimented with this transcription factor and have identified specific shifts in its function and the role of specific mutations within GATA3 to be of critical importance to progression and clinical representation of breast cancer. This manuscript has been sent for publication. Further, as reported, we have performed miR-seq on transformed lines and identified a unique role for miR-190b in ER negative breast cancer phenotype. This manuscript has been sent for publication as well.

Expected final results and their potential impact and use. During the reported period of the grant, we have followed, using the previously gained experience, the in vitro transformation using RNA seq, miR-seq and have further developed the computational methods needed to integrate such findings into a network view. The initial issues with transcriptome sequencing have now been resolved and RNA-seq results are now being utilized into algorithmic approaches. In particular, we have added to novel methods (reported in the manuscripts above) to quantify the joint expressions of miRs and genes and to use this joint behavior to stratify patients into clinical phenotypes. We have also built on our previous PathOlogist method and developed a novel tools we called PhenoNet, which frees the researcher from the previous bound method of pre-determined pathways and from the use of microarray based quantification.
In this reported period we have published much analyses utilizing network metrics and the network approach and have further followed up on this research into specific experimentation.


A summary of progress towards objectives.
The previous report discussed some of our studies on genomics data from multiple sources of GBM, ovarian cancer and breast cancer. Since then, we have further identified specific mutation in BCL2 in ovarian cancer patients, with which we have been able to predict (retrospectively) the needed line of paclitaxel treatment. We have further followed these data to study the nature of this mutation. In a similar manner, we have followed up on our findings regarding the inter connection between hsa-miR-9 and the p38 pathway and have identified modification in critical nodes of this network.