Heather A. Hirsch

An Epigenetic Switch Involving NF-κB, Lin28, Let-7 MicroRNA, and IL6 Links Inflammation to Cell Transformation

Inflammation is linked clinically and epidemiologically to cancer, and NF-kappaB appears to play a causative role, but the mechanisms are poorly understood. We show that transient activation of Src oncoprotein can mediate an epigenetic switch from immortalized breast cells to a stably transformed line that forms self-renewing mammospheres that contain cancer stem cells. Src activation triggers an inflammatory response mediated by NF-kappaB that directly activates Lin28 transcription and rapidly reduces let-7 microRNA levels. Let-7 directly inhibits IL6 expression, resulting in higher levels of IL6 than achieved by NF-kappaB activation. IL6-mediated activation of the STAT3 transcription factor is necessary for transformation, and IL6 activates NF-kappaB, thereby completing a positive feedback loop. This regulatory circuit operates in other cancer cells lines, and its transcriptional signature is found in human cancer tissues. Thus, inflammation activates a positive feedback loop that maintains the epigenetic transformed state for many generations in the absence of the inducing signal.

Metformin Selectively Targets Cancer Stem Cells, and Acts Together with Chemotherapy to Block Tumor Growth and Prolong Remission

The cancer stem cell hypothesis suggests that, unlike most cancer cells within a tumor, cancer stem cells resist chemotherapeutic drugs and can regenerate the various cell types in the tumor, thereby causing relapse of the disease. Thus, drugs that selectively target cancer stem cells offer great promise for cancer treatment, particularly in combination with chemotherapy. Here, we show that low doses of metformin, a standard drug for diabetes, inhibits cellular transformation and selectively kills cancer stem cells in four genetically different types of breast cancer. The combination of metformin and a well-defined chemotherapeutic agent, doxorubicin, kills both cancer stem cells and non-stem cancer cells in culture. Furthermore, this combinatorial therapy reduces tumor mass and prevents relapse much more effectively than either drug alone in a xenograft mouse model. Mice seem to remain tumor-free for at least 2 months after combinatorial therapy with metformin and doxorubicin is ended. These results provide further evidence supporting the cancer stem cell hypothesis, and they provide a rationale and experimental basis for using the combination of metformin and chemotherapeutic drugs to improve treatment of patients with breast (and possibly other) cancers.

STAT3 Activation of miR-21 and miR-181b-1 via PTEN and CYLD Are Part of the Epigenetic Switch Linking Inflammation to Cancer

A transient inflammatory signal can initiate an epigenetic switch from nontransformed to cancer cells via a positive feedback loop involving NF-kappaB, Lin28, let-7, and IL-6. We identify differentially regulated microRNAs important for this switch and putative transcription factor binding sites in their promoters. STAT3, a transcription factor activated by IL-6, directly activates miR-21 and miR-181b-1. Remarkably, transient expression of either microRNA induces the epigenetic switch. MiR-21 and miR-181b-1, respectively, inhibit PTEN and CYLD tumor suppressors, leading to increased NF-kappaB activity required to maintain the transformed state. These STAT3-mediated regulatory circuits are required for the transformed state in diverse cell lines and tumor growth in xenografts, and their transcriptional signatures are observed in colon adenocarcinomas. Thus, STAT3 is not only a downstream target of IL-6 but, with miR-21, miR-181b-1, PTEN, and CYLD, is part of the positive feedback loop that underlies the epigenetic switch that links inflammation to cancer.

Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion

Tumors are often heterogeneous, being composed of multiple cell types with different phenotypic and molecular properties. Cancer stem-like cells (CSCs) are a highly tumorigenic cell type found in developmentally diverse tumors or cancer cell lines, and they are often resistant to standard chemotherapeutic drugs. The origins of CSCs and their relationships to nonstem cancer cells (NSCCs) are poorly understood. In an inducible breast oncogenesis model, CSCs are generated from nontransformed cells at a specific time during the transformation process, but CSC formation is not required for transformation. MicroRNA profiles indicate that CSCs and NSCCs are related, but different cell types arising from a common nontransformed population. Interestingly, medium from the transformed population stimulates NSCCs to become CSCs, and conversion of NSCCs to CSCs occurs in mouse xenografts. Furthermore, IL6 is sufficient to convert NSCCs to CSCs in genetically different breast cell lines, human breast tumors, and a prostate cell line. Thus, breast and prostate CSCs and NSCCs do not represent distinct epigenetic states, and these CSCs do not behave as or arise from classic stem cells. Instead, tumor heterogeneity involves a dynamic equilibrium between CSCs and NSCCs mediated by IL6 and activation of the inflammatory feedback loop required for oncogenesis. This dynamic equilibrium provides an additional rationale for combining conventional chemotherapy with metformin, which selectively inhibits CSCs.

Loss of miR-200 Inhibition of Suz12 Leads to Polycomb-Mediated Repression Required for the Formation and Maintenance of Cancer Stem Cells

In an inducible oncogenesis model, the miR-200 family is inhibited during CSC formation but not transformation, and inhibition of miR-200b increases CSC formation. Interestingly, miR-200b directly targets Suz12, a subunit of a polycomb repressor complex (PRC2). Loss of miR-200 during CSC formation increases Suz12 expression, Suz12 binding, H3-K27 trimethylation, and Polycomb-mediated repression of the E-cadherin gene. miR-200b expression or Suz12 depletion blocks the formation and maintenance of mammospheres, and in combination with chemotherapy suppresses tumor growth and prolongs remission in mouse xenografts. Conversely, ectopic expression of Suz12 in transformed cells is sufficient to generate CSCs. The miR-200b-Suz12-cadherin pathway is important for CSC growth and invasive ability in genetically distinct breast cancer cells, and its transcriptional signature is observed in metastatic breast tumors. The interaction between miR-200 and Suz12 is highly conserved, suggesting that it represents an ancient regulatory mechanism to control the growth and function of stem cells.

Metformin decreases the dose of chemotherapy for prolonging tumor remission in mouse xenografts involving multiple cancer cell types

Metformin, the first-line drug for treating diabetes, selectively kills the chemotherapy resistant subpopulation of cancer stem cells (CSC) in genetically distinct types of breast cancer cell lines. In mouse xenografts, injection of metformin and the chemotherapeutic drug doxorubicin near the tumor is more effective than either drug alone in blocking tumor growth and preventing relapse. Here, we show that metformin is equally effective when given orally together with paclitaxel, carboplatin, and doxorubicin, indicating that metformin works together with a variety of standard chemotherapeutic agents. In addition, metformin has comparable effects on tumor regression and preventing relapse when combined with a four-fold reduced dose of doxorubicin that is not effective as a monotherapy. Finally, the combination of metformin and doxorubicin prevents relapse in xenografts generated with prostate and lung cancer cell lines. These observations provide further evidence for the CSC hypothesis for cancer relapse, an experimental rationale for using metformin as part of combinatorial therapy in a variety of clinical settings, and for reducing the chemotherapy dose in cancer patients.

Metformin inhibits the inflammatory response associated with cellular transformation and cancer stem cell growth

Metformin, the first-line drug for treating diabetes, inhibits cellular transformation and selectively kills cancer stem cells in breast cancer cell lines. In a Src-inducible model of cellular transformation, metformin inhibits the earliest known step in the process, activation of the inflammatory transcription factor NF-κB. Metformin strongly delays cellular transformation in a manner similar to that occurring upon a weaker inflammatory stimulus. Conversely, inhibition of transformation does not occur if metformin is added after the initial inflammatory stimulus. The antitransformation effect of metformin can be bypassed by overexpression of Lin28B or IL1β, downstream targets of NF-κB. Metformin preferentially inhibits nuclear translocation of NF-κB and phosphorylation of STAT3 in cancer stem cells compared with non-stem cancer cells in the same population. The ability of metformin to block tumor growth and prolong remission in xenografts in combination with doxorubicin is associated with decreased function of the inflammatory feedback loop. Lastly, metformin-based combinatorial therapy is effective in xenografts involving inflammatory prostate and melanoma cell lines, whereas it is ineffective in noninflammatory cell lines from these lineages. Taken together, our observations suggest that metformin inhibits a signal transduction pathway that results in an inflammatory response. As metformin alters energy metabolism in diabetics, we speculate that metformin may block a metabolic stress response that stimulates the inflammatory pathway associated with a wide variety of cancers.

A transcriptional signature and common gene networks link cancer with lipid metabolism and diverse human diseases

Transcriptional profiling of two isogenic models of transformation identifies a gene signature linking cancer with inflammatory and metabolic diseases. In accord with this common transcriptional program, many drugs used for treatment of diabetes and cardiovascular diseases inhibit transformation and tumor growth. Unexpectedly, lipid metabolism genes are important for transformation and are upregulated in cancer tissues. As in atherosclerosis, oxidized LDL and its receptor OLR1 activate the inflammatory pathway through NF-kappaB, leading to transformation. OLR1 is important for maintaining the transformed state in developmentally diverse cancer cell lines and for tumor growth, suggesting a molecular connection between cancer and atherosclerosis. We suggest that the interplay between this common transcriptional program and cell-type-specific factors gives rise to phenotypically disparate human diseases.

Distinct Mechanisms for Repression of RNA Polymerase III Transcription by the Retinoblastoma Tumor Suppressor Protein

ABSTRACT The retinoblastoma (RB) protein represses global RNA polymerase III transcription of genes that encode nontranslated RNAs, potentially to control cell growth. However, RNA polymerase III-transcribed genes exhibit diverse promoter structures and factor requirements for transcription, and a universal mechanism explaining global repression is uncertain. We show that RB represses different classes of RNA polymerase III-transcribed genes via distinct mechanisms. Repression of human U6 snRNA (class 3) gene transcription occurs through stable promoter occupancy by RB, whereas repression of adenovirus VAI (class 2) gene transcription occurs in the absence of detectable RB-promoter association. Endogenous RB binds to a human U6 snRNA gene in both normal and cancer cells that maintain functional RB but not in HeLa cells whose RB function is disrupted by the papillomavirus E7 protein. Both U6 promoter association and transcriptional repression require the A/B pocket domain and C region of RB. These regions of RB contribute to U6 promoter targeting through numerous interactions with components of the U6 general transcription machinery, including SNAPC and TFIIIB. Importantly, RB also concurrently occupies a U6 promoter with RNA polymerase III during repression. These observations suggest a novel mechanism for RB function wherein RB can repress U6 transcription at critical steps subsequent to RNA polymerase III recruitment.

Rank-statistics based enrichment-site prediction algorithm developed for chromatin immunoprecipitation on chip experiments

BackgroundHigh density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. Tiling arrays are increasingly used in chromatin immunoprecipitation (IP) experiments (ChIP on chip). ChIP on chip facilitates the generation of genome-wide maps of in-vivo interactions between DNA-associated proteins including transcription factors and DNA. Analysis of the hybridization of an immunoprecipitated sample to a tiling array facilitates the identification of ChIP-enriched segments of the genome. These enriched segments are putative targets of antibody assayable regulatory elements. The enrichment response is not ubiquitous across the genome. Typically 5 to 10% of tiled probes manifest some significant enrichment. Depending upon the factor being studied, this response can drop to less than 1%. The detection and assessment of significance for interactions that emanate from non-canonical and/or un-annotated regions of the genome is especially challenging. This is the motivation behind the proposed algorithm.ResultsWe have proposed a novel rank and replicate statistics-based methodology for identifying and ascribing statistical confidence to regions of ChIP-enrichment. The algorithm is optimized for identification of sites that manifest low levels of enrichment but are true positives, as validated by alternative biochemical experiments. Although the method is described here in the context of ChIP on chip experiments, it can be generalized to any treatment-control experimental design. The results of the algorithm show a high degree of concordance with independent biochemical validation methods. The sensitivity and specificity of the algorithm have been characterized via quantitative PCR and independent computational approaches.ConclusionThe algorithm ranks all enrichment sites based on their intra-replicate ranks and inter-replicate rank consistency. Following the ranking, the method allows segmentation of sites based on a meta p-value, a composite array signal enrichment criterion, or a composite of these two measures. The sensitivities obtained subsequent to the segmentation of data using a meta p-value of 10-5, an array signal enrichment of 0.2 and a composite of these two values are 88%, 87% and 95%, respectively.