Identification of F-box only protein 7 as a negative regulator of NF-kappaB signalling

The nuclear factor κB (NF-κB) signalling pathway controls important cellular events such as cell proliferation, differentiation, apoptosis and immune responses. Pathway activation occurs rapidly upon TNFα stimulation and is highly dependent on ubiquitination events. Using cytoplasmic to nuclear translocation of the NF-κB transcription factor family member p65 as a read-out, we screened a synthetic siRNA library targeting enzymes involved in ubiquitin conjugation and de-conjugation for modifiers of regulatory ubiquitination events in NF-κB signalling. We identified F-box protein only 7 (FBXO7), a component of Skp, Cullin, F-box (SCF)-ubiquitin ligase complexes, as a negative regulator of NF-κB signalling. F-box protein only 7 binds to, and mediates ubiquitin conjugation to cIAP1 and TRAF2, resulting in decreased RIP1 ubiquitination and lowered NF-κB signalling activity.

A robust genomic signature for the detection of colorectal cancer patients with microsatellite instability phenotype and high mutation frequency

Microsatellite instability (MSI) occurs in 10-20% of colorectal tumours and is associated with good prognosis. Here we describe the development and validation of a genomic signature that identifies colorectal cancer patients with MSI caused by DNA mismatch repair deficiency with high accuracy. Microsatellite status for 276 stage II and III colorectal tumours has been determined. Full-genome expression data was used to identify genes that correlate with MSI status. A subset of these samples (n = 73) had sequencing data for 615 genes available. An MSI gene signature of 64 genes was developed and validated in two independent validation sets: the first consisting of frozen samples from 132 stage II patients; and the second consisting of FFPE samples from the PETACC-3 trial (n = 625). The 64-gene MSI signature identified MSI patients in the first validation set with a sensitivity of 90.3% and an overall accuracy of 84.8%, with an AUC of 0.942 (95% CI, 0.888-0.975). In the second validation, the signature also showed excellent performance, with a sensitivity 94.3% and an overall accuracy of 90.6%, with an AUC of 0.965 (95% CI, 0.943-0.988). Besides correct identification of MSI patients, the gene signature identified a group of MSI-like patients that were MSS by standard assessment but MSI by signature assessment. The MSI-signature could be linked to a deficient MMR phenotype, as both MSI and MSI-like patients showed a high mutation frequency (8.2% and 6.4% of 615 genes assayed, respectively) as compared to patients classified as MSS (1.6% mutation frequency). The MSI signature showed prognostic power in stage II patients (n = 215) with a hazard ratio of 0.252 (p = 0.0145). Patients with an MSI-like phenotype had also an improved survival when compared to MSS patients. The MSI signature was translated to a diagnostic microarray and technically and clinically validated in FFPE and frozen samples.

Abstract IA13: Finding biomarkers of response to targeted cancer drugs through functional genetics

Unresponsiveness to therapy remains a significant problem in the treatment of cancer, also with the new classes of cancer drugs. In my laboratory, we use functional genetic approaches to identify biomarkers that can predict responsiveness to targeted cancer therapeutics; drugs that specifically inhibit molecules or pathways that are often activated in cancer. Nevertheless, it remains poorly explained why a significant number of tumors do not respond to these therapies. We aim to elucidate the molecular pathways that contribute to unresponsiveness to targeted cancer therapeutics using a functional genetic approach. This will yield biomarkers that may be useful to predict how individual patients will respond to these drugs. Furthermore, this work may allow the development of drugs that act in synergy with the established drug to prevent or overcome drug resistance.

To identify biomarkers that control tumor cell responsiveness to cancer therapeutics, we use multiple complementary approaches. First, we use genome wide loss-of-function genetic screens (with shRNA interference libraries) in cancer cells that are sensitive to the drug-of-interest to search for genes whose down-regulation confers resistance to the drug-of-interest (resistance screens). In addition, we use shRNA screens with a low dose of the drug to screen for genes whose inhibition enhances the toxicity of the cancer drug (sensitizer screens). As a third approach, we use gain of function genetic screens in which we search for genes whose over-expression modulates drug responsiveness. Once we have identified candidate drug response biomarkers in relevant cell line models, we ask if the expression of these genes is correlated with clinical response to the drug-of-interest. For this, we use tumor samples of cancer patients treated with the drug in question and whose response to therapy is documented.

In a fourth and distinct approach we perform high throughput sequencing of the “kinome” (some 600 genes) of tumor samples to identify connections between cancer genotype and drug responses.

Examples of these approaches to identify biomarkers of response to different cancer drugs will be presented.

Abstract A9: The cocaine and amphetamine-regulated transcript (CART) is an independent prognostic factor in lymph node-negative breast cancer and predicts outcome in tamoxifen treated patients

The successful identification and translation of informative biomarkers to aid clinical decision making is central to the effective implementation of personalized cancer therapeutic regimens. Antibody-based proteomics occupies a pivotal space in the cancer biomarker discovery and validation pipeline, facilitating the high-throughput evaluation of candidate markers. We have applied this approach to identify the cocaine- and amphetamine-regulated transcript (CART) as an independent poor prognostic marker in early stage breast cancer. The management of lymph node-negative breast cancer is the subject of much debate and two large ongoing clinical trials with particular emphasis on the requirement for adjuvant chemotherapy. Here we demonstrate, by tissue microarray and automated image analysis using a consistently applied threshold for definition of CART levels (median intensity +/− 1 standard deviation), that the neuropeptide hormone CART is expressed in primary and metastatic breast cancer and is an independent poor prognostic factor in ER-positive (p=0.0003), lymph node-negative (p=0.0025) tumors in two large breast cancer cohorts (n>1000). Kaplan-Meier analysis and the log rank test were used to illustrate differences between overall survival (OS) and recurrence free survival (RFS) according to CART protein and mRNA expression. Cox regression proportional hazards models were used to estimate the relationship between outcome and CART expression, grade, age, nodal status, ER status, PR status, Her2, Ki-67 and tumor size. We also show that CART mediates an autocrine/paracrine effect leading to an amplification of the CART signal in breast cancer cells. Furthermore, we additionally demonstrate that CART increases the transcriptional activity of ERα in a ligand-independent manner via the mitogen-activated protein kinase (MAPK) pathway and that CART-High tumors express increased levels of ER-target genes. Finally, we show that ectopic expression of CART in two independent ER+ cell lines protects against tamoxifen-mediated cell death (p=0.025) and that high CART expression predicts outcome in 3 independent tamoxifen-treated cohorts (p=0.002). We clearly demonstrate that both pre- and postmenopausal patients with CART-positive tumors have a poor prognosis and are resistant to tamoxifen and therefore should receive aggressive adjuvant chemotherapy. We believe that CART profiling will facilitate stratification of lymph node-negative breast cancer patients into high- and low-risk categories and allow the personalization of therapy.

Abstract LB-386: The NuRD complex cooperates with DNMTs to maintain silencing of colorectal tumor suppressor genes

Many Tumor Suppressor Genes (TSGs) are silenced through synergistic layers of epigenetic regulation including abnormal DNA hypermethylation of promoter CpG islands, repressive chromatin modifications and enhanced nucleosome deposition over transcription start sites. Many of the protein complexes responsible for silencing of such TSGs remain to be identified. A subset of silenced TSGs controlling key regulatory signaling pathways in colorectal cancer cells can be partially reactivated by in cells disrupted for the DNA methyltransferase 1 and 3B (DNMT1 and 3B) or by DNMT inhibitors (DNMTi). Herein, we used proteomic and functional genomic approaches to identify additional proteins that cooperate with DNMTs in silencing these key silenced TSGs in colon cancer cells. We discovered that DNMTs and the core components of the NuRD nucleosome remodeling complex, chromo domain helicase DNA-binding protein 4 (CHD4), histone deacetylases 1 and 2 (HDAC1 and 2), occupy the promoters of several of these key hypermethylated TSGs and physically and functionally interact to maintain their silencing. Consistent with this, we find an inverse relationship between expression of HDAC1 and 2 and these TSGs in a large panel of primary colorectal tumors. We demonstrate that this DNMT-NuRD complex maintains the silencing of several negative regulators of the WNT signaling pathway. We find that depletion of CHD4 is synthetic lethal with DNMT inhibition in correlation with reactivation of TSGs, suggesting that their combined inhibition may be beneficial for the treatment of colon cancer.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-386. doi:1538-7445.AM2012-LB-386

Abstract 5608: Functional genetic screens to find modulators of sensitivity to inhibitors of the PI3K/mTOR pathway

The PI3K/mTOR pathway is an important signaling pathway which is often hyperactivated in breast cancer by genetic events. Inhibitors of several PI3K pathway members have been developed in recent years. Clear improvements in survival have not been demonstrated in most cancer patients, including breast cancer patients. Clearly, additional targets for combination therapies and biomarkers of resistance are needed. We performed siRNA based screens, to find genes that can modulate the sensitivity of breast cancer cells to PI3K and mTOR inhibitors. Resistance screens: We screened the retroviral NKI hairpin library, consisting of 21,000 hairpins against 8,000 genes, in four breast cancer cell lines, using rapamycin (an inhibitor of mTORC1), AZ4 (a small molecule inhibitor of mTORC1 and 2) and the dual PI3K/mTOR inhibitors PI103 and NVP-BEZ235. Common hits identified in these resistance screens and validated in multiple cell lines were PTEN, GSK3A/B and ARID1A. PTEN and GSK3A/B are known members of the PI3K pathway; resistance by PTEN loss is most likely caused by hyperactivation of PI3K signaling, whereas GSK3A/B knockdown probably leads to decreased degradation of Cyclin D (published previously). Knockdown of the SWI/SNF complex component ARID1A could also induce resistance against PI3K and mTOR inhibition in various cell lines. Similar to PTEN knockdown, ARID1A downregulation caused hyperactivation of PI3K signaling. Further studies should elucidate which SWI/SNF transcriptional targets are responsible for this induction of PI3K signaling. Synthetic lethal screens: We screened the Dharmacon ‘kinome’ and ‘druggable genome’ siRNA libraries (over 8,000 genes) in HCC1954 breast cancer cells, to find genes whose knockdown enhances the toxicity of mTOR inhibitors. Few genes could reproducibly enhance the sensitivity of breast cancer cells to mTOR inhibition; most of these genes, such as RHEB and PIK3CA, are important members of the PI3K/mTOR pathway. We found knockdown of the anti-apoptotic MCL1 gene to increase sensitivity to PI3K and mTOR inhibition by causing an increase in apoptosis. Although not clearly established as a specific PI3K pathway member, MCL1 expression may be regulated by downstream proteins in the PI3K/mTOR pathway. Knockdown of the integrin α/vitronectin receptor ITGAV had a sensitizing effect on PI3K/mTOR inhibition in HCC1954 and SKBR3 breast cancer cells. The mechanism of this interaction is still under investigation but may involve upregulation of integrin function in response to PI3K/mTOR inhibition. In conclusion, most of the genes that can modulate the sensitivity of breast cancer cells to PI3K/mTOR inhibition are known members of the PI3K pathway, however ARID1A and ITGAV are new modulators of sensitivity to PI3K pathway inhibition.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5608. doi:1538-7445.AM2012-5608

Targeting the epigenome for treatment of cancer

Cancer genome analyses have revealed that the enzymes involved in epigenetic gene regulation are frequently deregulated in cancer. Here we describe the enzymes that control the epigenetic state of the cell, how they are affected in cancer and how this knowledge can be exploited to treat cancer with a new arsenal of selective therapies.

The cocaine- and amphetamine-regulated transcript mediates ligand-independent activation of ERα, and is an independent prognostic factor in node-negative breast cancer

Personalized medicine requires the identification of unambiguous prognostic and predictive biomarkers to inform therapeutic decisions. Within this context, the management of lymph node-negative breast cancer is the subject of much debate with particular emphasis on the requirement for adjuvant chemotherapy. The identification of prognostic and predictive biomarkers in this group of patients is crucial. Here, we demonstrate by tissue microarray and automated image analysis that the cocaine- and amphetamine-regulated transcript (CART) is expressed in primary and metastatic breast cancer and is an independent poor prognostic factor in estrogen receptor (ER)-positive, lymph node-negative tumors in two separate breast cancer cohorts (n=690; P=0.002, 0.013). We also show that CART increases the transcriptional activity of ERα in a ligand-independent manner via the mitogen-activated protein kinase pathway and that CART stimulates an autocrine/paracrine loop within tumor cells to amplify the CART signal. Additionally, we demonstrate that CART expression in ER-positive breast cancer cell lines protects against tamoxifen-mediated cell death and that high CART expression predicts disease outcome in tamoxifen-treated patients in vivo in three independent breast cancer cohorts. We believe that CART profiling will help facilitate stratification of lymph node-negative breast cancer patients into high- and low-risk categories and allow for the personalization of therapy.

The histone demethylase Jarid1b (Kdm5b) is a novel component of the Rb pathway and associates with E2f-target genes in MEFs during senescence

Senescence is a robust cell cycle arrest controlled by the p53 and Rb pathways that acts as an important barrier to tumorigenesis. Senescence is associated with profound alterations in gene expression, including stable suppression of E2f-target genes by heterochromatin formation. Some of these changes in chromatin composition are orchestrated by Rb. In complex with E2f, Rb recruits chromatin modifying enzymes to E2f target genes, leading to their transcriptional repression. To identify novel chromatin remodeling enzymes that specifically function in the Rb pathway, we used a functional genetic screening model for bypass of senescence in murine cells. We identified the H3K4-demethylase Jarid1b as novel component of the Rb pathway in this screening model. We find that depletion of Jarid1b phenocopies knockdown of Rb1 and that Jarid1b associates with E2f-target genes during cellular senescence. These results suggest a role for Jarid1b in Rb-mediated repression of cell cycle genes during senescence.

Combined use of MammaPrint and molecular subtyping profile (BluePrint) to identify subgroups with marked differences in response to neoadjuvant treatment

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Background: Concordance between the IHC receptor status and the molecular subtype suggests that molecular profiles represent oncogenic processes that are driven by pathways in which ER, PR and HER2 play pivotal roles. It is, therefore, likely that the use of gene expression arrays will enable the identification of previously unappreciated subtypes of breast cancer that differ in clinical outcomes.

Methods: The cohort consists of 133 (T1-4, N0-3) breast cancer patients treated with T/FAC neoadjuvant chemotherapy. Genome wide expression data was publicly available and downloaded from bioinformatics.mdanderson.org/pubdata.html. The data was used to determine the response to T/FAC neoadjuvant chemotherapy for patients stratified by MammaPrint and molecular subtype (BluePrint). The MammaPrint and BluePrint result were used to subtype the patients into 4 groups: MammaPrint Low-risk/Luminal-type, MammaPrint High-risk/Luminal-type, Basal-type and ERBB2-type.

Results: Within this patient cohort, 20% (n=27) were classified as Basal-type, 62% (n=82) as Luminal-type, and 18% (n=24) as ERBB2-type. The overall pCR of this patient cohort was 26% and differed substantially among the subgroups. pCR was observed in 9% of all Luminal-type samples (i.e. 3% of MammaPrint Low Risk/Luminal-type and 11% of MammaPrint High Risk/Luminal-type), in 50% of the ERBB2-type samples and in 56% of the Basal-type samples. The pCR rates observed for the ERBB2-type and Basal-type patient groups were higher compared to classification based on IHC/CISH assessed ER and HER2 receptor status: 50% for ERBB2-type versus 39% for HER2+ and 56% for Basal-type versus 47% for ER-/HER2- samples.

Conclusions: We observed marked differences in response to neo-adjuvant treatment in groups stratified by MammaPrint and BluePrint. These findings confirm differences in chemotherapy response among molecular subgroups and indicate that the BluePrint profile described here will help to further establish a clinical correlation between molecular subtyping and treatment response.

A diagnostic gene profile for molecular subtyping of breast cancer associated with treatment response

Classification of breast cancer into molecular subtypes maybe important for the proper selection of therapy, as tumors with seemingly similar histopathological features can have strikingly different clinical outcomes. Herein, we report the development of a molecular subtyping profile (BluePrint), that enables rationalization in patient selection for either chemotherapy or endocrine therapy prescription. An 80-Gene Molecular Subtyping Profile (BluePrint) was developed using 200 breast cancer patient specimens and confirmed on four independent validation cohorts (n = 784). Additionally, the profile was tested as a predictor of chemotherapy response in 133 breast cancer patients, treated with T/FAC neoadjuvant chemotherapy. BluePrint classification of a patient cohort that was treated with neoadjuvant chemotherapy (n = 133) shows improved distribution of pathological Complete Response (pCR), among molecular subgroups compared with local pathology: 56% of the patients had a pCR in the Basal-type subgroup, 3% in the MammaPrint Low-risk, Luminal-type subgroup, 11% in the MammaPrint High-risk, Luminal-type subgroup, and 50% in the HER2-type subgroup. The group of genes identifying Luminal-type breast cancer is highly enriched for genes having an Estrogen Receptor binding site proximal to the promoter-region, suggesting that these genes are direct targets of the Estrogen Receptor. Implementation of this profile may improve the clinical management of breast cancer patients, by enabling the selection of patients who are most likely to benefit from either chemotherapy or from endocrine therapy.

Biological functions of the genes in the mammaprint breast cancer profile reflect the hallmarks of cancer

Background:

MammaPrint was developed as a diagnostic tool to predict risk of breast cancer metastasis using the expression of 70 genes. To better understand the tumor biology assessed by MammaPrint, we interpreted the biological functions of the 70-genes and showed how the genes reflect the six hallmarks of cancer as defined by Hanahan and Weinberg.

Results:

We used a bottom-up system biology approach to elucidate how the cellular processes reflected by the 70-genes work together to regulate tumor activities and progression. The biological functions of the genes were analyzed using literature research and several bioinformatics tools. Protein-protein interaction network analyses indicated that the 70-genes form highly interconnected networks and that their expression levels are regulated by key tumorigenesis related genes such as TP53, RB1, MYC, JUN and CDKN2A. The biological functions of the genes could be associated with the essential steps necessary for tumor progression and metastasis, and cover the six well-defined hallmarks of cancer, reflecting the acquired malignant characteristics of a cancer cell along with tumor progression and metastasis-related biological activities.

Conclusion:

Genes in the MammaPrint gene signature comprehensively measure the six hallmarks of cancer-related biology. This finding establishes a link between a molecular signature and the underlying molecular mechanisms of tumor cell progression and metastasis.

Using functional genetics to understand breast cancer biology

Genetic screens were for long the prerogative of those that studied model organisms. The discovery in 2001 that gene silencing through RNA interference (RNAi) can also be brought about in mammalian cells paved the way for large scale loss-of-function genetic screens in higher organisms. In this article, we describe how functional genetic studies can help us understand the biology of breast cancer, how it can be used to identify novel targets for breast cancer therapy, and how it can help in the identification of those patients that are most likely to respond to a given therapy.

Abstract A30: NF1 is a tumor suppressor in neuroblastoma that determines retinoic acid response and disease outcome

Neuroblastoma is a malignancy of early childhood that derives from the developing neural crest and accounts for 15% of all paediatric oncology deaths. The vitamin A derivative retinoic acid (RA) controls neural development and promotes differentiation, cell cycle arrest and apoptosis of neuronal and neuroblastoma cells. These diverse effects of RA are exerted primarily through the ability to differentially regulate gene expression mediated by the retinoic acid receptors (RARs). RA is used in the clinic to treat high-risk neuroblastoma patients, however with variable success. This variability in clinical response to RA is enigmatic as no mutations in components of the RA signaling cascade have been found. By using large-scale RNAi-based genetic screens, we aimed to identify predictive biomarkers for RA sensitivity in neuroblastoma. We found an unexpected crosstalk between the tumor suppressor neurofibromin (NF1) and RA signaling. NF1 is a negative regulator of RAS signaling that promotes conversion of the active GTP bound form into the inactive GDP bound form of RAS. Activation of RAS-MEK signaling by loss of NF1 repressed ZNF423, a critical transcriptional co-activator of the retinoic acid receptors, and blocked RA induced differentiation and target gene activation. Restoration of ZNF423 levels resensitized NF1 knockdown cells to the inhibitory effect of RA. Moreover, ZNF423 and NF1 expression predicted sensitivity to RA in a large panel of neuroblastoma cell lines. Neuroblastomas with low levels of both NF1 and ZNF423 have extremely poor outcome. We found NF1 mutations in neuroblastoma cell lines and also in primary tumors. Inhibition of MEK signaling downstream of NF1 restored responsiveness to RA, suggesting a therapeutic strategy to overcome RA resistance in NF1 deficient neuroblastomas.

Citation Information: Clin Cancer Res 2010;16(7 Suppl):A30