Identification and pharmacological inactivation of the MYCN gene network as a therapeutic strategy for neuroblastic tumor cells

This research was originally published in Journal of Biological Chemistry. Olesya Chayka, Cosimo Walter D’Acunto, Odette Middleton, Maryam Arab, and Arturo Sala. Identification and Pharmacological Inactivation of the MYCN Gene Network as a Therapeutic Strategy for Neuroblastic Tumor Cells. Journal of Biological Chemistry. 2015; Vol 290 (4) :pp. 2198 - 2212. © the American Society for Biochemistry and Molecular Biology.This article has been made available through the Brunel Open Access Publishing Fund.The MYC family of transcription factors consists of three well characterized members, c-MYC, L-MYC, and MYCN, deregulated in the majority of human cancers. In neuronal tumors such as neuroblastoma, MYCN is frequently activated by gene amplification, and reducing its expression by RNA interference has been shown to promote growth arrest and apoptosis of tumor cells. From a clinical perspective, RNA interference is not yet a viable option, and small molecule inhibitors of transcription factors are difficult to develop. We therefore planned to identify, at the global level, the genes interacting functionally with MYCN required to promote fitness of tumor cells facing oncogenic stress. To find genes whose inactivation is synthetically lethal to MYCN, we implemented a genome-wide approach in which we carried out a drop-out shRNA screen using a whole genome library that was delivered into isogenic neuroblastoma cell lines expressing or not expressing MYCN. After the screen, we selected for in-depth analysis four shRNAs targeting AHCY, BLM, PKMYT1, and CKS1B. These genes were chosen because they are directly regulated by MYC proteins, associated with poor prognosis of neuroblastoma patients, and inhibited by small molecule compounds. Mechanistically, we found that BLM and PKMYT1 are required to limit oncogenic stress and promote stabilization of the MYCN protein. Cocktails of small molecule inhibitors of CKS1B, AHCY, BLM, and PKMYT1 profoundly affected the growth of all neuroblastoma cell lines but selectively caused death of MYCN-amplified cells. Our findings suggest that drugging the MYCN network is a promising avenue for the treatment of high risk, neuroblastic cancers.SPARKS and the Neuroblastoma Society

On Glauber modes in Soft-Collinear Effective Theory

Gluon interactions involving spectator partons in collisions at hadronic machines are investigated. We find a class of examples in which a mode, called Glauber gluons, must be introduced to the effective theory for consistency.Comment: 19 pages, three figures. Uses JHEP3.cl

Physical interaction between MYCN oncogene and polycomb repressive complex 2 (PRC2) in neuroblastoma: Functional and therapeutic implications

This article is made available through the Brunel Open Access Publishing Fund. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc.CLU (clusterin) is a tumor suppressor gene that we have previously shown to be negatively modulated by the MYCN proto-oncogene, but the mechanism of repression was unclear. Here, we show that MYCN inhibits the expression of CLU by direct interaction with the non-canonical E box sequence CACGCG in the 5′-flanking region. Binding of MYCN to the CLU gene induces bivalent epigenetic marks and recruitment of repressive proteins such as histone deacetylases and Polycomb members. MYCN physically binds in vitro and in vivo to EZH2, a component of the Polycomb repressive complex 2, required to repress CLU. Notably, EZH2 interacts with the Myc box domain 3, a segment of MYC known to be essential for its transforming effects. The expression of CLU can be restored in MYCN-amplified cells by epigenetic drugs with therapeutic results. Importantly, the anticancer effects of the drugs are ablated if CLU expression is blunted by RNA interference. Our study implies that MYC tumorigenesis can be effectively antagonized by epigenetic drugs that interfere with the recruitment of chromatin modifiers at repressive E boxes of tumor suppressor genes such as CLU.SPARKS, The Neuroblastoma Society, a Wellcome Trust grant (to A. S.), and the Italian Association for Cancer Research

Sign determination methods for the respiratory signal in data-driven PET gating

Patient respiratory motion during PET image acquisition leads to blurring in the reconstructed images and may cause significant artifacts, resulting in decreased lesion detectability, inaccurate standard uptake value calculation and incorrect treatment planning in radiation therapy. To reduce these effects data can be regrouped into (nearly) 'motion-free' gates prior to reconstruction by selecting the events with respect to the breathing phase. This gating procedure therefore needs a respiratory signal: on current scanners it is obtained from an external device, whereas with data driven (DD) methods it can be directly obtained from the raw PET data. DD methods thus eliminate the use of external equipment, which is often expensive, needs prior setup and can cause patient discomfort, and they could also potentially provide increased fidelity to the internal movement. DD methods have been recently applied on PET data showing promising results. However, many methods provide signals whose direction with respect to the physical motion is uncertain (i.e. their sign is arbitrary), therefore a maximum in the signal could refer either to the end-inspiration or end-expiration phase, possibly causing inaccurate motion correction. In this work we propose two novel methods, CorrWeights and CorrSino, to detect the correct direction of the motion represented by the DD signal, that is obtained by applying principal component analysis (PCA) on the acquired data. They only require the PET raw data, and they rely on the assumption that one of the major causes of change in the acquired data related to the chest is respiratory motion in the axial direction, that generates a cranio-caudal motion of the internal organs. We also implemented two versions of a published registration-based method, that require image reconstruction. The methods were first applied on XCAT simulations, and later evaluated on cancer patient datasets monitored by the Varian Real-time Position ManagementTM (RPM) device, selecting the lower chest bed positions. For each patient different time intervals were evaluated ranging from 50 to 300 s in duration. The novel methods proved to be generally more accurate than the registration-based ones in detecting the correct sign of the respiratory signal, and their failure rates are lower than 3% when the DD signal is highly correlated with the RPM. They also have the advantage of faster computation time, avoiding reconstruction. Moreover, CorrWeights is not specifically related to PCA and considering its simple implementation, it could easily be applied together with any DD method in clinical practice

Van Allen Probes, THEMIS, GOES, and Cluster Observations of EMIC waves, ULF pulsations, and an electron flux dropout

We examined an electron flux dropout during the 12-14 November 2012 geomagnetic storm using observations from seven spacecraft: the two Van Allen Probes, Time History of Events and Macroscale Interactions during Substorms (THEMIS)-A (P5), Cluster 2, and Geostationary Operational Environmental Satellites (GOES) 13, 14, and 15. The electron fluxes for energies greater than 2.0 MeV observed by GOES 13, 14, and 15 at geosynchronous orbit and by the Van Allen Probes remained at or near instrumental background levels for more than 24 h from 12 to 14 November. For energies of 0.8 MeV, the GOES satellites observed two shorter intervals of reduced electron fluxes. The first interval of reduced 0.8 MeV electron fluxes on 12-13 November was associated with an interplanetary shock and a sudden impulse. Cluster, THEMIS, and GOES observed intense He+ electromagnetic ion cyclotron (EMIC) waves from just inside geosynchronous orbit out to the magnetopause across the dayside to the dusk flank. The second interval of reduced 0.8 MeV electron fluxes on 13-14 November was associated with a solar sector boundary crossing and development of a geomagnetic storm with Dst<100 nT. At the start of the recovery phase, both the 0.8 and 2.0 MeV electron fluxes finally returned to near prestorm values, possibly in response to strong ultralow frequency (ULF) waves observed by the Van Allen Probes near dawn. A combination of adiabatic effects, losses to the magnetopause, scattering by EMIC waves, and acceleration by ULF waves can explain the observed electron behavior

Pathological Features of Breast Cancer seen in Northwestern Tanzania: A Nine Years Retrospective Study.

Breast cancer is more common in Western Countries compared to African populations. However in African population, it appears that the disease tends to be more aggressive and occurring at a relatively young age at the time of presentation. The aim of this study was to describe the trend of Breast Cancer in Northwestern Tanzania. This was a retrospective study which involved all cases of breast cancer diagnosed histologically at Bugando Medical Center from 2002 to 2010. Histological results and slides were retrieved from the records in the Pathology department, clinical information and demographic data for patients were retrieved from surgical wards and department of medical records. Histology slides were re-evaluated for the histological type, grade (By modified Bloom-Richardson score), and presence of necrosis and skin involvement. Data was entered and analyzed by SPSS computer software version 15. There were 328 patients histologically confirmed to have breast cancer, the mean age at diagnosis was 48.7 years (+/- 13.1). About half of the patients (52.4%) were below 46 years of age, and this group of patients had significantly higher tendency for lymph node metastasis (p = 0.012). The tumor size ranged from 1 cm to 18 cm in diameter with average (mean) of 5.5 cm (+/- 2.5), and median size of 6 cm. Size of the tumor (above 6 cm in diameter) and presence of necrosis within the tumor was significantly associated with high rate of lymph node metastasis (p = 0.000). Of all patients, 64% were at clinical stage III (specifically IIIB) and 70.4% had lymph node metastasis at the time of diagnosis. Only 4.3% of the patients were in clinical stage I at the time of diagnosis. Majority of the patients had invasive ductal carcinoma (91.5%) followed by mucinous carcinoma (5.2%), Invasive lobular carcinoma (3%) and in situ ductal carcinoma (0.3%). In all patients, 185 (56.4%) had tumor with histological grade 3. Breast cancer in this region show a trend towards relative young age at diagnosis with advanced stage at diagnosis and high rate of lymph node metastasis. Poor Referral system, lack of screening programs and natural aggressive biological behavior of tumor may contribute to advanced disease at the time of diagnosis

Crystallographic analysis of polypyrimidine tract-binding protein-Raver1 interactions involved in regulation of alternative splicing.

Accepted versio

Second order QCD corrections to inclusive semileptonic b \to Xc l \bar \nu_l decays with massless and massive lepton

We extend previous computations of the second order QCD corrections to semileptonic b \to c inclusive transitions, to the case where the charged lepton in the final state is massive. This allows accurate description of b \to c \tau \bar \nu_\tau decays. We review techniques used in the computation of O(\alpha_s^2) corrections to inclusive semileptonic b \to c transitions and present extensive numerical studies of O(\alpha_s^2) QCD corrections to b \to c l \bar \nu_l decays, for l =e, \tau.Comment: 30 pages, 4 figures, 5 table

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation, and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.Comment: Submitted to 'Systems Medicine' as a book chapte