Amplification and over‐expression of MAP3K3 gene in human breast cancer promotes formation and survival of breast cancer cells

Gene amplifications in the 17q chromosomal region are observed frequently in breast cancers. An integrative bioinformatics analysis of this region nominated the MAP3K3 gene as a potential therapeutic target in breast cancer. This gene encodes mitogen‐activated protein kinase kinase kinase 3 (MAP3K3/MEKK3), which has not yet been reported to be associated with cancer‐causing genetic aberrations. We found that MAP3K3 was amplified in approximately 8–20% of breast cancers. Knockdown of MAP3K3 expression significantly inhibited cell proliferation and colony formation in MAP3K3‐amplified breast cancer cell lines MCF‐7 and MDA‐MB‐361 but not in MAP3K3 non‐amplified breast cancer cells. Knockdown of MAP3K3 expression in MAP3K3‐amplified breast cancer cells sensitized breast cancer cells to apoptotic induction by TNFα and TRAIL, as well as doxorubicin, VP‐16 and fluorouracil, three commonly used chemotherapeutic drugs for treating breast cancer. In addition, ectopic expression of MAP3K3, in collaboration with Ras, induced colony formation in both primary mouse embryonic fibroblasts and immortalized human breast epithelial cells (MCF‐10A). Combined, these results suggest that MAP3K3 contributes to breast carcinogenesis and may endow resistance of breast cancer cells to cytotoxic chemotherapy. Therefore, MAP3K3 may be a valuable therapeutic target in patients with MAP3K3‐amplified breast cancers, and blocking MAP3K3 kinase activity with a small molecule inhibitor may sensitize MAP3K3‐amplified breast cancer cells to chemotherapy. Copyright © 2013 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.     

Life insurance: genomic stratification and risk classification

With the development and increasing accessibility of new genomic tools such as next-generation sequencing, genome-wide association studies, and genomic stratification models, the debate on genetic discrimination in the context of life insurance became even more complex, requiring a review of current practices and the exploration of new scenarios. In this perspective, a multidisciplinary group of international experts representing different interests revisited the genetics and life insurance debate during a 2-day symposium ‘Life insurance: breast cancer research and genetic risk prediction seminar'' held in Quebec City, Canada on 24 and 25 September 2012. Having reviewed the current legal, social, and ethical issues on the use of genomic information in the context of life insurance, the Expert Group identified four main questions: (1) Have recent developments in genomics and related sciences changed the contours of the genetics and life insurance debate? (2) Are genomic results obtained in a research context relevant for life insurance underwriting? (3) Should predictive risk assessment and risk stratification models based on genomic data also be used for life insurance underwriting? (4) What positive actions could stakeholders in the debate take to alleviate concerns over the use of genomic information by life insurance underwriters? This paper presents a summary of the discussions and the specific action items recommended by the Expert Group.Access to genetic information by life insurers has been a topic of discussion for many years.¹ The possibility of using genetic data to underwrite an applicant''s insurance policy has given rise to concerns about the emergence of ‘genetic discrimination''. Genetic discrimination in the field of life insurance is not necessarily illegal in that in insurance underwriting questions about health, family history of disease, or genetic information may constitute legal exceptions to antidiscrimination legislation.^{2, 3} Nevertheless, the expression ‘genetic discrimination'' has acquired public notoriety⁴ and we will use more neutral language in this paper.Countries including Canada, the United States, Russia, and Japan⁵ have chosen not to adopt laws specifically prohibiting access to genetic data for underwriting by life insurers.⁶ In these countries, life insurance underwriters treat genetic data like other types of medical or lifestyle data. However, a growing number of countries such as Belgium, France, and Norway⁵ have chosen to adopt laws to prevent or limit insurers'' access to genetic data for life insurance underwriting. Other countries including Finland and the United Kingdom have developed voluntary arrangements with the industry (ie moratoria) with similar objectives.⁷Life insurance is a private contract between the policy-holder and the insurer. Its principal role is to provide financial security to the beneficiaries in the event of the insured''s death.⁸ Because of this important role, life insurance is often required, or strongly recommended for those seeking loans to acquire primary social goods, like housing or cars.⁹ In Europe, a consequence of the advent of the welfare state is that private insurance has increasingly played a complementary and supplementary role to social insurance by offering additional security and protection to the population. Thus, in this region, insurance is often considered as a social good that allows individuals to live a comfortable life and as a tool to promote social integration.¹⁰ In other regions of the world, this social role of life insurance is also recognized to a lesser extent. Given this social role, equitable access to life insurance is perceived as a sensitive issue and cases of denial looked upon negatively in popular media. Although documented incidents of denial or of increased premiums on the basis of genetic information have remained limited to the context of a few relatively well known, highly penetrant, familial, adult-onset, genetic conditions,¹¹ they have nevertheless generated significant public concern. Fear that insurers will have access to genetic information generated in a clinical or research setting for use in underwriting has been reported by several studies as a reason for non-participation in genetic research or recommended clinical genetic testing.^{12, 13, 14}The clinical utility of genetic testing for monogenic disorders such as Huntington disease, and hereditary forms of cancer are well established.¹⁵ However, genomic risk profiles based on the known common susceptibility variants have limited utility in risk prediction at the individual level, although they could be used for risk stratification in prevention programmes in populations.¹⁶ Today, a new era of genomic research has made it increasingly affordable to scan the entire genome of an individual. Researchers and physicians can interpret these data together with medical and lifestyle information in the form of sophisticated risk prediction models.¹⁷ Moreover, improvement in computing technologies coupled with the Internet make predictive information increasingly available, whether through direct-to-consumer marketing of genetic tests, genetic data sharing online communities, or international research database projects. Given these important technological and scientific changes, and their impact on various stakeholders. The term ‘stakeholders'' is used in this text to refer to the following groups of individuals: actuaries (person who computes insurance risk and premium rates based on statistical data), academic researchers, community representatives, ethics committees, genetic counsellors, genomic researchers, human rights experts, insurers, governmental representatives, non-governmental organisations, patient representatives, physicians, policy makers, popular media, reinsurers (company in charge of calculating the risk and premium amount for insuring a particular customer), research participants, and underwriters (company or person in charge of calculating the risk involved in providing insurance for a particular customer and to decide how much should be paid for the premium). This list is not meant to be exhaustive as relevant new groups may emerge as this topic further develops in the coming years. A multidisciplinary group of international experts representing different interests (hereinafter ‘the Expert Group'') revisited the genetics and life insurance debate. The following text presents a summary of the issues discussed and the ‘Action Items'' agreed upon by the Expert Group at the ‘Life Insurance, Risk Stratification, and Personalized Medicine Symposium''.     

Importance of the DNA “bond” in programmable nanoparticle crystallization

If a solution of DNA-coated nanoparticles is allowed to crystallize, the thermodynamic structure can be predicted by a set of structural design rules analogous to Pauling’s rules for ionic crystallization. The details of the crystallization process, however, have proved more difficult to characterize as they depend on a complex interplay of many factors. Here, we report that this crystallization process is dictated by the individual DNA bonds and that the effect of changing structural or environmental conditions can be understood by considering the effect of these parameters on free oligonucleotides. Specifically, we observed the reorganization of nanoparticle superlattices using time-resolved synchrotron small-angle X-ray scattering in systems with different DNA sequences, salt concentrations, and densities of DNA linkers on the surface of the nanoparticles. The agreement between bulk crystallization and the behavior of free oligonucleotides may bear important consequences for constructing novel classes of crystals and incorporating new interparticle bonds in a rational manner.Materials scientists have accomplished much by studying the way atoms and molecules crystallize. In these systems, however, the identity of the atom and its bonding behavior cannot be independently controlled, limiting our ability to tune material properties at will. In contrast, when a nanoparticle is modified with a dense shell of upright, oriented DNA, it can behave as a programmable atom equivalent (PAE) (1, 2) that can be used to synthesize diverse crystal structures with independent control over composition, scale, and lattice symmetry (3–14). The thermodynamic product of this crystallization process has been extensively studied by both experimental and theoretical means, and thus a series of design rules has been proposed and validated with a simple geometric model known as the complementary contact model (CCM). These rules allow one to predict the thermodynamically favored structure as the arrangement of particles that maximizes complementary contacts and therefore DNA hybridization (2, 6). These efforts have been very successful in predicting the thermodynamically favored product; recent studies have even demonstrated that PAEs can form single-crystal Wulff polyhedra that are analogous to those formed in atomic systems with the same crystallographic symmetry (15). However, the fact that there is a crystalline thermodynamic product does not mean that any choice of DNA and nanoparticles will result in crystalline systems in practice (3, 4). For example, crystallization has been observed for a relatively narrow class of PAEs (16) and in a manner that is primarily dependent upon the length of the DNA linker and temperature at which assembly occurs (8). Thus, absent from our understanding of these systems is a connection between the crystallization process and the properties of the DNA bonds that form the foundation of these structures.Here, we study the crystallization process and find that the complexity of the polyvalent DNA interactions can be simply understood by considering the behavior of a single DNA bond. By systematically studying the roles of nucleobase sequence, solution ionic strength, DNA density, and temperature on crystallization, we find that the effects of these factors are mirrored by the rates of hybridization and dehybridization of free DNA. In addition to examining steady-state structures, we evaluate the formation and reorganization of these crystals in a time-resolved manner using small-angle X-ray scattering (SAXS) to study how crystallization dynamics are affected by each design variable. Finally, we develop a predictive model that allows one to compare the range of temperatures over which crystallization will occur for different conditions. In addition to providing an avenue for improving PAE crystallization and realizing new architectures, the effectiveness of this reductionist model suggests that this approach can be applied to study crystallization in a broader class of systems, thus making an impact in the materials by design community.     

A common X-linked inborn error of carnitine biosynthesis may be a risk factor for nondysmorphic autism

We recently reported a deletion of exon 2 of the trimethyllysine hydroxylase epsilon (TMLHE) gene in a proband with autism. TMLHE maps to the X chromosome and encodes the first enzyme in carnitine biosynthesis, 6-N-trimethyllysine dioxygenase. Deletion of exon 2 of TMLHE causes enzyme deficiency, resulting in increased substrate concentration (6-N-trimethyllysine) and decreased product levels (3-hydroxy-6-N-trimethyllysine and γ-butyrobetaine) in plasma and urine. TMLHE deficiency is common in control males (24 in 8,787 or 1 in 366) and was not significantly increased in frequency in probands from simplex autism families (9 in 2,904 or 1 in 323). However, it was 2.82-fold more frequent in probands from male-male multiplex autism families compared with controls (7 in 909 or 1 in 130; P = 0.023). Additionally, six of seven autistic male siblings of probands in male-male multiplex families had the deletion, suggesting that TMLHE deficiency is a risk factor for autism (metaanalysis Z-score = 2.90 and P = 0.0037), although with low penetrance (2-4%). These data suggest that dysregulation of carnitine metabolism may be important in nondysmorphic autism; that abnormalities of carnitine intake, loss, transport, or synthesis may be important in a larger fraction of nondysmorphic autism cases; and that the carnitine pathway may provide a novel target for therapy or prevention of autism.     

The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors

CD47, a "don't eat me" signal for phagocytic cells, is expressed on the surface of all human solid tumor cells. Analysis of patient tumor and matched adjacent normal (nontumor) tissue revealed that CD47 is overexpressed on cancer cells. CD47 mRNA expression levels correlated with a decreased probability of survival for multiple types of cancer. CD47 is a ligand for SIRPα, a protein expressed on macrophages and dendritic cells. In vitro, blockade of CD47 signaling using targeted monoclonal antibodies enabled macrophage phagocytosis of tumor cells that were otherwise protected. Administration of anti-CD47 antibodies inhibited tumor growth in orthotopic immunodeficient mouse xenotransplantation models established with patient tumor cells and increased the survival of the mice over time. Anti-CD47 antibody therapy initiated on larger tumors inhibited tumor growth and prevented or treated metastasis, but initiation of the therapy on smaller tumors was potentially curative. The safety and efficacy of targeting CD47 was further tested and validated in immune competent hosts using an orthotopic mouse breast cancer model. These results suggest all human solid tumor cells require CD47 expression to suppress phagocytic innate immune surveillance and elimination. These data, taken together with similar findings with other human neoplasms, show that CD47 is a commonly expressed molecule on all cancers, its function to block phagocytosis is known, and blockade of its function leads to tumor cell phagocytosis and elimination. CD47 is therefore a validated target for cancer therapies.     

Three differentiation states risk-stratify bladder cancer into distinct subtypes

Current clinical judgment in bladder cancer (BC) relies primarily on pathological stage and grade. We investigated whether a molecular classification of tumor cell differentiation, based on a developmental biology approach, can provide additional prognostic information. Exploiting large preexisting gene-expression databases, we developed a biologically supervised computational model to predict markers that correspond with BC differentiation. To provide mechanistic insight, we assessed relative tumorigenicity and differentiation potential via xenotransplantation. We then correlated the prognostic utility of the identified markers to outcomes within gene expression and formalin-fixed paraffin-embedded (FFPE) tissue datasets. Our data indicate that BC can be subclassified into three subtypes, on the basis of their differentiation states: basal, intermediate, and differentiated, where only the most primitive tumor cell subpopulation within each subtype is capable of generating xenograft tumors and recapitulating downstream populations. We found that keratin 14 (KRT14) marks the most primitive differentiation state that precedes KRT5 and KRT20 expression. Furthermore, KRT14 expression is consistently associated with worse prognosis in both univariate and multivariate analyses. We identify here three distinct BC subtypes on the basis of their differentiation states, each harboring a unique tumor-initiating population.