Association of prolactin receptor (PRLR) variants with prolactinomas

Prolactinomas are the most frequent type of pituitary tumors, which represent 10–20% of all intracranial neoplasms in humans. Prolactinomas develop in mice lacking the prolactin receptor (PRLR), which is a member of the cytokine receptor superfamily that signals via Janus kinase-2-signal transducer and activator of transcription-5 (JAK2-STAT5) or phosphoinositide 3-kinase-Akt (PI3K-Akt) pathways to mediate changes in transcription, differentiation and proliferation. To elucidate the role of the PRLR gene in human prolactinomas, we determined the PRLR sequence in 50 DNA samples (35 leucocytes, 15 tumors) from 46 prolactinoma patients (59% males, 41% females). This identified six germline PRLR variants, which comprised four rare variants (Gly57Ser, Glu376Gln, Arg453Trp and Asn492Ile) and two low-frequency variants (Ile76Val, Ile146Leu), but no somatic variants. The rare variants, Glu376Gln and Asn492Ile, which were in complete linkage disequilibrium, and are located in the PRLR intracellular domain, occurred with significantly higher frequencies (P 1.3-fold, P < 0.02) and proliferation (1.4-fold, P < 0.02), but did not affect pSTAT5 signaling. Treatment of cells with an Akt1/2 inhibitor or everolimus, which acts on the Akt pathway, reduced Asn492Ile signaling and proliferation to WT levels. Thus, our results identify an association between a gain-of-function PRLR variant and prolactinomas and reveal a new etiology and potential therapeutic approach for these neoplasms

A comprehensive analysis of common genetic variation in prolactin (PRL) and PRL receptor (PRLR) genes in relation to plasma prolactin levels and breast cancer risk: the Multiethnic Cohort

<p>Abstract</p> <p>Background</p> <p>Studies in animals and humans clearly indicate a role for prolactin (PRL) in breast epithelial proliferation, differentiation, and tumorigenesis. Prospective epidemiological studies have also shown that women with higher circulating PRL levels have an increase in risk of breast cancer, suggesting that variability in PRL may also be important in determining a woman's risk.</p> <p>Methods</p> <p>We evaluated genetic variation in the PRL and PRL receptor (PRLR) genes as predictors of plasma PRL levels and breast cancer risk among African-American, Native Hawaiian, Japanese-American, Latina, and White women in the Multiethnic Cohort Study (MEC). We selected single nucleotide polymorphisms (SNPs) from both the public (dbSNP) and private (Celera) databases to construct high density SNP maps that included up to 20 kilobases (kb) upstream of the transcription initiation site and 10 kb downstream of the last exon of each gene, for a total coverage of 59 kb in PRL and 210 kb in PRLR. We genotyped 80 SNPs in PRL and 173 SNPs in PRLR in a multiethnic panel of 349 unaffected subjects to characterize linkage disequilibrium (LD) and haplotype patterns. We sequenced the coding regions of PRL and PRLR in 95 advanced breast cancer cases (19 of each racial/ethnic group) to uncover putative functional variation. A total of 33 and 60 haplotype "tag" SNPs (tagSNPs) that allowed for high predictability (R_h²≥ 0.70) of the common haplotypes in PRL and PRLR, respectively, were then genotyped in a multiethnic breast cancer case-control study of 1,615 invasive breast cancer cases and 1,962 controls in the MEC. We also assessed the association of common genetic variation with circulating PRL levels in 362 postmenopausal controls without a history of hormone therapy use at blood draw. Because of the large number of comparisons being performed we used a relatively stringent type I error criteria (p < 0.0005) for evaluating the significance of any single association to correct for performing approximately 100 independent tests, close to the number of tagSNPs genotyped for both genes.</p> <p>Results</p> <p>We observed no significant associations between PRL and PRLR haplotypes or individual SNPs in relation to breast cancer risk. A nominally significant association was noted between prolactin levels and a tagSNP (tagSNP 44, rs2244502) in intron 1 of PRL. This SNP showed approximately a 50% increase in levels between minor allele homozygotes vs. major allele homozygotes. However, this association was not significant (p = 0.002) using our type I error criteria to correct for multiple testing, nor was this SNP associated with breast cancer risk (p = 0.58).</p> <p>Conclusion</p> <p>In this comprehensive analysis covering 59 kb of the PRL locus and 210 kb of the PRLR locus, we found no significant association between common variation in these candidate genes and breast cancer risk or plasma PRL levels. The LD characterization of PRL and PRLR in this multiethnic population provide a framework for studying these genes in relation to other disease outcomes that have been associated with PRL, as well as for larger studies of plasma PRL levels.</p

Prolactin-induced mouse mammary carcinomas model estrogen resistant luminal breast cancer.

INTRODUCTION: Tumors that express estrogen receptor alpha (ERα+) comprise 75% of breast cancers in women. While treatments directed against this receptor have successfully lowered mortality rates, many primary tumors initially or later exhibit resistance. The paucity of murine models of this luminal tumor subtype has hindered studies of factors that promote their pathogenesis and modulate responsiveness to estrogen-directed therapeutics. Since epidemiologic studies closely link prolactin and the development of ERα+ tumors in women, we examined characteristics of the aggressive ERα+ and ERα- carcinomas which develop in response to mammary prolactin in a murine transgenic model (neu-related lipocalin- prolactin (NRL-PRL)). To evaluate their relationship to clinical tumors, we determined phenotypic relationships among these carcinomas, other murine models of breast cancer, and features of luminal tumors in women. METHODS: We examined a panel of prolactin-induced tumors for characteristics relevant to clinical tumors: histotype, ERα/progesterone receptor (PR) expression and estrogen responsiveness, Activating Protein 1 (AP-1) components, and phosphorylation of signal transducer and activator of transcription 5 (Stat5), extracellular signal regulated kinase (ERK) 1/2 and AKT. We compared levels of transcripts in the ERα-associated luminal signature that defines this subtype of tumors in women and transcripts enriched in various mammary epithelial lineages to other well-studied genetically modified murine models of breast cancer. Finally, we used microarray analyses to compare prolactin-induced ERα+ and ERα- tumors, and examined responsiveness to estrogen and the anti-estrogen, Faslodex, in vivo. RESULTS: Prolactin-induced carcinomas were markedly diverse with respect to histotype, ERα/PR expression, and activated signaling cascades. They constituted a heterogeneous, but distinct group of murine mammary tumors, with molecular features of the luminal subtype of human breast cancer. In contrast to morphologically normal and hyperplastic structures in NRL-PRL females, carcinomas were insensitive to ERα-mediated signals. These tumors were distinct from mouse mammary tumor virus (MMTV)-neu tumors, and contained elevated transcripts for factors associated with luminal/alveolar expansion and differentiation, suggesting that they arose from physiologic targets of prolactin. These features were shared by ERα+ and ERα- tumors, suggesting a common origin, although the former exhibited transcript profiles reflecting greater differentiation. CONCLUSIONS: Our studies demonstrate that prolactin can promote diverse carcinomas in mice, many of which resemble luminal breast cancers, providing a novel experimental model to examine the pathogenesis, progression and treatment responsiveness of this tumor subtype

Decision-oriented evaluation of research cooperation partners

Gegenstand der Arbeit ist die Gestaltung eines Methodenapparates für die Bewertung alternativer Forschungskooperationspartner. Forschung wird definiert als die systematische Anwendung wissenschaftlicher Methoden mit dem Ziel, neues Wissen zu erlangen. Unter einer zwischenbetrieblichen Kooperation wird die freiwillige, zielorientierte, vertraglich vereinbarte langfristige Zusammenarbeit zwischen rechtlich selbstständigen Unternehmen verstanden, die mit einer teilweisen Einschränkung der wirtschaftlichen Selbstständigkeit einhergeht. Für die entscheidungslogisch fundierte Bewertung von Forschungskooperationspartnern ist in umfangreicher Weise die Beschaffung und Verarbeitung entscheidungsrelevanter Informationen und die Bereitstellung eines geeigneten Methodenapparates erforderlich. Forschungskooperationsziele stellen die zentralen Maßstäbe zur Beurteilung eines Forschungskooperationspartners dar. Sie sind für die Wahl geeigneter Methoden zur Bewertung alternativer Kooperationspartner entscheidend. Der Analyse betrieblicher Ziele beim Eingehen einer Forschungskooperation wird mittels einer Literaturanalyse ein besonderer Stellenwert eingeräumt. Typische Forschungskooperationsziele sind sachlicher, zeitlicher und formaler Natur. Inhalt dieser typischen Ziele von Forschungskooperationen ist es, das jeweilige Forschungsvorhaben qualitativ besser, schneller oder finanziell günstiger abzuschließen. Neben diesen typischen Zielrichtungen einer Forschungskooperation sind zwei weitere zu berücksichtigen: Risikoreduktion und Durchführbarkeit. Bewertungsrelevante Eigenschaften eines auf seine Eignung als Forschungskooperationspartner hin zu bewertenden Betriebs sind solche Eigenschaften, die darauf schließen lassen, in welchem Ausmaß es in Kooperation mit diesem Partner möglich wäre, die Forschungskooperationsziele zu erreichen. Diese Eigenschaften werden zweckmäßigerweise für das weitere Vorgehen in zwei Typen differenziert: zum ersten in die objektiv beobachtbaren Potenziale eines Kooperationspartners, die aus der Güterperspektive dessen Befähigung, sein Können, kennzeichnen, ein bestimmtes Forschungsprojekt sinnvoll zur eigenen Leistungsbereitschaft zu ergänzen. Potenzialfaktoren, die in einem Forschungsprojekt zum Einsatz kommen, sind materielle Betriebsmittel wie bspw. Versuchs- oder Rechenanlagen. Ausführende Arbeit zur Realisation von Forschungsvorhaben, bspw. durch Laborpersonal zählt ebenfalls zu den relevanten Potenzialfaktoren. Zu den bedeutendsten immateriellen Betriebsmitteln in einem Forschungsprojekt gehört ferner Wissen. Schließlich ist auch das finanzielle Potenzial eines Kooperationspartners zu bewerten. Neben der Potenzialbasis bildet die Bereitschaft des Partnerbetriebs, konstruktiv zu kooperieren (also dessen Wollen), den zweiten bewertungsrelevanten Eigenschaftstyp. Als Nullalternative bei der Bewertung alternativer Forschungskooperationspartner fungiert die Möglichkeit der alleinigen Realisation des Forschungsprojekts. Als Konsequenz erfasst der bewertende Vergleich von alternativen Kooperationspartnern die Differenz der Zielerreichung mit einzelnen Kooperationspartnern zur Nullalternative, um das Ausmaß der besseren Zielerreichung abzubilden. Kern des Bewertungsmodells ist die Bewertung der positiven und negativen Konsequenzen der Entscheidung für einen bestimmten Kooperationspartner. Die positiven Konsequenzen, oder auch der Nutzen, sind anhand des Vergleichs der Zielwirkungen von Nullalternative und Forschungskooperation mit dem jeweiligen Kooperationspartnern zu bestimmen. Da diese Zielwirkungen in unterschiedlichen Skalen gemessen werden, ist mittels einer Nutzwertanalyse eine einheitliche Vergleichsgröße zu berechnen. Negative Konsequenzen der Forschungskooperation mit dem jeweiligen Kooperationspartner ergeben sich aus seiner Kooperationsbereitschaft. Insbesondere ist der aus einer erwartungsgemäß submaximalen Kooperationsbereitschaft eines Kooperationspartners resultierende Aufwand für zwischenbetriebliche Koordinationsmaßnahmen bewertungsrelevant. Nach der Bestimmung des Nutzens aus den Potenzialen eines Kooperationspartners und des Disnutzens aus seiner fehlenden Kooperationsbereitschaft stehen nun die Größen zur Bildung eines partnerspezifischen Gesamtwerts zur Verfügung: Der Wert eines potenziellen Kooperationspartners ergibt sich durch Summierung von Nutzen und Disnutzen. Die Interpretation des Partnerwerts lässt sich beispielhaft aufzeigen. Entsprechend seiner Definition und der zugrundeliegenden Rechenweise spricht ein höherer Wert für einen Kooperationspartner, der eher geeignet ist, um die eigenen Forschungskooperationsziele zu erreichen, als ein niedrigerer Partnerwert. Solange der Partnerwert positiv ist, ist die Durchführung einer Kooperation zur Erlangung der Forschungsziele erfolgsversprechender, als die eigenständige Realisation des Forschungsprojekts. Ist der Partnerwert hingegen negativ, so wäre eine Realisation der Nullalternative zielführender. Zur Darstellung der Anwendungsbreite des entwickelten Bewertungsmodells werden abschließend drei spezielle Konstellationen der Bewertung von Forschungskooperationspartnern näher betrachtet: Forschungskooperationen mit mehr als einem Kooperationspartner, internationale Forschungskooperationen und Forschungskooperationen mit nicht privatwirtschaftlich verfassten Betrieben. Dabei wird gezeigt, dass das entwickelte Bewertungsmodell durchweg auch für diese Fälle anwendbar ist, wobei jeweils typischerweise einige Faktoren im Bewertungsmodell eine besondere Bedeutung erfahren.This thesis focuses on developing a method which can be used to evaluate potential partners to cooperate with in an intercompany research cooperation. Research is understood as systematically applying scientific methods in order to gain new knowledge. An intercompany cooperation is understood as a goal-directed, contractually settled long-term collaboration that is established on a voluntary basis between legally independent, yet consequently commercially mutually dependent companies. Decision-theory based evaluation of research cooperation partners requires processing a great deal of relevant information and the design of a suitable methodology. Research cooperation goals are seen as the essential benchmark on the basis of which alternative research cooperation partners are to be evaluated. Consequently, they are essential for the methodology to be chosen. Through the studying of literature, goals that are to be achieved through engaging in a research cooperation are thoroughly analyzed. Essential goals are content goals, timeframe goals and financial goals. Additional goals of more special character are risk reduction and feasibility. All characteristics of a company to be evaluated as a potential research cooperation partner are to be benchmarked in order to evaluate their value for achieving research cooperation goals. Conveniently, these characteristics can be distinguished between two types: first, the objectively observable potentials of a potential research cooperation partner. These characterize his capabilities, to enrich the planned research project in a purposeful way, when compared to one?s own capabilities. Capabilities of importance for research projects can typically be seen in material operating resources (e.g. experimental plants or specialized IT-facilities), human resources (e.g. laboratory staff), immaterial resources (particularly knowledge) and finally financial resources. Second, the will (or: motivation) is the second set of relevant cooperation partner characteristics. The best alternative to engaging in a research cooperation with any partner is to realize the intended research project by oneself, i.e. without a cooperation partner. This alternative is referred to as the null alternative. Consequently, all potential research cooperation partners are to be compared with the extent to which research goals can be achieved through one?s null alternative. The key aspect of the methodology to be developed thus is the evaluation of positive and negative consequences of choosing a particular company as a partner to cooperate with. Positive consequences (or: advantages) can be identified as a better achievement of goals than would be possible when realizing the null alternative. Since different goals are to be measured with different scales, standardization through a scoring model becomes necessary. Negative consequences (or: disadvantages) of cooperating with a particular partner result from his lack of cooperation will. In particular means and instruments of intercompany coordination are to be evaluated. Having determined advantages and disadvantages of a particular research cooperation partner, both findings can be added in order to generate an overall partner value. The higher this partner value, the more suitable is the company as a research cooperation partner. As long as the partner value is above zero, i.e. positive, cooperation leads to a better goal-achievement than realizing the null alternative (i.e. realizing the research project by oneself). A negative partner value however indicates that realizing the null alternative would mean a better goal achievement than engaging in a research cooperation with this particular partner. The wide usability of the methodology developed is demonstrated by a concluding discussion of three particularly relevant constellations in intercompany research cooperations: research coopera-tions with more than just two research partners (i.e. research networks), international research cooperations, and research cooperations in public-private-partnerships. Specific requirements of using the developed set of methodology in these three constellations are highlighted conclusively

Polymorphisms in the Janus kinase 2 (JAK)/signal transducer and activator of transcription (STAT) genes: putative association of the STAT gene region with familial breast cancer

The Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway mediates the signals of a wide range of cytokines, growth factors and hormones. Thus, aberrant activation of the JAK/STAT pathway may predispose to malignancy due to deregulation of proliferation, differentiation or apoptosis. In this study, we investigated whether genetic variation in the JAK2 gene and the STAT gene region (STAT3, STAT5A and STAT5B) is associated with breast cancer (BC) risk. We carried out a case-control study using a German sample set with 441 familial, unrelated BC cases and 552 controls matched by age, ethnicity and geographical region. A second similar set (381 cases, 460 controls) was applied to validate the findings. Haplotypes in the JAK2 gene were not associated with the risk of BC. In the STAT gene region, the rare haplotype CAGCC containing the variant alleles of each single nucleotide polymorphism (SNP) was associated with an increased risk odds ratio (OR = 5.83, 95% confidence interval (CI) 1.51–26.28). According to Akaike’s information criterion, the best model to describe the relationship between the haplotypes and BC was based on the SNPs rs6503691 (STAT5B) and rs7211777 (STAT3). Carriers of the AC haplotype, which represents the variant alleles of both SNPs, were at an increased risk (OR = 1.41, 95% CI 1.09–1.82). A decreased risk was observed for carriers of the AT haplotype (OR = 0.60, 95% CI 0.38–0.94). Furthermore, individuals with the AC/GC diplotype were at a significantly increased risk (OR = 1.88, 95% CI 1.13–3.14). The observed genetic variation may also influence the inter-individual variation in response to STAT-signalling targeted therapy