Molecular diagnosis of the myeloproliferative neoplasms: UK guidelines for the detection of JAK2 V617F and other relevant mutations

Molecular genetic assays for the detection of the JAK2 V617F (c.1849G>T) and other pathogenetic mutations within JAK2 exon 12 and MPL exon 10 are part of the routine diagnostic workup for patients presenting with erythrocytosis, thrombocytosis or otherwise suspected to have a myeloproliferative neoplasm. A wide choice of techniques are available for the detection of these mutations, leading to potential difficulties for clinical laboratories in deciding upon the most appropriate assay, which can lead to problems with inter-laboratory standardization. Here, we discuss the most important issues for a clinical diagnostic laboratory in choosing a technique, particularly for detection of the JAK2 V617F mutation at diagnosis. The JAK2 V617F detection assay should be both specific and sensitive enough to detect a mutant allele burden as low as 13%. Indeed, the use of sensitive assays increases the detection rate of the JAK2 V617F mutation within myeloproliferative neoplasms. Given their diagnostic relevance, it is also beneficial and relatively straightforward to screen JAK2 V617F negative patients for JAK2 exon 12 mutations (in the case of erythrocytosis) or MPL exon 10 mutations (thrombocytosis or myelofibrosis) using appropriate assays. Molecular results should be considered in the context of clinical findings and other haematological or laboratory results.

JAK2V617F homozygosity arises commonly and recurrently in PV and ET, but PV is characterized by expansion of a dominant homozygous subclone

Subclones homozygous for JAK2V617F are more common in polycythemia vera (PV) than essential thrombocythemia (ET), but their prevalence and significance remain unclear. The JAK2 mutation status of 6495 BFU-E, grown in low erythropoietin conditions, was determined in 77 patients with PV or ET. Homozygous-mutant colonies were common in patients with JAK2V617F-positive PV and were surprisingly prevalent in JAK2V617F-positive ET and JAK2 exon 12-mutated PV. Using microsatellite PCR to map loss-of-heterozygosity breakpoints within individual colonies, we demonstrate that recurrent acquisition of JAK2V617F homozygosity occurs frequently in both PV and ET. PV was distinguished from ET by expansion of a dominant homozygous subclone, the selective advantage of which is likely to reflect additional genetic or epigenetic lesions. Our results suggest a model in which development of a dominant JAK2V617F-homzygous subclone drives erythrocytosis in many PV patients, with alternative mechanisms operating in those with small or undetectable homozygous-mutant clones.

Ireland

‘Islands constitute natural unities; sea channels constitute natural divisions’. So wrote Kevin Howard (2006: 9) with particular reference to the two largest islands of the British Isles: Great Britain and Ireland. However, at the time of writing, the political unity of Great Britain is being called into question, while Ireland has been divided since the 1920s. Part of the island of Ireland is united not with the rest of the island, but with Great Britain across the other side of the ‘natural division’ that is the Irish Sea, for Ireland’s history, development and political structures have long been moulded by Great Britain. The journey to this ‘unnatural’ unity and the associated division of Ireland forms the material for this chapter.

On the Assumption of Bivariate Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence

Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal.
Methods: We introduce a novel approach for relaxing the bivariate normality assumption in selection models using copula functions. We apply this method to estimating HIV prevalence and new confidence intervals (CI) in the 2007 Zambia Demographic and Health Survey (DHS) by using interviewer identity as the selection variable that predicts participation (consent to test) but not the outcome (HIV status).
Results: We show in a simulation study that selection models can generate biased results when the bivariate normality assumption is violated. In the 2007 Zambia DHS, HIV prevalence estimates are similar irrespective of the structure of the association assumed between participation and outcome. For men, we estimate a population HIV prevalence of 21% (95% CI = 16%–25%) compared with 12% (11%–13%) among those who consented to be tested; for women, the corresponding figures are 19% (13%–24%) and 16% (15%–17%).
Conclusions: Copula approaches to Heckman-type selection models are a useful addition to the methodological toolkit of HIV epidemiology and of epidemiology in general. We develop the use of this approach to systematically evaluate the robustness of HIV prevalence estimates based on selection models, both empirically and in a simulation study.

Statin use after diagnosis of breast cancer and survival: a population-based cohort study

BACKGROUND: Preclinical studies have shown that statins, particularly simvastatin, can prevent growth in breast cancer cell lines and animal models. We investigated whether statins used after breast cancer diagnosis reduced the risk of breast cancer-specific, or all-cause, mortality in a large cohort of breast cancer patients.

METHODS: A cohort of 17,880 breast cancer patients, newly diagnosed between 1998 and 2009, was identified from English cancer registries (from the National Cancer Data Repository). This cohort was linked to the UK Clinical Practice Research Datalink, providing prescription records, and to the Office of National Statistics mortality data (up to 2013), identifying 3694 deaths, including 1469 deaths attributable to breast cancer. Unadjusted and adjusted hazard ratios (HRs) for breast cancer-specific, and all-cause, mortality in statin users after breast cancer diagnosis were calculated using time-dependent Cox regression models. Sensitivity analyses were conducted using multiple imputation methods, propensity score methods and a case-control approach.

RESULTS: There was some evidence that statin use after a diagnosis of breast cancer had reduced mortality due to breast cancer and all causes (fully adjusted HR = 0.84 [95% confidence interval = 0.68-1.04] and 0.84 [0.72-0.97], respectively). These associations were more marked for simvastatin 0.79 (0.63-1.00) and 0.81 (0.70-0.95), respectively.

CONCLUSIONS: In this large population-based breast cancer cohort, there was some evidence of reduced mortality in statin users after breast cancer diagnosis. However, these associations were weak in magnitude and were attenuated in some sensitivity analyses.