Kaplan-Meier plots of time to non-persistence having switched generics or not.

<p><b>NS</b>: Never Switchers, <b>RS-</b>: Recurrent Switchers without generic switch on index day, <b>FTS</b>: First Time Switchers and <b>RS+</b>: Recurrent Switchers with generic switch on index day.</p

Influence of length of grace period on estimated non-persistence.

<p>Hazard ratios between generic switch and non-persistence</p><p>Non-persistence was established as the first episode in a subjects’ medication history with a gap in prescription renewal that exceeded a predefined limit (number of tablets and a grace period of 90 days)</p><p>Hazard ratios are presented as the full model described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119688#pone.0119688.t002" target="_blank">Table 2</a></p><p>*p<0.05,</p><p>**p<0.01 and</p><p>***p<0.001</p><p>Influence of length of grace period on estimated non-persistence.</p

Hazard ratio of non-persistence.

<p>The adjusted model: adjusted for gender, age, number of different drugs, BMQ concerns and Views on generic medicine.</p><p>*p<0.05,</p><p>**p<0.01 and</p><p>***p<0.001</p><p>^aTable footnotes belong here: BMQ specific concerns assesses beliefs about the index drug—it represents beliefs about the danger of dependence and long-term toxicity and disruptive effects of medication</p><p>^bTable footnotes belong here: Views on cheaper generic medicine compared to more expensive medicine in terms of side effects, quality and effectiveness</p><p>Hazard ratio of non-persistence.</p

Characteristics of the study population, stratified on whether a generic switch took place on the index day.

<p>Data are presented as the number (of subjects), with the percentage given in parenthesis</p><p>Characteristics of the study population, stratified on whether a generic switch took place on the index day.</p

DataSheet_1_Therapeutic cancer vaccination against mutant calreticulin in myeloproliferative neoplasms induces expansion of specific T cells in the periphery but specific T cells fail to enrich in the bone marrow.pdf

BackgroundTherapeutic cancer vaccination against mutant calreticulin (CALR) in patients with CALR-mutant (CALRmut) myeloproliferative neoplasms (MPN) induces strong T-cell responses against mutant CALR yet fails to demonstrate clinical activity. Infiltration of tumor specific T cells into the tumor microenvironment is needed to attain a clinical response to therapeutic cancer vaccination.AimDetermine if CALRmut specific T cells isolated from vaccinated patients enrich in the bone marrow upon completion of vaccination and explore possible explanations for the lack of enrichment.MethodsCALRmut specific T cells from four of ten vaccinated patients were expanded, enriched, and analyzed by T-cell receptor sequencing (TCRSeq). The TCRs identified were used as fingerprints of CALRmut specific T cells. Bone marrow aspirations from the four patients were acquired at baseline and at the end of trial. T cells were enriched from the bone marrow aspirations and analyzed by TCRSeq to identify the presence and fraction of CALRmut specific T cells at the two different time points. In silico calculations were performed to calculate the ratio between transformed cells and effector cells in patients with CALRmut MPN.ResultsThe fraction of CALRmut specific T cells in the bone marrow did not increase upon completion of the vaccination trial. In general, the T cell repertoire in the bone marrow remains relatively constant through the vaccination trial. The enriched and expanded CALRmut specific T cells recognize peripheral blood autologous CALRmut cells. In silico analyses demonstrate a high imbalance in the fraction of CALRmut cells and CALRmut specific effector T-cells in peripheral blood.ConclusionCALRmut specific T cells do not enrich in the bone marrow after therapeutic cancer peptide vaccination against mutant CALR. The specific T cells recognize autologous peripheral blood derived CALRmut cells. In silico analyses demonstrate a high imbalance between the number of transformed cells and CALRmut specific effector T-cells in the periphery. We suggest that the high burden of transformed cells in the periphery compared to the number of effector cells could impact the ability of specific T cells to enrich in the bone marrow.</p

Crude and adjusted odds ratios for breast cancer clinicopathological subtypes of women with diabetes treated with or without insulin compared to women without diabetes in subgroups of menopausal status using (multinomial) logistic regression.

<p>Crude and adjusted odds ratios for breast cancer clinicopathological subtypes of women with diabetes treated with or without insulin compared to women without diabetes in subgroups of menopausal status using (multinomial) logistic regression.</p

Flow chart of patient identification and selection.

<p>Stratified by age at breast cancer diagnosis (≤50 and >50 years), women with diabetes were 2:1 frequency-matched on year of birth and age at breast cancer diagnosis (both in 10-year categories) to women without diabetes, to select ~300 patients with tumor tissue available. ^ǂ Exact numbers <5 cannot be shown according to regulations of Statistics Denmark.</p

Crude and adjusted odds ratios for breast cancer clinicopathological subtypes of women with diabetes compared to women without diabetes in subgroups of menopausal status using (multinomial) logistic regression.

<p>Crude and adjusted odds ratios for breast cancer clinicopathological subtypes of women with diabetes compared to women without diabetes in subgroups of menopausal status using (multinomial) logistic regression.</p

Model calibration.

<p>A) Plateaus to the left show the amount of hematopoietic stem cells <i>x</i>₀ (upper plateau) and that for MPN stem cells <i>y</i>₀ (lower plateau) whereas the plateaus to the right show the amount of hematopoietic stem cells <i>x</i>₀(lower plateau) and MPN mature cells <i>y</i>₀ (upper plateau). B) Plateaus to the left show the amount of hematopoietic mature cells <i>x</i>₁ (upper plateau) and that for MPN mature cells <i>y</i>₁ (lower plateau) whereas the plateaus to the right show the amount of hematopoietic mature cells <i>x</i>₁ (lower plateau) and MPN mature cells <i>y</i>₁ (upper plateau). The yellow and purple boxes show our data used for calibrating (and validating) the model with further details in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.s001" target="_blank">S1 Appendix</a>. Yellow boxes show our “no MPN cancer values”, and purple boxes show our “full blown” MPN values in the advanced myelofibrosis stage. Yellow position marker shows the number of hematopoietic stem cells as used by Dingli & Michor [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.ref092" target="_blank">92</a>], and black position markers show the number of cells as used by Gentry et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.ref086" target="_blank">86</a>].</p

Investigation of increased inflammatory load at various onsets and durations.

<p>Blue curve is default parameters corresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183620#pone.0183620.g003" target="_blank">Fig 3</a>, red dotted is a doubling of inflammatory load, full red curve is a doubling of inflammatory load in year 0–20, then back to default level, black dotted curve is inflammatory doubling from year 10, the full black is inflammatory doubling year 10–30. <b>Upper:</b> Increasing inflammatory load has a boosting effect on MPN MC (A) as well as on HMC (B). <b>Lower:</b> Displaying the results in terms of the clinically available quantity, total blood cell count, also shows a boosted effect with increasing inflammatory load (C). The allele burden of JAK2 mutated blood cells similarly shows that increased inflammation increases disease development (D). There is a clear effect of MPN promotion with increasing inflammatory load, earlier onset, and exposure time. Lowering inflammatory load makes disease progression less rapid. Maintaining a doubling (red dotted curve) shifts the allele burden curve to the left by two years. Shortening the exposure time of inflammatory load weakens the disease progression. The inflammation has a fast impact on the total number of blood cells, which typically changes by 25% within the first year after doubling or reducing the inflammatory load by 50%.</p