ANK2 and EPAS1 as differentially methylated regions (DMGs) in CMT. Subsequently, we established quantitative methylation specific PCR (qMSP) of ANK2 and EPAS1 to validate the target hypermethylation in CMT tissue, as well as cell free DNA (cfDNA) from CMT plasma. Both ANK2 and EPAS1 were hypermethylated in CMT and highlighted as potential tissue biomarkers in CMT. ANK2 additionally showed significant hypermethylation in the plasma cfDNA of CMT, indicating that it could be a potential liquid biopsy biomarker as well. A similar trend towards hypermethylation was indicated in HBC at a specific CpG of the ANK2 target on the orthologous human region, which validates the comparative approach using aberrant methylation in CMT. Canine mammary tumors (CMT) constitute the most common tumor types found in female dogs. Understanding this cancer through extensive research is important not only for clinical veterinary applications, but also in the scope of comparative oncology. The use of DNA methylation as a biomarker has been noted for numerous cancers in the form of both tissue and liquid biopsies, yet the study of methylation in CMT has been limited. By analyzing our canine methyl-binding domain sequencing (MBD-seq) data, we identified intron regions of canine  Canine mammary tumors (CMT) are the most common neoplasia diagnosed in female dogs and are found to be malignant in approximately 50% of cases ,2,3,4. ADogs have been highlighted as excellent animal models for human cancer ,6, and tERα [DAPK1, MGMT [BRCA1 [DNA methylation is an important method of epigenetic regulation and thus has often been studied in relation to cancer . AberranERα , DAPK1, K1, MGMT , and BRCT [BRCA1 , yet genT [BRCA1  making tT [BRCA1 . Liquid biopsies involve the investigation of cell-free DNA (cfDNA) found in the blood, plasma, serum, or other liquids from the body and often contain circulating tumor DNA. They are of clinical importance as they are less invasive and also allow for a more comprehensive view of the cancer biology as opposed to the view offered by tissue biopsies that is limited to a single site of the tumor at a single moment in the progression of the cancer . MethylaThis study set out to identify and investigate differentially methylated regions (DMRs) in CMT, initially validating our findings in CMT tissues and then also in cfDNA isolated from plasma obtained from dogs with CMT with the intent of establishing candidate tissue and liquid biomarkers for cfDNA. Additionally, we correlated our differentially methylated target regions in our CMT data with HBC data to see if the differential methylation seen in these diseases is consistent across species at the targeted sites.EPAS1, ANK2, DST and RUSC2 indicated a downregulation of gene expression in CMT noted from the RNA-seq data. Of these, ANK2 and EPAS1 were at a significant level  and were thus selected for further analysis  with thresholds of both 10% and 5%  was chosen as the method by which methylation was to be analyzed, mainly due to its ability to quantify the methylation of numerous samples simultaneously without the need for exhaustive cloning and sequencing procedures. qMSP is furthermore a very sensitive technique that is able to distinguish a very small amount of methylated CpGs from unmethylated CpGs [ANK2 and EPAS1 target regions were methylated more in the CMT than the paired normal samples, although the amount of differential methylation varied across the sample pairs  of 0.764 and EPAS1 had an AUC of 0.733  for the target regions using Wanderer [ANK2 to be hypermethylated at three of the four CpG probes in this region , which corresponds to the hypermethylation that we observed in the orthologous region in dog  to the human genome (HG19). This indicated that our 500 bp regions identified from the canine MBD-seq for the ectively . The EPAWanderer  and founn in dog A. Interen in dog A. The exine data B, and thpression C. The huCGA data D. Furthegulation E,F.EPAS1 target region did not show any methylation in this region for either HBC (four samples) or normal  [EPAS1 CMT tissue and HBC data from TCGA seems to not hold up in either canine or human cfDNA. On the other hand, sequencing results of the ANK2 target region from human cfDNA was more in line with what was suggested by the TCGA data. We sequenced colonies from four separate samples of HBC cfDNA and from 5 pooled normal cfDNA samples. Of the three CpGs indicated to be hypermethylated in this region from the TCGA data, only cg08448479, designated as CpG 5, was also shown to be more methylated in HBC cfDNA according to our sequencing results  , but thi results G. CpG 5  results G. We att results . In bothANK2 and EPAS1, respectively, that were significantly hypermethylated in CMT tissue and, in the case of ANK2, also in cfDNA isolated from dogs suffering from the disease. The ANK2 region was furthermore shown to be hypermethylated in human cfDNA at a corresponding CpG on the orthologous human region.CMT is a common malignancy in female dogs and, moreover, in the scope of using dogs as animal models for human disease, its study can be translated to HBC. The field of comparative oncology using canine models have been increasing in popularity, yet the epigenome of canines with CMT remains incomplete. Methylation of DNA has been highlighted as a biomarker in both tissue and liquid biopsies ,23,24, aANK2 encodes multiple AnkyrinB isoforms, which are integral membrane proteins that connect the lipid bilayer to the membrane skeleton [ANK2 mRNA expression was downregulated in CMT and also in HBC based on TCGA data. It is of note that other members of the ankyrin family, namely ANK1 and ANK3, have been shown to be overexpressed in multiple human cancers including breast cancer [ANK2 overexpression has been shown in pancreatic cancer where it contributed to the malignant phenotype [ANK2 gene expression was downregulated in colorectal cancer [ANK2 seems to vary amongst cancer types, and further investigation in a larger variety of cancers is required to better understand its relationship to cancer. Moreover, the role of cancer associated aberrant methylation in ANK2 remains largely unknown. One previous study compiled datasets for nasopharyngeal cancer and found ANK2 to be hypomethylated and upregulated, highlighting it as a potential biomarker for this cancer type [ANK2, in both tissue and cfDNA, and it was highlighted as a potential tissue and liquid biopsy biomarker for CMT. Yet, the impact of this specific methylation in CMT as well as the fuller extent of ANK2 methylation, on the promotor region for instance, remains unknown and should be studied further. Furthermore, the orthologous human region does contain hypermethylated CpGs but outside of a CpG island which makes the qMSP method difficult, even more so when considering that of the six CpGs in the region only one seemed to be differentially methylated based on the sequencing data in ANK2 is probably due to there only being one CpG (CpG 5) that is differentially methylated, and the technique is reliant on more than one CpG being differentially methylated. Additional sequencing of this region in more samples would be the best way to highlight the differential methylation in the respective CpGs. Other regions on ANK2 that contain conserved CpG islands could potentially be more successfully analyzed via the qMSP method and could potentially prove more influential in the downregulation of expression seen in ANK2 in either HBC or CMT, which makes our novel investigation of ANK2 in these diseases very insightful.skeleton . In thist cancer ,36, whilhenotype . Howeverl cancer  and thyrl cancer , which ccer type . Our stuEPAS1, on the other hand, is relatively well studied in its relation to cancer, including breast cancer. EPAS1 is part of the hypoxia inducible factor (HIF) family and plays an important role in the hypoxic response pathway which is often associated with tumorigenesis [igenesis . EPAS1 aigenesis ,42. It higenesis ,44. Howeigenesis ,46,47,48igenesis  found EPigenesis ,51.EPAS1 in CMT and in invasive HBC, yet the role of DNA methylation still remains unclear as, despite the indication of hypermethylation of the proposed region in the first intron of human EPAS1 shown in the TCGA data, the cfDNA analysis showed no methylation difference between normal and HBC patients. Similarly, the canine cfDNA data did not show any increased methylation in this region. However, significant hypermethylation was noted in CMT tissue compared to paired adjacent normal tissue. This discrepancy between the tissue methylation and cfDNA methylation could potentially be explained by a lesser abundance of circulating tumor DNA (ctDNA) compared to normal cfDNA isolated from the plasma samples used, seeing as even though tumors usually secrete a larger amount of cfDNA in the plasma there is currently no ideal method for separating ctDNA from total cfDNA. ctDNA is furthermore highly fragmented which could hinder its detection [EPAS1 target region in circulating tumor cells (CTCs) could prove insightful. The hypermethylation and mRNA downregulation noted in the CMT tissue is however concurrent with previous studies where EPAS1 has been shown to be hypermethylated in its promotor with downregulated mRNA expression in non-small cell lung cancer [EPAS1 to be hypomethylated in colon cancer [EPAS1 and its indications of hypermethylation in cancer as opposed to normal remains a novel and intriguing finding and highlights this target as a candidate tissue biomarker for CMT. Yet, future studies should endeavor to delve deeper into the methylation of the targeted intron region in HBC by analyzing tissue and cell culture samples. Elucidating the functional role of this hypermethylation in CMT and potentially HBC, specifically in ER+ subtypes that have previously indicated downregulation in HBC [EPAS1 and its potential role in CMT and HBC.This present study indicated an overall downregulation of etection . Analyzig cancer  and colog cancer . Althougn cancer . In CMT n in HBC  and in Hn in HBC , is impoANK2 and EPAS1 that could potentially serve as tissue biomarkers for CMT. Furthermore, the ANK2 target also showed hypermethylation in cfDNA isolated from CMT, which highlights it as a potential liquid biopsy biomarker for CMT. In HBC, one CpG from the orthologous human ANK2 region likewise indicated a hypermethylated trend in cfDNA isolated from HBC patients, which emphasizes the link between CMT and HBC epigenetic regulation.Overall, this study set out to investigate CMT tissues and cfDNA isolated from plasma obtained from dogs with CMT for potential biomarkers and attempt to translate the findings to HBC by ways of comparative oncology. This study identified novel hypermethylated intron regions for This study, including the materials and methods, was reviewed and approved by the Seoul National University Institutional Review Board/Institutional Animal Care and Use Committee , Samsung Medical Center IRB , and the Seoul Metropolitan Government—Seoul National University Boramae Medical Center IRB .Tissue samples used in this study consisted of surgically removed CMT tissue and paired adjacent normal tissue obtained from a variety of dog breeds. Tumors identified as carcinomas were selected, yet the selection was not subtype specific  seeing as the MBD-seq data (accession number PRJNA601533)  did not Human blood samples were obtained from Samsung Medical Center, Seoul. After written informed consent was given, 4–6 mL of human blood was obtained from HBC patients scheduled to undergo surgery and from healthy control subjects that indicated no breast abnormalities upon examination. A detailed roster of the human subjects can be found in g, 18 °C without brake. Plasma was collected from the supernatant and stored at −80 °C until use.Both canine and human plasma samples were separated from the whole blood immediately after blood collection. An equal volume of Ficoll-Paque PLUS  was added to each blood sample and centrifuged for 30 min, at 500× p-value < 0.05 were set as the cutoffs for a significant correlation.We performed an integrative analysis of MBD-seq (accession number PRJNA601533)  and RNA-Genomic DNA was isolated from 25 mg samples of paired CMT and adjacent normal tissues using the DNEasy Blood & Tissue kit  according to the manufacturer’s protocol. The recovered gDNA was quantified using a nanodrop spectrophotometer. Circulating cfDNA was isolated from both human  and canine  plasma samples using the QIAamp Circulating Nucleic Acid Kit  according to the manufacturer’s instructions. In short, 500 uL of plasma was brought up to 1 mL with PBS and lysed with proteinase K and Buffer ACL before being bound, washed, and eluted from QIAamp mini columns on a vacuum manifold. Recovered cfDNA was quantified with a Qubit 3.0 Fluorometer using the Qubit HS dsDNA Assay , according to the manufacturer’s protocol.DNA samples were Bisulfite treated using the EZ DNA Methylation-Lightning kit , according to the manufacturer’s instructions. Bisulfite treated DNA was quantified using the Qubit ssDNA Assay on the Qubit 3.0 Fluorometer . Paired samples were brought to equal concentrations by diluting with water as appropriate before qMSP was performed.ANK2 and EPAS1 targets in both CMT and normal tissue. Bisulfite Sequencing PCR (BSP) was conducted using the BSP primers  according to the manufacturer’s protocol. Fully unmethylated DNA was made by performing PCR with primers that flank the region of interest. The resulting amplimers were thus devoid of any methylation and served as completely unmethylated control template. Human primers were optimized on fully methylated and fully unmethylated human HCT116 DKO DNA .ANK2 and EPAS1 that we identified from the MBD-seq data to their orthologous regions on the human genome (Hg38). BLAST (https://blast.ncbi.nlm.nih.gov) was used to align the nucleotide sequences and determine the amount of sequence homology.Using liftOver , we wereANK2 and EPAS1 intron targets was obtained using Wanderer [ANK2 (202920_at) and EPAS1 (200878_at), and the relapse free survival data of 3951 patients obtained from the GEO database [HBC data for the Wanderer , which udatabase . The Kapdatabase .


Background: The purpose of this study is to estimate the impact on health care costs if United States (US) adults increased their dairy consumption to meet Dietary Guidelines for Americans (DGA) recommendations. Methods: Risk estimates from recent meta-analyses quantifying the association between dairy consumption and health outcomes were combined with the increase in dairy consumption under two scenarios where population mean dairy intakes from the 2015–2016 What We Eat in America were increased to meet the DGA recommendations: (1) according to proportions by type as specified in US Department of Agriculture Food Intake Patterns and (2) assuming the consumption of a single dairy type. The resulting change in risk was combined with published data on annual health care costs to estimate impact on costs. Health care costs were adjusted to account for potential double counting due to overlapping comorbidities of the health outcomes included. Results: Total dairy consumption among adults in the US was 1.49 cup-equivalents per day (c-eq/day), requiring an increase of 1.51 c-eq/day to meet the DGA recommendation. Annual cost savings of $12.5 billion (B) (range of $2.0B to $25.6B) were estimated based on total dairy consumption resulting from a reduction in stroke, hypertension, type 2 diabetes, and colorectal cancer and an increased risk of Parkinson’s disease and prostate cancer. Similar annual cost savings were estimated for an increase in low-fat dairy consumption . Among dairy sub-types, an increase of approximately 0.5 c-eq/day of yogurt consumption alone to help meet the DGA recommendations resulted in the highest annual cost savings of $32.5B (range of $16.5B to $52.8B), mostly driven by a reduction in type 2 diabetes. Conclusions: Adoption of a dietary pattern with increased dairy consumption among adults in the US to meet DGA recommendations has the potential to provide billions of dollars in savings. The 2015–2020 Dietary Guidelines for Americans (DGA) encourages all Americans to consume 3 cup-equivalents (c-eq) of dairy products daily, with most choices fat-free or low-fat, to ensure adequate intakes of key nutrients including calcium, potassium, protein and vitamins A and D provided primarily by dairy  OR “dairy”[All Fields]) AND  OR “chronic disease”[All Fields]) AND  with limits only for human studies published in English. The aim of the literature search was to identify moderate- to high-quality studies for each outcome rather than to conduct a formal assessment of the evidence. Search results were independently screened by two authors (CGS and MMM) to determine whether each identified paper met the following inclusion criteria: (1) meta-analysis published in the past 13 years of prospective cohort studies, (2) conducted in a cohort of healthy adults (18 years and older) at risk for chronic disease, and (3) provided quantitative measures of the association between dairy products and health outcomes. Dairy products were defined to include total dairy, low-fat dairy, high-fat dairy, milk, cheese, and yogurt. Studies reporting intermediate markers of disease were excluded. No a priori selection of health outcomes was included in the search protocol; all outcomes, including adverse or favorable, were eligible for inclusion in the review. A flow chart of the study selection is provided in Based on our review of the evidence on the beneficial effects of dairy consumption on chronic disease outcomes, inverse associations between dairy consumption and heart disease, type 2 diabetes, colorectal cancer, and hip fractures were identified. A review of the literature on the associations between dairy intake and coronary heart disease resulted in largely null findings in dose–response analyses as well as high versus low comparisons, which is consistent with a recent systematic review on the strength of evidence of dairy products and chronic health outcomes . Based oIncreased risks of prostate cancer and Parkinson’s disease with dairy consumption were identified. Specifically, in 2018, the WCRF CUP concluded that there was limited evidence showing that high dairy consumption increases the risk of prostate cancer . This fiSummary relative risk (RR) measures were extracted for the selected health outcomes associated with dairy consumption including a reduced risk of stroke, hypertension, type 2 diabetes, colorectal cancer, and hip fractures and an increased risk of Parkinson’s disease . All assAnnual direct medical costs as well as indirect costs for the selected health outcomes were based on a review of recent literature using data from the American Heart Association (2014–2015) , the Amen = 5017). All analyses to estimate the consumption of dairy products were conducted using Stata® Version 12.1. Mean total dairy intake  among the US adult population was estimated to be 1.49 c-eq/day (Estimates of dairy consumption among adults in the US were based on food consumption records collected in the What We Eat in America (WWEIA) component of the National Health and Nutrition Examination Survey (NHANES) conducted in 2015–2016  , and thec-eq/day . Therefoc-eq/day .Costi = total annual change in costs for selected health outcome;Δi = index for selected health outcome ;RRi, adj = adjusted RR for health outcome (i) (come (i) ;DCcited = change in dairy consumption (g/day) associated with RR for health outcome (i) (Δcome (i) ;Ii = annual indirect costs associated with health outcome (i) (come (i) ;Di = annual direct costs associated with health outcome (i) (come (i) ;DCUS adults = change in dairy consumption (g/day) to meet DGA recommendation of 3 c-eq/day  consumption among adults in the US was assumed to increase to meet the DGA’s recommendation of 3 c-eq/day. For each scenario, the same increases in values corresponding to total dairy, as shown in Scenario 1: Population mean intakes of milk, cheese and yogurt were increased to proportions by type as specified in US Department of Agriculture Food Intake Patterns [Patterns . In thesScenario 2: Intake of each type of dairy product was increased assuming the consumption of only that dairy type. Scenario 2 is an alternate modelling approach designed to illustrate the impact of a single dairy type  on net annual costs if the entire gap in dairy consumption were to be met exclusively through the increased consumption of 1.51 c-eq per day of a single dairy type with the exception of yogurt. Given the current low mean intake of yogurt among the adult US population of 21 g/day , an increase of 1.51 c-eq is more than 15 times the current intake and results in a modelled consumption that is outside the range of intakes reported in many of the prospective studies that provided risk estimates in the selected meta-analyses. For milk and cheese, the modelled increase for Scenario 2 is approximately two times the current intake. Therefore, for yogurt only, the modelled increase in Scenario 2 was selected to be 100 g (~0.4 c-eq) or ~5 times the current intake and is equivalent to the intake of the high-end (90th percentile) consumer in the adult US population. The model was repeated three times, once each for milk, cheese, and yogurt.i = index for selected health outcome ;n = number of health outcomes included for each dairy type;i.Total net changes in annual costs within each dairy product type were estimated by summing costs for all relevant health outcomes after adjustment for overlapping risk factors and co-morbidities:The lower and upper 95% confidence intervals around the RR estimates were included to provide a potential range in costs associated with each health outcome. These lower and upper cost estimates for all health outcomes were also summed, with the caveat that the resulting range no longer reflects a 95% confidence interval but rather a range of the potential annual costs associated with that particular dairy type. Further, annual costs within a health outcome across the three dairy types  cannot be summed to estimate the net changes for total dairy given that risk estimates for each dairy type are summary measures from different individual studies.Under Scenario 1, in which estimated dairy consumption among the US adult population was increased to meet the 3 c-eq/day DGA recommendation in proportions of milk, cheese, and yogurt consistent with USDA Food Intake Patterns, $12.5 billion (B) (range: $2.0B to $25.6B) in annual cost savings was estimated based on a modelled increase in total dairy consumption  resultinIn Scenario 2, where the intake of each dairy type  was increased assuming the consumption of only a single dairy type to meet the DGA recommendation of 3 c-eq/day in the case of milk and cheese and an increase of 100 g of yogurt, the largest estimated annual cost savings was associated with increased yogurt consumption . This savings was based on a reduction of both type 2 diabetes and hip fractures and held constant in the secondary analysis due to the lack of a reported association with adverse outcomes.This study illustrates the significant potential health care cost savings associated with increasing dairy consumption among adults in the US to meet the current DGA recommendations. Our analysis of the 2015–2016 WWEIA, NHANES consumption data indicates that 87.1% of adults consume less than the DGA recommendation of 3 c-eq of dairy per day . Thus, tType 2 diabetes contributed to over half of the overall cost savings from total dairy consumption. While risk reduction estimates per 200 g/day of total dairy for type 2 diabetes were similar or smaller compared to other health outcomes, the large contribution of type 2 diabetes to total savings is driven by the high direct and indirect costs associated with the condition, even after adjusting for overlapping pathways. Reduced risk of type 2 diabetes from a dietary pattern with an additional 100 g/day of yogurt accounted for the largest estimated annual cost savings  . This estimate reflects the high costs associated with type 2 diabetes and a 6% reduction in risk from the consumption of 50 g/day of yogurt. This risk reduction assumes linearity, however, the meta-analysis reported that there is a levelling of benefits after 80–125 g yogurt intake . While tAdverse associations of dairy consumption with prostate cancer and Parkinson’s disease impacted estimated cost savings for total dairy by reducing the total estimate by $3.6B, largely due to costs associated with Parkinson’s disease ($3.4B). However, the estimated mean costs remained an overall savings of $12.5B (range: $2.0B to $25.6B). Cost savings associated with milk consumption were most impacted by the adverse associations with approximately a 50% reduction in mean estimated cost savings when including the adverse outcomes compared to the secondary analysis and a lower range that results in increased costs in both Scenario 1 and 2. There is limited evidence on the potential causal mechanism attributed to dairy consumption for both outcomes as noted in large, systematic reviews ,29,30. TPrevious studies have estimated the potential health care savings associated with dairy consumption ,43. In MWhile the current model is based on an approach and assumptions that are consistent with other economic analyses, there are limitations and uncertainties to consider. The use of observational data to measure the association between dairy consumption and health outcomes will include the potential for residual confounding in the estimates of the RR. However, given the chronic nature of the health outcomes associated with dietary variables including dairy, randomized controlled trials are not realistic or feasible and many of those that have been published are limited to calcium supplementation or reduced fat dairy interventions and tend to be in at-risk populations  with insufficient follow-up for chronic disease outcomes. These characteristics of controlled trials limit the generalizability of these results to the US adult population. To capture the potential uncertainty inherent in observational studies, the lower and upper limits of the 95% confidence interval around the summary risk estimates were included to provide a range of potential cost savings Further, the definition of “total dairy” can vary significantly from study to study included in each meta-analysis. While the inclusion of milk, cheese, and yogurt in the category of total dairy is standard, some definitions also included products such as fermented dairy, ice cream and butter which increased heterogeneity among the studies included in the summary risk estimate and attenuate results observed due to misclassification and measurement bias.Consistent with similar economic analyses, the current model assumes that the relationship between dairy consumption and risk of disease is linear; that is, as consumption increases, risk will change by a set amount, which in turn will have a linear effect on the change in health care costs. Our criterion that preferentially selects meta-analyses that evaluated the dose–response association and provided risk estimates per dose of dairy is a strength of this analysis when compared to a high versus low comparison. This refinement requires more detailed information from each study included in the meta-analysis and helps to reduce the potential misclassification bias that occurs from basing comparisons on extreme quantiles of intake from studies as it is possible that a high consumer in one study population could be classified as a low consumer in another study population. The current model’s stricter inclusion criterion should help to reduce overestimation of effects but, in turn, may both over and underestimate cost savings if the association follows a non-linear dose response and has a threshold level above or below the level at which effects occur. For example, in the association between yogurt and type 2 diabetes, the linear model estimated a 6% reduction per 50 g/day increase in yogurt consumption while the non-linear model reported a 14% risk reduction when comparing 80 g/day to 0 g/day with no further risk reduction observed above 80–125 g/day . In thisThe current model does not account for substitution effects that may occur from increasing dairy consumption. The model assumes adoption of a dietary pattern that reflects a mean per capita increase in dairy intake of 1.51 c-eq/day to meet the 3 c-eq/day DGA recommendation across the US adult population. Each estimated change in annual net costs based on the current model assumes that an individual’s increased dairy consumption would all come from a specified dairy type . Intake of milk, cheese, and yogurt in Scenario 1 assumes that incremental intakes by type result in total intake of each dairy in proportions as specified in USDA Food Intake Patterns while Scenario 2 assumes that the entire dairy gap would be met, or partially met in the case of yogurt, by consuming a particular dairy type. However, given that individuals would be increasing intake of the dairy food group, decreases in the consumption of other food groups would be necessary to maintain caloric intake. Any substitution or replacement in an individual’s diet would most likely have an effect on risk factors, disease pathways and outcomes. The relative risks in the underlying studies included in the meta-analyses selected for inclusion were adjusted for demographic, lifestyle, and dietary factors such as energy intake or intake of particular foods or dietary components . The resulting summary risk estimates used in the current analysis are intended to reflect dietary patterns differing in the amount of dairy consumed independent of the effects of the other measured factors. In the application of risk estimates for a specified increase in a food component, the scenario approach like that conducted in the current analysis assumes that dietary patterns in the modelled population shifts in a similar way to accommodate the increased consumption of dairy. Further, given the mean per capita shift applied to the entire population, the model does not consider the extreme ends of the dairy intake distribution and how that could affect the proportion of adults who would benefit from particular levels of intake increases. There may be adults with a daily intake just below the recommended amount who would benefit from as little as one additional serving daily, while there are also adults whose intake is so low that even an increase of 1.51 servings daily might not be sufficient to reduce the risk of dairy-associated health concerns. While the model could have considered various distributions of intake in the population, without sufficient data to inform which distributions would be appropriate, the inclusion of this element would have added precision to the model without necessarily increasing accuracy.The model inputs are largely mean point estimates. Health care costs and consumption data are often right skewed, which indicates the mean value will be higher than another central tendency estimate such as the median or the geometric mean. Therefore, the use of the mean value for both the consumption data and health care costs data would result in an underestimate of the increase in consumption required to meet the DGA recommendation and a potential overestimate of the costs associated with each health outcomes. Further, indirect costs were not available or not distinguishable in the published literature for colorectal cancer and hip fractures, respectively.The benefits of changes in dietary patterns are typically not immediate, and even more so when considering the chronic health outcomes in this model. Although exact timelines are unknown and may vary by condition, it is likely that there are incremental improvements over time. The current model assumes that any shift in intake has had time to result in the benefits included in the model. In fact, the opposite may be true, in that there may be health economic benefits even among undiagnosed or preclinical populations. Such may be the case for pre-diabetes, which is associated with substantial costs  but is nThe current model does not consider the potential health and economic impact of side-effects relating to consuming additional dairy, particularly for lactose-intolerant individuals. However, potential strategies to cope with dairy intolerance include incorporation of dairy in small portions or selection of yogurt or cheese in place of milk. This model also did not consider the economic costs associated with increasing dairy consumption, in terms of costs for purchasing food, implementing a public health intervention, or macro-level policy implications of shifts in demand for dairy. The current study is theoretical and can be used to illustrate the potential economic impact of any public health intervention focused on increasing the US population’s adherence to the DGA recommendations for dairy consumed as a combination of milk, cheese, and yogurt. The net annual health care cost savings presented in this study would only be realized if interventions aimed at increasing dairy consumption are fully effective and if the observed associations between dairy consumption and health outcomes are true.The current model identifies a potential reduction in dairy-related health care costs and illustrates that a simple, realistic dietary change at the population level consisting of adoption of a dietary pattern with increased daily dairy consumption results in an economic benefit. While there are limitations to the modelling approach and data incorporated that are consistent with other economic analyses, the results of the current study can be used to support efforts to understand the health economic implications of increased dairy consumption and the importance of helping adults in the US meet the daily recommended dairy intakes.


The Veteran Administration (VA) Office of Patient-Centered Care and Cultural Transformation is invested in improving veteran health through a whole-person approach while taking advantage of the electronic resources suite available through the VA. Currently, there is no standardized process to collect and integrate electronic patient-reported outcomes (ePROs) of complementary and integrative health (CIH) into clinical care using a web-based survey platform. This quality improvement project enrolled veterans attending CIH appointments within a VA facility and used web-based technologies to collect ePROs.This study aimed to (1) determine a practical process for collecting ePROs using patient email services and a web-based survey platform and (2) conduct analyses of survey data using repeated measures to estimate the effects of CIH on patient outcomes.In total, 100 veterans from one VA facility, comprising 11 cohorts, agreed to participate. The VA patient email services (Secure Messaging) were used to manually send links to a 16-item web-based survey stored on a secure web-based survey storage platform . Each survey included questions about patient outcomes from CIH programs. Each cohort was sent survey links via Secure Messaging (SM) at 6 time points: weeks 1 through 4, week 8, and week 12. Process evaluation interviews were conducted with five primary care providers to assess barriers and facilitators to using the patient-reported outcome survey in usual care.This quality improvement project demonstrated the usability of SM and Qualtrics for ePRO collection. However, SM for ePROs was labor intensive for providers. Descriptive statistics on health competence , physical and mental health , and stress  indicated that scores did not significantly change over time. Survey response rates varied  across each of the 12 weekly survey periods. In total, 74 of 100 participants provided ≥1 survey, and 90% (66/74) were female. The majority, 62% (33/53) of participants, who reported the use of any CIH modality, reported the use of two or more unique modalities. Primary care providers highlighted specific challenges with SM and offered solutions regarding staff involvement in survey implementation.This quality improvement project informs our understanding of the processes currently available for using SM and web-based data platforms to collect ePROs. The study results indicate that although it is possible to use SM and web-based survey platforms for ePROs, automating scheduled administration will be necessary to reduce provider burden. The lack of significant change in ePROs may be due to standard measures taking a biomedical approach to wellness. Future work should focus on identifying ideal ePRO processes that would include standardized, whole-person measures of wellness. The Veterans Health Administration (VHA) is committed to increasing the use of health information technology (HIT) to promote personalized and patient-driven health services, including complementary and integrative health (CIH) . The inttraditional treatments in combination with complementary treatments [The VHA prioritizes access to CIH ,6. Compleatments . Integraeatments . Recent eatments ,6.Within the VHA, HIT for health care delivery is not only beneficial for providers and their delivery of health care services but is also advantageous for meeting patient-specific needs. Using HIT for health care delivery facilitates timely reporting of outcomes  and eliminates the potential for misplaced documentation. The process empowers patients to make informed health care decisions, improves patient satisfaction, and streamlines organizational processes that, historically, were barriers to the delivery of health care services . ElectroPatient-centered outcome measures for CIH are increasingly important as the use of nontraditional therapies and treatments increases in clinical settings . SeveralThis study aimed to (1) determine a practical process for collecting and integrating ePROs using SM and a web-based survey platform and (2) conduct analyses of pilot survey data using repeated measures to estimate the effects of CIH on patient outcomes. This paper provides lessons learned from the implementation of ePRO survey methods within the VA.This 1-year pilot project engaged veterans attending CIH appointments within one VA facility. We used process documentation, quantitative repeated measures surveys, and qualitative interviews to meet project objectives.This project used a convenience sample. Project team members reached out to local primary care providers  who were known to make referrals to CIH program services at the project site. These PCPs were identified as early adopters in the CIH program. Early adopter PCPs were asked to provide a list of potential participants who participated in at least one CIH program or activity at the project site based on their personal knowledge of veteran wellness activities.Of the responsive providers (n=5), a participant pool of 227 veterans was identified. The project site had a robust integrative health program at the time of participant pool identification. Providers often used referral to the integrative health program as a proxy for the inclusion criteria of the CIH program or activity participation. Additionally, at the time of participant pool identification, providers based their list of potential participants on general knowledge of veterans having used CIH program services in the recent past. Between participant pool identification and completion of recruitment, up to four months had passed, generating a subgroup of participants that were no longer participating in CIH programs during the project. Our final convenience sample of 100 veterans was recruited via telephone to participate in the project based on the following inclusion criteria: (1) participated in at least one CIH program or activity at the project site and (2) had access to SM to complete the web-based surveys.Two electronic messaging platforms for data collection were identified by operational partners and clinical providers: SM, a messaging platform provided to veterans through MyHealtheVet , and ANNIE, a mobile app that allows messaging, alerts, and push notifications, and can send messages to mobile phones that are not smartphones. ANNIE was not accessible for utilization at the time of project implementation. Owing to the unavailability of ANNIE, only the SM web-based platform was used. SM, as a messaging service, requires its own unique set of processes to send individual messages to each veteran in the project. We developed a customized protocol for using SM to collect and integrate ePROs, which was tested in this project.Participants were grouped into cohorts to facilitate data collection. Each cohort was the result of the project team making telephone calls once per week . Participants who agreed to participate were included in that week’s cohort and notified of the intent of the study, number and frequency of assessments, time burden for each assessment, and the need to access their SM account routinely . Thus, a cohort consisted of veterans who received electronic requests for survey completion at the same time points. The 11 cohorts ranged in size from 2 to 21 participants, with more participants in the initial cohorts and fewer in the latter cohorts, with an average cohort size of 9. Process evaluation interviews were conducted with 5 PCPs involved in the SM implementation of the ePRO survey to assess the acceptability, appropriateness, and feasibility of using SM to direct veterans to the web-based ePRO survey in future VA sites.Scales section below as well as items to indicate the types of CIH programs in which the participant was currently participating. Survey data were collected and managed via Qualtrics), a web-based survey platform.The effects of CIH activities may change over time and are often measured at variable time points . As suchProcess evaluation interviews were conducted via telephone. One researcher conducted the interview, and another researcher took extensive notes on this interview, capturing as much verbatim information as possible. Interviews lasted approximately 30 min and focused on capturing stakeholders’ perspectives on the acceptability, appropriateness, and feasibility of using the ePRO survey with veterans receiving CIH services through the SM platform. Our conceptual framework is derived from the categorization of implementation outcomes by Proctor et al , the impParticipants completed a 16-item web-based survey consisting of items that measured health competence on the 2-item Perceived Health Competence Scale (PHCS-2), physical and mental health on the 10-item Patient Reported Outcome Measurement Information System (PROMIS-10), and perceived stress on the 4-item Perceived Stress Scale 4 (PSS-4). These scales were chosen to obtain data across a broad range of patient-reported health outcomes, in which CIH modalities may have an impact. Survey links did not have an expiration time designated so they could be completed as assigned or at any point during the project. Demographic data were extracted from the electronic medical records of all participants.The PHCS-2 is a balanced subscale consisting of 2 questions (1 positively worded and 1 negatively worded) chosen from the larger 8-item PHCS and measures the degree to which an individual feels capable of reasonably managing his or her own health outcomes. The PHCS has previously shown to be a valid and reliable measure of health competence . Total sThe PROMIS-10 short form consists of 10 items that assess the general domains of physical and mental health and functioning. PROMIS was developed and validated by investigators at the National Institutes of Health to provide clinicians and researchers with accessible item banks to measure patient-reported health status ,27. Raw The PSS-4 measures the degree to which situations in one’s life over the past month are appraised as stressful. Items were designed to detect how unpredictable, uncontrollable, and overloaded respondents find their lives. PSS-4 was derived from the longer PSS-14 . As the t tests were used to compare demographics between survey responders and nonresponders. Survey response rates were considered a measure of ePRO collection process feasibility, with a focus on practicality [P value <.05 was used to assess significance. Analyses were performed using SAS software, version 9.4.Data are presented as mean (SD) for continuous data and frequencies and percentages for categorical data. Chi-square tests and two-tailed ticality . Survey Interview notes were analyzed using a directed content analysis approach , throughP<.001) and less likely to be white  but did not differ by gender  or Hispanic ethnicity .Response rates were lowest in week 1 (18%) and generally increased in the subsequent weeks . The highest response rate was in the final week of participation, where 42% of participants responded to the survey. This is commensurate with the established expectations of response rates based on our previous work ,32. The A total of 74 participants responded to at least one of the survey links sent to them via SM, resulting in 175 responses over the data collection period. Of these, 150 (85.7%) were completed for all 16 items from the 3 scales . Completeness was lowest for the PROMIS-10  and highest for the PHCS-2 , although the PSS-4 was similar with a completeness of 168/175 (96.0%). A total of 21 surveys were missing between 1 and 14 items across the 3 scales, with the majority missing just 1 item . There were 2 surveys (1.1%) missing 1 item on the PHCS-2, 16 surveys (9.1%) missing 1 item on the PROMIS-10, and 1 survey (0.57%) missing 1 item on the PSS-4. There was 1 survey missing 2 items on the PROMIS-10 and 4 surveys (2.3%) missing all 16 items from the 3 scales. One additional survey was missing all items for the PROMIS-2, and 2 surveys were missing all items for the PSS-4.Descriptive statistics of the computed scores from each of the scales are presented in Participants also reported their use of CIH modalities during the 12-week period by responding to the question: “Please list the whole health modalities you are currently engaged in. Please check all that apply.” Overall, 53/74 (72%) of respondents reported the use of at least one CIH modality during the 12 weeks. Some participants did not report participating in a CIH program or activity at the time of the project, which may be due to the time lag from participant pool identification and recruitment of individual participants. The reported use of CIH modalities increased over time, with 33/42 (79%) of respondents reporting using at least one modality in week 12, but only 7/18 (39%) of respondents reported using any modality in week 1. One participant reported using 12 different modalities in week 12. Over the 12 weeks, the majority, 33/53 (62%), of those that reported any modality, reported the use of two or more unique modalities . Interview data indicated that 4 main themes emerged from our discussions with the PCPs involved in the ePRO implementation: (1) SM can be burdensome for providers (acceptability); (2) PCPs delegate SM duties to their staff, such as registered nurses and licensed practical nurses, to make this process more feasible; (3) staff within the primary care clinic are more appropriate for being involved in the ePRO survey implementation; (4) veteran patients are often challenged with using both SM and the ePRO survey, therefore making this implementation less feasible for them. Using electronic tools for survey administration and dissemination, such as web-based survey platforms, secure email, and text messaging, has the potential to enhance the collection of ePROs by increasing the efficiency of reaching larger populations and collecting data at multiple time points without having to redirect valuable clinical time for data collection. We sought to determine the feasibility of using an SM platform tied to a web-based survey administration via Qualtrics for the collection of ePROs at 6 time points over 12 weeks within the context of VA regulations and systems. Feasibility was assessed qualitatively and quantitatively within the implementation outcomes framework using process evaluation interviews among stakeholders, the duration of time to completion, completeness of responses, and response rates. Overall, of the 100 veterans recruited to participate, 74% (74/100) of participants responded to at least one survey, with 53% (53/100) responding to more than one. However, only 1 participant responded to 5 of the 6 surveys, and only 2 responded to all 6. We learned that, whereas longitudinal ePRO data collection is possible, it is likely that participants will not respond to all surveys requested.Weekly response rates  increaseWe found that most surveys  were complete for all items from the 3 scales, and of those missing items, the majority  were only missing 1 item. Although the degree of missing data was small, we learned that this could inform the sample size needed for future research studies. We did not observe a significant change in the computed scores from the scales over the 12 weeks . This maexpire would help to reduce overlap and prevent late responses. Secure messages are available in the MyHealtheVet portal, requiring participants to access the portal to see their messages. Participants who did not access the portal regularly did not receive their messages in a timely manner. In addition, the staff time required to send SM was burdensome. We learned the burden, due to the constant follow-up by the staff to ensure that messages were being received, was a major limitation. The staff had to keep track of when the last survey link was sent using a customized Microsoft Access database and manually send the next link by copying/pasting the Qualtrics auto-generated link into the SM.The small number of participants may also have contributed to the inability to detect any change over time. Only 53 participants reported data at more than one time point. Allowing survey links to It is important to recognize the unique distribution of sex in this participant group. Our group overwhelmingly comprised female veterans. The national veteran population is 10% female . This biThis quality improvement project has limitations. First, using a convenience sample may not provide the representativeness that a random sample may provide. Our time from participant pool identification to recruitment was extended, potentially missing opportunities to engage participants. A timelier process for reaching out to the participant pool may have provided a more robust engagement. The personal connection between study teams and participants tends to improve responsiveness ,38. PartProject findings and lessons learned can inform future research. Future studies should explore the means of dissemination of survey requests that have the capacity for automation to reduce the potential for human and technical errors and reduce workflow burden, which are more efficient for both the staff and participants. In addition, the system should be able to contact participants using their preferred means of communication, whether that is an email address or a text message. Future projects should also examine the optimal spacing of ePRO longitudinal data collection to optimize sensitivity to measures.We demonstrated the feasibility of using SM for ePRO data collection among veterans who have received CIH services; although it is possible to use SM for ePROs, in the system’s current state, it is not practical. Lack of automation, workflow burden, and potential for human error make SM a cumbersome system to use when attempting to collect repeated measures from large participant cohorts. Systems that offer customizable features to automate administration on schedule are needed to reduce the provider workflow burden and potential for human error. These results can help inform future studies, including sample size considerations, best practices for workflow and automation, and ideal characteristics for messaging systems.


IGJ and immunoglobulin heavy (IGH) chain-encoding RNAs. The association with prognosis was investigated and public data sets were used to corroborate the findings. Except for IGHE and IGHD, mRNAs encoding heavy chains were generally detected at substantial levels and correlated with other immune-related genes. High IGHG1 mRNA was associated with factors related to poor prognosis such as estrogen receptor negativity, HER2 amplification, and high grade, whereas high IGHA2 mRNA levels were primarily associated with lower age at diagnosis. High IGHA2 and IGJ mRNA levels were associated with a more favorable prognosis both in univariable and multivariable Cox models. When adjusting for other prognostic factors, high IGHG1 mRNA levels were positively associated with improved prognosis. To our knowledge, these results are the first to demonstrate that expression of individual Ig class types has prognostic implications in breast cancer.The extent and composition of the immune response in a breast cancer is one important prognostic factor for the disease. The aim of the current work was to refine the analysis of the humoral component of an immune response in breast tumors by quantifying mRNA expression of different immunoglobulin classes and study their association with prognosis. We used RNA-Seq data from two local population-based breast cancer cohorts to determine the expression of  HER2 amplification, and differences in proliferation rate which together provide the basis for the classification of breast cancer in different subgroups. The subgrouping has become more elaborate with the use of global mRNA expression analysis that has led to the identification of at least five subtypes of breast cancer—basal-like, HER2-enriched, luminal A, luminal B, and normal-like tumors3. In addition, differences in the genomic stability, somatic driver mutations, and rearrangement patterns, show that different breast cancers indeed represent fundamentally differential biological subsets4. The heterogeneity has important implications for prognosis and for choice of adjuvant systemic therapy. For instance, patients with ER-positive tumors are advocated endocrine adjuvant therapy, whereas HER2-amplified tumors can be targeted with antibody-based therapy. On the other hand, for basal-like tumors that are typically negative for both ER expression and HER2 amplification, only chemotherapy is available today.Breast cancer is a heterogeneous disease, which is illustrated by differential expression of estrogen receptor (ER) and progesterone receptor (PR), occasional prevalence of 16 or certain types of macrophages20, in general or when restricted to specific breast cancer subsets, have been shown to be important for the prognosis of the disease. Immune metagenes have been discovered that may be prognostic in breast cancer in general or in more limited subgroups27. Taken together, there is an abundance of studies indicating that aspects of an immune response contain prognostic information. In addition, immune checkpoint inhibition has been demonstrated to have a therapeutic potential in breast cancer, particularly in triple-negative breast cancer, along with other types of malignancies29.Other factors also contribute to heterogeneity. These include the extent of immune response and presence of specific immune cells in and around the tumor. The amount of tumor-infiltrating lymphocytesOne important component of an adaptive immune response is the humoral immune system whose effector molecules are constituted by antibodies. There are several classes of antibodies, including IgM, IgD, IgG1–4, IgE, and IgA1–2. An antibody is built by two identical heavy chains (immunoglobulin heavy (IGH)) and two identical light chains. The heavy chain determines the class of the antibody. During activation of the adaptive immune system B cells, which produce antibodies that recognize relevant antigens, undergo class switch by DNA recombination. Prior to class switch, the B-cell normally expresses both IgD and IgM, whereas following class switch a B-cell produces only one type of antibody of an IgM, IgG, IgE, or IgA class. Antibodies of different classes have different functions. For instance, IgAs are dimers predominantly produced in the mucosa of organs that are in contact with the exterior, such as the airways and the gastrointestinal system, but also in the lactating breast. IgE is mainly produced during parasite infections and can also be involved in an allergic reaction. IgD is a membrane bound B-cell receptor together with IgM. IgM is expressed as a pentamer early during a primary immune response and is a potent activator of the complement system. Both IgA and IgM depend on IGJ (joining chain of multimeric IgA and IgM) to assemble as a functional multimer. The role of the different Ig classes in a tumor immune response is largely unknown but they have so far been thought to be of less importance compared to T-cell-mediated immunity.32, an on-going population-based study  to which we invite all new breast cancer patients in Southern Sweden. If feasible, and as long as the diagnostic evaluation is not jeopardized, fresh biopsies from the primary tumors are collected and subjected to RNA-Seq analysis. Here, we have analyzed the expression of individual IGHs and IGJ from two cohorts of breast cancer with available RNA-Seq data and show that they have different associations with tumor characteristics and that at least some of them appear to have additional prognostic value, independently of the markers used in the clinic today.RNA-Seq methodology enables a detailed analysis of RNAs expressed in a sample. Thus, it is possible to estimate the expression of RNA encoding each class of Ig heavy chains. We have launched the Sweden Cancerome Analysis Network—Breast (SCAN-B) projectIGJ-encoding mRNAs were compared in the SCAN-B cohort , retrieved from the Molecular Signature Database (http://software.broadinstitute.org/gsea/msigdb)34. Sets with a p value < 10–15 for either IGHG1- or IGHA2-correlating genes were compared for enrichment . The multivariable modeling demonstrated that, when adjusted for other prognostic markers, IGHG1 mRNA was also associated with better survival . Lastly, both IGJ and IGHM mRNAs were also associated with improved survival in both models. The same models were applied for IGJ in the Metabric cohort which revealed a similar association with prognosis in model 1  and a tendency to an association in model 2 .The Cox modeling revealed an independent association of IGJ mRNA was associated with improved outcome in all data sets. For IGHA2 and IGHM mRNA the pattern was the same in the KM-plotter data set and a nonsignificant tendency to an association was seen in the Cohort270. IGHG1 mRNA expression was, as for overall survival, not associated with recurrence-free survival.We next analyzed the association of the mRNA expression levels with recurrence-free survival for the cohorts where these data are available Fig. . IGJ mRNIGJ mRNA was associated with improved survival in all models and the same was true for IGHA2 mRNA, except when stratifying for grade where the p value was 0.098. IGHG1 mRNA was not associated with prognosis in any of the models.There are too few subjects in Cohort270 for multivariable modeling and there are no data on recurrence in the SCAN-B cohort. We therefore performed Cox modeling with the Cohort270 stratifying for each of the factors age, node status, tumor size, ER status, HER2 status, and grade separately Table . IGJ mRNIGHA2 and IGJ mRNA expression were positively associated with prognosis in the partially overlapping triple-negative  and basal-like  breast cancers, whereas neither IGHM nor IGHG1 mRNA was significantly associated with prognosis in these groups. However, there was a tendency to an association of IGHG1 mRNA and given the limited size of the cohort a prognostic value of IGHG1 mRNA in these subgroups cannot be excluded. In the ER-positive HER2-negative subgroups none of the IGH mRNAs were significantly associated with prognosis but if the analysis was restricted to patients with age at diagnosis below the median all four  mRNAs were associated with improved prognosis. In the generally ER-positive Luminal A and B subtypes the association was not significant with the exception for IGHG1 in Luminal A cancers restricted to patients with age at diagnosis below 65 years .The size of the SCAN-B cohort allows for analysis of breast cancer subgroups Table . The anaIGHA mRNA levels were not as strongly associated with immune cell metagenes as most other Ig heavy chain species  are stored at GEO (GSE81538).From September 2007 all breast cancer patients operated at Skåne University Hospital in Malmö were asked for consent for molecular analyses of their tumor. Fresh pieces from the primary tumors were stored at −80 °C prior to RNA extraction and RNA-Seq analysis32 , which largely uses the same procedures as described for Cohort270 above. From patients operated between late 2010 and early 2015, 3271 cases were identified that were primary breast cancer without diagnosis of disseminated disease. For this population-based cohort (SCAN-B) overall survival was used as end point. Clinical data were obtained from the Swedish Breast Cancer Register and overall survival data from the Swedish Population Register31. The molecular and clinical data are stored at GEO (GSE96058).Starting September 2010 all newly diagnosed breast cancer patients in the South Swedish Health Care Region were asked to participate in the SCAN-B projectThe two cohorts are summarized in Table 45, Tophat246, Cufflinks48, and hg19 reference genome annotation as described30.Following surgery the specimen was transported on ice to the pathology unit, where a piece was excised from the tumor and immediately put in −80 °C (Cohort270) or RNAlater (SCAN-B). This was only done when the excision with certainty would not influence the diagnostic work. Thus, too small tumors and tumors with too diffuse borders were excluded which also ensures that the excised piece only contains material from within the tumor. RNA was extracted using the AllPrep DNA/RNA Kit (Qiagen) and sequencing libraries were prepared using 1 µg RNA as a target amount. The libraries were sequenced on an IlluminaHiSeq2000 or NextSeq500 and the reads were processed with an implemented Tuxedo pipeline utilizing Bowtie249 package in R, chromosome positions of exons within the IGH locus were retrieved from Ensembl. Reads aligned to the locus were thereafter retrieved from BAM files using the RSamtools and GenomicRanges50 packages in R. Only reads with mapping quality above 10 were used for subsequent analysis. Based on the CIGAR parameter the number of bases from a read that had been aligned to an exon was allocated to the specific exon. The number of bases allocated to each exon for the same IGH class type was summed and divided by the sum of the length of the exons (in bases) and the number of reads from the library that had been aligned to the genome. The number was multiplied with 106 and following addition of 1 the value was log2-transformed. We have thus obtained a number representing the log2 of counts per base per million reads. The same procedure was done for the IGJ locus as an internal control to compare the expression estimate from our method with the Tuxedo pipeline expression estimate. The IGJ locus is useful as a control since it is included as a target transcript in our Tuxedo pipeline, whereas the IGH class is not. An almost perfect correlation (R = 0.99) was obtained for the expression levels of IGJ using our method and the log2 FPKM obtained with the Tuxedo pipeline.Using the biomaRtFor the SCAN-B cohort the piece of the tumor that was used for RNA extraction was also used for construction of a tissue microarray. For each tumor a pathologist analyzes a hematoxylin–eosin-stained tissue microarray section for estimation of the percentage of invasive cancer, in situ cancer, fat tissue, stroma, lymphocytes, and normal breast tissue. These data were obtained for 2513 of the 3271 tumors in the cohort.t-tests comparing levels between different groups. Age was binned in five year intervals. In multivariable Cox models ER status, HER2 amplification, grade, node positivity, tumor size, and Ig mRNA expression were included as variables, whereas stratification was made for age, PAM50 subgroups, chemotherapy, and cohort (Metabric).All statistical analyses were done with R (version 3.6.1). Cox proportional hazards modeling was done using the coxph function. For multivariable analyses subjects with missing data were excluded. ER status (positive versus negative), HER2 amplification (positive versus negative), node positivity (>1 node versus no positive node), and tumor size (>20 versus ≤20 mm) were used as categorical values. ER and PR status were considered positive if there were more than 10% positive cells which is the standard definition for Swedish clinical practice and therefore is uniformly registered in clinical records. Grade was dichotomized (grade 3 versus grades 1 and 2). The Ig mRNA expression data was either dichotomized on the median for Kaplan–Meier curves and for analysis of association with clinical variables using Fisher or chi-squared tests or used as continuous variables in Cox proportional hazard models and in 33 were done using the MCPcounter package in R applying the MCPcounter.estimate function to all samples.Calculations with the microenvironment cell population counterhttp://www.geneontology.org/) were retrieved from the Molecular Signature Database, using version 7.0 (http://software.broadinstitute.org/gsea/msigdb)34. The enrichment of gene sets was analyzed with Fisher’s test.For gene set enrichment analysis, Gene Ontology Biological Process sets (Further information on research design is available in the Reporting Summary