Immunophenotypically defined stem cell subsets in paediatric AML are highly heterogeneous and demonstrate differences in BCL-2 expression by cytogenetic subgroups.

In adult acute myeloid leukaemia (AML), immunophenotypic differences enable discrimination of leukaemic stem cells (LSCs) from healthy haematopoietic stem cells (HSCs). However, immunophenotypic stem cell characteristics are less explored in paediatric AML. Employing a 15-colour flow cytometry assay, we analysed the expression of eight aberrant surface markers together with BCL-2 on CD34+ CD38- bone marrow stem cells from 38 paediatric AML patients and seven non-leukaemic, age-matched controls. Furthermore, clonality was investigated by genetic analyses of sorted immunophenotypically abnormal stem cells from six patients. A total of 50 aberrant marker positive (non-HSC-like) subsets with 41 different immunophenotypic profiles were detected. CD123, CLEC12A, and IL1RAP were the most frequently expressed markers. IL1RAP, CD93, and CD25 expression were not restricted to stem cells harbouring leukaemia-associated mutations. Differential BCL-2 expression was found among defined cytogenetic subgroups. Interestingly, only immunophenotypically abnormal non-HSC-like subsets demonstrated BCL-2 overexpression. Collectively, we observed pronounced immunophenotypic heterogeneity within the stem cell compartment of paediatric AML patients. Additionally, certain aberrant markers used in adults seemed to be ineligible for detection of leukaemia-representing stem cells in paediatric patients implying that inference from adult studies must be done with caution.

Direct costs of antineoplastic and supportive treatment for progressive multiple myeloma in a tax-based health system.

Aim: To estimate current real-world costs of drugs and supportive care for the treatment of multiple myeloma in a tax-based health system. Methods: Forty-one patients were included from a personalized medicine study (2016-2019). Detailed information was collected from patient journals and hospital registries to estimate the total and mean costs using inverse probability weighting of censored data. Results: Total observed (censored) costs for the 41 patients was €8.84 million during 125 treatment years, with antineoplastic drugs as the main cost driver (€5.6 million). Individual costs showed large variations. Mean 3-year cost per patient from first progression was €182,103 (€131,800-232,405). Conclusion: Prediction of real-world costs is hindered by the availability of detailed costing data. Micro-costing analyses are needed for budgeting and real-world evaluation of cost-effectiveness.

Lay abstract In recent years, there has been a dramatic improvement in the treatment of multiple myeloma due to the introduction of new drugs. These drugs have significantly increased survival but have also had an immense impact on healthcare budgets. In this study, we used detailed treatment information for multiple myeloma patients in combination with billing data from the hospital pharmacy at a Danish hospital to calculate individual cost histories for both drugs and supportive care. Using these data, we estimated the mean 3-year cost of a multiple myeloma patient to be €182.103, but we also found large variation between patients, causing an uncertainty of €50.000 in either direction. We believe that detailed costing studies, similar to the present one, are necessary for evaluation of cost-effectiveness of drugs in clinical practice.

One size fits all? Direct evidence for the heterogeneity of genetic drift throughout the genome.

Effective population size (Ne) is a central parameter in population and conservation genetics. It measures the magnitude of genetic drift, rates of accumulation of inbreeding in a population, and it conditions the efficacy of selection. It is often assumed that a single Ne can account for the evolution of genomes. However, recent work provides indirect evidence for heterogeneity in Ne throughout the genome. We study this by examining genome-wide diversity in the Danish Holstein cattle breed. Using the differences in allele frequencies over a single generation, we directly estimated Ne among autosomes and smaller windows within autosomes. We found statistically significant variation in Ne at both scales. However, no correlation was found between the detected regional variability in Ne, and proxies for the intensity of linked selection (local recombination rate, gene density), or the presence of either past strong selection or current artificial selection on traits of economic value. Our findings call for further caution regarding the wide applicability of the Ne concept for understanding quantitatively processes such as genetic drift and accumulation of consanguinity in both natural and managed populations.

Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip.

BACKGROUND: Imputation of genotypes from low-density to higher density chips is a cost-effective method to obtain high-density genotypes for many animals, based on genotypes of only a relatively small subset of animals (reference population) on the high-density chip. Several factors influence the accuracy of imputation and our objective was to investigate the effects of the size of the reference population used for imputation and of the imputation method used and its parameters. Imputation of genotypes was carried out from 50,000 (moderate-density) to 777,000 (high-density) SNPs (single nucleotide polymorphisms). METHODS: The effect of reference population size was studied in two datasets: one with 548 and one with 1289 Holstein animals, genotyped with the Illumina BovineHD chip (777 k SNPs). A third dataset included the 548 animals genotyped with the 777 k SNP chip and 2200 animals genotyped with the Illumina BovineSNP50 chip. In each dataset, 60 animals were chosen as validation animals, for which all high-density genotypes were masked, except for the Illumina BovineSNP50 markers. Imputation was studied in a subset of six chromosomes, using the imputation software programs Beagle and DAGPHASE. RESULTS: Imputation with DAGPHASE and Beagle resulted in 1.91% and 0.87% allelic imputation error rates in the dataset with 548 high-density genotypes, when scale and shift parameters were 2.0 and 0.1, and 1.0 and 0.0, respectively. When Beagle was used alone, the imputation error rate was 0.67%. If the information obtained by Beagle was subsequently used in DAGPHASE, imputation error rates were slightly higher (0.71%). When 2200 moderate-density genotypes were added and Beagle was used alone, imputation error rates were slightly lower (0.64%). The least imputation errors were obtained with Beagle in the reference set with 1289 high-density genotypes (0.41%). CONCLUSIONS: For imputation of genotypes from the 50 k to the 777 k SNP chip, Beagle gave the lowest allelic imputation error rates. Imputation error rates decreased with increasing size of the reference population. For applications for which computing time is limiting, DAGPHASE using information from Beagle can be considered as an alternative, since it reduces computation time and increases imputation error rates only slightly.

Trends and drivers of pharmaceutical expenditures from systemic anti-cancer therapy.

Expenditures on medicine for systemic anti-cancer therapy (SACT) have seen large increases in recent years. The characterization of patients with high SACT costs is crucial to identify cost-driving factors, but little is known about the distribution of expenditures at the patient-level. We priced 260,834 registrations of SACT for 12,589 patients from 2008 to 2019 by combining them with product-level billings of EUR 142.1 million. Based on this, we defined high-cost patients as the 2.5% most expensive by accumulated SACT expenditures. We found that high-cost patients accounted for 28.8% of the total SACT expenditures and were observed across all major cancer groups except for pancreatic cancer. The risk of becoming a high-cost patient was increased for younger age groups, i.e., 18-44 and 45-64 years, for patients with BMI ≥ 25, and for patients with multiple cancer diagnoses, while no alteration of risk was observed due to comorbidities or sex. Changes in the characteristics of high-cost patients during the study period were found with an increased risk of becoming high-cost in later years for elderly patients and patients with lung cancer and a decreased risk for breast cancer patients.

Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer: Comparing Five Machine Learning Approaches.

PURPOSE: Administering systemic anticancer treatment (SACT) to patients near death can negatively affect their health-related quality of life. Late SACT administrations should be avoided in these cases. Machine learning techniques could be used to build decision support tools leveraging registry data for clinicians to limit late SACT administration. MATERIALS AND METHODS: Patients with advanced lung cancer who were treated at the Department of Oncology, Aalborg University Hospital and died between 2010 and 2019 were included (N = 2,368). Diagnoses, treatments, biochemical data, and histopathologic results were used to train predictive models of 30-day mortality using logistic regression with elastic net penalty, random forest, gradient tree boosting, multilayer perceptron, and long short-term memory network. The importance of the variables and the clinical utility of the models were evaluated. RESULTS: The random forest and gradient tree boosting models outperformed other models, whereas the artificial neural network-based models underperformed. Adding summary variables had a modest effect on performance with an increase in average precision from 0.500 to 0.505 and from 0.498 to 0.509 for the gradient tree boosting and random forest models, respectively. Biochemical results alone contained most of the information with a limited degradation of the performances when fitting models with only these variables. The utility analysis showed that by applying a simple threshold to the predicted risk of 30-day mortality, 40% of late SACT administrations could have been prevented at the cost of 2% of patients stopping their treatment 90 days before death. CONCLUSION: This study demonstrates the potential of a decision support tool to limit late SACT administration in patients with cancer. Further work is warranted to refine the model, build an easy-to-use prototype, and conduct a prospective validation study.

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark.

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort.

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals.

Stature is affected by many polymorphisms of small effect in humans 1 . In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10-8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP-seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.

Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle.

The 1000 bull genomes project supports the goal of accelerating the rates of genetic gain in domestic cattle while at the same time considering animal health and welfare by providing the annotated sequence variants and genotypes of key ancestor bulls. In the first phase of the 1000 bull genomes project, we sequenced the whole genomes of 234 cattle to an average of 8.3-fold coverage. This sequencing includes data for 129 individuals from the global Holstein-Friesian population, 43 individuals from the Fleckvieh breed and 15 individuals from the Jersey breed. We identified a total of 28.3 million variants, with an average of 1.44 heterozygous sites per kilobase for each individual. We demonstrate the use of this database in identifying a recessive mutation underlying embryonic death and a dominant mutation underlying lethal chrondrodysplasia. We also performed genome-wide association studies for milk production and curly coat, using imputed sequence variants, and identified variants associated with these traits in cattle.