Preventive drugs in the last year of life of older adults with cancer: Is there room for deprescribing?

BACKGROUND: The continuation of preventive drugs among older patients with advanced cancer has come under scrutiny because these drugs are unlikely to achieve their clinical benefit during the patients' remaining lifespan. METHODS: A nationwide cohort study of older adults (those aged ≥65 years) with solid tumors who died between 2007 and 2013 was performed in Sweden, using routinely collected data with record linkage. The authors calculated the monthly use and cost of preventive drugs throughout the last year before the patients' death. RESULTS: Among 151,201 older persons who died with cancer (mean age, 81.3 years [standard deviation, 8.1 years]), the average number of drugs increased from 6.9 to 10.1 over the course of the last year before death. Preventive drugs frequently were continued until the final month of life, including antihypertensives, platelet aggregation inhibitors, anticoagulants, statins, and oral antidiabetics. Median drug costs amounted to $1482 (interquartile range [IQR],$700-$2896]) per person, including$213 (IQR, $77-$490) for preventive therapies. Compared with older adults who died with lung cancer (median drug cost, $205; IQR,$61-$523), costs for preventive drugs were higher among older adults who died with pancreatic cancer (adjusted median difference,$13; 95% confidence interval, $5-$22) or gynecological cancers (adjusted median difference, $27; 95$18-$36). There was no decrease noted with regard to the cost of preventive drugs throughout the last year of life. CONCLUSIONS: Preventive drugs commonly are prescribed during the last year of life among older adults with cancer, and often are continued until the final weeks before death. Adequate deprescribing strategies are warranted to reduce the burden of drugs with limited clinical benefit near the end of life

Identifying hypermethylated CpG islands using a quantile regression model

<p>Abstract</p> <p>Background</p> <p>DNA methylation has been shown to play an important role in the silencing of tumor suppressor genes in various tumor types. In order to have a system-wide understanding of the methylation changes that occur in tumors, we have developed a differential methylation hybridization (DMH) protocol that can simultaneously assay the methylation status of all known CpG islands (CGIs) using microarray technologies. A large percentage of signals obtained from microarrays can be attributed to various measurable and unmeasurable confounding factors unrelated to the biological question at hand. In order to correct the bias due to noise, we first implemented a quantile regression model, with a quantile level equal to 75%, to identify hypermethylated CGIs in an earlier work. As a proof of concept, we applied this model to methylation microarray data generated from breast cancer cell lines. However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs. In this paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes.</p> <p>Results</p> <p>We introduce three statistical measurements to compare the performance of the proposed quantile regression model at different quantile levels (95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%), using known methylated genes and unmethylated housekeeping genes reported in breast cancer cell lines and ovarian cancer patients. Our results show that the quantile levels ranging from 80% to 90% are better at identifying known methylated and unmethylated genes.</p> <p>Conclusions</p> <p>In this paper, we propose to use a quantile regression model to identify hypermethylated CGIs by incorporating probe effects to account for noise due to unmeasurable factors. Our model can efficiently identify hypermethylated CGIs in both breast and ovarian cancer data.</p

Preprocessing differential methylation hybridization microarray data

<p>Abstract</p> <p>Background</p> <p>DNA methylation plays a very important role in the silencing of tumor suppressor genes in various tumor types. In order to gain a genome-wide understanding of how changes in methylation affect tumor growth, the differential methylation hybridization (DMH) protocol has been developed and large amounts of DMH microarray data have been generated. However, it is still unclear how to preprocess this type of microarray data and how different background correction and normalization methods used for two-color gene expression arrays perform for the methylation microarray data. In this paper, we demonstrate our discovery of a set of internal control probes that have log ratios (M) theoretically equal to zero according to this DMH protocol. With the aid of this set of control probes, we propose two LOESS (or LOWESS, locally weighted scatter-plot smoothing) normalization methods that are novel and unique for DMH microarray data. Combining with other normalization methods (global LOESS and no normalization), we compare four normalization methods. In addition, we compare five different background correction methods.</p> <p>Results</p> <p>We study 20 different preprocessing methods, which are the combination of five background correction methods and four normalization methods. In order to compare these 20 methods, we evaluate their performance of identifying known methylated and un-methylated housekeeping genes based on two statistics. Comparison details are illustrated using breast cancer cell line and ovarian cancer patient methylation microarray data. Our comparison results show that different background correction methods perform similarly; however, four normalization methods perform very differently. In particular, all three different LOESS normalization methods perform better than the one without any normalization.</p> <p>Conclusions</p> <p>It is necessary to do within-array normalization, and the two LOESS normalization methods based on specific DMH internal control probes produce more stable and relatively better results than the global LOESS normalization method.</p

Assessing agreement between malaria slide density readings

BACKGROUND: Several criteria have been used to assess agreement between replicate slide readings of malaria parasite density. Such criteria may be based on percent difference, or absolute difference, or a combination. Neither the rationale for choosing between these types of criteria, nor that for choosing the magnitude of difference which defines acceptable agreement, are clear. The current paper seeks a procedure which avoids the disadvantages of these current options and whose parameter values are more clearly justified. METHODS AND RESULTS: Variation of parasite density within a slide is expected, even when it has been prepared from a homogeneous sample. This places lower limits on sensitivity and observer agreement, quantified by the Poisson distribution. This means that, if a criterion of fixed percent difference criterion is used for satisfactory agreement, the number of discrepant readings is over-estimated at low parasite densities. With a criterion of fixed absolute difference, the same happens at high parasite densities. For an ideal slide, following the Poisson distribution, a criterion based on a constant difference in square root counts would apply for all densities. This can be back-transformed to a difference in absolute counts, which, as expected, gives a wider range of acceptable agreement at higher average densities. In an example dataset from Tanzania, observed differences in square root counts correspond to a 95% limits of agreement of -2,800 and +2,500 parasites/microl at average density of 2,000 parasites/microl, and -6,200 and +5,700 parasites/microl at 10,000 parasites/microl. However, there were more outliers beyond those ranges at higher densities, meaning that actual coverage of these ranges was not a constant 95%, but decreased with density. In a second study, a trial of microscopist training, the corresponding ranges of agreement are wider and asymmetrical: -8,600 to +5,200/microl, and -19,200 to +11,700/microl, respectively. By comparison, the optimal limits of agreement, corresponding to Poisson variation, are +/- 780 and +/- 1,800 parasites/microl, respectively. The focus of this approach on the volume of blood read leads to other conclusions. For example, no matter how large a volume of blood is read, some densities are too low to be reliably detected, which in turn means that disagreements on slide positivity may simply result from within-slide variation, rather than reading errors. CONCLUSIONS: The proposed method defines limits of acceptable agreement in a way which allows for the natural increase in variability with parasite density. This includes defining the levels of between-reader variability, which are consistent with random variation: disagreements within these limits should not trigger additional readings. This approach merits investigation in other settings, in order to determine both the extent of its applicability, and appropriate numerical values for limits of agreement

Does Reduced IGF-1R Signaling in Igf1r+/− Mice Alter Aging?

Mutations in insulin/IGF-1 signaling pathway have been shown to lead to increased longevity in various invertebrate models. Therefore, the effect of the haplo- insufficiency of the IGF-1 receptor (Igf1r+/−) on longevity/aging was evaluated in C57Bl/6 mice using rigorous criteria where lifespan and end-of-life pathology were measured under optimal husbandry conditions using large sample sizes. Igf1r+/− mice exhibited reductions in IGF-1 receptor levels and the activation of Akt by IGF-1, with no compensatory increases in serum IGF-1 or tissue IGF-1 mRNA levels, indicating that the Igf1r+/− mice show reduced IGF-1 signaling. Aged male, but not female Igf1r+/− mice were glucose intolerant, and both genders developed insulin resistance as they aged. Female, but not male Igf1r+/− mice survived longer than wild type mice after lethal paraquat and diquat exposure, and female Igf1r+/− mice also exhibited less diquat-induced liver damage. However, no significant difference between the lifespans of the male Igf1r+/− and wild type mice was observed; and the mean lifespan of the Igf1r+/− females was increased only slightly (less than 5%) compared to wild type mice. A comprehensive pathological analysis showed no significant difference in end-of-life pathological lesions between the Igf1r+/− and wild type mice. These data show that the Igf1r+/− mouse is not a model of increased longevity and delayed aging as predicted by invertebrate models with mutations in the insulin/IGF-1 signaling pathway

Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression