MOTIVATION: Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis. RESULTS: We introduce a novel Segmental Maximum A Posteriori approach, SMAP, for DNA copy number profiling. Our method is based on discrete-index Hidden Markov Modeling and incorporates genomic distance and overlap between clones. We exploit a priori information through user-controllable parameterization that enables the identification of copy number deviations of various lengths and amplitudes. The model parameters may be inferred at a genome-wide scale to avoid overfitting of model parameters often resulting from chromosome-wise model inference. We report superior performances of SMAP on synthetic data when compared with two recent methods. When applied on our new experimental data, SMAP readily recognizes already known genetic aberrations including both large-scale regions with aberrant DNA copy number and changes affecting only single features on the array. We highlight the differences between the prediction of SMAP and the compared methods and show that SMAP accurately determines copy number changes and benefits from overlap consideration.
Programmed cell death ligand 1 (PD-L1) expression within the same lung cancer tissue is variable. In this study we evaluated if the PD-L1 expression on small biopsy specimens represent the PD-L1 status of the corresponding resection specimen. Our results indicate a relative good agreement between biopsy and surgical specimens, with a discordance in approximately 10% of the cases. Background: The immunohistochemical analysis of programmed cell death ligand 1 (PD-L1) expression in tumor tissue of non-small-cell lung cancer patients has now been integrated in the diagnostic workup. Analysis is commonly done on small tissue biopsy samples representing a minimal fraction of the whole tumor. The aim of the study was to evaluate the correlation of PD-L1 expression on biopsy specimens with corresponding resection specimens. Materials and Methods: In total, 58 consecutive cases with preoperative biopsy and resected tumor specimens were selected. From each resection specimen 2 tumor cores were compiled into a tissue microarray (TMA). Immunohistochemical staining with the antibody SP263 was performed on biopsy specimens, resection specimens (whole sections), as well as on the TMA. Results: The proportion of PD-L1-positive stainings were comparable between the resection specimens (48% and 19%), the biopsies (43% and 17%), and the TMAs (47% and 14%), using cutoffs of 1% and 50%, respectively (P > .39 all comparisons). When the resection specimens were considered as reference, PD-L1 status differed in 16%/5% for biopsies and in 9%/9% for TMAs (1%/50% cutoff). The sensitivity of the biopsy analysis was 79%/82% and the specificity was 90%/98% at the 1%/50% cutoff. The Cohens kappa value for the agreement between biopsy and tumor. was 0.70 at the 1% cutoff and 0.83 at the 50% cutoff. Conclusion: The results indicate a moderate concordance between the analysis of biopsy and whole tumor tissue, resulting in misclassification of samples in particular when the lower 1% cutoff was used. Clinicians should be aware of this uncertainty when interpreting PD-L1 reports for treatment decisions.
Previous spatio-temporal COVID-19 prediction models have focused on the prediction of subsequent number of cases, and have shown varying accuracy and lack of high geographical resolution. We aimed to predict trends in COVID-19 test positivity, an important marker for planning local testing capacity and accessibility. We included a full year of information (June 29, 2020-July 4, 2021) with both direct and indirect indicators of transmission, e.g. mobility data, number of calls to the national healthcare advice line and vaccination coverage from Uppsala County, Sweden, as potential predictors. We developed four models for a 1-week-window, based on gradient boosting (GB), random forest (RF), autoregressive integrated moving average (ARIMA) and integrated nested laplace approximations (INLA). Three of the models (GB, RF and INLA) outperformed the naïve baseline model after data from a full pandemic wave became available and demonstrated moderate accuracy. An ensemble model of these three models slightly improved the average root mean square error to 0.039 compared to 0.040 for GB, RF and INLA, 0.055 for ARIMA and 0.046 for the naïve model. Our findings indicate that the collection of a wide variety of data can contribute to spatio-temporal predictions of COVID-19 test positivity.
Background The heritability of body composition has been studied extensively by researchers. However, few studies have explored the genetic variation of advanced body composition phenotypes derived from magnetic resonance imaging (MRI). In this study, polygenic risk scores (PRS) and single nucleotide polymorphisms (SNPs) that are associated with image-derived features from water- and fat separated MRI are reported. Method and materials The analysis was performed with the image processing framework Imiomics to attain spatial normalisation of large imaging cohorts from the UK Biobank. The study included 13,300 men and 13,849 women following GWAS and image data quality controls. Imiomics was further applied to generate voxel-wise Pearson correlation coefficient volumes. Relative effect sizes from six SNPs (rs1358980-T, rs1936805-T, rs2820443-C, rs6567160-C, rs10195252-C and rs13021737-G) were examined for associations with segmented tissue volumes and tissue fat fractions. In addition, the LDpred-derived PRS were compared with genome-wide significant only derived PRS for body mass index (BMI), waist-to-hip ratio (WHR) and height. Results Imiomics and GWAS integration delivered a detailed mapping of individual SNPs to the tissue volume and fat fraction of regional adipose tissue depots, heart, liver, lungs and thigh muscle. A putatively less harmful relationship between gluteofemoral SAT and the two obesity-related SNPs, rs6567160-C and rs1936805-T, compared with other tissues was found. The genetic variant, rs1358980-T, located upstream of VEGFA, was the highest ranked SNP inversely associated with gluteofemoral SAT volume in both sexes (r= -0.0245, p<0.05 and r= -0.0257, p<0.05 in men and women, respectively). Observed effect sizes were overall higher with LDpred-derived PRS compared with genome-wide significant only scores. Conclusion An image-based exploratory integration approach guided by Imiomics enabled efficient and large-scale analysis of advanced body composition and genetic variation.