Search

Aim: Our goal was to describe a precision medicine program in a regional academic hospital, characterize features of included patients and present early data on clinical impact. Materials and methods: We prospectively included 163 eligible patients with late-stage cancer of any diagnosis from June 2020 to May 2022 in the Proseq Cancer trial. Molecular profiling of new or fresh frozen tumor biopsies was done by WES and RNAseq with parallel sequencing of non-tumoral DNA as individual reference. Cases were presented at a National Molecular Tumor Board (NMTB) for discussion of targeted treatment. Subsequently, patients were followed for at least 7 months. Results: 80% (N = 131) of patients had a successful analysis done, disclosing at least one pathogenic or likely pathogenic variant in 96%. A strongly or potentially druggable variant was found in 19% and 73% of patients, respectively. A germline variant was identified in 2.5%. Median time from trial inclusion to NMTB decision was one month. One third (N = 44) of patients who underwent molecularly profiling were matched with a targeted treatment, however, only 16% were either treated (N = 16) or are waiting for treatment (N = 5), deteriorating performance status being the primary cause of failure. A history of cancer among 1st degree relatives, and a diagnosis of lung or prostate cancer correlated with greater chance of targeted treatment being available. The response rate of targeted treatments was 40%, the clinical benefit rate 53%, and the median time on treatment was 3.8 months. 23% of patients presented at NMTB were recommended clinical trial participation, not dependent on biomarkers. Conclusions: Precision medicine in end-stage cancer patients is feasible in a regional academic hospital but should continue within the frame of clinical protocols as few patients benefit. Close collaboration with comprehensive cancer centers ensures expert evaluations and equality in access to early clinical trials and modern treatment.

A better understanding of the biological factors underlying antidepressant treatment in patients with major depressive disorder (MDD) is needed. We perform gene expression analyses and explore sources of variability in peripheral blood related to antidepressant treatment and treatment response in patients suffering from recurrent MDD at baseline and after 8 weeks of treatment. The study includes 281 patients, which were randomized to 8 weeks of treatment with vortioxetine (N = 184) or placebo (N = 97). To our knowledge, this is the largest dataset including both gene expression in blood and placebo-controlled treatment response measured by a clinical scale in a randomized clinical trial. We identified three novel genes whose RNA expression levels at baseline and week 8 are significantly (FDR SOCS3 (FDR = 0.0039) and PROK2 (FDR = 0.0028), which have previously both been linked to depression. Downregulation of these genes was associated with poorer treatment response. We did not identify any genes that were differentially expressed between placebo and vortioxetine groups at week 8 or between baseline and week 8 of treatment. Nor did we replicate any genes identified in previous peripheral blood gene expression studies examining treatment response. Analysis of genome-wide expression variability showed that type of treatment and treatment response explains very little of the variance, a median of

Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low depth NGS data, which takes the uncertainty of the genotypes into account via geno-type likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C++ in a multi-threaded software and is freely available on Githubhttps://github.com/KHanghoj/NGSremix.

Paroxetine and sertraline are the only FDA approved drugs for treatment of posttraumatic stress disorder (PTSD). Although both drugs show better outcomes than placebo, not all patients benefit from treatment. We examined predictors and latent classes of SSRI treatment response in patients with PTSD. Symptom severity was measured over a 12-week period in 390 patients suffering from PTSD treated with open-label sertraline or paroxetine and a double-blinded placebo. First, growth curve modeling (GCM) was used to examine population-level predictors of treatment response. Second, growth mixture modeling (GMM) was used to group patients into latent classes based on their treatment response trajectories over time and to investigate predictors of latent class membership. Gender, childhood sexual trauma, and sexual assault as index trauma moderated the population-level treatment response using GCM. GMM identified three classes: fast responders, responders with low pretreatment symptom severity and responders with high pretreatment symptom severity. Class membership was predicted based on time since index trauma, severity of depression, and severity of anxiety. The study shows that higher severity of comorbid disorders does not result in an inferior response to treatment and suggests that patients with longer time since index trauma might particularly benefit from treatment with sertraline or paroxetine.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

Cervical spine injuries, such as whiplash or disk herniation, are a worldwide health problem. Digital videofluoroscopic is often used to examine spine movement by means of manual identification and marking of vertebral landmarks, which is a complicated and time-consuming process. The aim of our study was to develop a fast, semi-automatic cervical vertebrae tracking method to accurately calculate the inter vertebral rotation in C1–C7 vertebrae. We manually defined templates for each cervical vertebra, so that these templates would be automatically tracked throughout neck movement. Subjects performed extension and flexion in the sagittal plane, which was recorded with digital videofluoroscopy. We implemented cross-correlation and Kalman filters for template tracking, and validated our method by comparing our results with a manual method, where a trained clinician manually marked the vertebrae. Our method provided higher intraobserver repeatability for C2/C3 to C6/C7 segments. Accordingly, the intraobserver repeatability was also comparable to other methods developed to track the lumbar vertebrae.