Probabilistic pathway-based multimodal factor analysis.

MOTIVATION: Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. RESULTS: We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. AVAILABILITY AND IMPLEMENTATION: The tool is implemented in python and available at https://github.com/ratschlab/path-fa.

Learning single-cell perturbation responses using neural optimal transport.

Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-ß and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing.

MOTIVATION: Several recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (<0.05× per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing. RESULTS: We developed Single Cell Data Tumor Clusterer (SECEDO, lat. 'to separate'), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7250 cells and eight tumor subclones from a single patient and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to five real single-cell sequencing datasets from a breast cancer patient, each consisting of ≈2000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03×, achieving an Adjusted Rand Index (ARI) score of ≈0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of ≈0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDOs performance when pooling data from all five datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants. AVAILABILITY AND IMPLEMENTATION: SECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version are archived at: https://doi.org/10.5281/zenodo.6516955. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeting eIF4A-Dependent Translation of KRAS Signaling Molecules.

Pancreatic adenocarcinoma (PDAC) epitomizes a deadly cancer driven by abnormal KRAS signaling. Here, we show that the eIF4A RNA helicase is required for translation of key KRAS signaling molecules and that pharmacological inhibition of eIF4A has single-agent activity against murine and human PDAC models at safe dose levels. EIF4A was uniquely required for the translation of mRNAs with long and highly structured 5' untranslated regions, including those with multiple G-quadruplex elements. Computational analyses identified these features in mRNAs encoding KRAS and key downstream molecules. Transcriptome-scale ribosome footprinting accurately identified eIF4A-dependent mRNAs in PDAC, including critical KRAS signaling molecules such as PI3K, RALA, RAC2, MET, MYC, and YAP1. These findings contrast with a recent study that relied on an older method, polysome fractionation, and implicated redox-related genes as eIF4A clients. Together, our findings highlight the power of ribosome footprinting in conjunction with deep RNA sequencing in accurately decoding translational control mechanisms and define the therapeutic mechanism of eIF4A inhibitors in PDAC. SIGNIFICANCE: These findings document the coordinate, eIF4A-dependent translation of RAS-related oncogenic signaling molecules and demonstrate therapeutic efficacy of eIF4A blockade in pancreatic adenocarcinoma.

SCIM: universal single-cell matching with unpaired feature sets.

MOTIVATION: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. RESULTS: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively. AVAILABILITY AND IMPLEMENTATION: https://github.com/ratschlab/scim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Commissioning, dosimetric characterization and machine performance assessment of the LIAC HWL mobile accelerator for Intraoperative Radiotherapy.

BACKGROUND: The LIAC HWL (Sordina IORT Technologies, Vicenza, Italy) is a recently designed mobile linear accelerator for intraoperative electron radiotherapy (IOeRT), producing high dose rate electron beams at four different energy levels. It features a software tool for the visualization of 2D dose distributions, which is based on Monte Carlo simulations. The aims of this work were to (i) assess the dosimetric characteristics of the accelerator, (ii) experimentally verify calculated data exported from the software and (iii) report on commissioning as well as performance of the system during the first year of operation. METHODS: The electron energies of the LIAC HWL used in this study are 6, 8, 10 and 12 MeV. Diameters of the cylindrically shaped applicators range from 3 to 10cm. We studied two applicator sets with different length ratios of proximal and terminal applicator sections. Reference dosimetry, linearity as well as short- and long-term stability were measured with a PTW Advanced Markus chamber, relative depth dose and profiles were measured using an unshielded diode. Percentage-depth-dose (PDD) and transversal dose profile (TDP) data were exported from the simulation software LIACSim and compared with our measurements. RESULTS: The device reaches dose rates up to 40Gy/min (for 12 MeV). Surface doses for the 10cm applicators are higher than 90%, X-ray background is below 0.6% for all energies. Simulations and measurements of PDD agreed well, with a maximum difference in the depth of the 50% isodose of 0.7mm for the flat-ended applicators and 1mm for the beveled applicators. The simulations slightly underestimate the dose in the lateral parts of the field (difference < 1.8% for flat-ended applicators). The two different applicator sets were dosimetrically equivalent. Long-term stability measurements for the first year of operation ranged from -2.1% to 1.6% (mean: -0.1%). CONCLUSIONS: The system is dosimetrically well suited for IOeRT and performed stably and reliably. The software tool for visualization of dose distributions can be used to support treatment planning, following thorough validation.

Genomic basis for RNA alterations in cancer.

Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed 'bridged' fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer.

Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients.

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions ("neojunctions") in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders ("putative neoantigens").

Job burnout and job wornout as risk factors for long-term sickness absence.

OBJECTIVE: Contingent self-esteem has been assumed to be a risk for burnout-related disorders, and a contingent self-worth notion of job burnout was applied to study the prospective relationship between job burnout and registered episodes of sickness absence of ≥ 60 consecutive days. METHODS: Job burnout was defined as being in the high quartiles on the Maslach Burnout Inventory - General Survey (MBI-GS) scales of exhaustion and cynicism and, in addition, as being above the median on a scale for performance-based self-esteem. Another high exhaustion-cynicism group, a "job wornout" group, was defined as being high on the same MBI-GS scales but having performance-based self-esteem scores below the median. Data were analyzed by a multivariate, logistic regression approach. PARTICIPANTS: 4,109 public employees in Sweden. RESULTS: The job burnout group showed an over-risk of long-term sickness absence incidence, both compared with a low exhaustion-cynicism reference group and with the job wornout group after adjustment for several potential confounders. No association with incidence of long-term sickness absence was found for the job wornout group. CONCLUSIONS: The differential vulnerability to long-term sickness absence among high exhaustion-cynicism groups suggests that a self-worth perspective of job burnout can be advantageous for prevention of the costly long-term sickness absences.

The influence of household work and of having children on sickness absence among publicly employed women in Sweden.

AIM: To investigate whether family obligations influence the risk of sickness absence among female municipal employees in Sweden. METHODS: A 1-year prospective cohort study of 1464 female municipal employees <50 years of age in Sweden in 2000 was conducted using questionnaire responses and absence data from the employers' personnel records. The relative risk of having children <16 years of age in the home, marital status, household work, financial situation, working hours and work-family conflicts for repeated sick-leave spells (>or=4 spells) and long-term sickness absence (>or=28 days) were calculated by applying Poisson regression models. RESULTS: Women reporting financial strain or work-family conflicts were at elevated risk for long-term sickness absence. Having children was not a risk factor for repeated sick-leave spells or long-term sickness absence among married/cohabiting women. Single women with children had a two-fold greater risk of repeated sick-leave spells than single women without children. CONCLUSIONS: The findings suggest that the combination of gainful employment and children does not influence the risk of repeated sick-leave spells or long-term sickness absence among married/cohabiting publicly employed women. However, this was not true for single women with children, which indicates that their circumstances are particularly strained.

Return to work expectation predicts work in chronic musculoskeletal and behavioral health disorders: prospective study with clinical implications.

STUDY DESIGN: Prospective cohort study with 18-month follow-up. OBJECTIVE: To investigate if long-term sick listed persons' own predictions of their future return to work (RTW) have an impact on their RTW when controlling for other established factors. METHOD: Postal questionnaires at baseline were sent to persons who had been on sick leave for more than 90 days, and were employed in five municipalities and four county councils in Sweden. A follow-up regarding their RTW was performed 18 months later. RESULTS: After 18 months 135 out of 508 persons (27%) had returned to work, full or part-time. In a multivariate logistic regression, the sick listed persons' own prediction of their RTW proved to be highly significant (OR=8.28, 95% CI: 3.31-20.69). Only six out of 132 persons with a negative view of their RTW did return to wok. Other predictive factors that were found for RTW were: being on sick leave for a period of less than 1 year (OR=2.09, 95% CI: 1.19-3.67), having less pain than persons in the quartile with most pain (OR=2.65, 95% CI: 1.21-5.81), perceiving that one was welcome back to work (OR=1.98, 95% CI: 1.10-3.58), and being under 55 years of age (OR=2.37, 95% CI: 1.07-5.23 for age between 45 and 54 years and the same trend for age below 45 years OR=1.85, 95% CI: 0.82-4.20). CONCLUSION: Persons with a positive prediction should get help to realise their potential for RTW. Offering traditional rehabilitation measures to a person with a negative prediction of his/her RTW, could be a waste of resources if done ahead of improving self-confidence and view of what is possible. The problems in this group might decrease or be easier to handle if decisions about the future are taken within a year.

Long-term sick-listing among women in the public sector and its associations with age, social situation, lifestyle, and work factors: a three-year follow-up study.

BACKGROUND: Since 1997 the number of long-term sick-listed people in Sweden has increased dramatically, especially among women employed in the public sector. AIMS: The aim of this study was to investigate associations between age, social situation, lifestyle, work factors, and long-term sick-listing (> or =28 days), regardless of diagnosis, during a period of three years among women employed in the public sector. METHODS: Exposure information at baseline was recorded by a questionnaire. All new spells of sick listing (> or =28 days were consecutively reported from the employer for three years from baseline. In total 6,246 women from the public sector in Sweden answered the questionnaire (85% response rate); 5,224 were classified as having good or rather good health for working and were included. Of these, 918 persons had spells of sick-listing (> or =28 days during follow-up. RESULTS: Univariate and multivariate analyses for calculating relative risks (RR) were carried out. In the Cox regression model age (RR 1.4), strained financial situation (RR 1.3), obesity (RR 1.3), bullying (RR 1.5), physical demands at work higher than own capacity (RR 1.5), and mental demands at work higher than own capacity (1.2) remained risk indicators for long-term sick-listing. CONCLUSION: This study suggests prevention of some work and lifestyle factors as general measures to reduce long-term sick-listing among women in the public sector.