Sampling Effects on Gene Expression Data from a Human Tumour Xenograft

Human tumour tissue transplanted to and passed through immunodeficient mice as xenografts make powerful  model systems to study tumour biology, in particular to investigate the dynamics of treatment responses,  e.g. to chemotherapeutic agents. Before embarking on large-scale gene expression analysis of chemotherapy  response in human sarcoma xenografts, we investigated the reproducibility of expression  patterns derived from such samples. We compared expression profiles from tumours from the same or different  mice and of various sizes, as well as central and peripheral parts of the same tumours. Twenty-three  microarray hybridisations were performed on cDNA arrays representing 13000 genes, using direct labelling  of target cDNAs. An ANOVA-based linear mixed-effects model was constructed, and variances of  experimental and biological factors contributing to variability were estimated. With our labelling procedure  used, the effect of switching the dyes was pronounced compared to all other factors. We detected a small  variation in gene expression between two tumours in the same mouse as well as between tumours from different  mice. Furthermore, central or peripheral position in the tumour had only moderate influence on the  variability of the expression profiles. The biological variability was comparable to experimental variability  caused by labelling, confirming the importance of both biological and technical replicates. We further  analysed the data by pair-wise Fisher’s linear discriminant method and identified genes that were significantly  differentially expressed between samples taken from peripheral or central parts of the tumours.  Finally, we evaluated the result of pooling biological samples to estimate the recommended number of  arrays and hybridisations for microarray experiments in this model.

Raman and fluorescence contributions to resonant inelastic soft x-ray scattering on LaAlO3_3/SrTiO3_3 heterostructures

We present a detailed study of the Ti 3$d$ carriers at the interface of LaAlO$_3$/SrTiO$_3$ heterostructures by high-resolution resonant inelastic soft x-ray scattering (RIXS), with special focus on the roles of overlayer thickness and oxygen vacancies. Our measurements show the existence of interfacial Ti 3$d$ electrons already below the critical thickness for conductivity and an increase of the total interface charge up to a LaAlO$_3$ overlayer thickness of 6 unit cells before it levels out. By comparing stoichiometric and oxygen deficient samples we observe strong Ti 3$d$ charge carrier doping by oxygen vacancies. The RIXS data combined with photoelectron spectroscopy and transport measurements indicate the simultaneous presence of localized and itinerant charge carriers. However, it is demonstrated that the relative amount of localized and itinerant Ti $3d$ electrons in the ground state cannot be deduced from the relative intensities of the Raman and fluorescence peaks in excitation energy dependent RIXS measurements, in contrast to previous interpretations. Rather, we attribute the observation of either the Raman or the fluorescence signal to the spatial extension of the intermediate state reached in the RIXS excitation process.Comment: 9 pages, 6 figure

Fingolimod and tumor-infiltrating lymphocytes in checkpoint-inhibitor treated cancer patients.

Immune checkpoint inhibitors (ICIs) are emerging as the new standard of care for treating various metastatic cancers. It is known that effective anti-tumor immune responses are associated with a stronger presence of tumor-infiltrating lymphocytes (TILs) in solid tumor tissue. Cancer patients with relapsing-remitting multiple sclerosis (RRMS) are often under continuous treatment with fingolimod, an immune-modulating drug that inhibits lymphocyte egress from secondary lymphatic organs. Little is known about the effect of fingolimod on ICI cancer therapy, as fingolimod may limit the number of TILs. Here we present three patients with RRMS, who developed various cancers during fingolimod treatment. Histology of all tumors consistently showed low numbers of TILs. A second biopsy taken from one of the tumors, a melanoma, revealed a significant increase of TILs after stopping fingolimod and starting pembrolizumab, indicating a surge in the number and re-invigoration of T cells. Our study suggests that fingolimod limits the number of TILs in solid tumors and may, thus, inhibit anti-cancer immune responses

Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

BACKGROUND: The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS: Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ( unaided ), versus after use of the DDSS ( aided ). RESULTS: The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p \u3c 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an open book approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS: These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists\u27 ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02205086

The Personal Genome Project-UK, an open access resource of human multi-omics data

Integrative analysis of multi-omics data is a powerful approach for gaining functional insights into biological and medical processes. Conducting these multifaceted analyses on human samples is often complicated by the fact that the raw sequencing output is rarely available under open access. The Personal Genome Project UK (PGP-UK) is one of few resources that recruits its participants under open consent and makes the resulting multi-omics data freely and openly available. As part of this resource, we describe the PGP-UK multi-omics reference panel consisting of ten genomic, methylomic and transcriptomic data. Specifically, we outline the data processing, quality control and validation procedures which were implemented to ensure data integrity and exclude sample mix-ups. In addition, we provide a REST API to facilitate the download of the entire PGP-UK dataset. The data are also available from two cloud-based environments, providing platforms for free integrated analysis. In conclusion, the genotype-validated PGP-UK multi-omics human reference panel described here provides a valuable new open access resource for integrated analyses in support of personal and medical genomics