Gene expression based risk classification in multiple myeloma

This thesis focuses on gene expression profiling (GEP) to identify multiple myeloma (MM) patients with high-risk disease. Today primarily patient specific factors such as age, the presence of comorbidities, frailty and renal failure are used for treatment decisions. As a result, almost all newly diagnosed MM patients receive similar treatment. This treatment has been shown to be effective in the MM patient group as a whole. However, some patients respond only minimally or do not respond at all requiring treatment adjustments. This approach therefore fails to produce the best response in each patient. In the future molecular biomarkers are likely to guide treatment decisions by identifying treatment specific markers for both toxicities and response. In the absence of reliable predictions, treatments can be adapted based on risk stratification. In this way, a most optimal treatment for each patient can be selected in order to achieve a better quality of life, deeper responses and possibly even a cure. As a primary result we have shown that the EMC92-gene classifier is a valid prognostic marker. It effectively identifies a high-risk group of 18% of patients with unfavorable median survival of 24 months, independent of other prognostic markers. In combination with ISS, the EMC92 marker was able to identify 38% of patients with a favorable median survival which was not reached after 96 months. This thesis also highlights the power of routinely applied markers such as cytogenetics and ISS. Risk adapted strategies, hopefully coupled to predictive markers, must determine the best way to improve survival of this as yet incurable disease

Fast and accurate frequency-dependent radiation transport for hydrodynamics simulations in massive star formation

Context: Radiative feedback plays a crucial role in the formation of massive stars. The implementation of a fast and accurate description of the proceeding thermodynamics in pre-stellar cores and evolving accretion disks is therefore a main effort in current hydrodynamics simulations. Aims: We introduce our newly implemented three-dimensional frequency dependent radiation transport algorithm for hydrodynamics simulations of spatial configurations with a dominant central source. Methods: The module combines the advantage of the speed of an approximate Flux Limited Diffusion (FLD) solver with the high accuracy of a frequency dependent first order ray-tracing routine. Results: We prove the viability of the scheme in a standard radiation benchmark test compared to a full frequency dependent Monte-Carlo based radiative transfer code. The setup includes a central star, a circumstellar flared disk, as well as an envelope. The test is performed for different optical depths. Considering the frequency dependence of the stellar irradiation, the temperature distributions can be described precisely in the optically thin, thick, and irradiated transition regions. Resulting radiative forces onto dust grains are reproduced with high accuracy. The achievable parallel speedup of the method imposes no restriction on further radiative (magneto-) hydrodynamics simulations. Conclusions: The proposed approximate radiation transport method enables frequency dependent radiation hydrodynamics studies of the evolution of pre-stellar cores and circumstellar accretion disks around an evolving massive star in a highly efficient and accurate manner.Comment: 16 pages, 11 figure

Hierarchical fragmentation and collapse signatures in a high-mass starless region

Aims: We study the fragmentation and collapse properties of the dense gas during the onset of high-mass star formation. Methods: We observed the massive (~800 M⊙) starless gas clump IRDC 18310-4 with the Plateau de Bure Interferometer (PdBI) at subarcsecond resolution in the 1.07 mm continuum and N2H+(3-2) line emission. Results: Zooming from a single-dish low-resolution map to previous 3 mm PdBI data, and now the new 1.07 mm continuum observations, the substructures hierarchically fragment on the increasingly smaller spatial scales. While the fragment separations may still be roughly consistent with pure thermal Jeans fragmentation, the derived core masses are almost two orders of magnitude larger than the typical Jeans mass at the given densities and temperatures. However, the data can be reconciled with models using non-homogeneous initial density structures, turbulence, and/or magnetic fields. While most subcores remain (far-)infrared dark even at 70 μm, we identify weak 70 μm emission toward one core with a comparably low luminosity of ~16 L⊙, supporting the notion of the general youth of the region. The spectral line data always exhibit multiple spectral components toward each core with comparably small line widths for the individual components (in the 0.3 to 1.0 km s-1 regime). Based on single-dish C18O(2-1) data we estimate a low virial-to-gas-mass ratio ≤ 0.25. We propose that the likely origin of these spectral properties may be the global collapse of the original gas clump that results in multiple spectral components along each line of sight. Even within this dynamic picture the individual collapsing gas cores appear to have very low levels of internal turbulence

DART-RAY: a 3D ray-tracing radiative transfer code for calculating the propagation of light in dusty galaxies

We present DART-Ray, a new ray-tracing 3D dust radiative transfer (RT) code designed specifically to calculate radiation field energy density (RFED) distributions within dusty galaxy models with arbitrary geometries. In this paper, we introduce the basic algorithm implemented in . DART-Ray which is based on a pre-calculation of a lower limit for the RFED distribution. This pre-calculation allows us to estimate the extent of regions around the radiation sources within which these sources contribute significantly to the RFED. In this way, ray-tracing calculations can be restricted to take place only within these regions, thus substantially reducing the computational time compared to a complete ray-tracing RT calculation. Anisotropic scattering is included in the code and handled in a similar fashion. Furthermore, the code utilizes a Cartesian adaptive spatial grid and an iterative method has been implemented to optimize the angular densities of the rays originated from each emitting cell. In order to verify the accuracy of the RT calculations performed by DART-Ray, we present results of comparisons with solutions obtained using the dusty 1D RT code for a dust shell illuminated by a central point source and existing 2D RT calculations of disc galaxies with diffusely distributed stellar emission and dust opacity. Finally, we show the application of the code on a spiral galaxy model with logarithmic spiral arms in order to measure the effect of the spiral pattern on the attenuation and RFED. © 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society

Can subtle changes in gene expression be consistently detected with different microarray platforms?

Background: The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. Results: Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms. Conclusion: The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression

A gene expression based predictor for high risk myeloma treated with intensive therapy and autologous stem cell rescue

Myeloma is characterized by a highly variable clinical outcome. Despite the effectiveness of high-dose therapy, 15% of patients relapse within 1 year. We show that these cases also have a significantly shorter post-relapse survival compared to the others (median 14.9 months vs. 40 months, p = 8.03 × 10(- 14)). There are no effective approaches to define this potentially distinct biological group such that treatment could be altered. In this work a series of uniformly treated patients with myeloma were used to develop a gene expression profiling (GEP)-based signature to identify this high risk clinical behavior. Gene enrichment analyses applied to the top differentially expressed genes showed a significant enrichment of epigenetic regulators as well as "stem cell" myeloma genes. A derived 17-gene signature effectively identifies patients at high risk of early relapse as well as impaired overall survival. Integrative genomic analyses showed that epigenetic mechanisms may play an important role on transcription of these genes

Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma

Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight into the biological basis of MM.</p

Transcriptome-wide association study of multiple myeloma identifies candidate susceptibility genes

Background: While genome-wide association studies (GWAS) of multiple myeloma (MM) have identified variants at 23 regions influencing risk, the genes underlying these associations are largely unknown. To identify candidate causal genes at these regions and search for novel risk regions, we performed a multi-tissue transcriptome-wide association study (TWAS). Results: GWAS data on 7319 MM cases and 234,385 controls was integrated with Genotype-Tissue Expression Project (GTEx) data assayed in 48 tissues (sample sizes, N = 80–491), including lymphocyte cell lines and whole blood, to predict gene expression. We identified 108 genes at 13 independent regions associated with MM risk, all of which were in 1 Mb of known MM GWAS risk variants. Of these, 94 genes, located in eight regions, had not previously been considered as a candidate gene for that locus. Conclusions: Our findings highlight the value of leveraging expression data from multiple tissues to identify candidate genes responsible for GWAS associations which provide insight into MM tumorigenesis. Among the genes identified, a number have plausible roles in MM biology, notably APOBEC3C, APOBEC3H, APOBEC3D, APOBEC3F, APOBEC3G, or have been previously implicated in other malignancies. The genes identified in this TWAS can be explored for follow-up and validation to further understand their role in MM biology

Advances in estrogen receptor biology: prospects for improvements in targeted breast cancer therapy

Estrogen receptor (ER) has a crucial role in normal breast development and is expressed in the most common breast cancer subtypes. Importantly, its expression is very highly predictive for response to endocrine therapy. Current endocrine therapies for ER-positive breast cancers target ER function at multiple levels. These include targeting the level of estrogen, blocking estrogen action at the ER, and decreasing ER levels. However, the ultimate effectiveness of therapy is limited by either intrinsic or acquired resistance. Identifying the factors and pathways responsible for sensitivity and resistance remains a challenge in improving the treatment of breast cancer. With a better understanding of coordinated action of ER, its coregulatory factors, and the influence of other intracellular signaling cascades, improvements in breast cancer therapy are emerging