Biology subjects

Markowetz, F.

Publications and source records attributed to Markowetz, F..

31 records · Page 2Linked to original sources

bioRxiv2017

Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival

IntroductionGlioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivo method to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge.\n\nMethodsA total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS).\n\nResultsThe MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p < 0.001, HR = 0.33) in multivariate Cox regression analysis. Among the features selected by MVDA, higher mean value of DTI-q in the non-enhancing region contributed to a worse OS (HR = 1.40, p = 0.020) and worse PFS (HR = 1.36, p = 0.031). Multivoxel MRS showed N-acetylaspartate/creatine (NAA/Cr) ratio between the two clusters, both in the CE region (p < 0.001) and NE region (p = 0.013). Glutamate/Cr (Glu/Cr) ratio and glutamate + glutamine/Cr (Glx/Cr) of the cluster 1 was significantly lower than cluster 2 (p = 0.037, and 0.027 respectively) In the NE region.\n\nDiscussionThis study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.

bioinformatics↗

bioRxiv2017

The small molecule KHS101 induces bioenergetic dysfunction in glioblastoma cells through inhibition of mitochondrial HSPD1

Pharmacological inhibition of uncontrolled cell growth with small molecule inhibitors is a potential strategy against glioblastoma multiforme (GBM), the most malignant primary brain cancer. Phenotypic profiling of the neurogenic small molecule KHS101 revealed the chemical induction of lethal cellular degradation in molecularly-diverse GBM cells, independent of their tumor subtype, whereas non-cancerous brain cells remained viable. Mechanism-of-action (MOA) studies showed that KHS101 specifically bound and inhibited the mitochondrial chaperone HSPD1. In GBM but not non-cancerous brain cells, KHS101 elicited the aggregation of an enzymatic network that regulates energy metabolism. Compromised glycolysis and oxidative phosphorylation (OXPHOS) resulted in the metabolic energy depletion in KHS101-treated GBM cells. Consistently, KHS101 induced key mitochondrial unfolded protein response factor DDIT3 in vitro and in vivo, and significantly reduced intracranial GBM xenograft tumor growth upon systemic administration, without discernible side effects. These findings suggest targeting of HSPD1-dependent oncometabolic pathways as an anti-GBM therapy.

cancer biology↗

bioRxiv2017

Immuno-phenotypes of Pancreatic Ductal Adenocarcinoma: Metaanalysis of transcriptional subtypes

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of < 5% and median overall survival of typically six months from diagnosis. Recent transcriptional studies of PDAC have provided several competing stratifications of the disease. However, the development of therapeutic strategies will depend on a unique and coherent classification of PDAC. Here, we use an integrative meta-analysis of four different PDAC gene expression studies to derive the consensus PDAC classification. Despite the fact that immunotherapies have yet to have an impact in treatment of PDAC, the gene expression signatures that stratify PDAC across studies are immunologic. We define these as \"adaptive\", \"innate\" and \"immune-exclusion\" immunologic signatures, which are prognostic across independent cohorts. An appreciation of the immune composition of PDAC with prognostic significance is an opportunity to understand distinct immune escape mechanisms in development of the disease and design novel immune-oncology therapeutic strategies to overcome current barriers.

cancer biology↗

bioRxiv2017

Intratumoral Heterogeneity of Tumor Infiltration of Glioblastoma Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging

IntroductionGlioblastoma exhibits profound tumor heterogeneity, which causes inconsistent treatment response. The aim of this study was to propose an interpretation method of diffusion tensor imaging (DTI) using joint histogram analysis of DTI-p and-q. With this method we explored the patterns of tumor infiltration which causes disruption of brain microstructure, and examined the prognostic value of tumor infiltrative patterns for patient survival.\n\nMaterials and methodsA total of 115 primary glioblastoma patients (mean age 59.3 years, 87 males) were prospectively recruited from July 2010 to August 2015. Patients underwent preoperative MRI scans and maximal safe resection. DTI was processed and decomposed into p and q components. The univariate and joint histograms of DTI-p and-q were constructed using the voxels of contrast-enhancing and non-enhancing regions respectively. Eight joint histogram features were obtained and correlated with tumor progression and patient survival. Their prognostic values were compared with clinical factors using receiver operating characteristic curves.\n\nResultsThe subregion of increased DTI-p and decreased DTI-q accounted for the largest proportion. Additional diffusion patterns can be identified via joint histogram analysis. Particularly, higher proportion of decreased DTI-p and increased DTI-q in non-enhancing region contributed to worse progression-free survival (hazard ratio = 1.08, p< 0.001) and overall survival (hazard ratio = 1.11, p < 0.001).\n\nConclusionsJoint histogram analysis of DTI can provide a comprehensive measure of tumor infiltration and microstructure change, which showed prognostic values. The subregion of decreased DTI-p and increased DTI-q in non-enhancing regions may indicate a more invasive habitat.

neuroscience↗

bioRxiv2017

Novel Quantitative ChIP-seq Methods Measure Absolute Fold-Change in ER Binding Upon Fulvestrant Treatment

A key challenge in quantitative ChIP-seq is the normalisation of data in the presence of genome-wide changes in occupancy. Analysis-based normalisation methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor binding, these assumptions do not hold true. The challenges in normalisation are confounded by experimental variability during sample preparation, processing, and recovery.\n\nWe present a novel normalisation strategy utilising an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalisation. We compare our approach to normalisation by total read depth and two alternative methods that utilise external experimental controls to study transcription factor binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in Patient-Derived Xenographs. This is supported by an adaptable pipeline to normalise and quantify differential transcription factor binding genome-wide and generate metrics for differential binding at individual sites.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC=\"FIGDIR/small/182261_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@177285borg.highwire.dtl.DTLVardef@1fa7d79org.highwire.dtl.DTLVardef@43d17forg.highwire.dtl.DTLVardef@6eff8d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

genomics↗

bioRxiv2017

Low Perfusion Compartments in Glioblastoma Quantified by Advanced Magnetic Resonance Imaging: Correlation with Patient Survival

BackgroundGlioblastoma exhibits profound intratumoral heterogeneity in blood perfusion, which may cause inconsistent therapy response. Particularly, low perfusion may create hypoxic microenvironment and induce resistant clones. Thus, developing validated imaging approaches that define low perfusion compartments is crucial for clinical management.\n\nMethodsA total of 112 newly-diagnosed supratentorial glioblastoma patients were prospectively recruited for maximal safe resection. Preoperative MRI included anatomical, dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging (CSI). The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from DTI and DSC respectively. Using thresholding methods, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified. Volumetric analysis was performed. Lactate and macromolecule/lipid levels were determined from multivoxel spectroscopy. Progression-free survival (PFS) and overall survival (OS) were analysed using Kaplan-Meier and multivariate Cox regression analyses.\n\nResultsTwo compartments displayed higher lactate and macromolecule/lipid levels than normal controls (each P < 0.001), suggesting hypoxic and pro-inflammatory microenvironment. The proportional volume of ADCL-rCBVL compartment was associated with a larger infiltration area (P < 0.001, rho = 0.42). Lower lactate in this compartment was associated with a less invasive phenotype visualized on DTI. Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with a worse survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005).\n\nConclusionsThe ADCL-rCBVL compartment represent a treatment resistant sub-region associated with glioblastoma invasiveness. This approach was based on clinically available imaging modalities and could thus provide crucial pretreatment information for clinical decision making.

cancer biology↗

bioRxiv2017

Copy-number signatures and mutational processes in ovarian carcinoma

Tumours with profound copy-number aberration elude molecular stratification due to their genomic complexity. By representing this complexity as a mixture of copy-number signatures, we provide molecular explanations for differing clinical outcomes. Here we present a method for copy-number signature identification, deriving eight signatures in 117 shallow whole-genome sequenced high-grade serous ovarian cancers (HGSOC), which validated on independent cohorts of 95 deep whole-genome sequenced, and 402 SNP array-profiled cases. Three copy-number signatures predicted longer overall survival, while the others predicted poorer outcome. We found evidence for the mutational processes giving rise to copy-number change for six of the eight signatures via correlations with other genomic features. Our results provide insights into the pathogenesis of HGSOC by uncovering multiple mutational processes that shape genomes following TP53 mutation. Importantly, our work shows that most HGSOC have a mixture of mutational processes suggesting that targeting a single mutator phenotype may be therapeutically suboptimal.

cancer biology↗

bioRxiv2017

SVclone: inferring structural variant cancer cell fraction

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant breakpoints from whole-genome sequencing data. We validate our approach using simulated and real tumour samples, and demonstrate its utility on 2,778 whole-genome sequenced tumours. We find a subset of liver, breast and ovarian cancer cases with decreased overall survival that have subclonally enriched copy-number neutral rearrangements, an observation that could not be discovered with currently available methods.

cancer biology↗

bioRxiv2017

Allele-specific multi-sample copy number segmentation

MotivationAllele-specific copy number alterations are commonly used to trace the evolution of tumours. A key step of the analysis is to segment genomic data into regions of constant copy number. For precise phylogenetic inference, breakpoints shared between samples need to be aligned to each other.\n\nResultsHere we present asmultipcf, an algorithm for allele-specific segmentation of multiple samples that infers private and shared segment boundaries of phylogenetically related samples. The output of this algorithm can directly be used for allele-specific copy number calling using ASCAT.\n\nAvailabilityasmultipcf is available as part of the ASCAT R package (version 2.5) from github.com/Crick-CancerGenomics/ascat

bioinformatics↗

bioRxiv2017

The evolutionary history of 2,658 cancers

Cancer develops through a process of somatic evolution. Here, we use whole-genome sequencing of 2,778 tumour samples from 2,658 donors to reconstruct the life history, evolution of mutational processes, and driver mutation sequences of 39 cancer types. The early phases of oncogenesis are driven by point mutations in a small set of driver genes, often including biallelic inactivation of tumour suppressors. Early oncogenesis is also characterised by specific copy number gains, such as trisomy 7 in glioblastoma or isochromosome 17q in medulloblastoma. By contrast, increased genomic instability, a nearly four-fold diversification of driver genes, and an acceleration of point mutation processes are features of later stages. Copy-number alterations often occur in mitotic crises leading to simultaneous gains of multiple chromosomal segments. Timing analysis suggests that driver mutations often precede diagnosis by many years, and in some cases decades, providing a window of opportunity for early cancer detection.

cancer biology↗

bioRxiv2017

Neutral tumor evolution?

Tumor growth is an evolutionary process governed by somatic mutation, clonal selection and random genetic drift, constrained by the co-evolution of the microenvironment1,2. Tumor subclones are subpopulations of tumor cells with a common set of mutations resulting from the expansion of a single cell during tumor development, and have been observed in a significant fraction of cancers and across multiple cancer types3. Peter Nowell proposed that tumors evolve through sequential genetic events4, whereby one cell acquires a selective advantage so that its lineage becomes predominant. According to this traditional model, the selective advantage is conferred by a small set of driver mutations, but, as the subclones that bear them expand successively, they accumulate passenger mutations as well, which can be detected in sequencing experiments1. Genomes of indiv ...

cancer biology↗

bioRxiv2017

Causal Modeling Dissects Tumour-Microenvironment Interactions In Breast Cancer

Elucidating interactions between cancer cells and their microenvironment is a key goal of cancer research with implications for understanding cancer evolution and improving immunotherapy. Previous studies used association-based approaches to infer relationships in transcriptomic data, but could not infer the direction of interaction. Here we present a causal modeling approach that infers directed interactions between signaling pathway activity and immune activity by anchoring the analysis on somatic genomic changes. Our approach integrates copy number profiles, transcriptomic data, image data and a protein-protein interaction network to infer directed relationships. As a result, we propose 11 novel genomic drivers of T cell phenotypes in the breast cancer tumour microenvironment and validate them in independent cohorts and orthogonal data types. Our framework is flexible and provides a generally applicable way to extend association-based analysis in other cancer types and to other data and clinical parameters.

bioinformatics↗

bioRxiv2016

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods to detect allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and 6 targeted FAIRE-seq samples we show that BaalChIP effectively corrects allele-specific analysis for copy number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

cancer biology↗