Hans Lehrach

Active medulloblastoma enhancers reveal subgroup-specific cellular origins

Medulloblastoma is a highly malignant paediatric brain tumour, often inflicting devastating consequences on the developing child. Genomic studies have revealed four distinct molecular subgroups with divergent biology and clinical behaviour. An understanding of the regulatory circuitry governing the transcriptional landscapes of medulloblastoma subgroups, and how this relates to their respective developmental origins, is lacking. Here, using H3K27ac and BRD4 chromatin immunoprecipitation followed by sequencing (ChIP-seq) coupled with tissue-matched DNA methylation and transcriptome data, we describe the active cis-regulatory landscape across 28 primary medulloblastoma specimens. Analysis of differentially regulated enhancers and super-enhancers reinforced inter-subgroup heterogeneity and revealed novel, clinically relevant insights into medulloblastoma biology. Computational reconstruction of core regulatory circuitry identified a master set of transcription factors, validated by ChIP-seq, that is responsible for subgroup divergence, and implicates candidate cells of origin for Group 4. Our integrated analysis of enhancer elements in a large series of primary tumour samples reveals insights into cis-regulatory architecture, unrecognized dependencies, and cellular origins.

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Summary Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors

Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I–IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab.

Human Lineage-Specific Transcriptional Regulation through GA-Binding Protein Transcription Factor Alpha (GABPa)

A substantial fraction of phenotypic differences between closely related species are likely caused by differences in gene regulation. While this has already been postulated over 30 years ago, only few examples of evolutionary changes in gene regulation have been verified. Here, we identified and investigated binding sites of the transcription factor GA-binding protein alpha (GABPa) aiming to discover cis-regulatory adaptations on the human lineage. By performing chromatin immunoprecipitation-sequencing experiments in a human cell line, we found 11,619 putative GABPa binding sites. Through sequence comparisons of the human GABPa binding regions with orthologous sequences from 34 mammals, we identified substitutions that have resulted in 224 putative human-specific GABPa binding sites. To experimentally assess the transcriptional impact of those substitutions, we selected four promoters for promoter-reporter gene assays using human and African green monkey cells. We compared the activities of wild-type promoters to mutated forms, where we have introduced one or more substitutions to mimic the ancestral state devoid of the GABPa consensus binding sequence. Similarly, we introduced the human-specific substitutions into chimpanzee and macaque promoter backgrounds. Our results demonstrate that the identified substitutions are functional, both in human and nonhuman promoters. In addition, we performed GABPa knock-down experiments and found 1,215 genes as strong candidates for primary targets. Further analyses of our data sets link GABPa to cognitive disorders, diabetes, KRAB zinc finger (KRAB-ZNF), and human-specific genes. Thus, we propose that differences in GABPa binding sites played important roles in the evolution of human-specific phenotypes.

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough

Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies.

Models of Models: A Translational Route for Cancer Treatment and Drug Development

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using “models of models” has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.

Transcriptional signature induced by a metastasis-promoting c-Src mutant in a human breast cell line

Deletions at the C‐terminus of the proto‐oncogene protein c‐Src kinase are found in the viral oncogene protein v‐Src as well as in some advanced human colon cancers. They are associated with increased kinase activity and cellular invasiveness. Here, we analyzed the mRNA expression signature of a constitutively active C‐terminal mutant of c‐Src, c‐Src(mt), in comparison with its wild‐type protein, c‐Src(wt), in the human non‐transformed breast epithelial cell line MCF‐10A. We demonstrated previously that the mutant altered migratory and metastatic properties. Genome‐wide transcriptome analysis revealed that c‐Src(mt) de‐regulated the expression levels of approximately 430 mRNAs whose gene products are mainly involved in the cellular processes of migration and adhesion, apoptosis and protein synthesis. 82.9% of these genes have previously been linked to cellular migration, while the others play roles in RNA transport and splicing processes, for instance. Consistent with the transcriptome data, cells expressing c‐Src(mt), but not those expressing c‐Src(wt), showed the capacity to metastasize into the lungs of mice in vivo. The mRNA expression profile of c‐Src(mt)‐expressing cells shows significant overlap with that of various primary human tumor samples, possibly reflecting elevated Src activity in some cancerous cells. Expression of c‐Src(mt) led to elevated migratory potential. We used this model system to analyze the transcriptional changes associated with an invasive cellular phenotype. These genes and pathways de‐regulated by c‐Src(mt) may provide suitable biomarkers or targets of therapeutic approaches for metastatic cells.

The direction of cross affects obesity after puberty in male but not female offspring

BackgroundWe investigated parent-of-origin and allele-specific expression effects on obesity and hepatic gene expression in reciprocal crosses between the Berlin Fat Mouse Inbred line (BFMI) and C57Bl/6NCrl (B6N).ResultsWe found that F1-males with a BFMI mother developed 1.8 times more fat mass on a high fat diet at 10 weeks than F1-males of a BFMI father. The phenotype was detectable from six weeks on and was preserved after cross-fostering. RNA-seq data of liver provided evidence for higher biosynthesis and elongation of fatty acids (p = 0.00635) in obese male offspring of a BFMI mother versus lean offspring of a BFMI father. Furthermore, fatty acid degradation (p = 0.00198) and the peroxisome pathway were impaired (p = 0.00094). The circadian rhythm was affected as well (p = 0.00087). Among the highest up-regulated protein coding genes in obese males were Acot4 (1.82 fold, p = 0.022), Cyp4a10 (1.35 fold, p = 0.026) and Cyp4a14 (1.32 fold, p = 0.012), which hydroxylize fatty acids and which are known to be increased in liver steatosis. Obese males showed lower expression of the genetically imprinted and paternally expressed 3 (Peg3) gene (0.31 fold, p = 0.046) and higher expression of the androgen receptor (Ar) gene (2.38 fold, p = 0.068). Allelic imbalance was found for expression of ATP-binding cassette transporter gene Abca8b. Several of the differentially expressed genes contain estrogen response elements.ConclusionsParent-of-origin effects during gametogenesis and/or fetal development in an obese mother epigenetically modify the transcription of genes that lead to enhanced fatty acid synthesis and impair β-oxidation in the liver of male, but not female F1 offspring. Down-regulation of Peg3 could contribute to trigger this metabolic setting. At puberty, higher amounts of the androgen receptor and altered access to estrogen response elements in affected genes are likely responsible for male specific expression of genes that were epigenetically triggered. A suggestive lack of estrogen binding motifs was found for highly down-regulated genes in adult hepatocytes of obese F1 males (p = 0.074).

Separation of low and high grade colon and rectum carcinoma by eukaryotic translation initiation factors 1, 5 and 6

Colorectal cancer (CRC) is the third most common cause of cancer related death worldwide. Furthermore, with more than 1.2 million cases registered per year, it constitutes the third most frequent diagnosed cancer entity worldwide. Deregulation of protein synthesis has received considerable attention as a major step in cancer development and progression. Eukaryotic translation initiation factors (eIFs) are involved in the regulation of protein synthesis and are functionally linked to the phosphatidylinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling pathway. The identification of factors accounting for colorectal carcinoma (CRC) development is a major gap in the field. Besides the importance of eIF3 subunits and the eIF4 complex, eIF1, eIF5 and eIF6 were found to be altered in primary and metastatic CRC. We observed significant difference in the expression profile between low and high grade CRC. eIF1, eIF5 and eIF6 are involved in translational control in CRC. Our findings also indicate a probable clinical impact when separating them into low and high grade colon and rectum carcinoma. eIF and mTOR expression were analysed on protein and mRNA level in primary low and high grade colon carcinoma (CC) and rectum carcinoma (RC) samples in comparison to non-neoplastic tissue without any disease-related pathology. To assess the therapeutic potential of targeting eIF1, eIF5 and eIF6 siRNA knockdown in HCT116 and HT29 cells was performed. We evaluated the eIF knockdown efficacy on protein and mRNA level and investigated proliferation, apoptosis, invasion, as well as colony forming and polysome associated fractions. These results indicate that eIFs, in particular eIF1, eIF5 and eIF6 play a major role in translational control in colon and rectum cancer.

Erratum to: ‘The direction of cross affects obesity after puberty in male but not female offspring’

Unfortunately, the original version of this article [1] contained an error. The title was written incorrectly ‘The direction of cross obesity after puberty in male but not female offspring’. The title has been corrected in the original article and is also included correctly above.