Colorectal cancer (CRC) is a highly heterogeneous disease both from a molecular and clinical perspective. Several distinct molecular entities, such as microsatellite instability (MSI), have been ...defined that make up biologically distinct subgroups with their own clinical course. Recent data indicated that CRC can be best segregated into four groups called consensus molecular subtypes (CMS1-4), each of which has a unique biology and gene expression pattern. In order to develop improved, subtype-specific therapies and to gain insight into the molecular wiring and origin of these subtypes, reliable models are needed. This study was designed to determine the heterogeneity and identify the presence of CMSs in a large panel of CRC cell lines, primary cultures and patient-derived xenografts (PDX). We provide a repository encompassing this heterogeneity and moreover describe that a large part of the models can be robustly assigned to one of the four CMSs, independent of the stromal contribution. We subsequently validate our CMS stratification by functional analysis which for instance shows mesenchymal enrichment in CMS4 and metabolic dysregulation in CMS3. Finally, we observe a clear difference in sensitivity to chemotherapy-induced apoptosis, specifically between CMS2 and CMS4. This relates to the in vivo efficacy of chemotherapy, which delays outgrowth of CMS2, but not CMS4 xenografts. Combined our data indicate that molecular subtypes are faithfully modelled in CRC cell cultures and PDXs, representing tumour cell intrinsic and stable features. This repository provides researchers with a platform to study CRC using the existing heterogeneity.
Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis of all common cancers. However, divergent outcomes exist between patients, suggesting distinct underlying tumor biology. Here, we ...delineated this heterogeneity, compared interconnectivity between classification systems, and experimentally addressed the tumor biology that drives poor outcome. RNA-sequencing of 90 resected specimens and unsupervised classification revealed four subgroups associated with distinct outcomes. The worst-prognosis subtype was characterized by mesenchymal gene signatures. Comparative (network) analysis showed high interconnectivity with previously identified classification schemes and high robustness of the mesenchymal subtype. From species-specific transcript analysis of matching patient-derived xenografts we constructed dedicated classifiers for experimental models. Detailed assessments of tumor growth in subtyped experimental models revealed that a highly invasive growth pattern of mesenchymal subtype tumor cells is responsible for its poor outcome. Concluding, by developing a classification system tailored to experimental models, we have uncovered subtype-specific biology that should be further explored to improve treatment of a group of PDAC patients that currently has little therapeutic benefit from surgical treatment.
Patient‐derived xenograft (PDX) models have become an important asset in translational cancer research. However, to provide a robust preclinical platform, PDXs need to accommodate the tumor ...heterogeneity that is observed in patients. Colorectal cancer (CRC) can be stratified into four consensus molecular subtypes (CMS) with distinct biological and clinical features. Surprisingly, using a set of CRC patients, we revealed the partial representation of tumor heterogeneity in PDX models. The epithelial subtypes, the largest subgroups of CRC subtype, were very ineffective in establishing PDXs, indicating the need for further optimization to develop an effective personalized therapeutic approach to CRC. Moreover, we showed that tumor cell proliferation was associated with successful PDX establishment and able to distinguish patient with poor clinical outcomes within CMS2 group.
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Patient‐derived xenograft (PDX) models have become an important asset in translational cancer research. However, colorectal cancer (CRC) can be stratified into four consensus molecular subtypes (CMS) with distinct biological and clinical features, and to what extent the existing CRC PDX collection represents the inter‐patient heterogeneity remains an open question. This study identifies a subtype‐specific bias in the establishment of PDXs from CRC patients, leaving the major subtype CMS2 strongly underrepresented. Additionally, the findings suggest that further classification within CMS can be achieved. For CMS2, the proliferation‐related marker Ki67 may thus help refine patient classification, estimate prognosis, and guide treatment decisions.
Evasion of apoptosis is a hallmark of cancer, which is frequently mediated by upregulation of the antiapoptotic BCL-2 family proteins. In colorectal cancer (CRC), previous work has highlighted ...differential antiapoptotic protein dependencies determined by the stage of the disease. While intestinal stem cells (ISCs) require BCL-2 for adenoma outgrowth and survival during transformation, ISC-specific MCL1 deletion results in disturbed intestinal homeostasis, eventually contributing to tumorigenesis. Colon cancer stem cells (CSCs), however, no longer require BCL-2 and depend mainly on BCL-XL for their survival. We therefore hypothesized that a shift in antiapoptotic protein reliance occurs in ISCs as the disease progresses from normal to adenoma to carcinoma. By targeting antiapoptotic proteins with specific BH3 mimetics in organoid models of CRC progression, we found that BCL-2 is essential only during ISC transformation while MCL1 inhibition did not affect adenoma outgrowth. BCL-XL, on the other hand, was crucial for stem cell survival throughout the adenoma-to-carcinoma sequence. Furthermore, we identified that the limited window of BCL-2 reliance is a result of its downregulation by miR-17-5p, a microRNA that is upregulated upon APC-mutation driven transformation. Here we show that BCL-XL inhibition effectively impairs adenoma outgrowth in vivo and enhances the efficacy of chemotherapy. In line with this dependency, expression of BCL-XL, but not BCL-2 or MCL1, directly correlated to the outcome of chemotherapy-treated CRC patients. Our results provide insights to enable the rational use of BH3 mimetics in CRC management, particularly underlining the therapeutic potential of BCL-XL targeting mimetics in both early and late-stage disease.
Neuroblastoma is the most common extracranial solid tumor in children. A subgroup of high-risk patients is characterized by aberrations in the chromatin remodeller ATRX that is encoded by 35 exons. ...In contrast to other pediatric cancer where ATRX point mutations are most frequent, multi-exon deletions (MEDs) are the most frequent type of ATRX aberrations in neuroblastoma. 75% of these MEDs are predicted to produce in-frame fusion proteins, suggesting a potential gain-of-function effect compared to nonsense mutations. For neuroblastoma there are only a few patient-derived ATRX aberrant models. Therefore, we created isogenic ATRX aberrant models using CRISPR-Cas9 in several neuroblastoma cell lines and one tumoroid and performed total RNA-sequencing on these and the patient-derived models. Gene set enrichment analysis (GSEA) showed decreased expression of genes related to both ribosome biogenesis and several metabolic processes in our isogenic ATRX exon 2-10 MED model systems, the patient-derived MED models and in tumor data containing two patients with an ATRX exon 2-10 MED. In sharp contrast, these same processes showed an increased expression in our isogenic ATRX knock-out and exon 2-13 MED models. Our validations confirmed a role of ATRX in the regulation of ribosome homeostasis. The two distinct molecular expression patterns within ATRX aberrant neuroblastomas that we identified imply that there might be a need for distinct treatment regimens.
To understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression ...genome-wide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resulting genetic perturbation signatures reflect many different properties. These include the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. The data are assembled into a genetic perturbation network that shows different connectivities for different classes of regulators. Four feed-forward loop (FFL) types are overrepresented, including incoherent type 2 FFLs that likely represent feedback. Systematic transcription factor classification shows a surprisingly high abundance of gene-specific repressors, suggesting that yeast chromatin is not as generally restrictive to transcription as is often assumed. The data set is useful for studying individual genes and for discovering properties of an entire regulatory system.
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•A systems analyses resource reports expression signatures for 1,484 yeast gene knockouts•Analysis reveals pathway branching, connectivity, and responsiveness to perturbations•Four feed-forward loop types are overrepresented in the genetic perturbation network•Transcription factor classification shows an abundance of gene-specific repressors
A comprehensive mRNA expression profiling study reports the effects of over 1,400 individual gene deletions in yeast, focusing on nonessential regulators of gene expression. Analysis of the data indicates a surprisingly high abundance of repressors, suggesting that chromatin itself may not be generally restrictive to transcription, as previously supposed.
The heterogeneous nature of colorectal cancer (CRC) complicates prognosis and is suggested to be a determining factor in the efficacy of adjuvant therapy for individual patients. Based on gene ...expression profiling, CRC is currently classified into four consensus molecular subtypes (CMSs), characterized by specific biological programs, thus suggesting the existence of unifying developmental drivers for each CMS. Using human organoid cultures, we investigated the role of such developmental drivers at the premalignant stage of distinct CRC subtypes and found that TGFβ plays an important role in the development of the mesenchymal CMS4, which is of special interest due to its association with dismal prognosis. We show that in tubular adenomas (TAs), which progress to classical CRCs, the dominating response to TGFβ is death by apoptosis. By contrast, induction of a mesenchymal phenotype upon TGFβ treatment prevails in a genetically engineered organoid culture carrying a BRAFV600E mutation, constituting a model system for sessile serrated adenomas (SSAs). Our data indicate that TGFβ signaling is already active in SSA precursor lesions and that TGFβ is a critical cue for directing SSAs to the mesenchymal, poor‐prognosis CMS4 of CRC.
Synopsis
Sessile serrated adenomas (SSAs)—precursor lesions of the serrated neoplasia pathway to colorectal cancer (CRC)—are predicted to give rise to either the good‐ or the poor‐prognosis CRC consensus molecular subtype (CMS1 and CMS4, respectively).
Organoid cultures from patient‐derived pre‐neoplastic lesions and genetically engineered organoid cultures present valuable model systems for early stage disease.
An organoid culture genetically engineered to carry the BRAFV600E mutation served as a model system for the earliest stage of the serrated neoplasia pathway.
A TGFβ signature separated patient‐derived tubular adenomas—precursor lesions of the classical path to CRC—from SSAs.
High activity of the TGFβ signaling pathway was detected in SSAs that would likely have—judged by gene expression‐based prediction—developed to CMS4‐like CRCs.
Sessile serrated adenomas (SSAs)—precursor lesions of the serrated neoplasia pathway to colorectal cancer (CRC)—are predicted to give rise to either the good‐ or the poor‐prognosis CRC consensus molecular subtype (CMS1 and CMS4, respectively).
The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite ...consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention.
RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of ...tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial.
We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples.
We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.
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