A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells

A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences

Isogenic cells cultured together show heterogeneity in their proliferation rate. To determine the differences between fast and slow-proliferating cells, we developed a method to sort cells by proliferation rate, and performed RNA-seq on slow and fast proliferating subpopulations of pluripotent mouse embryonic stem cells (mESCs) and mouse fibroblasts. We found that slowly proliferating mESCs have a more naïve pluripotent character. We identified an evolutionarily conserved proliferation-correlated transcriptomic signature that is common to all eukaryotes: fast cells have higher expression of genes for protein synthesis and protein degradation. This signature accurately predicted growth rate in yeast and cancer cells, and identified lineage-specific proliferation dynamics during development, using C. elegans scRNA-seq data. In contrast, sorting by mitochondria membrane potential revealed a highly cell-type specific mitochondria-state related transcriptome. mESCs with hyperpolarized mitochondria are fast proliferating, while the opposite is true for fibroblasts. The mitochondrial electron transport chain inhibitor antimycin affected slow and fast subpopulations differently. While a major transcriptional-signature associated with cell-to-cell heterogeneity in proliferation is conserved, the metabolic and energetic dependency of cell proliferation is cell-type specific.

By performing RNA sequencing on cells sorted by their proliferation rate, this study identifies a gene expression signature capable of predicting proliferation rates in diverse eukaryotic cell types and species. This signature, applied to single-cell RNA sequencing data from embryos of the roundworm C. elegans, reveals lineage-specific proliferation differences during development. In contrast to the universality of the proliferation signature, mitochondria and metabolism related genes show a high degree of cell-type specificity; mouse pluripotent stem cells (mESCs) and differentiated cells (fibroblasts) exhibit opposite relations between mitochondria state and proliferation. Furthermore, we identified a slow proliferating subpopulation of mESCs with higher expression of pluripotency genes. Finally, we show that fast and slow proliferating subpopulations are differentially sensitive to mitochondria inhibitory drugs in different cell types.

Highlights:

A FACS-based method to determine the transcriptomes of fast and slow proliferating subpopulations.

A universal proliferation-correlated transcriptional signature indicates high protein synthesis and degradation in fast proliferating cells across cell types and species.

Applied to scRNA-seq, the expression signature predicts the global proliferation slowdown during C. elegans development.

Mitochondria membrane potential predicts proliferation rate in a cell-type specific manner, with ETC complex III inhibitor having distinct effects on fibroblasts vs mESCs.

SRSF1-dependent inhibition of C9ORF72-repeat RNA nuclear export: genome-wide mechanisms for neuroprotection in amyotrophic lateral sclerosis

Background

Loss of motor neurons in amyotrophic lateral sclerosis (ALS) leads to progressive paralysis and death. Dysregulation of thousands of RNA molecules with roles in multiple cellular pathways hinders the identification of ALS-causing alterations over downstream changes secondary to the neurodegenerative process. How many and which of these pathological gene expression changes require therapeutic normalisation remains a fundamental question.

Methods

Here, we investigated genome-wide RNA changes in C9ORF72-ALS patient-derived neurons and Drosophila, as well as upon neuroprotection taking advantage of our gene therapy approach which specifically inhibits the SRSF1-dependent nuclear export of pathological C9ORF72-repeat transcripts. This is a critical study to evaluate (i) the overall safety and efficacy of the partial depletion of SRSF1, a member of a protein family involved itself in gene expression, and (ii) a unique opportunity to identify neuroprotective RNA changes.

Results

Our study shows that manipulation of 362 transcripts out of 2257 pathological changes, in addition to inhibiting the nuclear export of repeat transcripts, is sufficient to confer neuroprotection in C9ORF72-ALS patient-derived neurons. In particular, expression of 90 disease-altered transcripts is fully reverted upon neuroprotection leading to the characterisation of a human C9ORF72-ALS disease-modifying gene expression signature. These findings were further investigated in vivo in diseased and neuroprotected Drosophila transcriptomes, highlighting a list of 21 neuroprotective changes conserved with 16 human orthologues in patient-derived neurons. We also functionally validated the high neuroprotective potential of one of these disease-modifying transcripts, demonstrating that inhibition of ALS-upregulated human KCNN1–3 (Drosophila SK) voltage-gated potassium channel orthologs mitigates degeneration of human motor neurons and Drosophila motor deficits.

Conclusions

Strikingly, the partial depletion of SRSF1 leads to expression changes in only a small proportion of disease-altered transcripts, indicating that not all RNA alterations need normalization and that the gene therapeutic approach is safe in the above preclinical models as it does not disrupt globally gene expression. The efficacy of this intervention is also validated at genome-wide level with transcripts modulated in the vast majority of biological processes affected in C9ORF72-ALS. Finally, the identification of a characteristic signature with key RNA changes modified in both the disease state and upon neuroprotection also provides potential new therapeutic targets and biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13024-021-00475-y.

P-selectin axis plays a key role in microglia immunophenotype and glioblastoma progression

Glioblastoma (GB) is a highly invasive type of brain cancer exhibiting poor prognosis. As such, its microenvironment plays a crucial role in its progression. Among the brain stromal cells, the microglia were shown to facilitate GB invasion and immunosuppression. However, the reciprocal mechanisms by which GB cells alter microglia/macrophages behavior are not fully understood. We propose that these mechanisms involve adhesion molecules such as the Selectins family. These proteins are involved in immune modulation and cancer immunity. We show that P-selectin mediates microglia-enhanced GB proliferation and invasion by altering microglia/macrophages activation state. We demonstrate these findings by pharmacological and molecular inhibition of P-selectin which leads to reduced tumor growth and increased survival in GB mouse models. Our work sheds light on tumor-associated microglia/macrophage function and the mechanisms by which GB cells suppress the immune system and invade the brain, paving the way to exploit P-selectin as a target for GB therapy.

Receptor tyrosine kinases activate heterotrimeric G proteins via phosphorylation within the interdomain cleft of Gαi

The molecular mechanisms by which receptor tyrosine kinases (RTKs) and heterotrimeric G proteins, two major signaling hubs in eukaryotes, independently relay signals across the plasma membrane have been extensively characterized. How these hubs cross-talk has been a long-standing question, but answers remain elusive. Using linear ion-trap mass spectrometry in combination with biochemical, cellular, and computational approaches, we unravel a mechanism of activation of heterotrimeric G proteins by RTKs and chart the key steps that mediate such activation. Upon growth factor stimulation, the guanine-nucleotide exchange modulator dissociates Gαi•βγ trimers, scaffolds monomeric Gαi with RTKs, and facilitates the phosphorylation on two tyrosines located within the interdomain cleft of Gαi. Phosphorylation triggers the activation of Gαi and inhibits second messengers (cAMP). Tumor-associated mutants reveal how constitutive activation of this pathway impacts cell's decision to "go" vs. "grow." These insights define a tyrosine-based G protein signaling paradigm and reveal its importance in eukaryotes.

Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature

BACKGROUND

Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients.

METHODS

We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity.

RESULTS

Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naïve patient cohort.

CONCLUSIONS

EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk.

Open Data for Differential Network Analysis in Glioma

The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism

Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.

A landscape of synthetic viable interactions in cancer

Synthetic viability, which is defined as the combination of gene alterations that can rescue the lethal effects of a single gene alteration, may represent a mechanism by which cancer cells resist targeted drugs. Approaches to detect synthetic viable (SV) interactions in cancer genome to investigate drug resistance are still scarce. Here, we present a computational method to detect synthetic viability-induced drug resistance (SVDR) by integrating the multidimensional data sets, including copy number alteration, whole-exome mutation, expression profile and clinical data. SVDR comprehensively characterized the landscape of SV interactions across 8580 tumors in 32 cancer types by integrating The Cancer Genome Atlas data, small hairpin RNA-based functional experimental data and yeast genetic interaction data. We revealed that the SV interactions are favorable to cells and can predict clinical prognosis for cancer patients, which were robustly observed in an independent data set. By integrating the cancer pharmacogenomics data sets from Cancer Cell Line Encyclopedia (CCLE) and Broad Cancer Therapeutics Response Portal, we have demonstrated that SVDR enables drug resistance prediction and exhibits high reliability between two databases. To our knowledge, SVDR is the first genome-scale data-driven approach for the identification of SV interactions related to drug resistance in cancer cells. This data-driven approach lays the foundation for identifying the genomic markers to predict drug resistance and successfully infers the potential drug combination for anti-cancer therapy.

Biosynthetic energy cost for amino acids decreases in cancer evolution

Rapidly proliferating cancer cells have much higher demand for proteinogenic amino acids than normal cells. The use of amino acids in human proteomes is largely affected by their bioavailability, which is constrained by the biosynthetic energy cost in living organisms. Conceptually distinct from gene-based analyses, we introduce the energy cost per amino acid (ECPA) to quantitatively characterize the use of 20 amino acids during protein synthesis in human cells. By analyzing gene expression data from The Cancer Genome Atlas, we find that cancer cells evolve to utilize amino acids more economically by optimizing gene expression profile and ECPA shows robust prognostic power across many cancer types. We further validate this pattern in an experimental evolution of xenograft tumors. Our ECPA analysis reveals a common principle during cancer evolution.

Proliferating cancer cells have a high demand for amino acids. Here, Zhang et al. show that cancer cells evolve towards gene expression profiles that use amino acids with lower biosynthetic energy costs, and demonstrate the potential prognostic utility of quantifying the extent of this adaptation.

Ribosomal DNA copy number amplification and loss in human cancers is linked to tumor genetic context, nucleolus activity, and proliferation

Ribosomal RNAs (rRNAs) are transcribed from two multicopy DNA arrays: the 5S ribosomal DNA (rDNA) array residing in a single human autosome and the 45S rDNA array residing in five human autosomes. The arrays are among the most variable segments of the genome, exhibit concerted copy number variation (cCNV), encode essential components of the ribosome, and modulate global gene expression. Here we combined whole genome data from >700 tumors and paired normal tissues to provide a portrait of rDNA variation in human tissues and cancers of diverse mutational signatures, including stomach and lung adenocarcinomas, ovarian cancers, and others of the TCGA panel. We show that cancers undergo coupled 5S rDNA array expansion and 45S rDNA loss that is accompanied by increased estimates of proliferation rate and nucleolar activity. These somatic changes in rDNA CN occur in a background of over 10-fold naturally occurring rDNA CN variation across individuals and cCNV of 5S-45S arrays in some but not all tissues. Analysis of genetic context revealed associations between cancer rDNA CN amplification or loss and the presence of specific somatic alterations, including somatic SNPs and copy number gain/losses in protein coding genes across the cancer genome. For instance, somatic inactivation of the tumor suppressor gene TP53 emerged with a strong association with coupled 5S expansion / 45S loss in several cancers. Our results uncover frequent and contrasting changes in the 5S and 45S rDNA along rapidly proliferating cell lineages with high nucleolar activity. We suggest that 5S rDNA amplification facilitates increased proliferation, nucleolar activity, and ribosomal synthesis in cancer, whereas 45S rDNA loss emerges as a byproduct of transcription-replication conflict in rapidly replicating tumor cells. The observations raise the prospects of using the rDNA arrays as re-emerging targets for the design of novel strategies in cancer therapy.

The 45S and 5S ribosomal DNA (rDNA) arrays contain hundreds of rDNA copies, with substantial variability across individuals in human populations. Although physically unlinked, the arrays also exhibit joint variation across individual genotypes. However, whether this co-variation in copy number (CN) is universally observed across all tissues is unknown. It also remains unknown if rDNA CN might vary across tissues and in cancer lineages. Here we showed that most cancers undergo coupled 5S rDNA array amplification and 45S rDNA loss, and concerted 5S-45S CN variation in some but not all tissues. The coupled 5S amplification and 45S loss is associated with the presence of certain somatic genetic alterations, as well as increased estimates of cancerous cell proliferation rate and nucleolar activity. Our research uncovers frequent and contrasting changes in rDNA CN in cancers of diverse tissue origin and associated with diverse mutational contexts of tumor suppressors and oncogenes. The observations raise the prospects of using the rDNA arrays as re-emerging targets in novel strategies for cancer therapy.

Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models

We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity to all drugs in each cell line. By conditioning on this variable, biomarkers were identified that were more likely to be effective in clinical trials than those identified using a conventional uncorrected approach. We find that differences in general levels of drug sensitivity are driven by biologically relevant processes. We developed a gene expression based method that can be used to correct for this confounder in future studies.

Codon-Driven Translational Efficiency Is Stable across Diverse Mammalian Cell States

Whether codon usage fine-tunes mRNA translation in mammals remains controversial, with recent papers suggesting that production of proteins in specific Gene Ontological (GO) pathways can be regulated by actively modifying the codon and anticodon pools in different cellular conditions. In this work, we compared the sequence content of genes in specific GO categories with the exonic genome background. Although a substantial fraction of variability in codon usage could be explained by random sampling, almost half of GO sets showed more variability in codon usage than expected by chance. Nevertheless, by quantifying translational efficiency in healthy and cancerous tissues in human and mouse, we demonstrated that a given tRNA pool can equally well translate many different sets of mRNAs, irrespective of their cell-type specificity. This disconnect between variations in codon usage and the stability of translational efficiency is best explained by differences in GC content between gene sets. GC variation across the mammalian genome is most likely a result of the interplay between genome repair and gene duplication mechanisms, rather than selective pressures caused by codon-driven translational rates. Consequently, codon usage differences in mammalian transcriptomes are most easily explained by well-understood mutational biases acting on the underlying genome.

CD10-Equipped Melanoma Cells Acquire Highly Potent Tumorigenic Activity: A Plausible Explanation of Their Significance for a Poor Prognosis

CD10 has been widely used in cancer diagnosis. We previously demonstrated that its expression in melanoma increased with tumor progression and predicted poor patient survival. However, the mechanism by which CD10 promotes melanoma progression remains unclear. In order to elucidate the role of CD10 in melanoma, we established CD10-overexpressing A375 melanoma cells and performed DNA microarray and qRT–PCR analyses to identify changes in the gene expression profile. The microarray analysis revealed that up-regulated genes in CD10-A375 were mostly involved in cell proliferation, angiogenesis, and resistance to apoptosis; down-regulated genes mostly belonged to the categories associated with cell adhesion and migration. Accordingly, in functional experiments, CD10-A375 showed significantly greater cell proliferation in vitro and higher tumorigenicity in vivo; CD10 enzymatic inhibitors, thiorphan and phosphoramidon, significantly blocked the tumor growth of CD10-A375 in mice. In migration and invasion assays, CD10-A375 displayed lower migratory and invasive capacity than mock-A375. CD10 augmented melanoma cell resistance to apoptosis mediated by etoposide and gemcitabine. These findings indicate that CD10 may promote tumor progression by regulating the expression profiles of genes related to cell proliferation, angiogenesis, and resistance to apoptosis.

Multiple myeloma and bone marrow mesenchymal stem cells' crosstalk: Effect on translation initiation

Multiple myeloma (MM) malignant plasma cells reside in the bone marrow (BM) and convert it into a specialized pre-neoplastic niche that promotes the proliferation and survival of the cancer cells. BM resident mesenchymal stem cells (BM-MSCs) are altered in MM and in vitro studies indicate their transformation by MM proximity is within hours. The response time frame suggested that protein translation may be implicated. Thus, we assembled a co-culture model of MM cell lines with MSCs from normal donors (ND) and MM patients to test our hypothesis. The cell lines (U266, ARP-1) and BM-MSCs (ND, MM) were harvested separately after 72 h of co-culture and assayed for proliferation, death, levels of major translation initiation factors (eIF4E, eIF4GI), their targets, and regulators. Significant changes were observed: BM-MSCs (ND and MM) co-cultured with MM cell lines displayed elevated proliferation and death as well as increased expression/activity of eIF4E/eIF4GI; MM cell lines co-cultured with MM-MSCs also displayed higher proliferation and death rates coupled with augmented translation initiation factors; in contrast, MM cell lines co-cultured with ND-MSCs did not display elevated proliferation only death and had no changes in eIF4GI levels/activity. eIF4E expression was increased in one of the cell lines. Our study demonstrates that there is direct dialogue between the MM and BM-MSCs populations that includes translation initiation manipulation and critically affects cell fate. Future research should be aimed at identifying therapeutic targets that may be used to minimize the collateral damage to the cancer microenvironment and limit its recruitment into the malignant process. © 2015 Wiley Periodicals, Inc.

Integrative network modeling approaches to personalized cancer medicine

The ability to collect millions of molecular measurements from patients is a now a reality for clinical medicine. This reality has created the challenge of how to integrate these vast amounts of data into models that accurately predict complex pathophysiology and can translate this complexity into clinically actionable outputs. Integrative informatics and data-driven approaches provide a framework for analyzing large-scale datasets and combining them into multiscale models that can be used to determine the key drivers of disease and identify optimal therapies for treating tumors. In this perspective we discuss how an integrative modeling approach is being used to inform individual treatment decisions, highlighting a recent case report that illustrates the challenges and opportunities for personalized oncology.

Highly Expressed Genes in Rapidly Proliferating Tumor Cells as New Targets for Colorectal Cancer Treatment

PURPOSE

The clinical management of colorectal cancer patients has significantly improved because of the identification of novel therapeutic targets such as EGFR and VEGF. Because rapid tumor proliferation is associated with poor patient prognosis, here we characterized the transcriptional signature of rapidly proliferating colorectal cancer cells in an attempt to identify novel candidate therapeutic targets.

EXPERIMENTAL DESIGN

The doubling time of 52 colorectal cancer cell lines was determined and genome-wide expression profiling of a subset of these lines was assessed by microarray analysis. We then investigated the potential of genes highly expressed in cancer cells with faster growth as new therapeutic targets.

RESULTS

Faster proliferation rates were associated with microsatellite instability and poorly differentiated histology. The expression of 1,290 genes was significantly correlated with the growth rates of colorectal cancer cells. These included genes involved in cell cycle, RNA processing/splicing, and protein transport. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and protoporphyrinogen oxidase (PPOX) were shown to have higher expression in faster growing cell lines and primary tumors. Pharmacologic or siRNA-based inhibition of GAPDH or PPOX reduced the growth of colon cancer cells in vitro. Moreover, using a mouse xenograft model, we show that treatment with the specific PPOX inhibitor acifluorfen significantly reduced the growth of three of the seven (42.8%) colon cancer lines investigated.

CONCLUSIONS

We have characterized at the transcriptomic level the differences between colorectal cancer cells that vary in their growth rates, and identified novel candidate chemotherapeutic targets for the treatment of colorectal cancer.

Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality

Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.