Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer.

BACKGROUND: Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses. METHODS: To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. RESULTS: Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells. CONCLUSIONS: Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.

Predicting drug response from single-cell expression profiles of tumours.

BACKGROUND: Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention. RESULTS: Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. CONCLUSIONS: DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .

MicroRNA 483-3p overexpression unleashes invasive growth of metastatic colorectal cancer via NDRG1 downregulation and ensuing activation of the ERBB3/AKT axis.

In colorectal cancer, the mechanisms underlying tumor aggressiveness require further elucidation. Taking advantage of a large panel of human metastatic colorectal cancer xenografts and matched stem-like cell cultures (m-colospheres), here we show that the overexpression of microRNA 483-3p (miRNA-483-3p; also known as MIR-483-3p), encoded by a frequently amplified gene locus, confers an aggressive phenotype. In m-colospheres, endogenous or ectopic miRNA-483-3p overexpression increased proliferative response, invasiveness, stem cell frequency, and resistance to differentiation. Transcriptomic analyses and functional validation found that miRNA-483-3p directly targets NDRG1, known as a metastasis suppressor involved in EGFR family downregulation. Mechanistically, miRNA-483-3p overexpression induced the signaling pathway triggered by ERBB3, including AKT and GSK3ß, and led to the activation of transcription factors regulating epithelial-mesenchymal transition (EMT). Consistently, treatment with selective anti-ERBB3 antibodies counteracted the invasive growth of miRNA-483-3p-overexpressing m-colospheres. In human colorectal tumors, miRNA-483-3p expression inversely correlated with NDRG1 and directly correlated with EMT transcription factor expression and poor prognosis. These results unveil a previously unrecognized link between miRNA-483-3p, NDRG1, and ERBB3-AKT signaling that can directly support colorectal cancer invasion and is amenable to therapeutic targeting.

Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.

Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.

Mutated axon guidance gene PLXNB2 sustains growth and invasiveness of stem cells isolated from cancers of unknown primary.

The genetic changes sustaining the development of cancers of unknown primary (CUP) remain elusive. The whole-exome genomic profiling of 14 rigorously selected CUP samples did not reveal specific recurring mutation in known driver genes. However, by comparing the mutational landscape of CUPs with that of most other human tumor types, it emerged a consistent enrichment of changes in genes belonging to the axon guidance KEGG pathway. In particular, G842C mutation of PlexinB2 (PlxnB2) was predicted to be activating. Indeed, knocking down the mutated, but not the wild-type, PlxnB2 in CUP stem cells resulted in the impairment of self-renewal and proliferation in culture, as well as tumorigenic capacity in mice. Conversely, the genetic transfer of G842C-PlxnB2 was sufficient to promote CUP stem cell proliferation and tumorigenesis in mice. Notably, G842C-PlxnB2 expression in CUP cells was associated with basal EGFR phosphorylation, and EGFR blockade impaired the viability of CUP cells reliant on the mutated receptor. Moreover, the mutated PlxnB2 elicited CUP cell invasiveness, blocked by EGFR inhibitor treatment. In sum, we found that a novel activating mutation of the axon guidance gene PLXNB2 sustains proliferative autonomy and confers invasive properties to stem cells isolated from cancers of unknown primary, in EGFR-dependent manner.

ß-catenin perturbations control differentiation programs in mouse embryonic stem cells.

The Wnt/ß-catenin pathway is involved in development, cancer, and embryonic stem cell (ESC) maintenance; its dual role in stem cell self-renewal and differentiation is still controversial. Here, by applying an in vitro system enabling inducible gene expression control, we report that moderate induction of transcriptionally active exogenous ß-catenin in ß-catenin null mouse ESCs promotes epiblast-like cell (EpiLC) derivation in vitro. Instead, in wild-type cells, moderate chemical pre-activation of the Wnt/ß-catenin pathway promotes EpiLC in vitro derivation. Finally, we suggest that moderate ß-catenin levels in ß-catenin null mouse ESCs favor early stem cell commitment toward mesoderm if the exogenous protein is induced only in the "ground state" of pluripotency condition, or endoderm if the induction is maintained during the differentiation. Overall, our results confirm previous findings about the role of ß-catenin in pluripotency and differentiation, while indicating a role for its doses in promoting specific differentiation programs.

Cancer of unknown primary stem-like cells model multi-organ metastasis and unveil liability to MEK inhibition.

Cancers of unknown primary (CUPs), featuring metastatic dissemination in the absence of a primary tumor, are a biological enigma and a fatal disease. We propose that CUPs are a distinct, yet unrecognized, pathological entity originating from stem-like cells endowed with peculiar and shared properties. These cells can be isolated in vitro (agnospheres) and propagated in vivo by serial transplantation, displaying high tumorigenicity. After subcutaneous engraftment, agnospheres recapitulate the CUP phenotype, by spontaneously and quickly disseminating, and forming widespread established metastases. Regardless of different genetic backgrounds, agnospheres invariably display cell-autonomous proliferation and self-renewal, mostly relying on unrestrained activation of the MAP kinase/MYC axis, which confers sensitivity to MEK inhibitors in vitro and in vivo. Such sensitivity is associated with a transcriptomic signature predicting that more than 70% of CUP patients could be eligible to MEK inhibition. These data shed light on CUP biology and unveil an opportunity for therapeutic intervention.

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

O-linked mucin-type glycosylation regulates the transcriptional programme downstream of EGFR.

Aberrant mucin-type O-linked glycosylation is a common occurrence in cancer where the upregulation of sialyltransferases is often seen leading to the early termination of O-glycan chains. Mucin-type O-linked glycosylation is not limited to mucins and occurs on many cell surface glycoproteins including EGFR, where the number of sites can be limited. Upon EGF ligation, EGFR induces a signaling cascade and may also translocate to the nucleus where it directly regulates gene transcription, a process modulated by Galectin-3 and MUC1 in some cancers. Here, we show that upon EGF binding, breast cancer cells carrying different O-glycans respond by transcribing different gene expression signatures. MMP10, the principal gene upregulated when cells carrying sialylated core 1 glycans were stimulated with EGF, is also upregulated in ER-positive breast carcinoma reported to express high levels of ST3Gal1 and hence mainly core 1 sialylated O-glycans. In contrast, isogenic cells engineered to carry core 2 glycans upregulate CX3CL1 and FGFBP1 and these genes are upregulated in ER-negative breast carcinomas, also known to express longer core 2 O-glycans. Changes in O-glycosylation did not significantly alter signal transduction downstream of EGFR in core 1 or core 2 O-glycan expressing cells. However, striking changes were observed in the formation of an EGFR/galectin-3/MUC1/ß-catenin complex at the cell surface that is present in cells carrying short core 1-based O-glycans but absent in core 2 carrying cells.

GADD34 is a modulator of autophagy during starvation.

Cells respond to starvation by shutting down protein synthesis and by activating catabolic processes, including autophagy, to recycle nutrients. This two-pronged response is mediated by the integrated stress response (ISR) through phosphorylation of eIF2α, which represses protein translation, and by inhibition of mTORC1 signaling, which promotes autophagy also through a stress-responsive transcriptional program. Implementation of such a program, however, requires protein synthesis, thus conflicting with general repression of translation. How is this mismatch resolved? We found that the main regulator of the starvation-induced transcriptional program, TFEB, counteracts protein synthesis inhibition by directly activating expression of GADD34, a component of the protein phosphatase 1 complex that dephosphorylates eIF2α. We discovered that GADD34 plays an essential role in autophagy by tuning translation during starvation, thus enabling lysosomal biogenesis and a sustained autophagic flux. Hence, the TFEB-GADD34 axis integrates the mTORC1 and ISR pathways in response to starvation.

Cancer of Unknown Primary (CUP): genetic evidence for a novel nosological entity? A case report.

Cancer of unknown primary (CUP) is an obscure disease characterized by multiple metastases in the absence of a primary tumor. No consensus has been reached whether CUPs are simply generated from cancers that cannot be detected or whether they are the manifestation of a still unknown nosological entity. Here, we report the complete expression and genetic analysis of multiple synchronous metastases harvested at warm autopsy of a patient with CUP. The expression profiles were remarkably similar and astonishingly singular. The whole exome analysis yielded a high number of mutations present in all metastases (fully shared), additional mutations (partially shared) accumulated one after another in a series, and few private mutations were unique to each metastasis. Surprisingly, the phylogenetic trajectory linking CUP metastases was atypical, depicting a common "stream", sprouting a series of linear "brooks", at variance from the extensive branched evolution observed in metastases from most cancers of known origin. The distinctive genetic and evolutionary features depicted suggest that CUP is a novel nosological entity.

TRPV4 and purinergic receptor signalling pathways are separately linked in airway epithelia to CFTR and TMEM16A chloride channels.

KEY POINTS: Eact is a putative pharmacological activator of TMEM16A. Eact is strongly effective in recombinant Fischer rat thyroid (FRT) cells but not in airway epithelial cells with endogenous TMEM16A expression. Transcriptomic analysis, gene silencing and functional studies in FRT cells reveal that Eact is actually an activator of the Ca2+ -permeable TRPV4 channel. In airway epithelial cells TRPV4 and TMEM16A are expressed in separate cell types. Intracellular Ca2+ elevation by TRPV4 stimulation leads to CFTR channel activation. ABSTRACT: TMEM16A is a Ca2+ -activated Cl- channel expressed in airway epithelial cells, particularly under conditions of mucus hypersecretion. To investigate the role of TMEM16A, we used Eact, a putative TMEM16A pharmacological activator. However, in contrast to purinergic stimulation, we found little effect of Eact on bronchial epithelial cells under conditions of high TMEM16A expression. We hypothesized that Eact is an indirect activator of TMEM16A. By a combination of approaches, including short-circuit current recordings, bulk and single cell RNA sequencing, intracellular Ca2+ imaging and RNA interference, we found that Eact is actually an activator of the Ca2+ -permeable TRPV4 channel and that the modest effect of this compound in bronchial epithelial cells is due to a separate expression of TMEM16A and TRPV4 in different cell types. Importantly, we found that TRPV4 stimulation induced activation of the CFTR Cl- channel. Our study reveals the existence of separate Ca2+ signalling pathways linked to different Cl- secretory processes.

A Tool for Visualization and Analysis of Single-Cell RNA-Seq Data Based on Text Mining.

Gene expression in individual cells can now be measured for thousands of cells in a single experiment thanks to innovative sample-preparation and sequencing technologies. State-of-the-art computational pipelines for single-cell RNA-sequencing data, however, still employ computational methods that were developed for traditional bulk RNA-sequencing data, thus not accounting for the peculiarities of single-cell data, such as sparseness and zero-inflated counts. Here, we present a ready-to-use pipeline named gf-icf (gene frequency-inverse cell frequency) for normalization of raw counts, feature selection, and dimensionality reduction of scRNA-seq data for their visualization and subsequent analyses. Our work is based on a data transformation model named term frequency-inverse document frequency (TF-IDF), which has been extensively used in the field of text mining where extremely sparse and zero-inflated data are common. Using benchmark scRNA-seq datasets, we show that the gf-icf pipeline outperforms existing state-of-the-art methods in terms of improved visualization and ability to separate and distinguish different cell types.

The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells.

MicroRNAs act posttranscriptionally to suppress multiple target genes within a cell population. To what extent this multi-target suppression occurs in individual cells and how it impacts transcriptional heterogeneity and gene co-expression remains unknown. Here we used single-cell sequencing combined with introduction of individual microRNAs. miR-294 and let-7c were introduced into otherwise microRNA-deficient Dgcr8 knockout mouse embryonic stem cells. Both microRNAs induce suppression and correlated expression of their respective gene targets. The two microRNAs had opposing effects on transcriptional heterogeneity within the cell population, with let-7c increasing and miR-294 decreasing the heterogeneity between cells. Furthermore, let-7c promotes, whereas miR-294 suppresses, the phasing of cell cycle genes. These results show at the individual cell level how a microRNA simultaneously has impacts on its many targets and how that in turn can influence a population of cells. The findings have important implications in the understanding of how microRNAs influence the co-expression of genes and pathways, and thus ultimately cell fate.

MEGA-V: detection of variant gene sets in patient cohorts.

Summary: : Detecting significant associations between genetic variants and disease may prove particularly challenging when the variants are rare in the population and/or act together with other variants to cause the disease. We have developed a statistical framework named Mutation Enrichment Gene set Analysis of Variants (MEGA-V) that specifically detects the enrichments of genetic alterations within a process in a cohort of interest. By focusing on the mutations of several genes contributing to the same function rather than on those affecting a single gene, MEGA-V increases the power to detect statistically significant associations. Availability and Implementation: MEGA-V is available at https://github.com/ciccalab/MEGA. Contact: francesca.ciccarelli@kcl.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Patients with genetically heterogeneous synchronous colorectal cancer carry rare damaging germline mutations in immune-related genes.

Synchronous colorectal cancers (syCRCs) are physically separated tumours that develop simultaneously. To understand how the genetic and environmental background influences the development of multiple tumours, here we conduct a comparative analysis of 20 syCRCs from 10 patients. We show that syCRCs have independent genetic origins, acquire dissimilar somatic alterations, and have different clone composition. This inter- and intratumour heterogeneity must be considered in the selection of therapy and in the monitoring of resistance. SyCRC patients show a higher occurrence of inherited damaging mutations in immune-related genes compared to patients with solitary colorectal cancer and to healthy individuals from the 1,000 Genomes Project. Moreover, they have a different composition of immune cell populations in tumour and normal mucosa, and transcriptional differences in immune-related biological processes. This suggests an environmental field effect that promotes multiple tumours likely in the background of inflammation.

A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data.

BACKGROUND: Transcription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods. RESULTS: We developed a computational strategy, called Differential Multi-Information (DMI), to infer post-translational modulators of a transcription factor from a compendium of gene expression profiles (GEPs). DMI is built on the hypothesis that the modulator of a TF (i.e. kinase/phosphatases), when expressed in the cell, will cause the TF target genes to be co-expressed. On the contrary, when the modulator is not expressed, the TF will be inactive resulting in a loss of co-regulation across its target genes. DMI detects the occurrence of changes in target gene co-regulation for each candidate modulator, using a measure called Multi-Information. We validated the DMI approach on a compendium of 5,372 GEPs showing its predictive ability in correctly identifying kinases regulating the activity of 14 different transcription factors. CONCLUSIONS: DMI can be used in combination with experimental approaches as high-throughput screening to efficiently improve both pathway and target discovery. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest che be found at http://dmi.tigem.it.

Fibroblast growth factor signalling controls nervous system patterning and pigment cell formation in Ciona intestinalis.

During the development of the central nervous system (CNS), combinations of transcription factors and signalling molecules orchestrate patterning, specification and differentiation of neural cell types. In vertebrates, three types of melanin-containing pigment cells, exert a variety of functional roles including visual perception. Here we analysed the mechanisms underlying pigment cell specification within the CNS of a simple chordate, the ascidian Ciona intestinalis. Ciona tadpole larvae exhibit a basic chordate body plan characterized by a small number of neural cells. We employed lineage-specific transcription profiling to characterize the expression of genes downstream of fibroblast growth factor signalling, which govern pigment cell formation. We demonstrate that FGF signalling sequentially imposes a pigment cell identity at the expense of anterior neural fates. We identify FGF-dependent and pigment cell-specific factors, including the small GTPase, Rab32/38 and demonstrated its requirement for the pigmentation of larval sensory organs.

Differential network analysis for the identification of condition-specific pathway activity and regulation.

MOTIVATION: Identification of differential expressed genes has led to countless new discoveries. However, differentially expressed genes are only a proxy for finding dysregulated pathways. The problem is to identify how the network of regulatory and physical interactions rewires in different conditions or in disease. RESULTS: We developed a procedure named DINA (DIfferential Network Analysis), which is able to identify set of genes, whose co-regulation is condition-specific, starting from a collection of condition-specific gene expression profiles. DINA is also able to predict which transcription factors (TFs) may be responsible for the pathway condition-specific co-regulation. We derived 30 tissue-specific gene networks in human and identified several metabolic pathways as the most differentially regulated across the tissues. We correctly identified TFs such as Nuclear Receptors as their main regulators and demonstrated that a gene with unknown function (YEATS2) acts as a negative regulator of hepatocyte metabolism. Finally, we showed that DINA can be used to make hypotheses on dysregulated pathways during disease progression. By analyzing gene expression profiles across primary and transformed hepatocytes, DINA identified hepatocarcinoma-specific metabolic and transcriptional pathway dysregulation. AVAILABILITY: We implemented an on-line web-tool http://dina.tigem.it enabling the user to apply DINA to identify tissue-specific pathways or gene signatures. CONTACT: dibernardo@tigem.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.