Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

Cancer cell states and emergent properties of the dynamic tumor system.

Phenotypic heterogeneity within malignant cells of a tumor is emerging as a key property of tumorigenesis. Recent work using single-cell transcriptomics has led to the identification of distinct cancer cell states across a range of cancer types, but their functional relevance and the advantage that they provide to the tumor as a system remain elusive. We present here a definition of cancer cell states in terms of coherently and differentially expressed gene modules and review the origins, dynamics, and impact of states on the tumor system as a whole. The spectrum of cell states taken on by a malignant population may depend on cellular lineage, epigenetic history, genetic mutations, or environmental cues, which has implications for the relative stability or plasticity of individual states. Finally, evidence has emerged that malignant cells in different states may cooperate or compete within a tumor niche, thereby providing an evolutionary advantage to the tumor through increased immune evasion, drug resistance, or invasiveness. Uncovering the mechanisms that govern the origin and dynamics of cancer cell states in tumorigenesis may shed light on how heterogeneity contributes to tumor fitness and highlight vulnerabilities that can be exploited for therapy.

Exploring tissue architecture using spatial transcriptomics.

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Bimodal Evolutionary Developmental miRNA Program in Animal Embryogenesis.

miRNAs play essential roles in the mechanics of gene regulation, however, on an organismal-scale, the processes in which they are deployed are not well understood. Here, we adopt an evolutionary developmental approach to study miRNA function by examining their expression throughout embryogenesis in both Caenorhabditis elegans and Drosophila melanogaster. We find that, in both species, miRNA transcriptomic shifts in a punctuated fashion during the mid-developmental transition, specifying two dominant modes of early and late expression profiles. Strikingly, late-expressed miRNAs are enriched for phylogenetic conservation and function by fine-tuning the expression of their targets, implicating a role in the canalization of cell types during differentiation. In contrast, early expressed miRNAs are inversely expressed with their targets suggesting strong target-inhibition. Taken together, our work exposes a bimodal role for miRNA function during animal development, involving late-expressed physiological roles and early expressed repressive roles.

Unveiling the Impact of the Genomic Architecture on the Evolution of Vertebrate microRNAs.

Eukaryotic genomes frequently exhibit interdependency between transcriptional units, as evidenced by regions of high gene density. It is well recognized that vertebrate microRNAs (miRNAs) are usually embedded in those regions. Recent work has shown that the genomic context is of utmost importance to determine miRNA expression in time and space, thus affecting their evolutionary fates over long and short terms. Consequently, understanding the inter- and intraspecific changes on miRNA genomic architecture may bring novel insights on the basic cellular processes regulated by miRNAs, as well as phenotypic evolution and disease-related mechanisms.

Host gene constraints and genomic context impact the expression and evolution of human microRNAs.

Increasing evidence has shown that recent miRNAs tend to emerge within coding genes. Here we conjecture that human miRNA evolution is tightly influenced by the genomic context, especially by host genes. Our findings show a preferential emergence of intragenic miRNAs within old genes. We found that miRNAs within old host genes are significantly more broadly expressed than those within young ones. Young miRNAs within old genes are more broadly expressed than their intergenic counterparts, suggesting that young miRNAs have an initial advantage by residing in old genes, and benefit from their hosts' expression control and from the exposure to diverse cellular contexts and target genes. Our results demonstrate that host genes may provide stronger expression constraints to intragenic miRNAs in the long run. We also report associated functional implications, highlighting the genomic context and host genes as driving factors for the expression and evolution of human miRNAs.

Comprehensive evaluation of the effectiveness of gene expression signatures to predict complete response to neoadjuvant chemoradiotherapy and guide surgical intervention in rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) may lead to complete tumor regression in rectal cancer patients. Prediction of complete response to nCRT may allow a personalized management of rectal cancer and spare patients from unnecessary radical total mesorectal excision with or without sphincter preservation. To identify a gene expression signature capable of predicting complete pathological response (pCR) to nCRT, we performed a gene expression analysis in 25 pretreatment biopsies from patients who underwent 5FU-based nCRT using RNA-Seq. A supervised learning algorithm was used to identify expression signatures capable of predicting pCR, and the predictive value of these signatures was validated using independent samples. We also evaluated the utility of previously published signatures in predicting complete response in our cohort. We identified 27 differentially expressed genes between patients with pCR and patients with incomplete responses to nCRT. Predictive gene signatures using subsets of these 27 differentially expressed genes peaked at 81.8% accuracy. However, signatures with the highest sensitivity showed poor specificity, and vice-versa, when applied in an independent set of patients. Testing previously published signatures on our cohort also showed poor predictive value. Our results indicate that currently available predictive signatures are highly dependent on the sample set from which they are derived, and their accuracy is not superior to current imaging and clinical parameters used to assess response to nCRT and guide surgical intervention.

miRIAD-integrating microRNA inter- and intragenic data.

MicroRNAs (miRNAs) are a class of small (∼22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter- and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein-protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations. Database URL: http://www.miriad-database.org.

Evolutionary history of exon shuffling.

Exon shuffling has been characterized as one of the major evolutionary forces shaping both the genome and the proteome of eukaryotes. This mechanism was particularly important in the creation of multidomain proteins during animal evolution, bringing a number of functional genetic novelties. Here, genome information from a variety of eukaryotic species was used to address several issues related to the evolutionary history of exon shuffling. By comparing all protein sequences within each species, we were able to characterize exon shuffling signatures throughout metazoans. Intron phase (the position of the intron regarding the codon) and exon symmetry (the pattern of flanking introns for a given exon or block of adjacent exons) were features used to evaluate exon shuffling. We confirmed previous observations that exon shuffling mediated by phase 1 introns (1-1 exon shuffling) is the predominant kind in multicellular animals. Evidence is provided that such pattern was achieved since the early steps of animal evolution, supported by a detectable presence of 1-1 shuffling units in Trichoplax adhaerens and a considerable prevalence of them in Nematostella vectensis. In contrast, Monosiga brevicollis, one of the closest relatives of metazoans, and Arabidopsis thaliana, showed no evidence of 1-1 exon or domain shuffling above what it would be expected by chance. Instead, exon shuffling events are less abundant and predominantly mediated by phase 0 introns (0-0 exon shuffling) in those non-metazoan species. Moreover, an intermediate pattern of 1-1 and 0-0 exon shuffling was observed for the placozoan T. adhaerens, a primitive animal. Finally, characterization of flanking intron phases around domain borders allowed us to identify a common set of symmetric 1-1 domains that have been shuffled throughout the metazoan lineage.

The role of exon shuffling in shaping protein-protein interaction networks.

BACKGROUND: Physical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. RESULTS: We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling. CONCLUSIONS: Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.