research

A major challenge in human genetics is to identify all distal regulatory elements and determine their effects on target gene expression in a given cell type. To this end, large-scale CRISPR screens have been conducted to perturb thousands of candidate enhancers. Using these data, predictive models have been developed that aim to generalize such findings to predict which enhancers regulate which genes across the genome. However, existing CRISPR methods and large-scale datasets have limitations in power, scale, or selection bias, with the potential to skew our understanding of the properties of distal regulatory elements and confound our ability to evaluate predictive models. Here, we develop a new framework for highly powered, unbiased CRISPR screens, including an optimized experimental method (Direct-Capture Targeted Perturb-seq (DC-TAP-seq)), a random design strategy, and a comprehensive analytical pipeline that accounts for statistical power. We applied this framework to survey 1,425 randomly selected candidate regulatory elements across two human cell lines. Our results reveal fundamental properties of distal regulatory elements in the human genome. Most element-gene regulatory interactions are estimated to have small effect sizes (<10%), which previous experiments were not powered to detect. Most cis-regulatory interactions occur over short genomic distances (<100 kb). A large fraction of the discovered regulatory elements bind CTCF but do not show chromatin marks typical of classical enhancers. Housekeeping genes have similar frequencies of distal regulatory elements compared to other genes, but with 2-fold weaker effect sizes. Comparisons to the predictions of the ENCODE-rE2G model suggest that, while performance is similar across two cell types, new models will be needed to detect elements with weaker effect sizes, regulatory effects of CTCF sites, and enhancers for housekeeping genes. Overall, this study describes the first unbiased, perturbation-based survey of thousands of distal regulatory element-gene connections, and provides a framework for expanding such efforts to build more complete maps of distal regulation in the human genome.

Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer-gene regulatory interactions from single-cell datasets has been challenging. Here, we introduce a new family of classification models, scE2G, to predict enhancer-gene regulation. These models use features from single-cell ATAC-seq or multiomic RNA and ATAC-seq data, and are trained on a CRISPR perturbation dataset including >10,000 evaluated element-gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped eQTLs, and GWAS variant-gene associations and demonstrate state-of-the-art performance at prediction tasks across multiple cell types and categories of perturbations. We apply scE2G to build maps of enhancer-gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte counts. The scE2G models will enable accurate mapping of enhancer-gene regulatory interactions across thousands of human cell types.

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1–6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

Plastic waste has reached epidemic proportions worldwide, and the production of plastic continues to rise steadily. Plastic represents a diverse array of commonly used synthetic polymers that are extremely useful as durable, economically beneficial alternatives to other materials; however, despite the wide-ranging utility of plastic, the increasing accumulation of plastic waste in the environment has had numerous detrimental impacts. In particular, plastic marine debris can transport invasive species, entangle marine organisms, and cause toxic chemical bioaccumulation in the marine food web. The negative impacts of plastic waste have motivated research on new ways to reduce and eliminate plastic. One unique approach to tackle the plastic waste problem is to turn to nature’s solutions for degrading polymers by leveraging the biology of naturally occurring organisms to degrade plastic. Advances in metagenomics, next generation sequencing, and bioengineering have provided new insights and new opportunities to identify and optimize organisms for use in plastic bioremediation. In this review, we discuss the plastic waste problem and possible solutions, with a focus on potential mechanisms for plastic bioremediation. We pinpoint two key habitats to identify plastic-biodegrading organisms: (1) habitats with distinct enrichment of plastic waste, such as those near processing or disposal sites, and (2) habitats with naturally occurring polymers, such as cutin, lignin, and wax. Finally, we identify directions of future research for the isolation and optimization of these methods for widespread bioremediation applications.

Defining the temporal gene regulatory programs that drive human organogenesis is essential for understanding the origins of congenital disease. We combined a time-resolved, single-cell multi-omic atlas of human iPSC-derived cardiac organoids with deep learning models that predict chromatin accessibility from DNA sequence, enabling the discovery of the regulatory syntax underlying early heart development. This framework uncovered cell-state-specific rules of cardiogenesis, including context-dependent activities of TEAD, HAND, and TBX transcription factor families, and linked these motifs to their target genes. We identified distinct programs guiding lineage divergence, such as ventricular versus pacemaker cardiomyocytes, and validated predictions by perturbing Myocardin (_MYOCD_), establishing its essential role in ventricular specification. Integration of chromatin, transcriptional, and genetic data further highlighted regulatory regions and disease-associated variants that perturb differentiation state transitions, supporting evidence that suggests congenital heart disease emerges early in development. This work bridges developmental gene regulation with disease genetics, providing a foundation for mechanistic and therapeutic insights into congenital diseases.

Despite advances in disease treatment, our understanding of how damaged organs recover and the mechanisms governing this process remain poorly defined. Here, we mapped the transcriptional and regulatory landscape of human cardiac recovery using single cell multiomics. Macrophages emerged as the most reprogrammed cell type. Deep learning identified the transcription factor RUNX1 as a key regulator of this process. Macrophage-specific Runx1 deletion recapitulated the human cardiac recovery phenotype in a chronic heart failure model. Runx1 deletion reprogrammed macrophages to a reparative phenotype, reduced fibrosis, and promoted cardiomyocyte adaptation. RUNX1 chromatin profiling revealed a conserved regulon that diminished during recovery. Mechanistically, the epigenetic reader BRD4 controlled Runx1 expression in macrophages. Chromatin activity mapping, combined with CRISPR perturbations, identified the precise regulatory element governing Runx1 expression. Therapeutically, small molecule Runx1 inhibition was sufficient to promote cardiac recovery. Our findings uncover a druggable RUNX1 epigenetic mechanism that orchestrates recovery of heart function.

Congenital heart defects (CHD) arise in part due to inherited genetic variants that alter genes and noncoding regulatory elements in the human genome. These variants are thought to act during fetal development to influence the formation of different heart structures. However, identifying the genes, pathways, and cell types that mediate these effects has been challenging due to the immense diversity of cell types involved in heart development as well as the superimposed complexities of interpreting noncoding sequences. As such, understanding the molecular functions of both noncoding and coding variants remains paramount to our fundamental understanding of cardiac development and CHD. Here, we created a gene regulation map of the healthy human fetal heart across developmental time, and applied it to interpret the functions of variants associated with CHD and quantitative cardiac traits. We collected single-cell multiomic data from 734,000 single cells sampled from 41 fetal hearts spanning post-conception weeks 6 to 22, enabling the construction of gene regulation maps in 90 cardiac cell types and states, including rare populations of cardiac conduction cells. Through an unbiased analysis of all 90 cell types, we find that both rare coding variants associated with CHD and common noncoding variants associated with valve traits converge to affect valvular interstitial cells (VICs). VICs are enriched for high expression of known CHD genes previously identified through mapping of rare coding variants. Eight CHD genes, as well as other genes in similar molecular pathways, are linked to common noncoding variants associated with other valve diseases or traits via enhancers in VICs. In addition, certain common noncoding variants impact enhancers with activities highly specific to particular subanatomic structures in the heart, illuminating how such variants can impact specific aspects of heart structure and function. Together, these results implicate new enhancers, genes, and cell types in the genetic etiology of CHD, identify molecular convergence of common noncoding and rare coding variants on VICs, and suggest a more expansive view of the cell types instrumental in genetic risk for CHD, beyond the working cardiomyocyte. This regulatory map of the human fetal heart will provide a foundational resource for understanding cardiac development, interpreting genetic variants associated with heart disease, and discovering targets for cell-type specific therapies.

Although genome wide association studies (GWAS) in large populations have identified hundreds of variants associated with common diseases such as coronary artery disease (CAD), most disease-associated variants lie within non-coding regions of the genome, rendering it difficult to determine the downstream causal gene and cell type. Here, we performed paired single nucleus gene expression and chromatin accessibility profiling from 44 human coronary arteries. To link disease variants to molecular traits, we developed a meta-map of 88 samples and discovered 11,182 single-cell chromatin accessibility quantitative trait loci (caQTLs). Heritability enrichment analysis and disease variant mapping demonstrated that smooth muscle cells (SMCs) harbor the greatest genetic risk for CAD. To capture the continuum of SMC cell states in disease, we used dynamic single cell caQTL modeling for the first time in tissue to uncover QTLs whose effects are modified by cell state and expand our insight into genetic regulation of heterogenous cell populations. Notably, we identified a variant in the COL4A1/COL4A2 CAD GWAS locus which becomes a caQTL as SMCs de-differentiate by changing a transcription factor binding site for EGR1/2. To unbiasedly prioritize functional candidate genes, we built a genome-wide single cell variant to enhancer to gene (scV2E2G) map for human CAD to link disease variants to causal genes in cell types. Using this approach, we found several hundred genes predicted to be linked to disease variants in different cell types. Next, we performed genome-wide Hi-C in 16 human coronary arteries to build tissue specific maps of chromatin conformation and link disease variants to integrated chromatin hubs and distal target genes. Using this approach, we show that rs4887091 within the ADAMTS7 CAD GWAS locus modulates function of a super chromatin interactome through a change in a CTCF binding site. Finally, we used CRISPR interference to validate a distal gene, AMOTL2, liked to a CAD GWAS locus. Collectively we provide a disease-agnostic framework to translate human genetic findings to identify pathologic cell states and genes driving disease, producing a comprehensive scV2E2G map with genetic and tissue level convergence for future mechanistic and therapeutic studies.

Sequence-specific activation by transcription factors is essential for gene regulation. Key to this are activation domains, which often fall within disordered regions of transcription factors and recruit co-activators to initiate transcription. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 Kds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

Raynaud's syndrome is a dysautonomia where exposure to cold causes vasoconstriction and hypoxia, particularly in the extremities. We performed meta-analysis in four cohorts and discovered eight loci (ADRA2A, IRX1, NOS3, ACVR2A, TMEM51, PCDH10-DT, HLA, and RAB6C) where ADRA2A, ACVR2A, NOS3, TMEM51, and IRX1 co-localized with expression quantitative trait loci (eQTLs), particularly in distal arteries. CRISPR gene editing further showed that ADRA2A and NOS3 loci modified gene expression and in situ RNAscope clarified the specificity of ADRA2A in small vessels and IRX1 around small capillaries in the skin. A functional contraction assay in the cold showed lower contraction in ADRA2A-deficient and higher contraction in ADRA2A-overexpressing smooth muscle cells. Overall, our study highlights the power of genome-wide association testing with functional follow-up as a method to understand complex diseases. The results indicate temperature-dependent adrenergic signaling through ADRA2A, effects at the microvasculature by IRX1, endothelial signaling by NOS3, and immune mechanisms by the HLA locus in Raynaud's syndrome.

Enhancers are key drivers of gene regulation thought to act via 3D physical interactions with the promoters of their target genes. However, genome-wide depletions of architectural proteins such as cohesin result in only limited changes in gene expression, despite a loss of contact domains and loops. Consequently, the role of cohesin and 3D contacts in enhancer function remains debated. Here, we developed CRISPRi of regulatory elements upon degron operation (CRUDO), a novel approach to measure how changes in contact frequency impact enhancer effects on target genes by perturbing enhancers with CRISPRi and measuring gene expression in the presence or absence of cohesin. We systematically perturbed all 1,039 candidate enhancers near five cohesin-dependent genes and identified 34 enhancer-gene regulatory interactions. Of 26 regulatory interactions with sufficient statistical power to evaluate cohesin dependence, 18 show cohesin-dependent effects. A decrease in enhancer-promoter contact frequency upon removal of cohesin is frequently accompanied by a decrease in the regulatory effect of the enhancer on gene expression, consistent with a contact-based model for enhancer function. However, changes in contact frequency and regulatory effects on gene expression vary as a function of distance, with distal enhancers (e.g., >50Kb) experiencing much larger changes than proximal ones (e.g., <50Kb). Because most enhancers are located close to their target genes, these observations can explain how only a small subset of genes - those with strong distal enhancers - are sensitive to cohesin. Together, our results illuminate how 3D contacts, influenced by both cohesin and genomic distance, tune enhancer effects on gene expression.

Despite substantial improvements in the treatment landscape of prostate cancer, the evolution of hormone therapy-resistant and metastatic prostate cancer remains a major cause of cancer-related death globally. The mainstay of treatment for advanced prostate cancer is targeting of androgen receptor signaling, including androgen deprivation therapy plus second-generation androgen receptor blockade (e.g., enzalutamide, apalutamide, darolutamide), and/or androgen synthesis inhibition (abiraterone). While these agents have significantly prolonged the lives of patients with advanced prostate cancer, is nearly universal. This therapy resistance is mediated by diverse mechanisms, including both androgen receptor-dependent mechanisms, such as androgen receptor mutations, amplifications, alternative splicing, and amplification, as well as non-androgen receptor-mediated mechanisms, such as lineage plasticity toward neuroendocrine-like or epithelial-mesenchymal transition (EMT)-like lineages. Our prior work identified the EMT transcriptional regulator Snail as critical to hormonal therapy resistance and is commonly detected in human metastatic prostate cancer. In the current study, we sought to interrogate the actionable landscape of EMT-mediated hormone therapy resistant prostate cancer to identify synthetic lethality and collateral sensitivity approaches to treating this aggressive, therapy-resistant disease state. Using a combination of high-throughput drug screens and multi-parameter phenotyping by confluence imaging, ATP production, and phenotypic plasticity reporters of EMT, we identified candidate synthetic lethalities to Snail-mediated EMT in prostate cancer. These analyses identified multiple actionable targets, such as XPO1, PI3K/mTOR, aurora kinases, c-MET, polo-like kinases, and JAK/STAT as synthetic lethalities in Snail+ prostate cancer. We validated these targets in a subsequent validation screen in an LNCaP-derived model of resistance to sequential androgen deprivation and enzalutamide. This follow-up screen provided validation of inhibitors of JAK/STAT and PI3K/mTOR as therapeutic vulnerabilities for both Snail+ and enzalutamide-resistant prostate cancer.

Osteosarcoma (OS) is a lethal disease with few known targeted therapies. Here, we show that decreased ATRX expression is associated with more aggressive tumor cell phenotypes, including increased growth, migration, invasion, and metastasis. These phenotypic changes correspond with activation of NF-κB signaling, extracellular matrix remodeling, increased integrin αvβ3 expression, and ETS family transcription factor binding. Here, we characterize these changes in vitro, in vivo, and in a data set of human OS patients. This increased aggression substantially sensitizes ATRX-deficient OS cells to integrin signaling inhibition. Thus, ATRX plays an important tumor-suppression role in OS, and loss of function of this gene may underlie new therapeutic vulnerabilities. The relationship between ATRX expression and integrin binding, NF-κB activation, and ETS family transcription factor binding has not been described in previous studies and may impact the pathophysiology of other diseases with ATRX loss, including other cancers and the ATR-X α thalassemia intellectual disability syndrome.

Case: A 15-year-old boy with chondroblastoma of the right hemipelvis presented with significant periacetabular bone destruction. Neoadjuvant denosumab treatment facilitated initial joint preserving surgery. Unfortunately, he experienced 2 local recurrences and underwent wide surgical resection 2 years after his initial diagnosis. Conclusion: Inhibition of the receptor activator of NF-κB (RANK)/RANK ligand (RANK-L) pathway with denosumab has been used neoadjuvantly for the treatment of giant cell tumor of bone, but its role in the treatment of chondroblastoma is less understood. This patient's clinical response and effect on cellular RANK/RANK-L activity support the consideration of denosumab in the treatment algorithm for other osteolytic bone tumors such as chondroblastoma.

Background: Osteosarcoma is a rare but aggressive bone cancer that occurs primarily in children. Like other rare cancers, treatment advances for osteosarcoma have stagnated, with little improvement in survival for the past several decades. Developing new treatments has been hampered by extensive genomic heterogeneity and limited access to patient samples to study the biology of this complex disease. Methods: To overcome these barriers, we combined the power of comparative oncology with patient-derived models of cancer and high-throughput chemical screens in a cross-species drug discovery pipeline. Results: Coupling in vitro high-throughput drug screens on low-passage and established cell lines with in vivo validation in patient-derived xenografts we identify the proteasome and CRM1 nuclear export pathways as therapeutic sensitivities in osteosarcoma, with dual inhibition of these pathways inducing synergistic cytotoxicity. Conclusions: These collective efforts provide an experimental framework and set of new tools for osteosarcoma and other rare cancers to identify and study new therapeutic vulnerabilities.

In the United States alone prostate cancer is responsible for nearly 70 days each day. The vast majority of these deaths are due to metastatic spread of therapy-resistant disease. Metastasis and therapy resistance are mediated by phenotypic plasticity in which prostate cancer cells alter their cellular lineages from epithelial-like cells that are dependent on androgen receptor signaling to mesenchymal-like or neuroendocrine-like cells that are independent of androgen receptor signaling. This phenoptyic plasticity also promotes migratory, invasive, and chemoresistant properties. All of these properties are regulated by a series of secreted signaling molecules and intracellular transcription factors normally involved in lineage programming during development; however, during prostate cancer progression, these factors are reactivated, which induces lineage reprogramming and promotes metastasis and hormone therapy resistance. Interestingly, many of these factors are found in common during both epithelial-mesenchymal and neuroendocrine reprogramming; however, the exact relationships between the epithelial, mesenchymal, and neuroendocrine phenotypes remain poorly understood. Here we discuss the current understanding of the relationships between these overlapping, but distinct phenotypic transitions and highlight key unanswered questions about the mechanistic interconnections between epithelial-mesenchymal transition and neuroendocrine prostate cancer biology.

Adaptation of cancer cells to targeted therapy follows ecological paradigms observed in natural populations that encounter resource depletion and changing environments, including activation of pro-survival mechanisms, migration to new locations, and escape of predation. We identified the p38 MAPK pathway as a common molecular driver of these three responses during the adaptation to hormone therapy resistance in prostate cancer. The p38 pathway is activated in therapy-resistant cells and mechanistically drives these three convergent responses through sustained AR activity, enhanced invasion and metastasis, and immune evasion. Targeting p38 signaling may represent a new therapeutic strategy to treat men with metastatic, hormone therapy-resistant prostate cancer.

Cancer drug discovery is an inefficient process, with more than 90% of newly-discovered therapies failing to gain regulatory approval. Patient-derived models of cancer offer a promising new approach to identify new treatments; however, for rare cancers, such as sarcomas, access to patient samples is limited, which precludes development of patient-derived models. To address the limited access to patient samples, we have turned to pet dogs with naturally-occurring sarcomas. Although sarcomas make up <1% of all human cancers, sarcomas represent 15% of cancers in dogs. Because dogs have similar immune systems, an accelerated pace of cancer progression, and a shared environment with humans, studying pet dogs with cancer is ideal for bridging gaps between mouse models and human cancers. Here, we present our cross-species personalized medicine pipeline to identify new therapies for sarcomas. We explore this process through the focused study of a pet dog, Teddy, who presented with six synchronous leiomyosarcomas. Using our pipeline we identified proteasome inhibitors as a potential therapy for Teddy. Teddy was treated with bortezomib and showed a varied response across tumors. Whole exome sequencing revealed substantial genetic heterogeneity across Teddy's recurrent tumors and metastases, suggesting that intra-patient heterogeneity and tumoral adaptation were responsible for the heterogeneous clinical response. Ubiquitin proteomics coupled with exome sequencing revealed multiple candidate driver mutations in proteins related to the proteasome pathway. Together, our results demonstrate how the comparative study of canine sarcomas offers important insights into the development of personalized medicine approaches that can lead to new treatments for sarcomas in both humans and canines.

Despite a considerable expenditure of time and resources and significant advances in experimental models of disease, cancer research continues to suffer from extremely low success rates in translating preclinical discoveries into clinical practice. The continued failure of cancer drug development, particularly late in the course of human testing, not only impacts patient outcomes, but also drives up the cost for those therapies that do succeed. It is clear that a paradigm shift is necessary if improvements in this process are to occur. One promising direction for increasing translational success is comparative oncology—the study of cancer across species, often involving veterinary patients that develop naturally-occurring cancers. Comparative oncology leverages the power of cross-species analyses to understand the fundamental drivers of cancer protective mechanisms, as well as factors contributing to cancer initiation and progression. Clinical trials in veterinary patients with cancer provide an opportunity to evaluate novel therapeutics in a setting that recapitulates many of the key features of human cancers, including genomic aberrations that underly tumor development, response and resistance to treatment, and the presence of comorbidities that can affect outcomes. With a concerted effort from basic scientists, human physicians and veterinarians, comparative oncology has the potential to enhance the cost-effectiveness and efficiency of pipelines for cancer drug discovery and other cancer treatments.

The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.

Cancer metastasis and therapy resistance are the major unsolved clinical challenges, and account for nearly all cancer-related deaths. Both metastasis and therapy resistance are fueled by epithelial plasticity, the reversible phenotypic transitions between epithelial and mesenchymal phenotypes, including epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET). EMT and MET have been largely considered as binary processes, where cells detach from the primary tumor as individual units with many, if not all, traits of a mesenchymal cell (EMT) and then convert back to being epithelial (MET). However, recent studies have demonstrated that cells can metastasize in ways alternative to traditional EMT paradigm; for example, they can detach as clusters, and/or occupy one or more stable hybrid epithelial/mesenchymal (E/M) phenotypes that can be the end point of a transition. Such hybrid E/M cells can integrate various epithelial and mesenchymal traits and markers, facilitating collective cell migration. Furthermore, these hybrid E/M cells may possess higher tumor-initiation and metastatic potential as compared to cells on either end of the EMT spectrum. Here, we review in silico, in vitro, in vivo and clinical evidence for the existence of one or more hybrid E/M phenotype(s) in multiple carcinomas, and discuss their implications in tumor-initiation, tumor relapse, therapy resistance, and metastasis. Together, these studies drive the emerging notion that cells in a hybrid E/M phenotype may occupy ‘metastatic sweet spot’ in multiple subtypes of carcinomas, and pathways linked to this (these) hybrid E/M state(s) may be relevant as prognostic biomarkers as well as a promising therapeutic targets.