Global loss of promoter-enhancer connectivity and rebalancing of gene expression during early colorectal cancer carcinogenesis.

Although three-dimensional (3D) genome architecture is crucial for gene regulation, its role in disease remains elusive. We traced the evolution and malignant transformation of colorectal cancer (CRC) by generating high-resolution chromatin conformation maps of 33 colon samples spanning different stages of early neoplastic growth in persons with familial adenomatous polyposis (FAP). Our analysis revealed a substantial progressive loss of genome-wide cis-regulatory connectivity at early malignancy stages, correlating with nonlinear gene regulation effects. Genes with high promoter-enhancer (P-E) connectivity in unaffected mucosa were not linked to elevated baseline expression but tended to be upregulated in advanced stages. Inhibiting highly connected promoters preferentially represses gene expression in CRC cells compared to normal colonic epithelial cells. Our results suggest a two-phase model whereby neoplastic transformation reduces P-E connectivity from a redundant state to a rate-limiting one for transcriptional levels, highlighting the intricate interplay between 3D genome architecture and gene regulation during early CRC progression.

Prospective Study of Perioperative Circulating Tumor DNA Dynamics in Patients Undergoing Hepatectomy for Colorectal Liver Metastases.

OBJECTIVE: To evaluate the association of perioperative ctDNA dynamics on outcomes after hepatectomy for CLM. SUMMARY BACKGROUND DATA: Prognostication is imprecise for patients undergoing hepatectomy for CLM, and ctDNA is a promising biomarker. However, clinical implications of perioperative ctDNA dynamics are not well established. METHODS: Patients underwent curative-intent hepatectomy after preoperative chemotherapy for CLM (2013-2017) with paired prehepatectomy/postoperative ctDNA analyses via plasma-only assay. Positivity was determined using a proprietary variant classifier. Primary endpoint was recurrence-free survival (RFS). Median follow-up was 55 months. RESULTS: Forty-eight patients were included. ctDNA was detected before and after surgery (ctDNA+/+) in 14 (29%), before but not after surgery (ctDNA+/-) in 19 (40%), and not at all (ctDNA-/-) in 11 (23%). Adverse tissue somatic mutations were detected in TP53 (n = 26; 54%), RAS (n = 23; 48%), SMAD4 (n = 5; 10%), FBXW7 (n = 3; 6%), and BRAF (n = 2; 4%). ctDNA+/+ was associated with worse RFS (median: ctDNA+/+, 6.0 months; ctDNA+/-, not reached; ctDNA-/-, 33.0 months; P = 0.001). Compared to ctDNA+/+, ctDNA+/- was associated with improved RFS [hazard ratio (HR) 0.24 (95% confidence interval (CI) 0.1-0.58)] and overall survival [HR 0.24 (95% CI 0.08-0.74)]. Adverse somatic mutations were not associated with survival. After adjustment for prehepatectomy chemotherapy, synchronous disease, and ≥2 CLM, ctDNA+/- and ctDNA-/- were independently associated with improved RFS compared to ctDNA+/+ (ctDNA+/-: HR 0.21, 95% CI 0.08-0.53; ctDNA-/-: HR 0.21, 95% CI 0.08-0.56). CONCLUSIONS: Perioperative ctDNA dynamics are associated with survival, identify patients with high recurrence risk, and may be used to guide treatment decisions and surveillance after hepatectomy for patients with CLM.

Minimal Residual Disease Detection using a Plasma-only Circulating Tumor DNA Assay in Patients with Colorectal Cancer.

PURPOSE: Detection of persistent circulating tumor DNA (ctDNA) after curative-intent surgery can identify patients with minimal residual disease (MRD) who will ultimately recur. Most ctDNA MRD assays require tumor sequencing to identify tumor-derived mutations to facilitate ctDNA detection, requiring tumor and blood. We evaluated a plasma-only ctDNA assay integrating genomic and epigenomic cancer signatures to enable tumor-uninformed MRD detection. EXPERIMENTAL DESIGN: A total of 252 prospective serial plasma specimens from 103 patients with colorectal cancer undergoing curative-intent surgery were analyzed and correlated with recurrence. RESULTS: Of 103 patients, 84 [stage I (9.5%), II (23.8%), III (47.6%), IV (19%)] had evaluable plasma drawn after completion of definitive therapy, defined as surgery only (n = 39) or completion of adjuvant therapy (n = 45). In "landmark" plasma drawn 1-month (median, 31.5 days) after definitive therapy and >1 year follow-up, 15 patients had detectable ctDNA, and all 15 recurred [positive predictive value (PPV), 100%; HR, 11.28 (P < 0.0001)]. Of 49 patients without detectable ctDNA at the landmark timepoint, 12 (24.5%) recurred. Landmark recurrence sensitivity and specificity were 55.6% and 100%. Incorporating serial longitudinal and surveillance (drawn within 4 months of recurrence) samples, sensitivity improved to 69% and 91%. Integrating epigenomic signatures increased sensitivity by 25%-36% versus genomic alterations alone. Notably, standard serum carcinoembryonic antigen levels did not predict recurrence [HR, 1.84 (P = 0.18); PPV = 53.9%]. CONCLUSIONS: Plasma-only MRD detection demonstrated favorable sensitivity and specificity for recurrence, comparable with tumor-informed approaches. Integrating analysis of epigenomic and genomic alterations enhanced sensitivity. These findings support the potential clinical utility of plasma-only ctDNA MRD detection.See related commentary by Bent and Kopetz, p. 5449.

Stress-mediated exit to quiescence restricted by increasing persistence in CDK4/6 activation.

Mammalian cells typically start the cell-cycle entry program by activating cyclin-dependent protein kinase 4/6 (CDK4/6). CDK4/6 activity is clinically relevant as mutations, deletions, and amplifications that increase CDK4/6 activity contribute to the progression of many cancers. However, when CDK4/6 is activated relative to CDK2 remained incompletely understood. Here, we developed a reporter system to simultaneously monitor CDK4/6 and CDK2 activities in single cells and found that CDK4/6 activity increases rapidly before CDK2 activity gradually increases, and that CDK4/6 activity can be active after mitosis or inactive for variable time periods. Markedly, stress signals in G1 can rapidly inactivate CDK4/6 to return cells to quiescence but with reduced probability as cells approach S phase. Together, our study reveals a regulation of G1 length by temporary inactivation of CDK4/6 activity after mitosis, and a progressively increasing persistence in CDK4/6 activity that restricts cells from returning to quiescence as cells approach S phase.

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation.

Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell.

Irreversible APC(Cdh1) Inactivation Underlies the Point of No Return for Cell-Cycle Entry.

Proliferating cells must cross a point of no return before they replicate their DNA and divide. This commitment decision plays a fundamental role in cancer and degenerative diseases and has been proposed to be mediated by phosphorylation of retinoblastoma (Rb) protein. Here, we show that inactivation of the anaphase-promoting complex/cyclosome (APC(Cdh1)) has the necessary characteristics to be the point of no return for cell-cycle entry. Our study shows that APC(Cdh1) inactivation is a rapid, bistable switch initiated shortly before the start of DNA replication by cyclin E/Cdk2 and made irreversible by Emi1. Exposure to stress between Rb phosphorylation and APC(Cdh1) inactivation, but not after APC(Cdh1) inactivation, reverted cells to a mitogen-sensitive quiescent state, from which they can later re-enter the cell cycle. Thus, APC(Cdh1) inactivation is the commitment point when cells lose the ability to return to quiescence and decide to progress through the cell cycle.

Systematic Discovery of Human Gene Function and Principles of Modular Organization through Phylogenetic Profiling.

Functional links between genes can be predicted using phylogenetic profiling, by correlating the appearance and loss of homologs in subsets of species. However, effective genome-wide phylogenetic profiling has been hindered by the large fraction of human genes related to each other through historical duplication events. Here, we overcame this challenge by automatically profiling over 30,000 groups of homologous human genes (orthogroups) representing the entire protein-coding genome across 177 eukaryotic species (hOP profiles). By generating a full pairwise orthogroup phylogenetic co-occurrence matrix, we derive unbiased genome-wide predictions of functional modules (hOP modules). Our approach predicts functions for hundreds of poorly characterized genes. The results suggest evolutionary constraints that lead components of protein complexes and metabolic pathways to co-evolve while genes in signaling and transcriptional networks do not. As a proof of principle, we validated two subsets of candidates experimentally for their predicted link to the actin-nucleating WASH complex and cilia/basal body function.

Parallel measurement of dynamic changes in translation rates in single cells.

Protein concentrations are often regulated by dynamic changes in translation rates. Nevertheless, it has been challenging to directly monitor changes in translation in living cells. We have developed a reporter system to measure real-time changes of translation rates in human or mouse individual cells by conjugating translation regulatory motifs to sequences encoding a nuclear targeted fluorescent protein and a controllable destabilization domain. Application of the method showed that individual cells undergo marked fluctuations in the translation rate of mRNAs whose 5' terminal oligopyrimidine (5' TOP) motif regulates the synthesis of ribosomal proteins. Furthermore, we show that small reductions in amino acid levels signal through different mTOR-dependent pathways to control TOP mRNA translation, whereas larger reductions in amino acid levels control translation through eIF2A. Our study demonstrates that dynamic measurements of single-cell activities of translation regulatory motifs can be used to identify and investigate fundamental principles of translation.

Exploring transcription regulation through cell-to-cell variability.

The regulation of cellular protein levels is a complex process involving many regulatory mechanisms, each introducing stochastic events, leading to variability of protein levels between isogenic cells. Previous studies have shown that perturbing genes involved in transcription regulation affects the amount of cell-to-cell variability in protein levels, but to date there has been no systematic characterization of variability in expression as a phenotype. In this research, we use single-cell expression levels of two fluorescent reporters driven by two different promoters under a wide range of genetic perturbations in Saccharomyces cerevisiae, to identify proteins that affect variability in the expression of these reporters. We introduce computational methodology to determine the variability caused by each perturbation and distinguish between global variability, which affects both reporters in a coordinated manner (e.g., due to cell size variability), and local variability, which affects the individual reporters independently (e.g., due to stochastic events in transcription initiation). Classifying genes by their variability phenotype (the effect of their deletion on reporter variability) identifies functionally coherent groups, which broadly correlate with the different stages of transcriptional regulation. Specifically, we find that most processes whose perturbation affects global variability are related to protein synthesis, protein transport, and cell morphology, whereas most processes whose perturbations affect local variability are related to DNA maintenance, chromatin regulation, and RNA synthesis. Moreover, we demonstrate that the variability phenotypes of different protein complexes provide insights into their cellular functions. Our results establish the utility of variability phenotype for dissecting the regulatory mechanisms involved in gene expression.

From large-scale assays to mechanistic insights: computational analysis of interactions.

The activity in the living cell is carried out by a myriad network of interactions between macromolecules. These include interactions between proteins that form a functional complex, a protein modifying another protein in a transient interaction, a transcription factor that binds a specific DNA locus triggering a change in chromatin or transcription, and so on. Characterization of these interactions in terms of timing, context, and function is crucial for understanding how cells carry out basic biological processes. The recent years have led to the introduction of many assays for probing these interactions in a systematic and large-scale manner. However, there is a large gap between assay results and understanding of biological systems. The challenge for computational methods is to bridge this gap by combining results of different assays and introducing statistical methodologies. In this review we discuss recent advances in approaches dealing with these challenges, and key directions for the future.

Modularity and directionality in genetic interaction maps.

MOTIVATION: Genetic interactions between genes reflect functional relationships caused by a wide range of molecular mechanisms. Large-scale genetic interaction assays lead to a wealth of information about the functional relations between genes. However, the vast number of observed interactions, along with experimental noise, makes the interpretation of such assays a major challenge. RESULTS: Here, we introduce a computational approach to organize genetic interactions and show that the bulk of observed interactions can be organized in a hierarchy of modules. Revealing this organization enables insights into the function of cellular machineries and highlights global properties of interaction maps. To gain further insight into the nature of these interactions, we integrated data from genetic screens under a wide range of conditions to reveal that more than a third of observed aggravating (i.e. synthetic sick/lethal) interactions are unidirectional, where one gene can buffer the effects of perturbing another gene but not vice versa. Furthermore, most modules of genes that have multiple aggravating interactions were found to be involved in such unidirectional interactions. We demonstrate that the identification of external stimuli that mimic the effect of specific gene knockouts provides insights into the role of individual modules in maintaining cellular integrity. AVAILABILITY: We designed a freely accessible web tool that includes all our findings, and is specifically intended to allow effective browsing of our results (http://compbio.cs.huji.ac.il/GIAnalysis). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nucleosome positioning from tiling microarray data.

MOTIVATION: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5-10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed. RESULTS: In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions. We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA.

Towards an integrated protein-protein interaction network: a relational Markov network approach.

Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.