Escherichia coli is mostly a commensal of birds and mammals, including humans, where it can act as an opportunistic pathogen. It is also found in water and sediments. We investigated the phylogeny, genetic diversification, and habitat-association of 1,294 isolates representative of the phylogenetic diversity of more than 5,000 isolates from the Australian continent. Since many previous studies focused on clinical isolates, we investigated mostly other isolates originating from humans, poultry, wild animals and water. These strains represent the species genetic diversity and reveal widespread associations between phylogroups and isolation sources. The analysis of strains from the same sequence types revealed very rapid change of gene repertoires in the very early stages of divergence, driven by the acquisition of many different types of mobile genetic elements. These elements also lead to rapid variations in genome size, even if few of their genes rise to high frequency in the species. Variations in genome size are associated with phylogroup and isolation sources, but the latter determine the number of MGEs, a marker of recent transfer, suggesting that gene flow reinforces the association of certain genetic backgrounds with specific habitats. After a while, the divergence of gene repertoires becomes linear with phylogenetic distance, presumably reflecting the continuous turnover of mobile element and the occasional acquisition of adaptive genes. Surprisingly, the phylogroups with smallest genomes have the highest rates of gene repertoire diversification and fewer but more diverse mobile genetic elements. This suggests that smaller genomes are associated with higher, not lower, turnover of genetic information. Many of these genomes are from freshwater isolates and have peculiar traits, including a specific capsule, suggesting adaptation to this environment. Altogether, these data contribute to explain why epidemiological clones tend to emerge from specific phylogenetic groups in the presence of pervasive horizontal gene transfer across the species. E. coli focused on clinical isolates emphasizing virulence and antibiotic resistance in medically important lineages. Yet, most E. coli strains are either human commensals or not associated with humans at all. Here, we analyzed a large collection of non-clinical isolates of the species to assess the mechanisms of gene repertoire diversification in the light of isolation sources and phylogeny. We show that gene repertoires evolve so rapidly by the high turnover of mobile genetic elements that epidemiologically indistinguishable strains can be phenotypically extremely heterogeneous, illustrating the velocity of bacterial adaptation and the importance of accounting for the information on the whole genome at the epidemiological scale. Phylogeny and habitat shape the genetic diversification of E. coli to similar extents. Surprisingly, freshwater strains seem specifically adapted to this environment, breaking the paradigm that E. coli environmental isolates are systematically fecal contaminations. As a consequence, the evolution of this species is also shaped by environmental habitats, and it may diversify by acquiring genes and mobile elements from environmental bacteria (and not just from gut bacteria). This may facilitate the acquisition of virulence factors and antibiotic resistance in the strains that become pathogenic.Previous large scale studies on the evolution of  Escherichia coli is a commensal of the gut microbiota of mammals and birds (primary habitat) . Fo. Fo62]. papX, the P fimbriae, yersiniabactin, colibactin and multiple type 5a protein secretion systems (−3), while being rare in strains isolated from freshwater and wild birds’ feces  as previously shown , and the minimal sequence identity  observed in each of the 2,486 persistent gene families. The observed average sequence identity is 98.3% across families of persistent genes. The average minimal value observed across persistent gene families is 95.5%.A. Number of gene families according to their occurrence in genomes. Singletons (in green), (EPS)Click here for additional data file.S3 FigA. Graphical representation of the different steps of the phylogenetic trees build process from the persistent genome. Among persistent gene families, there are families that are core  and the remaining that have missing genes . B. Number of persistent gene families according to their number of missing genes in the Australian dataset. Only 12% of families are core, i.e., present in all genomes (in red). C. Violin-plot of the number of missing genes per genome in the Australian dataset. On average, the number of missing genes is around 8 per genome. It can reach up 93 in a single genome, but this represents less than 4% of persistent families.(EPS)Click here for additional data file.S4 FigE. coli and 86 outgroups genomes performed from the matrix of mash distances computed between all pairs of genomes using bionj. The number of genomes in each species (or clade) was indicated. The different phylogroups of E. coli were displayed: A (in blue), B1 (in green), E (in purple), D (in yellow), F (in orange), G (in brown) and B2 (in red). B. Boxplot of the mash distances computed between all pairs of genomes belonging to the same species (or clades). In both cases, the maximal mash distance was lower than 0.05. For E. coli species, the median was around 0.027 and the maximal value was 0.04. C. Phylogenetic tree of 100 Australian E. coli genomes representative to the diversity of the dataset and 86 outgroups genomes performed from the persistent-genome of the genus with IQ-TREE under the GTR+F+I+G4 model. We made 1,000 ultra-fast boostrap to assess the robustness of the topology of the tree. We found that all boostrap supports were higher than 95%. D. We rooted the species phylogenetic tree from the genus phylogenetic tree. The resulting rooted species tree was reported, and for simplicity, the main phylogenetic groups were collapsed.A. Distance tree of 1,294 Australian (EPS)Click here for additional data file.S5 Figi.e., persistent (in gold), accessory (in grey) and singleton (in green). The average was represented by a black dot. The pairwise Wilcoxon Rank Sum test with bonferroni correction was applied to all comparisons (P<0.001 :***). B. Same analysis as in A, but distinguishing the genomic location of the gene of each set : inside of contigs  or at the edge of contigs . The average gene size for each case was reported in the table. C. Percentage of genes located inside contigs (dark color) or at the edge of contigs (light color) in the 3 sets. The last column corresponds to the fraction of the 3 sets located at the edge of contigs. D. Heatmap of the observed/expected (O/E) ratios of genes located inside or at the edges of contigs in the 3 sets. The ratio (O/E) was reported for all comparisons with a color code ranging from blue (under-representation) to red (over-representation). The level of significance of each Fisher’s exact test was also indicated (P<0.001 :***). It was performed on each 2*2 contingency table. E. Fraction of singletons with no hit (in light gey), with a small domain (in grey) or fully included (black) in larger accessory or persistent gene families (−4), the average number of persistent is also significantly higher  than the rarefied RefSeq dataset. Singletons represent 43%, and 35% of the rarefied Australian and RefSeq pan-genomes, resp.A. Boxplots of gene size (bp) in the three categories of gene families, families . F. Viol(EPS)Click here for additional data file.S6 Fig−4). The summary of the linear fit was: Y = 90.722391–76.2919X, R2 = 0.50,P<10−4. Hence, with or without singletons, the results were similar.Here, the GRR were computed excluding singletons in all genomes. Due to the large amount of comparisons (points), we divided the plot area in regular hexagons. Color intensity is proportional to the number of cases (count) in each hexagon. The linear fit ) and the spline fit ) were reported for the whole  or the intra-ST (in blue) comparisons. There was a significant negative correlation between GRR and the patristic distance Click here for additional data file.S7 Fig−3 :***). D. Average number of persistent (in gold), accessory (in grey) and singleton (in green) in the rarefied pan-genomes of each dataset.A. Violin plots of the nucleotide diversity per site in the 3 datasets computed from the multiple alignments of 112 core gene families see . The pai(EPS)Click here for additional data file.S8 Fig−4). B. Boxplots of the nucleotide diversity (left), the MASH (center) and the patristic distances (right) computed with/between genomes in each phylogroup. The pairwise Wilcoxon Rank Sum test with bonferroni correction was applied to all comparisons. Here, only the non-significant (ns : P> = 0.05) comparisons were indicated, all other were higly significant P<10−4. C. Density of the patristic distances between all pairs of genomes of the same phylogroup (intra-phylogroup). The dash vertical line corresponds to the median of each distribution. (A-B-C) In all cases, similar results were obtained with rarefied datasets .A. Violin plots of the nucleotide diversity per site (left), the MASH (center) and the patristic distances (right) computed with/between genomes belonging to the same phylogroup , to different phylogroups , or all together . In all cases, intra- and inter-phylogroup distributions were significantly different Click here for additional data file.S9 Fig2>0.88, P<10−4). The Rarefaction curve of the pan-genomes of the full dataset was also reported . B. Rarefaction curves of the pan-genomes of each phylogroup and of the full dataset (All). C. Rarefaction curves of the gene-families associated to MGE in each phylogroup and in the full dataset (All). D. Rarefaction curves of the pan-genomes of each isolation sources. In each case, (i) we used 1,000 permutations (genomes orderings) and then averaged the results , (ii) the pan-genomes remained open Click here for additional data file.S10 Figi.e., GRR, Manhattan, Jaccard, MASH and patristic, computed between pairs of genomes belonging to the same phylogroup (intra-phylogroup) with the whole dataset or excluding singletons (woS). Spearman’s rank correlation rho matrix. Positive correlations were displayed in red and negative correlations in blue color. Color intensity and the size of the circle were proportional to the correlation coefficients. The p-value of each correlation was highly significant (P<10−4). We found similar results with rarefied datasets, i.e., considering only 50 randomly selected genomes in each phylogroup. We also found higher correlation coefficients using all the comparisons (intra- and inter-phylogroup).A. Average GRR (%) computed between pairs of genomes belonging to the same phylogroup (intra-phylogroup) and to different phylogroups (inter-phylogroup). The color code used was displayed in the insert (top right). B. Correlation between the different distances and indexes, (EPS)Click here for additional data file.S11 Fig−4). B. Boxplot and histogram of the size of the detected regions in complete and draft genomes. These distributions were significantly different . On average the regions were almost 4 times larger in the complete genomes than in draft genomes and few regions (644) in draft genomes had a typical size of known dsDNA phages (around 44kb). (A-B) showed that prophage elements were less assembled and were probably divided into several small contigs. The large regions (>60 kb) in complete genomes corresponded to tandem elements (consecutive on the genomic sequence). Thus, the number of detected regions did not correspond to the number of prophages either in the complete genomes (due to tandem elements) or in the drafts genomes (the elements being fragmented). C. Strong association between the cumulative size of the detected regions (X) with the number of detected regions (Y). Linear regression (dash red line) and statistics were reported. D. Boxplot of the predicted number of prophage elements in both the complete and the draft genomes using the linear equation showed in (C) from the cumulative size of the regions detected by VirSorter. These distributions were significantly different . On average, there was 6.0 prophages in complete genomes, and 4.25 in draft genomes. The medians of the two data sets were closer reflecting probably the assembly problem related to the presence of prophages in tandem combined with the fact that they are often genetically close (most of them are lambdoids). In each panel, the red arrow corresponds to the median and the blue arrow to the average of each distribution.A. Boxplot of the number of regions detected as prophage-related by Virsorter in the 370 complete RefSeq GenBank genomes and in the 1,294 draft Australian genomes. These distributions were significantly different, on average the number of regions detected was significantly higher in draft than in complete genomes Click here for additional data file.S12 Fig−4). On average the contigs were almost 10 times larger in the complete genomes than in draft genomes (81 kb vs. 8.9 kb). We identified 2347, 562 and 53 contigs larger than 20, 50 and 100 kb, resp. (A-B) showed that plasmid elements were poorly assembled and probably divided into several small contigs. C. Boxplot of the fraction of the proteome encoding plasmid elements per genome  in complete and draft genomes. These distributions were similar  with an average of 3.2% in both.A. Boxplot of the number of contigs classified as plasmid by PlaScope in the 370 complete RefSeq GenBank (Complete) genomes and in the 1,294 draft Australian genomes (Draft). All the extrachromosomal replicons of the complete genomes were perfectly identified as plasmid elements by PlaScope. Hence, results based on the extrachromosomal replicons or on the contigs detected as plasmid by PlaScope were identical (Complete*). The average number of contigs was eight times larger in draft genomes than in complete genomes (15.4 vs 1.9) and reached up to 124 contigs. B. Boxplot and histogram of the size of the contigs detected as plasmid in complete and draft genomes. These distributions were significantly different Click here for additional data file.S13 Figi.e., prophage (left column), plasmid (middle columns) and IS elements (right column). A. Histogram and boxplot of genomic features of each type of MGEs, i.e, the cumulative size of the elements per genome (Kb), the total number (#) of genes encoded by the elements per genome, the fraction of the genome encoding these elements per genome. For each case, the dash line corresponds to the smoothed curve, the red arrow to the median and the blue arrow to the average of each distribution. B. Histogram and boxplot of the number of conjugation systems per genome. C. Number of conjugative systems: (MPF) and isolated relaxases (MOB) detected in our dataset. The different MPF types were indicated and also their genomic location, i.e., located on a contig classified as plasmid or as chromosome by PlaScope.Three types of MGEs were detected, (EPS)Click here for additional data file.S14 Figi.e., # of genes per genome) and the total number of genes associated to the MGE elements. B. Histogram and boxplot of the genome size (in grey), and of the genome size without MGE (in red), i.e., after removing all the genes encoding MGE elements (in red). These distributions were significantly different . C. Same representation as in (a), but distinguishing the different types of MGEs, i.e., prophage, plasmid and IS elements. (A-C) We found a strong correlation in each case. Linear regression (dash red line) and statistics were reported. Similar results were obtained with the genome size (Mb). D. Number of singletons (in green) and accessory gene families encoding MGEs. The fraction of the pan-genome encoding such elements was reported in each case (%).A. Association between the genome size ((EPS)Click here for additional data file.S15 FigNumber of accessory gene families associated to prophage and plasmid present in one  to seven phylogroups (A), or in one  to seven sources (B). The Z-score obtained for the observed number with respect to the expected distribution (as in (EPS)Click here for additional data file.S16 FigNodes are phylogroups and edges the O/E ratio of the number of pairs of MGE genes (from the same gene family) acquired in the terminal branches of the tree. Only significant O/E values (and edges) are plottted (|Z-score|>1.96). Under-represented values are in dash blue and over-represented in red see .(EPS)Click here for additional data file.S17 FigA. Heatmap of the average genome size of strains from different sources in each phylogroup. The deviation to the overall intra-phylogroup mean  was reported for all comparisons with a color code ranging from blue (below average) to red (above average). The level of significance of each ANOM test was indicated . It was performed within each phylogroup (each line). (B-C-D) Same representation as in (A), but in relation with the average number of genes associated to MGEs (B), to prophage (C), or plasmid elements (D).(EPS)Click here for additional data file.S18 Figint1+) or not (int1-). The level of significance of the Wilcoxon test was indicated (P<10−3). B. Heatmap of the proportion of genomes int1+ in each phylogroup and source. A cross marks the absence of data. C. Same as in (B) but we merged sources related to human activity (with), or not directly associated to human (without). The level of significance of each ANOM for proportions test was indicated . Here, we compared response proportions for the X levels to the overall response proportion from the contingency table. This method uses the normal approximation to the binomial. Therefore, in some cases sample sizes were too small to be tested. D. Heatmap of the average number of ARGs per genome in each phylogroup and source. E. Heatmap of the average number of ARGs when we merged sources related (with) or not (without) to human activity. The level of significance of each non-parametric ANOM test (ANOM with Transformed Ranks) was indicated . The deviation to the overall mean  was reported for all comparisons with a color code ranging from blue (below average) to red (above average). The color code used was displayed in the top of each panel.A. Violin plots of the number of ARGs in genomes encoding integron-integrase ((EPS)Click here for additional data file.S19 Fig(A-B). Heatmap of the average number of VFs per strain from different sources in each phylogroup. The deviation to the overall mean  or to the intra-phylogroup mean  was reported for all comparisons with a color code ranging from blue (below average) to red (above average). The level of significance of each ANOM test was indicated . It was performed within each phylogroup . C. Heatmap of the average number of Colicins per genome in each phylogroup and source. D. Same representation as in (B), but in relation with the average number of Colicins per genome.(EPS)Click here for additional data file.S20 FigA. Heatmap of the average number of capsule systems per genome in each phylogroup and source. B. The deviation to the intra-phylogroup mean  was reported for all comparisons with a color code ranging from blue (below average) to red (above average). The level of significance of each ANOM test was indicated . It was performed within each phylogroup (each line). C. Prevalence (%) of each capsule groups across phylogroups and sources.(EPS)Click here for additional data file. 15 Apr 2020* Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. *Dear Dr Touchon,Thank you very much for submitting your Research Article entitled 'Phylogenetic background and habitat drive the genetic  diversification of Escherichia coli' to PLOS Genetics. Your manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important topic but identified some aspects of the manuscript that should be improved.We therefore ask you to modify the manuscript according to the review recommendations before we can consider your manuscript for acceptance. Your revisions should address the specific points made by each reviewer.In addition we ask that you:1) Provide a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.archive. 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All points should be backed up by data provided with the submission.Please be aware that our Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.[LINK]Please let us know if you have any questions while making these revisions.Yours sincerely,Xavier DidelotAssociate EditorPLOS GeneticsKirsten BombliesSection Editor: EvolutionPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors present a fantastic analysis of genetic diversity and pan genome dynamics ics in E. coli. I thoroughly enjoyed reading this paper and have no doubt it will make a major contribution to the E. coli community and the burgeoning scientific discourse on bacterial pan genomes. I have a few comments the authors may wish to consider:Line 86: In consequence beginning to sentence does not read wellLine 120: what are essentially core genes are given a new term persistent. Shouldnt they be termed Core to avoid confusion and align with common terminologyLine 157-198: Apologies as I see how much work this analysis has been. But this GRR finding is leaving me with alot of questions. Is this differences in functional gene classes? Is it deviation in sequence ofsimilar genes as a result of drift? I feel i would like more info on the GRR diversification and description of the results.Line 254: The suggestion of B2 not being basal to the tree. Isnt this counter to the widely accepted pattern of evolution proposed by some of the authors of this paper, that B2 is the ancestral E. coli and A/B1 emerged from ancestral pathogensLine. 263: not probably more frequent. Absolutely more frequentLine 310-11: I am slightly confused at what level the prevalence of MGE is being described and analysed, and that no MGE is found in the core genome. Is this only true across the entire species data set? There are no MGEs which are core in a given lineage/phylogroup? Similarly in the following lines why would one expect MGE fixed across the species given the ecology of lineages is so varied? I am a bit confused by this section.Line 417: potential number of possible sources is absolutely a function of phylogroup is it not?Line 636: I can only assume the 454 genomes represents the fact some genomes were sequenced as legacy projects some time ago. But to fend off criticism I would provide a very clear and honest assessmentt of how why and when these were sequenced.Reviewer #2: Dear Professor Didelot,First of all I apologise for the delay in my review. Disruptions and subsequent preparations linked to the COVID-19 pandemic have got in the way of providing you with a quick review. I have now read with attention the manuscript submitted to you by Marie Touchon et al. entitled "Phylogenetic background and habitat drive the genetic diversification of Escherichia coli"E. coli can be found in all endothermic animals and is consequently associated with their environment . Currently, there is a strong bias towards clinical samples in E. coli studies. Here, authors use an impressive sampling of >5,000 isolates to sequence and characterize a subset of representative >1,200 E. coli from mostly non-clinical, isolates originating from humans, poultry, wild animals and water sampled from the Australian continent (no plants). Using comparisons of pangenomic diversity and gene repertoires, authors show that (1) there are large variations in gene content in E. coli lineages, even within STs, (2) gene flow explains the association of certain lineages with specific ecological niches and habitats, something that is not new, but had never been looked at in such way, (3) smaller genomes are associated with a higher (and not lower) turnover of genetic information, and (4) an interesting proportion of small genomes were freshwater isolates, which is very interesting and provides new evidence suggesting that E. coli can be "naturalized" and adapt/replicate in non-host environments.The results from this study are not ground-breakingly novel. It's been more than a decade that we (including many studies from authors of this manuscript) find evidence for phylogroup-specific ecologies and physiologies in E. coli but it is the first study of this scale, which makes it seminal and authoritative.Overall, I enjoyed this manuscript a lot, and will be happy to recommend it for publication in Plos Genetics after a few clarifications are made:Methods:- I think I need some clarification on the phylogenetics section, because I am a bit confused as to why the authors chose to do things the way they did. I would understand that aligning with E-INS-i in MAFFT is not recommended for >200 sequences, but is it why authors used this odd strategy of back-translating concatenated protein-to-protein alignments instead of performing the alignment on gene-by-gene concatenates? I am also missing details as to how the back-translation was performed: which codon usage did you pick? How is that affecting recombination/HGT/diversity analyses you are doing afterwards? Or did you come back to the original nucleotide sequence? At the very least, a clarification of the rationale here would be more than welcome. Similarly, another amber flag (to not say red) for me was to read that authors manually added gaps "-" in the alignment. Please explain a bit better.- I couldn't access the sequences on ENA accession PRJEB34791 nor on the SRA using the individual accessions from the S1 dataset. Please make sure to make things available when the paper is published, or for eventual future reviews.- I would have liked to see a bit more details on the sources of isolations presented in the S1 Dataset. Which birds and non-human mammals were actually sampled? Were they captive or wild?- It would be very good to provide with more information on the actual animal source for each isolate, rather than just being vague and mention "bird faecal" for example. I suspect there is a collection of various Australian animals there, but at the moment we cannot tell.Results:- I would have liked to see more genetics in this analysis: you have characterised the pangenome of a rather unique dataset in one of the most iconic bacterial species ever studied, and yet, I am left hungry for gene names, and knowing which functions you find to be potentially involved in what. For instance, what functions would be phylogroup-specific in healthy and environmental E. coli? Are they predicted metabolic functions like we expect?Discussion:- I find the generalism of phylogroup B1 fascinating, so I enjoyed the freshwater angle at the end of this study. Instead of an open sentence at the end of the manuscript, would authors care to hazard a more exhaustive interpretation on the evolution of phylogroup B1? A majority of B1 isolates are also found in ruminants and on plants, which could maybe have an obvious link with freshwater composition too. The deadliest outbreak strain of E. coli (O104:H4) was also a B1 isolate. Is B1 only the most dramatic example (i.e. for which we can find a possible explanation) of the impact of ecology on phylogroup evolution?- Linked to my comment above for the Results section, I would have liked to see a bit more on the possible genetic functions involved in the "naturalization" in the freshwater niche (to paraphrase Mike Sadowsky). Are these functions also found in other bacteria that are found in natural environments? Can this study contribute to a better idea of what is actually E. coli associated with in freshwater (free-living vs. surviving in protozoa vs. in sediments)? If I recall well, this is still quite an unclear point.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: No: I couldn't access the sequences on ENA accession PRJEB34791 nor on the SRA using the individual accessions from the S1 dataset.**********what does this mean?). 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  4 Jun 2020PGENETICS-D-20-00247R1 Phylogenetic background and habitat drive the genetic diversification of Escherichia coli Dear Dr Touchon, We are pleased to inform you that your manuscript entitled "Phylogenetic background and habitat drive the genetic diversification of Escherichia coli" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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Metastases and cancer recurrence are the main causes of cancer death. Circulating Tumor Cells (CTCs) and disseminated tumor cells are the drivers of cancer cell dissemination. The assessment of CTCs’ clinical role in early metastasis prediction, diagnosis, and treatment requires more information about their biology, their roles in cancer dormancy, and immune evasion as well as in therapy resistance. Indeed, CTC functional and biochemical phenotypes have been only partially characterized using murine metastasis models and liquid biopsy in human patients. CTC detection, characterization, and enumeration represent a promising tool for tailoring the management of each patient with cancer. The comprehensive understanding of CTCs will provide more opportunities to determine their clinical utility. This review provides much-needed insights into this dynamic field of translational cancer research. For metastasis formation, cancer cells must leave the primary tumor and disseminate. To this aim, epithelial tumor cells might go through epithelial-to-mesenchymal transition (EMT), lose their polarity and cell-cell/matrix adhesion, secrete specific enzymes  to digest the extracellular matrix (ECM), and gain migratory properties ,2 Figura. Then, During the last two decades, CTC detection, characterization, and enumeration opened a promising avenue to better understand the biology of metastatic cancer at the exact moment of metastasis initiation. Their use as a real-time “liquid biopsy” might help to predict metastasis formation and to develop novel anticancer therapies . HoweverThere is substantial evidence that many cancers are driven by cancer stem cells (CSCs) or tumor-initiating cells that are called metastasis-initiating cells (MICs) at metastatic sites. CSCs are a cancer cell population with stem cell features, such as self-renewal and differentiation into multiple cell types . Stem ceIt is not clear whether CTCs are representative of the whole tumor cell population or, similarly to CSCs, are a rare subpopulation . MoreoveGenetic analyses have revealed similar mutation profiles in primary and metastatic tumors and also in the corresponding CTCs . NeverthThe hypothesis that single CTCs must undergo EMT for metastasis initiation a has beeOCT4, NANOG, SOX2 binding sites in CTC clusters enhances their stemness features and the expression of cell–cell junction components, thus promoting collective migration and invasion [Collective cancer cell invasion might be explained by two mechanisms. First, during embryo development and wound-healing, EMT leads to collective migration of neural crest cells  and cellinvasion  , K8, K14, and also of E-cadherin and P-cadherin (adhesion molecules) in disseminating cell clusters ,39,40. TIt has been reported that the detection of rare CTC clusters indicates a higher metastatic potential compared with high numbers of single CTCs . This poPolyclonal metastases might originate from the accumulation of multiple single CTCs or by direct seeding of a CTC cluster at a single site . SeveralOnce in circulation, CTCs are stopped at branch points in vessels due to low shear stress and size limitations (CTCs up to 20 μm in diameter versus capillaries of ~3–7 μm) . Then, CAlthough lymphatic vessels can uptake only particles of ~5–300 nm in diameter , melanomAggressive tumors release thousands of cancer cells in the circulation each day; however, the metastasis rate is very low  and it iMoreover, depending on their origin (late-stage primary tumor or pre-invasive lesion), DTCs may give rise to metastatic lesions or enter dormancy, respectively . Cancer AXL signaling that is associated with quiescence of disseminated cells [Production by bone stromal osteocytes of the ligands for tyrosine kinase receptors, such as growth arrest-specific protein 6 (GAS6), bone morphogenetic protein 4 (BMP7), and transforming growth factor-β2 (TGFβ2), support DTC dormancy a. GAS6 aed cells . TGFβ2 ied cells . BMP7 sied cells . Regulated cells . T cell-ed cells   to its receptor mediates Extracellular signal-Regulated Kinase (ERK) activation and cell proliferation that is then reduced by binding of the ECM integrin αvβ3 to vitronectin, resulting in uPA decrease  Figure c. Loss oThe immune system should eliminate or at least control CTC activity . The intDuring metastasis formation, the overall natural killer (NK) cell count is increased. CTCs can modulate NK cell anti-tumor activity through production of inhibitory cytokines  that block the NK cell immunoglobulin receptors (KIRs)  Figure a. Toll-lThe major histocompatibility complex (MHC) is a set of cell surface proteins that helps to recognize viral and tumor-associated antigens for initiation of the immune cell response . NK cellThe high shear forces in lung capillaries induce micro-particle production by CTCs. Following their uptake and ingestion, macrophages are activated and contribute to metastatic lesion initiation and development . In physThe characterization of individual white blood cells (WBCs) in CTC clusters of breast tumor-bearing mice has shown the 85.5–91.7% of WBCs express neutrophil markers . These cCTCs downregulate MHC-I expression on CD8 T cells, a receptor that is crucial for initiation of the adaptive cytotoxic T lymphocyte response . ProgramFas Cell Surface Death Receptor (FAS or CD95) belongs to the TNF transmembrane receptor superfamily. After binding to its ligand Fas Cell Surface Death Receptor Ligand (FASL), FAS can activate the extrinsic apoptosis pathway through recruitment of caspase 8 and 10, and consequently mediates cells death. In resistant tumor cells, FAS also induces non-apoptotic signaling pathways linked to tumor growth, survival, and migration . FASL onCTCs modulate the immune system also by increasing platelet number and activity  Figure d. PlatelThe major mechanisms of chemotherapy resistance in tumor cells and CSCs  have alsMoreover, EMT and dormancy mediate the upregulation of ECM remodeling markers and the downregulation of signaling pathways that induce tumor cell mitosis ,95,96. TFinally, hypoxia stress signals ensure CTC survival through activation of autophagy to provide the nutrients  required for cell metabolism . Immune To conclude, as CTCs contribute to promoting resistance to conventional anticancer treatments, investigating and targeting these mechanisms represent a potential therapeutic approach.Currently, CTCs are considered a promising bio-source for cancer detection. Many technologies have been and are developed for CTC capture and isolation based on their biophysical and biological features  Figure . The chaBiological feature-based CTC enrichment is usually performed using immunoaffinity approaches based on CTC trapping or removal of background blood cells  a Table . ®  system is an immunomagnetic device based on EpCAM-positive CTC enrichment that offers semi-automated CTC capturing, staining, and image analysis, and thus can overcome the problems of CTC stability during sample shipment and storage [Immunomagnetic devices and microfluidic chips are two examples of positive enrichment technologies in which monoclonal antibodies (mAbs) targeting CTC surface antigens are coupled to magnetic beads or micro-posts/surfaces, respectively a1. For i storage ,101. Aff storage . In this storage ,104; how storage . Moreove storage . This pr storage . Aptamer storage .However, cancer cells undergoing EMT may lose EpCAM expression . To overCTC affinity selection (and thus CTC purity) is influenced by the surface mAb concentration, and low antigen level leads to mixed monolayers and recovery reduction. Moreover, some antibodies target only specific cancer types  or lack cancer-specificity because their targets are expressed also by other cells  . These lCentrifugation or reed blood cells (RBCs) lysis as pre-processing steps can lead to CTC loss. This problem can be addressed by using another immunomagnetic enrichment technology  that captures CTCs from whole diluted blood samples pre-labeled with magnetic particles by bead-bound CTC absorption using magnetic rods .To overcome the issues of CTC low number in blood samples and of CTC stability during sample shipment and storage, leukapheresis and cytopheresis are an excellent option for increasing the chance of ex vivo CTC capture and detection of rare CTCs. During cytopheresis, blood is passed through a machine that retain CTCs, while WBCs and peripheral blood stem cells go back to the patient’s vein . StandarPositive enrichment has other shortcomings. Indeed, some CTCs are not captured by the difficult-to-remove antibodies, and the capture accuracy is reduced by the heterogeneous expression of cell surface biomarkers due to EMT . MoreoveOverall, while immunoaffinity-based CTC enrichment presents valuable advantages, it also has some limitations. In label-dependent methods, antibody binding to surface markers causes the activation of intracellular signaling pathways , and theCTC enrichment methods based on biophysical properties, also called “label-free” approaches, can isolate CTCs from blood by exploiting their specific density, size, deformability, and electric charges b Table . ®  use density gradient centrifugation  gradient system that leads to higher CTC enrichment compared with the standard Ficoll-Paque® method [Density-based CTC enrichment technologies, such as Ficoll-Paquefugation b1 in whi® method .®, ScreenCell®, and CellSieve™) and 3D membrane microfilters , to isolate single CTCs or CTC clusters based on their size and/or deformability    is exploited by the DEPArray system for selection and isolation of single CTCs, independently of their features, followed by massive parallel sequencing to clearly reveal differences among CTCs . In thisCTCs can be isolated with high purity using hydrodynamic microfluidic devices based on inertial forces that force blood cells to migrate across flow stream lines to equilibrium positions b4. For iZeinali et al. developed the inertial microfluidic Labyrinth device that can isolate single CTCs and CTC clusters  from blood samples of patients with NSCLC using a high-throughput, biomarker-independent, and size-based  isolation method . The aut® FX or the Vortex method, however these methods still require further validation [Other inertial focusing methods have been developed that can capture viable cells, for example ClearCelllidation ,132. ® system is the only US Food and Drug Administration (FDA) approved CTC detection system, and newly developed technologies are routinely compared with this system [CTC enrichment methodologies have their own advantages and limitations , and sevs system . Unfortus system . ® [One alternative approach is to combine the advantages of biophysical- and biological-based enrichment methods, such as geometrically enhanced differential immunocapture , cell enrichment and extraction , and RosetteSep™ (density centrifugation and immunoaffinity selection). For instance, the GEDI chip can increase (2–400-fold) CTC counts compared with CellSearch® . A micro® ,103. CTC® . Besides CTC enrichment and enumeration, the detailed molecular and functional characterization of isolated CTCs can be performed using high-throughput single-cell analytical methods . MoreoveWhile technologies are focused on CTCs in peripheral blood, before and during dissemination, DTCs that successfully land at distant organs, such as bone marrow and lymph nodes, can form recurrent tumors and metastases . After sTumor tissue biopsy and fine-needle aspiration are invasive procedures that allow sampling only part of the tumor mass. Therefore, they are not representative of the whole disease. Moreover, these procedures may lead to tumor cell seeding around the sampling area and increase the risk of local dissemination . Hence, At diagnosis, CTC number is correlated with PFS and OS . In earlIn patients with breast cancer, the correlation between tumor size, CTC counts, and survival revealed that CTC number is closely associated with the primary tumor size, the number of metastases, and PFS reduction . ConversBalakrishnan et.al. studied the clinical implications of CTC clusters, and showed that in vitro cluster formation from CTCs of patients with advanced stage lung and breast cancer correlates with shorter OS. They also found that patients who were sensitive and resistant to chemotherapy exhibited loose and tight clusters, respectively. Moreover, tight clusters were clearly correlated with poor patient survival . ConversCTCs may provide useful information for tumor staging at diagnosis. For instance, in gastric cancer, the percentage of patients with ≥4 CTCs increases with the tumor stage and reaches 95.24% at stage IV . MoreoveDetermining the tumor cell phenotypic status and its correlation with CTCs can help treatment decision-making . In patiAnalysis of PD-L1 expression in CTCs from patients with metastatic lung cancer before and after immunotherapy showed that the overall response to anti-PD-1 immunotherapy was higher in patients with >1.32 CTCs/mL and in those with >50% of PD-L1-positive CTCs . IncreasFinally, metastatic CSC subsets that express specific markers, such as CD44v6 in colorectal cancer , CD133 aEGFR-T790M drug-resistance mutation in 92% of them. This was correlated with PFS reduction during treatment [The genetic analysis of mutations in CTCs could improve cancer management. Analysis of PIK3CA mutations in single CTCs from 20 patients with metastatic breast cancer showed that six harbored a PIK3CA mutation that may help to select the best treatment . Epidermreatment . B-Raf proto-oncogene, serine/threonine kinase (BRAF) and KIT proto-oncogene, receptor tyrosine kinase (KIT) mutations. Comparison of the mutation profile in CTCs and in the resected primary melanoma showed some heterogeneity [anaplastic lymphoma kinase (ALK) gene with echinoderm microtubule-associated protein-like 4 (EML4) and the production of oncogenic EML4–ALK fusion transcripts [EML4–ALK analysis in CTCs from patients with NSCLC indicated that EML4–ALK+ CTCs are associated with resistance to crizotinib (ALK inhibitor), and thus are a promising candidate for monitoring treatment efficacy and for the early detection of drug resistance [Single CTC mutation analysis highlighted the presence of mutations in genes encoding the therapeutic target or signaling proteins that are involved in resistance to targeted therapy . The effogeneity , particuogeneity . Severalogeneity . A smallnscripts . EML4–ALsistance .In conclusion, CTC enumeration and mutation profiles give information on the prognosis and help to stratify patients in clinics.Circulating tumor DNA (ctDNA) is actively released from living tumor cells into the blood stream and can be used as a surrogate biopsy biomarker. Its quantification and genomic analysis are useful for the management of patients with tumors in the era of precision oncology . Like CTKRAS mutations in patients with NSCLC [EGFR mutations in patients with NSCLC [On the other hand, it has been reported that ctDNA is much more sensitive than CTCs for the detection of th NSCLC . Also, cth NSCLC ,192. In th NSCLC . Moreoveth NSCLC . In concth NSCLC .The higher metastatic potential of CTC clusters compared with single CTCs is associated with poor prognosis . CTC cluCTC cluster survival in the bloodstream and extravasation are supported by platelets  . DisruptIn solid primary tumors, autophagy induces the endocytosis of TRAIL receptors, resulting in resistance to TRAIL-based therapy . HoweverBesides metastases in distant organs, the leaky-prone neo-vasculature of the primary tumor and tumor-draining lymph nodes promote CTCs’ re-entry in the tumor of origin after circulation  . Tumor-dAs CTCs express CD47, an anti-phagocytic receptor   and the post-metastatic niche initiates and takes form upon CTC arrival. As each cancer exhibits a proclivity to metastasize in specific organs, the CTC niche type in distant sites should be characterized . Understanding CTC biological properties, how they migrate (single cells or clusters) and escape the anti-tumor immunity, as well as their pre- and post-metastatic niche will offer opportunities to improve cancer management.CTC enrichment and detection technologies have improved in the last decades. For routine use, it would be better to develop technologies that simultaneously enrich and detect CTCs by downstream molecular characterization at the single-cell level. Moreover, CTC detection might be enhanced in the vessels and lymphatic network close to the tumor .The implementation of a tumor-specific signature for CTC identification improves personalized medicine through disease monitoring to guide treatment decisions, and even for metastatic cancer therapy. Targeting CTCs in the blood circulation might represent a promising therapeutic strategy; however, CTC biology needs to be fully understood. Moreover, insights into the role of EMT, stemness, clustering, and immune escape in CTCs help to better understand the metastatic cascade and consequently to guide research on future anti-cancer agents.


Metastatic castration-resistant prostate cancer (mCRPC) is the most aggressive and deadly form of prostate cancer. As a bone-predominant metastatic disease, liquid biopsy-based biomarkers have advantages in monitoring cancer dynamics. Previous studies have demonstrated the associations between circulating tumor cells (CTCs) and mCRPC outcomes, but little is known about the prognostic value of CTC-clusters. In this study, we investigated the associations of CTCs and CTC-clusters with mCRPC prognosis, individually and jointly, using longitudinal samples. We confirmed the associations of CTC counts with mCRPC outcomes in both baseline and longitudinal analyses. Our results also showed that the presence of CTC-clusters alone had prognostic value and that CTC-clusters may further improve CTC-based prognostic stratification in mCRPC. Our findings suggest the potential of combing CTC and CTC-clusters as non-invasive means to monitor progression and predict survival in mCRPC and build a premise for in-depth genomic and molecular analyses of CTCs and CTC-clusters.p = 0.0185). mCRPC patients with both unfavorable CTCs and CTC-clusters had the highest risk for death , as compared to those with <5 CTCs. Analyses using longitudinal data yielded similar results. In conclusion, CTC-clusters provided additional prognostic information for further stratifying death risk among patients with unfavorable CTCs.Liquid biopsy-based biomarkers have advantages in monitoring the dynamics of metastatic castration-resistant prostate cancer (mCRPC), a bone-predominant metastatic disease. Previous studies have demonstrated associations between circulating tumor cells (CTCs) and clinical outcomes of mCRPC patients, but little is known about the prognostic value of CTC-clusters. In 227 longitudinally collected blood samples from 64 mCRPC patients, CTCs and CTC-clusters were enumerated using the CellSearch platform. The associations of CTC and CTC-cluster counts with progression-free survival (PFS) and overall survival (OS), individually and jointly, were evaluated by Cox models. CTCs and CTC-clusters were detected in 24 (37.5%) and 8 (12.5%) of 64 baseline samples, and in 119 (52.4%) and 27 (11.9%) of 227 longitudinal samples, respectively. CTC counts were associated with both PFS and OS, but CTC-clusters were only independently associated with an increased risk of death. Among patients with unfavorable CTCs (≥5), the presence of CTC-clusters signified a worse survival (log-rank  Prostate cancer (PCa) is the most commonly diagnosed cancer and the second leading cause of cancer-related death among men in the United States . In patiRemarkable progress has been made in the use of tissue-based molecular analyses  to guide treatment decisions of many cancers. However, such tissue-based genomic profiling is challenging in mCRPC due to several reasons. First, mCRPC is a bone-predominant metastatic disease, thus tissue samples are not always obtainable. Second, the yield of tumor tissues from metastatic sites can often be quite low, particularly when sampling from bone metastases . Third, CTCs are shed from primary or metastatic tumors into the blood and have extremely high malignant potential. Since CTCs constitute “seed cells” for metastasis, they are arguably the most important subset of tumor cells to monitor and treat ,11. UnliIn addition to disseminating as individual cells, tumor cells also collectively migrate as clusters, in which cell-cell adhesion remains intact ,21. ClusAs yet, no study has been reported to evaluate whether CTC-clusters can further improve CTC-based prognostic stratification in mCRPC. Furthermore, no in-depth analysis has been conducted to explore the prognostic value of CTC-clusters, by using longitudinally collected data. Herein, based on an ongoing mCRPC cohort with longitudinal samples, we conducted, to our best knowledge, the first study that evaluated the prognostic value CTC-clusters in high-risk mCRPC patients with high CTC levels.A total of 64 mCRPC patients were included in this analysis . At the p < 0.0001 for progression-free survival (PFS); 6.0 months vs. not reached, log-rank p < 0.0001 for OS)  . mCRPC p for OS) . When co for OS) . We thenp = 0.0003 for PFS; 4.2 months vs. not reached, log-rank p < 0.0001 for OS) (p = 0.0299) (CTC-clusters were identified in 8 (12.5%) of 64 patients. Representative immunofluorescent images of CTC-clusters are shown in  for OS) . mCRPC p for OS) . After a 0.0299) .n = 44) included those with favorable CTC counts (<5 CTCs), and no patients in this group had a CTC-cluster. The medium-risk group (n = 12) included those with unfavorable CTC counts (≥5 CTCs) but without a CTC-cluster. The high-risk group (n = 7) included those with both unfavorable CTC counts and CTC-clusters. Note that the one patient with one CTC that was a 2-cell cluster was excluded from this analysis because this subgroup only had one subject.We were interested in learning whether CTC-clusters could further stratify prognostic risk in patients with unfavorable CTC counts. To this end, we categorized patients into three risk groups. The low-risk group  (p = 0.0185), suggesting improved prognostic stratification using CTC-clusters. In the univariate Cox analyses, compared with the patients in the low-risk group, those in medium- and high-risk groups had a 3.53-fold  and 6.30-fold  increased risk for progression, as well as a 3.96-fold  and 21.53-fold  increased risk for death (p = 0.0072) in the multivariate Cox analyses  . Among por death . mCRPC panalyses .In the joint analyses of CTCs and CTC-clusters described above, we found that baseline CTC-clusters could further stratify patients with unfavorable baseline CTCs into different risk groups. However, using measurements with only one time point may underestimate prognostic values. In comparison, using longitudinal data obtained from repeated measurements of each individual over time is an effective approach to improve prediction power ,28.n = 1) from joint analyses. As shown in p < 0.0001). The association between the presence of CTC-clusters and outcomes remained significant in OS-related analyses, even after adjustment for covariates (p < 0.05). In the joint analysis using longitudinal CTCs and CTC-clusters at each time point, we found that the death risk for patients with both unfavorable CTCs and CTC-clusters almost doubled, as compared to those with unfavorable CTCs but without a CTC-cluster . These results from longitudinal analyses were consistent with the baseline analyses and further confirmed that CTC-clusters conferred additional prognostic information to CTC enumeration alone and improved prognostic stratification in patients with unfavorable CTCs.To confirm the additional prognostic value of CTC-clusters, and to clarify whether the non-significant finding in PFS analyses was due to relatively small sample size, we evaluated the associations of longitudinal changes in CTCs and CTC-clusters with clinical outcomes, using the Cox proportional hazards model with time-dependent covariates. The time-dependent CTC- and cluster-related variables, including risk groups, were re-defined for every patient at each time point of blood draw from baseline to first progression or death . In totaWe also plotted the dynamic changes of CTCs and CTC-clusters of individual patients. The prognostic values of CTC-clusters have been reported, but mostly only in breast cancer. Despite a seminal study on CTC-clusters in PCa , no studCTC-clusters are derived from multicellular groups of tumor cells that are held together through plakoglobin-dependent intercellular adhesion . ClusterIn vivo studies have shown that CTC-clusters had a 23- to 50-fold increase in metastatic potential compared to single CTCs in breast cancer . Recent The maintenance of cell-clusters, including cell-cell cohesive interactions, has been observed in the majority of invasive PCa ,36. AcetWe then evaluated the role of CTC-clusters in prognostic stratification. After stratifying mCRPC patients with unfavorable CTC counts according to the presence or absence of CTC-clusters, we noted that OS differed significantly between these two groups B. ComparThe major strengths of this study include the focus on mCRPC that ensures a homogenous study population and the innovative use of time-dependent analyses of longitudinal samples with repeated measurements ,37. Our We recruited men with mCRPC who visited the Sidney Kimmel Cancer Center at Thomas Jefferson University Hospital from March 2018. The enrolled patients had histologically confirmed prostate adenocarcinoma, a progressive disease despite castration levels of serum testosterone (<50 ng/dL), and radiographic metastases according to computed tomography (CT) or technetium-99 bone scan. Patients were excluded if they concurrently had other primary tumors. Demographic data , clinical data , and laboratory data  were collected by reviewing medical charts. Blood samples were collected from each patient at baseline before initiation of a new therapy and at follow-up visits (approximately every 6–8 weeks) for CTC and CTC-cluster enumerations. Assessments of CTC/CTC-cluster, PSA, and tumor lesions were repeated. Follow-up imaging tests were conducted following the PCWG3 guideline . This reApproximately 8–10 mL of peripheral blood was drawn into a 10 mL evacuated blood draw tube , maintained at room temperature, and processed within 96 h of collection. CTC and CTC-cluster enumerations were conducted using the CellSearch System (MENARINI Silicon biosystems), which consists of the CellTracks Autoprep and the CellSearch CTC kit to immunomagnetically enrich cells expressing the epithelial cell adhesion molecule (EpCAM) and fluorescently label nuclei (DAPI), leukocytes with monoclonal antibodies specific for leukocytes (CD45), and epithelial cells . CTCs were defined as nucleated cells lacking CD45 and expressing cytokeratin (CK+/DAPI+/CD45−) . CTC-cluhttp://www.r-project.org) software packages. Two-sided p values of <0.05 were considered to be of statistical significance.Clinical outcomes analyzed in this study included PFS and OS. PFS was defined as the time from the date of baseline blood draw to the date of radiologic progression  ; on boneTo our best knowledge, this is the first comprehensive analysis of the role of prognosis stratification by CTC-clusters in mCRPC patients. Our findings suggest the potential of combing CTC and CTC-clusters as non-invasive means to monitor progression and predict survival in mCRPC and build a premise for in-depth genomic and molecular analyses of CTCs and CTC-clusters.


The potential clinical utility of circulating tumor cells (CTCs) in the diagnosis and management of cancer has drawn a lot of attention in the past 10 years. CTCs disseminate from tumors into the bloodstream and are believed to carry vital information about tumor onset, progression, and metastasis. In addition, CTCs reflect different biological aspects of the primary tumor they originate from, mainly in their genetic and protein expression. Moreover, emerging evidence indicates that CTC liquid biopsies can be extended beyond prognostication to pharmacodynamic and predictive biomarkers in cancer patient management. A key challenge in harnessing the clinical potential and utility of CTCs is enumerating and isolating these rare heterogeneous cells from a blood sample while allowing downstream CTC analysis. That being said, there have been serious doubts regarding the potential value of CTCs as clinical biomarkers for cancer due to the low number of promising outcomes in the published results. This review aims to present an overview of the current preclinical CTC detection technologies and the advantages and limitations of each sensing platform, while surveying and analyzing the published evidence of the clinical utility of CTCs. The advent of new diagnostic and treatment modalities have improved the 5-year relative survival rate for all types of cancers combined; the survival rate increased substantially from 39% to 70% among white patients and from 27% up to 63% among black patients . It is wSeveral CTC detection platforms have emerged over the past decade, each exploiting a distinctive characteristic of CTCs for sensitive selection and capture. Each technology differs in the biophysical or bimolecular trait leveraged for CTC capture, enrichment, and downstream cellular and molecular characterization, but all of them aim to enumerate CTC and draw clinically relevant conclusions regarding the prevalence of CTC for cancer management. The focus of these methodologies is the detection of CTC clinically, rapidly, and with high sensitivity, selectivity and specificity, while remaining minimally invasive. The differences between CTC and normal blood cells in gene/protein expression, morphology, volume, and biophysical properties had led to the establishment and commercialization of several CTC detection and enumeration devices during the past decade. These commercialized technologies can be categorized based on method of CTC identification as label dependent (affinity-based) or label independent. Then, each category is subdivided into different classes based on its functional detection approach .The most widely used approach for CTC detection and isolation is immune-based detection, whereby antibodies are used to selectively bind cell surface antigens . Tumor cOne of the leading platforms utilizing label-dependent technology is the CellSearch systems (Veridex), which employ EpCAM-coated ferrofluid nanoparticles for the selection of EpCAM-positive CTCs followed by confirmation with immunostaining for the high expression of CK 8, 18 and the absence of CD45 expression . More thImportant correlations between CTC count and cancer relapse have already been observed using the CellSearch system, but the technology has some limitations . Firstly®  is another commercialized positive selection platform that relies on immunomagnetic beads coated with a cocktail of antibodies for the enhanced capture and enrichment of CTCs in breast, prostate, ovarian and colon cancer . A. A44]. Aic beads . In comp sample) . In a hellSearch . It is wllSearch . With dullSearch . In a stllSearch . Each caGiven that CTCs express EpCAM and CK to varying levels, with some displaying the complete downregulation of these proteins, alternative strategies have been developed and tested to isolate and enumerate CTCs based on their biophysical properties . These pMicrofiltration enrichment methods process whole blood through a range of microscale constrictions to capture target cells based on their size or a combination of size and deformability. The recovery efficiency is reduced due to the buildup of filtration resistance resulting from the frictional drag on the blood sample as it passes through the filter, a main limitation of this strategy . ® technology, for the enrichment and cultivation of CTCs in vitro. Using this technology, CTCs were detected in 66.7% of patients, with comparable frequencies in patients with operable and inoperable tumors (60% vs. 77.8%). Comparable CTC fractions were observed among patients with metastatic and nonmetastatic tumors (66.7% vs. 66.7%). The CTCs were then cultured in vitro for further downstream applications, thus confirming their viability [The most significant physical difference between CTCs and WBCs is size, where CTCs are larger on average. Several platforms aim at sieving CTCs from a blood sample and have been shown to be more selective and efficient than the CellSearch system. These platforms rely on microfiltration, which involves a single membrane with varying pore sizes between 6 and 9 µm and is used to separate CTCs and filter out smaller blood cells. A novel filter-based size exclusion technology called ISET (isolation by size of tumor cells), developed by RareCell Diagnostics, Paris, France, was capable of isolating CTCs independent of their expression of any particular marker. Using this technique, CTCs were detected in patients with hepatocellular carcinoma, breast carcinoma, and melanoma ,69,70. Iiability . Althougiability .Microfluidic chips have also been developed for CTC size-based detection and termed “three-dimensional microfiltration”, where 3D geometries are constructed and designed to allow the separation of CTCs from background blood components. The Parsortix system is an example of such an approach: it has a stair-like architecture that decreases gradually in width down to 4.5 µm to aid in the capture of larger cells  and provide the necessary physical support above and below the captured cells to prevent morphological changes. CTCs larger than the channel width become trapped in the gaps, while smaller cells pass through. Its design maximizes the length of separation and allows reverse flow for the subsequent release of captured CTCs for downstream interrogation and analysis .®  and Percoll, Ficoll-Hypaque® , mostly used in biomedical laboratories to recover peripheral blood mononuclear cells. Despite its long history of use in laboratory environments, there are some drawbacks linked to this technique, mainly the possible loss of CTCs that move either to the plasma region or to the bottom of the density gradient due to the formation of aggregates. It is worth noting that this cell loss could be due to the cytotoxicity of the density medium. Interestingly, the Percoll density  gradient medium has some advantages over Ficoll, which include reduced toxicity as well as a wider density gradient range [Density gradient centrifugation is a typical method for segregating whole blood into its constituents based on differences in sedimentation coefficients. As whole blood is dropped in the liquid gradient while being subjected to centrifugation, cells are distributed along the gradient depending on their density. Erythrocytes or polymorphonuclear leukocytes with lower cellular density are precipitated at the bottom, whereas the heavier mononuclear leukocytes and CTCs remain at the top . Severalnt range ,74. Anotnt range . Inertial focusing uses the effects of fluid inertia in microchannels of a certain shape to align microparticles and cells at high flow rates. When randomly dispersed particles, such as cells in blood, flow through a channel with a particle Reynolds number of one or greater, they are subjected to two counteracting inertial lift forces: a force that directs particles toward the channel walls, and another that repels the particles toward the channel centerline. In square or rectangular channels, combining these forces leads to the migration of particles to two to four dynamic equilibrium positions located between the channel centerline and the wall. Following focusing, cells are collected in a smaller volume and significantly concentrated in a size-dependent fashion ,77. SollAnother modality that makes use of inertial focusing is the ClearCell FX, developed by Clearbrdige Biomedics. ClearCell FX is a spiral microfluidic device that combines both inertial focusing with the secondary Dean’s flow resulting from curved channels to trap CTCs from a blood sample; this allows for the proper positioning of CTCs within the channel. This modality can process 7.5 mL of blood in less than 10 min but requires red blood cells lysis prior to sample processing. Khoo et al. tested this system on patients with metastatic breast cancer or NSCLC. CTCs were detected in 100% of the patients, with a varied range of CTCs isolated for breast cancer samples (12–1275 CTCs/mL) (Median: 55 CTCs/mL) and NSCLC samples (10–1535 CTCs/mL) (Median: 82 CTCs/mL), respectively . InertiaIn addition to size-dependent isolation, other physical traits observed in CTCs are exploited to distinguish them from leukocytes. Two innovative approaches to cell separation are dielectrophoresis and direct imaging, which both depend on cell composition, morphology, and phenotypes. These two platforms are discussed in the following subsections.Imaging-based detection makes use of specific fluorescent tags to identify and count CTCs in blood. Several imaging-based CTC detection technologies have been developed and tested and each is unique in its sample preparation, detection algorithm, and fluorophores used. Somlo G. et al. developed a novel fiber optic array scanning technology (FASTcell™) to detect CTCs in patients with locally advanced/inflammatory breast cancer (LABC/IBC), metastatic breast cancer (MBC) and non-small cell lung cancer. The principle of operation of the FASTcell™ technology is based on an array of optical fibers that form a wide collection aperture to allow a wider field of view; this enables the rapid high-fidelity localization of CTCs identified by conventional markers such as CK, DAPI and CD45 without the need for enrichment. The FASTcell™ system can scan a sample of blood on a glass side at a rapid rate of 25 million cells/min, comprising the image resolution and subsequent confirmation of potential detected CTCs. Clinical studies using FASTcell™ have successfully identified CTCs in 62% of LABC/IBC patients, in 82% of MBC patients and 42% of non-small cell lung cancer patients ,79.Another novel imaging approach that combines both flow cytometry with fluorescence imaging for high throughput analysis has been developed recently. The technology is referred to as ImageStream, and its detection of CTC depends on the expression of EpCAM, CK, AFP, glypican-3 and DNA-PK together with an analysis of size, morphology and DNA content. In a clinical study of patients with hepatocellular carcinoma (HCC), between one and 1642 CTCs were detected in the blood samples of HCC patients (45/69) compared to zero CTCs in the controls (0/31) . In a coDielectrophoresis (DEP) is a liquid biopsy separation method that relies on particles with different polarization that move differently under a nonuniform electric field . MicrochAs in label-based enrichment technology, label-free enrichment methodologies have their own advantages and disadvantages that are presented in ClinicalTrials.gov” website revealed 296 studies involved in CTC detection and capture in patients with metastatic disease [In 2015, a search for “circulating tumor cells” in the “ disease . CurrentAmong the clinical uses of CTC detection is cancer prognosis, which is significant for clinical decision making as prognostic estimation is useful for assessing the risks and benefits of the proposed treatment. Huge efforts have been made to understand the clinical utility of CTCs to predict prognosis and guide therapeutic decisions. We selected seventeen studies to demonstrate and confirm the prognostic, and sometimes the diagnostic, implications of CTCs in various types of cancer using different CTC detection technologies including CellSearch. In these studies, like in most cases, patients who had a higher count of CTCs (unfavorable count) have had a worse prognosis, measured primarily by PFS and OS survival estimates. The results of these trials shed light on the real possibilities of CTC counting and gave deeper insights on the potential of using CTCs as liquid biopsies b.p < 0.001) and relapse-free survival (RFS)  compared to CTC negative patients. Even in patients with non-metastatic tumors and lymph node invasion, CTC presence indicated worse OS and RFS. A multivariate analysis identified CTCs as strong independent prognostic indicators of tumor recurrence . The outcome of this study suggests the clinical relevance of CTCs as preoperative prognostic and staging parameters in esophageal cancer [p < 0.001). Importantly, multivariate analysis revealed that CTC detection was the strongest prognostic predictor independent of other clinicopathological parameters; in fact, there was no association between primary tumor characteristics or clinicopathological parameters  and CTC detection in non-metastatic patients [The pretreatment staging of patients with cancer and risk stratification based on clinical and histopathological findings greatly improved prognostication and treatment allocation for many cancer subtypes; however, there remains much room for improvement . Evidencl cancer . In a sipatients . These sThe prognostic significance of CTC is also true for patients undergoing a new line of treatment. Using the CellSearch platform, predictive outcome measures of CTC count and chromogranin A (CgA) were investigated in 138 patients with metastatic neuroendocrine neoplasms receiving a new line of treatment . Of all patients, 51% had received previous anticancer therapy, 41% were receiving long-term SST , and 60% of patients tested positive for CTC (at least one CTC detected). Fifteen weeks after starting the new line of therapy, patients with zero CTC count had the highest OS (49.1 months), patients with a CTC count from one to eight had lower OS , and patients with a CTC count above eight had the worst OS . The changes in CTC count significantly correlated with the response to treatment and OS, suggesting their potential use as surrogate markers to direct clinical decision-making. On the other hand, changes in CgA were not significantly associated with survival . p < 0.0001) and PFS  compared to patients with a CTC count lower than five CTCs/7.5 mL. After the new line of treatment, any increase in CTC count correlated with shortened OS and PFS. When the CTC count was added to the full clinicopathologic predictive models, the prognosis accuracy was improved according to using the likelihood ratio (LR) χ2 statistical analysis. It is worth mentioning that serum tumor markers (CEA and CA15-3) did not show any significant prognostic value, even when added to the clinicopathologic predictive model [In another study, a pooled analysis of 1944 patients with metastatic breast cancer from 20 different studies at 17 European centers validated the prognostic clinical utility of CTCs. Before using a new line of treatment, 47% of patients tested positive for CTCs using the CellSearch system (threshold ≥ 5CTCs/7.5 mL). Patients with elevated CTCs at baseline had an inferior OS  to determine therapeutic approaches. The CellSearch system was used to count CTCs expressing IGF-1R in patients treated with monoclonal antibodies against IGF-1R, either alone or in combination with docetaxel. Out of 26 patients, 23 had positive IGF-1R CTCs and had responded better to the combinatorial treatment compared to the remaining three patients whose CTCs were negative for IGF-1R. Such a study suggests the potential use of CTCs as predictive markers for the choice of administered chemotherapy . AnotherSeveral clinical studies have reported the use of CTC with circulating tumor DNA (ctDNA), tumor-derived DNA released into the blood via apoptosis or the necrosis of shedding cells from primary and metastatic lesions, as a complementary biomarker for treatment assessment. In a phase II clinical trial of erlotinib and pertuzumab in patients with advanced NSCLC, a decrease in CTC count upon treatment was correlated with longer PFS. In addition, patients with EGFR mutations showed a substantial reduction in CTC count throughout treatment. The mutational analysis of EGFR showed that ctDNA had higher sensitivity in detecting mutations compared to CTC and, upon treatment, a decrease in mutational load suggested a partial response to treatment. This study, and another mentioned in In the past two decades, our improved understanding of the biology of CTCs and their role in cancer metastasis has opened the door to a myriad of technologies aimed at exploring the clinical potential of CTCs as biomarkers for cancer. CellSearch remains the only successful platform to obtain FDA approval, while many are still in preclinical and clinical trial stages. Antigen-free approaches are showing a lot of potential for clinical success as they overcome the heterogeneous expression of expressed membrane proteins in CTCs. Affinity-based detection platforms are primarily designed to detect, and count CTCs based on the expression of epithelial markers, mainly EpCAM and CK, but such markers are proven to be downregulated upon undergoing EMT. Such inadequacy necessitates either finding a universal selection epithelial and mesenchymal cell surface marker to prevent cross-reactivity with other types of cells in the blood or adopting physical, mechanical and electrical properties as selection markers for isolating CTCs from their surroundings. In addition, any selection approach also may need to distinguish single cells and clusters  in patients with metastatic breast cancer and no CTC decrease in response to the first-line chemotherapy, switching to second-line chemotherapy did not affect CTC count, nor did it improve OS . This meDespite all the advances in CTC detection technologies and their diverse capture and enrichment systems, many significant challenges are yet to be met, particularly those with respect to analytical and clinical sensitivities. Adopting such tools into routine clinical practice will demand laborious studies into their analytical validity, clinical validity and clinical utility. These tools, coupled with bioinformatics tools and annotated databases, will provide evidence as to whether detected genomic aberrations in blood may aid in predicting the most suitable cancer therapy on a personalized level.


Long noncoding (lnc)RNAs and glycolysis are both recognized as key regulators of cancers. Some lncRNAs are also reportedly involved in regulating glycolysis metabolism. However, glycolysis-associated lncRNA signatures and their clinical relevance in cancers remain unclear. We investigated the roles of glycolysis-associated lncRNAs in cancers.Glycolysis scores and glycolysis-associated lncRNA signatures were established using a single-sample gene set enrichment analysis (GSEA) of The Cancer Genome Atlas pan-cancer data. Consensus clustering assays and genomic classifiers were used to stratify patient subtypes and for validation. Fisher’s exact test was performed to investigate genomic mutations and molecular subtypes. A differentially expressed gene analysis, with GSEA, transcription factor (TF) activity scoring, cellular distributions, and immune cell infiltration, was conducted to explore the functions of glycolysis-associated lncRNAs.Glycolysis-associated lncRNA signatures across 33 cancer types were generated and used to stratify patients into distinct clusters. Patients in cluster 3 had high glycolysis scores and poor survival, especially in bladder carcinoma, low-grade gliomas, mesotheliomas, pancreatic adenocarcinomas, and uveal melanomas. The clinical significance of lncRNA-defined groups was validated using external datasets and genomic classifiers. Gene mutations, molecular subtypes associated with poor prognoses, TFs, oncogenic signaling such as the epithelial-to-mesenchymal transition (EMT), and high immune cell infiltration demonstrated significant associations with cluster 3 patients. Furthermore, five lncRNAs, namely MIR4435-2HG, AC078846.1, AL157392.3, AP001273.1, and RAD51-AS1, exhibited significant correlations with glycolysis across the five cancers. Except MIR4435-2HG, the lncRNAs were distributed in nuclei. MIR4435-2HG was connected to glycolysis, EMT, and immune infiltrations in cancers.We identified a subgroup of cancer patients stratified by glycolysis-associated lncRNAs with poor prognoses, high immune infiltration, and EMT activation, thus providing new directions for cancer therapy.The online version contains supplementary material available at 10.1186/s12916-021-01925-6. Cancer is regarded as a type of metabolic disease. Tumor cells can drive certain metabolic pathways to sustain their biological processes for growth and to adapt to complex tumor microenvironments (TMEs) . A well-Long noncoding (lnc)RNAs, which are longer than 200 nucleotides, can modulate gene expressions through various mechanisms. Several oncogenic signaling pathways, such as the cell cycle , immune In this study, we used pan-cancer data from The Cancer Genome Atlas (TCGA) to identify glycolysis-associated lncRNAs across 33 tumor types. We performed a consensus clustering analysis to classify these glycolysis-associated lncRNAs into distinct clusters. We then identified glycolysis-associated lncRNAs that exhibited key clinical effects in five cancer types. Finally, we explored the potential pathways and functions of glycolysis-correlated lncRNAs in association with oncogenic signaling such as EMT and immune regulation.https://xena.ucsc.edu/). Raw counts of RNA-Seq data were normalized to counts per million (CPMs). Gene-level copy number values were calculated using GISTIC2.0. Beta values derived from Illumina human methylation 450K arrays were used to analyze TCGA DNA methylation changes. LncRNA annotation was retrieved from GENCODE, which contains 17,910 lncRNAs. In total, 15,121 lncRNAs were detected in TCGA RNA-Seq data. We defined an lncRNA as that expressed in a certain cancer type if the gene count was > 10 in more than 90% of patients.Genomic profiles including RNA sequencing (RNA-Seq) data, gene-level copy number, DNA methylation, and patient clinical characteristics of 33 TCGA cancer types were downloaded from UCSC Xena  or downregulated (FC < 0.7 and FDR < 10− 4) in cluster 3 versus cluster 2, and cluster 2 versus cluster 1. In total, 174 upregulated and 49 downregulated genes were identified in low-grade gliomas . These mutation data, generated by the Multi-Center Mutation Calling in Multiple Cancers (MC3) project, were derived from exon sequencing of TCGA cancer patient samples, and genes were categorized into binary calls as either nonsilent mutation or wild-type. Fisher’s exact test was performed to investigate genomic mutations that were significantly enriched  in glycolysis score-classified cluster 3 or cluster 1 cancer patients. The identified mutation within each cancer type was shown as a heatmap. To compare established molecular subtypes with the glycolysis signature-classified groups, we selected two cancer types (LGG and BLCA) to perform the analyses; the types have been classified as different groups in other studies [Genomic mutation data of BLCA, LGGs, mesotheliomas (MESOs), pancreatic ductal adenocarcinomas (PAADs), and uveal melanomas (UVMs) were retrieved from UCSC Xena , and a pathway with an FDR of < 0.01 was considered significant enrichment. The Hallmark pathway database [https://david.ncifcrf.gov/tools.jsp).To explore the transcriptome that exhibits distinct expression patterns within glycolysis score-stratified clusters, we used CPM-normalized counts from RNA-Seq data to perform a differentially expressed gene (DEG) analysis by using the edgeR package. Gene candidates were ranked based on log2 multiples of change to conduct a GSEA with 10database . In brieThe lncALTAS database  was usedp value of < 0.01 were considered to significantly differ among the three clusters.To infer immune cell infiltration based on transcriptome profiles, we followed the method reported by Şenbabaoğlu et al. . Brieflyx, and glycolysis-associated gene expression was y. The first-order partial correlation between x and y conditioned on lncRNAs wasA first-order partial correlation was performed to explore interlinks among lncRNAs, glycolysis scores, and glycolysis-associated genes. The glycolysis score was assumed to be − 6.We compared the cumulative distribution of Pearson correlation coefficients between glycolysis scores and gene expressions with or without removing the effect of lncRNA expression by using the Kolmogorov–Smirnov test. To identify gene candidates that were correlated with lncRNA expression and were independent of copy number variations and DNA methylation, we performed a multivariate linear regression to adjust for these covariates. We considered a gene candidate to be significantly associated with an lncRNA when its absolute correlation coefficient was > 0.3 and its FDR was < 10p < 0.01). Cluster 3 patients in five cancer types—BLCA, LGGs, MESOs, PAADs, and UVMs—demonstrated a significant association with a poor prognosis , MESO (n = 84), and UVM (n = 80) were small, we mainly focused on the effects of lncRNAs on BLCA (n = 406) and LGG (n = 524). Furthermore, the microarray data of PAAD, MESO, and UVM cancer patients lacked survival information, limiting our validation of the clinical importance of glycolytic lncRNA-stratified clusters. Thus, we focused mainly on LGG and BLCA to validate the clinical significance of the clusters. We identified 26 gene candidates in BLCA and 45 gene candidates in LGGs by using the developed genomic classifiers, which allowed discrimination within these subgroups based on gene expression levels . Directly correlating lncRNAs with these genes would generate hundreds of correlation results and Relationships between glycolysis and the immune resistance of cancers have recently been reported. Tumor cells utilize glucose and metabolically compete with T cells through impairing mammalian target of rapamycin (mTOR) activity and glycolytic activity in T cells, leading to the overriding of the capability of T cell-mediated cytotoxicity . In addi-omics analysis integrating transcriptome, metabolite, and genomic analysis revealed that UVM cells with BAP1 mutation maintain their energy demand through oxidative phosphorylation and glycolytic pathway [SLC2A1, PDK1, LDHA, and SLC16A3 [In our genomic mutation analysis, we identified specific gene mutations that were associated with glycolysis-associated lncRNA-stratified clusters in different cancers. Some of these genes have been reported to be involved in glucose metabolism. For example, in mice bearing RB1 null lung cancer , upregul pathway . Additio SLC16A3 . By contIn our TF analysis, we determined that activated prominent TFs in highly glycolytic clusters across BLCA, LGG, PAAD, and UVM were mainly involved in TGF-β signaling and SMAD protein complex assembly. SMAD proteins are downstream signal transducers of the TGF-β signaling pathway, which functions as an immune-suppressive regulator in cancers. For instance, Smad3-mediated TGF-β signaling was reported to suppress the cytotoxic activity of NK cells by blocking the production of CD16-mediated IFN-gamma . AnotherAmong the glycolysis-associated lncRNAs we identified, some lncRNAs have been reported to be directly involved in glycolysis signaling. For example, plasmacytoma variant translocation 1, which exhibited positive associations with glycolysis scores in six of thirty-three cancer types in our findings, was suggested to function as a microRNA sponge in suppressing miR-497 expression, leading to the promotion of HK2 upregulation and osteosarcoma progression . NuclearAmong the five lncRNAs that were consistently correlated with glycolysis scores across different cancers in our findings, MIR4435-2HG was identified as being highly associated with glycolysis and its correlated genes. By performing a multivariate linear regression adjustment, we also uncovered that immune and EMT-involved genes are positively correlated with MIR4435-HG expression. Several studies have demonstrated the oncogenic roles of MIR4435-2HG in cancer processes. MIR4435-2HG promotes gastric cancer cell migration and proliferation through Wnt/β-catenin signaling . The uprWe identified a subgroup of cancer patients stratified by glycolysis-correlated lncRNA signatures with the poorest prognosis, a highly infiltrative immune microenvironment, and EMT activation and thus provide novel aspects for cancer therapy.Additional file 1: Table S1. Glycolysis candidate genes in each cancer types.Additional file 2: Figures S1. Consensus cumulative distribution function (CDF) and delta area for five cancer types.Additional file 3: Table S2. DEGs in LGG. Table S3. DEGs in BLCA.Additional file 4: Table S4. Gene candidates in TCGA BLCA data. Table S5. Ggene candidates in TCGA LGG data.Additional file 5. Table S6. Number of tumor tissues from TCGA pan cancer data.Additional file 6: Figures S2. Flowchart for filtering cancer types and selecting glycolysis-associated long non-coding (lnc) RNAs.Additional file 7: Figures S3. Stratification of patients into different clusters by a consensus clustering analysis.Additional file 8: Table S7. Glycolytic score associated lncRNA in UVM.Additional file 9: Figures S4. Flow chart for establishing genomic classifiers.Additional file 10: Figures S5. Flow chart for Transcription factor activity analyses.Additional file 11: Figures S6. Negative correlation of lncRNA-MYC activity pairs.Additional file 12: Figures S7. Positive correlation of lncRNA-MYC activity pairs.Additional file 13: Figures S8. All the high resolution images in the result section.